*3.6. Anti-Candida Activity of Extracts*

Antifungal activity was demonstrated via a broth microdilution method, using the standard drug fluconazole as a positive control. Among the different goji extracts tested against three *Candida albicans* strains, P samples displayed the best inhibitory activity. In particular, P1D, P1U, W1D, W1U, E1D, and B1D showed MIC50 values of 138 μg mL–1, 186 μg mL–1, 238 μg mL–1, 250 μg mL–1, >1000 μg mL–1, and >1000 μg mL–1, respectively, against all *Candida albicans* strains. Samples harvested at different commercial harvesting periods showed different MIC values. Interestingly, the P1D sample, harvested on 6 July 2015, was endowed with the best antifungal activity compared to the other harvesting periods (P2D, P3D, P4D and P5D) with a MIC100 of 476 μg mL–1, 552 μg mL–1, 609 μg mL–1, >1000 μg mL–1, and >1000 μg mL–1, respectively against all the considered strains (Table 4).

**Table 4.** Antifungal activity of goji berry extracts and reference compounds expressed as an MIC (μg/mL).

\* The data are expressed as a median.

Due to the presence in these extracts of considerable amounts of well-recognized antimicrobial metabolites, we further tested rutin and carvacrol against the same fungal strains [45]. Rutin was almost inactive with MIC values ≥64 μg/mL. Conversely, carvacrol displayed a strong inhibitory activity against *C. albicans* with MIC50 ranging from 0.125 μg/mL to 0.25 μg/mL. Despite the high amount of carvacrol present in B1D (Table 1), it displayed a weak anti-fungal activity thus suggesting that the concurrent presence of other secondary metabolites can enhance or decrease the bioactivity of these goji extracts.

Furthermore, considering the quantified components and the explicated anti-*Candida* effects, W4U represented the sample characterized by a higher amount of detected secondary metabolites and a positive upward trend in the biological activity. Many components, other than those quantified using the HPLC analyses, could contribute to the TPC and TFC of the W4U sample, whose activity was evidently correlated not only with the single analytes, even though they represented the most representative molecules in weight terms, but also in the complexity of the phytocomplex, evidently characterized by many different minor components. However, this clearly shows how important the work-up is in the preservation of many components (especially for flavonoid compounds, considering the effect of U on P) represented in extremely low concentrations. The high flavonoid component seemed to correlate, in particular, with a marked activity on tyrosinase.


**Preface to "Forensic Science and Legal Medicine: A Multidisciplinary Puzzle!"**

Even if the terms "forensic sciences"and "legal medicine"seem to be synonymous, they could be defined as two sides of the same coin. It has been demonstrated that they are different components of the application of medical knowledge upon the legal system. Legal medicine has greater relevance to civil and tort law, impacting upon patient care, whereas forensic medicine relates to criminal law and damage to (or by) patients.

The discipline of forensic science is remarkably complex and includes methodologies ranging from DNA analysis to chemical composition and pattern recognition. Many forensic practices were developed under the auspices of law enforcement and vetted primarily by the legal system rather than being subjected to scientific scrutiny and empirical testing. Furthermore, the recent experience of the COVID-19 pandemic suggests a pivotal role of the forensic autopsy in order to gather information about unknown diseases. As previously described, only a full autopsy can investigate the potential mechanisms of damage to organs or systems not readily accessible to biopsies, such as central nervous system or cardiovascular system, leading to appropriate healthcare strategies that are useful in the control of the disease. Another important perspective that underlines the importance of forensic sciences is related to the development of vaccine candidates and new therapies for the prevention and treatment of different diseases, with certain benefits for healthcare.

Furthermore, legal medicine plays a pivotal role in risk management. In healthcare, the term "risk management"refers to all processes employed to detect, monitor, assess, mitigate, and prevent risks in healthcare facilities and safeguard patient safety. Considering the importance of this issue, a further aim of this Special Issue is to assess the role and progress of research and training in the field of risk management.

This Special Issue, entitled "Forensic Science and Legal Medicine: A Multidisciplinary Puzzle!", collected accurate and up-to-date scientific information on all aspects of this theme, publishing original investigations, case series and case reports, and reviews in all forensic and medico-legal branches.

