Search results for: network identification

Authors: Habtamu Tkubet Ebuy

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Purpose Conventional dynamic energy simulation tools go beyond the static dimension of simplified methods by providing better and more accurate prediction of building performance. However, their ability to forecast actual performance is undermined by a low representation of human interactions. The purpose of this study is to examine the potential benefits of incorporating information on occupant diversity into occupant behavior models used to simulate building performance. The co-simulation of the stochastic behavior of the occupants substantially increases the accuracy of the simulation. Design/methodology/approach In this article, probabilistic models of the "opening and closing" behavior of the window of inhabitants have been developed in a separate multi-agent platform, SimOcc, and implemented in the building simulation, TRNSYS, in such a way that the behavior of the window with the interconnectivity can be reflected in the simulation analysis of the building. Findings The results of the study prove that the application of complex behaviors is important to research in predicting actual building performance. The results aid in the identification of the gap between reality and existing simulation methods. We hope this study and its results will serve as a guide for researchers interested in investigating occupant behavior in the future. Research limitations/implications Further case studies involving multi-user behavior for complex commercial buildings need to more understand the impact of the occupant behavior on building performance. Originality/value This study is considered as a good opportunity to achieve the national strategy by showing a suitable tool to help stakeholders in the design phase of new or retrofitted buildings to improve the performance of office buildings.

Keywords: occupant behavior, co-simulation, energy consumption, thermal comfort

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Authors: Dina K. Badawy, Dina M. Ibrahim, Alsayed A. Sallam

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Distributed database is a collection of logically related databases that cooperate in a transparent manner. Query processing uses a communication network for transmitting data between sites. It refers to one of the challenges in the database world. The development of sophisticated query optimization technology is the reason for the commercial success of database systems, which complexity and cost increase with increasing number of relations in the query. Mariposa, query trading and query trading with processing task-trading strategies developed for autonomous distributed database systems, but they cause high optimization cost because of involvement of all nodes in generating an optimal plan. In this paper, we proposed a modification on the autonomous strategy K-QTPT that make the seller’s nodes with the lowest cost have gradually high priorities to reduce the optimization time. We implement our proposed strategy and present the results and analysis based on those results.

Keywords: autonomous strategies, distributed database systems, high priority, query optimization

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Authors: Veronika Solopova, Oana-Iuliana Popescu, Tim Landgraf, Christoph Benzmüller

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Beginning in 2014, slowly building geopolitical tensions in Eastern Europe led to a full-blown conflict between the Russian Federation and Ukraine that generated an unprecedented amount of news articles and data from social media data, reflecting the opposing ideologies and narratives as a background and the essence of the ongoing war. These polarized informational campaigns have led to countless mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for many readers all over the world. In this study, we analyzed scraped news articles from Ukrainian, Russian, Romanian and English-speaking news outlets, on the eve of 24th of February 2022, compared to day five of the conflict (28th of February), to see how the media influenced and mirrored the changes in public opinion. We also contrast the sources opposing and supporting the stands of the Russian government in Ukrainian, Russian and Romanian media spaces. In a data-driven way, we describe how the narratives are spread throughout Eastern and Central Europe. We present predictive linguistic features surrounding war propaganda. Our results indicate that there are strong similarities in terms of rhetoric strategies in the pro-Kremlin media in both Ukraine and Russia, which, while being relatively neutral according to surface structure, use aggressive vocabulary. This suggests that automatic propaganda identification systems have to be tailored for each new case, as they have to rely on situationally specific words. Both Ukrainian and Russian outlets lean towards strongly opinionated news, pointing towards the use of war propaganda in order to achieve strategic goals.

Keywords: linguistic, news, propaganda, Russia, ukraine

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Authors: Nnadi Ezekiel Oluwaseun Ejiofor

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The foundation of this study is the identification of risk variables and their implications on abandoned construction projects in Nigeria. The study's particular goals are to pinpoint the risk factors that lead to the abandonment of public building projects in Nigeria. This study used a hybrid research design that included case studies and descriptive survey research methods. Professionals who work directly in the built environment and are employed by Ministries and Departmental Agencies (MDAs), the public sector, or the private sector are the study's target demographic. This study used a descriptive survey and case study research design to gather data. Nigeria is experiencing a high rate of project abandonment due to housing deficit issues. Factors contributing to this include The study reveals factors contributing to public project abandonment in Abuja FCT include poor cashflow 4.96, inconsistent government policies 4.89, lack of accountability, high corruption, incompetent contractors, non-availability of building materials, lack of utilities, wrong materials, infrastructural facilities, poor planning, and undefined contracts. The study reveals that abandoned projects have a huge impact on the construction industry, such as wastage of resources with a mean value of 3.35, distrust of economic growth, 3.28, and so on. The study found a significant relationship between risk factors and public building construction in Abuja through a T-test value of 0.037, rejecting the null hypothesis and indicating a positive correlation.

