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## Meet the editors

Dr. Juber Akhtar has more than 15 years of research and teaching experience. He obtained his BPharm and MPharm from Jamia Hamdard, New Delhi, India, and Manipal University, Karnataka, India. He obtained his Ph.D. from Integral University, Lucknow, India. Dr. Akhtar was nominated as the head of the Faculty of Pharmacy, at Integral University. He also served as chairman cum biological scientist for the Institutional Animal

Ethics Committee (IAEC). He is a member of the Causality Assessment Committee (CAC) and the Animal House facility, at Integral University. He has experience teaching abroad and was previously a professor at Buraydah College of Pharmacy and Dentistry, Kingdom of Saudi Arabia (KSA). Dr. Akhtar has more than ninety publications in reputed journals to his credit. He is an academic book editor and an editorial board member for many esteemed journals. He has supervised a dozen Ph.D. and MPharm students in research projects. Dr. Akhtar's areas of research interest include the development of nanoparticulate drug delivery systems.

Dr. Badruddeen obtained both a BPharm and MPharm from Hamdard University, Delhi, India. He obtained a Ph.D. in Pharmacology from Integral University, Lucknow, India, in 2014. He has expertise in cardiovascular, neurodegenerative, and endocrine diseases. He has experience in conducting research on the safety, toxicity, and clinical efficacy of herbal and allopathic drugs. He has many original articles in reputed journals

to his credit. Dr. Badruddeen is an academic book editor and an editorial board member for many esteemed journals. He has supervised a dozen research scholars and is a member of the Indian Science Congress Association, member secretary of the Institutional Animal Ethics Committee (IAEC), coordinator of the Adverse Drug Monitoring Center (AMC), chairman of the Causality Assessment Committee (CAC), and a member of the Animal House facility, Integral University.

Dr. Mohammad Irfan Khan obtained an MPharm from Hamdard University, Delhi, India. He obtained his Ph.D. in Phytochemistry from Singhania University, Jhunjhunu, India. He has expertise in Ayurvedic medicine and nutraceuticals and has worked with some of the world's major pharmaceutical companies. He has more than 12 years of experience in research and formulation development of Ayurvedic, herbal, and nutra-

ceutical products and successfully developed more than 100 formulations. He has more than forty original papers in international journals to his credit. Dr. Khan has supervised many research scholars at the post-graduate and Ph.D. levels.

Dr. Mohammad Ahmad holds a specialization in Pharmacology from Jamia Hamdard, New Delhi, India. He received his Ph.D. from Integral University, Lucknow, India. Presently, he is an Assistant Professor of Pharmacology, at the Faculty of Pharmacy, at Integral University. He has been teaching PharmD, BPharm, and MPharm students and conducting research in the field of diabetes and cancer. From 2011 to 2014, he worked as a research assistant

on a project sponsored by the Council for Science and Technology, Uttar Pradesh, India. He has published more than twenty original articles in reputed journals. He is a member of the British Society of Nanomedicine, Indian Science Congress Association, and International NanoScience Community, Budapest, Hungry. He is also a social nominee of the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA). Dr. Ahmad is actively involved in research and his areas of interest include pharmacology and drug and pharmaceutical regulatory affairs. He is also engaged in research pertaining to diabetes mellitus, oral delivery of anticancer agents, and chemoprevention using natural bioactive compounds.

### Contents

#### **Chapter 6 89**

Machine Learning and Artificial Intelligence in Therapeutics and Drug Development Life Cycle *by Subhomoi Borkotoky, Amit Joshi, Vikas Kaushik and Anupam Nath Jha*

## Preface

The development of a new drug product or biologic is an extended, multifarious, and costly progression that usually takes on an average 10 to 12 years. Occasionally, extra time may be required from product development to commercialization. This book presents a comprehensive overview of drug design, highlighting the steps involved from the discovery phase to product approval.

The book is divided into four sections. Section 1 discusses drug development, highlighting analytical processes, some adverse drug reactions associated with drugs in a few conditions, and repurposing of medicaments in various ailments. Section 2 discusses the topical application of drugs. It includes some pharmaceutical nanoformulations and cosmeceuticals useful in various diseases including topical diseases. Section 3 focuses on ocular drug delivery systems and the barriers encountered during the delivery of drugs in the eyes. Section 4 explores *in silico* drug development involving computer-aided drug design, machine learning, and artificial intelligence. It also examines the computational and statistical methods used to investigate and analyze chemicals in pharmaceutical medicine.

This book is written by experts in the field and is a useful resource for students, researchers, and academicians. I am thankful to all the authors for their excellent contributions.

> **Dr. Juber Akhtar** Associate Professor, Faculty of Pharmacy, Integral University, Lucknow, India

**Badruddeen, Mohammad Irfan Khan and Mohammad Ahmad**

Integral University, Lucknow, India

**1**

Section 1

Investigation of Medicine

Progress

### Section 1

## Investigation of Medicine Progress

#### **Chapter 1**

## Introductory Chapter: Drug Development Life Cycle

*Juber Akhtar and Badruddeen*

#### **1. Introduction**

Drug development comprises all the activities involved in transforming a compound from drug candidate (the end product of the discovery phase) to a product approved for marketing, that is, the dosage form which will be available in the market for sale after the approval of the appropriate regulatory authorities (**Figure 1**).

#### **2. Topical formulations**

The drug development of topical formulations involving nanoemulgel delivery system in which fusion of two different delivery systems and the physical state of drug containing nanoemulsion is elaborated. A nanoemulsion which is a thermodynamically stable system might be transformed into the nanogel. A formulator thus can make the incorporation of lipophilic drugs in the system and further might be used in treatment. The poor oral bioavailability, and unpredictable pharmacokinetic and absorption variation of various drugs can be overcome by this technique. Simultaneously, its non-greasy nature and easily spreading ability support the patient compliance. The treatment of acne, pimple, psoriasis, fungal infection, and inflammation caused by osteoarthritis and rheumatoid arthritis is possible.

