Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detec...Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detection, is facing a lack of simple and fast sensing methods that are compatible with field applications in resource-limited areas. In this work, we developed a sensing approach to identify PCR-amplified Salmonella genomic DNA with the naked eye in a snapshot. Based on the ratiometric fiuorescence signals from SYBR Green Ⅰ and Hydroxyl naphthol blue, positive samples stood out from negative ones with a distinct color pattern under UV exposure. The proposed sensing scheme enabled highly specific identification of Salmonella with a detection limit at the single-copy level. Also, as a supplement to the intuitive naked-eye visualization results, numerical analysis of the colored images was available with a smartphone app to extract RGB values from colored images. This work provides a simple, rapid, and user-friendly solution for PCR identification, which promises great potential in molecular diagnosis of Salmonella and other pathogens in field.展开更多
This work is devoted to numerical analysis of thermo-hydromechanical problem and cracking process in saturated porous media in the context of deep geological disposal of radioactive waste.The fundamental background of...This work is devoted to numerical analysis of thermo-hydromechanical problem and cracking process in saturated porous media in the context of deep geological disposal of radioactive waste.The fundamental background of thermo-poro-elastoplasticity theory is first summarized.The emphasis is put on the effect of pore fluid pressure on plastic deformation.A micromechanics-based elastoplastic model is then presented for a class of clayey rocks considered as host rock.Based on linear and nonlinear homogenization techniques,the proposed model is able to systematically account for the influences of porosity and mineral composition on macroscopic elastic properties and plastic yield strength.The initial anisotropy and time-dependent deformation are also taken into account.The induced cracking process is described by using a non-local damage model.A specific hybrid formulation is proposed,able to conveniently capture tensile,shear and mixed cracks.In particular,the influences of pore pressure and confining stress on the shear cracking mechanism are taken into account.The proposed model is applied to investigating thermo-hydromechanical responses and induced damage evolution in laboratory tests at the sample scale.In the last part,an in situ heating experiment is analyzed by using the proposed model.Numerical results are compared with experimental data and field measurements in terms of temperature variation,pore fluid pressure change and induced damaged zone.展开更多
Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation...Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitor...Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.展开更多
Scarcity of empirical studies turning the concepts into cost-effective practices is a barrier in achieving the desired trajectory and scale of ecosystem restoration.The present study aimed to assess(i)potential of tre...Scarcity of empirical studies turning the concepts into cost-effective practices is a barrier in achieving the desired trajectory and scale of ecosystem restoration.The present study aimed to assess(i)potential of tree-bamboo-medicinal herb mixed plantation founded on the concept of adaptive comanagement in restoration of degraded community forest in a temperate village of Indian Himalaya and(ii)persistence of offer of local people to voluntarily maintain and expand the trial after its economic benefit/cost ratio became>1.0.Biodiversity,carbon stock and economic benefits were assessed in the restored forest 1,3 and 10 years after 7-year-long funding phase(i.e.,8,10 and 20 years after initial planting in 1991),and other land uses in the village landscape.Significant economic loss occurred from gregarious flowering induced mass mortality of bamboo in the 2nd year after funding phase but it was outweighed by the gain from walnut fruiting.People maintained recovery by transplanting Nepalese Alder(Alnus nepalensis)in gaps.The 20-year-old restored forest land had 17%of aboveground and 75%of belowground carbon stocks,and 39%of flowering species present in the intact forest.Restored forest had only four of the eight Near-threatened/Threatened species present in intact forest.Further,intact forest was monetarily the most efficient land use despite absence of payments for its ecosystem services.People did not expand the trial or medicinal plant cultivation in farms induced by it.They abandoned cropping in 39%farm area and leased 24%abandoned area to a company.Flowering plant species richness and carbon stocks changed at the ecosystem scale but not at the village landscape scale.Emission from agricultural abandonment nullified carbon sequestration by forest restoration.Community forest restoration should render both material/monetary and nonmaterial/non-monetary benefits to people.Cultural landscapes should be taken as spatial units for ecosystem restoration planning,monitoring and evaluation.展开更多