Keywords: cost, tetfund, construction projects, public university

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Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Victor Chomel, Maziyar Panahi, David Chavalarias

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Misinformation has taken an increasingly worrying place in social media. Propagation patterns are closely linked to the structure of communities. This study proposes a method of community analysis based on a combination of centrality indicators for the network and its main communities. The objective is to establish a link between the stability of the communities over time, the social ascension of its members internally, and the propagation of information in the community. To this end, data from the debates about global warming and political communities on Twitter have been collected, and several tens of millions of tweets and retweets have helped us better understand the structure of these communities. The quantification of this stability allows for the study of the propagation of information of any kind, including disinformation. Our results indicate that the most stable communities over time are the ones that enable the establishment of nodes capturing a large part of the information and broadcasting its opinions. Conversely, communities with a high turnover and social ascendancy only stabilize themselves strongly in the face of adversity and external events but seem to offer a greater diversity of opinions most of the time.

Keywords: community analysis, disinformation, misinformation, Twitter

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Authors: Mahmoud M. Othman, Almoataz Y. Abdelaziz, Yasser G. Hegazy

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This paper presents a load control strategy based on modification of the Big Bang Big Crunch optimization method. The proposed strategy aims to determine the optimal load to be controlled and the corresponding time of control in order to minimize the energy purchased from substation. The presented strategy helps the distribution network operator to rely on the renewable energy sources in supplying the system demand. The renewable energy sources used in the presented study are modeled using the diagonal band Copula method and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithm.

Keywords: big bang big crunch, distributed generation, load control, optimization, planning

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Authors: Aliasghar Golmohammadian, Sona Rostampour Yasouri

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One of the most important intestinal pathogenic strains is E. coli O₁₅₇:H₇. This pathogenic bacterium is transmitted to humans through water and food. E. coli O₁₅₇:H₇ is the main cause of Hemorrhagic colitis (HC), Hemolytic Uremic Syndrome (HUS), Thrombotic Thrombocytopenic Purpura (TTP) and in some cases death. Since E. coli O₁₅₇:H₇ can be transmitted through the consumption of different foods, including vegetables, agricultural products, and fresh dairy products, this study aims to identify and isolate E. coli O₁₅₇:H₇ from wastewater by PCR technique. One hundred twenty samples of water and wastewater were collected by Falcom Sterile from Shahrood and Neka cities. The samples were checked for colony formation after appropriate centrifugation and cultivation in the specific medium of Sorbitol MacConkey Agar (SMAC) and other diagnostic media of E. coli O₁₅₇:H₇. Also, the plates were observed macroscopically and microscopically. Then, the necessary phenotypic tests were performed on the colonies, and finally, after DNA extraction, the PCR technique was performed with specific primers related to rfbE and stx2 genes. The number of 5 samples (6%) out of all the samples examined were determined positive by PCR technique with observing the bands related to the mentioned genes on the agarose gel electrophoresis. PCR is a fast and accurate method to identify the bacteria E. coli O₁₅₇:H₇. Considering that E. coli bacteria is a resistant bacteria and survives in water and food for weeks and months, the PCR technique can provide the possibility of quick detection of contaminated water. Moreover, it helps people in the community control and prevent the transfer of bacteria to healthy and underground water and agricultural and even dairy products.