**Figure 1.** *A brief summary on drug development.*

#### **3. The ophthalmic preparations**

The ophthalmic preparations and various barriers affecting drug penetration and distribution inside the eye were also explained. As per World Health Organization, the prevalence of distance or near vision impairment is increasing. Both the anterior and posterior areas of the eyes are affected by various degenerative infections. These may be age-related macular degeneration and diabetic retinopathy at the posterior segment, which can cause severe vision loss. The ocular drug delivery is one of the challenges for delivery of medicaments since it has number of anatomical and physiological barriers. Keeping in mind one full chapter has been compiled in the book in which various barriers that can protect the external and internal structures of the eye from the passage of drugs are elaborated. However, it is very difficult to attain effective pharmacotherapy because of these barriers. Many conventional dosage forms (eye drops and ointments) cannot achieve therapeutic concentrations in the posterior region of eye since only an extremely small amount of drug (1/100,000) can reach the retina and choroid. Although investigations into novel dosage forms that can be applied topically are underway, the topical dosage form might be formed and may target posterior segment diseases [1, 2].

#### **4. The machine learning and artificial intelligence and computer-aided drug design (CADD)**

The machine learning and artificial intelligence (AI) and computer-aided drug design (CADD) are also the new challenges for formulation developer. One can identify and practically implement the number of computational and statistical methods for analyzing biomedical entities, so that target identification will be easy, cost-effective, and validated ones. To complete the drug development processes, CADD can be used to attain biochemical safety and effectiveness, and to stay away from toxicity. The *in silico* techniques that are accepted in academics, firms, and administration [3, 4] may lead to momentous improvement in drug blueprint and innovations. Since a huge raw data (primary data) were obtained during and after biological, chemical, and pharmaceutical medicine development, there is need of machine learning algorithms that can be optimized and same to be applied in the countryside of CADD. In this way, significant improvement in the competence of drug design and discovery processes is possible. If a formulator apply computational methods and tools during drug design and discovery and development, one can think over an accurate and reliable pre-processed data [5, 6]. Further artificial intelligence (AI) approaches might be useful for pre-processing of huge data [7] and its modeling [8, 9] and overall design of the dosage forms.

*Introductory Chapter: Drug Development Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.106480*

#### **Author details**

Juber Akhtar\* and Badruddeen Faculty of Pharmacy, Integral University, Lucknow, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Lee J, Rhee YS. Ophthalmic dosage forms for drug delivery to posterior segment. Journal of Pharmaceutical Investigation. 2022;**52**:161-173

[2] Loftsson T. Topical drug delivery to the retina: Obstacles and routes to success. Expert Opinion on Drug Delivery. 2022;**19**(1):9-21

[3] Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. Journal of Cheminformatics. 2020;**12**:9. DOI: 10.1186/s13321-020-0408-x

[4] Yang X, Wang YF, Byrne R, Schneider G, Yang SY. Concepts of artificial intelligence for computerassisted drug discovery. Chemical Reviews. 2019;**119**:10520-10594. DOI: 10.1021/acs.chemrev.8b00728

[5] Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal. 2016;**14**:177-184. DOI: 10.1016/j.csbj.2016.04.004

[6] Macalino SJY, Gosu V, Hong SH, Choi S. Role of computer-aided drug design in modern drug discovery. Archives of Pharmacal Research. 2015;**38**:1686-1701. DOI: 10.1007/ s12272-015-0640-5

[7] Hu YH, Lin WC, Tsai CF, Ke SW, Chen CW. An efficient data preprocessing approach for large scale medical data mining. Technology and Health Care. 2015;**23**:153-160. DOI: 10.3233/THC-140887

[8] Car J, Sheikh A, Wicks P, Williams MS. Beyond the hype of big data and artificial intelligence: Building foundations for knowledge and wisdom. BMC Medicine. 2019;**17**:143. DOI: 10.1186/s12916-019-1382-x

[9] Saez C, Garcia-Gomez JM. Kinematics of big biomedical data to characterize temporal variability and seasonality of data repositories: Functional data analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics. 2018;**119**:109-124. DOI: 10.1016/j.ijmedinf.2018.09.015

### **Chapter 2**

A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources of Scientific Documents Using Deep Learning Method with Drug Repurposing: Case Study Metformin

*Zahra Rezaei and Behnaz Eslami*

#### **Abstract**

Drugs for medical purposes aim at saving one's life and improving their life quality. Side effects or adverse drug reactions (ADRs) on patients are studied as an important issue in pharmacology. In order to prevent the adverse drug effects, clinical trials are conducted on the drug production process, but the process of these trials is very costly and time consuming. So, various text mining methods are used to identify ADRs on scientific documents and articles. Using existing articles in the reference websites such as PubMed to predict an effective drug in the disease is a vital way to declare the drug effective. However, the effective integration of biomedical literature and biological drug network information is one of the major challenges in diagnosing a new drug. In this study, we use medical text documents to train the BioBERT model so that we can use it to discover potential drugs for treating diseases. Then, we are able to create a graphical network of drugs and their side effects with this method as well as it provides us with an opportunity to identify effective drugs that have been used in many diseases so far while having the ability to be used effectively on other diseases.

**Keywords:** adverse drug reactions, drug repurposing, deep learning, natural language processing, social network

#### **1. Introduction**

What makes reusing old drugs worthwhile is the cost of developing a new drug, according to research [1]. The cost of developing a new drug reaches billions of dollars, which includes analysis, testing, validation costs, and so on. More importantly, duration of developing new drugs may take long nearly 9 to 12 years to launch a new drug.

This practice, therefore, is deemed to be of importance in the pharmaceutical industry because it accelerates the development of drugs and reduces the cost of drug production, especially for pandemic diseases such as COVID-19, and the need to use this scientific process in the field of artificial intelligence is essential.

Due to the rapid growth of scientific articles in medical research, the analysis of medical textual documents using text mining methods has become very popular. The emergence of powerful deep learning methods and their maturity in text mining has created various development ways for different types of text analysis. The only drawback of deep learning models is their training using a large number of input data, which has made an unsurmountable challenge in medical topics. Fortunately, various medical sites such as PubMed allow the use of textual data and have seriously contributed to the development of deep learning models.

Improvements in healthcare and nutrition have generated remarkable increases in life expectancy worldwide. Although our understanding of the molecular basis of these morbidities has quickly advanced, effective novel treatments are still lacking. Today, the topic of reusing drugs based on text mining methods and based on valid scientific articles is important and vital, because based on the characteristics of pharmacokinetics and pharmacodynamics, the process of data generation has already been approved and validated by scientific communities and the study of side effects, and their impact on other diseases will significantly save the time and cost of the data generation process. Creating new drug profiles based on previously valid drugs is a way of bypassing the drug production cycle.