> **Francesco Sessa** *Editor*


**Notes/thanks/other declarations**

The Authors hereby acknowledge the funding support from TETFUND.

*A Comparative Evaluation of Biodiesel and Used Cooking Oil as Feedstock for HDRD… DOI: http://dx.doi.org/10.5772/intechopen.104393*


Table 3.

The design parameters are represented by the number of neurons and the number of delays of the network inputs and outputs, while the value of the weights of each neuron are derived from the network training from the data acquired from the

The design of the fault diagnosis schemes proposed for the application example considered in this chapter have been summarised in Section 4. However, the tool addressed in this chapter enhances the design of the banks of these fault estimators

This tool consists of a fault sensitivity analysis that has to be performed on the wind turbine simulator. It is aimed at defining the most sensitive measurements

practice, the considered fault signals have been injected into the wind turbine simulator, assuming that only a single fault may occur. Then, the relative mean square errors (RMSE) between the fault-free and faulty measured signals are eval-

selected. The results of the fault sensitivity analysis are summarised in Table 2 for

Nx ¼ Sx S∗ x

Sx ¼ xfð Þ� k xnð Þk

k k xnð Þk 2

xfð Þ� k xnð Þk

The value of Nx indicates the effect of the considered fault case with respect to

the general measured signal x kð Þ, with k ¼ 1, 2, …, N. The subscripts 'f' and 'n'

Fault fi 12345 Measurements uj, yl β1,m1 β2,m2 β3,m1 ωr,m1 ωr,m1 RMSE 11.29 0.98 2.48 1.44 1.45

indicate the faulty and the fault-free case, respectively. Therefore, the

Fault fi 6789 Measurements uj or yl β2,m1 β3,m2 τg,m ωg,m1 RMSE 0.80 0.73 0.84 0.77

ð Þk .

ð Þk with respect ujð Þk and yl

k k xnð Þk 2

 2

> 2

In particular, the fault sensitivity analysis is conducted on the basis of a selection algorithm that is performed by introducing the normalised sensitivity function Nx,

ð Þk considered in Section 2.2. In

ð Þk can be

(17)

(18)

(19)

ð Þk with respect to the fault conditions fi

uated, so that, for each fault, the most sensitive signal ujð Þk and yl

S∗ x ¼ max

system under diagnosis, see Hunt et al. [20].

3.3 Fault sensitivity analysis

Fault Detection, Diagnosis and Prognosis

depicted in Figure 3.

the wind turbine system.

defined in Eq. 17:

with

and

Table 2. Fault sensitivity fi

56

ujð Þk and yl

Fault sensitivity test.

measurements that are most affected by the considered fault lead to a value of Nx equal to 1. Otherwise, a smaller value of Nx, that is, close to zero, represents a signal x kð Þ not affected by the fault. Those signals characterised by high value of Nx are thus selected as the most sensitive measurements, and they will be considered in the design of the fault diagnosis modules of the bank sketched in Figure 3.

The complete results of the fault sensitivity analysis are summarised in Table 3. For each fault case, the selected signals of the wind turbine benchmark are marked as inputs or outputs.

This method represents a key feature of the proposed approach to fault diagnosis. In fact, the fault estimators of the bank of Figure 3 can be designed by exploiting a reduced number of signals, thus leading to a noteworthy simplification of the overall complexity, and a decrease in the computational cost of the training and identification phases.


**3. Results**

The sociodemographic data did not show statistically significant differences (*P* > 0.05) for the sex, age, weight and height between groups but it did show statistically significant differences (*P* < 0.05) for the body mass index (BMI) between groups (Table 1). Regarding Table 2 and Figure 3, ultrasound evaluations of the CFP and PF at the calcaneus thickness showed statistically significant differences (*P* < 0.01) with a decrease observed for the tendinopathy group with respect to the control group. For the PF midfoot and forefoot thickness, no significant differences (*P* > 0.05) were observed between groups.

