Search results for: long short-term memory networks

Authors: Sa'ad Ali

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This study explores wasta as an example of a social network and how it impacts business practice in the Arab Middle East, drawing links with social network impact in different regions of the world. In doing so, particular attention will be paid to the socio-economic and cultural influences on business practice. In exploring relationships in business, concepts such as social network analysis, social capital and group identity are used to explore the different forms of social networks and how they influence business decisions and practices in the regions and countries where they prevail. The use of social networks to achieve objectives is known as guanxi in China, wasta in the Arab Middle East and blat in ex-Soviet countries. Wasta can be defined as favouritism based on tribal and family affiliation and is a widespread practice that has a substantial impact on political, social and business interactions in the Arab Middle East. Within the business context, it is used in several ways, such as to secure a job or promotion or to cut through bureaucracy in government interactions. The little research available is fragmented, and most studies reveal a negative attitude towards its usage in business. Paradoxically, while wasta is widely practised, people from the Arab Middle East often deny its influence. Moreover, despite the regular exhibition of a negative opinion on the practice of wasta, it can also be a source of great pride. This paper addresses this paradox by conducting a positional literature review, exploring the current literature on wasta and identifying how the identified paradox can be explained. The findings highlight how wasta, to a large extent, has been treated as an umbrella concept, whilst it is a highly complex practice which has evolved from intermediary wasta to intercessory wasta and therefore from bonding social capital relationships to more bridging social capital relationships. In addition, the research found that Islam, as the predominant religion in the region and the main source of ethical guidance for the majority of people from the region, plays a substantial role in this paradox. Specifically, it is submitted that wasta can be viewed positively in Islam when it is practised to aid others without breaking Islamic ethical guidelines, whilst it can be viewed negatively when it is used in contradiction with the teachings of Islam. As such, the unique contribution to knowledge of this study is that it ties together the fragmented literature on wasta, highlighting and helping us understand its complexity. In addition, it sheds light on the role of Islam in wasta practices, aiding our understanding of the paradoxical nature of the practice.

Keywords: Islamic ethics, social capital, social networks, Wasta

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Tehreem Fatima, Enjun Xia

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The research investigates the relationship between total oil consumption and transport oil consumption, GDP, oil price, and oil reserve in order to forecast future oil demand in China. Annual time series data is used over the period of 1980 to 2015, and for this purpose, an oil demand function is estimated by applying structural time series model (STSM). The technique also uncovers the Underline energy demand trend (UEDT) for China oil demand and GDP, oil reserve, oil price and UEDT are considering important drivers of China oil demand. The long-run elasticity of total oil consumption with respect to GDP and price are (0.5, -0.04) respectively while GDP, oil reserve, and price remain (0.17; 0.23; -0.05) respectively. Moreover, the Estimated results of long-run elasticity of transport oil consumption with respect to GDP and price are (0.5, -0.00) respectively long-run estimates remain (0.28; 37.76;-37.8) for GDP, oil reserve, and price respectively. For both model estimated underline energy demand trend (UEDT) remains nonlinear and stochastic and with an increasing trend of (UEDT) and based on estimated equations, it is predicted that China total oil demand somewhere will be 9.9 thousand barrel per day by 2025 as compare to 9.4 thousand barrel per day in 2015, while transport oil demand predicting value is 9.0 thousand barrel per day by 2020 as compare to 8.8 thousand barrel per day in 2015.

