Michelle B. Jugan, T. NiñaRozanne, D. Reyes, Jr Joseph C. Pepito, Reylan Capuno, Lilibeth C. Pinili, Ann Frances, P. Cabigon, Regina E. Sitoy, Irene O. Mamites
This study examined the inclusion teachers' attitudes towards inclusive education in the public schools of Liloan District, Cebu Province Division. A descriptive-correlational design was utilized to collect data from purposively sampled 30 elementary teacher respondents through the M-STATIC structured questionnaire. Most teachers were experienced females aged 34-43 years, married with some graduate studies. They had 1-5 years of teaching experience in inclusion yet only 1-2 inclusive education training sessions. Results found teachers generally supported inclusive philosophies and recognized social benefits but had concerns regarding training, resources, and support. While philosophically positive, worries existed about the support and resources needed in the classrooms. Pearson’s r correlations and one-way ANOVA found no significant relationships between demographic profiles and attitudes. Based on these findings, a Teacher Inclusion Support Plan was recommended and customized for each school to enhance the long-term implementation of high-quality inclusion practices through ongoing, evidence-driven capacity building and professional development. DOI: https://doi.org/10.52783/pst.487
{"title":"Attitudes of Elementary Teachers Towards Inclusive Education of Learners with Special Education Needs in a Public School","authors":"Michelle B. Jugan, T. NiñaRozanne, D. Reyes, Jr Joseph C. Pepito, Reylan Capuno, Lilibeth C. Pinili, Ann Frances, P. Cabigon, Regina E. Sitoy, Irene O. Mamites","doi":"10.52783/pst.487","DOIUrl":"https://doi.org/10.52783/pst.487","url":null,"abstract":"This study examined the inclusion teachers' attitudes towards inclusive education in the public schools of Liloan District, Cebu Province Division. A descriptive-correlational design was utilized to collect data from purposively sampled 30 elementary teacher respondents through the M-STATIC structured questionnaire. Most teachers were experienced females aged 34-43 years, married with some graduate studies. They had 1-5 years of teaching experience in inclusion yet only 1-2 inclusive education training sessions. Results found teachers generally supported inclusive philosophies and recognized social benefits but had concerns regarding training, resources, and support. While philosophically positive, worries existed about the support and resources needed in the classrooms. Pearson’s r correlations and one-way ANOVA found no significant relationships between demographic profiles and attitudes. Based on these findings, a Teacher Inclusion Support Plan was recommended and customized for each school to enhance the long-term implementation of high-quality inclusion practices through ongoing, evidence-driven capacity building and professional development.\u0000DOI: https://doi.org/10.52783/pst.487","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the application of artificial intelligence (AI) in mitigating the effects of global warming, which stands as one of the most pressing and complex challenges of our time. The purpose of this research is to examine how various AI technologies, including machine learning, neural networks, and big data analytics, can be leveraged to enhance climate modeling, optimize energy systems, improve agricultural practices, and support carbon capture and storage efforts. By conducting a comprehensive literature review, this paper aims to highlight current advancements, practical applications, and relevant case studies that demonstrate the potential of AI to reduce greenhouse gas emissions and promote sustainable practices across different sectors. The study synthesizes findings from recent academic research, industry reports, and real-world implementations to provide an in-depth analysis of the benefits and challenges associated with integrating AI into climate action strategies. The methodology involves a thorough examination of the existing literature, identifying key areas where AI has shown significant promise in addressing various aspects of global warming. This includes enhancing the accuracy of climate predictions, optimizing the efficiency of renewable energy systems, improving precision agriculture techniques, and increasing the effectiveness of carbon capture and storage technologies. The conclusions drawn from this research underscore the transformative potential of AI in combating global warming. The findings highlight the necessity for interdisciplinary collaboration, advancements in AI technologies, and the development of supportive policy frameworks to maximize the impact of these innovations. The paper emphasizes that while AI offers significant potential to address global warming, realizing this potential requires addressing several challenges, including data quality and availability, integration with existing systems, ethical considerations, and economic and policy barriers. Furthermore, this paper discusses the critical role of AI in enabling more effective climate adaptation strategies. As the impacts of global warming become increasingly apparent, AI-driven tools and solutions can help communities and ecosystems adapt to changing environmental conditions. This includes providing early warning systems for natural disasters, optimizing resource allocation during climate-related crises, and supporting the development of resilient infrastructure. In addition to technological advancements, the paper also explores the importance of public engagement and citizen science in enhancing the effectiveness of AI applications in environmental monitoring and climate action. By involving citizens in data collection and environmental monitoring, AI models can access more diverse and localized data, improving their accuracy and relevance. Public engagement can also raise awareness about AI's role in addressing climate change and foster g