The Miombo ecoregion covers eastern and southern Africa,with variations in plant species composition,structure,and biomass across a broad precipitation gradient.Most studies of woody plant communities focus exclusivel...The Miombo ecoregion covers eastern and southern Africa,with variations in plant species composition,structure,and biomass across a broad precipitation gradient.Most studies of woody plant communities focus exclusively on larger overstorey trees(≥5 or≥10cm stem diameter),overlooking the contribution of small trees and shrubs in the understorey,which can comprise a significant portion of total biomass and diversity.Here,we evaluate the contribution of both large overstorey and small understorey woody plants to species diversity and above-ground biomass(AGB),with 17 plots(0.5-1ha)across five sites representing both extremes of rainfall gradient spanning the Miombo ecoregion,in northeast Namibia(500-700mm mean annual precipitation,MAP)and southern Democratic Republic of Congo(DRC)(>1,200mm MAP).Mean AGB per site ranged from 21 to 119Mg·ha^(-1),increasing with rainfall,while the proportional AGB contribution of small trees,saplings,and shrubs decreased.In dry Namibia,small trees,saplings,and shrubs(<5cm DBH)contributed up to 28.2%of total AGB(mean±standard deviation:18.3%±3.4%),whereas in wet DRC,they contributed only up to 2.5%(2.3%±1.4%).Namibian sites,on average,contained a large proportion of woody species diversity exclusively in small trees and shrubs(<5cm DBH),with 55 species representing 59.4%of the total diversity.In contrast,DRC sites had higher overall small woody plant diversity(66 species)but fewer species found exclusively as small individuals(25.2%),with many saplings that grow to larger trees.Understorey composition also differed,with saplings of overstorey trees dominating in DRC,while shrubs dominated in Namibia.Our findings show that woody biomass and diversity in dry woodlands are substantially underestimated when studies focus only on larger trees.This highlights the need to consider all woody vegetation to better understand woody plant diversity and biomass variation.展开更多
Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscul...Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.展开更多
BACKGROUND Hepatitis C virus(HCV)is a blood-borne virus which globally affects around 79 million people and is associated with high morbidity and mortality.Chronic infection leads to cirrhosis in a large proportion of...BACKGROUND Hepatitis C virus(HCV)is a blood-borne virus which globally affects around 79 million people and is associated with high morbidity and mortality.Chronic infection leads to cirrhosis in a large proportion of patients and often causes hepatocellular carcinoma(HCC)in people with cirrhosis.Of the 6 HCV genotypes(G1-G6),genotype-3 accounts for 17.9%of infections.HCV genotype-3 responds least well to directly-acting antivirals and patients with genotype-3 infection are at increased risk of HCC even if they do not have cirrhosis.AIM To systematically review and critically appraise all risk factors for HCC secondary to HCV-G3 in all settings.Consequently,we studied possible risk factors for HCC due to HCV-G3 in the literature from 1946 to 2023.METHODS This systematic review aimed to synthesise existing and published studies of risk factors for HCC secondary to HCV genotype-3 and evaluate their strengths and limitations.We searched Web of Science,Medline,EMBASE,and CENTRAL for publications reporting risk factors for HCC due to HCV genotype-3 in all settings,1946-2023.RESULTS Four thousand one hundred and forty-four records were identified from the four databases with 260 records removed as duplicates.Three thousand eight hundred and eighty-four records were screened with 3514 excluded.Three hundred and seventy-one full-texts were assessed for eligibility with seven studies included for analysis.Of the seven studies,three studies were retrospective case-control trials,two retrospective cohort studies,one a prospective cohort study and one a cross-sectional study design.All were based in hospital settings with four in Pakistan,two in South Korea and one in the United States.The total number of participants were 9621 of which 167 developed HCC(1.7%).All seven studies found cirrhosis to be a risk factor for HCC secondary to HCV genotype-3 followed by higher age(five-studies),with two studies each showing male sex,high alpha feto-protein,directly-acting antivirals treatment and achievement of sustained virologic response as risk factors for developing HCC.CONCLUSION Although,studies have shown that HCV genotype-3 infection is an independent risk factor for end-stage liver disease,HCC,and liver-related death,there is a lack of evidence for specific risk factors for HCC secondary to HCV genotype-3.Only cirrhosis and age have demonstrated an association;however,the number of studies is very small,and more research is required to investigate risk factors for HCC secondary to HCV genotype-3.展开更多
Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s g...Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.展开更多
This paper addresses numerical analysis of thermo-hydromechanical processes in the context of deep geological disposal of radioactive waste.The emphasis is put on modeling of damaged zones induced by excavation,pore p...This paper addresses numerical analysis of thermo-hydromechanical processes in the context of deep geological disposal of radioactive waste.The emphasis is put on modeling of damaged zones induced by excavation,pore pressure and temperature changes.The theoretical background of thermo-poroelasticity for saturated porous media is first recalled.The framework for modeling initiation and evolution of induced cracks is then presented by using a variational approach of phase-field method.A specific model with two crack phase fields and considering thermo-hydromechanical interaction is proposed.A particular attention is paid on the description of shear cracks.The proposed model is implemented in the standard finite element method.An example of application is finally presented on the analysis of thermo-hydromechanical responses and cracked zones evolution around a typical disposal repository in the context of French concept for high level waste disposal.展开更多