Keywords: china, forecasting, oil, structural time series model (STSM), underline energy demand trend (UEDT)

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Authors: Noel Jeygar Robert, V. K.Vidya

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Cognitive radio is a software-defined radio technology that allows cognitive users to operate on the vacant bands of spectrum allocated to licensed users. Cognitive radio plays a vital role in the efficient utilization of wireless radio spectrum available between cognitive users and licensed users without making any interference to licensed users. The spectrum allocation followed by spectrum sharing is done in a fashion where a cognitive user has to wait until spectrum holes are identified and allocated when the licensed user moves out of his own allocated spectrum. In this paper, we propose a self –coexistence strategy using bargaining and Cournot game model for achieving spectrum allocation in cognitive radio networks. The game-theoretic model analyses the behaviour of cognitive users in both cooperative and non-cooperative scenarios and provides an equilibrium level of spectrum allocation. Game-theoretic models such as bargaining game model and Cournot game model produce a balanced distribution of spectrum resources and energy consumption. Simulation results show that both game theories achieve better performance compared to other popular techniques

Keywords: cognitive radio, game theory, bargaining game, Cournot game

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: M. Testa, L. Peotta, S. Giusiano, B. Lazzolino, U. Manera, A. Canosa, M. Grassano, F. Palumbo, A. Bombaci, S. Cabras, F. Di Pede, L. Solero, E. Matteoni, C. Moglia, A. Calvo, A. Chio

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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease of adulthood, which primarily affects the central nervous system and is characterized by progressive bilateral degeneration of motor neurons. The degeneration processes in ALS extend far beyond the neurons of the motor system, and affects cognition, behaviour and language. To outline the prevalence of language deficits in an ALS cohort and explore their profile along with demographic and neuropsychological data. A full neuropsychological battery and language assessment was administered to 56 ALS patients. Neuropsychological assessment included tests of executive functioning, verbal fluency, social cognition and memory. Language was assessed using tests for verbal comprehension, production and pragmatics. Patients were cognitively classified following the Revised Consensus Criteria and divided in three groups showing different levels of language deficits: group 1 - no language deficit; group 2 - one language deficit; group 3 - two or more language deficits. Chi-square for independence and non-parametric measures to compare groups were applied. Nearly half of ALS-CN patients (48%) reported one language test under the clinical cut-off, and only 13% of patents classified as ALS-CI showed no language deficits, while the rest 87% of ALS-CI reported two or more language deficits. ALS-BI and ALS-CBI cases all reported two or more language deficits. Deficits in production and in comprehension appeared more frequent in ALS-CI patients (p=0.011, p=0.003 respectively), with a higher percentage of comprehension deficits (83%). Nearly all ALS-CI reported at least one deficit in pragmatic abilities (96%) and all ALS-BI and ALS-CBI patients showed pragmatic deficits. Males showed higher percentage of pragmatic deficits (97%, p=0.007). No significant differences in language deficits have been found between bulbar and spinal onset. Months from onset and level of impairment at testing (ALS-FRS total score) were not significantly different between levels and type of language impairment. Age and education were significantly higher for cases showing no deficits in comprehension and pragmatics and in the group showing no language deficits. Comparing performances at neuropsychological tests among the three levels of language deficits, no significant differences in neuropsychological performances were found between group 1 and 2; compared to group 1, group 3 appeared to decay specifically on executive testing, verbal/visuospatial learning, and social cognition. Compared to group 2, group 3 showed worse performances specifically in tests of working memory and attention. Language deficits have found to be spread in our sample, encompassing verbal comprehension, production and pragmatics. Our study reveals that also cognitive intact patients (ALS-CN) showed at least one language deficit in 48% of cases. Pragmatic domain is the most compromised (84% of the total sample), present in nearly all ALS-CI (96%), likely due to the influence of executive impairment. Lower age and higher education seem to preserve comprehension, pragmatics and presence of language deficits. Finally, executive functions, verbal/visuospatial learning and social cognition differentiate the group with no language deficits from the group with a clinical language impairment (group 3), while attention and working memory differentiate the group with one language deficit from the clinical impaired group.

