title: An Ant-Inspired Track-to-Track Recovery Approach for Construction of Cell Lineage Trees

text: Correct track-to-track association is crucial to the construction of cell lineage trees as well as the discovery of novel biological phenomenon that occur at rare frequencies. In this paper, an ant colony optimization based heuristic approach is proposed to link potential tracks through minimizing the cost function that mainly occurs on the fragmented intervals with the constraint of maximum inter-frame displacement. Specifically, both cell motion and morphology are emphasized in the defined cost function, and two decisions are made respectively to recover the mitotic and non-mitotic cases. Our method has proven to be feasible that can repair the broken tracklets caused by large migration, occlusion and mitosis missing, as well as false positives and missed detections, and can effectively help the construction of reliable cell lineage trees.


title: Research on Crowd-Sensing Task Assignment Based on Fuzzy Inference PSO Algorithm

text: To solve the problem of load unbalance in the case of few users and multi-task, a fuzzy inference PSO algorithm (FPSO) crowd sensing single objective task assignment method is proposed. With task completion time, user load balancing and perceived cost as the optimization goals, the fuzzy learning algorithm dynamically adjusts the learning factor in the PSO algorithm, so that the PSO algorithm can perform global search in the scope of the task space, thus obtaining the optimal task assignment solution set. Finally, the FPSO algorithm is compared with the PSO, GA and ABC algorithms on the optimization objectives, such as the algorithm convergence, task completion time, perceived cost and load balance. The experimental results show that the FPSO algorithm not only has faster convergence rate than the other algorithms, and shorten the task completion time, reduce the platform’s perceived cost, improve the user’s load balance, and have a good application effect in the crowd sensing task assignment.


title: On Assessing the Temporal Characteristics of Reaching the Milestone by a Swarm

text: The physical swarm operation is investigated. Swarm units are considered as three-wheeled mobile robots, moving through rough terrain. For the longitudinal movement of such type vehicle the dynamic model is obtained. The article discusses the issue of forecasting the time a physical swarm reaches a milestone and proposes a hypothesis on the form of the law of the distribution of time during which a swarm unit reaches a milestone. Obtaining this time distribution is carried out with use the Petri-Markov net fundamental apparatus. With use Petri-Markov nets time densities of reaching the milestone both one unit and swarm as a whole are obtained. More common formula of distribution of time of milestone reaching by l units of K is obtained too. To confirm the hypothesis about the type of theoretical time distribution a computer experiment was carried out using the Monte Carlo method.


title: Improved Bacterial Foraging Optimization Algorithm with Comprehensive Swarm Learning Strategies

text: Bacterial foraging optimization (BFO), a novel bio-inspired heuristic optimization algorithm, has been attracted widespread attention and widely applied to various practical optimization problems. However, the standard BFO algorithm exists some potential deficiencies, such as the weakness of convergence accuracy and a lack of swarm communication. Owing to the improvement of these issues, an improved BFO algorithm with comprehensive swarm learning strategies (LPCBFO) is proposed. As for the LPCBFO algorithm, each bacterium keeps on moving with stochastic run lengths based on linear-decreasing Lévy flight strategy. Moreover, illuminated by the social learning mechanism of PSO and CSO algorithm, the paper incorporates cooperative communication with the current global best individual and competitive learning into the original BFO algorithm. To examine the optimization capability of the proposed algorithm, six benchmark functions with 30 dimensions are chosen. Finally, experimental results demonstrate that the performance of the LPCBFO algorithm is superior to the other five algorithms.


title: Aula Touch Game: Digital Tablets and Their Incidence in the Development of Citizen Competences of Middle Education Students in the District of Barranquilla-Colombia

text: Citizen competences are considered as a fundamental aspect in the social development of man with his environment, which allows him to carry out actions that are articulated with the different guidelines established by law, which leads the citizen to live in a coherent and peaceful way in a nation that tends for freedom of thought framed in a democratic society. That is why it is considered of high importance that in educational establishments there are spaces that tend for training in peaceful coexistence framed in the law of the educated. This article resulted from the research project: “Social Appropriation of citizen and mathematical competences making use of MIDTablets”, in which the mediation of Information and Communication Technologies is proposed to support the training of citizens with competences citizens who ensure adequate behavior in society. This project was developed in 31 educational institutions in the district of Barranquilla-Colombia, with support from resources of both the Ministry of Information Technology and Communications (MinTic), and the Secretariat of District Education of Barranquilla, in compliance with national goals, departmental and district regarding the quality of education of the national population


title: A Parallel Evolutionary Algorithm with Value Decomposition for Multi-agent Problems

text: Many real-world problems involve cooperation and/or competition among multiple agents. These problems often can be formulated as multi-agent problems. Recently, Reinforcement Learning (RL) has made significant progress on single-agent problems. However, multi-agent problems still cannot be easily solved by traditional RL algorithms. First, the multi-agent environment is considered as a non-stationary system. Second, most multi-agent environments only provide a shared team reward as feedback. As a result, agents may not be able to learn proper cooperative or competitive behaviors by traditional RL. Our algorithm adopts Evolution Strategies (ES) for optimizing policy which is used to control agents and a value decomposition method for estimating proper fitness for each policy. Evolutionary Algorithm is considered as a promising alternative for signal-agent problems. Owing to its simplicity, scalability, and efficiency on zeroth-order optimization, EAs can even outperform RLs on some tasks. In order to solve multi-agent problems by EA, a value decomposition method is used to decompose the team reward. Our method is parallel on multiple cores, which can speed up our algorithm significantly. We test our algorithm on two benchmarking environments, and the experiment results show that our algorithm is better than traditional RL and other representative gradient-free methods.


title: Binary Pigeon-Inspired Optimization for Quadrotor Swarm Formation Control

text: This paper proposes a binary pigeon-inspired optimization (BPIO) algorithm, for the quadrotor swarm formation control problem. The expected position is provided by the BPIO. Quadrotor moves to the position with control strategy, and the strategy is based on the proportional integral derivative (PID) control method. The BPIO algorithm which is based on pigeon-inspired optimization (PIO) algorithm can effectively solve the combination problem in the binary solution space. The BPIO keeps the fast convergence of the PIO, and can explore the space effectively at the same time. The parameters to be optimized are encoded with binary bits. A special fitness function is designed to avoid the happening of crash. The simulation experiment shows how the BPIO works. The results of simulation verify the feasibility and effectiveness of the BPIO to solve the swarm formation problem.


title: Econometric Algorithms Applied to the Incidence of Income on Satisfaction with Quality of Life in Latin American Capitals

text: The purpose of this research is to determine the incidence of income on the satisfaction of quality of life in Latin American capitals. The data was taken from the CAF Survey in 2017 (ECAF), made by Development Bank of Latin America. To do this, an econometric model was constructed which represents the relationship between the studied variables. Among the main findings, it was identified that, except for Mexico City, in all the analyzed cities a deteriorated state of health reduces the probability that the individual feels satisfied with his or her life. Therefore, health is a crucial element to increase the citizens’ perception of the quality of life in Latin American cities.


title: A New EDA with Dimension Reduction Technique for Large Scale Many-Objective Optimization

text: The performance of many-objective evolutionary algorithms deteriorates appreciably in solving large-scale many-objective optimization problems (MaOPs) which encompass more than hundreds variables. One of the known rationales is the curse of dimensionality. Estimation of distribution algorithms sample new solutions with a probabilistic model built from the statistics extracting over the existing solutions so as to mitigate the adverse impact of genetic operators. In this paper, an Gaussian Bayesian network-based estimation of distribution algorithm (GBNEDA-DR) is proposed to effectively tackle continued large-scale MaOPs. In the proposed algorithm, dimension reduction technique (i.e. LPP) is employed in the decision space to speed up the estimation search of the proposed algorithm. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different decision space dimensions, and achieves comparable results on some compared with many existing algorithms.


title: Site Selection of the Colombian Antarctic Research Station Based on Fuzzy-Topsis Algorithm

text: By 2025 the Republic of Colombia aims to be an advisory member of the Antarctic Treaty System (ATS) and the installation of a scientific station is necessary to upscale the scientific capabilities. The aim of this paper is showing the results of the implementation of a Fuzzy TOPSIS algorithm for site selection of the Colombian Antarctic Scientific Station. A three-phase methodology was proposed, and the obtained results allowed to identify the optimum location for the station, considering key success factors and regulatory constraints.


title: Methods of Machine Learning in System Abnormal Behavior Detection

text: The aim of the research is to develop mathematical and program support for detecting abnormal behavior of users. It will be based on analysis of their behavioral biometric characteristics. One of the major problems in UEBA/DSS intelligent systems is obtaining useful information from a large amount of unstructured, inconsistent data. Management decision-making should be based on real data collected from the analysed feature. However, based on the information received, it is rather difficult to make any management decision, as the data are heterogeneous and their volumes are extremely large. Application of machine learning methods in implementation of mobile UEBA/DSS system is proposed. This will make it possible to achieve a data analysis high quality and find complex dependencies in it. A list of the most significant factors submitted to the input of the analysing methods was formed during the research.


title: BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

text: Brain storm optimization algorithms (BSO) have shown great potential in many global black-box optimization problems. However, the existing BSO variants can suffer from three problems: (1) large-scale optimization problem; (2) hyperparameter optimization problem; (3) high computational cost of the clustering operations. To address these problems, in this paper, we propose a simple yet effective BSO variant named Brain Storm Optimization Algorithm with Cooperative Learning Strategy (BSO-CLS). It is inspired by the new ideas generating process of brain storm in which the participators propose their own ideas by cooperatively learning other participators’ ideas. Thus, BSO-CLS iteratively updates the candidate solutions by linearly combining other solutions with the weights deriving from the fitness values of other solutions. To validate the effectiveness of the proposed method, we test it on 6 benchmark functions with the 1000 dimensions. The experimental results show that BSO-CLS can outperform the vanilla BSO and the other BSO variant with the learning strategy.


