Search results for: green infrastructure network

Authors: Pshtiwan Shafiq Ahmed

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The invasion of Iraq was a turning point in Iraq, destroying the economic infrastructure of several important strategic and historic cities, including Mosul, Anbar and Diyala, which will take decades to rebuild It left 18,805 people dead and 37,000 injured, destroying hundreds of villages and cities, displacing 2.3 million people, and increasing the number of orphans The increase in the number of windows and the destruction of society and the structure of the population so that the number of children, women and the elderly has increased. Religious clashes have increased and religious cleansing has begun, trying to eradicate Christianity, Yazidis and Kakais from the whole of Iraq, causing the largest number of Christians, Yazidis and Kakais to leave Iraq and many of them went missing.

Keywords: ISIS, population change, geographical change, Iraq

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Authors: Liqin Zhang, Liang Yan

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This paper addresses the speed synchronization control problem for a network-based multi-motor system from the perspective of cluster consensus theory. Each motor is considered as a single agent connected through fixed and undirected network. This paper presents an improved control protocol from three aspects. First, for the purpose of improving both tracking and synchronization performance, this paper presents a distributed leader-following method. The improved control protocol takes the importance of each motor’s speed into consideration, and all motors are divided into different groups according to speed weights. Specifically, by using control parameters optimization, the synchronization error and tracking error can be regulated and decoupled to some extent. The simulation results demonstrate the effectiveness and superiority of the proposed strategy. In practical engineering, the simplified models are unrealistic, such as single-integrator and double-integrator. And previous algorithms require the acceleration information of the leader available to all followers if the leader has a varying velocity, which is also difficult to realize. Therefore, the method focuses on an observer-based variable structure algorithm for consensus tracking, which gets rid of the leader acceleration. The presented scheme optimizes synchronization performance, as well as provides satisfactory robustness. What’s more, the existing algorithms can obtain a stable synchronous system; however, the obtained stable system may encounter some disturbances that may destroy the synchronization. Focus on this challenging technological problem, a state-dependent-switching approach is introduced. In the presence of unmeasured angular speed and unknown failures, this paper investigates a distributed fault-tolerant consensus tracking algorithm for a group non-identical motors. The failures are modeled by nonlinear functions, and the sliding mode observer is designed to estimate the angular speed and nonlinear failures. The convergence and stability of the given multi-motor system are proved. Simulation results have shown that all followers asymptotically converge to a consistent state when one follower fails to follow the virtual leader during a large enough disturbance, which illustrates the good performance of synchronization control accuracy.

Keywords: consensus control, distributed follow, fault-tolerant control, multi-motor system, speed synchronization

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Authors: Kerem Ok, Cem Cevikbas, Vedat Coskun, Mohammed Alsadi, Busra Ozdenizci

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Un-keyed SIM cards do not contain the required security infrastructure to provide end-to-end encryption with Service Providers. Hence, new, emerging, or smart services those require end-to-end encryption between SIM card and a Service Provider is impossible. SIMSec key exchange protocol creates symmetric keys between SIM card and Service Provider. After a successful protocol execution, SIM card and Service Provider creates the symmetric keys and can perform end-to-end data encryption when required. In this paper, our aim is to analyze the SIMSec protocol’s security. According to the results, SIM card and Service Provider can generate keys securely using SIMSec protocol.

Keywords: End-to-end encryption, key exchange, SIM card, smart card

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Authors: A. Arshad, M. Lehtonen. M. Humayun

