Search results for: radial network

Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

Procedia PDF Downloads 84

Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

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Authors: Fariba Ebrahimi, Mehdi Ghorbani, Ali Salajegheh

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Comprehensively water management considers economic, environmental, technical and social and also sustainability of water resources for future generations. Grassland management implies cooperative approach and involves all stakeholders and also introduces issues to managers, decision and policy makers. Solving these issues needs integrated and system approach. According to the recognition of actors or key persons in necessary to apply cooperative management of Water. Therefore, based on stakeholder analysis and social network analysis can be used to demonstrate the most effective actors for environmental decisions. In this research, social powers according are specified to social network approach at Water utilizers’ level of Natural in Jirood catchment of Latian basin. In this paper, utilizers of water resources were recognized using field trips and then, trust and collaboration matrix produced using questionnaires. In the next step, degree centrality index were Examined. Finally, geometric position of each actor was illustrated in the network. The results of the research based on centrality index have a key role in recognition of cooperative management of Water in Jirood and also will help managers and planners of water in the case of recognition of social powers in order to organization and implementation of sustainable management of Water.

Keywords: social network analysis, water co-management, social power, centrality index, local stakeholders network, Jirood catchment

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Authors: Shidrokh Goudarzi, Wan Haslina Hassan

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Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers or vertical handoffs are necessary for seamless mobility. In this paper, we conduct a review of existing vertical handover decision-making mechanisms that aim to provide ubiquitous connectivity to mobile users. To offer a systematic comparison, we categorize these vertical handover measurement and decision structures based on their respective methodology and parameters. Subsequently, we analyze several vertical handover approaches in the literature and compare them according to their advantages and weaknesses. The paper compares the algorithms based on the network selection methods, complexity of the technologies used and efficiency in order to introduce our vertical handover decision framework. We find that vertical handovers on heterogeneous wireless networks suffer from the lack of a standard and efficient method to satisfy both user and network quality of service requirements at different levels including architectural, decision-making and protocols. Also, the consolidation of network terminal, cross-layer information, multi packet casting and intelligent network selection algorithm appears to be an optimum solution for achieving seamless service continuity in order to facilitate seamless connectivity.

Keywords: heterogeneous wireless networks, vertical handovers, vertical handover metric, decision-making algorithms

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Authors: Yair Bleiberg, Michael Werman

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Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper hyperparameter, just like batch normalization and dropout do. We believe that under certain circumstances, PE is not necessary, and a smarter construction of the network architecture together with a smart training method is sufficient to achieve similar results. In this paper, we show that very simple MLPs can quite easily output a frequency when given input of the half-frequency and quarter-frequency. Using this, we design a network architecture in blocks, where the input to each block is the output of the two previous blocks along with the original input. We call this a Fibonacci Network. By training each block on the corresponding frequencies of the signal, we show that Fibonacci Networks can reconstruct arbitrarily high frequencies.

Keywords: neural networks, positional encoding, high frequency intepolation, fully connected

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Authors: Pin-Ju Juan, Peng-Yu Juan, Yi-Shan Chen

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The main purpose of this paper is to present a fuzzy Analytic Network Process (ANP) model for the hostel organizational performance selection. In this article, we created 39 criteria for selecting hostel organizational performance acquired from literature's review and experts method practical investigations, and the methods of fuzzy analytic network process are used to consolidate decision-makers’ assessments about criteria weightings. Finally, we selected organizational performance of a hostel in Taiwan to determine the effectiveness of the proposed evaluation model in this paper.

Keywords: Fuzzy ANP, hostel, organizational performance, strategy management

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Said Rabah Azzam, Andrew Graham

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Fragmentation of packets caused by encryption applied on the network layer of the IOS model in Internet Protocol version 4 (IPv4) networks as well as the possibility of using fragmentation and Access Control Lists (ACLs) as a method of restricting network access to certain hosts or areas of a network.Using default settings, fragmentation is expected to occur and each fragment to be reassembled at the other end. If this does not occur then a high number of ICMP messages should be generated back towards the source host indicating that the packet is too large and that it needs to be made smaller. This result is also expected when the MTU is changed for certain links between devices.When using ACLs and packet fragments to restrict access to hosts or network segments it is possible that ACLs cannot be set up in this way. If ACLs cannot be setup to allow only fragments then it is a limitation of the hardware’s firmware holding back this particular method. If the ACL on the restricted switch can be set up in such a way to allow only fragments then a connection that forces packets to fragment should be allowed to pass through the ACL. This should then make a network connection to the destination machine allowing data to be sent to and from the destination machine. ICMP messages from the restricted access switch and host should also be blocked from being sent back across the link which will be shown in an SSH session into the switch.