title: Synchronized Swarm Operation

text: Physical swarm system, including number of units, operated in physical time according to corporative algorithm, is considered. It is shown, that for proper corporative algorithm interpretation it is necessary to synchronize computational processes in units. Structural-parametric model of synchronized swarm operation, based on Petri-Markov nets apparatus, is worked out. In the Petri-Markov net transitions are abstract analogues of synchronization procedure, while places simulate corporative algorithm parts interpretation by swarm units. Primary Petri-Markov model is transformed into complex semi-Markov process. Formulae for calculation of stochastic and time characteristics of the process are obtained. It is shown, that after transformation all methods of ordinary semi-Markov processes investigation may be used for synchronized systems. With use the concept of distributed forfeit effectiveness of synchronization is evaluated.


title: A Performance Class-Based Particle Swarm Optimizer

text: One of the main concerns with Particle Swarm Optimization (PSO) is to increase or maintain diversity during search in order to avoid premature convergence. In this study, a Performance Class-Based learning PSO (PCB-PSO) algorithm is proposed, that not only increases and maintains swarm diversity but also improves exploration and exploitation while speeding up convergence simultaneously. In the PCB-PSO algorithm, each particle belongs to a class based on its fitness value and particles might change classes at evolutionary stages or search step based on their updated position. The particles are divided into an upper, middle and lower. In the upper class are particles with top fitness values, the middle are those with average while particles in the bottom class are the worst performing in the swarm. The number of particles in each group is predetermined. Each class has a unique learning strategy designed specifically for a given task. The upper class is designed to converge towards the best solution found, Middle class particles exploit the search space while lower class particles explore. The algorithm’s strength is its flexibility and robustness as the population of each class allows us to prioritize a desired swarm behavior. The Algorithm is tested on a set of 8 benchmark functions which have generally proven to be difficult to optimize. The algorithm is able to be on par with some cutting edge PSO variants and outperforms other swarm and evolutionary algorithms on a number of functions. On complex multimodal functions, it is able to outperform other PSO variants showing its ability to escape local optima solutions.


title: Learning Automata-Based Fireworks Algorithm on Adaptive Assigning Sparks

text: Fireworks algorithm (FWA) is an emerging swarm intelligence inspired by the phenomenon of fireworks explosion. The numbers of sparks generated by fireworks have a great impact on the algorithm performance. It is widely accepted that promising fireworks should generate more sparks. However, in many researches, the quality of a firework is judged only on its current fitness value. This work proposes a Learning Automata-based Fireworks Algorithm (LA-FWA) introduced Learning automata (LA) to assign sparks for a better algorithm performance. Sparks are assigned to fireworks according to a state probability vector, which is updated constantly based on feedbacks from an environment so that it accumulates historical information. The probability vector converges as the search proceeds so that the local search ability of the LAFWA turns strong in the late search stage. Experimental results performed on CEC2013 benchmark functions show that the LAFWA outperforms several pioneering FWA variants.


title: A Multi-agent Ant Colony Optimization Algorithm for Effective Vehicular Traffic Management

text: An intelligent agent refers to an autonomous entity directing its activity towards achieving goals, acting upon an environment using data obtained with the help of a sensory mechanism. Intelligent agent software is a software system that performs tasks independently on behalf of a user in a networking environment based on user interface and past experiences. By the design of an intelligent sensing software program we can regulate the flow of traffic in a transportation infrastructure network. The problems leading to inefficiencies like loss of time, decrease in safety of vehicles and pedestrians, massive pollution, high wastage of fuel energy, degradation in the quality of life can be achieved by the optimized design. Ant Colony Optimization (ACO) has proven to be a very powerful optimization model for combinatorial optimization problems. The algorithm has the objective of regulating high real time traffic enabling every vehicle in the network with increased efficiency to minimize factors like time delay and traffic congestion.


title: Determinative Brain Storm Optimization

text: Brain Storm Optimization (BSO) is a swarm intelligence optimization algorithm, based on the human brainstorming process. The ideas of a brainstorming process comprise the solutions of the algorithm, which iteratively applies solution grouping, generation and selection operators. Several modifications of BSO have been proposed to enhance its performance. In this paper, we propose a novel modification enabling faster convergence of BSO to optimal solutions, without requiring setting an upper bound of algorithm iterations. It considers a brainstorming scenario where participating groups with similar ideas recognize that their ideas are similar, and together, collaborate for the determination of a better solution. The proposed modification, called Determinative BSO (DBSO), implements this scenario by applying a cluster merging strategy for merging groups of similar solutions, while following elitist selection. Experimental results using eleven benchmark functions show that the proposed modified BSO performs better than both the original and a state-of-the-art algorithm.


title: Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients

text: Human race is looking forward to an era where science and technology can wipeout the threats laid by lethal diseases. Major statistics shows that about 10 million people die from various forms of cancer annually. Every sixth death in the world is caused by cancer. Treatment to cancer always depend on its type and spread. Treatment includes single or combination of surgery, chemotherapy and radiation therapy. In this paper, survival prediction in prophylactic resection patients are carried out using various deep learning methods. Prophylactic resection has been found to be very effective in colon cancer, breast cancer and ovarian cancer. In this paper, we try to validate the results in a test environment using multi layered deep neural network. Classical Navie Bayer’s algorithm has been used to classify the dataset and convolution neural network (CNN) has been used to create the survival prediction model. Results affirm better survival results in prophylactic resection patients.


title: O-Flocking: Optimized Flocking Model on Autonomous Navigation for Robotic Swarm

text: Flocking model has been widely used in robotic swarm control. However, the traditional model still has some problems such as manually adjusted parameters, poor stability and low adaptability when dealing with autonomous navigation tasks in large-scale groups and complex environments. Therefore, it is an important and meaningful research problem to automatically generate Optimized Flocking model (O-flocking) with better performance and portability. To solve this problem, we design Comprehensive Flocking (C-flocking) model which can meet the requirements of formation keeping, collision avoidance of convex and non-convex obstacles and directional movement. At the same time, Genetic Optimization Framework for Flocking Model (GF) is proposed. The important parameters of C-flocking model are extracted as seeds to initialize the population, and the offspring are generated through operations such as crossover and mutation. The offspring model is input into the experimental scene of autonomous navigation for robotic swarms, and the comprehensive fitness function value is obtained. The model with smallest value is selected as the new seed to continue evolution repeatedly, which finally generates the O-flocking model. The extended simulation experiments are carried out in more complex scenes, and the O-flocking and C-flocking are compared. Simulation results show that the O-flocking model can be migrated and applied to large-scale and complex scenes, and its performance is better than that of C-flocking model in most aspects.


title: A Structural Testing Model Using SDA Algorithm

text: Path testing is the most needed and useful coverage criterion in structural testing. Tracing and obtaining the resultant paths is the main problem in path coverage testing. Evolutionary techniques are adopted in many software product evaluation methods such as generating and selection of input test data. The priority of the feasible paths is also to be determined. In this paper, we proposes an optimization algorithm for identifying the effective test data execution paths in control flow graph for the program module under test and finding the most efficient test paths using modified smell detection agent based optimization algorithm. New innovations are being conducted for bio-motivated algorithmic techniques from the characteristics of animal behavior. Smell detection agent based algorithm helps to identify most feasible paths and it uses sequential search to obtain all paths in a graph. The tester achieves the paths to be tested through a number of smell spot values from the source node to the target node. We will use control flow graph to produce perfect test paths and cyclomatic complexity number for obtaining the number of feasible test paths. The best feasible paths are prioritized using smell detection agent algorithm such that all the paths are thoroughly tested which ensures structural testing. This algorithm generates paths equal to the cyclomatic complexity. It can be illustrated that the proposed approach guarantees full path coverage.


title: Image Clustering by Generative Adversarial Optimization and Advanced Clustering Criteria

text: Clustering is the task that has been used in numerous applications including digital image analysis and processing. Image clustering refers to the problem of segmenting image for different purposes which leads to various clustering criteria. Finding the optimal clusters represented by their centers is a hard optimization problem and it is one of the main research focuses on clustering methods. In this paper we proposed a novel generative adversarial optimization algorithm for finding the optimal cluster centers while using standard and advance clustering criteria. The proposed method was tested on seven benchmark images and results were compared with the artificial bee colony, particle swarm optimization and genetic algorithm. Based on the obtained results, the generative adversarial optimization algorithm founded better cluster centers for image clustering compared to named methods from the literature.


title: The Research of Flexible Scheduling of Workshop Based on Artificial Fish Swarm Algorithm and Knowledge Mining

text: The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decision tree reasoning technique and data mining are often used in multi-objective optimization research to solve flexible job shop scheduling issues. Unfortunately, when job shop scheduling problems involve complex logic, it becomes difficult to implement data-driven automatic scheduling without human intervention. Based on the analysis of mass data and specialized knowledge in the scheduling domain, an ontology-based scheduling knowledge model and a method of knowledge representation can be established. Considering the relationship between data mining and knowledge, this paper illustrates the acquisition process of scheduling rules. These scheduling rules were applied to improve the initialization process of the artificial fish algorithm. Then, a scheduling experiment was designed, the results of which show that the efficiency and accuracy of the algorithm has been improved. The desired uncertain information analysis, decision-making support for production planning and scheduling on the shop floor are provided and an adaptive scheduling algorithm for complex manufacturing systems is established by building a knowledge-based system.


title: Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis

text: Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery.