{"title":"Using Ai to Help Reduce the Effect of Global Warming","authors":"1-Dr. Ayman Naji Khallaf, 2-Dr. Nader Moneer Alqerafi","doi":"10.52783/pst.464","DOIUrl":"https://doi.org/10.52783/pst.464","url":null,"abstract":"This paper explores the application of artificial intelligence (AI) in mitigating the effects of global warming, which stands as one of the most pressing and complex challenges of our time. The purpose of this research is to examine how various AI technologies, including machine learning, neural networks, and big data analytics, can be leveraged to enhance climate modeling, optimize energy systems, improve agricultural practices, and support carbon capture and storage efforts. By conducting a comprehensive literature review, this paper aims to highlight current advancements, practical applications, and relevant case studies that demonstrate the potential of AI to reduce greenhouse gas emissions and promote sustainable practices across different sectors.\u0000The study synthesizes findings from recent academic research, industry reports, and real-world implementations to provide an in-depth analysis of the benefits and challenges associated with integrating AI into climate action strategies. The methodology involves a thorough examination of the existing literature, identifying key areas where AI has shown significant promise in addressing various aspects of global warming. This includes enhancing the accuracy of climate predictions, optimizing the efficiency of renewable energy systems, improving precision agriculture techniques, and increasing the effectiveness of carbon capture and storage technologies.\u0000The conclusions drawn from this research underscore the transformative potential of AI in combating global warming. The findings highlight the necessity for interdisciplinary collaboration, advancements in AI technologies, and the development of supportive policy frameworks to maximize the impact of these innovations. The paper emphasizes that while AI offers significant potential to address global warming, realizing this potential requires addressing several challenges, including data quality and availability, integration with existing systems, ethical considerations, and economic and policy barriers.\u0000Furthermore, this paper discusses the critical role of AI in enabling more effective climate adaptation strategies. As the impacts of global warming become increasingly apparent, AI-driven tools and solutions can help communities and ecosystems adapt to changing environmental conditions. This includes providing early warning systems for natural disasters, optimizing resource allocation during climate-related crises, and supporting the development of resilient infrastructure.\u0000In addition to technological advancements, the paper also explores the importance of public engagement and citizen science in enhancing the effectiveness of AI applications in environmental monitoring and climate action. By involving citizens in data collection and environmental monitoring, AI models can access more diverse and localized data, improving their accuracy and relevance. Public engagement can also raise awareness about AI's role in addressing climate change and foster g","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent times road infrastructures mean bridges and when the matter comes to repair and maintenance for any roads the main attraction is always bridges and pavements. The culverts are always neglected or least valued in terms of repair and maintenance. Culverts are mostly used for cross drainage on highways and railways. As the culverts are not considered a primary priority for maintenance the degradation of culverts causes major concern for road infrastructure safety. As the culverts are an essential part of road infrastructures it becomes important to survey each culvert regularly. Thus, this research focuses on accessing culverts of selected roads of Madhya Pradesh (India) and creating or providing a proper inventory and condition rating system or formats to note down and access the condition of each culvert. This assessment or system will help in maintenance planning and enhance the culverts’ life span. This assessment system will help in an efficient culvert management plan. DOI: https://doi.org/10.52783/pst.460