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With the increasing Distributed Generation (DG) penetration, distribution systems are advancing towards the smart grid technology for least latency in tackling voltage control problem in a distributed manner. This paper proposes a Multi-agent based distributed voltage level control. In this method a flat architecture of agents is used and agents involved in the whole controlling procedure are On Load Tap Changer Agent (OLTCA), Static VAR Compensator Agent (SVCA), and the agents associated with DGs and loads at their locations. The objectives of the proposed voltage control model are to minimize network losses and DG curtailments while maintaining voltage value within statutory limits as close as possible to the nominal. The total loss cost is the sum of network losses cost, DG curtailment costs, and voltage damage cost (which is based on penalty function implementation). The total cost is iteratively calculated for various stricter limits by plotting voltage damage cost and losses cost against varying voltage limit band. The method provides the optimal limits closer to nominal value with minimum total loss cost. In order to achieve the objective of voltage control, the whole network is divided into multiple control regions; downstream from the controlling device. The OLTCA behaves as a supervisory agent and performs all the optimizations. At first, a token is generated by OLTCA on each time step and it transfers from node to node until the node with voltage violation is detected. Upon detection of such a node, the token grants permission to Load Agent (LA) for initiation of possible remedial actions. LA will contact the respective controlling devices dependent on the vicinity of the violated node. If the violated node does not lie in the vicinity of the controller or the controlling capabilities of all the downstream control devices are at their limits then OLTC is considered as a last resort. For a realistic study, simulations are performed for a typical Finnish residential medium-voltage distribution system using Matlab ®. These simulations are executed for two cases; simple Distributed Voltage Control (DVC) and DVC with optimized loss cost (DVC + Penalty Function). A sensitivity analysis is performed based on DG penetration. The results indicate that costs of losses and DG curtailments are directly proportional to the DG penetration, while in case 2 there is a significant reduction in total loss. For lower DG penetration, losses are reduced more or less 50%, while for higher DG penetration, loss reduction is not very significant. Another observation is that the newer stricter limits calculated by cost optimization moves towards the statutory limits of ±10% of the nominal with the increasing DG penetration as for 25, 45 and 65% limits calculated are ±5, ±6.25 and 8.75% respectively. Observed results conclude that the novel voltage control algorithm proposed in case 1 is able to deal with the voltage control problem instantly but with higher losses. In contrast, case 2 make sure to reduce the network losses through proposed iterative method of loss cost optimization by OLTCA, slowly with time.

Keywords: distributed voltage control, distribution system, multi-agent systems, smart grids

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Authors: Kurt Maxwell Kusterer, Sylvain Mignot, Annick Vignes

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To our best knowledge YouTube is the largest video hosting platform in the world. A high number of creators, viewers, subscribers and commentators act in this specific eco-system which generates huge sums of money. Views, subscribers, and comments help to increase the popularity of content creators. The most popular creators are sponsored by brands and participate in marketing campaigns. For a few of them, this becomes a financially rewarding profession. This is made possible through the YouTube Partner Program, which shares revenue among creators based on their popularity. We believe that the role of comments in increasing the popularity is to be emphasized. In what follows, YouTube is considered as a bilateral network between the videos and the commentators. Analyzing a detailed data set focused on French YouTubers, we consider each comment as a link between a commentator and a video. Our research question asks what are the predominant features of a video which give it the highest probability to be commented on. Following on from this question, how can we use these features to predict the action of the agent in commenting one video instead of another, considering the characteristics of the commentators, videos, topics, channels, and recommendations. We expect to see that the videos of more popular channels generate higher viewer engagement and thus are more frequently commented. The interest lies in discovering features which have not classically been considered as markers for popularity on the platform. A quick view of our data set shows that 96% of the commentators comment only once on a certain video. Thus, we study a non-weighted bipartite network between commentators and videos built on the sub-sample of 96% of unique comments. A link exists between two nodes when a commentator makes a comment on a video. We run an Exponential Random Graph Model (ERGM) approach to evaluate which characteristics influence the probability of commenting a video. The creation of a link will be explained in terms of common video features, such as duration, quality, number of likes, number of views, etc. Our data is relevant for the period of 2020-2021 and focuses on the French YouTube environment. From this set of 391 588 videos, we extract the channels which can be monetized according to YouTube regulations (channels with at least 1000 subscribers and more than 4000 hours of viewing time during the last twelve months).In the end, we have a data set of 128 462 videos which consist of 4093 channels. Based on these videos, we have a data set of 1 032 771 unique commentators, with a mean of 2 comments per a commentator, a minimum of 1 comment each, and a maximum of 584 comments.

Keywords: YouTube, social networks, economics, consumer behaviour

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Authors: Banu Ozturk Kurtaslan, Ruhugul Ozge Ocak

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In this study, educational problems related to landscape design education which is an important study area of landscape architecture discipline. It is important to research about the problems in S.U. Agriculture Faculty Landscape Architecture Department which is a new department, started its B.Sc. education in 2011; and developing some suggestions on this issue in terms of future of the department. In the context of the study a questionnaire has been developed to conduct to the B.Sc. students. The questions has been prepared under the topics of education program, instructor, student, physical infrastructure and other problems.