title: Colour Quantisation by Human Mental Search

text: Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image which are then represented by a colour palette. The selection of appropriate entries in this palette is a challenging issue while the quality of the quantised image is directly related to the colour palette. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm. HMS is a recent population-based metaheuristic algorithm with three main operators: mental search to explore the vicinity of candidate solutions based on Levy flight, grouping to determine a promising region based on a clustering algorithm, and movement towards the best strategy. The performance of our proposed algorithm is evaluated on a set of benchmark images and in comparison to four conventional algorithms and seven soft computing-based colour quantisation algorithms. The obtained experimental results convincingly show that our proposed algorithm is capable of outperforming these approaches.


title: Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

text: Cargo management in all mode of transports like airlines, ships and trucks is a challenging task. The way in which an optimal allocation of packages in different containers are done using a software controlled method. An agent based software module is enabled as a service for the optimum allocation of cargo packages in the container terminals. There are multiple factors that will affect this allocation - size, shape, weight of the cargo packets and the container. When we design an optimal allocation module in a software these components need to be addressed along with capacity of the container. Hence, a multi-objective optimization algorithm will improve the performance of cargo management software. In this paper we suggest a Mixed Species Particle Swarm Optimisation (MSPSO) procedure for optimal allocation of cargo packages in containers of different size and capacity. The redesigned version of cargo management software performs well with search space on normal time complexity. The simulated results gives an improved optimised allocation than normalised allocation of cargo packets. The improved implementation performed better in terms of efficient cargo package allocation.


title: An Improved Bacterial Foraging Optimization with Differential and Poisson Distribution Strategy and its Application to Nurse Scheduling Problem

text: Bacterial Foraging Optimization (BFO) has been predominately applied to some real-world problems, but this method has poor convergence speed over complex optimization problems. In this paper, an improved Bacterial Foraging Optimization with Differential and Poisson Distribution strategies (PDBFO) is proposed to promote the insufficiency of BFO. In PDBFO, the step size of bacteria is segmented and adjusted in accordance with fitness value to accelerate convergence and enhance the search capability. Moreover, the differential operator and the Poisson Distribution strategy are incorporated to enrich individual diversity, which prevents algorithm from being trapped in the local optimum. Experimental simulations on eleven benchmark functions demonstrate that the proposed PDBFO has better convergence behavior in comparison to other six algorithms. Additionally, to verify the effectiveness of the method in solving the real-world complex problems, the PDBFO is also applied to the Nurse Scheduling Problem (NSP). Results indicate that the proposed PDBFO is more effective in obtaining the optimal solutions by comparing with other algorithms.


title: An Improved CMA-ES for Solving Large Scale Optimization Problem

text: In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems.


title: Multi-objective Dynamic Scheduling Model of Flexible Job Shop Based on NSGAII Algorithm and Scroll Window Technology

text: The production process is often accompanied by a lot of disturbances, which make it difficult for flexible job shop to execute production according to the original job plan. It is necessary to dynamically adjust the production plan according to real-time conditions. To this end, this paper proposes a multi-objective dynamic scheduling model. In this model, scroll window technology and NSGAII algorithm is adopted to adapt the dynamic production evironment. A specific chromosome retention strategy and a variable objective selection mechanism are designed to ensure that the proposed model can select different objectives according to different disturbance events to solve the optimal solution. Finally, a case test is used to verify the feasibility and effectiveness of the model.


title: A New Local Search Adaptive Genetic Algorithm for the Pseudo-Coloring Problem

text: Several applications result in a gray level image partitioned into different regions of interest. However, the human brain has difficulty in recognizing many levels of gray. In some cases, this problem is alleviated with the attribution of artificial colors to these regions, thus configuring an application in the area of visualization and graphic processing responsible for categorizing samples using colors. However, the task of making a set of distinct colors for these regions stand out is a problem of the NP-hard class, known as the pseudo-coloring problem (PsCP). In this work, it is proposed to use the well-known meta-heuristic Genetic Algorithm together with operators specialized in the local search for solutions as well as self-adjusting operators responsible for guiding the parameterization of the technique during the resolution of PsCPs. The proposed methodology was evaluated in two different scenarios of color assignment, having obtained the best results in comparison to the techniques that configure the state of the art.


title: A Genetic Algorithm-Based Solver for Small-Scale Jigsaw Puzzles

text: In this paper, we present a genetic algorithm-based puzzle solver, which is mainly used to solve small-scale puzzle problems. We introduce a new measurement function that improves its accuracy by normalizing the Mahalanobis distance and the Euclidean distance between two puzzle pieces. By calculating the difference between edges of two puzzle pieces and using the genetic algorithm to assemble pieces correctly, two “parent” solutions are merged into one improved “child” solution. Using the idea of local search, it avoids the problem of local optimum solutions brought by the genetic algorithm, which greatly improves the accuracy of the puzzle.


title: A Novel Biogeography-Based Optimization Algorithm with Momentum Migration and Taxonomic Mutation

text: Biogeography-based optimization (BBO) algorithm is not good at dealing with regions where function values change dramatically or barely. A novel biogeography-based optimization algorithm is proposed in this paper based on Momentum migration and taxonomic mutation. The momentum item is added to the original migration operation of BBO. It makes the algorithm more advantageous in dealing with regions where function values change dramatically or barely. At the same time, taxonomic mutation strategy divides the solutions into three categories: promising class, middle class and inferior class. Promising solutions do not take part in this mutation operation. Solutions of middle class use balanced differential mutation, and inferior solutions adopt exploration-biased random mutation. This strategy further increases the diversity of population. The simulation experiments are carried out with different types of CEC2014 benchmark functions. The proposed algorithm is compared with other algorithms and shows stronger global search ability, faster convergence speed and higher convergence accuracy.


title: A Novel Image Segmentation Based on Clustering and Population-Based Optimisation

text: Image segmentation is an essential step in image processing and computer vision with many image segmentation algorithms having been proposed in the literature. Among these, clustering is one of the prominent approaches to achieve segmentation. Traditional clustering algorithms have been used extensively for this purpose, although they have disadvantages such as dependence on initialisation conditions and a tendency to find only local optima. To overcome these disadvantages, population-based metaheuristic algorithms can be applied. In this paper, we propose a novel clustering algorithm based on human mental search (HMS) for image segmentation. HMS is a relatively new population-based metaheuristic inspired from the manner of searching in online auctions. HMS comprises three operators: mental search, which explores the neighbourhood of candidate solutions using Levy flight; grouping, which clusters candidate solutions; and moving candidate solutions towards a promising area. To verify the efficacy of the proposed algorithm, we conduct several experiments based on different criteria including mean cost function value, statistical analysis and image segmentation criteria. The obtained results confirm superior performance of our proposed algorithm compared to competitors.


title: Map Generation and Balance in the Terra Mystica Board Game Using Particle Swarm and Local Search

text: Modern board games offer an interesting opportunity for automatically generating content and models for ensuring balance among players. This paper tackles the problem of generating balanced maps for a popular and sophisticated board game called Terra Mystica. The complexity of the involved requirements coupled with a large search space makes of this a complex combinatorial optimisation problem which has not been investigated in the literature, to the best of the authors’ knowledge. This paper investigates the use of particle swarm optimisation and steepest ascent hill climbing with a random restart for generating maps in accordance with a designed subset of requirements. The results of applying these methods are very encouraging, fully showcasing the potential of search-based metaheuristics in procedural content generation.


title: Multi-objective Combinatorial Generative Adversarial Optimization and Its Application in Crowdsensing

text: With the increasing of the decision variables in multi-objective combinatorial optimization problems, the traditional evolutionary algorithms perform worse due to the low efficiency for generating the offspring by a stochastic mechanism. To address the issue, a multi-objective combinatorial generative adversarial optimization method is proposed to make the algorithm capable of learning the implicit information embodied in the evolution process. After classifying the optimal non-dominated solutions in the current generation as real data, the generative adversarial network (GAN) is trained by them, with the purpose of learning their distribution information. The Adam algorithm that employs the adaptively learning rate for each parameter is introduced to update the main parameters of GAN. Following that, an offspring reproduction strategy is designed to form a new feasible solution from the decimal output of the generator. To further verify the rationality of the proposed method, it is applied to solve the participant selection problem of the crowdsensing and the detailed offspring reproduction strategy is given. The experimental results for the crowdsensing systems with various tasks and participants show that the proposed algorithm outperforms the others in both convergence and distribution.


title: Case Classification Processing and Analysis Method for Respiratory Belt Data

text: Human respiratory signal is the important physiological indicator to reflect the physical condition. The respiratory belt, compared with the other human respiratory data measurement methods, has the advantages of being portable, cheap, non-invasive, etc. However, it is unclear which features of the breathing data can effectively classify the normal/abnormal state of breathing state. To solve the problem, we proposed a novel approach based on long-short-term-memory (LSTM) and breathing features of respiratory data. First, LSTM structure were used, then compared the result with the traditional method which extract the feature to experiment (in our paper which is RIE (ratio of inspiratory time to expiratory time)). In the end, a novel methodology proposed which combined the RIE feature with the LSTM structure. Experiment the three methods above using 342 normal and abnormal 24-h breathing data, the results show that the third method has higher classification accuracy.


title: Canine Algorithm for Node Disjoint Paths

text: Node Disjoint Paths (NDP) is one of the extensively studied Graph Theory problem. In this problem, the input is a directed n vertex graph and the set of source destination pair of vertices. The goal is to find the maximum number of paths connecting each such pair, so that such discovered paths are node-disjoint. In this paper, a novel Canine Inspired Algorithm is proposed which is a bio-inspired one, based on the olfactory capabilities of canines in tracing and reaching a destination. Currently many of the existing algorithms try to identify disjoint paths in a linear manner, whereas the Canine algorithm can be executed in a concurrent manner, depending on the number of canines deployed to find the disjoint paths. The time complexity of the algorithm is estimated to be [Formula: see text] . We hope that this algorithm finds many applications in problems related to various fields such as communication networks, scheduling and transportation and provides better results.


