The study was conducted to identify indoor air quality and the level of thermal comfort in various selected locations in Faculty of Engineering and Built Environment （FKAB）, University Kebangsaan Malaysia （UKM） wi...The study was conducted to identify indoor air quality and the level of thermal comfort in various selected locations in Faculty of Engineering and Built Environment （FKAB）, University Kebangsaan Malaysia （UKM） with built-up area of 250,936 fie. The indoor air quality and thermal comfort were measured at various selected locations by using indoor air quality equipment （Thermal Comfort SERI）. The thermal comfort assessments are based on Malaysian Code of Practice Indoor Air Quality 2005 and Moderate Thermal Environments-Determination of the PMV and PPD indices specification of the condition for thermal comfort （ISO7730：1994） From the data analysis, the FKAB building is considered inadequately vented space. The concentration of CO2 for all sampling area evaluated exceeds the recommended concentration （〉 1000 ppm）. The ventilation system used in FKAB building is designed by delivering fix amount of fresh air into building from external building without consideration on the number of occupants. This common ventilation design will increase the amount of CO2 dramatically all day long and these reflect the inefficiency of energy used. The faculty needs to be equipped with a comprehensive energy management system that can allow detailed documentation of continuous performance of all energy system and consumption in the building.展开更多
Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how str...Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how stress affects lifespan,this review offers the first comprehensive,multiscale comparison of strategies that optimize geometry to improve fatigue performance.This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets,notches,and overall structural layouts.We analyze and combine various methods,including topology and shape optimization,the ability of additive manufacturing to finetune internal geometries,and reliability-based design approaches.A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design,allowing for consistent comparison and encouraging validation across different fields.Furthermore,we highlight important areas for future research,such as incorporating manufacturing flaws,using multiscale models,and integrating machine learning techniques.This work is the first to provide a broad geometric viewpoint in fatigue engineering,laying the groundwork for future design methods that are driven by data and centered on reliability.展开更多
Magnesium(Mg)-based bioresorbable stents represent a potentially groundbreaking advancement in cardiovascular therapy;offering tem-porary vessel support and complete biodegradability—addressing limitations of traditi...Magnesium(Mg)-based bioresorbable stents represent a potentially groundbreaking advancement in cardiovascular therapy;offering tem-porary vessel support and complete biodegradability—addressing limitations of traditional stents like in-stent restenosis and long-term com-plications.However,challenges such as rapid corrosion and suboptimal endothelialisation have hindered their clinical adoption.This review highlights the latest breakthroughs in surface modification,alloying,and coating strategies to enhance the mechanical integrity,corrosion resistance,and biocompatibility of Mg-based stents.Key surface engineering techniques,including polymer and bioactive coatings,are ex-amined for their role in promoting endothelial healing and minimising inflammatory responses.Future directions are proposed,focusing on personalised stent designs to optimize efficacy and long-term outcomes,positioning Mg-based stents as a transformative solution in interventional cardiology.展开更多
Cardiac tissue engineering aims to efficiently replace or repair injured heart tissue using scaffolds,relevant cells,or their combination.While the combination of scaffolds and relevant cells holds the potential to ra...Cardiac tissue engineering aims to efficiently replace or repair injured heart tissue using scaffolds,relevant cells,or their combination.While the combination of scaffolds and relevant cells holds the potential to rapidly remuscularize the heart,thereby avoiding the slow process of cell recruitment,the proper ex vivo cellularization of a scaffold poses a substantial challenge.First,proper diffusion of nutrients and oxygen should be provided to the cell-seeded scaffold.Second,to generate a functional tissue construct,cells can benefit from physiological-like conditions.To meet these challenges,we developed a modular bioreactor for the dynamic cellularization of full-thickness cardiac scaffolds under synchronized mechanical and electrical stimuli.In this unique bioreactor system,we designed a cyclic mechanical load that mimics the left ventricle volume inflation,thus achieving a steady stimulus,as well as an electrical stimulus with an action potential profile to mirror the cells’microenvironment and electrical stimuli in the heart.These mechanical and electrical stimuli were synchronized according to cardiac physiology and regulated by constant feedback.When applied to a seeded thick porcine cardiac extracellular matrix(pcECM)scaffold,these stimuli improved the proliferation of mesenchymal stem/stromal cells(MSCs)and induced the formation of a dense tissue-like structure near the scaffold’s surface.Most importantly,after 35 d of cultivation,the MSCs presented the early cardiac progenitor markers Connexin-43 andα-actinin,which were absent in the control cells.Overall,this research developed a new bioreactor system for cellularizing cardiac scaffolds under cardiac-like conditions,aiming to restore a sustainable dynamic living tissue that can bear the essential cardiac excitation–contraction coupling.