Advanced artificial intelligence technologies such as ChatGPT and other large language models(LLMs)have significantly impacted fields such as education and research in recent years.ChatGPT benefits students and educat...Advanced artificial intelligence technologies such as ChatGPT and other large language models(LLMs)have significantly impacted fields such as education and research in recent years.ChatGPT benefits students and educators by providing personalized feedback,facilitating interactive learning,and introducing innovative teaching methods.While many researchers have studied ChatGPT across various subject domains,few analyses have focused on the engineering domain,particularly in addressing the risks of academic dishonesty and potential declines in critical thinking skills.To address this gap,this study explores both the opportunities and limitations of ChatGPT in engineering contexts through a two-part analysis.First,we conducted experiments with ChatGPT to assess its effectiveness in tasks such as code generation,error checking,and solution optimization.Second,we surveyed 125 users,predominantly engineering students,to analyze ChatGPTs role in academic support.Our findings reveal that 93.60%of respondents use ChatGPT for quick academic answers,particularly among early-stage university students,and that 84.00%find it helpful for sourcing research materials.The study also highlights ChatGPT’s strengths in programming assistance,with 84.80%of users utilizing it for debugging and 86.40%for solving coding problems.However,limitations persist,with many users reporting inaccuracies in mathematical solutions and occasional false citations.Furthermore,the reliance on the free version by 96%of users underscores its accessibility but also suggests limitations in resource availability.This work provides key insights into ChatGPT’s strengths and limitations,establishing a framework for responsible AI use in education.Highlighting areas for improvement marks a milestone in understanding and optimizing AI’s role in academia for sustainable future use.展开更多
Trace metal contamination in soil is of great concern owing to its long persistence in the environment and toxicity to humans and other organisms.Concentrations of six potentially toxic trace metals,Cr,Ni,Cu,As,Cd,and...Trace metal contamination in soil is of great concern owing to its long persistence in the environment and toxicity to humans and other organisms.Concentrations of six potentially toxic trace metals,Cr,Ni,Cu,As,Cd,and Pb,in urban soils were measured in Dhaka City,Bangladesh.Soils from different land-use types,namely,agricultural field,park,playground,petrol station,metal workshop,brick field,burning sites,disposal sites of household waste,garment waste,electronic waste,and tannery wast,and construction waste demolishing sites,were investigated.The concentration ranges of Cr,Ni,Cu,As,Pb,and Cd in soils were 2.4–1258,8.3–1044,9.7–823,8.7–277,1.8–80,and 13–842 mg kg^-1,respectively.The concentrations of metals were subsequently used to establish hazard quotients(HQs)for the adult population.The metal HQs decreased in the order of As>Cr>Pb>Cd>Ni>Cu.Ingestion was the most vital exposure pathway of studied metals from soils followed by dermal contact and inhalation.The range of pollution load index(PLI)was 0.96–17,indicating severe contamination of soil by trace metals.Considering the comprehensive potential ecological risk(PER),soils from all land-use types showed considerable to very high ecological risks.The findings of this study revealed that in the urban area studied,soils of some land-use types were severely contaminated with trace metals.Thus,it is suggested that more attention should be paid to the potential health risks to the local inhabitants and ecological risk to the surrounding ecosystems.展开更多
COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over th...COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.展开更多
In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory dumps.This research paper presents a novel machine learning-based framewor...In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory dumps.This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware.Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps,encompassing a wide array of obfuscated malware types including Spyware,Ransomware,and Trojan Horses with their subcategories.We begin by employing rigorous data preprocessing methods,including the normalization of memory dumps and encoding of categorical data.To tackle the issue of class imbalance,a Synthetic Minority Over-sampling Technique is utilized,ensuring a balanced representation of various malware types.Feature selection is meticulously conducted through Chi-Square tests,mutual information,and correlation analyses,refning the model’s focus on the most indicative attributes of obfuscated malware.The heart of our framework lies in the deployment of an Ensemble-based Classifer,chosen for its robustness and efectiveness in handling complex data structures.The model’s performance is rigorously evaluated using a suite of metrics,including accuracy,precision,recall,F1-score,and the area under the ROC curve(AUC)with other evaluation metrics to assess the model’s efciency.The proposed model demonstrates a detection accuracy exceeding 99%across all cases,surpassing the performance of all existing models in the realm of malware detection.展开更多