We report a novel double-shelled nanoboxes photocatalyst architecture with tailored interfaces that accelerate quantum efficiency for photocatalytic CO_(2) reduction reaction(CO_(2)RR)via Mo–S bridging bonds sites in...We report a novel double-shelled nanoboxes photocatalyst architecture with tailored interfaces that accelerate quantum efficiency for photocatalytic CO_(2) reduction reaction(CO_(2)RR)via Mo–S bridging bonds sites in S_(v)–In_(2)S_(3)@2H–MoTe_(2).The X-ray absorption near-edge structure shows that the formation of S_(v)–In_(2)S_(3)@2H–MoTe_(2) adjusts the coordination environment via interface engineering and forms Mo–S polarized sites at the interface.The interfacial dynamics and catalytic behavior are clearly revealed by ultrafast femtosecond transient absorption,time-resolved,and in situ diffuse reflectance–Infrared Fourier transform spectroscopy.A tunable electronic structure through steric interaction of Mo–S bridging bonds induces a 1.7-fold enhancement in S_(v)–In_(2)S_(3)@2H–MoTe_(2)(5)photogenerated carrier concentration relative to pristine S_(v)–In_(2)S_(3).Benefiting from lower carrier transport activation energy,an internal quantum efficiency of 94.01%at 380 nm was used for photocatalytic CO_(2)RR.This study proposes a new strategy to design photocatalyst through bridging sites to adjust the selectivity of photocatalytic CO_(2)RR.展开更多
Rotaviruses are non-enveloped double-stranded RNA virus that causes acute diarrheal diseases in children(<5 years).More than 90%of the global rotavirus infection in humans was caused by Rotavirus group A.Rotavirus ...Rotaviruses are non-enveloped double-stranded RNA virus that causes acute diarrheal diseases in children(<5 years).More than 90%of the global rotavirus infection in humans was caused by Rotavirus group A.Rotavirus infection has caused more than 200000 deaths annually and predominantly occurs in the low-income countries.Rotavirus evolution is indicated by the strain dynamics or the emergence of the unprecedented strain.The major factors that drive the rotavirus evolution include the genetic shift that is caused by the reassortment mechanism,either in the intra-or the inter-genogroup.However,other factors are also known to have an impact on rotavirus evolution.This review discusses the structure and types,epidemiology,and evolution of rotaviruses.This article also reviews other supplemental factors of rotavirus evolution,such as genetic reassortment,mutation rate,glycan specificity,vaccine introduction,the host immune respo-nses,and antiviral drugs.展开更多
Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by reta...Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation.In addition,the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy.Nevertheless,due to the lower complexity of the fusion algorithm,the efficiency of fusion methods is high in practical applications.The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information,edge contour,and overall contrast.展开更多
The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learn...The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.展开更多
基金supported by the Macao Science and Technology Development Fund(FDCT)(Nos.FDCT 0029/2021/A1,FDCT0002/2021/AKP,004/2023/SKL,0036/2021/APD)University of Macao(No.MYRG-GRG2023-00034-IME,SRG2024-00057IME)+2 种基金Dr.Stanley Ho Medical Development Foundation(No.SHMDF-OIRFS/2024/001)Zhuhai Huafa Group(No.HF-006-2021)Guangdong Science and Technology Department(No.2022A0505030022)。
文摘Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detection, is facing a lack of simple and fast sensing methods that are compatible with field applications in resource-limited areas. In this work, we developed a sensing approach to identify PCR-amplified Salmonella genomic DNA with the naked eye in a snapshot. Based on the ratiometric fiuorescence signals from SYBR Green Ⅰ and Hydroxyl naphthol blue, positive samples stood out from negative ones with a distinct color pattern under UV exposure. The proposed sensing scheme enabled highly specific identification of Salmonella with a detection limit at the single-copy level. Also, as a supplement to the intuitive naked-eye visualization results, numerical analysis of the colored images was available with a smartphone app to extract RGB values from colored images. This work provides a simple, rapid, and user-friendly solution for PCR identification, which promises great potential in molecular diagnosis of Salmonella and other pathogens in field.
基金supported by the French National Agency for radioactive waste management(ANDRA).
文摘This work is devoted to numerical analysis of thermo-hydromechanical problem and cracking process in saturated porous media in the context of deep geological disposal of radioactive waste.The fundamental background of thermo-poro-elastoplasticity theory is first summarized.The emphasis is put on the effect of pore fluid pressure on plastic deformation.A micromechanics-based elastoplastic model is then presented for a class of clayey rocks considered as host rock.Based on linear and nonlinear homogenization techniques,the proposed model is able to systematically account for the influences of porosity and mineral composition on macroscopic elastic properties and plastic yield strength.The initial anisotropy and time-dependent deformation are also taken into account.The induced cracking process is described by using a non-local damage model.A specific hybrid formulation is proposed,able to conveniently capture tensile,shear and mixed cracks.In particular,the influences of pore pressure and confining stress on the shear cracking mechanism are taken into account.The proposed model is applied to investigating thermo-hydromechanical responses and induced damage evolution in laboratory tests at the sample scale.In the last part,an in situ heating experiment is analyzed by using the proposed model.Numerical results are compared with experimental data and field measurements in terms of temperature variation,pore fluid pressure change and induced damaged zone.