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology,combining real-time biosensing,therapeutic capabilities,and user comfort in a single platform.These devices tak...MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology,combining real-time biosensing,therapeutic capabilities,and user comfort in a single platform.These devices take the advantage of the exceptional electrical conductivity,mechanical flexibility,and biocompatibility of two-dimensional MXenes to enable noninvasive,tear-based monitoring of key physiological markers such as intraocular pressure and glucose levels.Recent developments focus on the integration of transparent MXene films into the conventional lens materials,allowing multifunctional performance including photothermal therapy,antimicrobial and anti-inflammation protection,and dehydration resistance.These innovations offer promising strategies for ocular disease management and eye protection.In addition to their multifunctionality,improvements in MXene synthesis and device engineering have enhanced the stability,transparency,and wearability of these lenses.Despite these advances,challenges remain in long-term biostability,scalable production,and integration with wireless communication systems.This review summarizes the current progress,key challenges,and future directions of MXene-based smart contact lenses,highlighting their transformative potential in next-generation digital healthcare and ophthalmic care.展开更多
This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-speci...This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.展开更多
The concept of using biological process in soil improvement which is known as bio-mediated soil improvement technique has shown greater potential in geotechnical engineering applications in terms of performance and en...The concept of using biological process in soil improvement which is known as bio-mediated soil improvement technique has shown greater potential in geotechnical engineering applications in terms of performance and environmental sustainability. This paper presents a review on the soil microorganisms responsible for this process, and factors that affect their metabolic activities and geometric compatibility with the soil particle sizes. Two mechanisms of biomineralization, i.e. biologically controlled and biologically induced mineralization, were also discussed. Environmental and other factors that may be encountered in situ during microbially induced calcite precipitation （MICP） and their influences on the process were identified and presented. Improvements in the engineering properties of soil such as strength/stiffness and permeability as evaluated in some studies were explored. Potential applications of the process in geotechnical engineering and the challenges of field application of the process were identified.展开更多
Oxygen evolution reaction(OER)is a critical process in electrocatalytic water splitting.However,the development of low-cost,highly efficient OER electrocatalysts by a simple method that can be used for industrial appl...Oxygen evolution reaction(OER)is a critical process in electrocatalytic water splitting.However,the development of low-cost,highly efficient OER electrocatalysts by a simple method that can be used for industrial application on a large scale is still a huge challenge.Recently,high entropy alloy(HEA)has acquired extensive attention,which may provide answers to the current dilemma.Here,we report bulk Fe_(50)Mn_(30)Co_(10)Cr_(10),which is prepared by 3D printing on a large scale,as electrocatalyst for OER with high catalytic performance.Especially,an easy approach,corrosion engineering,is adopted for the first time to build an active layer of honeycomb nanostructures on its surface,leading to ultrahigh OER performance with an overpotential of 247 mV to achieve a current density of 10 mA cm^(-2),a low Tafel slope of 63 mV dec^(-1),and excellent stability up to 60 h at 100 mA cm^(-2)in 1 M KOH.The excellent catalytic activity mainly originates from:(1)the binder-free self-supported honeycomb nanostructures and multi-component hydroxides,which improve intrinsic catalytic activity,provide rich active sites,and reduce interfacial resistance;and(2)the diverse valence states for multiple active sites to enhance the OER kinetics.Our findings show that corrosion engineering is a novel strategy to improve the bulk HEA catalytic performance.We expect that this work would open up a new avenue to fabricate large-scale HEA electrocatalysts by 3D printing and corrosion engineering for industrial applications.展开更多
