Search results for: transformer neural networks

Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Natalie Riley, Anita Quigley, Robert M. I. Kapsa, George W. Greene

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As the fields of medicine and bionics grow rapidly in technological advancement, the future and success of it depends on the ability to effectively interface between the artificial and the biological worlds. The biggest obstacle when it comes to implantable, electronic medical devices, is maintaining a ‘clean’, low noise electrical connection that allows for efficient sharing of electrical information between the artificial and biological systems. Implant fouling occurs with the adhesion and accumulation of proteins and various cell types as a result of the immune response to protect itself from the foreign object, essentially forming an electrical insulation barrier that often leads to implant failure over time. Lubricin (LUB) functions as a major boundary lubricant in articular joints, a unique glycoprotein with impressive anti-adhesive properties that self-assembles to virtually any substrate to form a highly ordered, ‘telechelic’ polymer brush. LUB does not passivate electroactive surfaces which makes it ideal, along with its innate biocompatibility, as a coating for implantable bionic electrodes. It is the aim of the study to investigate LUB’s anti-fouling properties and its potential as a safe, bioinspired material for coating applications to enhance the performance and longevity of implantable medical devices as well as reducing the frequency of implant replacement surgeries. Native, bovine-derived LUB (N-LUB) and recombinant LUB (R-LUB) were applied to gold-coated mylar surfaces. Fibroblast, chondrocyte and neural cell types were cultured and grown on the coatings under both passive and electrically stimulated conditions to test the stability and anti-adhesive property of the LUB coating in the presence of an electric field. Lactate dehydrogenase (LDH) assays were conducted as a directly proportional cell population count on each surface along with immunofluorescent microscopy to visualize cells. One-way analysis of variance (ANOVA) with post-hoc Tukey’s test was used to test for statistical significance. Under both passive and electrically stimulated conditions, LUB significantly reduced cell attachment compared to bare gold. Comparing the two coating types, R-LUB reduced cell attachment significantly compared to its native counterpart. Immunofluorescent micrographs visually confirmed LUB’s antiadhesive property, R-LUB consistently demonstrating significantly less attached cells for both fibroblasts and chondrocytes. Preliminary results investigating neural cells have so far demonstrated that R-LUB has little effect on reducing neural cell attachment; the study is ongoing. Recombinant LUB coatings demonstrated impressive anti-adhesive properties, reducing cell attachment in fibroblasts and chondrocytes. These findings and the availability of recombinant LUB brings into question the results of previous experiments conducted using native-derived LUB, its potential not adequately represented nor realized due to unknown factors and impurities that warrant further study. R-LUB is stable and maintains its anti-fouling property under electrical stimulation, making it suitable for electroactive surfaces.

Keywords: anti-fouling, bioinspired, cell attachment, lubricin

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Authors: Adenike Adesanya, Nonthaphat Wong, Xiang-Yun Lan, Shea Ping Yip, Chien-Ling Huang

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Background: The most challenging mutation of the oncokinase BCR-ABL protein T315I, which is commonly known as the “gatekeeper” mutation and is notorious for its strong resistance to almost all tyrosine kinase inhibitors (TKIs), especially imatinib. Therefore, this study aims to identify T315I-dependent downstream microRNA (miRNA) pathways associated with drug resistance in chronic myeloid leukemia (CML) for prognostic and therapeutic purposes. Methods: T315I-carrying K562 cell clones (K562-T315I) were generated by the CRISPR-Cas9 system. Imatinib-treated K562-T315I cells were subjected to small RNA library preparation and next-generation sequencing. Putative lncRNA-miRNA-mRNA networks were analyzed with (i) DESeq2 to extract differentially expressed miRNAs, using Padj value of 0.05 as cut-off, (ii) STarMir to obtain potential miRNA response element (MRE) binding sites of selected miRNAs on lncRNA H19, (iii) miRDB, miRTarbase, and TargetScan to predict mRNA targets of selected miRNAs, (iv) IntaRNA to obtain putative interactions between H19 and the predicted mRNAs, (v) Cytoscape to visualize putative networks, and (vi) several pathway analysis platforms – Enrichr, PANTHER and ShinyGO for pathway enrichment analysis. Moreover, mitochondria isolation and transcript quantification were adopted to determine the new mechanism involved in T315I-mediated resistance of CML treatment. Results: Verification of the CRISPR-mediated mutagenesis with digital droplet PCR detected the mutation abundance of ≥80%. Further validation showed the viability of ≥90% by cell viability assay, and intense phosphorylated CRKL protein band being detected with no observable change for BCR-ABL and c-ABL protein expressions by Western blot. As reported by several investigations into hematological malignancies, we determined a 7-fold increase of H19 expression in K562-T315I cells. After imatinib treatment, a 9-fold increment was observed. DESeq2 revealed 171 miRNAs were differentially expressed K562-T315I, 112 out of these miRNAs were identified to have MRE binding regions on H19, and 26 out of the 112 miRNAs were significantly downregulated. Adopting the seed-sequence analysis of these identified miRNAs, we obtained 167 mRNAs. 6 hub miRNAs (hsa-let-7b-5p, hsa-let-7e-5p, hsa-miR-125a-5p, hsa-miR-129-5p, and hsa-miR-372-3p) and 25 predicted genes were identified after constructing hub miRNA-target gene network. These targets demonstrated putative interactions with H19 lncRNA and were mostly enriched in pathways related to cell proliferation, senescence, gene silencing, and pluripotency of stem cells. Further experimental findings have also shown the up-regulation of mitochondrial transcript and lncRNA MALAT1 contributing to the lncRNA-miRNA-mRNA networks induced by BCR-ABL T315I mutation. Conclusions: Our results have indicated that lncRNA-miRNA regulators play a crucial role not only in leukemogenesis but also in drug resistance, considering the significant dysregulation and interactions in the K562-T315I cell model generated by CRISPR-Cas9. In silico analysis has further shown that lncRNAs H19 and MALAT1 bear several complementary miRNA sites. This implies that they could serve as a sponge, hence sequestering the activity of the target miRNAs.

