The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To a...The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.展开更多
Human rights are not isolated; rather, they are human rights situated in the soci- ety, the network interwoven with economy, politics, society, culture and so on, and human rights protection has to depend upon certain...Human rights are not isolated; rather, they are human rights situated in the soci- ety, the network interwoven with economy, politics, society, culture and so on, and human rights protection has to depend upon certain interior and exterior environments and institutional arrangement. As early as in 1948, the Universal Declaration of Human Rights has pointed out, ＂Everyone is entitled to a social and international order in which the rights and freedoms set forth in this Declaration can be fully realized.＂ The realization of human rights not only involves the claim to rights themselves,展开更多
The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential...The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.展开更多
Given the recent changes in the world,it is fair to say that there have been many changes in air transport.It is a fact that there is a great intensity of demand in the civil aviation sector,especially with the increa...Given the recent changes in the world,it is fair to say that there have been many changes in air transport.It is a fact that there is a great intensity of demand in the civil aviation sector,especially with the increasing demand for fast transportation trends.On the other hand,reasons such as the pandemic that affected the whole world and the disruption of air transportation made operations expensive,affecting operations on a sectoral basis and the world economy.It is known that although many airlines went bankrupt under these conditions,new airlines were also founded.In this study,a fleet analysis using the Response Surface Methodology and the Load factor Prof-itability Rate is conducted to examine the purposes of the existing airlines’fleet structures and what is required to build a fleet.The main reason for all these discussions is to show the most realistic parameters and limits to ensure the economic sustainability of airlines.The Response Surface Methodology was used for fleet analysis and optimization for the first time in the literature.展开更多
Simulations of land use/land cover(LULC)and ecosystem services(ES),which integrate national land policies,reflect the development of land and ecological functions under different scenarios and are crucial for accurate...Simulations of land use/land cover(LULC)and ecosystem services(ES),which integrate national land policies,reflect the development of land and ecological functions under different scenarios and are crucial for accurately predicting and optimizing urban ecosystem sustainable development.展开更多
基金This research is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan,Grant No.AP19674517.
文摘The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.
文摘Human rights are not isolated; rather, they are human rights situated in the soci- ety, the network interwoven with economy, politics, society, culture and so on, and human rights protection has to depend upon certain interior and exterior environments and institutional arrangement. As early as in 1948, the Universal Declaration of Human Rights has pointed out, ＂Everyone is entitled to a social and international order in which the rights and freedoms set forth in this Declaration can be fully realized.＂ The realization of human rights not only involves the claim to rights themselves,
文摘The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.
文摘Given the recent changes in the world,it is fair to say that there have been many changes in air transport.It is a fact that there is a great intensity of demand in the civil aviation sector,especially with the increasing demand for fast transportation trends.On the other hand,reasons such as the pandemic that affected the whole world and the disruption of air transportation made operations expensive,affecting operations on a sectoral basis and the world economy.It is known that although many airlines went bankrupt under these conditions,new airlines were also founded.In this study,a fleet analysis using the Response Surface Methodology and the Load factor Prof-itability Rate is conducted to examine the purposes of the existing airlines’fleet structures and what is required to build a fleet.The main reason for all these discussions is to show the most realistic parameters and limits to ensure the economic sustainability of airlines.The Response Surface Methodology was used for fleet analysis and optimization for the first time in the literature.
基金The Special Fund for the Basic Research and Development Program at the Central Non-profit Research Institutes of China(no.CAFYBB2020ZB008)provided financial support for this study
文摘Simulations of land use/land cover(LULC)and ecosystem services(ES),which integrate national land policies,reflect the development of land and ecological functions under different scenarios and are crucial for accurately predicting and optimizing urban ecosystem sustainable development.