This study examines the trade-led growth(TLG)hypothesis for the Kingdom of Saudi Arabia.Using time-series annual data for the period 1985-2019,the ARDL approach and Toda-Yamamoto Granger causality test are applied to ...This study examines the trade-led growth(TLG)hypothesis for the Kingdom of Saudi Arabia.Using time-series annual data for the period 1985-2019,the ARDL approach and Toda-Yamamoto Granger causality test are applied to accomplish the study.The ARDL estimation reveals that trade openness positively causes economic growth in both the long and short run,and the TLG hypothesis is found valid for the Kingdom.The Toda-Yamamoto Granger causality test results have evidenced several unidirectional causalities.Of them,trade openness causes economic growth and supports the ARDL finding and hence the TLG hypothesis for the Kingdom.Moreover,trade openness causes gross fixed capital formation,and the labor force stimulates both economic growth and trade volume.The findings recommend that the Kingdom may increase its trade to reap further benefits and enhance its income growth.展开更多
Corrosion behavior of A6061 aluminum alloy in cation containing simulated aqueous medium was investigated by surface analysis techniques and electrochemical tests.The mass of specimens was increased after the immersio...Corrosion behavior of A6061 aluminum alloy in cation containing simulated aqueous medium was investigated by surface analysis techniques and electrochemical tests.The mass of specimens was increased after the immersion tests,and a smaller mass change was observed in the Zn^(2+)containing solution.Electron microscopic images showed the deposited products on the specimen that were Al-hydroxide confirmed by X-ray photoelectron spectroscopy.Zn-layer was formed on the specimen immersed in Zn^(2+)containing solution,and corrosion inhibition of Al resulted in a smaller number of Al products deposited on the alloy surface.Electrochemical results showed higher impedance in the Zn^(2+)containing solution due to the formation of the Zn related layer on the surface with oxide film.展开更多
基金supported by Competitive Research by the University of Aizu.
文摘Advanced artificial intelligence technologies such as ChatGPT and other large language models(LLMs)have significantly impacted fields such as education and research in recent years.ChatGPT benefits students and educators by providing personalized feedback,facilitating interactive learning,and introducing innovative teaching methods.While many researchers have studied ChatGPT across various subject domains,few analyses have focused on the engineering domain,particularly in addressing the risks of academic dishonesty and potential declines in critical thinking skills.To address this gap,this study explores both the opportunities and limitations of ChatGPT in engineering contexts through a two-part analysis.First,we conducted experiments with ChatGPT to assess its effectiveness in tasks such as code generation,error checking,and solution optimization.Second,we surveyed 125 users,predominantly engineering students,to analyze ChatGPTs role in academic support.Our findings reveal that 93.60%of respondents use ChatGPT for quick academic answers,particularly among early-stage university students,and that 84.00%find it helpful for sourcing research materials.The study also highlights ChatGPT’s strengths in programming assistance,with 84.80%of users utilizing it for debugging and 86.40%for solving coding problems.However,limitations persist,with many users reporting inaccuracies in mathematical solutions and occasional false citations.Furthermore,the reliance on the free version by 96%of users underscores its accessibility but also suggests limitations in resource availability.This work provides key insights into ChatGPT’s strengths and limitations,establishing a framework for responsible AI use in education.Highlighting areas for improvement marks a milestone in understanding and optimizing AI’s role in academia for sustainable future use.