{"title":"Assessments of Inventory and Condition for Culverts","authors":"S. Shivam","doi":"10.52783/pst.460","DOIUrl":"https://doi.org/10.52783/pst.460","url":null,"abstract":"In recent times road infrastructures mean bridges and when the matter comes to repair and maintenance for any roads the main attraction is always bridges and pavements. The culverts are always neglected or least valued in terms of repair and maintenance. Culverts are mostly used for cross drainage on highways and railways. As the culverts are not considered a primary priority for maintenance the degradation of culverts causes major concern for road infrastructure safety. As the culverts are an essential part of road infrastructures it becomes important to survey each culvert regularly. Thus, this research focuses on accessing culverts of selected roads of Madhya Pradesh (India) and creating or providing a proper inventory and condition rating system or formats to note down and access the condition of each culvert. This assessment or system will help in maintenance planning and enhance the culverts’ life span. This assessment system will help in an efficient culvert management plan.\u0000DOI: https://doi.org/10.52783/pst.460","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gastric cancer with mutations in the human epidermal growth factor receptor (HER2) can be regarded as one of the leading causes of cancer mortality in the world. Targeted tyrosine kinase inhibitors (TKIs) developed against HER2 yielded optimistic results in improving patients' survival rates and life quality. Nevertheless, drug resistance can influence the critical supportive documents of treatment plans and decrease the treatment effectiveness after about one year. Predicting the efficacy of HER2-TKI drugs or therapies for patients with HER2-mutated gastric cancer is a critical research field. In the present study, a personalized drug response prediction model based on molecular dynamics simulations and machine learning is presented to predict response to first-generation drugs approved by the Ministry of Health in patients with gastric cancer. In the molecular dynamics simulation, the patient's mutation status is considered. The patient's unique mutation status was modeled using molecular dynamics simulations to extract geometric features at the molecular level.Furthermore, additional clinical features are incorporated into the machine learning model to predict drug response. The complete features encompass demographic and clinical characteristics, geometrical properties of the drug-target binding site, and binding free energy (RBFE) of the drug-target complex from molecular dynamics simulations. Drug response prediction utilizes the XGBoost classifier, which achieved leading-edge performance for a 4-level drug response prediction task (PDRP) with 97.5% accuracy, 93% sensitivity, 96.5% specificity, and 94% F1 score. The present research has demonstrated that modeling the binding cavity geometry, in tandem with the binding free energy, can effectively predict drug response. Interestingly, the clinical information, while significant, did not significantly influence the model's performance. This exciting finding opens up new avenues for testing the proposed model on various types of cancers, potentially revolutionizing drug development strategies. DOI: https://doi.org/10.52783/pst.391
{"title":"Predicting Drug Resistance in Gastric Cancer with Mutation in the Human Epidermal Growth Factor Receptor 2 (HER2) And Machine Learning Technique","authors":"Elham Soltanian","doi":"10.52783/pst.391","DOIUrl":"https://doi.org/10.52783/pst.391","url":null,"abstract":"Gastric cancer with mutations in the human epidermal growth factor receptor (HER2) can be regarded as one of the leading causes of cancer mortality in the world. Targeted tyrosine kinase inhibitors (TKIs) developed against HER2 yielded optimistic results in improving patients' survival rates and life quality. Nevertheless, drug resistance can influence the critical supportive documents of treatment plans and decrease the treatment effectiveness after about one year. Predicting the efficacy of HER2-TKI drugs or therapies for patients with HER2-mutated gastric cancer is a critical research field. In the present study, a personalized drug response prediction model based on molecular dynamics simulations and machine learning is presented to predict response to first-generation drugs approved by the Ministry of Health in patients with gastric cancer. In the molecular dynamics simulation, the patient's mutation status is considered. The patient's unique mutation status was modeled using molecular dynamics simulations to extract geometric features at the molecular level.Furthermore, additional clinical features are incorporated into the machine learning model to predict drug response. The complete features encompass demographic and clinical characteristics, geometrical properties of the drug-target binding site, and binding free energy (RBFE) of the drug-target complex from molecular dynamics simulations. Drug response prediction utilizes the XGBoost classifier, which achieved leading-edge performance for a 4-level drug response prediction task (PDRP) with 97.5% accuracy, 93% sensitivity, 96.5% specificity, and 94% F1 score.\u0000The present research has demonstrated that modeling the binding cavity geometry, in tandem with the binding free energy, can effectively predict drug response. Interestingly, the clinical information, while significant, did not significantly influence the model's performance. This exciting finding opens up new avenues for testing the proposed model on various types of cancers, potentially revolutionizing drug development strategies.