Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural f...Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.展开更多
Objective:To evaluate the effectiveness of direct-acting antivirals(DAAs)in patients with chronic hepatitis C,assess changes in liver function and hepatic fibrosis following treatment,and identify independent predicto...Objective:To evaluate the effectiveness of direct-acting antivirals(DAAs)in patients with chronic hepatitis C,assess changes in liver function and hepatic fibrosis following treatment,and identify independent predictors of treatment failure.Methods:This retrospective cohort study included patients who received DAA therapy at Hospital Kuala Lumpur between January 2020 and December 2023.Sustained virologic response(SVR)was assessed at least 12 weeks post-treatment by reverse transcription-polymerase chain reaction for hepatitis C virus(HCV)RNA.Demographic,clinical,and laboratory data were collected and analyzed.Multiple logistic regression analysis was performed to identify independent predictors of treatment failure.Results:A total of 335 patients in the study.The overall SVR rate was 89%.After achieving SVR,significant improvements were observed in liver enzyme levels and non-invasive liver fibrosis scores,whereas the overall Model for End-Stage Liver Disease(MELD)scores remained unchanged.Significant independent predictors of treatment failure included non-compliance with DAA therapy[adjusted odds ratio(aOR)68.3;95%confidence interval(95%CI)16.3-285.0;P<0.001],treatment with sofosbuvir/velpatasvir(aOR 6.1;95%CI 1.4-26.5;P=0.015),MELD score of 10-15(aOR 4.6;95%CI 1.1-18.2;P=0.031),HCV genotype 3 infection(aOR 4.5;95%CI 1.1-17.6;P=0.031),and elevated serum total bilirubin level(aOR 1.1;95%CI 1.0-1.1;P=0.003).Conclusions:DAA therapy yielded a high SVR rate,and treatment failure was strongly associated with non-adherence to therapy and advanced liver disease.These findings underscore the necessity of adherence support,early diagnosis,and individualized clinical management to optimize treatment outcomes in patients with chronic hepatitis C.展开更多
With the rapid development of digital culture,a large number of cultural texts are presented in the form of digital and network.These texts have significant characteristics such as sparsity,real-time and non-standard ...With the rapid development of digital culture,a large number of cultural texts are presented in the form of digital and network.These texts have significant characteristics such as sparsity,real-time and non-standard expression,which bring serious challenges to traditional classification methods.In order to cope with the above problems,this paper proposes a new ASSC(ALBERT,SVD,Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model.Based on the framework of TextRCNN,the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding.Combined with the dual attention mechanism,the model’s ability to capture and model potential key information in short texts is strengthened.The Singular Value Decomposition(SVD)was used to replace the traditional Max pooling operation,which effectively reduced the feature loss rate and retained more key semantic information.The cross-entropy loss function was used to optimize the prediction results,making the model more robust in class distribution learning.The experimental results indicate that,in the digital cultural text classification task,as compared to the baseline model,the proposed ASSC-TextRCNN method achieves an 11.85%relative improvement in accuracy and an 11.97%relative increase in the F1 score.Meanwhile,the relative error rate decreases by 53.18%.This achievement not only validates the effectiveness and advanced nature of the proposed approach but also offers a novel technical route and methodological underpinnings for the intelligent analysis and dissemination of digital cultural texts.It holds great significance for promoting the in-depth exploration and value realization of digital culture.展开更多
Typhonium flagelliforme(TF)is a Southeast Asian medicinal plant traditionally used for cancer,respiratory disorders,gastrointestinal complaints,wound healing,inflammation,and general health.Contemporary studies valida...Typhonium flagelliforme(TF)is a Southeast Asian medicinal plant traditionally used for cancer,respiratory disorders,gastrointestinal complaints,wound healing,inflammation,and general health.Contemporary studies validate these uses,showing potent anticancer,immunomodulatory,anti-inflammatory,gastroprotective,antibacterial,antioxidant,and wound-healing activities.Ethanol,dichloromethane,methanol,and ethyl acetate extracts exhibit strong cytotoxicity against breast(MCF-7,T47D),lung(NCI-H23),colon(WiDr),and leukemia(CEM-ss,WEHI-3)cells via apoptosis,telomerase inhibition,HER2/neu and BCL-2 suppression,and antiangiogenesis.Notably,2-octenoic acid and 2-hexenoic acid show exceptional activity(IC₅₀=2.66 and 3.10μg/mL)against MCF-7 cells.TF also restores lymphocyte proliferation,enhances macrophage activity,increases both CD4+and CD8+T-cell levels,and modulates cytokines(TNF-α,IL-1α,IL-10).Gastroprotective,anti-ulcer,antibacterial,antioxidant,and wound-healing effects further support traditional claims.Key phytochemicals include flavonoids(isovitexin,kaempferol,vitexin),phenolics(vanillin,4-hydroxybenzaldehyde),phytosterols(β-sitosterol,campesterol,stigmasterol,daucosterol),chlorophyll derivatives(pheophorbides),and long-chain fatty acids(linoleic,linolenic,oleic,stearic).These findings highlight TF as a source of multifunctional bioactive compounds,warranting further pharmacokinetic,safety,and clinical evaluation for evidence-based therapeutic development.展开更多
Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
Background:Maternal mental health literacy is a cognitive resource that may support preschoolers’emotional development,yet its influence on emotional regulation and the related mechanisms remains unclear.This study e...Background:Maternal mental health literacy is a cognitive resource that may support preschoolers’emotional development,yet its influence on emotional regulation and the related mechanisms remains unclear.This study examined whether maternal depressive mood and democratic parenting form a chain pathway linking maternal mental health literacy to preschoolers’emotional regulation ability.Methods:Mothers of 544 preschoolers in China’s Mainland completed an online questionnaire that assessed maternal mental health literacy,depressive mood,democratic parenting,and child emotional regulation.Structural path analysis was conducted with child age and gender controlled.Indirect effects were tested using 5000 bootstrap samples.Results:Maternal mental health literacy did not directly predict preschoolers’emotional regulation.Three indirect effects were significant.The pathway through depressive mood had an effect of 0.005,the pathway through democratic parenting had an effect of 0.004,and the chain pathway through depressive mood and democratic parenting had an effect of 0.002.All confidence intervals excluded 0.Conclusion:Maternal mental health literacy influences preschoolers’emotional regulation only through maternal depressive mood and democratic parenting,indicating that cognitive resources affect child emotional outcomes through emotional and behavioral processes rather than a direct pathway.展开更多
Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These iss...Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study pr...critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.展开更多
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ...Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.展开更多
Nanotechnology has revolutionized drug delivery,particularly through nanoformulations of phytoconstituents,enhancing their therapeutic potential.Despite their broad bioactivities,plant-based compounds often suffer fro...Nanotechnology has revolutionized drug delivery,particularly through nanoformulations of phytoconstituents,enhancing their therapeutic potential.Despite their broad bioactivities,plant-based compounds often suffer from poor bioavailability and stability.Nanoformulations address these limitations by improving solubility,targeted delivery,and controlled release.This approach opens new possibilities for treating chronic diseases like cancer,diabetes,and neurodegenerative disorders.This review aims to examine recent advancements in nanotechnology-based formulation strategies designed to enhance the delivery,stability,and therapeutic efficacy of phytochemicals and also discusses regulatory issues,safety concerns,scalability,and cost-effectiveness.Emphasis was placed on nanoformulation techniques employed for key phytoconstituents such as curcumin,resveratrol,epigallocatechin gallate,and quercetin.The most commonly employed nanocarriers included polymeric nanoparticles,solid lipid nanoparticles,and liposomes.These formulations significantly improved the solubility,stability,and controlled release profiles of phytochemicals.In vitro and in vivo studies demonstrated enhanced anti-inflammatory,anticancer,and antioxidant activities.Moreover,surface-modified and targeted nanoparticles showed promise in increasing site-specific targeting and enhancing bioavailability of the encapsulated compounds.Nanoformulations present a promising strategy for overcoming the pharmacokinetic limitations of phytochemicals.Despite encouraging preclinical results,further studies are needed to address issues related to long-term safety,clinical efficacy,and regulatory approval for successful clinical translation.展开更多
The functional relationships between flow （veh/km）, density （veh/h） and speed （kin/h） in traffic congestion have a long history of research. However, their findings and techniques persist to be relevant to this ...The functional relationships between flow （veh/km）, density （veh/h） and speed （kin/h） in traffic congestion have a long history of research. However, their findings and techniques persist to be relevant to this day. The analysis is pertinent, particularly in finding the best fit for the three major highways in Malaysia, namely the KL-Karak Highway, KL-Seremban Highway and KL-Ipoh Highway. The trans-logarithm function of density-speed model was compared to the classical models of Greenshields, Greenberg, Underwood and Drake et al. using data provided by the Transport Statistics Malaysia 2014. The results of regression analysis revealed that the Greenshields and Greenberg models were statistically significant. The trans-logarithm function was also tested and the results were nonetheless without exception. Its usefulness in addition to statistical significance related to the derived economic concepts of maximum speed and the related number of vehicles, flow and density and the limits of free speed were relevant in comparing the individual levels of traffic congestion between highways. For instance, KL-Karak Highway was least congested compared to KL-Seremban Highway and KL-Ipoh Highway. Their maximum speeds, based on three lanes carriage capacity of one direction, were 33.4 km/h for KL-Karak, 15.9 km/h for KL-Seremban, and 21.1 km/h for KL-Ipoh. Their corresponding flows were approximated at 1080.9 veh/h, 1555.4 veh/h, and 1436.6 veh/h.展开更多
基金supported by the Malaysia-Japan International Institute of Technology(MJIIT),Universiti Teknologi Malaysia.