**Table 1.** Sociodemographic features, pain scores and VISA-A scale of the sample.

Abbreviations: VAS, visual analogue scale. \* Mean ± standard deviation (SD) was applied. \*\* Student´s *t*-test for independent samples was performed. † Median ± interquartile range (IR) was used. ‡ Mann–Whitney *U* test was utilized. ‡‡ Fisher exact test was used.

**Table 2.** Ultrasonography measurements.

Abbreviations: PF, plantar fascia. \* Mean ± standard deviation (SD) (minimum–maximum) was applied. \*\* Student´s *t*-test for independent samples was performed. † Median ± interquartile range (IR) (minimum–maximum) was used. ‡ Mann–Whitney *U* test was utilized.

**Figure 3.** Bar graphs completed with the 95% CI to illustrate the thickness differences of calcaneus fat pad and plantar fascia at the calcaneus insertion, midfoot and forefoot between patients with chronic non-insertional Achilles tendinopathy and controls.

According to the linear regression analysis (Table 3), the prediction model for CFP thickness (*R*2 = 0.382) was determined by group (presence of Achilles tendinopathy) and sex, and also the prediction model for PF thickness at calcaneus insertion (*R*2 = 0.323) was determined by group and weight. The rest of the independent variables did not predict these statistically significant differences between the case and control groups.

**Table 3.** Multivariate predictive analysis for CFP and PF thicknesses for patients with Achilles tendinopathy and controls.

 \* Multiplay: Group (control = 0; Tendinopathy = 1); Sex (women = 0; men = 1). † *P*-value < 0.05 for a 95% confidence interval was shown. ‡ *P*-value < 0.001 for a 95% confidence interval was shown.

## **4. Discussion**

USI was considered a valid and reliable imaging method to assess soft tissue structures, architectures and sizes. Considering the strong relationship between the Achilles tendon and PF by their locations and insertions, this study may provide a new approach to the assessment and treatment of individuals with AT. This is the first study where the thickness of the PF and CPF were evaluated with USI in patients with AT, variables of interest due to ankle and foot biomechanics [13].

Foot overpronation was considered a risk factor to the predisposition of AT [31] through disturbances in ankle biomechanics producing an extra mechanical stress on the soft tissue structures, such as the PF. Those findings were related to a thickness decrease in the PF at the calcaneus, a structure that serves as a link between the PF and the Achilles tendon. In addition, Cornwall et al. [32] argued that an excessive foot pronation may increase the foot mobility and the level of stress applied through PF. Many studies have also reported an altered foot arch in patients with disturbances in PF modifies the capability of absorbing ground reaction forces [32,33]. These structural alterations were related to the Achilles tendon capacity to store and release energy during the gait [34]. Our findings showed an altered thickness in the PF at the calcaneus insertion, which could be explained by the changes in the ankle biomechanics, the extrinsic and intrinsic foot muscles observed in patients with AT.

CFP protects the rear-foot from the stress produced during the initial phase of locomotion and the heel-strike actions. CFP thickness was described as a valid and reliable method to quantify and assess the heel fat pad by USI [24]. Several authors argued that CFP has been implicated in foot and plantar disturbances, such as diabetes [35], fractures [36] and plantar heel pain [37]. To our knowledge, this is the first study to evaluate the CPF thickness in patients with AT. The results of our study showed a decrease in CPF thickness in the tendinopathy group, so it could be considered a relevant variable for the diagnosis and management of individuals with AT.

The findings of the present study did not intend to provide an explanation about the etiology of AT. In addition, several studies argued that the etiology of the tendinopathy was caused by multiple factors. The authors try to offer a novel approach to evaluate and quantify soft tissue structures that usually present disturbances in AT by USI. Thus, USI assessment of the reduction in PF in subjects with AT could be useful to carry out a follow-up for the interventions prescribed to treat AT.


**2. Methodology**

*Deep Learning Applications*

consumption for a full-scale waste rock pile.

costly and time-consuming.