Metformin is one such drug currently being investigated for novel applications. What is clear from the clinical evidence is that metformin is prescribed in the treatment of diabetes. The aim of this research is to investigate the effects of metformin on various diseases that are reflected in PubMed documents. What will be studied in this report are the results of the use of metformin in the prevention of various diseases.

This chapter aims to provide the reported results, available in medical literatures for potential of metformin to prevent or treat different kinds of disorders.

Furthermore, some of the previous researches in the field of drug reuse have been reviewed in the second chapter. In the third chapter, the proposed research model is discussed and in the fourth chapter, the explanation of the model architecture is discussed. The implementation results and final outputs of the proposed method are explained in the last section.

#### **2. Related works**

Drug reuse is used to treat diseases other than an approved disease (such as drug use in new drugs, development of indications, or change of indications), including the development of new medical applications for previously approved drugs, as well as the evolutionary cycle. A drug is defined for the use and development of drugs that are in the drug archive. This strategy is not very new, but it has gained significant momentum in the last decade as approved scientific sources on drug reuse have identified side effects.

About one-third of the approvals in recent years correspond to drug repurposing, and repurposed drugs currently generate around 25% of the annual revenue for the pharmaceutical industry [2].

*A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources… DOI: http://dx.doi.org/10.5772/intechopen.107858*

Drug reuse involves identifying new uses for existing drugs. Prominent examples of the use of these methods include sildenafil and thalidomide as a result of serendipity [3].

Graphs of drugs, genes, and diseases are created and clustering methods are developed to predict new edges between drugs and diseases [4].

Disease genes and drug genes are modeled. Relationships from Medical Scientific Documents and Induction of Indirect Relationships Between Drugs and Diseases Proposed a ranking method based on the similarity of the drug target to rank these relationships [5].

When predicting a new drug-target interactions (DTI), drug-drug interaction (DDI) [6], there are three levels of prediction using machine learning techniques. First, it preprocesses input data such as drug side effects, drug chemical structure, and disease genes and provides training data through feature extraction. The appropriate machine learning algorithm is then used for training. Third, we apply a predictive model to get the results of drug repositioning in the test dataset. The data is transformed into a consistent, normalized format, such as computer-readable vectors and matrices, before being entered into the machine learning model to train the representation. Representation learning [7] (or feature learning) is a set of techniques for transforming raw data into something that can be effectively used through machine learning. Representation learning is mainly divided into a supervised learning approach and an unsupervised learning approach and extracts the properties of the input data of the downstream.

#### **3. Material and methods**

We look for relevant publication in PubMed through using metformin as key word. The data of this research are documents and scientific articles written in English between 1994 and 2020. In this direction, we applied named entity recognition (NER) BioBERT method.

The used NER method includes three main phases (**Figure 1**); it is started with textual documents from PubMed which are entered as input data, followed by preprocessing phase to improve data, and eventually, in the third phase, grouping data into train and test categories is done, and NER via deep learning algorithm – BioBERT method – runs to extract patterns.

#### **3.1 Data sources**

We looked at 18000 publications in PubMed using metformin as a keyword. The abstracts of 16,000 out of them were analyzed by NER BioBERT. This search covered studies which have been done between 1994 and 2020.

**Figure 1.** *The workflow of the proposed model-based strategy.*

#### **3.2 Preprocessing**

The preprocessing of comments in both datasets was performed as follows:

#### **3.3 Deep classification**

Bidirectional Encoder Representations from Transformers for Biomedical Text Mining can be considered a particular language pretrained model on a large-scale biomedical corpus. According to the mentioned architecture, the knowledge from a large number of biomedical documents by BioBERT [8] is transferred to biomedical text mining models with the least amount of modification in the architecture. Whereas competitive performances with previous novel models appeared by BERT and BIOBERT essentially have better performance on the following three biomedical text mining functions: biomedical named entity recognition and biomedical clustering based on the effect of Metformin.

Different diseases and the trend of metformin impact in publication during several years, based on drug effect on various diseases.

BioBERT effectively moved the data from a part of biomedical textual documents to biomedical text mining models by some alterations in a particular structure. Whereas BERT had outlined excellent function with previous models, BioBERT discernibly overwhelmed them on entity recognition and clustering concerning metformin effect on individuals' wellbeing.

We investigated publications based on the association between metformin and type 1 and 2 diabetes. And separately, we explored them in accordance with the effect of metformin on other disease.

PubTator [9] and BEST [10] are two of the potential sources that automatically can extract compounds and proteins from PubMed or PubMed Central (PMC). However, these two sources are not able to extract the combined and interactive relationships between the drug and the disease. To address these issues, we began building a pipeline using NER to identify studies containing DTI and extract related data. We, then, trained the BioBERT model on known studies containing DTIs and used this model to predict new drug studies.

Indeed, given an input sentence X = {x1, x2, …, xN} where xi is the i-th word/token and N speaks to the length of the sentence. The objective of NER is to categorize each word/token in X and allot it to corresponding name y ϵ Y, where Y may be a predefined list of all conceivable name sorts (e.g. CHEMICAL as Drugs, Infection).

Additionally, this structure was used after preprocessing to identify the relationship between the drug and the disease. In future research, we are going to create this *A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources… DOI: http://dx.doi.org/10.5772/intechopen.107858*

graph of relationships and use the number of drug references to a chemical structure as a weight to discover drug relationships.

#### **4. Result**

There have been a few detailed examinations into the relationship between metformin and the results of cures in different diseases. Moreover, these preclinical reports and dependable biological pathways have been known which clarify the atomic component of metformin and addressed in our research work. Nevertheless, the vital reply to this issue is the level of metformin adequacy against nondiabetic disarranges.

Metformin is the generic name of the drug which is produced and supplied under different brand names such as Metformex, Glucophage, and so on. As shown in **Figure 2**, the drugs extracted from the authoritative scientific articles are in the drug groups related to diabetes and some other drug groups.There are several classes of drugs used to control diabetes, and members belonging to each group have similar

#### **Figure 2.**

*The word-cloud of the BioBERT model in drugs.*

**Figure 3.**

*The word-cloud of the BioBERT model in disease.*

**Figure 4.** *The word-cloud of the BioBERT model in disease.*

*A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources… DOI: http://dx.doi.org/10.5772/intechopen.107858*

**Figure 5.** *The word-cloud of the BioBert model in disease.*

functions. One of these drug classes is biguanides. Metformin, the only member of this drug group, works in three ways:

Diabetes medications are generally prescribed to lower blood glucose For example, in articles, it refers to synthetic alternatives and antidiabetic drugs to reduce perfusion or kidney function, exacerbate the antihypertensive effects, exacerbate metabolic acidosis, and so on (**Figures 3**–**5**).