Keywords: amyotrophic lateral sclerosis, language assessment, neuropsychological assessment, language deficit

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Authors: Alexander G. Yushchenko

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From a multi-science point of view, we analyze threats to security resulting from globalization of international information space and information and communication aggression of Russia. A definition of Ruschism is formulated as an ideology supporting aggressive actions of modern Russia against the Euro-Atlantic community. Stages of the hybrid war Russia is leading against Ukraine are described, including the elements of subversive activity of the special services, the activation of the military phase and the gradual shift of the focus of confrontation to the realm of information and communication technologies. We reveal an emergence of a threat for democratic states resulting from the destabilizing impact of a target state’s mass media and social networks being exploited by Russian secret services under freedom-of-speech disguise. Thus, we underline the vulnerability of cyber- and information security of the network society in regard of hybrid war. We propose to define the latter a synergetic war. Our analysis is supported with a long-term qualitative monitoring of representation of top state officials on popular TV channels and Facebook. From the memetics point of view, we have detected a destructive psycho-information technology used by the Kremlin, a kind of information catastrophe, the essence of which is explained in detail. In the conclusion, a comprehensive plan for information protection of the public consciousness and mentality of Euro-Atlantic citizens from the aggression of the enemy is proposed.

Keywords: cyber and information security, hybrid war, psycho-information technology, synergetic war, Ruschism

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Authors: Ioannis Binas, Marios Moschakis

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Electrical faults in transmission and distribution networks can have great impact on the electrical equipment used. Fault effects depend on the characteristics of the fault as well as the network itself. It is important to anticipate the network’s behavior during faults when planning a new equipment installation, as well as troubleshooting. Moreover, working backwards, we could be able to estimate the characteristics of the fault when checking the perceived effects. Different transformer winding connections dominantly used in the Greek power transfer and distribution networks and the effects of 1-phase to neutral, phase-to-phase, 2-phases to neutral and 3-phase faults on different locations of the network were simulated in order to present voltage sag characteristics. The study was performed on a generic network with three steps down transformers on two voltage level buses (one 150 kV/20 kV transformer and two 20 kV/0.4 kV). We found that during faults, there are significant changes both on voltage magnitudes and on phase angles. The simulations and short-circuit analysis were performed using the PSCAD simulation package. This paper presents voltage characteristics calculated for the simulated network, with different approaches on the transformer winding connections during symmetrical and asymmetrical faults on various locations.

Keywords: Phase angle shift, power quality, transformer winding connections, voltage sag propagation

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Authors: Yasmina Houda Bendahma, Tewfik Bouchaour, Meriem Merad, Ulrich Maschke

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The purpose of this work is to study retention of dye dissolved in distilled water, by an hydrophilic acrylic polymer network. The polymer network considered is Poly (2-hydroxyethyl methacrylate) (PHEMA): it is prepared by photo-polymerization under UV irradiation in the presence of a monomer (HEMA), initiator and an agent cross-linker. PHEMA polymer network obtained can be used in the retention of dye molecules present in the wastewater. The results obtained are interesting in the study of the kinetics of swelling and de-swelling of cross linked polymer networks PHEMA in colored aqueous solutions. The dyes used for retention by the PHEMA networks are eosin Y and Malachite Green, dissolved in distilled water. Theoretical conformational study by a simplified molecular model of system cross linked PHEMA / dye (eosin Y and Malachite Green), is used to simulate the retention phenomenon (or Docking) dye molecules in cavities in nano-domains included in the PHEMA polymer network.

Keywords: dye retention, molecular modeling, photochemically crosslinked polymer network, swelling deswelling, PHEMA, HEMA

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Authors: Ahmad Alhawarat, Mustafa Mamat, Mohd Rivaie, Ismail Mohd

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Conjugate gradient method has been enormously used to solve large scale unconstrained optimization problems due to the number of iteration, memory, CPU time, and convergence property, in this paper we find a new class of nonlinear conjugate gradient coefficient with global convergence properties proved by exact line search. The numerical results for our new βK give a good result when it compared with well-known formulas.