For day-to-day mine site management, flood control and contaminant remediation plan are dependent on the evaluation and prediction of drainage flow rates and drainage chemistries. Various methodologies have been developed over the past several decades to predict the drainage from waste rock storages. For predicting drainage flow rates, a numerical model to simulate groundwater flow through unsaturated bed or layers of earth is developed in [3]. In reference [4], a water balance approach is proposed to calculate the conservation of total water flow through waste rock piles by dividing the whole hydrological process into independent components. In terms of predicting drainage chemistries, a numerous numerical models enabled with mass transport effect are developed to evaluate the geochemical reaction and transport inside waste rock piles [5–7]. Furthermore, using dimensional equation to correlate drainage chemistries with seepage flow rates from waste rock piles is explored in [8]. In reference [9], the effectiveness of a cover system is assessed and a Multiphysics model is developed to predict the iron loading and lime

However, there are several limitations with above predictive models. For example: 1. Many predictive models are based on lab testing and then scaled up to predict the result in the field, but there is little comprehensive understanding on how to scale up; 2. Simplification and assumptions of geo-bio-chemical processes for the geochemical reaction and leaching process in waste rock storages are critical to the accuracy of the predictive models; 3 Lab or field characterization of material and transport properties related to predictive models is essential, which is also very

To understand and minimize the environmental impact from the contaminated drainage, routine monitoring of waste rock storages is required by many governmental regulators. With the rapid development of computer and sensing technologies, constant and comprehensive monitoring on the waste rock storages is now possible for many mine sites. Daily or even hourly monitoring data become available for many key parameters such as precipitation, temperature, wind, internal temperature, gas concentrations, air/water flow rates, drainage chemistries, etc. These monitoring data are accumulated to weekly, monthly and yearly datasets, and become so huge and complex that traditional data analysis approaches are inadequate to handle and investigate them. As one of the most famous machine learning technologies, artificial neural network not only has the advantages of high processing speed and high computational accuracy, but also enables a machine to mimic human learning behavior and problem solving functions. Thus using neural network to investigate the huge monitoring datasets and further predict drainage flow rates and drainage chemistries from waste rock storages shows very promising potentials. For example, the concentrations of sulphate, chlorine, total dissolved solids and total suspended solids in mine water are predicted by artificial neural network based on the input of pH, temperature and hardness in [10]. Heavy metal included in acid rock drainage is investigated by support vector machine and neural network for a copper mine in Iran [11]. Five machine learning approaches to predict copper concentration are compared in [12]. A feedforward neural network with weather input is proposed to

In this book chapter, a refined feedforward neural network based on [13] will be introduced to learn from historical monitoring data and then predict the drainage flow rate, in addition, the refined neural network will also be extended to predict the drainage chemistries in the field. Compared with above traditional predictive models, the proposed neural network approach requires much less simplification and assumption of geo-bio-chemical processes involved and it can significantly reduce characterization cost for mining companies, as the monitoring data inherently contain the information of all the underlying physical mechanisms within real

predict drainage flow rates for a full scale waste rock pile [13].

**102**

#### **2.1 Feedforward neural network**

The Feedforward neural network is an artificial neural network wherein connections between the artificial neurons do not form a cycle, which is different from its variant: recurrent neural networks. The artificial neurons are capable of simulating basic learning behaviors through receiving inputs, calculating a weighted sum and then passing the sum through a transformation known as activation function to produce outputs. The mathematical calculation for an artificial neuron in a feedforward neural network is generally illustrated as follows:

$$\mathbf{X}\_{t,p} = \mathfrak{g}\_{t,p} \left( \sum\_{i=1}^{q} \mathbf{X}\_{t-1,i} w\_{t,p,i} + \mathbf{b}\_{t,p} \right) \tag{1}$$

where *t* denotes the layer number and *p* denotes the order number in that layer. The combination of *t* and *p* can be used to determine the location of the neuron in the network. For the *p*th neuron located at the *t*th layer, there are q inputs from *Xt*−**1 1**, through *Xt q* −**1**, , and also weights from *wt p*, ,**1** to . *wt,p,q* In addition, *bt p*, is the bias input, ϕ*t p*, is the activation function and *Xt p*, is the output of that neuron. After calculating the output of *Xt p*, based on Eq. (1), it may further propagate to the input of the next layer or leave the network as the output of the whole feedforward neural network.