Keywords: E. coli O₁₅₇:H₇, PCR, water, wastewater

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Authors: Lormaine Anne A. Branzuela, Elysa V. Largo, Monet Concepcion M. Detras, Neil C. Concibido

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The electricity demand is still increasing, and currently, the Philippine government is investigating the feasibility of operating the Bataan Nuclear Power Plant (BNPP) to address the country’s energy problem. However, the lack of process safety studies on BNPP focused on the effects of hazardous substances on the integrity of the structure, equipment, and other components, have made the plant operationalization questionable to the public. The three major nuclear power plant incidents – TMI-2, Chernobyl, and Fukushima – have made many people hesitant to include nuclear energy in the energy matrix. This study focused on the safety evaluation of possible operations of a nuclear power plant installed with a Pressurized Water Reactor (PWR), which is similar to BNPP. Failure Mode and Effects Analysis (FMEA) is one of the Process Hazard Analysis (PHA) techniques used for the identification of equipment failure modes and minimizing its consequences. Using the FMEA technique, this study was able to recognize 116 different failure modes in total. Upon computation and ranking of the risk priority number (RPN) and criticality rating (CR), it showed that failure of the reactor coolant pump due to earthquakes is the most critical failure mode. This hazard scenario could lead to a nuclear meltdown and radioactive release, as identified by the FMEA team. Safeguards and recommended risk reduction strategies to lower the RPN and CR were identified such that the effects are minimized, the likelihood of occurrence is reduced, and failure detection is improved.

Keywords: PHA, FMEA, nuclear power plant, bataan nuclear power plant

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Authors: Anocha Rojanapanich

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The purpose of this research is to develop and design an accounting management information system for convenient shop in Bangkok Thailand. The study applied the System Development Life Cycle (SDLC) for development which began with study and analysis of current data, including the existing system. Then, the system was designed and developed to meet users’ requirements via the internet network by use of application software such as My SQL for database management, Product diversity, Apache HTTP Server for Web Server and PHP Hypertext Preprocessor for an interface between web server, database and users. The system was designed into two subsystems as the main system, or system for head office, and the branch system for branch shops. These consisted of three parts which are classified by user management as shop management, inventory management and Point of Sale (POS) management and importance of cost information for decision making also as well as.

Keywords: accounting management information system, convenient shop, cost information for decision making system, development life cycle

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Authors: Yiqi Wu, Simon Tait, Andrew Nichols

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Management of UK railway drainage assets is challenging due to the large amounts of historical assets with long asset life cycles. A major concern for asset managers is to maintain the required performance economically and efficiently while complying with the relevant regulation and legislation. As the majority of the drainage assets are buried underground and are often difficult or costly to examine, it is important for asset managers to understand and model the degradation process in order to foresee the upcoming reduction in asset performance and conduct proactive maintenance accordingly. In this research, a Markov chain approach is used to model the deterioration process of rail drainage assets. The study is based on historical condition scores and characteristics of drainage assets across the whole railway network in England, Scotland, and Wales. The model is used to examine the effect of various characteristics on the probabilities of degradation, for example, the regional difference in probabilities of degradation, and how material and shape can influence the deterioration process for chambers, channels, and pipes.

Keywords: deterioration, degradation, markov models, probability, railway drainage

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Authors: Sydney Burnett

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The history of the relationship between elder abuse and its tendency to go underreported is rooted in the oppressive nature of ageism and victimization. Approximately 8% of the world's population was aged sixty or over in 1950, whereas, in 2020, the number more than doubled to 16.9%. By 2050, that number is expected to reach 22%. Although difficult for individuals of any age to feel completely supported in society, this task proves especially difficult for the elderly demographic. And as the elderly population continues to grow, the systemic abuse and neglect that this group encounters, and thus its underreported nature, multiply at a similar rate. Although a recent increase in awareness has initiated stronger efforts towards addressing the meager resources, processes, and personnel present to manage elder abuse, both reported and unreported, the destructive complexities of ageism and victimization persist. Examining the byproducts of the rapidly growing elderly demographic in China, the United States, and Israel, in cohesion with the inherent challenges in the context of terminology, definition, and typologies of elder abuse should provide insight into the pernicious influences of elder abuse that contribute to the non-identification and non-recognition of elder maltreatment present in these three countries in different stages of development. This investigation aims to understand the intricacy of elder abuse and its correlation to a lack of acknowledgment as well as its consequences in society by exploring the variation between China, the United States, and Israel's attitudes surrounding the subject. Furthermore, the systemic abuse and neglect embedded in global ageism can be revealed by the differences between the three countries' approaches to reporting elder abuse.