Keywords: fragmentation, encryption, security, switch

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Authors: Yi Yang

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In the era of 'Internet plus', the rapid development of webcast has posed new challenges to the intellectual property law. Meanwhile, traditional copyright protection has also exposed the existing theoretical imbalance in webcast. Through the analysis of the outstanding problems in the copyright protection of the network live broadcast, this paper points out that the main causes of the problems are the unclear nature of the copyright of the network live broadcast, the copyright protection system of the game network live broadcast has not yet been constructed, and the copyright infringement of the pan entertainment live broadcast is mostly, and so on. Based on the current practice, this paper puts forward the specific thinking of the protection path of online live broadcast copyright. First of all, to provide a reasonable judicial solution for a large number of online live copyright cases, we need to integrate the right scope and regulatory behavior of broadcasting right and information network communication right. Secondly, in order to protect the rights of network anchors, the webcast should be regarded as works. Thirdly, in order to protect the copyright of webcast and prevent the infringement of copyright by webcast, the webcast platform will be used as an intermediary to provide solutions for solving the judicial dilemma. In the era of 'Internet plus', it is a theoretical attempt to explore the protection and method of copyright protection on webcast, which has positive guiding significance for judicial practice.

Keywords: 'Internet Plus' era, webcast, copyright, protection dilemma

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Authors: S. Neelima, P. S. Subramanyam

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A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses reduces when moved away from the substation, also the losses are high. The reason for a decrease in voltage and high losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with an appropriate size is always a challenge. Thus, the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In the first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Genetic Algorithm (GA) technique is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 9 and 34 bus system and compared with other methods in the literature.

Keywords: dimension reducing distribution load flow algorithm, DRDLFA, genetic algorithm, electrical distribution network, optimal capacitors placement, voltage profile improvement, loss reduction

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Saumya R. Jha, Krishanu Biswas, Nilesh P. Gurao

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Several recent investigations have revealed medium entropy alloys exhibiting better mechanical properties than their high entropy counterparts. This clearly establishes that although a higher entropy plays a vital role in stabilization of particular phase over complex intermetallic phases, configurational entropy is not the primary factor responsible for the high inherent strengthening in these systems. Above and beyond a high contribution from friction stresses and solid solution strengthening, strain hardening is an important contributor to the strengthening in these systems. In this regard, researchers have developed severe plastic deformation (SPD) techniques like High Pressure Torsion (HPT) to incorporate very high shear strain in the material, thereby leading to ultrafine grained (UFG) microstructures, which cause manifold increase in the strength. The presented work demonstrates a meticulous study of the variation in mechanical properties at different radial displacements from the center of HPT tested equiatomic ternary FeMnNi synthesized by casting route, which is a low stacking fault energy FCC alloy that shows significantly higher toughness than its high entropy counterparts like Cantor alloy. The gradient in grain sizes along the radial direction of these specimens has been modeled using microstructure entropy for predicting the mechanical properties, which has also been validated by indentation tests. The dislocation density is computed by FEM simulations for varying strains and validated by analyzing synchrotron diffraction data. Thus, the proposed model can be utilized to predict the strengthening behavior of similar systems deformed by HPT subjected to varying loading conditions.

Keywords: high pressure torsion, severe plastic deformation, configurational entropy, dislocation density, FEM simulation

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Authors: D. Farsi, G. Dokhtvasi, S. Abbasi, S. Shafiee Ardestani, E. Payani

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Background and aim:It has not been well studied whether fentanyl-thiopental (FT) is effective and safe for PSA in orthopedic procedures in Emergency Department (ED). The aim of this trial was to evaluate the effectiveness of intravenous FTversusfentanyl-midazolam (FM)in patients who suffered from shoulder dislocation or distal radial fracture-dislocation. Methods:In this randomized double-blinded study, Seventy-six eligible patients were entered the study and randomly received intravenous FT or FM. The success rate, onset of action and recovery time, pain score, physicians’ satisfaction and adverse events were assessed and recorded by treating emergency physicians. The statistical analysis was intention to treat. Results: The success rate after administrating loading dose in FT group was significantly higher than FM group (71.7% vs. 48.9%, p=0.04); however, the ultimate unsuccess rate after 3 doses of drugs in the FT group was higher than the FM group (3 to 1) but it did not reach to significant level (p=0.61). Despite near equal onset of action time in two study group (P=0.464), the recovery period in patients receiving FT was markedly shorter than FM group (P<0.001). The occurrence of adverse effects was low in both groups (p=0.31). Conclusion: PSA using FT is effective and appears to be safe for orthopedic procedures in the ED. Therefore, regarding the prompt onset of action, short recovery period of thiopental, it seems that this combination can be considered more for performing PSA in orthopedic procedures in ED.