Keywords: chronic myeloid leukemia, imatinib resistance, lncRNA-miRNA-mRNA, T315I mutation

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Authors: Usama Abdulwahab, Mohammed Wahab

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This paper presents the application of uncapacitated facility location problems (UFLP) and 1-median problems to support decision making in blood supply chain networks. A plethora of factors make blood supply-chain networks a complex, yet vital problem for the regional blood bank. These factors are rapidly increasing demand; criticality of the product; strict storage and handling requirements; and the vastness of the theater of operations. As in the UFLP, facilities can be opened at any of $m$ predefined locations with given fixed costs. Clients have to be allocated to the open facilities. In classical location models, the allocation cost is the distance between a client and an open facility. In this model, the costs are the allocation cost, transportation costs, and inventory costs. In order to address this problem the median algorithm is used to analyze inventory, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The Euclidean distance data for some Ontario cities (demand nodes) are used to test the developed algorithm. Sitation software, lagrangian relaxation algorithm, and branch and bound heuristics are used to solve this model. Computational experiments confirm the efficiency of the proposed approach. Compared to the existing modeling and solution methods, the median algorithm approach not only provides a more general modeling framework but also leads to efficient solution times in general.

Keywords: approximate dynamic programming, facility location, perishable product, inventory model, blood platelet, P-median problem

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Authors: Flavio Cannavo

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Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

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Authors: M. Almutairi, S. Hadjiloucas

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The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: harmonics, passive filter, power factor, power quality

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Authors: Vaishakh Medikeri

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Energy in this Universe is the most basic characteristic of every particle. Since the birth of life on this planet, there has been a quest undertaken by the living beings to analyze, understand and harness the precious natural facts of the nature. In this quest, one of the greatest undertaken is the process of harnessing the naturally available energy. Scientists around the globe have discovered many ways to harness the freely available energy. But even today we speak of “Power Crisis”. Nikola Tesla once said “Nature has stored up in this universe infinite energy”. Energy is everywhere around us in unlimited quantities; all of it waiting to be harnessed by us. Here in this paper a method has been proposed to harness earth's electric field and transmit the stored electric energy using strong magnetic fields and electric fields. In this paper a new technique has been proposed to harness earth's electric field which is everywhere around the world in infinite quantities. Near the surface of the earth there is an electric field of about 120V/m. This electric field is used to charge a capacitor with high capacitance. Later the energy stored is allowed to pass through a device which converts the DC stored into AC. The AC so produced is then passed through a step down transformer to magnify the incoming current. Later the current passes through the RLC circuit. Later the current can be transmitted wirelessly using the principle of resonant inductive coupling. The proposed apparatus can be placed in most of the required places and any circuit tuned to the frequency of the transmitted current can receive the energy. The new source of renewable energy is of great importance if implemented since the apparatus is not costly and can be situated in most of the required places. And also the receiver which receives the transmitted energy is just an RLC circuit tuned to the resonant frequency of the transmitted energy. By using the proposed apparatus the energy losses can be reduced to a very large extent.