AIM: To study the optical property and biocompatibility of a tissue engineering cornea. METHODS: The cross-linker of N- (3-Dimethylaminoropyl)-N'ethylcarbodiimide hydrochloride (EDC)/ N-Hydroxysuccinimide (NHS) wa...AIM: To study the optical property and biocompatibility of a tissue engineering cornea. METHODS: The cross-linker of N- (3-Dimethylaminoropyl)-N'ethylcarbodiimide hydrochloride (EDC)/ N-Hydroxysuccinimide (NHS) was mixed with Type I collagen at 10% (weight/volume). The final solution was molded to the shape of a corneal contact lens. The collagen concentrations of 10%, 12.5%, 15%, 17.5% and 20% artificial corneas were tested by UV/vis-spectroscopy for their transparency compared with normal rat cornea. 10-0 sutures were knotted on the edges of substitute to measure the corneal buttons's mechanical properties. Normal rat corneal tissue primary culture on the collagen scaffold was observed in 4 weeks. Histopathologic examinations were performed after 4 weeks of in vitro culturing. RESULTS: The collagen scaffold appearance was similar to that of soft contact lens. With the increase of collagen concentration, the transparency of artificial corneal buttons was diminished, but the toughness of the scaffold was enhanced. The scaffold transparency in the 10% concentration collagen group resembled normal rat cornea. To knot and embed the scaffold under the microscope, 20% concentration collagen group was more effective during implantation than lower concentrations of collagen group. In the first 3 weeks, corneal cell proliferation was highly active. The shapes of cells that grew on the substitute had no significant difference when compared with the cells before they were moved to the scaffold. However, on the fortieth day, most cells detached from the scaffold and died. Histopathologic examination of the primary culture scaffold revealed well grown corneal cells tightly attached to the scaffold in the former culturing. CONCLUSION: Collagen scaffold can be molded to the shape of soft contact corneal lens with NHS/EDC. The biological stability and biocompatibility of collagen from animal species may be used as material in preparing to engineer artificial corneal scaffold.展开更多
While stromal interactions are essential in cancer adaptation to hormonal therapies,the effects of bone stroma and androgen deprivation on cancer progression in bone are poorly understood.Here,we tissue-engineered and...While stromal interactions are essential in cancer adaptation to hormonal therapies,the effects of bone stroma and androgen deprivation on cancer progression in bone are poorly understood.Here,we tissue-engineered and validated an in vitro microtissue model of osteoblastic bone metastases,and used it to study the effects of androgen deprivation in this microenvironment.The model was established by culturing primary human osteoprogenitor cells on melt electrowritten polymer scaffolds,leading to a mineralized osteoblast-derived microtissue containing,in a 3D setting,viable osteoblastic cells,osteocytic cells,and appropriate expression of osteoblast/osteocyte-derived mRNA and proteins,and mineral content.Direct co-culture of androgen receptordependent/ independent cell lines (LNCaP,C4-2B,and PC3) led cancer cells to display functional and molecular features as observed in vivo.Co-cultured cancer cells showed increased affinity to the microtissues,as a function of their bone metastatic potential.Cocultures led to alkaline phosphatase and collagen-I upregulation and sclerostin downregulation,consistent with the clinical marker profile of osteoblastic bone metastases.LNCaP showed a significant adaptive response under androgen deprivation in the microtissues,with the notable appearance of neuroendocrine transdifferentiation features and increased expression of related markers (dopa decarboxylase,enolase 2).Androgen deprivation affected the biology of the metastatic microenvironment with stronger upregulation of androgen receptor,alkaline phosphatase,and dopa decarboxylase,as seen in the transition towards resistance.The unique microtissues engineered here represent a substantial asset to determine the involvement of the human bone microenvironment in prostate cancer progression and response to a therapeutic context in this microenvironment.展开更多
Magnesium metal anode holds great potentials toward future high energy and safe rechargeable magnesium battery technology due to its divalent redox and dendrite-free nature. Electrolytes based on Lewis acid chemistry ...Magnesium metal anode holds great potentials toward future high energy and safe rechargeable magnesium battery technology due to its divalent redox and dendrite-free nature. Electrolytes based on Lewis acid chemistry enable the reversible Mg plating/stripping,while they fail to match most cathode materials toward highvoltage magnesium batteries. Herein,reversible Mg plating/stripping is achieved in conventional carbonate electrolytes enabled by the cooperative solvation/surface engineering. Strongly electronegative Cl from the MgCl_(2) additive of electrolyte impairs the Mg…O = C interaction to reduce the Mg^(2+) desolvation barrier for accelerated redox kinetics,while the Mg^(2+)-conducting polymer coating on the Mg surface ensures the facile Mg^(2+) migration and the e ective isolation of electrolytes. As a result,reversible plating and stripping of Mg is demonstrated with a low overpotential of 0.7 V up to 2000 cycles. Moreover,benefitting from the wide electrochemical window of carbonate electrolytes,high-voltage(> 2.0 V) rechargeable magnesium batteries are achieved through assembling the electrode couple of Mg metal anode and Prussian blue-based cathodes. The present work provides a cooperative engineering strategy to promote the application of magnesium anode in carbonate electrolytes toward high energy rechargeable batteries.展开更多