Keywords: procedural sedation and analgesia, thiopental, fentanyl, midazolam, orthopedic procedure, emergency department, pain

Procedia PDF Downloads 252

Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Riyadh Alsultani, Ali Majdi

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It is necessary to develop a design methodology that includes the possibility of seismic events occurring in a region, the vulnerability of the civil hydraulic structure, and the effects of the occurrence hazard on society, environment, and economy in order to evaluate the flood and earthquake risks of coastal bridge networks. This paper presents a design approach for the assessment of the risk and sustainability of coastal bridge networks under time-variant flood-earthquake conditions. The social, environmental, and economic indicators of the network are used to measure its sustainability. These consist of anticipated loss, downtime, energy waste, and carbon dioxide emissions. The design process takes into account the possibility of happening in a set of flood and earthquake scenarios that represent the local seismic activity. Based on the performance of each bridge as determined by fragility assessments, network linkages are measured. The network's connections and bridges' damage statuses after an earthquake scenario determine the network's sustainability and danger. The sustainability measures' temporal volatility and the danger of structural degradation are both highlighted. The method is shown using a transportation network in Baghdad, Iraq.

Keywords: sustainability, Coastal bridge networks, flood-earthquake risk, structural design

Procedia PDF Downloads 93

Authors: Ishtiaq Wahid, Masood Ahmad, Nighat Ayub, Sajad Ali

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Lifetime of a wireless sensor network (WSN) is directly proportional to the energy consumption of its constituent nodes. Routing in wireless sensor network is very challenging due its inherit characteristics. In hierarchal routing the sensor filed is divided into clusters. The cluster-heads are selected from each cluster, which forms a hierarchy of nodes. The cluster-heads are used to transmit the data to the base station while other nodes perform the sensing task. In this way the lifetime of the network is increased. In this paper a comparative study of hierarchal routing protocols are conducted. The simulation is done in NS-2 for validation.

Keywords: WSN, cluster, routing, sensor networks

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Muhammad R. Ahmed, Mohammed Aseeri

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Wireless Sensor Network (WSN) consists of low-cost and multi functional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. In this paper we present an internal attack detection method based on maximum entropy model. The final experimental works showed that the proposed algorithm does work well at the designed level.

Keywords: internal attack, wireless sensor network, network security, entropy

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Authors: Jitendra Aswani

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In the first section of this study, impact of Global Financial Crisis (GFC) on the synchronization of fourteen Asian Stock Markets (ASM’s) of countries like Hong Kong, India, Thailand, Singapore, Taiwan, Pakistan, Bangladesh, South Korea, Malaysia, Indonesia, Japan, China, Philippines and Sri Lanka, has been analysed using the network science and its metrics like degree of node, clustering coefficient and network density. Then in the second section of this study by introducing the US stock market in existing network and developing a Minimum Spanning Tree (MST) spread of crisis from the US stock market to Asian Stock Markets (ASM) has been explained. Data used for this study is adjusted the closing price of these indices from 6th January, 2000 to 15th September, 2013 which further divided into three sub-periods: Pre, during and post-crisis. Using network analysis, it is found that Asian stock markets become more interdependent during the crisis than pre and post crisis, and also Hong Kong, India, South Korea and Japan are systemic important stock markets in the Asian region. Therefore, failure or shock to any of these systemic important stock markets can cause contagion to another stock market of this region. This study is useful for global investors’ in portfolio management especially during the crisis period and also for policy makers in formulating the financial regulation norms by knowing the connections between the stock markets and how the system of these stock markets changes in crisis period and after that.

Keywords: global financial crisis, Asian stock markets, network science, Kruskal algorithm

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Jing Niu, Jun X. Wang

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Arterial stiffness predicts mortality and morbidity, independently of other cardiovascular risk factors. And it is a major risk factor for age-related morbidity and mortality. The non-invasive industry gold standard measurement system of arterial stiffness utilizes pulse wave velocity method. However, the desktop device is expensive and requires trained professional to operate. The main objective of this research is the proof of concept of the proposed non-invasive method which uses measurement of magnetic flux disturbance and electrocardiogram (ECG) signal for measuring arterial stiffness. The method could enable accurate and easy self-assessment of arterial stiffness at home, and to help doctors in research, diagnostic and prescription in hospitals and clinics. A platform for assessing arterial stiffness through acquisition and analysis of radial artery pulse waveform and ECG signal has been developed based on the proposed method. Radial artery pulse waveform is acquired using the magnetic based sensing technology, while ECG signal is acquired using two dry contact single arm ECG electrodes. The measurement only requires the participant to wear a wrist strap and an arm band. Participants were recruited for data collection using both the developed platform and the industry gold standard system. The results from both systems underwent correlation assessment analysis. A strong positive correlation between the results of the two systems is observed. This study presents the possibility of developing an accurate, easy to use and affordable measurement device for arterial stiffness assessment.