\u0000DOI: https://doi.org/10.52783/pst.391","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Botnets now make up a wide range of cyber-attacks, which are a network of infected computers connected to the Internet, with remote control. So far, a lot of research has been done in this field, the proposed methods are based on the signatures of discovered botnets, anomalies, traffic behavior, and addresses. Each method has both advantages and disadvantages. This research proposes a structure for performing identification operations, which is presented in this research based on the Markov chain and is based on behavioral analysis. A disadvantage of the past methods is the inability to receive network information at a very high speed. In this research, it has tried using a solution to receive traffic at a very high speed of about 40 Gbps and analyze it. To be able to perform the analysis with a lower overhead. The proposed method can investigate the behavior of botnets by examining the area of behavior better than the previous solutions, and as a result, during the solutions used by botnets to hide their behavior, it can counter and identify suspicious flows. The accuracy of the proposed method was found to be 96.170%. DOI: https://doi.org/10.52783/pst.390
{"title":"Botnet detection based on Markov chain and Fuzzy rough set","authors":"Aziz Ezzatneshan","doi":"10.52783/pst.390","DOIUrl":"https://doi.org/10.52783/pst.390","url":null,"abstract":"Botnets now make up a wide range of cyber-attacks, which are a network of infected computers connected to the Internet, with remote control. So far, a lot of research has been done in this field, the proposed methods are based on the signatures of discovered botnets, anomalies, traffic behavior, and addresses. Each method has both advantages and disadvantages. This research proposes a structure for performing identification operations, which is presented in this research based on the Markov chain and is based on behavioral analysis. A disadvantage of the past methods is the inability to receive network information at a very high speed. In this research, it has tried using a solution to receive traffic at a very high speed of about 40 Gbps and analyze it. To be able to perform the analysis with a lower overhead. The proposed method can investigate the behavior of botnets by examining the area of behavior better than the previous solutions, and as a result, during the solutions used by botnets to hide their behavior, it can counter and identify suspicious flows. The accuracy of the proposed method was found to be 96.170%.\u0000DOI: https://doi.org/10.52783/pst.390","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manjula G. Hegde, Dr. E. Kavitha, Professor and Head Research Supervisor
Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. DOI: https://doi.org/10.52783/pst.387
{"title":"Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Cluster based Wireless Sensor Network","authors":"Manjula G. Hegde, Dr. E. Kavitha, Professor and Head Research Supervisor","doi":"10.52783/pst.387","DOIUrl":"https://doi.org/10.52783/pst.387","url":null,"abstract":" Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. \u0000DOI: https://doi.org/10.52783/pst.387","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel cooperative-based routing protocol designed for underwater Wireless Sensor Network Cloud Applications (uWSNCA). The protocol aims to minimize energy consumption by avoiding void nodes, which are areas without sensor coverage. The proposed approach consists of two main steps: first, avoiding void nodes using an adaptive hop-by-hop vector method, and second, applying a cooperative rule-based adaptive hop-by-hop vector method to create a hybrid algorithm that is more efficient than existing approaches, such as the watch-dog protocol. The research focuses on developing an energy-efficient distributed cooperative routing protocol for wireless sensor networks, specifically tailored for cooperation-based uWSNCA. This is achieved by preventing data packets from being forwarded through void nodes. The proposed routing protocol utilizes cooperation-based adaptive hop-by-hop vector-based forwarding concepts, where sensor nodes forward data packets in a multi-hop fashion within a virtual pipeline. In the developed algorithm for the underwater WSN cloud application, data packets are forwarded in a multi-hop manner using a virtual pipelining mechanism.Nodes outside the virtual pipeline do not forward data packets, thereby reducing flooding in the cooperative WSN. To prevent data packets from being forwarded towards void areas in the WSN, 2-hop information is used at each hop. This approach improves the performance of the wireless sensor network in terms of various performance criteria compared to existing routing protocols. The simulation tool NS2 is used to observe the simulation results of the energy packets being transferred from the source to the sink. The simulation results demonstrate the effectiveness of the proposed methodology. Overall, this paper contributes to the field of underwater WSNs by introducing a novel routing protocol that improves energy efficiency and performance for cloud applications.