Keywords: landscape design, landscape design education, problems, Selcuk University Landscape Architecture Department

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Authors: Marita Pigłowska, Beata Kurc, Paweł Daszkiewicz

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Railway vehicles are divided into two groups: traction (powered) vehicles and wagons. The traction vehicles include locomotives (line and shunting), railcars (sometimes referred to as railbuses), and multiple units (electric and diesel), consisting of several or a dozen carriages. In vehicles with diesel traction, fuel energy (petrol, diesel, or compressed gas) is converted into mechanical energy directly in the internal combustion engine or via electricity. In the latter case, the combustion engine generator produces electricity that is then used to drive the vehicle (diesel-electric drive or electric transmission). In Poland, such a solution dominates both in heavy linear and shunting locomotives. The classic diesel drive is available for the lightest shunting locomotives, railcars, and passenger diesel multiple units. Vehicles with electric traction do not have their own source of energy -they use pantographs to obtain electricity from the traction network. To determine the competitiveness of the hydrogen propulsion system, it is essential to understand how it works. The basic elements of the construction of a railway vehicle drive system that uses hydrogen as a source of traction force are fuel cells, batteries, fuel tanks, traction motors as well as main and auxiliary converters. The compressed hydrogen is stored in tanks usually located on the roof of the vehicle. This resource is supplemented with the use of specialized infrastructure while the vehicle is stationary. Hydrogen is supplied to the fuel cell, where it oxidizes. The effect of this chemical reaction is electricity and water (in two forms -liquid and water vapor). Electricity is stored in batteries (so far, lithium-ion batteries are used). Electricity stored in this way is used to drive traction motors and supply onboard equipment. The current generated by the fuel cell passes through the main converter, whose task is to adjust it to the values required by the consumers, i.e., batteries and the traction motor. The work will attempt to construct a fuel cell with unique electrodes. This research is a trend that connects industry with science. The first goal will be to obtain hydrogen on a large scale in tube furnaces, to thoroughly analyze the obtained structures (IR), and to apply the method in fuel cells. The second goal is to create low-energy energy storage and distribution station for hydrogen and electric vehicles. The scope of the research includes obtaining a carbon variety and obtaining oxide systems on a large scale using a tubular furnace and then supplying vehicles. Acknowledgments: This work is supported by the Polish Ministry of Science and Education, project "The best of the best! 4.0", number 0911/MNSW/4968 – M.P. and grant 0911/SBAD/2102—B.K.

Keywords: railway, hydrogen, fuel cells, hybrid vehicles

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Authors: N. Perko, N. Kavran, M. Bukljas, I. Berbic

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This paper is a result of implemented research on proposed introduced methodology for nautical ports capacity planning by introducing a multi-criteria approach of defined criteria and impacts at the Adriatic Sea. The purpose was analysing the determinants -characteristics of infrastructure and services of nautical ports capacity allocated, especially nowadays due to COVID-19 pandemic, as crucial for the successful operation of nautical ports. Giving the importance of the defined priorities for short-term and long-term planning is essential not only in terms of the development of nautical tourism but also in terms of developing the maritime system, but unfortunately, this is not always carried out. Evaluation of the use of resources should follow from a detailed analysis of all aspects of resources bearing in mind that nautical tourism used resources in a sustainable manner and generate effects in the tourism and maritime sectors. Consequently, the identified multiplier effect of nautical tourism, which should be defined and quantified in detail, should be one of the major competitive products on the Croatian Adriatic and the Mediterranean. Research of nautical tourism is necessary to quantify the effects and required planning system development. In the future, the greatest threat to the long-term sustainable development of nautical tourism can be its further uncontrolled or unlimited and undirected development, especially under pressure markedly higher demand than supply for new moorings in the Mediterranean. Results of this implemented research are applicable to nautical ports management and decision-makers of maritime transport system development. This paper will present implemented research and obtained result-developed methodology for nautical port capacity planning -port capacity planning multi-criteria decision-making. A proposed methodological approach of multi-criteria capacity planning includes four criteria (spatial - transport, cost - infrastructure, ecological and organizational criteria, and additional services). The importance of the criteria and sub-criteria is evaluated and carried out as the basis for sensitivity analysis of the importance of the criteria and sub-criteria. Based on the analysis of the identified and quantified importance of certain criteria and sub-criteria, as well as sensitivity analysis and analysis of changes of the quantified importance, scientific and applicable results will be presented. These obtained results have practical applicability by management of nautical ports in the planning of increasing capacity and further development and for the adaptation of existing nautical ports. Obtained research is applicable and replicable in other seas, and results are especially important and useful in this COVID-19 pandemic challenging maritime development framework.