In this study, the authors shall illustrate the difficulties, pros and cons of ＂Technical Drawing＂ courses in engineering curriculums. The authors also show the efficiency, effectiveness of students＇ grasping the e...In this study, the authors shall illustrate the difficulties, pros and cons of ＂Technical Drawing＂ courses in engineering curriculums. The authors also show the efficiency, effectiveness of students＇ grasping the engineering drawing concepts in depth. The authors have conducted a survey on students and their instructors for teaching the course. Whether the course should be taught with manual handling and then computerized or starting with computer aid and finishing the content of the course, the authors shall also go in detail of syllabus how the drawing rooms and aid tools will be. This part of study shall look into the ergonomics, safety and conditions of classrooms. Responses of 300 students will be shared in this presentation. Statistical analysis will detect the effects of different factors involved in the questionnaire.展开更多
Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manua...Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.展开更多
Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
文摘The study was conducted to identify indoor air quality and the level of thermal comfort in various selected locations in Faculty of Engineering and Built Environment （FKAB）, University Kebangsaan Malaysia （UKM） with built-up area of 250,936 fie. The indoor air quality and thermal comfort were measured at various selected locations by using indoor air quality equipment （Thermal Comfort SERI）. The thermal comfort assessments are based on Malaysian Code of Practice Indoor Air Quality 2005 and Moderate Thermal Environments-Determination of the PMV and PPD indices specification of the condition for thermal comfort （ISO7730：1994） From the data analysis, the FKAB building is considered inadequately vented space. The concentration of CO2 for all sampling area evaluated exceeds the recommended concentration （〉 1000 ppm）. The ventilation system used in FKAB building is designed by delivering fix amount of fresh air into building from external building without consideration on the number of occupants. This common ventilation design will increase the amount of CO2 dramatically all day long and these reflect the inefficiency of energy used. The faculty needs to be equipped with a comprehensive energy management system that can allow detailed documentation of continuous performance of all energy system and consumption in the building.
文摘Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how stress affects lifespan,this review offers the first comprehensive,multiscale comparison of strategies that optimize geometry to improve fatigue performance.This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets,notches,and overall structural layouts.We analyze and combine various methods,including topology and shape optimization,the ability of additive manufacturing to finetune internal geometries,and reliability-based design approaches.A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design,allowing for consistent comparison and encouraging validation across different fields.Furthermore,we highlight important areas for future research,such as incorporating manufacturing flaws,using multiscale models,and integrating machine learning techniques.This work is the first to provide a broad geometric viewpoint in fatigue engineering,laying the groundwork for future design methods that are driven by data and centered on reliability.
文摘Magnesium(Mg)-based bioresorbable stents represent a potentially groundbreaking advancement in cardiovascular therapy;offering tem-porary vessel support and complete biodegradability—addressing limitations of traditional stents like in-stent restenosis and long-term com-plications.However,challenges such as rapid corrosion and suboptimal endothelialisation have hindered their clinical adoption.This review highlights the latest breakthroughs in surface modification,alloying,and coating strategies to enhance the mechanical integrity,corrosion resistance,and biocompatibility of Mg-based stents.Key surface engineering techniques,including polymer and bioactive coatings,are ex-amined for their role in promoting endothelial healing and minimising inflammatory responses.Future directions are proposed,focusing on personalised stent designs to optimize efficacy and long-term outcomes,positioning Mg-based stents as a transformative solution in interventional cardiology.