According to the NERBIOBERT model, out of 16,781 articles reviewed by the PubMed site and analyzed in the article, 6185 papers refer to type 2 diabetes and 221 papers refer to type 1 diabetes as we know it. Type 2 diabetes is a chronic disease. It is characterized by high levels of sugar in the blood. Type 2 diabetes is also called type 2 diabetes mellitus and adult-onset diabetes. Although, 2388 papers used metformin in type 2 diabetes mellitus, and 1178 articles did not mention any disease at all. Therefore, based on the type of articles, if the adverse use of metformin for the treatment of another disease has been identified, it can be used in the treatment of that disease. What is important in this analysis is a demonstration of the disease and the drug so that by analyzing a large volume of authoritative articles, the use of the approved drug can be used in the treatment of other diseases.

#### **5. Conclusion**

Experimental results on the drugs and disease with using advanced deep learning models like Bret show that integrating pretrained biomedical language representation models (i.e. BERT and BioBERT) into a pipe of information extraction methods with multitask learning can improve the ability to collect drug repurposing knowledge from PubMed.

Hitherto, there has not been any clear answer for that in clinical trial, and also, the role of metformin on treatment or prevention of disease remains hypothetical on next step, and we will extract the association between diabetes and other relevant disease with respect to administration of metformin as treatment.

### **Acknowledgements**

The authors have no proprietary, financial, professional, or other personal interest of any nature in any product, service, or company. There is no conflict of interest in this study.

#### **Author details**

Zahra Rezaei1 \* and Behnaz Eslami<sup>2</sup>

1 Department of Statistics and Information Technology, Institute of Judiciary, Tehran, Iran

2 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources… DOI: http://dx.doi.org/10.5772/intechopen.107858*

#### **References**

[1] Dickson M, Gagnon JP. The cost of new drug discovery and development. Discovery Medicine. 2009;**22**(4):172-179

[2] Naylor S, Kauppi MJ, Schonfeld JM. Therapeutic drug repurposing, repositioning and rescue part II: Business review. Drug Discovery World. 2015;**16**:57-72

[3] Liu Z, Fang H, Reagan K, Xu X, Mendrick DL, William Slikker WT Jr. In silico drug repositioning: What we need to know. Drug Discovery Today. 2013;**18**:110-115

[4] Sun P, Guo J, Winnenburg R, Baumbach J. Drug repurposing by integrated literature mining and drug-gene-disease triangulation. Drug Discovery Today. 2017;**22**:615-619

[5] Yang H-T, Ju J-H, Wong Y-T, Shmulevich I, Chiang J-H. Literaturebased discovery of new candidates for drug repurposing. Briefings in Bioinformatics. 2017;**18**:488-497

[6] Zhu S, Bai Q, Li L, Xu T. Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents. Computational and Structural Biotechnology Journal. 2022;**20**:2839-2847

[7] Bengio ACPY. Vincent representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;**35**(8):1798-1828

[8] Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;**36**(4):1234-1240

[9] Wei C-H, Kao H-Y, Lu Z. PubTator.A web-based text mining tool for assisting biocuration. Nucleic Acids Research. 2013;**41**:W518-W522

[10] Lee S, Kim D, Lee K, Choi J, Kim S, Jeon M, et al. BEST: Next-generation biomedical entity search tool for knowledge discovery from biomedical literature. PLoS One. 2016;**11**(10):e0164680

Section 2


**1. Introduction**

Soil erosion processes lead to soil depletion, reduced fertility, and consequently affect crop quality and crop yields [1–5]. At the pedon scale, soil erosion can be attributed to the separation of soil particles by raindrops and runoff during interrill erosion processes, so preventing this process is an important goal in the management of water and soil resources [6–8]. The amount of runoff is a function of navigation time, therefore the accurate estimation of the time of concentration and runoff threshold results in a more accurate hydrograph of flooding [9]. Designing methods and structures for soil water

conservation requires accurate estimation of the amount and time of flood peak discharge and time of concentration. To achieve these goals, it is necessary to study the factors affecting the amount of runoff produced, the time to runoff generation, the time to the concentration of runoff and, consequently, the nature of the hydrograph [10,11].

It is well known that the runoff process consists of three parts: surface runoff, subsurface runoff, and baseflow. In arid and semi-arid areas, the soils show some inherent properties that produce an elevated irregularity of these hydrological mechanisms, generating mixed models of surface and subsurface flows [12]. A representative example of this kind of mixed and complex dynamic is the degraded soils of Iran. Since most soils in Iran have low permeability, precipitation exceeds infiltration, hence, surface runoff becomes dominant [9,13].

Research in recent years on the behavior and performance of surface runoff indicates the impact of various climatic and physiographic factors such as rainfall intensity, slope steepness, and surface Roughness Coefficient (RC) on surface runoff variables in different temporal and spatial scales (e.g., [14–23]). In this regard, Vermang et al. [24] studied the effect of RC on runoff and soil loss rate using a rainfall intensity of 50.2 mm h−1 on a silt loam soil. Soil particle sizes were divided into four size groups of 3–12, 12–20, 20–45 and 45–100 mm under a slope steepness of 5%. The results showed a significant decrease (*p* ≤ 0.05) in the rate of runoff changes along with an increase in RC. However, in RC with particle sizes larger due to the impact of raindrops and breaking of aggregates and the formation of a seal layer on surfaces, the permeability decreased, and the runoff rate reached a steady state. Ding et al. [25] also measured runoff and sediment at different times using rainfall simulation under laboratory conditions on two types of soils with different RCs. The results showed that high RC delayed runoff generation and concentration of runoff, but no significant changes in the amount of runoff were observed. Another example can be found in Vaezi and Ebadi [26]. They recently investigated the effect of nine rainfall intensities from 10 to 90 mm h−1 under five slope steepness' from 0% to 40% as the most important factors in producing surface runoff under laboratory conditions. Their results show that the runoff threshold in interrill erosion occurred using rainfall intensities close to 20 mm h−1. They also reported that the highest surface runoff occurred under a slope steepness of 20%, with no significant difference for runoff as the slope increased.