{"title":"Design and Development of a Hybrid Cooperative- Based Routing Protocol avoiding the void nodes in WSNs using the Concepts of Minimum Energy Consumption with good Data Security features","authors":"Manjula G. Hegde, Dr.E.Kavitha","doi":"10.52783/pst.374","DOIUrl":"https://doi.org/10.52783/pst.374","url":null,"abstract":"This paper presents a novel cooperative-based routing protocol designed for underwater Wireless Sensor Network Cloud Applications (uWSNCA). The protocol aims to minimize energy consumption by avoiding void nodes, which are areas without sensor coverage. The proposed approach consists of two main steps: first, avoiding void nodes using an adaptive hop-by-hop vector method, and second, applying a cooperative rule-based adaptive hop-by-hop vector method to create a hybrid algorithm that is more efficient than existing approaches, such as the watch-dog protocol. The research focuses on developing an energy-efficient distributed cooperative routing protocol for wireless sensor networks, specifically tailored for cooperation-based uWSNCA. This is achieved by preventing data packets from being forwarded through void nodes. The proposed routing protocol utilizes cooperation-based adaptive hop-by-hop vector-based forwarding concepts, where sensor nodes forward data packets in a multi-hop fashion within a virtual pipeline. In the developed algorithm for the underwater WSN cloud application, data packets are forwarded in a multi-hop manner using a virtual pipelining mechanism.Nodes outside the virtual pipeline do not forward data packets, thereby reducing flooding in the cooperative WSN. To prevent data packets from being forwarded towards void areas in the WSN, 2-hop information is used at each hop. This approach improves the performance of the wireless sensor network in terms of various performance criteria compared to existing routing protocols. The simulation tool NS2 is used to observe the simulation results of the energy packets being transferred from the source to the sink. The simulation results demonstrate the effectiveness of the proposed methodology. Overall, this paper contributes to the field of underwater WSNs by introducing a novel routing protocol that improves energy efficiency and performance for cloud applications.","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nguyen Trong, Du, Nguyen Huu, Nguyen Cuong, Van Minh, Hoang Nguyen, Phong Dien
Wind energy is increasingly recognized as a worldwide sustainable and environmentally friendly power source. Nevertheless, an important barrier to further investment in wind energy is the large rate of failure that occurs to turbines. The gearbox, a crucial component, has a major effect on the performance of wind turbines. It consists of complex planetary and cylindrical gear systems, making it prone to failure and causing major defects in wind turbines. Therefore, there is an urgent need to minimize downtime and improve productivity in wind turbine gearbox operations. Recent decades have witnessed growing interest in fault diagnosis of gearboxes due to their widespread use and industry significance. The time synchronous averaging (TSA) method is widely used as a fundamental approach to detect faults in wind turbine gearboxes from mechanical vibration signals. Usually, applying this method requires a device to measure the vibration phase. Nevertheless, there are certain circumstances where installing a phase-measuring device might pose challenges. For instance, if the gearbox operates, it cannot be paused for installation. Additionally, if the gearboxes are enclosed, it becomes difficult to insert the device. The present paper presents an innovative technical approach to improve the time synchornous averaging method without requiring information about phase vibration. It also evaluates the effectiveness of the proposed method using feature values. An experimental test rig was set up to evaluate the algorithm's effectiveness, simulating different gear faults and load conditions.