基金funded by the Israeli Ministry of Innovation,Science and Technology(Grant No.3-11873)the Israel Science Foundation(Grant No.1563/10)+1 种基金the Randy L.and Melvin R.Berlin Family Research Center for Regenerative Medicinethe Gurwin Family Foundation.
文摘Cardiac tissue engineering aims to efficiently replace or repair injured heart tissue using scaffolds,relevant cells,or their combination.While the combination of scaffolds and relevant cells holds the potential to rapidly remuscularize the heart,thereby avoiding the slow process of cell recruitment,the proper ex vivo cellularization of a scaffold poses a substantial challenge.First,proper diffusion of nutrients and oxygen should be provided to the cell-seeded scaffold.Second,to generate a functional tissue construct,cells can benefit from physiological-like conditions.To meet these challenges,we developed a modular bioreactor for the dynamic cellularization of full-thickness cardiac scaffolds under synchronized mechanical and electrical stimuli.In this unique bioreactor system,we designed a cyclic mechanical load that mimics the left ventricle volume inflation,thus achieving a steady stimulus,as well as an electrical stimulus with an action potential profile to mirror the cells’microenvironment and electrical stimuli in the heart.These mechanical and electrical stimuli were synchronized according to cardiac physiology and regulated by constant feedback.When applied to a seeded thick porcine cardiac extracellular matrix(pcECM)scaffold,these stimuli improved the proliferation of mesenchymal stem/stromal cells(MSCs)and induced the formation of a dense tissue-like structure near the scaffold’s surface.Most importantly,after 35 d of cultivation,the MSCs presented the early cardiac progenitor markers Connexin-43 andα-actinin,which were absent in the control cells.Overall,this research developed a new bioreactor system for cellularizing cardiac scaffolds under cardiac-like conditions,aiming to restore a sustainable dynamic living tissue that can bear the essential cardiac excitation–contraction coupling.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
文摘MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology,combining real-time biosensing,therapeutic capabilities,and user comfort in a single platform.These devices take the advantage of the exceptional electrical conductivity,mechanical flexibility,and biocompatibility of two-dimensional MXenes to enable noninvasive,tear-based monitoring of key physiological markers such as intraocular pressure and glucose levels.Recent developments focus on the integration of transparent MXene films into the conventional lens materials,allowing multifunctional performance including photothermal therapy,antimicrobial and anti-inflammation protection,and dehydration resistance.These innovations offer promising strategies for ocular disease management and eye protection.In addition to their multifunctionality,improvements in MXene synthesis and device engineering have enhanced the stability,transparency,and wearability of these lenses.Despite these advances,challenges remain in long-term biostability,scalable production,and integration with wireless communication systems.This review summarizes the current progress,key challenges,and future directions of MXene-based smart contact lenses,highlighting their transformative potential in next-generation digital healthcare and ophthalmic care.
文摘This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.
文摘The concept of using biological process in soil improvement which is known as bio-mediated soil improvement technique has shown greater potential in geotechnical engineering applications in terms of performance and environmental sustainability. This paper presents a review on the soil microorganisms responsible for this process, and factors that affect their metabolic activities and geometric compatibility with the soil particle sizes. Two mechanisms of biomineralization, i.e. biologically controlled and biologically induced mineralization, were also discussed. Environmental and other factors that may be encountered in situ during microbially induced calcite precipitation （MICP） and their influences on the process were identified and presented. Improvements in the engineering properties of soil such as strength/stiffness and permeability as evaluated in some studies were explored. Potential applications of the process in geotechnical engineering and the challenges of field application of the process were identified.