文摘Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.
文摘Objective:To evaluate the effectiveness of direct-acting antivirals(DAAs)in patients with chronic hepatitis C,assess changes in liver function and hepatic fibrosis following treatment,and identify independent predictors of treatment failure.Methods:This retrospective cohort study included patients who received DAA therapy at Hospital Kuala Lumpur between January 2020 and December 2023.Sustained virologic response(SVR)was assessed at least 12 weeks post-treatment by reverse transcription-polymerase chain reaction for hepatitis C virus(HCV)RNA.Demographic,clinical,and laboratory data were collected and analyzed.Multiple logistic regression analysis was performed to identify independent predictors of treatment failure.Results:A total of 335 patients in the study.The overall SVR rate was 89%.After achieving SVR,significant improvements were observed in liver enzyme levels and non-invasive liver fibrosis scores,whereas the overall Model for End-Stage Liver Disease(MELD)scores remained unchanged.Significant independent predictors of treatment failure included non-compliance with DAA therapy[adjusted odds ratio(aOR)68.3;95%confidence interval(95%CI)16.3-285.0;P<0.001],treatment with sofosbuvir/velpatasvir(aOR 6.1;95%CI 1.4-26.5;P=0.015),MELD score of 10-15(aOR 4.6;95%CI 1.1-18.2;P=0.031),HCV genotype 3 infection(aOR 4.5;95%CI 1.1-17.6;P=0.031),and elevated serum total bilirubin level(aOR 1.1;95%CI 1.0-1.1;P=0.003).Conclusions:DAA therapy yielded a high SVR rate,and treatment failure was strongly associated with non-adherence to therapy and advanced liver disease.These findings underscore the necessity of adherence support,early diagnosis,and individualized clinical management to optimize treatment outcomes in patients with chronic hepatitis C.
基金funded by China National Innovation and Entrepreneurship Project Fund Innovation Training Program(202410451009).
文摘With the rapid development of digital culture,a large number of cultural texts are presented in the form of digital and network.These texts have significant characteristics such as sparsity,real-time and non-standard expression,which bring serious challenges to traditional classification methods.In order to cope with the above problems,this paper proposes a new ASSC(ALBERT,SVD,Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model.Based on the framework of TextRCNN,the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding.Combined with the dual attention mechanism,the model’s ability to capture and model potential key information in short texts is strengthened.The Singular Value Decomposition(SVD)was used to replace the traditional Max pooling operation,which effectively reduced the feature loss rate and retained more key semantic information.The cross-entropy loss function was used to optimize the prediction results,making the model more robust in class distribution learning.The experimental results indicate that,in the digital cultural text classification task,as compared to the baseline model,the proposed ASSC-TextRCNN method achieves an 11.85%relative improvement in accuracy and an 11.97%relative increase in the F1 score.Meanwhile,the relative error rate decreases by 53.18%.This achievement not only validates the effectiveness and advanced nature of the proposed approach but also offers a novel technical route and methodological underpinnings for the intelligent analysis and dissemination of digital cultural texts.It holds great significance for promoting the in-depth exploration and value realization of digital culture.
基金the Ministry of Higher Education(MOHE),Malaysia,for funding this research through grant no.IF070-2020the Science and Technology Research Partnership for Sustainable Development(SATREPS)program,administered by the Japan Agency for Medical Research and Development(AMED)and the Japan International Cooperation Agency(JICA).
文摘Typhonium flagelliforme(TF)is a Southeast Asian medicinal plant traditionally used for cancer,respiratory disorders,gastrointestinal complaints,wound healing,inflammation,and general health.Contemporary studies validate these uses,showing potent anticancer,immunomodulatory,anti-inflammatory,gastroprotective,antibacterial,antioxidant,and wound-healing activities.Ethanol,dichloromethane,methanol,and ethyl acetate extracts exhibit strong cytotoxicity against breast(MCF-7,T47D),lung(NCI-H23),colon(WiDr),and leukemia(CEM-ss,WEHI-3)cells via apoptosis,telomerase inhibition,HER2/neu and BCL-2 suppression,and antiangiogenesis.Notably,2-octenoic acid and 2-hexenoic acid show exceptional activity(IC₅₀=2.66 and 3.10μg/mL)against MCF-7 cells.TF also restores lymphocyte proliferation,enhances macrophage activity,increases both CD4+and CD8+T-cell levels,and modulates cytokines(TNF-α,IL-1α,IL-10).Gastroprotective,anti-ulcer,antibacterial,antioxidant,and wound-healing effects further support traditional claims.Key phytochemicals include flavonoids(isovitexin,kaempferol,vitexin),phenolics(vanillin,4-hydroxybenzaldehyde),phytosterols(β-sitosterol,campesterol,stigmasterol,daucosterol),chlorophyll derivatives(pheophorbides),and long-chain fatty acids(linoleic,linolenic,oleic,stearic).These findings highlight TF as a source of multifunctional bioactive compounds,warranting further pharmacokinetic,safety,and clinical evaluation for evidence-based therapeutic development.
