In this research study,magnesium-aluminum(Mg-Al)bimetallic oxide powders are synthesized via the sol-gel auto combustion method using diethanolamine(DEA)as the fuel.In order to subsequently determine the influence of ...In this research study,magnesium-aluminum(Mg-Al)bimetallic oxide powders are synthesized via the sol-gel auto combustion method using diethanolamine(DEA)as the fuel.In order to subsequently determine the influence of calcination temperatures upon the structure,chemical bonding,morphology,optical properties,and fluorescence properties of the as-synthesized and calcined Mg-Al bimetallic oxide powders,the researcher employed X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM),transmission electron microscopy(TEM),UV–visible diffuse reflectance spectroscopy(UV-DRS),and photoluminescence spectroscopy(PL),respectively.It was apparent on the basis of the XRD and FT-IR analyses that those powders undergoing calcination at temperatures of 500℃,700℃,and 900℃contained the major phase magnesium aluminate(Mg Al_(2)O_(4))spinel with trace magnesium oxide(Mg O)and hydrotalcite(Mg_(6)Al_(2)(CO_(3))(OH)_(16)).When the calcination temperature rose to 1100℃,this resulted in a single phase MgAl_(2)O_(4)while MgO and(Mg_(6)Al_(2)(CO_(3))(OH)_(16))were no longer observed.UV-DRS analysis revealed that in optimized conditions,calcination resulted in better sample absorption and reflection levels when compared to the ultraviolet,visible,and infrared spectra observed in the case of the as-synthesized sample.The bandgap energy(E_(g))for calcined samples was in the range of 2.65 e V to 5.85 e V,in contrast to the value of 4.10 e V for the as-synthesized sample.Analysis of photoluminescence showed that for the as-synthesized samples and those calcined at low temperatures,visible light was emitted only in the violet,blue,and green regions with low intensity,while for samples calcined at higher temperatures,the emissions showed greater intensity and extended to the yellow and orange regions.Multiple defect centers were found in the bandgap which can explain these findings.展开更多
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st...Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.展开更多
On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng...On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng Qinghong,chairman of GAC Group.He encouraged GAC to purchase various spare parts in Thailand to enhance Thailand’s position in the global electric vehicle industry supply chain.“Thailand has a favorable business environment,”Zeng responded.“As the location of GAC’s first wholly-owned overseas facility,it was our premier choice.”展开更多
The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 n...The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.展开更多
In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological ...In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.展开更多
BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan...BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advanced cognitive functions,but also from their potential to drive innovation across various industries.展开更多
Lanthanum doped nickel-cobalt nano ferrites with chemical formula Ni_(0.5)Co_(0.5)LaxFe_(2-x)O_(4)(x=0.05,0,10,0.15 and 0,20) were prepared using a simple sol-gel auto combustion method.The basic structural properties...Lanthanum doped nickel-cobalt nano ferrites with chemical formula Ni_(0.5)Co_(0.5)LaxFe_(2-x)O_(4)(x=0.05,0,10,0.15 and 0,20) were prepared using a simple sol-gel auto combustion method.The basic structural properties were determined by X-ray diffraction method and the formation of single phased spinel ferrite was confirmed.The crystalline size decreased from 25 to 11 nm and lattice parameter a increases with increase of La doping.The surface morphology of these ferrites was observed by field-emission scanning electron microscopy(FESEM) and agglomerated irregular grains are observed with increase of the rare earth element La doping.Energy-dispersive X-ray spectroscopy(EDX) result confirms the presence of the required elements.The Fourier transform infrared spectroscopy(FTIR) spectrum indicates the formation of the spinel ferrite structure with M-O bonds.Optical direct band measurements from ultraviolet-visible spectroscopy(UV-Vis) spectroscopy indicate that the direct band gap decreases from 1.39 to 1.19 eV for x=0.05 to x=0.15,then increases to 1.28 eV for x=0.20.The room temperature magnetic properties of these ferrites were studied by a vibrating sample magnetometer(VSM).The enhanced saturation magnetization of 49.73 emu/g is observed for x=0.10 and then saturation magnetizations are gradually decreased for x=0.15 and x=0.20.Interestingly the remanent magnetization and coercivity also follow the same trend.展开更多
Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This pape...Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.展开更多
Experiments were conducted in this study to examine the thermal performance of a thermosyphon,made from Inconel alloy 625,could recover waste heat from automobile exhaust using a limited amount of fluid.The thermosyph...Experiments were conducted in this study to examine the thermal performance of a thermosyphon,made from Inconel alloy 625,could recover waste heat from automobile exhaust using a limited amount of fluid.The thermosyphon has an outer diameter of 27 mm,a thickness of 2.6 mm,and an overall length of 483 mm.The study involved directing exhaust gas onto the evaporator.This length includes a 180-mm evaporator,a 70-mm adiabatic section,a 223-mm condenser,and a 97-mm finned exchanger.The study examined the thermal performance of the thermosyphon under exhaust flow rates ranging from 0–10 g/sec and temperatures varying from 300℃–900℃.The influence of three parameters—inclination angle(5°–45°),water mass(2–5.3 g),and the quantity of non-condensable gas Argon(0–0.6 g)—was investigated to assess their impacts on the thermosyphon’s thermal efficiency.The experimental findings revealed that with 3 g of water and 0.0564 g of argon in the thermosyphon,the condenser reached its highest temperature at around 200℃.The ideal fuel loading rate for the thermosyphon falls between 0.2 and 0.7 g/s.Moreover,as inclination angles rise,outer wall temperatures of the thermosyphon increase.This is attributed to the explicit expansion of the effective heating area within the evaporation section,coupled with an amplified gravitational component of the water flux.Additionally,an upsurge in the quantity of non-condensable gas(NCG)can mitigate temperature gradients on the outer wall,resulting in a decline in the thermosyphon’s performance.The insulation applied to the adiabatic section demonstrated efficacy in augmenting temperature gradients on the outer wall,thereby improving the overall performance of the thermosyphon.As the water charge within the thermosyphon increases,there is a corresponding rise in heat transfer rates both from the exhaust to the thermosyphon and from the thermosyphon to the fuel.展开更多
基金financial supported from the Thailand Research Fund,Office of the Higher Education Commission(Grant number MRG6280220)。
文摘In this research study,magnesium-aluminum(Mg-Al)bimetallic oxide powders are synthesized via the sol-gel auto combustion method using diethanolamine(DEA)as the fuel.In order to subsequently determine the influence of calcination temperatures upon the structure,chemical bonding,morphology,optical properties,and fluorescence properties of the as-synthesized and calcined Mg-Al bimetallic oxide powders,the researcher employed X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM),transmission electron microscopy(TEM),UV–visible diffuse reflectance spectroscopy(UV-DRS),and photoluminescence spectroscopy(PL),respectively.It was apparent on the basis of the XRD and FT-IR analyses that those powders undergoing calcination at temperatures of 500℃,700℃,and 900℃contained the major phase magnesium aluminate(Mg Al_(2)O_(4))spinel with trace magnesium oxide(Mg O)and hydrotalcite(Mg_(6)Al_(2)(CO_(3))(OH)_(16)).When the calcination temperature rose to 1100℃,this resulted in a single phase MgAl_(2)O_(4)while MgO and(Mg_(6)Al_(2)(CO_(3))(OH)_(16))were no longer observed.UV-DRS analysis revealed that in optimized conditions,calcination resulted in better sample absorption and reflection levels when compared to the ultraviolet,visible,and infrared spectra observed in the case of the as-synthesized sample.The bandgap energy(E_(g))for calcined samples was in the range of 2.65 e V to 5.85 e V,in contrast to the value of 4.10 e V for the as-synthesized sample.Analysis of photoluminescence showed that for the as-synthesized samples and those calcined at low temperatures,visible light was emitted only in the violet,blue,and green regions with low intensity,while for samples calcined at higher temperatures,the emissions showed greater intensity and extended to the yellow and orange regions.Multiple defect centers were found in the bandgap which can explain these findings.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.