**EMBEDDED SYSTEMS – THEORY AND DESIGN METHODOLOGY**

Edited by **Kiyofumi Tanaka**

**INTECHOPEN.COM**

#### **Embedded Systems - Theory and Design Methodology**

http://dx.doi.org/10.5772/2339 Edited by Kiyofumi Tanaka

#### **Contributors**

Yongpan Liu, Pei Zhang, Shuangchen Li, Huazhong Yang, Pablo Peñil, Fernando Herrera, Hector Posadas, Eugenio Villar, Nikolaos Kavvadias, Vasiliki Giannakopoulou, Kostas Masselos, Jawar Singh, Balwinder Raj, Makoto Sugihara, Zhe Peng, Longhua Ma, Shao Meng, Iñigo Ugarte, Susanna Pantsar-Syvaniemi, Philippe Dhaussy, Jean-Charles Roger, Frédéric Boniol, Takaaki Goto, Yasunori Shiono, Tomoo Sumida, Tetsuro Nishino, Takeo Yaku, Kensei Tsuchida, Mouaaz Nahas, Prof. Dr. Ahmed M Nahhas,, Adi Maaita, Konstantin Mikhaylov, Jouni Kaarlo Tervonen, Dmitry Fadeev, Michael Dossis, Juergen Mottok, Frank Schiller, Thomas Zeitler, Tong-Ying Juang, Yung-Yuan Derek Chen, Rodrigo Santos, Javier Orozco, Matthias Hagner, Ursula Goltz

#### **© The Editor(s) and the Author(s) 2012**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2012 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Embedded Systems - Theory and Design Methodology Edited by Kiyofumi Tanaka

p. cm. Print ISBN 978-953-51-0167-3 eBook (PDF) ISBN 978-953-51-5628-4


**3. Inclusiveness in learning**

Inclusiveness in learning from the general perspective is all about giving every child regardless of class and nature same opportunity to go to school with a view to acquiring the desired knowledge and skills for future development survival through all inclusive learning process. It was the UN Department of Economic and Social Affairs Disability (UNDESAD) in 2018 that brought inclusion education to public purview as the theme of International Day of Persons with Disabilities an initiation that has been described as a clarion call for all children of all abilities for the sake of education to be in the classroom [15].

Inclusive education could be said to be one important avenue through which children are given that ample opportunity to go to school with the sole purpose of learning, developing the required skills to succeed in life. This implies that inclusion is carry every child along a message that is in line with the United Nations Sustainable Development Goals (UN-SDGs). The realization of this philosophy will be better pronounced when students with learning ability are included in every aspect of the global educational program or process [16].

As an ambition plan of action of the international Community, the 2030 Agenda of leave no one behind, is aimed at creating a peaceful and prosperous world, where dignity of an individual person and equality among all is applied as the fundamental principle, cutting across the three pillars of the work of the United Nations: Development, Human Rights and Peace and Security. Its main purpose is to ensure the full and equal participation of persons with disabilities in all spheres of society and creating a enabling environments by, for and with persons with disabilities.  It is all about therefore, all children, same classrooms and same schools whether with disability or not even speakers of minority languages [17].

Inclusion in education in a broad term means that all students should be able to access and gain equal opportunities to education and learning and on no ground should any child be discriminated upon as to denying him or her the right to effective formal education. This is built on the premise that special and personalized educational programs are more effective with children with special needs as such mixed experiences will enhance their social interactions leading to successful life. The idea behind inclusion in education is to make way for the utilization of special classrooms and schools for students disabilities. The underlining factor is that the protagonists of inclusion in education main intention is to move away from seclusion models of special education to the fullest extent practical. This on the believe that that it is to the social benefit of general education students and special education students alike, with the more able students serving as peer models and those less able serving as motivation for general education students to learn empathy [18]. It is pertinent to state that the application of these practices differs as some schools most frequently utilize the inclusion model for selecting students with mild to moderate special needs [19]. While those schools that implement to the fullest inclusiveness, though rare, do not separate "general education" and "special education" programs; rather, such schools are restructured in such a way that all students learn together [20].