Keywords: Adriatic Sea, capacity, infrastructures, maritime system, methodology, nautical ports, nautical tourism, service

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Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Amir Rashid

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Today is the world of innovations where everyone is looking for a change. Organizations are now looking toward virtualization in order to minimize their computing cost. Cloud Computing has also introduced itself by the means of reducing computing cost. It offers different approach to make computing better by improving utilization and reducing infrastructure and administrative costs. Cloud Computing is basically the amalgamation of Utility Computing and SaaS (Software as a Service). Cloud Computing is quite new to organizations as it is still at its deploying stage. Due to this reason, organizations are not confident whether to adopt it or not. This thesis investigates the problem for organization concerning the security and cost issues. Benefits and drawbacks are being highlighted which organizations can have or suffer in order to adopt Cloud Computing. In Conclusion, Cloud Computing is a better option available for small and medium organizations with a comparison to large companies both in terms of data security and cost.

Keywords: cloud computing, security, cost, elasticity, PaaS, IaaS, SaaS

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Authors: Tamara Trček Pečak, Andrej Mohar, Denis Trček

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This article investigates the latest technological means, which deploy smart structures that are based on (advanced) wireless sensors technologies and ubiquitous computing in general in order to support the above mentioned decision making. Based on two years of in-field research experiences it gives their analysis for these kinds of purposes and provides appropriate architectures and architectural solutions. Moreover, the directions for future research are stated, because these technologies are currently the most promising ones to enable cost-effective preservation of cultural heritage not only in uncontrolled places, but also in general.

Keywords: smart structures, wireless sensors, sensors networks, green computing, cultural heritage preservation, monitoring, cost effectiveness

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Authors: Mace Bentley, Zhuojun Duan, Tobias Gerken, Dudley Bonsal, Henry Way, Endre Szakal, Mia Pham, Hunter Donaldson, Chelsea Lang, Hayden Abbott, Leah Wilcynzski

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Air pollution has the potential to modify the lifespan and intensity of thunderstorms and the properties of lightning. Using data mining and geovisualization, we investigate how background climate and weather conditions shape variability in urban air pollution and how this, in turn, shapes thunderstorms as measured by the intensity, distribution, and frequency of cloud-to-ground lightning. A spatiotemporal analysis was conducted in order to identify thunderstorms using high-resolution lightning detection network data. Over seven million lightning flashes were used to identify more than 196,000 thunderstorms that occurred between 2006 - 2020 in the Washington, DC Metropolitan Region. Each lightning flash in the dataset was grouped into thunderstorm events by means of a temporal and spatial clustering algorithm. Once the thunderstorm event database was constructed, hourly wind direction, wind speed, and atmospheric thermodynamic data were added to the initiation and dissipation times and locations for the 196,000 identified thunderstorms. Hourly aerosol and air quality data for the thunderstorm initiation times and locations were also incorporated into the dataset. Developing thunderstorm climatologies using a lightning tracking algorithm and lightning detection network data was found to be useful for visualizing the spatial and temporal distribution of urban augmented thunderstorms in the region.

Keywords: lightning, urbanization, thunderstorms, climatology

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Authors: Chung-Hung Tsai, Ya-Ping Li

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Tourism industry is highly depended on the natural environment and climate. Compared to other industries, it is more susceptible to environment and climate. Taiwan belongs to a sea island country and located in the subtropical monsoon zone. The events of climate variability, frequency of typhoons and rainfalls raged are caused regularly serious disaster. In traditional disaster assessment, it usually focuses on the disaster damage and risk assessment, which is short of the features from different industries to understand the impact of the restoring force in post-disaster resilience and the main factors that constitute resilience. The object of this study is based on disaster recovery experience of tourism area and to understand the main factors affecting the tourist area of disaster resilience. The combinations of literature review and interviews with experts are prepared an early indicator system of the disaster resilience. Then, it is screened through a Fuzzy Delphi Method and Analytic Network Process for weight analysis. Finally, this study will establish the tourism disaster resilience evaluation index system considering the Taiwan's tourism industry characteristics. We hope that be able to enhance disaster resilience after tourist areas and increases the sustainability of industrial development. It is expected to provide government departments the tourism industry as the future owner of the assets in extreme climates responses.