Keywords: capacitor, inductive resonant coupling, RLC circuit, transmission of electricity

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Ahmad S. Darwish, Tarek Lemaoui, Hanifa Taher, Inas M. AlNashef, Fawzi Banat

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This research reports a systematic top-down approach for designing neoteric hydrophobic solvents –particularly, deep eutectic solvents (DES) and ionic liquids (IL)– as furfural extractants from aqueous media for the application of sustainable biomass conversion. The first stage of the framework entailed screening 32 neoteric solvents to determine their efficacy against toluene as the application’s conventional benchmark for comparison. The selection criteria for the best solvents encompassed not only their efficiency in extracting furfural but also low viscosity and minimal toxicity levels. Additionally, for the DESs, their natural origins, availability, and biodegradability were also taken into account. From the screening pool, two neoteric solvents were selected: thymol:decanoic acid 1:1 (Thy:DecA) and trihexyltetradecyl phosphonium bis(trifluoromethylsulfonyl) imide [P₁₄,₆,₆,₆][NTf₂]. These solvents outperformed the toluene benchmark, achieving efficiencies of 94.1% and 97.1% respectively, compared to toluene’s 81.2%, while also possessing the desired properties. These solvents were then characterized thoroughly in terms of their physical properties, thermal properties, critical properties, and cross-contamination solubilities. The selected neoteric solvents were then extensively tested under various operating conditions, and an exceptional stable performance was exhibited, maintaining high efficiency across a broad range of temperatures (15–100 °C), pH levels (1–13), and furfural concentrations (0.1–2.0 wt%) with a remarkable equilibrium time of only 2 minutes, and most notably, demonstrated high efficiencies even at low solvent-to-feed ratios. The durability of the neoteric solvents was also validated to be stable over multiple extraction-regeneration cycles, with limited leachability to the aqueous phase (≈0.1%). Moreover, the extraction performance of the solvents was then modeled through machine learning, specifically multiple non-linear regression (MNLR) and artificial neural networks (ANN). The models demonstrated high accuracy, indicated by their low absolute average relative deviations with values of 2.74% and 2.28% for Thy:DecA and [P₁₄,₆,₆,₆][NTf₂], respectively, using MNLR, and 0.10% for Thy:DecA and 0.41% for [P₁₄,₆,₆,₆][NTf₂] using ANN, highlighting the significantly enhanced predictive accuracy of the ANN. The neoteric solvents presented herein offer noteworthy advantages over traditional organic solvents, including their high efficiency in both extraction and regeneration processes, their stability and minimal leachability, making them particularly suitable for applications involving aqueous media. Moreover, these solvents are more environmentally friendly, incorporating renewable and sustainable components like thymol and decanoic acid. This exceptional efficacy of the newly developed neoteric solvents signifies a significant advancement, providing a green and sustainable alternative for furfural production from biowaste.

Keywords: sustainable biomass conversion, furfural extraction, ionic liquids, deep eutectic solvents

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Authors: Hemant Kumar, Nilendu Das

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A study based on ANN structure gives us the information to predict the size of the future in realizing a past event. ANN, IMD (Indian meteorological department) data and remote sensing were used to enable a number of parameters for calculating the size that may occur in the future. A threshold selected specifically above the high-frequency harvest reached the area during the selected seismic activity. In the field of human and local biodiversity it remains to obtain the right parameter compared to the frequency of impact. But during the study the assumption is that predicting seismic activity is a difficult process, not because of the parameters involved here, which can be analyzed and funded in research activity.

Keywords: ANN, Bayesion class, earthquakes, IMD

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Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

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Authors: Jianwei Qian, Rob Law

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With the expansion of globalization and the increment of market competition, the festival, especially the traditional one, has demonstrated its vitality under the new context. As a new tourist attraction, festivals play a critically important role in promoting the tourism economy, because the organization of a festival can engage more tourists, generate more revenues and win a wider media concern. However, in the current stage of China, traditional festivals as a way to disseminate national culture are undergoing the challenge of foreign festivals and the related culture. Different from those special events created solely for developing economy, traditional festivals have their own culture and connotation. Therefore, it is necessary to conduct a study on not only protecting the tradition, but promoting its development as well. This study conducts a comparative study of the development of China’s Valentine’s Day and Western Valentine’s Day under the Chinese context and centers on newspaper reports in China from 2000 to 2016. Based on the literature, two main research focuses can be established: one is concerned about the festival’s impact and the other is about tourists’ motivation to engage in a festival. Newspaper reports serve as the research discourse and can help cover the two focal points. With the assistance of content mining techniques, semantic networks for both Days are constructed separately to help depict the status quo of these two festivals in China. Based on the networks, two models are established to show the key component system of traditional festivals in the hope of perfecting the positive role festival tourism plays in the promotion of economy and culture. According to the semantic networks, newspaper reports on both festivals have similarities and differences. The difference is mainly reflected in its cultural connotation, because westerners and Chinese may show their love in different ways. Nevertheless, they share more common points in terms of economy, tourism, and society. They also have a similar living environment and stakeholders. Thus, they can be promoted together to revitalize some traditions in China. Three strategies are proposed to realize the aforementioned aim. Firstly, localize international festivals to suit the Chinese context to make it function better. Secondly, facilitate the internationalization process of traditional Chinese festivals to receive more recognition worldwide. Finally, allow traditional festivals to compete with foreign ones to help them learn from each other and elucidate the development of other festivals. It is believed that if all these can be realized, not only the traditional Chinese festivals can obtain a more promising future, but foreign ones are the same as well. Accordingly, the paper can contribute to the theoretical construction of festival images by the presentation of the semantic network. Meanwhile, the identified features and issues of festivals from two different cultures can enlighten the organization and marketing of festivals as a vital tourism activity. In the long run, the study can enhance the festival as a key attraction to keep the sustainable development of both the economy and the society.