Keywords: elder abuse, ageism, mistreatment, underreported

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Authors: Kanika Chowdhary, Satyawati Sharma

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Endophytic microbes are hosted inside plants in a symbiotic and hugely benefitting relationship. Exploring agriculturally beneficial endophytes is quite a prospective field of research. In the present work fungal endophytes associated with aromatic plant Ocimum basicilium L. were investigated for biocontrol potential. The anti-plant pathogenic activity of fungal endophytes was tested against causal agent of stem rot Sclerotinia sclerotiorum. 75 endophytic fungi were recovered through culture-dependent approach. Fungal identification was performed both microscopically and by rDNA ITS sequencing. Curvuaria lunata (Sb-6) and Colletotrichum lindemuthianum (Sb-8) inhibited 86% and 72% mycelia growth of S. sclerotinia on Sabouraud dextrose agar medium at 7.4 pH. Small-scale fermentation was carried out on sterilised oatmeal grain medium. In another set of experiment, fungi were grown in oatmeal grain medium amended with certain biostimulants such as aqueous seaweed extract (10% v/w); methanolic seaweed extract (5% v/w); cow urine (20% v/w); biochar (10% w/w) in triplicate along with control of each to ascertain the degree of metabolic difference and anti-plant pathogenic activity induced. Phytochemically extracts of both the fungal isolates showed the presence of flavanoids, phenols, tannins, alkaloids and terpenoids. Ethylacetate extract of C. lunata and C. lindemuthianum suppressed S. sclerotinia conidial germination at IC50 values of 0.514± 0.02 and 0.913± 0.04 mg/ml. Therefore, fungal endophytes of O. basicilium are highly promising bio-resource agent, which can be developed further for sustainable agriculture.

Keywords: endophytic fungi, ocimum basicilium, sclerotinia sclerotiorum, biostimulants

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Helen Yetunde Omoboye, Adebukola Adenike Adedeji, Israel Funso Adeniyi

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This study of the biodiversity, community structure, and production capacity of the zooplankton community is an aspect of bio-monitoring of the aquatic ecosystem. Samples were selected horizontally and vertically from Esa-Odo Reservoir using improvised Meyer’s water sampler. Planktonic samples were collected at two months intervals for two years. Net and total plankton were sampled by filtration and sedimentation methods. Planktonic samples were preserved as 5% formalin and 1% Lugol’s solution. Measurement, enumeration, and scaled pictures of the recorded zooplankton were taken using a photomicrograph. The taxonomic composition of zooplankton biota was determined using identification keys. Eighty three (83) species of zooplankton recorded in this study belong to 4 groups: Rotifera, Cladocera, Copepoda, and Insecta. Rotifera was the most represented group (61.21%). Horizontally, 24 species with the highest mean abundance characterized the lacustrine; while 12 species and 10 species were unique to the transition and riverine zones, respectively. Vertically, most species had their mean abundance decreased from the surface to the bottom of the reservoir. A total of nine (9), two (2), and one (1) species were peculiar to the surface, bottom and mid-depth, respectively. Zooplankton was most abundant during the dry season. In conclusion, Esa-Odo Reservoir comprised highly diversified zooplankton fauna with great potential to support a rich aquatic community and fishery production. The reservoir can be classified as fairly clean based on the abundance of the rotifer group. However, the lake should be subjected to regular proper monitoring because of the presence of some pollution tolerant copepod species identified among the zooplankton fauna.

Keywords: zooplankton, spatial, temporal, abundance, biodiversity, reservoir

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Authors: Toufik Karech, Abderahmen Benseghir, Tayeb Bouzid

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This work presents a 2D numerical simulation of an earth dam to assess the behavior of its foundation after a treatment by stone columns. This treatment aims to improve the bearing capacity, to increase the mechanical properties of the soil, to accelerate the consolidation, to reduce the settlements and to eliminate the liquefaction phenomenon in case of seismic excitation. For the evaluation of the pore pressures, the position of the phreatic line and the flow network was defined, and a seepage analysis was performed with the software MIDAS Soil Works. The consolidation calculation is performed through a simulation of the actual construction stages of the dam. These analyzes were performed using the Mohr-Coulomb soil model and the results are compared with the actual measurements of settlement gauges implanted in the dam. An analysis of the bearing capacity was conducted to show the role of stone columns in improving the bearing capacity of the foundation.