Keywords: resilience, Fuzzy Delphi Method, Analytic Network Process, industrial development

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Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

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Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Alireza Monzavi Chaleshtari, Mahnaz Aliakbari Dehkordi, Amin Asadi Hieh, Majid Kazemnezhad

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This research was conducted with the aim of determining the effectiveness of sexual cognitive behavioral therapy on psychological performance and openness to experience in women. The type of research was experimental in the form of pre-test-post-test. The statistical population of this research was made up of all working and married women with membership in the researcher's Instagram social network who had problems in marital-sexual relationships (N=900). From the statistical community, which includes working and married women who are members of the researcher's Instagram social network who have problems in marital-sexual relationships, there are 30 people including two groups (15 people in the experimental group and 15 people in the control group) as available sampling and selected randomly. They were placed in two experimental and control groups. The anxiety, stress, and depression scale (DASS) and the Costa and McCree personality questionnaire were used to collect data, and the cognitive behavioral therapy protocol of Dr. Mehrnaz Ali Akbari was used for the treatment sessions. To analyze the data, the covariance test was used in the SPSS22 software environment. The results showed that sexual cognitive behavioral therapy has a positive and significant effect on psychological performance and openness to experience in women. Conclusion: It can be concluded that interventions such as cognitive-behavioral sex can be used to treat marital problems.

Keywords: sexual cognitive behavioral therapy, psychological function, openness to experience, women

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Oludare Isaac Abiodun, Esther Omolara Abiodun

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The purpose of this paper is to demonstrate that cyberterrorism is existing and poses a threat to computer security and national security. Nowadays, people have become excitedly dependent upon computers, phones, the Internet, and the Internet of things systems to share information, communicate, conduct a search, etc. However, these network systems are at risk from a different source that is known and unknown. These network systems risk being caused by some malicious individuals, groups, organizations, or governments, they take advantage of vulnerabilities in the computer system to hawk sensitive information from people, organizations, or governments. In doing so, they are engaging themselves in computer threats, crime, and terrorism, thereby making the use of computers insecure for others. The threat of cyberterrorism is of various forms and ranges from one country to another country. These threats include disrupting communications and information, stealing data, destroying data, leaking, and breaching data, interfering with messages and networks, and in some cases, demanding financial rewards for stolen data. Hence, this study identifies many ways that cyberterrorists utilize the Internet as a tool to advance their malicious mission, which negatively affects computer security and safety. One could identify causes for disparate anomaly behaviors and the theoretical, ideological, and current forms of the likelihood of cyberterrorism. Therefore, for a countermeasure, this paper proposes the use of previous and current computer security models as found in the literature to help in countering cyberterrorism

Keywords: cyberterrorism, computer security, information, internet, terrorism, threat, digital forensic solution

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Levy flight, distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Charles Asenime, Asaju Joel, Fagbenro Abiola, Adetoyese Oguntimehin, Agosu Rebecca

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This paper examines the fundamental principles of the National Transport Policy (NTP) and determined its role in the execution of transport projects, and the establishment of ministries, departments, and agencies. Data used for the paper are from secondary sources of commissioned reports, studies, internet sources, and government releases. Results of the analysis show that the draft NTP has been used to establish transport schemes, master plans, and transport infrastructure. The paper concludes that though, the national transport Policy is still in a draft form, its production, however, has shaped the transport system in Nigeria and has shown how transport has improved the economy through the efficient utilisation of resources, improved mobility, and lifestyle.

Keywords: principles, draft, system, resources

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Authors: Yassine Jadi, Lin Jie

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In order to serve the society in an electronic manner, many developing countries have launched tremendous e-government projects. The strategies of development and implementation e-government system have reached different levels, and to ensure consistency of development, the governments need to evaluate e-government performance. The United nation has design e-government development ranking index (EGDI) that rely on three indexes, Online service index (OSI), Telecommunication Infrastructure index (TII), and human capital index( HCI) which are not reflecting the interaction between a government and their citizens. Based on data envelopment analyses (DEA) technique, we are using E-participating index (EPI) as an output of government effort to evaluate the performance of e-government system. Therefore, the ranking index can be achieved in efficiency manner.