In recent years, Complex Hillslope (CH) (i.e., profile curvature and plan shape) has also been considered as an effective factor in surface and subsurface runoff dynamics. Once a mutual relation is proven between the shape of the hillslope and the hydrological processes, it can be both useful and efficient in managing natural and urban watersheds [27,28]. Hillslope shape is an effective measure in studying the complex impact of topography on the different runoff variables. Troch et al. [14,29] introduced nine different CHs through the combination of three plan shapes (convergent, divergent, and parallel) and three profile curvatures (convex, straight, and concave). Agnese et al. [30] showed that for constant plan shapes, the convex profile generates more surface runoff than the other profiles. For the constant profile curvatures, the converged plan obtained higher amounts of surface runoff than the parallel and divergent plans. In addition, Talebi et al. [31] also demonstrated that convex and divergent hillslopes could be generally more stable than other types of hillslopes and that concave and convergent hillslopes could be less stable. According to the results obtained by Geranian et al. [32] on the effect of CH plan and profile on surface runoff changes, the impact of plan on runoff generation and time of concentration of runoff could be much greater than profile curvature, and convergent hillslopes with surface runoff concentration had an earlier runoff activation time than divergent and parallel hillslopes. Moreover, the runoff thresholds on convex hillslopes could be lower than on concave hillslopes. In addition, they observed that the peak discharge in divergent and parallel hillslopes was much higher than for convergent hillslopes due to the greater width at the outlet of the hillslope. Thus, the effect of profile on the rate of discharge changes needs further investigation.

The straight hillslope reaches a peak discharge earlier than the convex hillslope, which reaches a peak discharge earlier than the concave hillslope. Sabzevari et al. [9] found that the geometry of the hillslopes could change the peak discharge of hydrograph several times since the divergent hillslopes had a higher peak discharge than the parallel and convergent hillslopes. In addition, their results indicated that the highest peak discharge could be found in concave-divergent hillslopes. In their investigation of the effect of hillslope geometry on surface and subsurface runoff, they also showed that the rate of change in the hydrograph of the divergent hillslopes was decreasing, and the concave curve was downward. In another study, Sabzevari et al. [33] further examined the effect of hillslope geometry on the temporal variables of runoff, such as lag time and time to equilibrium. The results showed a 33% increase in time to equilibrium in the divergent hillslopes as compared to the convergent hillslopes. Talebi et al. [18] examined the effect of CH plan and profiles on separation and deposition of sediment particles caused by sheet erosion. They stated that the rate of particle separation because of runoff on convex profiles is about 15 times higher than that in straight profiles. Regarding this, Fariborzi et al. [34] employed the subsurface time area model for the prediction of subsurface flow in CHs. To validate these results, a rainfall simulator on a sandy loam soil was tested, which was used under three rainfall intensities and three inclinations. Then, subsurface time area model results were compared with those of a laboratory of subsurface flow. The results showed an accurate estimation with a determination coefficient of 0.85 for the method of subsurface time area in CHs.

Although there is a great number of research on the individual effect of RC and CH on the performance and dynamics of surface runoff and subsurface flow, the combined effects of RC and CH morphology on runoff variables during rainfall-runoff processes in laboratory conditions is still unknown. The main aim of this study is to perform a comparative analysis of the individual and combined effects of RC and CH on runoff production and parameters related to runoff, such as start time, time of concentration, and runoff peak, by using hydrographs. To achieve this goal, a total of 81 rainfall simulations under laboratory conditions were conducted with a rainfall intensity of 7 L min−1 on three soils with different RCs (i.e., low = 0.015, medium = 0.016, and high = 0.018) and CHs (i.e., profile curvature and plan shape). An inclination of almost 20% was used in three replications per treatment.

#### **2. Materials and Methods**

#### *2.1. Preparing Rainfall Simulator Conditions*

In the current research, a laboratory model was used to investigate the rainfall-runoff processes in CHs under three different types of RCs. The longitudinal profiles and slope plans were designed based on the Evans model [35]. According to Troch et al. [14], nine different CHs were designed with the simultaneous change of plan and profile of the hillslopes (Table 1). To investigate the plan shapes (convergent, divergent, and parallel) and three profile curvatures (convex, straight, and concave), a three-dimensional geometric model of the hillslopes were considered [34,36]. To introduce a suitable function that can represent the geometry of the CHs, the model proposed by Troch et al. [14] was used.

In this study, a rainfall simulator was used with a uniform rainfall intensity of 71 min−1 (210 mm/h). The simulator has a plot with a length of 2 m, a width of 1 m, and a depth of 0.8 m. The height of the surface of the plot to the precipitation nozzles is 20 cm. The simulator has a water tank with a capacity of 200 L beneath the unit where the water is pumped into the nozzles and rained down on the surface of the plot. There are two jacks below and on the left side of the plot for longitudinal slope adjustment. A slope steepness of almost 20% was used, as this value was considered a representative inclination of the most eroded areas in Iran [26] Surface runoff was also measured using the outlet pipes of P2 and P4 in outlet plot (Figure 1).

**Table 1.** Geometric characteristics of complex hillslopes (CHs).

H: maximum heights relative to the baseline; L: total length of the hillslope; n: profile curvature parameter; ω: plan curvature parameter calculated [14].

**Figure 1.** Schematic diagram of the water cycle within the rainfall simulator [32].

A rectangular weir was used to measure the time of concentration and amount of runoff generated in CHs. After preparing the rainfall simulator and adjusting the slope and rainfall intensity, a digital chronometer was turned on. Then, the start time of the runoff was recorded by observing runoff on the within plot and runoff travel from the plot most distant point to the outlet plot as the time of runoff generated and time of runoff concentration, respectively. In the following, runoff peak (L min−1 m−2) was measured using a hydrograph, so that the maximum amount of runoff in each hydrograph was considered as the runoff peak. Then, the amount of runoff was measured in intervals of 1 min with a total duration of 15 min after runoff generation under different CH and RC conditions. Finally, using the rectangular weir information at different times, the peak discharge rate was also determined [26].