Keywords: conjugate gradient method, conjugate gradient coefficient, global convergence

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Authors: Bai-Chen Xie, Biao Li

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With the deepening of international exchanges and investment, the international comparison of power grid firms has become the focus of regulatory authorities. Ignoring the differences in the economic environment, resource endowment, technology, and other aspects of different countries or regions may lead to efficiency bias. Based on the Meta-frontier model, this paper divides China and Japan into two groups by using the data of China and Japan from 2006 to 2020. While preserving the differences between the two countries, it analyzes and compares the efficiency of the transmission and distribution industries of the two countries. Combined with the four-component stochastic frontier model, the efficiency is divided into transient and persistent efficiency. We found that there are obvious differences between the transmission and distribution sectors in China and Japan. On the one hand, the inefficiency of the two countries is mostly caused by long-term and structural problems. The key to improve the efficiency of the two countries is to focus more on solving long-term and structural problems. On the other hand, the long-term and structural problems that cause the inefficiency of the two countries are not the same. Quality factors have different effects on the efficiency of the two countries, and this different effect is captured by the common frontier model but is offset in the overall model. Based on these findings, this paper proposes some targeted policy recommendations.

Keywords: transmission and distribution industries, transient efficiency, persistent efficiency, meta-frontier, international comparison

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Authors: Jongho Park, Ui-Cheol Shin, Jinwoong Choi, Sungnam Hong, Sun-Kyu Park

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While the numerous structures built the industrialization are aging, the effort for the maintenance is concentrated in many countries. However, the traffic jam, environmental damage, and enormous maintenance cost, and etc become a problem. So, in order to solve this, the modular bridge has been studied. This bridge is the structure which utilizes and assembles the standard precast member. Through this, the substitution of the existing bridge and advantage of the easy maintenance will be achieved. However, the reliability in the long-term behavior is insufficient due to the junction part between modular precast members. Therefore, in this research, the cyclic load loading experiment was performed on the junction and deflection was analyzed by long-term service in modular slab connection. The deflection of modular slab with junction was mostly generated when initial and final test.

Keywords: modular bridge, deflection, cyclic loading, junction

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Authors: Małgorzata Osowiecka

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Negative emotions are rather seen as creativity inhibitor. On the other hand, it is worth noting that negative emotions may be good for our functioning. Negative emotions enhance cognitive resources and improve evaluative processes. Moreover maintaining a negative emotional state allow for cognitive reinterpretation of the emotional stimuli, what is good for our creativity, especially cognitive flexibility. Writing a diary or writing about difficult emotional experiences in general can be the way to not only improve psychical health, but also – enhance creative behaviors. Thanks to translating difficult emotions to the verbal level and giving them ‘a name’ or ‘a label’, we can get easier access to both emotional content of an experience and to the semantic content, without the need of speaking out loud. Expressive writing improves academic results and the efficiency of working memory. The classical method of writing about emotions consists in a long-term process of describing negative experiences. Present research demonstrate the efficiency of this process over a shorter period of time - one writing session, on school children sample. Participants performed writing task. Writing task had two different topics: emotions connected with their negative emotions (expressive writing) and content not connected with negative emotional state (writing about one’s typical day). Creativity was measured by Guilford’s Alternative Uses Task. Results have shown that writing about negative emotions results in the higher level of divergent thinking in all three parameters: fluency, flexibility and originality. After the writing task mood of expressive writing participants remained negative more than the mood of the controls. Taking an expressive action after a difficult emotional experience can support functioning, which can be observed in enhancement of divergent thinking. Writing about emotions connected with negative experience makes one more creative, than writing about something unrelated with difficult emotional moments. Research has shown that young people should not demonize negative emotions. Sometimes, properly applied, negative emotions can be the basis of creation. Preparation was supported by a The Young Scientist University grant titled ‘Dynamics of emotions in the creative process’ from The Polish Ministry of Science and Higher Education.