title: Target Tracking Algorithm Based on Density Clustering

text: The traditional Siamese network-based target tracking algorithm needs to use the convolution feature of the target to scan around the target location when predicting the location of the target in the next frame image, and perform similarity calculation to obtain the similarity score matrix with the highest score. It is the next frame target position. The highest similarity score often does not represent the precise target position of the target, which is often affected by the sliding step size during scanning. Aiming at this problem, this paper proposes a target tracking method based on density clustering. By combining the Siamese network to predict the next frame target position, and adding the target’s motion trajectory information, the direction of the target motion is given more weight, the other directions are given a smaller weight, and finally the target position is predicted by the density clustering method. The results show that the proposed algorithm effectively improves the accuracy of the target location prediction of the Siamese network when tracking targets.


title: A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem

text: The Dynamic Vehicle Routing Problem (DVRP) has many real-world applications and practical values. The objective of DVRP is to find the optimal routes for a fleet of vehicles to service the given customer requests, without violating the vehicle capacity constraint. In this paper, a hybrid algorithm is proposed for solving the DVRP with the objective to minimize the total distance of the vehicles. The Brain Storm Optimization in objective space (BSO-OS) is applied to guide the choice of different strategies for the periodic reoptimization of routes. In the BSO-OS procedure, Adaptive Large Neighborhood Search (ALNS) and Ant Colony System (ACS) are used to generate new solutions. The experiments on the DVRP benchmark and comparative studies are conducted, from which 12 out of 21 new best solutions are obtained by the proposed algorithm, and the other nine solutions are also very competitive. The experimental results show that the proposed algorithm is very effective and competitive.


title: The Improvement of V-Shaped Transfer Function of Binary Particle Swarm Optimization

text: Binary Particle Swarm Optimization (BPSO) is a swarm intelligence to optimize discrete space problems by extending the Particle Swarm Optimization. Its transfer function is the key element of BPSO. In this paper, a new V-shaped transfer function with a parameter k has been proposed. The parameter k was used to control the opening size of the transfer function. At first, the setting of the parameter k has been obtained by the experiments, and then the new V-shaped transfer with the optimal k value is compared with the other kinds of the V-shaped transfer functions by the experiment of feature selection. The results have indicated that the new V-shaped transfer function improved the performance of Binary Particle Swarm Optimization.


title: Optimal Reservoir Optimization Using Multiobjective Genetic Algorithm

text: Scarcity of fresh water resources has thrown various challenges to hydrologist. Optimum usage of resource is the only way out to handle this situation. Among the various water resources the most controllable one is the dam reservoirs. This paper deals with optimal reservoir optimization using multi objective genetic algorithm (MOGA). Various parameters like reservoir storage capacity, spill loss, evaporation rate, water used for irrigation, water used for electricity production, rate of inflow, outflow all need to be managed in an optimal way so that water levels are managed and resource specifications are met. This is normally managed using a software, but sudden change in scenarios and change in requirements cannot be handled by such softwares. Hence we are incorporating an optimised software layer to handle such situation. Multi objective genetic algorithm was able to optimise the water usage within the usage constrains. The results were assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. The simulated result shows that MOGA derived rules are promising and competitive and can be effectively used for reservoir optimization operations.


title: Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images

text: The detection of chest CT scan images of the lung play a key role in clinical decision making for some lung disease, such as tumors, pulmonary tuberculosis, solitary pulmonary nodule, lung masses and so on. In this paper, a novel automated CT scan image segmentation algorithm based on hybrid Ant Colony algorithm and snake algorithm is proposed. Firstly, traditional snake algorithm is used to detect the possible edge points of focal of a disease. Then Ant Colony Optimization (ACO) algorithm is applied to search the possible edge points of focal of a disease repeatedly. Finally, real edges can be extracted according to the intensity of pheromones. Simulation experiment results demonstrate that the proposed algorithm is more efficient and effective than the methods we compared it to.


title: A Method for Localization and Classification of Breast Ultrasound Tumors

text: Ultrasound instruments are suitable for large-scale examination of breast tumors, especially for women from Asian whose glands are dense. However, ultrasound images have the low contrast and resolution, blurred boundary and artifacts, which bring great difficulties to the interpretation of the junior doctor. However, traditional methods of breast ultrasound tumor recognition often use manually extracted features to gradually realize ROI region location and tumor classification with low accuracy, poor robustness and weak universality. Deep learning is limited to the location of tumor ROI region or the classification of a given tumor ROI region. In this paper, YOLOV3 algorithm is used for breast ultrasound tumor recognition, which could realize ROI localization and tumor classification at the same time. In addition, K-Means is optimized by K-Means++ and K-Mediods algorithm to generate anchor boxes of YOLOV3, and based on the Darknet-53 network structure of YOLOV3, ResNet and DenseNet are combined to design ResNet-DenseNet_Darknet-53. The proposed method is tested on the breast ultrasound tumor data set. Experiments show that the improved YOLOV3 algorithm shows better detection results on multiple evaluation indicators.


Looking for Brighter Days


title: Die Ausgangslage

text: Die psychotherapeutische Praxis in den deutschsprachigen Ländern entwickelte sich bisher in einem gesellschaftlichen Klima von Frieden und Sicherheit und konnte sich unter diesen Bedingungen als fester Bestandteil des Gesundheitssystems und der Gesellschaft etablieren. Im Gegensatz etwa zu den Arbeitsumgebungen von Kolleg*innen in Ländern des Nahen Ostens oder auch vielen afrikanischen Staaten wurde Psychotherapie bei uns auf dem Boden eines generellen Gefühls sozialer Stabilität und in Abwesenheit grundsätzlicher Gefahrenszenarien praktiziert. Durch globale Krisen wie zuletzt der Corona-Pandemie schwindet diese bisher selbstverständliche Basis und eine allgemeine, auch den/die Therapeut*in erfassende Verunsicherung zieht in die Praxen ein.


title: Verunsicherung und der Verlust an Eindeutigkeiten

text: Am Beispiel der Corona-Krise wird deutlich: Kollektive Gefahrenszenarien konfrontieren mit mangifach Unbekanntem und Ungewissen. Schon in „normalen“ Zeiten hat unsere Gesellschaft mit dem schmerzhaft erlebten Verlust von vermeidlichen Eindeutigkeiten zu tun, was z. B. in den teils hoch emotional geführten Zuwanderungs- oder Transgenderdebatten deutlich wird. Die reflexhaften unmittelbaren Reaktionen sind Verunsicherung und Unsicherheit als kollektive und individuelle Gefühlslagen.


title: Tiefenpsychologische Überlegungen

text: Persönliche Katastrophen der Unsicherheit, Verlassenheit und (Lebens-) Bedrohung, Erfahrungen von Verlust, Gewalt und Ohnmacht und deren Bewältigung bilden die zum großen Teil unbewusste Grundlage unserer Verarbeitung aktueller individueller oder gesellschaftlicher Krisen. Gleichzeitig treffen aktuelle Notszenarien aber auch auf gesellschaftlich-unbewusste Schichten, in denen die gesellschaftlichen Erfahrungen mit Kriegen, Umweltkatastrophen oder im Falle von Corona von Pest, Cholera und Spanischer Grippe abgelagert sind


title: Citrullinated histone H3, a marker of extracellular trap formation, is increased in blood of stable asthma patients

text: BACKGROUND: Emerging data indicates that extracellular traps (ETs), structures formed by various immune cell types, may contribute to the pathology of noninfectious inflammatory diseases. Histone hypercitrullination is an important step in ETs formation and citrullinated histone H3 (H3cit) is considered a novel and specific biomarker of that process. In the present study we have evaluated circulating H3cit in stable asthmatics and investigated its relationship with asthma severity, pulmonary function and selected blood and bronchoalveolar lavage (BAL) biomarkers. METHODS: In 60 white adult stable asthmatics and 50 well-matched controls we measured serum levels of H3cit. In asthmatics we also performed bronchoscopy with BAL. We analyzed blood and BAL biomarkers, including interleukin (IL)-4, IL-5, IL-6, IL-10, IL-12p70, IL-17A and interferon γ. For statistical analysis, Mann–Whitney U-test, χ(2) test, one-way ANCOVA, ROC curve analysis and univariate linear regression were applied. Independent determinants of H3cit were established in a multiple linear regression model. RESULTS: Asthma was characterized by elevated circulating H3cit (17.49 [11.25–22.58] vs. 13.66 [8.66–18.87] ng/ml, p = 0.03). In asthmatics positive associations were demonstrated between serum H3cit and lung function variables, including total lung capacity (TLC) (β = 0.37 [95% CI 0.24–0.50]) and residual volume (β = 0.38 [95% CI 0.25–0.51]). H3cit was increased in asthma patients receiving systemic steroids (p = 0.02), as well as in subjects with BAL eosinophilia above 144 cells/ml (p = 0.02). In asthmatics, but not in controls, circulating H3cit correlated well with number of neutrophils (β = 0.31 [95% CI 0.19–0.44]) and monocytes (β = 0.42 [95% CI 0.29–0.55]) in peripheral blood. Furthermore, BAL macrophages, BAL neutrophils, TLC, high-sensitivity C-reactive protein, Il-12p70 and bronchial obstruction degree were independent determinants of H3cit in a multivariate linear regression model. CONCLUSIONS: Asthma is characterized by increased circulating H3cit likely related to the enhanced lung ETs formation. Inhibition of ETs might be a therapeutic option in selected asthma phenotypes, such as neutrophilic asthma.