Keywords: Chinese context, comparative study, festival tourism, semantic network analysis, valentine’s day

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Authors: Rasha Waheeb, Bjorn Andersen, Rafa Shakir

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Purpose The purpose of this study is to avoid delays that occur in emergency reconstruction projects especially in post disaster circumstances whether if they were natural or manmade due to their particular national and humanitarian importance. We presented a theoretical and practical concepts for projects management in the field of construction industry that deal with a range of global and local trails. This study aimed to identify the factors of effective delay in construction projects in Iraq that affect the time and the specific quality cost, and find the best solutions to address delays and solve the problem by setting parameters to restore balance in this study. 30 projects were selected in different areas of construction were selected as a sample for this study. Design/methodology/approach This study discusses the reconstruction strategies and delay in time and cost caused by different delay factors in some selected projects in Iraq (Baghdad as a case study).A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. Project participants from the case projects provided data about the projects through a data collection instrument distributed through a survey. Mixed approach and methods were applied in this study. Mathematical data analysis was used to construct models to predict delay in time and cost of projects before they started. The artificial neural networks analysis was selected as a mathematical approach. These models were mainly to help decision makers in construction project to find solutions to these delays before they cause any inefficiency in the project being implemented and to strike the obstacles thoroughly to develop this industry in Iraq. This approach was practiced using the data collected through survey and questionnaire data collection as information form. Findings The most important delay factors identified leading to schedule overruns were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are quite in line with findings from similar studies in other countries/regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. Originality/value we selected ANN’s analysis first because ANN’s was rarely used in project management , and never been used in Iraq to finding solutions for problems in construction industry. Also, this methodology can be used in complicated problems when there is no interpretation or solution for a problem. In some cases statistical analysis was conducted and in some cases the problem is not following a linear equation or there was a weak correlation, thus we suggested using the ANN’s because it is used for nonlinear problems to find the relationship between input and output data and that was really supportive.

Keywords: construction projects, delay factors, emergency reconstruction, innovation ANN, post disasters, project management

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Authors: Behrooz Daneshmand

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Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.

Keywords: software-defined networking, security, SDN, 5G/IMT-2020

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Authors: Ding He, Ping Hu

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This article presents a “networking approach” as an alternative to the “layering model” in the issue of the historic urban landscape [HUL], based on research conducted in the historic city of Jingdezhen, the center of the porcelain industry in China. This study points out that the existing HUL concept, which can be traced back to the fundamental conceptual divisions set forth by western science, tends to analyze the various elements of urban heritage (composed of hybrid natural-cultural elements) by layers and ignore the nuanced connections and interweaving structure of various elements. Instead, the networking analysis approach can respond to the challenges of complex heritage networks and to the difficulties that are often faced when modern schemes of looking and thinking of landscape in the Eurocentric heritage model encounters local knowledge of Chinese settlement. The fieldwork in this paper examines the local language regarding place names and everyday uses of urban spaces, thereby highlighting heritage systems grounded in local life and indigenous knowledge. In the context of Chinese “Fengshui”, this paper demonstrates the local knowledge of nature and local intelligence of settlement location and design. This paper suggests that industrial elements (kilns, molding rooms, piers, etc.) and spiritual elements (temples for ceramic saints or water gods) are located in their intimate natural networks. Furthermore, the functional, spiritual, and natural elements are perceived as a whole and evolve as an interactive system. This paper proposes a local and cognitive approach in heritage, which was initially developed in European Landscape Convention and historic landscape characterization projects, and yet seeks a more tentative and nuanced model based on urban ethnography in a Chinese city.