文摘On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng Qinghong,chairman of GAC Group.He encouraged GAC to purchase various spare parts in Thailand to enhance Thailand’s position in the global electric vehicle industry supply chain.“Thailand has a favorable business environment,”Zeng responded.“As the location of GAC’s first wholly-owned overseas facility,it was our premier choice.”
文摘The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.
文摘In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.
基金the National Natural Science Foundation of China(62103411)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1)。
文摘BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advanced cognitive functions,but also from their potential to drive innovation across various industries.
文摘Lanthanum doped nickel-cobalt nano ferrites with chemical formula Ni_(0.5)Co_(0.5)LaxFe_(2-x)O_(4)(x=0.05,0,10,0.15 and 0,20) were prepared using a simple sol-gel auto combustion method.The basic structural properties were determined by X-ray diffraction method and the formation of single phased spinel ferrite was confirmed.The crystalline size decreased from 25 to 11 nm and lattice parameter a increases with increase of La doping.The surface morphology of these ferrites was observed by field-emission scanning electron microscopy(FESEM) and agglomerated irregular grains are observed with increase of the rare earth element La doping.Energy-dispersive X-ray spectroscopy(EDX) result confirms the presence of the required elements.The Fourier transform infrared spectroscopy(FTIR) spectrum indicates the formation of the spinel ferrite structure with M-O bonds.Optical direct band measurements from ultraviolet-visible spectroscopy(UV-Vis) spectroscopy indicate that the direct band gap decreases from 1.39 to 1.19 eV for x=0.05 to x=0.15,then increases to 1.28 eV for x=0.20.The room temperature magnetic properties of these ferrites were studied by a vibrating sample magnetometer(VSM).The enhanced saturation magnetization of 49.73 emu/g is observed for x=0.10 and then saturation magnetizations are gradually decreased for x=0.15 and x=0.20.Interestingly the remanent magnetization and coercivity also follow the same trend.
文摘Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.
文摘Experiments were conducted in this study to examine the thermal performance of a thermosyphon,made from Inconel alloy 625,could recover waste heat from automobile exhaust using a limited amount of fluid.The thermosyphon has an outer diameter of 27 mm,a thickness of 2.6 mm,and an overall length of 483 mm.The study involved directing exhaust gas onto the evaporator.This length includes a 180-mm evaporator,a 70-mm adiabatic section,a 223-mm condenser,and a 97-mm finned exchanger.The study examined the thermal performance of the thermosyphon under exhaust flow rates ranging from 0–10 g/sec and temperatures varying from 300℃–900℃.The influence of three parameters—inclination angle(5°–45°),water mass(2–5.3 g),and the quantity of non-condensable gas Argon(0–0.6 g)—was investigated to assess their impacts on the thermosyphon’s thermal efficiency.The experimental findings revealed that with 3 g of water and 0.0564 g of argon in the thermosyphon,the condenser reached its highest temperature at around 200℃.The ideal fuel loading rate for the thermosyphon falls between 0.2 and 0.7 g/s.Moreover,as inclination angles rise,outer wall temperatures of the thermosyphon increase.This is attributed to the explicit expansion of the effective heating area within the evaporation section,coupled with an amplified gravitational component of the water flux.Additionally,an upsurge in the quantity of non-condensable gas(NCG)can mitigate temperature gradients on the outer wall,resulting in a decline in the thermosyphon’s performance.The insulation applied to the adiabatic section demonstrated efficacy in augmenting temperature gradients on the outer wall,thereby improving the overall performance of the thermosyphon.As the water charge within the thermosyphon increases,there is a corresponding rise in heat transfer rates both from the exhaust to the thermosyphon and from the thermosyphon to the fuel.