title: Acute suppurative thyroiditis with transient hyperthyroidism in thalassemia child: an uncommon association in a common disease—a rare case report

text: BACKGROUND: Thyroid infection is rare in paediatric age group in anatomically normal thyroid gland. However, acute suppurative thyroiditis (AST) is common in high-risk cases like thyroglossal duct remnant and thyroglossal pyriform sinus fistula. This may be associated with variable thyroid dysfunctions. Though transient hyperthyroidism is reported in some cases, its association was not documented in thalassemia child. CASE PRESENTATION: We are reporting an 8-year-old male child with a history of fever, pain, and left neck swelling for 5 days. He was evaluated and empirically started intravenous antibiotics (ceftriaxone, cloxacillin, and amikacin) along with other supportive treatment for localized signs of thyroid infection. Ultrasonography of the neck revealed intercommunicating pockets of collections with internal echoes in the left thyroid lobe. CT neck was done and suggested that the left lobe of the thyroid gland was replaced by hypodense lesions of fluid attenuation without any congenital abnormality of the thyroid gland. His thyroid function tests were suggestive of biochemical hyperthyroidism. He was treated with intravenous antibiotics and ultrasonography-guided fine-needle aspiration of pus. His pus culture showed growth of methicillin resistant Staphylococcus aureus. He also had microcytic hypochromic anaemia which was evaluated and found to have thalassemia trait. His biochemical hyperthyroidism normalized after completion of 2 weeks of treatment and required no intervention. CONCLUSION: Acute suppurative thyroiditis is an uncommon clinical condition in an anatomically normal thyroid gland which could be associated with thyroid dysfunction. Its association in a thalassemia child is also not documented in literature. Clinical evaluation and proper history taking and non-invasive thyroid imaging are the cornerstone for diagnosis. Antibiotics and pus drainage are the mainstay management. Associated biochemical hyperthyroidism is transient and resolves without any specific medication in asymptomatic cases.


title: Half a century and more of PhD theses by published papers: Comment on: “Bringing the doctoral thesis by published papers to the Social Sciences and the Humanities: A quantitative easing? A small study of doctoral thesis submission rules and practice in two disciplines in the UK” by John Rigby and Barbara Jones in Scientometrics published online 15-May-2020

text: The recent article by Rigby and Jones in Scientometrics (15-May-2020) again draws attention to basing PhD-theses on published works, in their case introducing the system into the Social Sciences and Humanities. In this short communication we endeavour to provide additional information that is essential for this debate.


A crisis of accountability for women's, children's, and adolescents' health


title: Einleitung

text: Durch immer komplexer werdende Baumaßnahmen, bei gleichzeitig wachsendem Termin- und Kostendruck, sowie steigenden Ansprüchen an die Qualität, kommt es in der baugewerblichen Praxis immer häufiger zu gestörten Abläufen. Zurückzuführen ist dies auf fehlende Pufferzeiten, die teilweise bereits in der Planungsphase gestrichen werden, um den Ansprüchen des Auftrages gerecht werden zu können. Eine Bauablaufstörung liegt immer dann vor, wenn von der eigentlich vertraglich festgelegten Soll-Leistung abgewichen werden muss, unabhängig davon, wer diese Abweichung zu vertreten hat. Um den wirtschaftlichen Erfolg der Maßnahme zu sichern und eine verantwortungsgerechte Kostenallokation herzustellen, kann die Seite, welche die Störung nicht zu verantworten hat, berechtigte Mehrkosten zurückfordern oder die Erstattung des entstandenen Schadens von der anderen Partei verlangen.


title: Baubetriebliche und baurechtliche Grundlagen

text: Im zweiten Kapitel wird zunächst die Bedeutung der Bauzeit für die einzelnen am Bau beteiligten Parteien dargelegt, bevor die verschiedenen Bauablaufstörungen klassifiziert werden. Zudem erfolgt eine Erläuterung zu den möglichen Anspruchsgrundlagen des Auftragnehmers für eine Erstattung von Mehrkosten aus einem gestörten Bauablauf. Dieser kann in dem Verantwortungsbereich des Auftraggebers liegen oder aus höherer Gewalt resultieren. In einem weiteren Schritt wird auf die aktuelle Rechtsprechung des Bundesgerichtshofes eingegangen und die Mindestanforderungen dargelegt, die dieser an die Nachweis- bzw. Beweisführung stellt.


title: Concluding Considerations and Directions for Future Research

text: luxury fashion firms compete in an environment in which two different sets of competitive variables, traditional and new, coexist. However, these two sets are coherent and complementary within their own boundaries and less so with each other and challenge firms willing to design a new competitive landscape; luxury fashion firms are tightropes in between zeroing the brand and respecting the heritage; in between craftmanship, increased number of collections and sustainability; in between street style, increased number of collections and perception of uniqueness and rarity; and in between speed and creativity; additional studies are needed to understand how Maisons will cope with the above trade-offs, which variables will lead to superior competitive advantage, how new technologies and their usage will shape competitive advantaged.


title: An Overview of the Luxury Fashion Industry

text: provides a definition of luxury and a detailed discussion of how it has changed in time till arriving at the concepts of new luxury and zeroing; offers an overview of the industry, its performance and its major regional trends; concludes with a description of the industry’s concentration process driven by merger and acquisitions.


title: Conclusion

text: This conclusion of the compendium of essays by Anthony E. Clark summarizes the content and significance of his research on the history of Catholicism in China. It was written as the COVID-19 virus was sweeping across the globe, and Clark reflects upon the comparisons between the virus’ outbreak at Wuhan in 2019 and two French Catholic missionaries, François-Régis Clet, and Jean-Gabriel Perboyre, who were martyred in the Wuchang district of Wuhan in the mid-nineteenth century. Also considered in this conclusion is the trend among scholars to depict China’s relationship with the West and Christianity as one of “conflict” or “cooperation,” highlighting the two extremes of either irreconcilable difference or congruous sameness. Clark concludes this collection of essays with the suggestion that the historical exchange between China and the West has been rather an admixture of conflict and cooperation, but defined mostly as a relationship of friendship.


title: Pathophysiology and therapy of systemic vasculitides

text: Systemic vasculitides represent uncommon conditions characterized by the inflammation of blood vessels that can lead to different complex disorders limited to one organ or potentially involving multiple organs and systems. Systemic vasculitides are classified according to the diameter of the vessel that they mainly affect (small, medium, large, or variable). The pathogenetic mechanisms of systemic vasculitides are still partly unknown, as well as their genetic basis. For most of the primary systemic vasculitides, a single gold standard test is not available, and diagnosis is often made after having ruled out other mimicking conditions. Current research has focused on new management protocol and therapeutic strategies aimed at improving long-term patient outcomes and avoiding progression to multiorgan failure with irreversible damage. In this narrative review, authors describe different forms of systemic vasculitides through a review of the literature, with the aim of highlighting the current knowledge and recent findings on etiopathogenesis, diagnosis and therapy.


Commentary: Mentoring Trainees When the Going Gets Tough


Assisted reproduction and COVID-19: A joint statement of ASRM, ESHRE and IFFS


Commentary: Let’s Just Celebrate This Win


Commentary: Cardiac Surgery in North America: Facing an inevitable “new normal”?


Original Article Highlights From This Issue


title: From Small Association to Global Pundit: 2009–2020

text: When Jean Todt became Fédération Internationale de l’Automobile (FIA) president in 2009, he made good use of the Mosley legacy. At the same time, he engineered a series of new reforms that would mark the greatest difference between the old and the new FIA. This chapter explores a selection of organisational innovations during Todt’s three presidential periods (2009–2021). This selection includes two presidential elections in 2009 and 2013 which had a hitherto unrivalled emphasis on good governance, a political debacle (the F1 Bahrain Grand Prix), a structural revamp (the establishing the FIA Statutes Review), the introduction of an external audit (aided by consulting firm, Deloitte), and a proactive media strategy to comprehend a media logic (by publishing AUTO magazine from 2012 onwards). Together, this combination of substantive and symbolic actions represents the FIA’s desire to renew its legitimacy among its members, motorists, and other stakeholders.


Special issue in honor of John C. Rothwell


Commissaris krijgt de regie


José Geertsema


title: The Sars-Cov-2 Pandemic and the Brave New Digital World of Environmental Enrichment to Prevent Brain Aging and Cognitive Decline

text: Individuals experiencing brain aging, cognitive decline, and dementia are currently confronted with several more complex challenges due to the current Sars-Cov-2 pandemic as compared to younger and cognitively healthy people. During the first six months of the pandemic, we are experiencing critical issues related to the management of mild cognitive impairment (MCI) and dementia. The evolving, highly contagious global viral spread has created a pressure test of unprecedented proportions for the existing brain health care infrastructure and related services for management, diagnosis, treatment, and prevention. Social distancing and lock-down measures are catalyzing and accelerating a technological paradigm shift, away from a traditional model of brain healthcare focused on late symptomatic disease stages and towards optimized preventive strategies to slow brain aging and increase resilience at preclinical asymptomatic stages. Digital technologies transform global healthcare for accessible equality of opportunities in order to generate better outcomes for brain aging aligned with the paradigm of preventive medicine.