Keywords: Chinese city, historic urban landscape, heritage conservation, network

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Authors: Suneet Jagdev

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Rapid urbanization has resulted in informal settlements on the periphery of nearly all big cities in the developing world due to lack of affordable housing options in the city. Residents of these communities have to travel long distances to get to work or search for jobs in these cities, and women, children and elderly people are excluded from urban opportunities. Affordable and safe public transport facilities can help them expand their possibilities. The aim of this research is to identify social capital as another important element of livable cities that can be protected and nurtured through transit-oriented development, as a tool to provide real resources that can help these transit-oriented communities become self-sustainable. Social capital has been referred to the collective value of all social networks and the inclinations that arise from these networks to do things for each other. It is one of the key component responsible to build and maintain democracy. Public spaces, pedestrian amenities and social equity are the other essential part of Transit Oriented Development models that will be analyzed in this research. The data has been collected through the analysis of several case studies, the urban design strategies implemented and their impact on the perception and on the community´s experience, and, finally, how these focused on the social capital. Case studies have been evaluated on several metrics, namely ecological, financial, energy consumption, etc. A questionnaire and other tools were designed to collect data to analyze the research objective and reflect the dimension of social capital. The results of the questionnaire indicated that almost all the participants have a positive attitude towards this dimensions of building a social capital with the aid of transit-oriented development. Statistical data of the identified key motivators against against demographic characteristics have been generated based on the case studies used for the paper. The findings suggested that there is a direct relation between urbanization, transit-oriented developments, and social capital.

Keywords: better opportunities, low-income settlements, social capital, social inclusion, transit oriented development

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Authors: Andrea Hernandez Hurtado

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Research on social networks (SNs) -- the interactions individuals share with others has shed important light in helping to explain differential use of variable linguistic forms, both in L1s and L2s. Nevertheless, the acquisition of nonstandard L2 Spanish in the Region of Murcia, Spain, and how learners interact with other speakers while sojourning there have received little attention. Murcian Spanish (MuSp) was widely influenced by Panocho, a divergent evolution of Hispanic Latin, and differs from the more standard Peninsular Spanish (StSp) in phonology, morphology, and lexicon. For instance, speakers from this area will most likely palatalize diminutive endings, producing animalico [̩a.ni.ma.ˈli.ko] instead of animalito [̩a.ni.ma.ˈli.to] ‘little animal’. Because L1 speakers of the area produce and prefer salient regional lexicon and morphology (particularly the palatalized diminutive -ico) in their speech, the current research focuses on how international residents in the Region of Murcia use Spanish: (1) whether or not they acquire (perceptively and/or productively) any of the salient regional features of MuSp, and (2) how their SNs explain such acquisition. This study triangulates across three tasks -recognition, production, and preference- addressing both lexicon and morphology, with each task specifically created for the investigation of MuSp features. Among other variables, the effects of L1, residence, and identity are considered. As an ongoing dissertation research, data are currently being gathered through an online questionnaire. So far, 7 participants from multiple nationalities have completed the survey, although a minimum of 25 are expected to be included in the coming months. Preliminary results revealed that MuSp lexicon and morphology were successfully recognized by participants (p<.001). In terms of regional lexicon production (10.0%) and preference (47.5%), although participants showed higher percentages of StSp, results showed that international residents become aware of stigmatized lexicon and may incorporate it into their language use. Similarly, palatalized diminutives (production 14.2%, preference 19.0%) were present in their responses. The Social Network Analysis provided information about participants’ relationships with their interactants, as well as among them. Results indicated that, generally, when residents were more immersed in the culture (i.e., had more Murcian alters) they produced and preferred more regional features. This project contributes to the knowledge of language variation acquisition in L2 speakers, focusing on a stigmatized Spanish dialect and exploring how stigmatized varieties may affect L2 development. Results will show how L2 Spanish speakers’ language is affected by their stay in Murcia. This, in turn, will shed light on the role of SNs in language acquisition, the acquisition of understudied and marginalized varieties, and the role of immersion on language acquisition. As the first systematic account on the acquisition of L2 Spanish lexicon and morphology in the Region of Murcia, it lays important groundwork for further research on the connection between SNs and the acquisition of regional variants, applicable to Murcia and beyond.