文摘Trace metal contamination in soil is of great concern owing to its long persistence in the environment and toxicity to humans and other organisms.Concentrations of six potentially toxic trace metals,Cr,Ni,Cu,As,Cd,and Pb,in urban soils were measured in Dhaka City,Bangladesh.Soils from different land-use types,namely,agricultural field,park,playground,petrol station,metal workshop,brick field,burning sites,disposal sites of household waste,garment waste,electronic waste,and tannery wast,and construction waste demolishing sites,were investigated.The concentration ranges of Cr,Ni,Cu,As,Pb,and Cd in soils were 2.4–1258,8.3–1044,9.7–823,8.7–277,1.8–80,and 13–842 mg kg^-1,respectively.The concentrations of metals were subsequently used to establish hazard quotients(HQs)for the adult population.The metal HQs decreased in the order of As>Cr>Pb>Cd>Ni>Cu.Ingestion was the most vital exposure pathway of studied metals from soils followed by dermal contact and inhalation.The range of pollution load index(PLI)was 0.96–17,indicating severe contamination of soil by trace metals.Considering the comprehensive potential ecological risk(PER),soils from all land-use types showed considerable to very high ecological risks.The findings of this study revealed that in the urban area studied,soils of some land-use types were severely contaminated with trace metals.Thus,it is suggested that more attention should be paid to the potential health risks to the local inhabitants and ecological risk to the surrounding ecosystems.
文摘COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.
文摘In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory dumps.This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware.Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps,encompassing a wide array of obfuscated malware types including Spyware,Ransomware,and Trojan Horses with their subcategories.We begin by employing rigorous data preprocessing methods,including the normalization of memory dumps and encoding of categorical data.To tackle the issue of class imbalance,a Synthetic Minority Over-sampling Technique is utilized,ensuring a balanced representation of various malware types.Feature selection is meticulously conducted through Chi-Square tests,mutual information,and correlation analyses,refning the model’s focus on the most indicative attributes of obfuscated malware.The heart of our framework lies in the deployment of an Ensemble-based Classifer,chosen for its robustness and efectiveness in handling complex data structures.The model’s performance is rigorously evaluated using a suite of metrics,including accuracy,precision,recall,F1-score,and the area under the ROC curve(AUC)with other evaluation metrics to assess the model’s efciency.The proposed model demonstrates a detection accuracy exceeding 99%across all cases,surpassing the performance of all existing models in the realm of malware detection.
文摘This study examines the trade-led growth(TLG)hypothesis for the Kingdom of Saudi Arabia.Using time-series annual data for the period 1985-2019,the ARDL approach and Toda-Yamamoto Granger causality test are applied to accomplish the study.The ARDL estimation reveals that trade openness positively causes economic growth in both the long and short run,and the TLG hypothesis is found valid for the Kingdom.The Toda-Yamamoto Granger causality test results have evidenced several unidirectional causalities.Of them,trade openness causes economic growth and supports the ARDL finding and hence the TLG hypothesis for the Kingdom.Moreover,trade openness causes gross fixed capital formation,and the labor force stimulates both economic growth and trade volume.The findings recommend that the Kingdom may increase its trade to reap further benefits and enhance its income growth.
基金supported by The Light Metal Educational Foundation,Inc
文摘Corrosion behavior of A6061 aluminum alloy in cation containing simulated aqueous medium was investigated by surface analysis techniques and electrochemical tests.The mass of specimens was increased after the immersion tests,and a smaller mass change was observed in the Zn^(2+)containing solution.Electron microscopic images showed the deposited products on the specimen that were Al-hydroxide confirmed by X-ray photoelectron spectroscopy.Zn-layer was formed on the specimen immersed in Zn^(2+)containing solution,and corrosion inhibition of Al resulted in a smaller number of Al products deposited on the alloy surface.Electrochemical results showed higher impedance in the Zn^(2+)containing solution due to the formation of the Zn related layer on the surface with oxide film.