基金supported by the Science and Technology Development Fund from Macao SAR(FDCT)(Nos.0102/2019/A2,0035/2019/AGJ,0154/2019/A3,0081/2019/AMJ,0033/2019/AMJ and 0125/2018/A3)Multi-Year Research Grants(MYRG2018-00003IAPME)from the University of Macao。
文摘Oxygen evolution reaction(OER)is a critical process in electrocatalytic water splitting.However,the development of low-cost,highly efficient OER electrocatalysts by a simple method that can be used for industrial application on a large scale is still a huge challenge.Recently,high entropy alloy(HEA)has acquired extensive attention,which may provide answers to the current dilemma.Here,we report bulk Fe_(50)Mn_(30)Co_(10)Cr_(10),which is prepared by 3D printing on a large scale,as electrocatalyst for OER with high catalytic performance.Especially,an easy approach,corrosion engineering,is adopted for the first time to build an active layer of honeycomb nanostructures on its surface,leading to ultrahigh OER performance with an overpotential of 247 mV to achieve a current density of 10 mA cm^(-2),a low Tafel slope of 63 mV dec^(-1),and excellent stability up to 60 h at 100 mA cm^(-2)in 1 M KOH.The excellent catalytic activity mainly originates from:(1)the binder-free self-supported honeycomb nanostructures and multi-component hydroxides,which improve intrinsic catalytic activity,provide rich active sites,and reduce interfacial resistance;and(2)the diverse valence states for multiple active sites to enhance the OER kinetics.Our findings show that corrosion engineering is a novel strategy to improve the bulk HEA catalytic performance.We expect that this work would open up a new avenue to fabricate large-scale HEA electrocatalysts by 3D printing and corrosion engineering for industrial applications.
基金Scientific and Technological Research Projects of Educational Committee of Liaoning Province of China(No.2008S243)
文摘AIM: To study the optical property and biocompatibility of a tissue engineering cornea. METHODS: The cross-linker of N- (3-Dimethylaminoropyl)-N'ethylcarbodiimide hydrochloride (EDC)/ N-Hydroxysuccinimide (NHS) was mixed with Type I collagen at 10% (weight/volume). The final solution was molded to the shape of a corneal contact lens. The collagen concentrations of 10%, 12.5%, 15%, 17.5% and 20% artificial corneas were tested by UV/vis-spectroscopy for their transparency compared with normal rat cornea. 10-0 sutures were knotted on the edges of substitute to measure the corneal buttons's mechanical properties. Normal rat corneal tissue primary culture on the collagen scaffold was observed in 4 weeks. Histopathologic examinations were performed after 4 weeks of in vitro culturing. RESULTS: The collagen scaffold appearance was similar to that of soft contact lens. With the increase of collagen concentration, the transparency of artificial corneal buttons was diminished, but the toughness of the scaffold was enhanced. The scaffold transparency in the 10% concentration collagen group resembled normal rat cornea. To knot and embed the scaffold under the microscope, 20% concentration collagen group was more effective during implantation than lower concentrations of collagen group. In the first 3 weeks, corneal cell proliferation was highly active. The shapes of cells that grew on the substitute had no significant difference when compared with the cells before they were moved to the scaffold. However, on the fortieth day, most cells detached from the scaffold and died. Histopathologic examination of the primary culture scaffold revealed well grown corneal cells tightly attached to the scaffold in the former culturing. CONCLUSION: Collagen scaffold can be molded to the shape of soft contact corneal lens with NHS/EDC. The biological stability and biocompatibility of collagen from animal species may be used as material in preparing to engineer artificial corneal scaffold.