Keywords: international residents, L2 Spanish, lexicon, morphology, nonstandard language acquisition, social networks

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Authors: Kyosuke Kamada, Christoph Kapeller, Michael Jordan, Mostafa Mohammadpour, Christy Li, Christoph Guger

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Cortico-cortical evoked potentials (CCEP) refer to responses generated by cortical electrical stimulation at distant brain sites. These responses provide insights into the functional networks associated with language or motor functions, and in the context of epilepsy, they can reveal pathological networks. Locating the origin and spread of seizures within the cortex is crucial for pre-surgical planning. This process can be enhanced by employing cortical stimulation at the seizure onset zone (SOZ), leading to the generation of CCEPs in remote brain regions that may be targeted for disconnection. In the case of a 24-year-old male patient suffering from intractable epilepsy, corpus callosotomy was performed as part of the treatment. DTI-MRI imaging, conducted using a 3T MRI scanner for fiber tracking, along with CCEP, is used as part of an assessment for surgical planning. Stimulation of the SOZ, with alternating monophasic pulses of 300µs duration and 15mA current intensity, resulted in CCEPs on the contralateral frontal cortex, reaching a peak amplitude of 206µV with a latency of 31ms, specifically in the left pars triangularis. The related fiber tracts were identified with a two-tensor unscented Kalman filter (UKF) technique, showing transversal fibers through the corpus callosum. The CCEPs were monitored through the progress of the surgery. Notably, the SOZ-associated CCEPs exhibited a reduction following the resection of the anterior portion of the corpus callosum, reaching the identified connecting fibers. This intervention demonstrated a potential strategy for mitigating the impact of intractable epilepsy through targeted disconnection of identified cortical regions.

Keywords: CCEP, SOZ, Corpus callosotomy, DTI

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya

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Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay Tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.

Keywords: ETX, opportunistic routing, PSR, throughput

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Authors: Zi-Yan Chao

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With the rapid development of urbanization and increasing demand for efficient transportation systems, the interaction between land use diversity and transportation resource allocation has become a critical issue in urban planning. Achieving a balance of land use types, such as residential, commercial, and industrial areas, is crucial role in ensuring social equity and sustainable urban development. Simultaneously, optimizing multimodal transportation networks, including bus, subway, and car routes, is essential for minimizing total travel time and costs, while ensuring fairness for all social groups, particularly in meeting the transportation needs of low-income populations. This study develops a bilevel programming model to address these challenges, with land use diversity as the foundation for measuring equity. The upper-level model maximizes land use diversity for balanced land distribution across regions. The lower-level model optimizes multimodal transportation networks to minimize travel time and costs while maintaining user equilibrium. The model also incorporates constraints to ensure fair resource allocation, such as balancing transportation accessibility and cost differences across various social groups. A solution approach is developed to solve the bilevel optimization problem, ensuring efficient exploration of the solution space for land use and transportation resource allocation. This study maximizes social equity by maximizing land use diversity and achieving user equilibrium with optimal transportation resource distribution. The proposed method provides a robust framework for addressing urban planning challenges, contributing to sustainable and equitable urban development.

Keywords: bilevel programming model, genetic algorithms, land use diversity, multimodal transportation optimization, social equity

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Authors: M. R. Ahmad, I. Musirin, M. M. Othman, N. A. Rahmat

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Plug-in Hybrid-Electric Vehicle (PHEV) has gained immense popularity in recent years. PHEV offers numerous advantages compared to the conventional internal-combustion engine (ICE) vehicle. Millions of PHEVs are estimated to be on the road in the USA by 2020. Uncoordinated PHEV charging is believed to cause severe impacts to the power grid; i.e. feeders, lines and transformers overload and voltage drop. Nevertheless, improper PHEV data model used in such studies may cause the findings of their works is in appropriated. Although smart charging is more attractive to researchers in recent years, its implementation is not yet attainable on the street due to its requirement for physical infrastructure readiness and technology advancement. As the first step, it is finest to study the impact of charging PHEV based on real vehicle travel data from National Household Travel Survey (NHTS) and at present charging rate. Due to the lack of charging station on the street at the moment, charging PHEV at home is the best option and has been considered in this work. This paper proposed a technique that comprehensively presents the impact of charging PHEV on power system networks considering huge numbers of PHEV samples with its traveling data pattern. Vehicles Charging Load Profile (VCLP) is developed and implemented in IEEE 30-bus test system that represents a portion of American Electric Power System (Midwestern US). Normalization technique is used to correspond to real time loads at all buses. Results from the study indicated that charging PHEV using opportunity charging will have significant impacts on power system networks, especially whereas bigger battery capacity (kWh) is used as well as for higher penetration level.