Keywords: earth dam, dam foundation, numerical simulation, stone columns, seepage analysis, consolidation, bearing capacity

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Authors: Muhammad Rana, Quazi Mamun, Rafiqul Islam

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In the Internet of Things (IoT), many devices are connected and accumulate a sheer amount of data. These Internet-driven raw data need to be transferred securely to the end-users via dependable networks. Consequently, the challenges of IoT security in various IoT domains are paramount. Cryptography is being applied to secure the networks for authentication, confidentiality, data integrity and access control. However, due to the resource constraint properties of IoT devices, the conventional cipher may not be suitable in all IoT networks. This paper designs a robust and effective lightweight cipher to secure the IoT environment and meet the resource-constrained nature of IoT devices. We also propose a symmetric and block-cipher based lightweight cryptographic algorithm. The proposed algorithm increases the complexity of the block cipher, maintaining the lowest computational requirements possible. The proposed algorithm efficiently constructs the key register updating technique, reduces the number of encryption rounds, and adds a new layer between the encryption and decryption processes.

Keywords: internet of things, cryptography block cipher, S-box, key management, security, network

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Authors: E. C. Mattos, V. S. M. G. Daros, R. Dal Col, A. L. Nascimento

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The aim of this study is to present the results of a retrospective survey on the foreign matter found in foods analyzed at the Adolfo Lutz Institute, from July 2001 to July 2015. All the analyses were conducted according to the official methods described on Association of Official Agricultural Chemists (AOAC) for the micro analytical procedures and Food and Drug Administration (FDA) for the macro analytical procedures. The results showed flours, cereals and derivatives such as baking and pasta products were the types of food where foreign matters were found more frequently followed by condiments and teas. Fragments of stored grains insects, its larvae, nets, excrement, dead mites and rodent excrement were the most foreign matter found in food. Besides, foreign matters that can cause a physical risk to the consumer’s health such as metal, stones, glass, wood were found but rarely. Miscellaneous (shell, sand, dirt and seeds) were also reported. There are a lot of extraneous materials that are considered unavoidable since are something inherent to the product itself, such as insect fragments in grains. In contrast, there are avoidable extraneous materials that are less tolerated because it is preventable with the Good Manufacturing Practice. The conclusion of this work is that although most extraneous materials found in food are considered unavoidable it is necessary to keep the Good Manufacturing Practice throughout the food processing as well as maintaining a constant surveillance of the production process in order to avoid accidents that may lead to occurrence of these extraneous materials in food.

Keywords: extraneous materials, food contamination, foreign matter, surveillance

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Authors: Khaled Ben Oualid Medani, Samir Sayah

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The Optimal Reactive Power Dispatch (ORPD) problem has been solved and analysed usually in the normal conditions. However, network collapses appear in contingency conditions. In this paper, ORPD under several contingencies is presented using the proposed method WOA. To ensure viability of the power system in contingency conditions, several critical cases are simulated in order to prevent and prepare the power system to face such situations. The results obtained are carried out in IEEE 30 bus test system for the solution of ORPD problem in which control of bus voltages, tap position of transformers and reactive power sources are involved. Moreover, another method, namely, Particle Swarm Optimization with Time Varying Acceleration Coefficient (PSO-TVAC) has been compared with the proposed technique. Simulation results indicate that the proposed WOA gives remarkable solution in terms of effectiveness in case of outages.

Keywords: optimal reactive power dispatch, power system analysis, real power loss minimization, contingency condition, metaheuristic technique, whale optimization algorithm

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

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Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce.

Keywords: spam keyword, e-commerce, keyword features, spam filtering

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Authors: Rupesh K. Chaudhary, Narinder P. Jain, Rajesh Mahajan, Rajat Manchanda

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Introduction: Delirium is a complex, multifactorial neuropsychiatric syndrome comprising a broad range of cognitive and neurobehavioral symptoms. In critically ill patients, it may develop secondary to multiple predisposing factors. Although it can be transient and irreversible but if left untreated may lead to long term cognitive dysfunction. Early identification and assessment of risk factors usually help in appropriate management of delirium which in turn leads to decreased hospital stay, cost of therapy and mortality. Aim and Objective: Aim of the present study was to estimate the incidence of delirium using a validated scale in medical ICU patients and to determine the associated risk factors and outcomes. Material and Method: A prospective study in an 18-bed medical-intensive care unit (ICU) was undertaken. A total of 357 consecutive patients admitted to ICU for more than 24 hours were assessed. These patients were screened with the help of Confusion Assessment Method for Intensive Care Unit -CAM-ICU, Richmond Agitation and Sedation Scale, Screening Checklist for delirium and APACHE II. Appropiate statistical analysis was done to evaluate the risk factors influencing mortality in delirium. Results: Delirium occurred in 54.6% of 194 patients. Risk of delirium was independently associated with a history of hypertension, diabetes but not with severity of illness APACHE II score. Delirium was linked to longer ICU stay 13.08 ± 9.6 ver 7.07 ± 4.98 days, higher ICU mortality (35.8% % vs. 17.0%). Conclusion: Our study concluded that delirium poses a great risk factor in the outcome of the patient and carries high mortality, so a timely intervention helps in addressing these issues.