It is very pertinent to state that inclusive education is quite different from the 'integration' or 'mainstreaming' model of education, which tended to be concerned with selected few while in inclusive education much attention is focused on optimal involvement of students with disabilities respecting their social, civil, and educational rights. The obvious is that when one is said to be included, it is not limited to physical and cognitive disabilities, but rather it includes the full range of human diversity with respect to ability, language, culture, gender, age and of other forms of human differences [21]. As has been noted, student performance and behavior in educational tasks can be profoundly affected by the way they feel, they are seen and judged by others. This is because, when one is viewed as inferior, the abilities seem to diminish [22]. It is on this ground that the United Nations Sustainable Development Goal 4 recognizes the essence for adequate physical infrastructures and the necessity for safe, inclusive learning environments [23].

### **4. Contemporary teaching and learning environments**

Unless you living under a rock, that is why you may not have noticed how dramatically the education system has changed in the past few decades especially in developed nations like the United States (US), United Kingdom (UK), Canada, Germany, Europe and others. The true picture is that the conventional teacherknows-all based learning system has been overtaken by higher-order thinking

#### *Effect of Inclusiveness and Contemporary Teaching and Learning Environments on Students… DOI: http://dx.doi.org/10.5772/intechopen.114104*

with future skills such as problem-solving and books being replaced by tablets or mobiles. All the same, one thing that has not drastically change in line with the trend in the education system is the environment where teaching and learning take effect. The truth is that some schools make use of contemporary classrooms well equipped with modern facilities such as smart boards; tablets, computers, among others, whereas, most schools are nowhere closer. Likewise, teacher in most schools still apply the old method of teaching which is standing in the front and lecturing the students all at once. In the present dispensation, both the pattern of delivering the lesson and environment negatively affect students as they are neither developed nor equipped with problem solving skills, innovative skills as well as creative and logical thinking in a world ruled by technology. The one way out of this morass, is the utilization of modern teaching environment enriched with technologies of our time.

The concept of contemporary learning and teaching environment has brought about transformation with schools in most nations of the world more so those of developed countries as they fuse the physical spaces with modern teaching approaches. In contemporary learning and teaching environment, teachers with flexible classroom spaces and integrated technology which facilitate the mix of independent, small-group and whole class learning which pave way for students' success. Ideally, contemporary learning environment is made up three basic elements. These are; connected devices (such as notebooks, tablets or even smartphones); audiovisual tools (including projectors and touch-screen displays); and purposeful furniture that allows students to learn in different ways at different times (such as standing desks, collaborative workstations and connected seating.

Inasmuch as the idea of equipping classrooms with modern devices is not anew lexicon, the trending fact is that most devices in students' possession are not optimally being utilized due the fact they are seen as distracter rather than effective tools needed for functional teaching and learning. In the past, what was common in most schools especially those of the developing nations was the use of laptops and tablets that were rarely connected to the internet as funds were not invested on acquiring these tools for teaching so available ones were self-provided. In situations where much have been invested in one-to-one programs, leaders have sometimes been disappointed by lackluster adoption, found it difficult to continue funding the programs over time or failed to make the networking upgrades necessary to ensure a high level of performance.

In the contrary, the assumption for designing contemporary learning and teaching environments is that students will have an uninterrupted and regular access to internet connectivity, supported by technology and teachers' training that will ensure that students' devices play pivotal role in the classroom in the course of teaching and learning. Furthermore, in contemporary learning and teaching environment audiovisual solutions directly support student learning and engagement. Audiovisual solutions depending on grade level and instructional goals, may include interactive whiteboards, document cameras, multi-touch digital displays, projectors and even microphone lanyards for soft-spoken teachers in larger classrooms. The idea is not to implement any single audiovisual tool with a one size-fits-all approach, but rather to outfit classrooms with the solutions that will best help teachers reach their students.

The Contemporary learning and teaching environment, so-to-speak a learning space (a classroom) that is more interactive, innovative, and connected with the primary purpose of helping students learn better and follow the modern ways of learning to meet future challenges. A modern learning environment basically comprises of but is not limited to: **audiovisual aids;** such as touch screen displays and projectors, **connected devices:** Such as tablets, notebooks, or smartphones [24]. Indeed, the three key aspects of contemporary learning and teaching environment are technology, flexible furniture and audiovisual tools which help propel classrooms and students into the future.