#### *2.2. Determining the RC*

Soil RC has a great impact on the surface storage, velocity, and direction of runoff [37]. In this research, in order to investigate the effect of RCs on the rainfall-runoff processes in CHs, soil particle diameter in the plot was considered as a factor influencing hydrological processes on RCs, and three different granulation types were used to investigate different RCs. Various relations have been proposed to determine the hydraulic RC through granulation. Therefore, in order to select the appropriate RC, the granulation curve of three soil types was determined by granulometry tests. The soil uniformity was then calculated using Equation (1) [38]:

$$\mathcal{C}\_{\text{ll}} = \frac{d\_{60}}{d\_{10}} \tag{1}$$

where *Cu* is the soil uniformity and *d*60 and *d*10 represent the diameters at which 60% and 10% of each soil type are comprised of particles with a diameter less than those values, respectively. For *Cu* smaller than 5, the soil is uniform. For 5 < *Cu* < 15, it is non-uniform, and for larger than 15, it is very non-uniform [38]. Finally, Soils 1, 2, and 3 (Figure 2) were uniform with a *Cu* < 5 (*Cu*: 1.5, 2.63, and 1.74, respectively). Thus, the Strickler equation (Equation (2)) was used to determine the RCs of Soils 1, 2, and 3. Their RCs, according to the granulation curve, were obtained to be 0.015, 0.016, and 0.018, respectively. In this equation, *d*50 (mm) is the median diameter of particles [39].

$$n = 0.0474 \ (d\varepsilon\_0)^{1/6}.\tag{2}$$

**Figure 2.** The three soil types with different roughness coefficients (RCs) used during the rainfall simulation experiments, (**1**): Cu: 1.5; (**2**): Cu: 2.63; (**3**) Cu: 1.74.

#### *2.3. Statistical Analysis*

After measuring the surface runoff, under different CHs and RCs, the data obtained from the experiments were categorized in Excel 2013 (Microsoft, USA). Before performing any statistical analysis, the normality of the data was tested through the Kolmogorov–Smirnov test. Regarding this, the non-normal data were changed to normal data using a transforming data method and this was then analyzed. In the following, the means of different groups were compared using Tukey's test. Two-way ANOVA tests were used to measure the individual and combined effects of CHs and RCs on runoff variations as was done by Kiani-Harchegani et al. [40].

#### **3. Results and Discussion**

#### *3.1. Descriptive Statistics of Runo*ff *Variables in RCs in CHs*

Table 2 presents the descriptive statistics, including the mean, standard deviation (SD) and coefficient of variation (CV) of different runoff variables, including start time, time of concentration, and runoff peak in three soil types with different RCs under different CHs.

The results indicate that the slowest start time for Soils 1 and 2 was in the concave-parallel hillslopes (Hillslope No. 2). The slowest start time was observed in Soil 3 in the straight-parallel hillslopes (Hillslope No. 5), showing the longest start time of runoff amongst all CHs. The lowest start time of runoff for all soils was obtained in the convex-convergent hillslopes (Hillslope No. 7) and the earliest start time was Soil 1. The results, as shown in Table 2, also indicate that the highest time of concentration was in Soil 2 with convex-convergent hillslopes (Hillslope No. 7) and the lowest time of concentration in Soil 1 with straight-divergent hillslopes (Hillslope No. 9). Finally, the highest runoff peak was observed in Soil 3 in the concave-divergent hillslopes (Hillslope No. 3) and the lowest runoff peak in Soil 1 with the convex-convergent hillslopes (Hillslope No. 7).

#### *3.2. Start Time of Runo*ff

According to the results of the two-way ANOVA presented in Table 3, there was a significant difference (*p* ≤ 0.001) in the effect of different CHs and different RCs on the start time of runoff. The interaction between RCs and CHs also shows a significant difference (*p* ≤ 0.001), indicating different effects of different RCs under CHs on the start time of runoff.

The results of the Tukey's test (Figure 3) also showed that runoff reaches the plot outlet in Soil 1 (n = 0.015) earlier under convex-convergent hillslopes (Hillslope No. 7) due to surface flow concentration, high water height at the plot outlet, and reduced permeability. The earliest start time of runoff in Soils 2 and 3 at the hillslope of No. 7 can be also accounted for the more effective simultaneous permeability effect and profile curvature. Therefore, because of an increased RC of the soil due to coarser soil particles increasing and a higher surface in convex profile, as well as the increased runoff height at the plot outlet, runoff occurs quickly.

By considering the soil profile and type constant, the effect of the plan shape on the runoff start time can be determined, such that the convergence reduces the start time of runoff and the divergence delays the start time of runoff. In addition, by considering the plan and type of soil constant, we can confirm the impact of profile on the start time of runoff, such that concave delays the start time of runoff and convex hillslopes have an earlier start time of runoff. These results are in line with the results obtained by Sabzevari et al. [9] and Geranian et al. [32].

SD, standard deviation; CV, coefficient of variation.

#### *Water* **2019** , *11*, 2550

**Figure 3.** Effect of different RCs and CHs on runoff variables by Tukey's test. (a, b and c are statistically different at *p* ≤ 0.05 on three soil types in each hillslope). Means followed by the same letters are not significantly different according to Tukey's HSD at *p* ≤ 0.05.

**Table 3.** Effect of different RCs and CHs and their interaction on runoff variables using two-way ANOVA.

df: degrees of freedom; F-value: variation between gropes mean/variation within the gropes; *p*-value: *p*-value ≤ 0.01 and *p*-value > 0.05 indicates a significant variation within the group and a low variation within the group, which leads to the rejection and acceptance of the null hypothesis, respectively.

#### *3.3. Time of Concentration*

The results of the two-way ANOVA presented in Table 3 indicate a significant difference (*p* ≤ 0.001) in the individual effect of different CHs and RCs on time of runoff concentration. The interaction between CHs and RCs also shows a significant difference (*p* ≤ 0.001), indicating different effects of different RCs and CHs on the time of concentration.

The results of means of different groups using Tukey's test in Figure 3 also show that the divergent hillslopes decrease the time of concentration and concave hillslopes increase it, which results in an increased time of concentration on the concave-divergent hillslopes (Hillslope No. 3) with increasing RCs (n = 0.016 to n = 0.018). This shows that hillslope profiles have a greater effect than plan shape. Therefore, as soil permeability increases, more time is needed to accumulate runoff at the bottom of the plots, thus increasing the time of concentration [22]. In the convex-parallel hillslopes (Hillslope No. 8), with increasing soil RC, the time of concentration was first increased and then decreased. The reason may be that with the low slope of the convex profile at the bottom of the plot as well as water accumulation, the water is discharged earlier [41,42]. The results also show that the convergent plan, due to the concentration of surface and subsurface runoff, retains more water and takes longer to discharge. As well, the runoff path is curved to reach the exit plot outlet in the convergent plan, but in other plans, it follows a smooth path. This finding is consistent with those obtained by Geranian et al. [32].