Keywords: plug-in hybrid electric vehicle, transportation electrification, impact of charging PHEV, electricity demand profile, load profile

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Authors: Sarah J. C. Slabbert

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The uptake of research as part of the diffusion or adoption of innovation by practitioners, whether individuals or organisations, has been a popular topic in agricultural development studies for many decades. In the classical, linear model of innovation theory, the innovation originates from an expert source such as a state-supported research organisation or academic institution. The changing context of agriculture led to the development of the agricultural innovation systems model, which recognizes innovation as a complex interaction between individuals and organisations, which include private industry and collective action organisations. In terms of this model, an innovation can be developed and adopted without any input or intervention from a state or parastatal research organisation. This evolution in the diffusion of agricultural innovation has put forward new challenges for state or parastatal research organisations, which have to demonstrate the impact of their research to the legislature or a regulatory authority: Unless the organisation and the research it produces cross the knowledge paths of the intended audience, there will be no awareness, no uptake and certainly no impact. It is therefore critical for such a research organisation to base its communication strategy on a thorough understanding of the knowledge needs, information sources and knowledge networks of the intended target audience. In 2016, the South African Water Research Commission (WRC) commissioned a study to investigate the knowledge needs, information sources and knowledge networks of Water User Associations and commercial irrigators with the aim of improving uptake of its research on efficient water use in irrigation. The first phase of the study comprised face-to-face interviews with the CEOs and Board Chairs of four Water User Associations along the Orange River in South Africa, and 36 commercial irrigation farmers from the same four irrigation schemes. Intermediaries who act as knowledge conduits to the Water User Associations and the irrigators were identified and 20 of them were subsequently interviewed telephonically. The study found that irrigators interact regularly with grower organisations such as SATI (South African Table Grape Industry) and SAPPA (South African Pecan Nut Association) and that they perceive these organisations as credible, trustworthy and reliable, within their limitations. State and parastatal research institutions, on the other hand, are associated with a range of negative attributes. As a result, the awareness of, and interest in, the WRC and its research on water use efficiency in irrigated agriculture are low. The findings suggest that a communication strategy that involves collaboration with these grower organisations would empower the WRC to participate much more efficiently and with greater impact in agricultural innovation networks. The paper will elaborate on the findings and discuss partnering frameworks and opportunities to manage perceptions and uptake.

Keywords: agricultural innovation systems, communication strategy, diffusion of innovation, irrigated agriculture, knowledge paths, research organisations, target audiences, water use efficiency

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Authors: Omojokun G. Aju, Adedayo O. Sule

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ZigBee wireless sensor and control network is one of the most popularly deployed wireless technologies in recent years. This is because ZigBee is an open standard lightweight, low-cost, low-speed, low-power protocol that allows true operability between systems. It is built on existing IEEE 802.15.4 protocol and therefore combines the IEEE 802.15.4 features and newly added features to meet required functionalities thereby finding applications in wide variety of wireless networked systems. ZigBee‘s current focus is on embedded applications of general-purpose, inexpensive, self-organising networks which requires low to medium data rates, high number of nodes and very low power consumption such as home/industrial automation, embedded sensing, medical data collection, smart lighting, safety and security sensor networks, and monitoring systems. Although the ZigBee design specification includes security features to protect data communication confidentiality and integrity, however, when simplicity and low-cost are the goals, security is normally traded-off. A lot of researches have been carried out on ZigBee technology in which emphasis has mainly been placed on ZigBee network performance characteristics such as energy efficiency, throughput, robustness, packet delay and delivery ratio in different scenarios and applications. This paper investigate and analyse the data accuracy, network implementation difficulties and security challenges of ZigBee network applications in star-based and mesh-based topologies with emphases on its home monitoring application using the ZigBee ProBee ZE-10 development boards for the network setup. The paper also expose some factors that need to be considered when designing ZigBee network applications and suggest ways in which ZigBee network can be designed to provide more resilient to network attacks.

Keywords: home monitoring, IEEE 802.14.5, topology, wireless security, wireless sensor network (WSN), ZigBee

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Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

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The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them. A detailed review of each of the list of papers selected for the study is included in section III of this document. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs), as summarized in Table 1, are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The prevailing challenges of the different architectures are discussed and summarized in Table 3 of the IV section, where the main problem is the obstruction of communication paths by buildings, trees, hills, etc.