Keywords: e-government, DEA, efficiency measurement, EGDI

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Authors: N. Ibrahim-Rassoul, A. Kessi, E. K. Si-Ahmed, N. Djilali, J. Legrand

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The static and dynamic formation of liquid water bridges is analyzed using a combination of visualization experiments in a microchannel with a mathematical model. This paper presents experimental and theoretical findings of water plug/capillary bridge formation in a 250 μm squared microchannel. The approach combines mathematical and numerical modeling with experimental visualization and measurements. The generality of the model is also illustrated for flow conditions encountered in manipulation of polymeric materials and formation of liquid bridges between patterned surfaces. The predictions of the model agree favorably the observations as well as with the experimental recordings.

Keywords: green energy, mathematical modeling, fuel cell, water plug, gas diffusion layer, surface of revolution

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Authors: Lin Tang, Xiaoxia Guo, Lingling Zhang

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Recent vaccine scandals overshadowed China’s accomplishment of public health, triggering discussions on the causes of vaccine incidents. Through legal interpretation of selected vaccine incidents and analysis of systemic vulnerabilities in vaccine circulation and lot release, a panoramic review of legislative progresses in the vaccine administration sheds the light on this debate. In essence, it is the combination of the lagging legal system and the absence of information technology infrastructure in the process of vaccine administration reform that has led to the recurrence of vaccine incidents. These findings have significant implications for further improvement of vaccine administration and China’s participation in global healthcare.

Keywords: legislation, lot release, public health, reform, vaccine administration, vaccine circulation

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Authors: Irfan Khan, Chiemeka David Ekene Arize, Hilly Swami

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Mental health care access and stigma present global challenges, with disparities significantly influenced by economic, cultural, and societal factors. This paper focuses on the mental health care systems of South Asia and the United States, comparing how cultural norms, infrastructure, and policy affect mental health care accessibility and effectiveness in both regions. In South Asia, mental health care is hindered by a combination of underfunding, a critical shortage of professionals, and deeply ingrained cultural stigmas that deter help-seeking. Traditional beliefs often link mental disorders to supernatural causes, and women face additional barriers due to gender disparities. Despite recent policy reforms, implementation remains a challenge, particularly in rural areas. In contrast, the U.S. has a more developed healthcare infrastructure but continues to grapple with stigma, particularly within professional settings like law enforcement. Interventions such as the use of community health workers (CHWs) and collaborative care models have improved access, especially among underserved populations. However, the U.S. still faces disparities in care for minority groups, where cultural competence and stigma reduction are critical for improving outcomes. The paper’s comparative analysis identifies transferable strategies from the U.S. that could be adapted to South Asia’s context, such as integrating mental health care into primary care and using digital interventions to bridge the treatment gap in rural areas. Additionally, South Asia's community-centered approaches offer insights that could enhance the cultural adaptability of interventions in the U.S., particularly for ethnic minorities and immigrant populations. Through a systematic review, this paper examines intervention strategies, stigma, policy support, and the cultural and social determinants of mental health in both regions. The findings emphasize the need for culturally tailored mental health interventions and policy reforms that promote access and reduce stigma. Recommendations include enhancing public awareness, integrating mental health services into primary care, expanding community-based programs, and leveraging digital health interventions. This research contributes to the global discourse on mental health by highlighting culturally sensitive approaches that can be adapted to improve mental health care access and outcomes in both South Asia and the United States.

Keywords: mental health stigma South Asia, mental health care accessibility South Asia, cultural influences mental health South Asia, mental health interventions USA, cross-cultural mental health care

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Authors: Ramoshweu Melvin Sethobya, Emmanuel Chirenje, Mihlali Hobo, Simon Sebothoma