Keywords: delirium, risk factors, outcome, intervention

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Authors: Regan Harper, Song Yang, Douglas Adams

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During the historically unprecedent time of Covid-19 pandemic, we survey college students with social issue generators to measure their core discussion groups. For the total 191 students, we elicit 847 conversation partners (alters) with our five social issue generators such as school closing, facemasks, collegiate sports, race and policing, and social inequality, producing an average of 4.43 alters per respondent. The core discussion groups of our sample are very gender balanced, with female alters slightly outnumbering male alters. However, the core discussion groups are racially homogenous, consisting of mostly white students (around or above 80 percent). Explanatory analyses reveal that gender and race of respondents significantly impact the size, gender composition, and racial composition of their core discussion networks. We discuss those major findings and implications of future studies in our conclusion section.

Keywords: core discussion groups, social issue generators, ego-centric network, Covid-19 pandemic

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Authors: Jarek Krajewski, Andre Wittenborn, Martin Sauerland

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This abstract describes a promising approach for estimating post-traumatic stress disorder (PTSD) based on prosodic speech characteristics. It illustrates the validity of this method by briefly discussing results from an Arabic refugee sample (N= 47, 32 m, 15 f). A well-established standardized self-report scale “Reaction of Adolescents to Traumatic Stress” (RATS) was used to determine the ground truth level of PTSD. The speech material was prompted by telling about autobiographical related sadness inducing experiences (sampling rate 16 kHz, 8 bit resolution). In order to investigate PTSD-induced speech changes, a self-developed set of 136 prosodic speech features was extracted from the .wav files. This set was adapted to capture traumatization related speech phenomena. An artificial neural network (ANN) machine learning model was applied to determine the PTSD level and reached a correlation of r = .37. These results indicate that our classifiers can achieve similar results to those seen in speech-based stress research.

Keywords: speech prosody, PTSD, machine learning, feature extraction

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Authors: Yu Cheng-Yu

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‘Heterotopia’, ‘Visual Cultural Art Education’ and ‘New Media’ of these three subjects seemingly are irrelevant. In fact, there are synchronicity and intertextuality inside. In addition to visual culture, art education inspires students the ability to reflect on popular culture image through visual culture teaching strategies in school. We should get involved in the community to construct the learning environment that conveys visual culture art. This thesis attempts to probe the heterogeneity of space and value from Michel Foucault and to research sustainable development strategy in ‘New Media Art Village’ heterogeneity from Jean Baudrillard, Marshall McLuhan's media culture theory and social construction ideology. It is possible to find a new media group that can convey ‘Visual Culture Art Education’ around the National Taipei University of Technology in this commercial district that combines intelligent technology, fashion, media, entertainment, art education, and marketing network. Let the imagination and innovation of ‘New Media Art Village’ become ‘implementable’ and new media Heterotopia of inter-subjectivity with the engagement of big data and digital media. Visual culture art education will also bring aesthetics into the community by New Media Art Village.

Keywords: social construction, heterogeneity, new media, big data, visual culture art education

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Authors: Mhaned Oubounyt, Jan Baumbach

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Differences in co-expression networks between two or multiple cells (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN (Gene Correlation Network) and GRN (Gene Regulatory Networks) pipeline, including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. In an example case, we illustrate how SCANet can be applied to identify regulatory drivers behind a cytokine storm associated with mortality in patients with acute respiratory illness. SCANet is available as a free, open-source, and user-friendly Python package that can be easily integrated into systems biology pipelines.