Keywords: creativity, divergent thinking, emotions, expressive writing

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Authors: Jessica Hemberg, Amanda Sundqvist, Yulia Korzhina, Lillemor Östman, Sofia Gylfe, Frida Gädda, Lisbet Nyström, Henrik Groundstroem, Pia Nyman-Kurkiala

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Purpose: There are large gaps in the literature on COVID-19 pandemic-related mental health outcomes and after-effects specific to adolescents and young adults. The study's aim was to explore adolescents’ and young adults’ experiences of well-being, health, and loneliness during the COVID-19 pandemic. Method: A qualitative exploratory design with qualitative content analysis was used. Twenty-three participants (aged 19-27; four men and 19 women) were interviewed. Results: Four themes emerged: Changed social networks – fewer and closer contacts, changed mental and physical health, increased physical and social loneliness, well-being, internal growth, and need for support. Conclusion: Adolescents’ and young adults’ experiences of well-being, health, and loneliness are subtle and complex. Participants experienced changed social networks, mental and physical health, and well-being. Also, internal growth, need for support, and increased loneliness were seen. Clear information on how to seek help and support from professionals should be made available.

Keywords: adolescents, COVID-19 pandemic, health, interviews, loneliness, qualitative, well-being, young adults

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Authors: Sefik Can Karakaya, Ibrahim Demir

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In this study, the relationship between musical preferences and personality traits has been investigated in terms of Spotify audio analysis features. The aim of this paper is to build such a classifier capable of segmenting people into their Myers-Briggs Type Index (MBTI) personality type based on their Spotify playlists. Music takes an important place in the lives of people all over the world and online music streaming platforms make it easier to reach musical contents. In this context, the motivation to build such a classifier is allowing people to gain access to their MBTI personality type and perhaps for more reliably and more quickly. For this purpose, logistic regression and deep neural networks have been selected for classifier and their performances are compared. In conclusion, it has been found that musical preferences differ statistically between personality traits, and evaluated models are able to distinguish personality types based on given musical data structure with over %60 accuracy rate.

Keywords: myers-briggs type indicator, music psychology, Spotify, behavioural user profiling, deep neural networks, logistic regression

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Authors: Rita U. Onolemhemhen, Saheed L. Bello, Akin P. Iwayemi

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The effectiveness of energy demand policy depends on identifying the key drivers of energy demand both in the short-run and the long-run. This paper examines the influence of regional differences on the link between energy demand and other explanatory variables for Nigeria, China and USA using the Vector Error Correction Model (VECM) approach. This study employed annual time series data on energy consumption (ED), real gross domestic product (GDP) per capita (RGDP), real energy prices (P) and urbanization (N) for a thirty-six-year sample period. The utilized time-series data are sourced from World Bank’s World Development Indicators (WDI, 2016) and US Energy Information Administration (EIA). Results from the study, shows that all the independent variables (income, urbanization, and price) substantially affect the long-run energy consumption in Nigeria, USA and China, whereas, income has no significant effect on short-run energy demand in USA and Nigeria. In addition, the long-run effect of urbanization is relatively stronger in China. Urbanization is a key factor in energy demand, it therefore recommended that more attention should be given to the development of rural communities to reduce the inflow of migrants into urban communities which causes the increase in energy demand and energy excesses should be penalized while energy management should be incentivized.

Keywords: economic growth, energy demand, income, real GDP, urbanization, VECM

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: Moheb R. Girgis, Tarek M. Mahmoud, Bahgat A. Abdullatif, Ahmed M. Rabie

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Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. We usually need multiple gateways, which takes time and costs a lot of money set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. In this paper, we concentrate on this challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

Keywords: wireless mesh networks, genetic algorithms, simulated annealing, topology design

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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Authors: Shilu Deepa Thomas, P. Jayaprakash, Dorota Łazewska, Katarzyna Kieć-Kononowicz, B. Sadek