Maurice van den Boch


title: Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market

text: The Covid-19 pandemic and the social distancing that followed have affected all walks of society, also education. In order to keep education running, educational institutions have had to quickly adapt to the situation. This has resulted in an unprecedented push to online learning. Many, including commercial digital learning platform providers, have rushed to provide their support and ‘solutions’, sometimes for free. The Covid-19 pandemic has therefore also created a sellers’ market in ed-tech. This paper employs a critical lens to reflect on the possible problems arising from hasty adoption of commercial digital learning solutions whose design might not always be driven by best pedagogical practices but their business model that leverages user data for profit-making. Moreover, already before Covid-19, there has been increasing critique of how ed-tech is redefining and reducing concepts of teaching and learning. The paper also challenges the narrative that claims, ‘education is broken, and it should and can be fixed with technology’. Such technologization, often seen as neutral, is closely related to educationalization, i.e. imposing growing societal problems for education to resolve. Therefore, this is a critical moment to reflect how the current choices educational institutions are making might affect with Covid-19 education and online learning: Will they reinforce capitalist instrumental view of education or promote holistic human growth? This paper urges educational leaders to think carefully about the decisions they are currently making and if they indeed pave the way to a desirable future of education.


title: A Phase 3 Open-label, Randomized, Controlled Study to Evaluate the Efficacy and Safety of Intravenously Administered Ravulizumab Compared with Best Supportive Care in Patients with COVID-19 Severe Pneumonia, Acute Lung Injury, or Acute Respiratory Distress Syndrome: A structured summary of a study protocol for a randomised controlled trial

text: OBJECTIVES: Primary Objective • To evaluate the effect of ravulizumab, a long-acting complement (C5) inhibitor plus best supportive care (BSC) compared with BSC alone on the survival of patients with COVID-19. Secondary Objectives • Number of days free of mechanical ventilation at Day 29 • Duration of intensive care unit stay at Day 29 • Change from baseline in Sequential Organ Failure Assessment (SOFA) score at Day 29 • Change from baseline in peripheral capillary oxygen saturation/ fraction of inspired oxygen (SpO2 /FiO2) at Day 29 • Duration of hospitalization at Day 29 • Survival (based on all-cause mortality) at Day 60 and Day 90 Safety • Incidence of treatment-emergent adverse events and treatment-emergent serious adverse events. PK/PD/Immunogenicity • Change in serum ravulizumab concentrations over time • Change in serum free and total C5 concentrations over time • Incidence and titer of anti-ALXN1210 antibodies Biomarkers • Change in absolute level of soluble biomarkers in blood associated with complement activation, inflammatory processes, and hypercoagulable states over time Exploratory • Incidence of progression to renal failure requiring dialysis at Day 29 • Time to clinical improvement (based on a modified 6-point ordinal scale) over 29 days • SF-12 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores at Day 29 (or discharge), Day 60, and Day 90 • EuroQol 5-dimension 5-level (EQ-5D-5L) scores at Day 29 (or discharge), Day 60, and Day 90 TRIAL DESIGN: This is a multicenter Phase 3, open-label, randomized, controlled, study. The study is being conducted in acute care hospital settings in the United States, United Kingdom, Spain, France, Germany, and Japan. PARTICIPANTS: Male or female patients at least 18 years of age, weighing ≥ 40 kg, admitted to a designated hospital facility for treatment will be screened for eligibility in this study. Key Inclusion criteria • Confirmed diagnosis of SARS-CoV-2 infection (eg, via polymerase chain reaction [PCR] and/or antibody test) presenting as severe COVID-19 requiring hospitalization • Severe pneumonia, acute lung injury, or ARDS confirmed by computed tomography (CT) or X-ray at Screening or within the 3 days prior to Screening, as part of the patient’s routine clinical care • Respiratory distress requiring mechanical ventilation, which can be either invasive (requiring endotracheal intubation) or non-invasive (with continuous positive airway pressure [CPAP] or bilevel positive airway pressure [BiPAP]) Key Exclusion criteria • Patient is not expected to survive for more than 24 hours • Patient is on invasive mechanical ventilation with intubation for more than 48 hours prior to Screening • Severe pre-existing cardiac disease (ie, NYHA Class 3 or Class 4, acute coronary syndrome, or persistent ventricular tachyarrhythmias) • Patient has an unresolved Neisseria meningitidis infection Excluded medications and therapies • Current treatment with a complement inhibitor • Intravenous immunoglobulin (IVIg) within 4 weeks prior to randomization on Day 1 Excluded prior/concurrent clinical study experience • Treatment with investigational therapy in a clinical study within 30 days before randomization, or within 5 half-lives of that investigational therapy, whichever is greater • Exceptions a. Investigational therapies will be allowed if received as part of best supportive care through an expanded access protocol or emergency approval for the treatment of COVID-19. b. Investigational antiviral therapies (such as remdesivir) will be allowed even if received as part of a clinical study. INTERVENTION AND COMPARATOR: The study consists of a Screening Period of up to 3 days, a Primary Evaluation Period of 4 weeks, a final assessment at Day 29, and a Follow-up Period of 8 weeks. For patients randomized to ravulizumab plus BSC, a weight-based dose of ravulizumab (≥40 to < 60 kg/2400 mg, 60 to < 100 kg/2700 mg, ≥ 100 kg/3000 mg) will be administered on Day 1. On Day 5 and Day 10, additional doses of 600 mg (≥40 to <60 kg) or 900 mg (>60 kg) ravulizumab will be administered and on Day 15 patients will receive 900 mg ravulizumab. There is no active or placebo comparator in this open-label clinical trial. The total duration of each patient’s participation is anticipated to be approximately 3 months. MAIN OUTCOMES: The primary efficacy outcome of this study is survival (based on all-cause mortality) at Day 29. RANDOMISATION: Patients will be randomized in a 2:1 ratio (ravulizumab plus BSC:BSC alone). Randomization will be stratified by intubated or not intubated on Day 1. Computer-generated randomization lists will be prepared by a third party under the direction of the sponsor. Investigators, or designees, will enrol patients and then obtain randomization codes using an interactive voice/web response system. The block size will be kept concealed so that investigators cannot select patients for a particular treatment assignment. Blinding (masking): This is an open-label study. Numbers to be randomised (sample size): Approximately 270 patients will be randomly assigned in a 2:1 ratio to ravulizumab plus BSC (n=180) or BSC alone (n=90). TRIAL STATUS: Protocol Number: ALXN1210-COV-305 Original Protocol: 09 Apr 2020 Protocol Amendment 1 (Global): 13 Apr 2020 Protocol Amendment 2 (Global): 17 Apr 2020 Protocol Amendment 3 (Global): 09 Jun 2020 Recruitment is currently ongoing. Recruitment was initiated on 11 May 2020. We expect recruitment to be completed by 30 Nov 2020. TRIAL REGISTRATION: Clinicaltrials.gov: Protocol Registry Number: NCT04369469; First posted; 30 Apr 2020 EU Clinical Trials Register: EudraCT Number: https://www.clinicaltrialsregister.eu/ctr-search/search?query=ALXN1210-COV-305, Start date: 07 May 2020 FULL PROTOCOL: The full redacted protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.


title: Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

text: Prediction of financial time series is a great challenge for statistical models. In general, the stock market times series present high volatility due to its sensitivity to economic and political factors. Furthermore, recently, the covid-19 pandemic has caused a drastic change in the stock exchange times series. In this challenging context, several computational techniques have been proposed to improve the performance of predicting such times series. The main goal of this article is to compare the prediction performance of five neural network architectures in predicting the six most traded stocks of the official Brazilian stock exchange B3 from March 2019 to April 2020. We trained the models to predict the closing price of the next day using as inputs its own previous values. We compared the predictive performance of multiple linear regression, Elman, Jordan, radial basis function, and multilayer perceptron architectures based on the root of the mean square error. We trained all models using the training set while hyper-parameters such as the number of input variables and hidden layers were selected using the testing set. Moreover, we used the trimmed average of 100 bootstrap samples as our prediction. Thus, our approach allows us to measure the uncertainty associate with the predicted values. The results showed that for all times series, considered all architectures, except the radial basis function, the networks tunning provide suitable fit, reasonable predictions, and confidence intervals.


'Ons past bescheidenheid en trots': Noord-Nederland koos eigen weg in coronacrisis


Overmacht door corona


Gedwongen het schip verlaten


KORT


Impact of the Covid-19 Pandemic on Early Childhood Care and Education


title: A novel control set-valued approach with application to epidemic models

text: This work is considered in the framework of studies dedicated to the control problems, especially in epidemiology where the scientist are concerned to develop effective control strategies to minimize the number of infected individuals. In this paper, we set this problem as an asymptotic target control problem under mixed state-control constraints, for a general class of ordinary differential equations that model the temporal evolution of disease spread. The set of initial data, from which the number of infected people decrease to zero, is generated by a new type of Lyapunov functions defined in the sense of viability theory. The associated controls are provided via selections of adequately designed feedback map. The existence of such selections is improved by using Micheal selection theorem. Finally, an application to the SIRS epidemic model, with numerical simulations, is given to show the efficiency of our approach. To the best of our knowledge, our work is the first one that used a set-valued approach based on the viability theory to deal with an epidemic control problem.


title: Kompetenznetz Public Health zu COVID-19

text: Das „Kompetenznetz Public Health zu COVID-19“ ist ein Ad hoc-Zusammenschluss mehrerer wissenschaftlicher Fachgesellschaften und Verbände aus dem Bereich Public Health, die somit ihre methodische, epidemiologische, statistische, sozialwissenschaftliche und (bevölkerungs-) medizinische Fachkenntnis bündeln. Wissenschaftler aus Deutschland, Österreich und der Schweiz arbeiten daran, schnell interdisziplinäre Expertise zu COVID-19 für die aktuelle Diskussion und Entscheidungsfindung zur Verfügung zu stellen. Dafür werden wissenschaftliche Erkenntnisse zusammengestellt, aufbereitet und in möglichst leicht verständlicher Form verbreitet.