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The recent surge in residential infrastructure development around the metropolitan areas of South Africa has necessitated conditions for thorough geotechnical assessments to be conducted prior to site developments to ensure human and infrastructure safety. This paper appraises the success in the application of multi-method geophysical techniques for the delineation of sinkhole vulnerability in a residential landscape. Geophysical techniques ERT, MASW, VES, Magnetics and gravity surveys were conducted to assist in mapping sinkhole vulnerability, using an existing sinkhole as a constraint at Venterspost town, West of Johannesburg city. A combination of different geophysical techniques and results integration from those proved to be useful in the delineation of the lithologic succession around sinkhole locality, and determining the geotechnical characteristics of each layer for its contribution to the development of sinkholes, subsidence and cavities at the vicinity of the site. Study results have also assisted in the determination of the possible depth extension of the currently existing sinkhole and the location of sites where other similar karstic features and sinkholes could form. Results of the ERT, VES and MASW surveys have uncovered dolomitic bedrock at varying depths around the sites, which exhibits high resistivity values in the range 2500-8000ohm.m and corresponding high velocities in the range 1000-2400 m/s. The dolomite layer was found to be overlain by a weathered chert-poor dolomite layer, which has resistivities between the range 250-2400ohm.m, and velocities ranging from 500-600m/s, from which the large sinkhole has been found to collapse/ cave in. A compiled 2.5D high resolution Shear Wave Velocity (Vs) map of the study area was created using 2D profiles of MASW data, offering insights into the prevailing lithological setup conducive for formation various types of karstic features around the site. 3D magnetic models of the site highlighted the regions of possible subsurface interconnections between the currently existing large sinkhole and the other subsidence feature at the site. A number of depth slices were used to detail the conditions near the sinkhole as depth increases. Gravity surveys results mapped the possible formational pathways for development of new karstic features around the site. Combination and correlation of different geophysical techniques proved useful in delineation of the site geotechnical characteristics and mapping the possible depth extend of the currently existing sinkhole.

Keywords: resistivity, magnetics, sinkhole, gravity, karst, delineation, VES

Procedia PDF Downloads 84

Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

Procedia PDF Downloads 125

Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

Procedia PDF Downloads 279

Authors: Victor V. Ribeiro, Thais C. Lopes, Rafael A. A. Monteiro

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The Albatroz, Baleia Jubarte, Coral Vivo, Golfinho Rotador and Tamar projects make up the Young Sea Network (YSN), part of the BIOMAR Network, which aims to integrate the environmental youths of the Brazilian coast. For this, three editions of the Young Sea Meeting (YSM) were performed. Seeking to stimulate belonging, self-knowledge, participation, autonomy and youth protagonism, the Albatroz Project hosted the III YSM, in Bertioga (SP), in April 2019 and aimed to collectively plan the meeting. Five pillars of Environmental Education were used: identity, community, dialogue, power to act and happiness, the OCA Method and the Young Educates Young; Young Chooses Young; and One Generation Learns from the Other principals. In December 2018, still in the II YSM, the participatory planning of the III YSM began. Two "representatives" of each group were voluntarily elected to facilitate joint decisions, propose, receive and communicate demands from their groups and coordinators. The Young Albatroz Collective (YAC) facilitated the organization process as a whole. The purpose of the meeting was collectively constructed, answering the following question: "What is the YSM for?". Only two of the five pairs of representatives responded. There was difficulty gathering the young people in each group, because it was the end of the year, with people traveling. Thus, due to the short planning time, the YAC built a pre-programming to be validated by the other groups, defining as the objective of the meeting the strengthening of youth protagonism within the YSN. In the planning process, the YAC held 20 meetings, with 60 hours of face-to-face work, in three months, and two technical visits to the headquarters of the III YSM. The participatory dynamics of consultation, when it occurred, required up to two weeks, evidencing the limits of participation. The project coordinations stated that they were not being included in the process by their young people. There is a need to work more to be able to aloud the participation, developing skills and understanding about its principles. This training must take place in an articulated way between the network, implying the important role of the five projects in jointly developing and implementing educator processes with this objective in a national dimension, but without forgetting the specificities of each young group. Finally, it is worth highlighting the great potential of the III YSM by stimulating the exercise of leading environmental youth in more than 50 young people from Brazilian coast, linked to the YSN, stimulating the learning and mobilization of young people in favor of coastal and marine conservation.

Keywords: Marine Conservation, Environmental Education, Youth, Participation, Planning

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Authors: Ioana Pisica, Dimitrios Gkritzapis

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The significance of UAS in scientific applications has been amply demonstrated in recent years. The combinations of portability and quasi-static positioning by means of flying in close loop path make them versatile and efficient in the inspection of power systems infrastructure. In this paper, we critically assess several platforms and sensor capabilities to identify their pros and cons in relation to the power systems assets to be monitored. In this respect, it is paramount the flights to be conducted by using UAS which bear certain suitable features, such as responsive and easy control, video capturing in real time, autonomous routing of pre-planned flight programming with differentiating payloads. The outcome of this research is a set of optimal requirements for power system assets monitoring with UAS.

Keywords: platforms, power system, sensors, UAVs

Procedia PDF Downloads 288

Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

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Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus, country consumes a significant amount of energy due to the high level of industralisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991-2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised, followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development

Procedia PDF Downloads 586