{"title":"Detect Gear Fault in Wind Turbine Gearbox based on Time Synchornous Averaging Without Phase Signal.","authors":"Nguyen Trong, Du, Nguyen Huu, Nguyen Cuong, Van Minh, Hoang Nguyen, Phong Dien","doi":"10.52783/pst.352","DOIUrl":"https://doi.org/10.52783/pst.352","url":null,"abstract":"Wind energy is increasingly recognized as a worldwide sustainable and environmentally friendly power source. Nevertheless, an important barrier to further investment in wind energy is the large rate of failure that occurs to turbines. The gearbox, a crucial component, has a major effect on the performance of wind turbines. It consists of complex planetary and cylindrical gear systems, making it prone to failure and causing major defects in wind turbines. Therefore, there is an urgent need to minimize downtime and improve productivity in wind turbine gearbox operations. Recent decades have witnessed growing interest in fault diagnosis of gearboxes due to their widespread use and industry significance. The time synchronous averaging (TSA) method is widely used as a fundamental approach to detect faults in wind turbine gearboxes from mechanical vibration signals. Usually, applying this method requires a device to measure the vibration phase. Nevertheless, there are certain circumstances where installing a phase-measuring device might pose challenges. For instance, if the gearbox operates, it cannot be paused for installation. Additionally, if the gearboxes are enclosed, it becomes difficult to insert the device. The present paper presents an innovative technical approach to improve the time synchornous averaging method without requiring information about phase vibration. It also evaluates the effectiveness of the proposed method using feature values. An experimental test rig was set up to evaluate the algorithm's effectiveness, simulating different gear faults and load conditions.","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141014802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Malik, Sagheer Abbas, Altaf Hussain, Muhammad Saleem, Rahat Qudsi
Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353
{"title":"Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network","authors":"J. Malik, Sagheer Abbas, Altaf Hussain, Muhammad Saleem, Rahat Qudsi","doi":"10.52783/pst.353","DOIUrl":"https://doi.org/10.52783/pst.353","url":null,"abstract":"Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios.\u0000DOI: https://doi.org/10.52783/pst.353","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abbasali Saffar, Mohammad Ehsanifar, Seyed Mohammad Mirhosseini, Mohammad Javad Taheri
Geopolymer mixtures offer an enhanced alternative to implement an eco-friendly solution in construction industry. These mixtures exhibit similar or better mechanical and structural properties in comprising of cement and can use recycling and by-product materials. Against this background, an eco-friendly advantageous were achieved from the engineers and researchers by using waste materials to replace cement and attention to reduce CO2 emission during its procedure. This study attempted to evaluate compressive and tensile strength, micro structure and post-fire characteristic of the geopolymer mixtures. For this aim, the geopolymer mixtures were exposed to elevated temperature between 200, 500 and 800 0C. Then, the post-fire and mechanical behaviour were investigated. The proposed research supported the slight decrease in the GPC's compressive stress by fibres, and the 1.25% PP fibres displayed the least performance, indicating nearly 13% decrease than the unreinforced mixtures. As exhibited by the post-fire behaviour of mixtures, the GPC's compressive strength increased first for all mixes but in the range of 400 - 800°C it decreased at a higher rate because of the geopolymer matrix-related dehydration. Besides, fiber melting under high temperatures and the thermal reaction procedure related to free water evaporation reduce the mechanical properties under temperatures ranging from 400°C to 800°C. DOI: https://doi.org/10.52783/pst.349
{"title":"Experimental Evaluation of Mechanical Properties, Micro Structure and Post-Fire Strength of Fiber Reinforced Geopolymer Concrete","authors":"Abbasali Saffar, Mohammad Ehsanifar, Seyed Mohammad Mirhosseini, Mohammad Javad Taheri ","doi":"10.52783/pst.349","DOIUrl":"https://doi.org/10.52783/pst.349","url":null,"abstract":"Geopolymer mixtures offer an enhanced alternative to implement an eco-friendly solution in construction industry. These mixtures exhibit similar or better mechanical and structural properties in comprising of cement and can use recycling and by-product materials. Against this background, an eco-friendly advantageous were achieved from the engineers and researchers by using waste materials to replace cement and attention to reduce CO2 emission during its procedure. This study attempted to evaluate compressive and tensile strength, micro structure and post-fire characteristic of the geopolymer mixtures. For this aim, the geopolymer mixtures were exposed to elevated temperature between 200, 500 and 800 0C. Then, the post-fire and mechanical behaviour were investigated. The proposed research supported the slight decrease in the GPC's compressive stress by fibres, and the 1.25% PP fibres displayed the least performance, indicating nearly 13% decrease than the unreinforced mixtures. As exhibited by the post-fire behaviour of mixtures, the GPC's compressive strength increased first for all mixes but in the range of 400 - 800°C it decreased at a higher rate because of the geopolymer matrix-related dehydration. Besides, fiber melting under high temperatures and the thermal reaction procedure related to free water evaporation reduce the mechanical properties under temperatures ranging from 400°C to 800°C.\u0000DOI: https://doi.org/10.52783/pst.349","PeriodicalId":20420,"journal":{"name":"Power system technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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