title: Research trends and scientific analysis of publications on burnout and compassion fatigue among healthcare providers

text: BACKGROUND: Burnout and compassion fatigue are closely related concepts. Burnout is thought to develop from occupational stress while compassion fatigue results from being in a caregiver role leading to inability to get engaged in a caring relation. The objective of the current study was to investigate research trends, themes, citations, and key players of publications on burnout and compassion fatigue among healthcare providers. METHODS: A validated search query was developed and implemented in the Scopus database. The study period was all times up to 2019. RESULTS: Research query found 4416 publications. Publications started in 1978. Steep growth in the number of publications was observed in the last decade. There were seven research themes in the retrieved publications; six for burnout and one for compassion fatigue. Approximately 36% of the retrieved publications were about nurses; 36% were about physicians, 10% were about medical residents and the remaining did not include a specific profession. The retrieved publications received an average of 22.2 citations per article. Four of the top 10 active journals were in the field of nursing and two in the field of general medicine, one in public health, one in neurology, one in psychology, and one was multidisciplinary. The USA ranked first with 1292 (29.3%) articles followed by Spain (n = 248; 5.6%) and the UK (n = 247; 5.6%). Mayo Clinic was the most active institution (n = 93; 2.1%) followed by Harvard University (n = 46; 1.0%) and University of Washington, Seattle (n = 45; 1.0%). A total of 16,108 authors participated in publishing the retrieved documents, an average of 3.6 authors per article. Shanafelt, T.D. was the most active author (n = 78; 1.8%) followed by Dyrbye, L.N. (n = 43; 1.0%), and West, C.P. (n = 37; 0.8%). A total of 472 (10.7%) articles declared funding. CONCLUSION: The current study was carried out to draw attention to the wellbeing of healthcare providers. Retrieved literature was dominated by high-income countries. Lack of information from low- and middle-income countries will hinder planning for interventional strategies and will negatively affect the health system and the patients. Health researchers in low- and middle-income countries need to focus on burn out and compassion fatigue.


title: Meeting report of the third annual Tri-Service Microbiome Consortium symposium

text: The Tri-Service Microbiome Consortium (TSMC) was founded to enhance collaboration, coordination, and communication of microbiome research among U.S. Department of Defense (DoD) organizations and to facilitate resource, material and information sharing among consortium members. The 2019 annual symposium was held 22–24 October 2019 at Wright-Patterson Air Force Base in Dayton, OH. Presentations and discussions centered on microbiome-related topics within five broad thematic areas: 1) human microbiomes; 2) transitioning products into Warfighter solutions; 3) environmental microbiomes; 4) engineering microbiomes; and 5) microbiome simulation and characterization. Collectively, the symposium provided an update on the scope of current DoD microbiome research efforts, highlighted innovative research being done in academia and industry that can be leveraged by the DoD, and fostered collaborative opportunities. This report summarizes the presentations and outcomes of the 3rd annual TSMC symposium.


title: Recommendations for the surveillance of multidrug-resistant bacteria in Italian long-term care facilities by the GLISTer working group of the Italian Association of Clinical Microbiologists (AMCLI)

text: Long-term care facilities (LTCFs) are an important reservoir of multidrug-resistant organisms (MDROs). Colonization of LTCF residents by MDROs is generally higher in Italy compared to other European countries. The present review by the working group for the study of infections in LTCFs (GLISTer) of the Italian Association of Clinical Microbiologists (AMCLI) aims to propose criteria for a laboratory-based surveillance of MDROs in Italian LTCFs. We recommend the adhesion to three levels of laboratory-based MDROs surveillance in LTCFs: i) mandatory MDRO surveillance by cumulative retrospective analysis of antimicrobial susceptibility data, obtained as part of routine care of clinical specimens. ii) strongly recommended surveillance by active rectal swab cultures or molecular screening to determine colonization with carbapenemase-producing Enterobacterales, should a resident be proven infected. iii) voluntary surveillance by prospective MDRO surveys, mainly based on point prevalence colonization studies, allowing to determine the MDROs baseline prevalence in the facility. Laboratory-based surveillance of MDROs in LTCFs is aimed at providing useful epidemiological information to healthcare providers operating in the facility, but it is only effective if the collected data are used for infection prevention and control purposes, targeting the peculiar aspects of LTCFs.


title: Transcription Factor Nrf2 as a Potential Therapeutic Target for Prevention of Cytokine Storm in COVID-19 Patients

text: Nrf2 is a key transcription factor responsible for antioxidant defense in many tissues and cells, including alveolar epithelium, endothelium, and macrophages. Furthermore, Nrf2 functions as a transcriptional repressor that inhibits expression of the inflammatory cytokines in macrophages. Critically ill patients with COVID-19 infection often present signs of high oxidative stress and systemic inflammation – the leading causes of mortality. This article suggests rationale for the use of Nrf2 inducers to prevent development of an excessive inflammatory response in COVID-19 patients.


title: Führung virtueller Teams

text: Eine gute Führungskraft zeichnet sich durch den souveränen und den konstruktiven Umgang mit gesellschaftlichen und unternehmerischen Herausforderungen sowie ein hohes Maß an Selbstreflexion aus. Dennoch nehmen die Anforderungen an die Führung zahlenmäßig und an Komplexität konstant zu. Dies kann einen Manager im digitalen Zeitalter schnell überfordern. Die Abb. 5.1 zeigt nur einen Ausschnitt der zahlreichen Herausforderungen, mit denen sich Führungskräfte konfrontiert sehen. Dazu zählt die Führung virtueller Teams, für die neben neuen Vorgehensweisen auch ein hohes Maß an Selbstreflexion erforderlich ist. Selbst Bundeskanzlerin Angela Merkel reagiert aktuell (04/2020) aufgrund von COVID-19 das Land aus dem Homeoffice und steht vor der gleichen Herausforderung.


title: Zusammenfassung

text: Seit mittlerweile fünf Jahren arbeite ich fast ausschließlich im Homeoffice bzw. in virtuellen Teams und habe auch eine wissenschaftliche Arbeit zu diesem Themenbereich geschrieben. Ich war viel unterwegs und habe von allen Ecken der Welt aus gearbeitet, bis mich COVID-19 gezwungen hat, meine Reisen zu unterbrechen. Dies gab mir Zeit, die praktischen wie auch die wissenschaftlichen Erfahrungen zusammenzufassen.


title: Einleitung

text: Unter einem virtuellen Team wird im Allgemeinen die Zusammenarbeit einer Gruppe von Personen auf Distanz mithilfe von Technologie verstanden. Anfang der 2000er Jahre wurde dies zumeist nur temporär praktiziert als Zusammenschluss mehrerer Fachkräfte für ein bestimmtes Projekt oder für einen entsprechenden Zeitraum zur Erarbeitung und zur Durchführung bestimmter Projekte. Vor allem in den letzten fünf Jahren kommt es zunehmend häufig vor, dass ganze Abteilungen dauerhaft durch virtuelle Teams repräsentiert werden oder dass diese von zwei Standorten des Unternehmens aus zusammenarbeiten.


title: Technologische Ausstattung virtueller Teams

text: In diesem Kapitel sollen die benötigte technologische Ausstattung in Form von Software sowie die notwendige Technologie für Videokonferenzen im Homeoffice behandelt werden. Das Kapitel schließt mit Empfehlungen zur Einrichtung des Homeoffice und einem Exkurs zu VR-Meetings.


title: Traditional Chinese Medicine and Clinical Pharmacology

text: Traditional Chinese Medicine (TCM) represents thousands of years in treatment protocols for health, healing, and longevity and has evolved into a complex healthcare system. The following chapter is designed to give a basic concise overview of TCM principles and practices, in relation to clinical pharmacology, and focuses on mutually relevant areas of interest. Much as society learned to harness electrical currents to empower machinery, TCM theories of energetic patterns help to empower the functionality of the human body. The major theories include the vital life force, known as Qi, Yin and Yang, Five Elements, herbal medicine, multivariate diagnostics, syndrome differentiation, and acupuncture, among others. In TCM, there is a direct relationship to the energy of nature and the energetic patterns within the human body and the human consciousness. This comprehensive approach is gaining momentum in clinical and network pharmacology, as new technologies emerge regarding TCM collaborative compounds versus singular chemical constituents in treatment modalities. Of major importance to clinical pharmacology are recent TCM evidenced-based empirical studies, correlations, and interactions between herbs and pharmaceuticals, updated data on toxicity, adverse reactions, quality assurance, and herbal medicine standardization. Global concerns over antimicrobial resistance to drugs have put a spotlight on TCM herbal medicines as alternatives with greatly reduced resistance factors. From ancient archives to the Nobel Prize to a formal endorsement by the World Health Organization as a global healthcare system, TCM has widened the lens of modern science. It offers the wisdom and experience of millennia to inspire the think tank of today’s medicine and pharmacology and tomorrow’s generations of integrative healthcare advancements.


title: Techniques of Gastrointestinal Endoscopy—Do We Need to Change Anything?

text: Novel coronavirus disease 2019 (COVID-19) has spread to different parts of the world and was declared a pandemic by World Health Organization (WHO). Health care workers are at increased risk of contracting the disease due to their nature of work and close contact with the patients. Staff in endoscopy need to be aware of this risk due to the aerosol-generating nature of procedures and the presence of virus particles in stool samples of infected persons. The risk of asymptomatic patients spreading the disease is also a cause for concern. This article intends to provide guidance and recommendations for techniques and practice of gastrointestinal (GI) endoscopy to prevent infection in endoscopy unit.