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A large body of evidence suggests the involvement of cognitive impairment, increased levels of inflammation and oxidative stress in the pathogenesis of autism spectrum disorder (ASD). ASD commonly coexists with psychiatric conditions like anxiety and cognitive challenges, and individuals with ASD exhibit significant levels of inflammation and immune system dysregulation. Previous Studies have identified elevated levels of pro-inflammatory markers such as IL-1β, IL-6, IL-2 and TNF-α, particularly in young children with ASD. The current therapeutic options for ASD show limited effectiveness, signifying the importance of exploring an efficient drugs to address the core symptoms. The role of histamine H3 receptors (H3Rs) in memory and the prospective role of H3R antagonists in pharmacological control of neurodegenerative disorders, e.g., ASD, is well-accepted. Hence, the effects of chronic systemic administration of H3R antagonist E159 on autistic-like repetitive behaviors, social deficits, memory and anxiety parameters, as well as neuroinflammation in Black and Tan BRachyury (BTBR) mice, were evaluated using Y maze, Barnes maze, self-grooming, open field and three chamber social test. E159 (2.5, 5 and 10 mg/kg, i.p.) dose-dependently ameliorated repetitive and compulsive behaviors by reducing the increased time spent in self-grooming and improved reduced spontaneous alternation in BTBR mice. Moreover, treatment with E159 attenuated disturbed anxiety levels and social deficits in tested male BTBR mice. Furthermore, E159 attenuated oxidative stress by significantly increasing GSH, CAT, and SOD and decreasing the increased levels of MDA in the cerebellum as well as the hippocampus. In addition, E159 decreased the elevated levels of proinflammatory cytokines (tumor necrosis factor (TNF-α), interleukin-1β (IL-1β), and IL-6). The observed results show that H3R antagonists like E159 may represent a promising novel pharmacological strategy for the future treatment of ASD.

Keywords: histamine H3 receptors, antagonist E159, autism, behaviors, mice

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Khalid Mohammed Idrees

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Endodontic treatment aims to reverse the disease process and thereby eliminate the associated signs of symptoms. When the treatment itself appears to initiate the onset of pain and /or swelling (endodontic flare-up), the result can be distressing to both the patient and the operator. Patient might even consider postoperative symptoms as a bench mark against which the clinician’s skills are measured. Obviously the treatment with the lowest prevalence of postoperative pain is usually the treatment of choice as long as effectiveness and cost are not compromised. Knowledge of the cause and mechanism behind intra appointment flare-up is of utmost importance for the clinician to properly prevent or manage this undesirable condition. This review lecture will discuss the causative factors of flare-up with special attention to the microorganism role, various modalities of preventive measures would be discussed. Those measures are based on scientific evidence combined with the long clinical experience of the lecturer.

Keywords: endodontic flare-up, causative factors, inflammatory mediators, preventive measures

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Authors: Denis Duagi, Ben Carter, Maria Farrelly, Stephen Lisk, June S. L. Brown

Abstract:

Background: Adolescence represents a distinctive phase of development, and variables linked to this developmental period could affect the efficiency of prevention and treatment for depression and anxiety, as well as the long-term prognosis. The objectives of this study were to investigate the long-term effectiveness of psychosocial interventions for adolescents on depression and anxiety symptoms and to assess the influence of different intervention parameters on the long-term effects. Methods: Searches were carried out on the 11ᵗʰ of August 2022 using five databases (Cochrane Library, Embase, Medline, PsychInfo, Web of Science), as well as trial registers. Randomized controlled trials of psychosocial interventions targeting specifically adolescents were included if they assessed outcomes at 1-year post-intervention or more. The Cochrane risk of bias-2 quality assessment tool was used. The primary outcome was depression, and studies were pooled using a standardised mean difference, with an associated 95% confidence interval, p-value, and I². The study protocol was pre-registered (CRD42022348668). Findings: A total of 57 reports (n= 46,678 participants) were included in the review. Psychosocial interventions led to small reductions in depressive symptoms, with a standardised mean difference (SMD) at 1-year of -0.08 (95%CI -0.20, -0.03, p=0.002, I²=72%), 18-months SMD=-0.12, 95% CI -0.22, -0.01, p=0.03, I²=63%) and 2-years SMD=-0.12 (95% CI -0.20, -0.03, p=0.01, I²=68%). Sub-group analyses indicated that targeted interventions produced stronger effects, particularly when delivered by trained mental health professionals (K=18, SMD=-0.24, 95% CI -0.38, -0.10, p=0.001, I²=60%). No effects were detected for anxiety at any assessment. Conclusion: Psychosocial interventions specifically targeting adolescents were shown to have small but positive effects on depression symptoms but not anxiety symptoms, which were sustained for up to 2 years. These findings highlight the potential population-level preventive effects if such psychosocial interventions become widely implemented in accessible settings such as schools.