基金N.B.:IHBI ECR grant,Advance Queensland(AQ)Maternity Fund Award from the Queensland Government(DSITI),Young Researcher Award(2017-YR-RoW-9)from Lush(UK)supporting non-animal testing alternatives,National Health and Medical Research Council(NHMRC)Peter Doherty Early Career Research Fellowship(RF)(APP1091734)+5 种基金John Mills Young Investigator Award(YI0715)from the Prostate Cancer Foundation of Australia(PCFA)P.A.T.:Vice Chancellor’s RF(QUT)and AQ RF(QLD)J.A.C.:NHMRC PRFD.W.H.:Humboldt RF,ARC Industrial Transformation Training Center in Additive Biomanufacturing(IC160100026)NHMRC,World Cancer Foundation,National Breast Cancer Foundation,PCFA.D.W.H.,J.A.C.,C.C.N.:Movember Revolutionary Team Award(from Movember and PCFA).APCRC-Qthe Translational Research Institute are supported by grants from the Australian Government
文摘While stromal interactions are essential in cancer adaptation to hormonal therapies,the effects of bone stroma and androgen deprivation on cancer progression in bone are poorly understood.Here,we tissue-engineered and validated an in vitro microtissue model of osteoblastic bone metastases,and used it to study the effects of androgen deprivation in this microenvironment.The model was established by culturing primary human osteoprogenitor cells on melt electrowritten polymer scaffolds,leading to a mineralized osteoblast-derived microtissue containing,in a 3D setting,viable osteoblastic cells,osteocytic cells,and appropriate expression of osteoblast/osteocyte-derived mRNA and proteins,and mineral content.Direct co-culture of androgen receptordependent/ independent cell lines (LNCaP,C4-2B,and PC3) led cancer cells to display functional and molecular features as observed in vivo.Co-cultured cancer cells showed increased affinity to the microtissues,as a function of their bone metastatic potential.Cocultures led to alkaline phosphatase and collagen-I upregulation and sclerostin downregulation,consistent with the clinical marker profile of osteoblastic bone metastases.LNCaP showed a significant adaptive response under androgen deprivation in the microtissues,with the notable appearance of neuroendocrine transdifferentiation features and increased expression of related markers (dopa decarboxylase,enolase 2).Androgen deprivation affected the biology of the metastatic microenvironment with stronger upregulation of androgen receptor,alkaline phosphatase,and dopa decarboxylase,as seen in the transition towards resistance.The unique microtissues engineered here represent a substantial asset to determine the involvement of the human bone microenvironment in prostate cancer progression and response to a therapeutic context in this microenvironment.
基金supported by National Key Research and Development Program (2019YFE0111200)the National Natural Science Foundation of China (51722105)+1 种基金Zhejiang Provincial Natural Science Foundation of China (LR18B030001)the Fundamental Research Funds for the Central Universities and the Fundamental Research Funds for the Central Universities。
文摘Magnesium metal anode holds great potentials toward future high energy and safe rechargeable magnesium battery technology due to its divalent redox and dendrite-free nature. Electrolytes based on Lewis acid chemistry enable the reversible Mg plating/stripping,while they fail to match most cathode materials toward highvoltage magnesium batteries. Herein,reversible Mg plating/stripping is achieved in conventional carbonate electrolytes enabled by the cooperative solvation/surface engineering. Strongly electronegative Cl from the MgCl_(2) additive of electrolyte impairs the Mg…O = C interaction to reduce the Mg^(2+) desolvation barrier for accelerated redox kinetics,while the Mg^(2+)-conducting polymer coating on the Mg surface ensures the facile Mg^(2+) migration and the e ective isolation of electrolytes. As a result,reversible plating and stripping of Mg is demonstrated with a low overpotential of 0.7 V up to 2000 cycles. Moreover,benefitting from the wide electrochemical window of carbonate electrolytes,high-voltage(> 2.0 V) rechargeable magnesium batteries are achieved through assembling the electrode couple of Mg metal anode and Prussian blue-based cathodes. The present work provides a cooperative engineering strategy to promote the application of magnesium anode in carbonate electrolytes toward high energy rechargeable batteries.
文摘In this study, the authors shall illustrate the difficulties, pros and cons of ＂Technical Drawing＂ courses in engineering curriculums. The authors also show the efficiency, effectiveness of students＇ grasping the engineering drawing concepts in depth. The authors have conducted a survey on students and their instructors for teaching the course. Whether the course should be taught with manual handling and then computerized or starting with computer aid and finishing the content of the course, the authors shall also go in detail of syllabus how the drawing rooms and aid tools will be. This part of study shall look into the ergonomics, safety and conditions of classrooms. Responses of 300 students will be shared in this presentation. Statistical analysis will detect the effects of different factors involved in the questionnaire.
基金The first author appreciates the financial support from Hunan Provincial Expressway Group Co.,Ltd.and the Hunan Department of Transportation(No.202152)in ChinaThe first author also appreciates the funding support from the National Natural Science Foundation of China(No.51778038)the Beijing high-level overseas talents in China.Any opinion,finding,and conclusion expressed in this paper are those of the authors and do not necessarily represent the view of any organization.
文摘Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