Infection Control Practices in Endoscopy Room in COVID-19 Era


Kickstarting the Endoscopy Unit after Lockdown Period: Why, When, and How?


title: Risk of COVID-19 Transmission During Gastrointestinal Endoscopy

text: COVID-19 is a viral illness caused by a novel severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). It is highly contagious virus with human-to-human transmission being primarily attributed to direct contact or air droplets. It is highly infectious and contagious due to high-viable viral loads in the upper respiratory tract secretions of infected patients and also similar viral load being detected in both asymptomatic and symptomatic patients. The virus stability characteristics and modes of transmission via fomites and air droplets make gastrointestinal (GI) endoscopy a high-risk procedure for COVID-19 transmission. This review discusses the currently available literature on risk of transmission of SARS-CoV-2 during GI endoscopy.


title: Personal Protective Equipment during Endoscopy

text: The novel coronavirus disease 2019 (COVID-19) which originated in China has been declared a pandemic by the World Health Organization (WHO). This virus gets transmitted through air droplets and direct contact. Health care workers doing aerosol-generating procedures are at a higher risk of acquiring the infection. Many procedures done by the gastrointestinal endoscopists are classified as aerosol-generating procedures, which in turn underline the need for proper safety precautions during these procedures. Apart from general safety measures advised by various organizations, proper use of personal protective equipment (PPE) is a pivotal factor in safeguarding health care personnel during endoscopy. This article provides a short overview of the different PPEs available and their proper use in endoscopy.


title: Triaging Patients Undergoing Endoscopy During COVID-19 Pandemic

text: "Triaging for endoscopy" is the process to determine the most deserving patients after careful scrutiny amongst a pool of patients who report for Gastrointestinal (GI) Endoscopy diagnostic or interventional procedures that include Esophago-gastro-duodenoscopy (OGD), Colonoscopy, ERCP, EUS and Enteroscopy. The purpose of triaging at endoscopy is to prevent transmission of SARS-COV2 infection from suspected patient to the health care workers involved at endoscopy. The three professional Indian Gastroenterology bodies namely Society of Gastrointestinal Endoscopy of India (SGEI), Indian Society of Gastroenterology (ISG) & Indian Association for the study of the liver (INASL) have recently provided guidance of scheduling of endoscopic procedures in the current Covid-9 pandemic. Similar suggestions have been promulgated by other international bodies.


title: Way Forward: Gastrointestinal Tract Endoscopy Work Flow Postlockdown Era

text: Gastrointestinal tract endoscopy being an aerosol generating procedure increases the risk to staff and uninfected patients from a coronavirus disease 2019 patient. Social and physical distancing through “lockdown” has suppressed the spread of disease but will not eradicate it. Various endoscopy societies formulated guidelines to triage the patients and limit the work to only emergency and urgent cases and postpone “routine” endoscopies. Postlockdown infected vector pool will persist till an effective vaccine is widely available. Nonurgent cases cannot be postponed indefinitely. We need to identify infected symptomatic and asymptomatic individuals and create a safe environment for uninfected patients. Endoscopy staff protection through education, optimized manpower flow, and personal protective equipment usage and hand hygiene needs urgent attention. Proper environment sanitization, endoscope, and device reprocessing will remain important.


COVID-19 Pandemic Brings Gastrointestinal Endoscopy Practice to Its Knees—Financially!


title: Pharmacokinetics of high-dose tigecycline in critically ill patients with severe infections

text: BACKGROUND: In critically ill patients, the use of high tigecycline dosages (HD TGC) (200 mg/day) has been recently increasing but few pharmacokinetic/pharmacodynamic (PK/PD) data are available. We designed a prospective observational study to describe the pharmacokinetic/pharmacodynamic (PK/PD) profile of HD TGC in a cohort of critically ill patients with severe infections. RESULTS: This was a single centre, prospective, observational study that was conducted in the 20-bed mixed ICU of a 1500-bed teaching hospital in Rome, Italy. In all patients admitted to the ICU between 2015 and 2018, who received TGC (200 mg loading dose, then 100 mg q12) for the treatment of documented infections, serial blood samples were collected to measure steady-state TGC concentrations. Moreover, epithelial lining fluid (ELF) concentrations were determined in patients with nosocomial pneumonia. Amongst the 32 non-obese patients included, 11 had a treatment failure, whilst the other 21 subjects successfully eradicated the infection. There were no between-group differences in terms of demographic aspects and main comorbidities. In nosocomial pneumonia, for a target AUC(0-24)/MIC of 4.5, 75% of the patients would be successfully treated in presence of 0.5 mcg/mL MIC value and all the patients obtained the PK target with MIC ≤ 0.12 mcg/mL. In intra-abdominal infections (IAI), for a target AUC(0-24)/MIC of 6.96, at least 50% of the patients would be adequately treated against bacteria with MIC ≤ 0.5 mcg/mL. Finally, in skin and soft-tissue infections (SSTI), for a target AUC(0-24)/MIC of 17.9 only 25% of the patients obtained the PK target at MIC values of 0.5 mcg/mL and less than 10% were adequately treated against germs with MIC value ≥ 1 mcg/mL. HD TGC showed a relevant pulmonary penetration with a median and IQR ELF/plasma ratio (%) of 152.9 [73.5–386.8]. CONCLUSIONS: The use of HD TGC is associated with satisfactory plasmatic and pulmonary concentrations for the treatment of severe infections due to fully susceptible bacteria (MIC < 0.5 mcg/mL). Even higher dosages and combination strategies may be suggested in presence of difficult to treat pathogens, especially in case of SSTI and IAI.


title: Tigecycline Therapy for Multi-drug-Resistant Pseudomonas aeruginosa Sepsis Associated with Multi-organ Failure in an Infant with Persistent Arterial Duct. Case Report

text: Sepsis is the leading cause of death in infants and children worldwide. The growing drug resistance in nosocomial gram-negative bacteria has resulted in treatment challenges. One of the most common multi-drug-resistant bacteria is Pseudomonas aeruginosa. Resistance to antibiotics used in Pseudomonas aeruginosa infections limits the therapeutic options. We present a tigecycline administration in a 5-month-old infant with patent arterial duct, heart failure, and respiratory failure due to respiratory syncytial virus bronchiolitis with subsequent respiratory distress syndrome and severe sepsis caused by multi-drug-resistant Pseudomonas aeruginosa. Despite combined antibiotic therapy with meropenem, amikacin, and colistin, inflammatory markers increased. Because of life-threatening condition, tigecycline was added to the therapy and was administered intravenously twice daily. Within 48 h, inflammatory markers started to decrease and tigecycline therapy continued for 13 days without adverse effects. Tigecycline used in combination with other antibiotics might be a valuable therapeutic approach in the management of multi-drug-resistant bacteria infections in pediatric patients when conventional antibiotics have failed. Further studies are needed to evaluate the efficacy and safety of tigecycline administration in critically ill pediatric patients.


title: The Changing Landscapes in DCD Liver Transplantation

text: PURPOSE OF REVIEW: The transplant community continues to look for ways to help address the discordance between donor liver graft availability and patients on the liver transplant waiting list. Donation after circulatory death (DCD) donor livers represents one potential means to help address this discordance. The present review describes the changing landscape of DCD liver transplantation (LT). RECENT FINDINGS: The number of DCD LTs performed annually within the USA has continued to grow on an annual basis. Importantly, national data has demonstrated that outcomes with DCD LT have been improving. This improvement has been driven by better understanding of how to successfully utilize these organs through better donor and recipient matching and careful evaluation of both hemodynamics during withdrawal of life support and the refinement of the procurement operation. SUMMARY: Despite these improvements in outcome, ischemic cholangiopathy (IC) continues to be the Achilles heel of DCD LT. Emerging technologies such as various forms of machine perfusion may allow for reduction of complications and better prognostication of the risk associated with DCD liver grafts.


title: Feeding the food insecure in Britain: learning from the 2020 COVID-19 crisis

text: The lockdown in Britain has rendered a large proportion of the population economically vulnerable and has at least quadrupled demand for emergency food relief. This paper looks critically at response to the crisis from the government and the voluntary sector with respect to provision of emergency food. In doing so, it has exposed gaps in understanding of the vagaries of the food supply for certain population groups and systemic weaknesses in the current system of emergency food aid. We make recommendations for healthier governmental capacity to react to a food security crisis, better relationships between the government and the voluntary sector, and further research into the dietary constraints of the precariate. Importantly, the social system needs to be responsive to short-term changes in people’s income if people are not to fall into food insecurity.


title: The Internet of Things: Enabling Opportunities and Challenges

text: There is much market hype about the potential of IoT to transform firms and industries. Taking perspective of actors in the quadruple helix—industry, government, universities and end users—we consider the enabling opportunities and challenges that IoT brings. Enabling opportunities for firms include firm purpose reconfiguration, competitive disruption and market dominance, value reconfiguration, value creation and business models environmental and customer insights and ecosystem integration. Regulation, infrastructural investment and capacity, societal awareness and adoption, entrepreneurial ecosystem development are challenges that IoT brings to quadruple helix actors. The chapter concludes by discussing the implications of IoT for these actors.


title: Disrupting the Electrical Circuit: New Onset Atrial Fibrillation in a Patient With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

text: In December 2019, an outbreak of pneumonia cases in Wuhan, China was attributed to a novel coronavirus that was eventually recognized as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently identified as coronavirus disease 2019 (COVID-19), it has been declared a pandemic by the World Health Organization given its rapid global transmission. Various cardiovascular complications have been reported, including heart failure, myocarditis, acute coronary syndrome and arrhythmias, both atrial and ventricular. Regarding arrhythmias, onset from time of infection is variable but usually ranges from several days to a week. We hereby present a case of a COVID-19 positive patient presenting with new onset atrial fibrillation.