In examining the CH profiles with increasing RCs, it was noticed that concave profiles with a lower slope angle at the end of the plot required a considerable runoff, thus an increasing trend was observed with RCs in different plan shapes. In straight profiles, due to the uniformity of the slope, time of concentration is affected by the RC and soil permeability [43].

The examination of the effect of the plan on time of concentration by considering soil profile and type constant indicates that time of concentration in divergent hillslopes is less than that in parallel and convergent hillslopes [21,44]. The reason may be due to the curved path of runoff in convergent plans. Considering the plan and soil type constant, concave hillslopes are also shown to have a higher time of runoff concentration than convex and straight hillslopes because, on the convex hillslopes, the water flow is faster than other profiles for the high slope and accumulation of runoff [45]. For example, the highest time of concentration for all soils was obtained for the convex-convergent hillslopes. The lowest time of concentration for Soil 1, 2 and 3 was observed in the convex-divergent, concave-divergent, and convex-parallel hillslopes, respectively. In most hillslopes, as the soil RC increases, the time of concentration is delayed, which is consistent with the results obtained by Geranian et al. [32]. However, from the results of simultaneous impact analysis of profiles, plans, and RCs, it is shown that in different RCs, the effect of the profile and plan on runoff variables varies. Moreover, in some hillslopes, by increasing permeability, the effect of profiles is observed more than the plan, and in other hillslopes,

the opposite is the case. For example, in the concave-divergent hillslopes (Hillslope No. 3), we can observe the effect of profile (concavity) on the plan (divergence), so by increasing soil RC, the time of concentration and permeability is increased. A similar case can also be observed for Hillslope No. 8 (convex-parallel).

#### *3.4. Runo*ff *Peak*

The results of the two-way ANOVA, presented in Table 3, indicate a significant difference (*p* ≤ 0.001) for the individual influence of different CHs and RCs on the runoff peak rate. In addition, the interaction between soil RC and CHs shows a significant difference (*p* ≤ 0.001), indicating the different effects of different soil RCs and CHs on runoff peak rate.

The results of Tukey's test (Figure 3) also show that in concave-convergent, concave-divergent, straight convergent, and straight-parallel hillslopes (Hillslopes No. 1, 3, 4, and 5, respectively), increasing soil RC relatively increases the runoff peak rate. In the concave-parallel and straight-divergent CHs (Hillslopes No. 2 and 6), the soil RC has no effect on the runoff peak rate. Meanwhile, in the convex-convergent CHs (Hillslope No. 7), increasing soil RCs significantly increases the runoff peak, and in CHs (Hillslopes No. 8 and 9), by increasing the soil RCs, the time of peak rate first decreases and then increases. This is due to the effect of the plan on the runoff peak rate in different RCs and therefore permeability [16,17]. The highest runoff peak rate was also observed in Soil 3 (n = 0.018) with concave-divergent hillslopes (Hillslope No. 3) and the lowest runoff peak in Soil 1 (n = 0.015) with convex-convergent hillslopes (Hillslope No. 7).

A different profile affects the speed of discharge rate with slight changes (Figure 3). But in the CHs plan, due to changes in the width of the down plot, great changes can be observed in the discharge rate. Divergent or parallel plans can pass all runoff but in the convergent plan, there is some runoff accumulation due to a lower width. The individual effect of the hillslope profiles can be attributed to the speed of changes in the discharge rate due to the same amount of input discharge for all hillslopes (assuming hillslope plan and soil type constant). The runoff peak in concave hillslopes has a lower increase than the other hillslopes due to runoff accumulation in the outlet and higher initial discharge [46]. Thus, the runoff peak of the convergent hillslopes is smaller in the outlet than the parallel and divergent plans, which also reflects the higher time to concentration (greater discharge time). This could be consistent with the results obtained by Tucker and Bras [47] and Bonetti et al. [48] if we consider the differences generated at different scales. According to the results of this study, in all soils with constant RC and different CHs, the highest runoff peak is for divergent hillslopes with straight or concave profiles and the lowest amount for convex-convergent hillslopes.

#### *3.5. Hydrographs*

Divergent hillslopes have a decreasing rate of change due to parallel and direct movements of runoff toward the outlet and the effect of the lower part of plot on the outlet runoff volume, while parallel hillslopes have a constant rate of head change due to the constant up and down widths of the plot [49]. However, in convergent hillslopes due to a greater area of upward of plot over time, the rate of increase of runoff peak increases [50,51]. The study of the CHs also shows that the start time of runoff and time to concentration increases with increasing soil RCs (Figure 4). But in convex profiles, due to the steep slope of the lower part of the plot, with increasing soil RCs and consequently increasing its permeability, the runoff is discharged earlier and the time of concentration is reduced. So, the hydrographs of Soil 1, 2, and 3 are almost overlapped. Similarly, in convergent hillslopes, the start time of runoff decreases because the convergent hillslope saturation is faster than parallel and divergent hillslopes. The results of this research and previous researches, such as Sabzevari et al. [9] and Geranian et al. [32], show that convergence and convexity of hillslopes accelerate the start time of runoff and divergence and concaveness delay it.

**Figure4.**Hydrographsofrainfall-runoffprocessesatdifferentRCsand

 CHs.

37

2

#### *3.6. Recommendations, Challenges and Future Research Work*

We recommend delaying the start time of runoff and its subsequent destructive consequences in one of the following ways in landscapes with high levels of convexity morphology. Firstly, increasing surface RC by changing soil granulation or by changing vegetation, as well as enhancing soil horizon formations, biological processes, and water retention capacity [52,53]. For this purpose, modified natural or synthetic coatings could increase the RC, thereby reducing the start time and time of concentration and runoff peak rate of the runoff. Secondly, it could be possible to introduce an embankment for diverging artificial paths or reducing the convexity of hillslopes with an appropriate stair levelling, which could increase the start time of runoff and relatively reduce the runoff peak rate. This is very common in cultivated fields where terraces are used to retain water, conserve the soil, and reduce hillslope inclination [54,55].

Another point to be discussed at this point as a challenge for the future should be the effect of other external factors to assess the connectivity processes at the pedon scale [56]. It is well known that laboratory experiments are not representative of natural conditions [10,57]; however, they can be very useful to detect the specific factors that condition the activated dynamics and processes. In the future, other related factors that affect the connectivity of the surface and subsurface processes should be considered, such as the number of rock fragments [58], changes in organic carbon contains and litter cover [59,60], or different parent materials [61].