Keywords: single-cell, co-expression networks, drug-gene interactions, co-regulatory networks

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Authors: A. Movaghar, R. A. Mahdavinejad

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To analyze the effect of various parameters of fluid on the material properties such as surface and depth defects and/or cracks, it is possible to determine the affection of pressure field on these specifications. Stress tensor analysis is also able to determine the points in which the probability of defection creation is more. Besides, from pressure field, it is possible to analyze the affection of various fluid specifications such as viscosity and density on defect created in the material. In this research, the concerned boundary conditions are analyzed first. Then the solution network and stencil used are mentioned. With the determination of relevant equation on the fluid flow between notch and matrix and their discretion according to the governed boundary conditions, these equations can be solved. Finally, with the variation creations on fluid parameters such as density and viscosity, the affection of these variations can be determined on pressure field. In this direction, the flowchart and solution algorithm with their results as vortex and current function contours for two conditions with most applications in pressing process are introduced and discussed.

Keywords: pressing, notch, matrix, flow function, vortex

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Authors: Qusay Alghazali, Tibor Cinkler, Abdulhalim Fayad

Abstract:

In modern optical networks, the flex grid spectrum usage is most widespread, where higher bit rate streams get larger spectrum slices while lower bit rate traffic streams get smaller spectrum slices. To our practice, under the ITU-T recommendation, G.694.1, spectrum slices of 50, 75, and 100 GHz are being used with central frequency at 193.1 THz. However, when these spectrum slices are not sufficient, multiple spectrum slices can use either one next to another or anywhere in the optical wavelength. In this paper, we propose the analysis of the wavelength assignment problem. We compare different algorithms for this spectrum assignment with and without protection. As a reference for comparisons, we concluded that the Integer Linear Programming (ILP) provides the global optimum for all cases. The most scalable algorithm is the greedy one, which yields results in subsequent ranges even for more significant network instances. The algorithms’ benchmark implemented using the LEMON C++ optimization library and simulation runs based on a minimum number of spectrum slices assigned to lightpaths and their execution time.

Keywords: spectrum assignment, integer linear programming, greedy algorithm, international telecommunication union, library for efficient modeling and optimization in networks

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Authors: James Kaweesa

Abstract:

The Internet of Things (IoT) has become a critical component of modern technology, enabling the connection of numerous devices to the internet. The interconnected nature of IoT devices, along with their heterogeneous and resource-constrained nature, makes them vulnerable to various types of attacks, such as malware, denial-of-service attacks, and network scanning. Intrusion Detection Systems (IDSs) are a key mechanism for protecting IoT networks and from attacks by identifying and alerting administrators to suspicious activities. In this review, the paper will discuss the different types of IDSs available for IoT systems and evaluate their effectiveness in detecting and preventing attacks. Also, examine the various evaluation methods used to assess the performance of IDSs and the challenges associated with evaluating them in IoT environments. The review will highlight the need for effective and efficient IDSs that can cope with the unique characteristics of IoT networks, including their heterogeneity, dynamic topology, and resource constraints. The paper will conclude by indicating where further research is needed to develop IDSs that can address these challenges and effectively protect IoT systems from cyber threats.

Keywords: cyber-threats, iot, intrusion detection system, networks

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Authors: Lee Myung-Won, Kwak Keun-Chang

Abstract:

This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation

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Authors: Mustapha Saidu, Bilyaminu Mohammed

Abstract:

The study was conducted in Udubo Grazing Reserve between July 2019 and October 2019 to assess forage utilization for pasture-based livestock production in reserve. The grazing land was cross-divided into grids, where 15 coordinates were selected as the sample points. Grids of one-kilometer interval were made. The grids were systematically selected 1 grid after 7 grids. 1 × 1-meter quadrat was made at the coordinate of the selected grids for measurement, estimation, and sample collection. The results of the study indicated that Zornia glochidiatah has the highest percent of species composition (42%), while Mitracarpus hirtus has the lowest percent (0.1%). Urochloa mosambicensis has 48 percent of height removed and 27 percent used by weight, Zornia glochidiata 60 percent of height removed and 57 percent used by weight, Alysicapus veginalis has 55 percent of height removed, and 40 percent used by weight, and Cenchrus biflorus has 40 percent of height removed and 28 percent used by weight. The target is 50 percent utilization of forage by weight during a grazing period as well as at the end of the grazing season. The study found that Orochloa mosambicensis, Alysicarpus veginalis, and Cenchrus biflorus had lower percent by weight which is normal, while Zornia glochidiata had a higher percent by weight which is an indication of danger. The study recommends that the identification of key plant species in pasture and rangeland is critical to implementing a successful grazing management plan. There should be collective action and promotion of historically generated grazing knowledge through public and private advocacies.

Keywords: forage, grazing reserve, live stock, pasture, plant species

Procedia PDF Downloads 67