Keywords: communication technologies, environmental monitoring, Internet of Things, IoT deployment challenges

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Authors: Paritosh Goel

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The conservation of historic buildings and historic Centre’s over recent years has become fully encompassed in the planning of built-up areas and their management following climate changes. The approach of the world of restoration, in the Indian context on integrated urban regeneration and its strategic potential for a smarter, more sustainable and socially inclusive urban development introduces, for urban transformations in general (historical centers and otherwise), the theme of sustainability. From this viewpoint, it envisages, as a primary objective, a real “green, ecological or environmental” requalification of the city through interventions within the main categories of sustainability: mobility, energy efficiency, use of sources of renewable energy, urban metabolism (waste, water, territory, etc.) and natural environment. With this the concept of a “resilient city” is also introduced, which can adapt through progressive transformations to situations of change which may not be predictable, behavior that the historical city has always been able to express. Urban planning on the other hand, has increasingly focused on analyses oriented towards the taxonomic description of social/economic and perceptive parameters. It is connected with human behavior, mobility and the characterization of the consumption of resources, in terms of quantity even before quality to inform the city design process, which for ancient fabrics, and mainly affects the public space also in its social dimension. An exact definition of the term “smart city” is still essentially elusive, since we can attribute three dimensions to the term: a) That of a virtual city, evolved based on digital networks and web networks b) That of a physical construction determined by urban planning based on infrastructural innovation, which in the case of historic Centre’s implies regeneration that stimulates and sometimes changes the existing fabric; c) That of a political and social/economic project guided by a dynamic process that provides new behavior and requirements of the city communities that orients the future planning of cities also through participation in their management. This paper is a preliminary research into the connections between these three dimensions applied to the specific case of the fabric of ancient cities with the aim of obtaining a scientific theory and methodology to apply to the regeneration of Indian historical Centre’s. The Smart city scheme if contextualize with heritage of the city it can be an initiative which intends to provide a transdisciplinary approach between various research networks (natural sciences, socio-economics sciences and humanities, technological disciplines, digital infrastructures) which are united in order to improve the design, livability and understanding of urban environment and high historical/cultural performance levels.

Keywords: historical cities regeneration, sustainable restoration, urban planning, smart cities, cultural heritage development strategies

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Authors: Kyle Burke Pfeiffer, Carmella Burdi, Karen Marsh

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The evacuation and shelter-in-place planning process employed by most jurisdictions within the United States is not informed by a scientifically-derived framework that is inclusive of the behavioral and policy-related indicators of public compliance with evacuation orders. While a significant body of work exists to define these indicators, the research findings have not been well-integrated nor translated into useable planning factors for public safety officials. Additionally, refinement of the planning factors alone is insufficient to support science-based evacuation planning as the behavioral elements of evacuees—even with consideration of policy-related indicators—must be examined in the context of specific regional transportation and shelter networks. To address this problem, the Federal Emergency Management Agency and Argonne National Laboratory developed an agent-based model to support regional analysis of zone-based evacuation in southeastern Georgia. In particular, this model allows public safety officials to analyze the consequences that a range of hazards may have upon a community, assess evacuation and shelter-in-place decisions in the context of specified evacuation and response plans, and predict outcomes based on community compliance with orders and the capacity of the regional (to include extra-jurisdictional) transportation and shelter networks. The intention is to use this model to aid evacuation planning and decision-making. Applications for the model include developing a science-driven risk communication strategy and, ultimately, in the case of evacuation, the shortest possible travel distance and clearance times for evacuees within the regional boundary conditions.

Keywords: agent-based modeling for evacuation, decision-support for evacuation planning, evacuation planning, human behavior in evacuation

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Authors: Mohamed M. Elsherbiny, Ahmed T. Helal Ibrahim

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Social isolation is one of the common problems faced visual impaired students especially in new situations. It refers to lack of interactions with others (students, staff members, and others) and dissatisfaction of social networks with others. In addition, it means "a lack of quantity and quality of social contacts". The situation became more complicated if we know that visual impaired students at Sultan Qaboos University were in special schools for the blind completely away from any integration with regular student, which may lead to isolation for being with regular students for the first time. Because the researcher is an academic advisor for all blind students in the College of Arts and Social Sciences at Sultan Qaboos University, he has noted (from the regular meetings with them) some aspects of isolation and many complaints from staff which motivated the researcher to try to alleviate the problem. Logotherapy is an important therapy used in clinical social work with various problems to help children and young people who are facing problems related to the lack of meaning in their life. So, the aim of the therapy is to find meaning in life and to be satisfied with that life. The basic meaning for visual impaired students in this study is to provide opportunities to build relationships and friendships with others and help them to be satisfied about interactions with their networks. The study aimed to identify whether there is a relationship between the use of logotherapy and alleviating social isolation for visual impaired students. This study is considered one of the quasi-experimental studies, the researcher has used experimental method. The researcher used one design which is before and after experiment on two groups, one control (did not apply to the therapy) and experimental group which is applied to the therapy. About the study tools, social isolation scale (SIS) was used to assess the degree of isolation. The sample was (20) of the visually impaired students at the College of Arts and Social Sciences, Sultan Qaboos University. The results showed the effectiveness of logotherapy in alleviating isolation for students.

Keywords: social isolation, logotherapy, visually impaired, disability

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