#### **4. Conclusions**

The main purpose of this research was to compare the variability of a surface runoff between different soil RCs under different CHs, both individually and together during rainfall-runoff processes. The results showed that the effect of the CH plan due to the change in runoff storage volume at the bottom of the plot was greater than the CH profile. With this, the divergent hillslopes showed a slower start time than the convergent and parallel hillslopes, and with increasing soil RC, the start time of runoff increased. With increasing soil RC, start time on convex profile hillslopes increased because of the higher slope at the end of the plot, and so did the convergent hillslopes due to the concentration of surface runoff at the bottom of the plot. In addition, the examination of the simultaneous effect of profile and plan of CHs and soil RC on time of concentration and runoff peak of surface runoff showed that the percentage of the impact of profile and plan changes with soil RC changes. Finally, the analysis of hydrographs on CHs showed that start time of the runoff and time of concentration increased with increasing soil RC. However, in convex profiles, due to the high slope of the lower end of the plot, by increasing soil RC and consequently increasing its permeability, the runoff is discharged earlier and the time of concentration is reduced. As a result, the hydrographs of all three types of soil are almost overlapped. In order to obtain comprehensive results, it is recommended that experiments be carried out on non-uniform soils using different rainfall intensities and different slope steepness in CHs.

**Author Contributions:** Conceptualization and methodology, M.M., N.A., A.T., J.R.-C.; formal analysis and investigation, M.M., N.A., A.T. and M.K.-H.; data curation, M.K.-H.; writing—original draft preparation, M.M., N.A. and A.T.; writing—review and editing, M.K.-H. and J.R.-C.; supervision, N.A.

**Funding:** This research received no external funding.

**Acknowledgments:** We would like to thank Mahta Amanian, a senior undergraduate student at the University of British Columbia, Canada, for editing this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**4. Conclusions**

Introduction of the concept Industry 4.0 can reduce losses and improve efficiency of the processes, because only comprehensive solutions can lead to real improvements. Developers of smart vehicles optimistically believe that autonomous vehicles on roads will improve the safety of the transport system, reducing the number of accidents due to the exclusion of the human factor.

At the same time, despite the existing positive experience of introducing intelligent technologies, there are still can appear situations when alleged improvements can lead to losses while vehicles production. There are also a lot of issues identified by analysts that could lead to critical situations while such vehicles operation.

First, it should be noted that operation of any complicated system is always closely connected to the risks. It is especially actual for transport systems. The complexity of transportation systems' risk analysis is due to the fact that an accident potentially may happen in any part of the route and the same events may lead to absolutely different consequences. That is why every decision for the existing transportation system's optimization should also be considered from the perspectives of risk management.

The most possible risks with the most drastic consequences can be grouped by types:

**1.** Technical:

In Industry 4.0, the system structure changes. The data collection level remains a separate dedicated level, as it is now, but the devices will be more intelligent and they will also significantly increase in numbers. All other functions will move to the high speed real-time network consisting of data processing center and cloud computing. The benefits of such a structure are as follows: (1) reduction of diversity of devices and processing hardware that are the most modern in the world; (2) separation of specific functions and (3) the use of augmented and virtual reality. All of it contribute to simplification of the management process, more efficient use of resources and, consequently, cost savings. This approach has not been implemented yet due to the low efficiency, reliability and throughput of communication channels between servers and data collecting devices. However, all these problems will be solved in new and

Industry 4.0 will be built in cyber-physical systems, which involves the integration of computation, networking and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computa-

An example of such a system today is the CarTel project at MIT [27] where a fleet of taxis collects real-time traffic information in the Boston area. This information is combined with historical data to calculate the fastest routes for particular times of the day. Another example that you may be familiar with is the Smart Grid. One of its definitions, based on [28], is: "A modernized electrical grid that uses information and communications technology to gather and act on information in an automated fashion … to improve the efficiency, reliability, economics, and sustainability of the production and distribution of

Finally, an example for a factory [29] is changing systems so that the energy consumption in a vehicle assembly line is reduced when the line does not operate. Today, many production lines continue running during breaks and weekends. Consider laser welding technology that remains powered up over weekends, so it can resume quickly on Monday. This practice consumes up to 12% of total energy consumption of the assembly line. With Industry 4.0 and cyber-physical systems, robots will go into standby mode as a matter of course during short production breaks and power down during longer breaks. Speed-controlled motors that reduce the energy required to run machines will be widespread. Such changes will significantly reduce energy consumption and will be taken into account up front as part of Smart

Introduction of the concept Industry 4.0 can reduce losses and improve efficiency of the processes, because only comprehensive solutions can lead to real improvements. Developers of smart vehicles optimistically believe that autonomous vehicles on roads will improve the safety of the transport system, reducing the number of accidents due to the exclusion of the

future systems.

126 Sustainable Cities - Authenticity, Ambition and Dream

tions and vice versa.

electricity."

Factory design practices.

**4. Conclusions**

human factor.

	- **a.** The risk of technological disasters if there are cyberattacks or failures in the control system.
	- **b.** The risk of increasing negative impact on environment because of expansion of the vehicles' fleet.
	- **a.** Complexity of the movement algorithms for rough terrain.
	- **b.** The absence of a panoramic view of the streets, impeding the routing.
	- **c.** Increased requirements to information processing speed.
	- **d.** Complexity of decision-making in unusual situations.
	- **a.** The high cost of infrastructure changes.
	- **b.** The high price of the vehicles.
	- **a.** Ambiguity of legal responsibility for causing damage and when organizing transportation.
	- **b.** Loss of privacy.

#### **6.** Social:

All problems described above have to be solved now, before the widespread of intelligent vehicles. The main but not the only trends in solving these problems are as follows:

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In this regard, the processes of the vehicles' intellectualization should be considered from the point of view of each stage of the life cycle and the most dangerous situations should be highlighted for the subsequent countermeasures development.


*5.3. Air Humidity*

The effect of the air humidity is here analyzed varying the critical electric field according to Reference [21]. A minimum value of 2 g/m3 and a maximum value of 25 g/m3 are here considered. Considering *m* = 0.8, a line diameter of 10 mm and the line details previously described, Figure 7 shows that the influence of the air humidity is substantially negligible. This is ascribed to the low variation of the critical electric field according to Reference [21].

**Figure 7.** Percentage variation of the number of flashovers as a function of the air humidity—the red dot line represents the case when the corona effect is not considered.