Keywords: psychosocial, adolescent, interventions, depression, anxiety, meta-analysis, randomized controlled trial

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Authors: Aishwarya Shekhar, Devesh Kumar Srivastava

Abstract:

Cloud computing has emerged as a promising direction for cost efficient and reliable service delivery across data communication networks. The dynamic location of service facilities and the virtualization of hardware and software elements are stressing the communication networks and protocols, especially when data centres are interconnected through the internet. Although the computing aspects of cloud technologies have been largely investigated, lower attention has been devoted to the networking services without involving IT operating overhead. Cloud computing has enabled elastic and transparent access to infrastructure services without involving IT operating overhead. Virtualization has been a key enabler for cloud computing. While resource virtualization and service abstraction have been widely investigated, networking in cloud remains a difficult puzzle. Even though network has significant role in facilitating hybrid cloud scenarios, it hasn't received much attention in research community until recently. We propose Network as a Service (NaaS), which forms the basis of unifying public and private clouds. In this paper, we identify various challenges in adoption of hybrid cloud. We discuss the design and implementation of a cloud platform.

Keywords: cloud computing, networking, infrastructure, hybrid cloud, open stack, naas

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Authors: Khalid Kaddoura, Tarek Zayed

Abstract:

Sewer networks are constructed to protect the communities and the environment from any contact with the sewer mediums. Pipelines, being laterals or sewer mains, and manholes form the huge underground infrastructure in every urban city. Due to the sewer networks importance, the infrastructure asset management field has extensive advancement in condition assessment and rehabilitation decision models. However, most of the focus was devoted to pipelines giving little attention toward manholes condition assessment. In fact, recent studies started to emerge in this area to preserve manholes from any malfunction. Therefore, the main objective of this study is to propose a condition assessment model for sewer manholes. The model divides the manhole into several components and determines the relative importance weight of each component using the Analytic Network Process (ANP) decision-making method. Later, the condition of the manhole is computed by aggregating the condition of each component with its corresponding weight. Accordingly, the proposed assessment model will enable decision-makers to have a final index suggesting the overall condition of the manhole and a backward analysis to check the condition of each component. Consequently, better decisions are made pertinent to maintenance, rehabilitation, and replacement actions.

Keywords: Analytic Network Process (ANP), condition assessment, decision-making, manholes

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Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel

Abstract:

In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network

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Authors: Maoxin Ye

Abstract:

This study examines the relationship between social network sites (SNS) usage and social capital. Because SNS usage can expand the users’ networks, and people who are connected in this networks may become resources to SNS users and lead them to advantage in some situation, it is important to estimate the relationship between SNS usage and ‘who’ is connected or what resources the SNS users can get. Additionally, ‘who’ can be divided in two aspects – people who possess high position and people who are different, hence, it is important to estimate the relationship between SNS usage and high position people and different people. This study adapts Lin’s definition of social capital and the measurement of position generator which tells us who was connected, and can be divided into the same two aspects as well. A national data of America (N = 2,255) collected by Pew Research Center is utilized to do a general regression analysis about SNS usage and social capital. The results indicate that SNS usage is negatively associated with each factor of social capital, and it suggests that, in fact, comparing with non-users, although SNS users can get more connections, the variety and resources of these connections are fewer. For this reason, we could lose benefits through SNS usage.

Keywords: social network sites, social capital, position generator, general regression

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

Abstract:

An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

Procedia PDF Downloads 407