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
文摘Background:Maternal mental health literacy is a cognitive resource that may support preschoolers’emotional development,yet its influence on emotional regulation and the related mechanisms remains unclear.This study examined whether maternal depressive mood and democratic parenting form a chain pathway linking maternal mental health literacy to preschoolers’emotional regulation ability.Methods:Mothers of 544 preschoolers in China’s Mainland completed an online questionnaire that assessed maternal mental health literacy,depressive mood,democratic parenting,and child emotional regulation.Structural path analysis was conducted with child age and gender controlled.Indirect effects were tested using 5000 bootstrap samples.Results:Maternal mental health literacy did not directly predict preschoolers’emotional regulation.Three indirect effects were significant.The pathway through depressive mood had an effect of 0.005,the pathway through democratic parenting had an effect of 0.004,and the chain pathway through depressive mood and democratic parenting had an effect of 0.002.All confidence intervals excluded 0.Conclusion:Maternal mental health literacy influences preschoolers’emotional regulation only through maternal depressive mood and democratic parenting,indicating that cognitive resources affect child emotional outcomes through emotional and behavioral processes rather than a direct pathway.
文摘Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金funded by the Ministry of Higher Education(MoHE)Malaysia through the Fundamental Research Grant Scheme—Early Career Researcher(FRGS-EC),grant number FRGSEC/1/2024/ICT02/UNIMAP/02/8.
文摘critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.
基金funded by United Arab Emirates University(UAEU)under the UAEU-AUA grant number G00004577(12N145)with the corresponding grant at Universiti Malaya(UM)under grant number IF019-2024.
文摘Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.
文摘Nanotechnology has revolutionized drug delivery,particularly through nanoformulations of phytoconstituents,enhancing their therapeutic potential.Despite their broad bioactivities,plant-based compounds often suffer from poor bioavailability and stability.Nanoformulations address these limitations by improving solubility,targeted delivery,and controlled release.This approach opens new possibilities for treating chronic diseases like cancer,diabetes,and neurodegenerative disorders.This review aims to examine recent advancements in nanotechnology-based formulation strategies designed to enhance the delivery,stability,and therapeutic efficacy of phytochemicals and also discusses regulatory issues,safety concerns,scalability,and cost-effectiveness.Emphasis was placed on nanoformulation techniques employed for key phytoconstituents such as curcumin,resveratrol,epigallocatechin gallate,and quercetin.The most commonly employed nanocarriers included polymeric nanoparticles,solid lipid nanoparticles,and liposomes.These formulations significantly improved the solubility,stability,and controlled release profiles of phytochemicals.In vitro and in vivo studies demonstrated enhanced anti-inflammatory,anticancer,and antioxidant activities.Moreover,surface-modified and targeted nanoparticles showed promise in increasing site-specific targeting and enhancing bioavailability of the encapsulated compounds.Nanoformulations present a promising strategy for overcoming the pharmacokinetic limitations of phytochemicals.Despite encouraging preclinical results,further studies are needed to address issues related to long-term safety,clinical efficacy,and regulatory approval for successful clinical translation.
文摘The functional relationships between flow （veh/km）, density （veh/h） and speed （kin/h） in traffic congestion have a long history of research. However, their findings and techniques persist to be relevant to this day. The analysis is pertinent, particularly in finding the best fit for the three major highways in Malaysia, namely the KL-Karak Highway, KL-Seremban Highway and KL-Ipoh Highway. The trans-logarithm function of density-speed model was compared to the classical models of Greenshields, Greenberg, Underwood and Drake et al. using data provided by the Transport Statistics Malaysia 2014. The results of regression analysis revealed that the Greenshields and Greenberg models were statistically significant. The trans-logarithm function was also tested and the results were nonetheless without exception. Its usefulness in addition to statistical significance related to the derived economic concepts of maximum speed and the related number of vehicles, flow and density and the limits of free speed were relevant in comparing the individual levels of traffic congestion between highways. For instance, KL-Karak Highway was least congested compared to KL-Seremban Highway and KL-Ipoh Highway. Their maximum speeds, based on three lanes carriage capacity of one direction, were 33.4 km/h for KL-Karak, 15.9 km/h for KL-Seremban, and 21.1 km/h for KL-Ipoh. Their corresponding flows were approximated at 1080.9 veh/h, 1555.4 veh/h, and 1436.6 veh/h.