Keywords: arterial stiffness, electrocardiogram, pulse wave velocity, Magnetic Flux Disturbance

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Authors: Saidi Nabiha, Khaled Zaatouri, Walid Fajraoui, Tahar Ezzeddine

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The market of Wireless Sensor Network WSN has a great potential and development opportunities. Researchers are focusing on optimization in many fields like efficient deployment and routing protocols. In this article, we will concentrate on energy efficiency for WSN because WSN nodes are habitually deployed in severe No Man’s Land with batteries are not rechargeable, so reducing energy consumption represents an important challenge to extend the life of the network. We will present the design of new WSN mote based on ultra low power STM32L microcontrollers and the ZIGBEE transceiver CC2520. We will compare it to existent motes and we will conclude that our mote is promising in energy consumption.

Keywords: component, WSN mote, power consumption, STM32L, sensors, CC2520

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

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Authors: Paulu Saramge Shalika Nirupani Seram

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The study delves into the complex dynamics of social networks and how they affect paddy farmers’ adoption of agricultural technologies, which are included in Yaya Development program, Weedy rice program and Good Agricultural Practices (GAP) program in Matara district. Identify the social networks among the farmers of ongoing Extension Programs in Matara district, examine the farmers’ adoption level to the ongoing extension programs in Matara district, analyze the impacts of social networks for the adoption to the technologies of ongoing extension programs and give suggestions and recommendations to improve the social network of paddy farmers in Matara District for ongoing extension programs are the objectives of this research. A structured questionnaire survey was conducted with 25 farmers from Matara-North (Wilpita), 25 farmers from Matara-Central (Kamburupitiya), and 25 farmers from Matara-South (Malimbada). UCINET (Version -6.771) software was used for social network analysis, and other than that, descriptive statistics and inferential statistics were used to analyze the findings. Matara-North has the highest social network density, and Matara-South has the lowest social network density according to the social network analysis. Dissemination of intensive technologies requires the most prominent actors of the social network, and in Matara district, agricultural instructors have the highest ability to disseminate technologies. The influence of actors in the social network, the trustworthiness of AI officers, and the trust of indigenous knowledge about paddy cultivation have a significant effect on the technology adoption of farmers. The research endeavors to contribute a nuanced understanding of the social networks and agricultural technology adoption in Matara District, offering practical insights for stakeholders involved in agricultural extension services.

Keywords: agricultural extension, paddy cultivation, social network, technology adoption

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Authors: Thomas Abraham, Garcia de la Vega Felipe, Lubna Nishath, Nzekwe Nduka, Powell Anne-Marie

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Introduction: The purpose of this paper is to discuss the effectiveness of a peer support network created to provide medical education, pastoral support, and reliable resources to registrars to help them pass the MRCGP exams. This paper will include a description of the network and its purpose, discuss how it has been used by trainees since its creation, and explain how this methodology can be applied to other areas of medical education and primary care. Background: The peer support network was created in February 2021, using Facebook, Telegram, and WhatsApp platforms to facilitate discussion of cases and answer queries about the exams, share resources, and offer peer support from qualified GPs and specialists. The network was created and is maintained by the authors of this paper and is open to anyone who is registered with the General Medical Council (GMC) and is studying for the MRCGP exams. Purpose: The purpose of the network is to provide medical education, pastoral support, and reliable resources to registrars to help them pass the exams. The network is free to use and is designed to take the onus away from a single medical educator and collate a vast amount of information from multiple medical educators/trainers; thereby creating a digital library of information for all trainees - exam related or otherwise. Methodology The network is managed by a team of moderators who respond to queries and facilitate discussion. Smaller study groups are created from the main group and provide a platform for trainees to work together, share resources, and provide peer support. The network has had thousands of trainees using it since February 2021, with positive feedback from all trainees. Results: The feedback from trainees has been overwhelmingly positive. Word of mouth has spread rapidly, growing the groups exponentially. Trainees add colleagues to the groups and often stay after they pass their exams to 'give back' to their fellow trainees. To date, thousands of trainees have passed the MRCGP exams using the resources and support provided by the network. Conclusion The success of this peer support network demonstrates the effectiveness of creating a network of thousands of doctors to provide medical education and support.

Keywords: peer support, medical education, pastoral support, MRCGP exams

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Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha

Abstract:

This paper considers a robust stabilization problem of a single agent in a multi-agent consensus system composed of identical agents, when the network topology of the system is completely unknown. It is shown that the transfer function of an agent in a consensus system can be described as a multiplicative perturbation of the isolated agent transfer function in frequency domain. Applying known robust stabilization results, we present sufficient conditions for a robust stabilization of an agent against unknown network topology.

Keywords: single agent control, multi-agent system, transfer function, graph angle

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

Abstract:

Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI

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