文摘Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
文摘Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.
文摘Scarcity of empirical studies turning the concepts into cost-effective practices is a barrier in achieving the desired trajectory and scale of ecosystem restoration.The present study aimed to assess(i)potential of tree-bamboo-medicinal herb mixed plantation founded on the concept of adaptive comanagement in restoration of degraded community forest in a temperate village of Indian Himalaya and(ii)persistence of offer of local people to voluntarily maintain and expand the trial after its economic benefit/cost ratio became>1.0.Biodiversity,carbon stock and economic benefits were assessed in the restored forest 1,3 and 10 years after 7-year-long funding phase(i.e.,8,10 and 20 years after initial planting in 1991),and other land uses in the village landscape.Significant economic loss occurred from gregarious flowering induced mass mortality of bamboo in the 2nd year after funding phase but it was outweighed by the gain from walnut fruiting.People maintained recovery by transplanting Nepalese Alder(Alnus nepalensis)in gaps.The 20-year-old restored forest land had 17%of aboveground and 75%of belowground carbon stocks,and 39%of flowering species present in the intact forest.Restored forest had only four of the eight Near-threatened/Threatened species present in intact forest.Further,intact forest was monetarily the most efficient land use despite absence of payments for its ecosystem services.People did not expand the trial or medicinal plant cultivation in farms induced by it.They abandoned cropping in 39%farm area and leased 24%abandoned area to a company.Flowering plant species richness and carbon stocks changed at the ecosystem scale but not at the village landscape scale.Emission from agricultural abandonment nullified carbon sequestration by forest restoration.Community forest restoration should render both material/monetary and nonmaterial/non-monetary benefits to people.Cultural landscapes should be taken as spatial units for ecosystem restoration planning,monitoring and evaluation.
基金funded by the following grants:the Natural Environ-ment Research Council-Funded SECO Project(NE/T01279X/1)the Fostering Research&Intra-African Knowledge Transfer Through Mobility&Education(FRAME)Conservation Action Research Network(CARN)through the ASPIRE Grant Programme.
文摘The Miombo ecoregion covers eastern and southern Africa,with variations in plant species composition,structure,and biomass across a broad precipitation gradient.Most studies of woody plant communities focus exclusively on larger overstorey trees(≥5 or≥10cm stem diameter),overlooking the contribution of small trees and shrubs in the understorey,which can comprise a significant portion of total biomass and diversity.Here,we evaluate the contribution of both large overstorey and small understorey woody plants to species diversity and above-ground biomass(AGB),with 17 plots(0.5-1ha)across five sites representing both extremes of rainfall gradient spanning the Miombo ecoregion,in northeast Namibia(500-700mm mean annual precipitation,MAP)and southern Democratic Republic of Congo(DRC)(>1,200mm MAP).Mean AGB per site ranged from 21 to 119Mg·ha^(-1),increasing with rainfall,while the proportional AGB contribution of small trees,saplings,and shrubs decreased.In dry Namibia,small trees,saplings,and shrubs(<5cm DBH)contributed up to 28.2%of total AGB(mean±standard deviation:18.3%±3.4%),whereas in wet DRC,they contributed only up to 2.5%(2.3%±1.4%).Namibian sites,on average,contained a large proportion of woody species diversity exclusively in small trees and shrubs(<5cm DBH),with 55 species representing 59.4%of the total diversity.In contrast,DRC sites had higher overall small woody plant diversity(66 species)but fewer species found exclusively as small individuals(25.2%),with many saplings that grow to larger trees.Understorey composition also differed,with saplings of overstorey trees dominating in DRC,while shrubs dominated in Namibia.Our findings show that woody biomass and diversity in dry woodlands are substantially underestimated when studies focus only on larger trees.This highlights the need to consider all woody vegetation to better understand woody plant diversity and biomass variation.
基金funded by the deanship of scientific research(DSR),King Abdulaziz University,Jeddah,under grant No.(G-1436-611-309).
文摘Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.
基金Supported by the Clinical Research Fellowship Grant from the Wellcome Trust,United Kingdom,No.227516/Z/23/Z.
文摘BACKGROUND Hepatitis C virus(HCV)is a blood-borne virus which globally affects around 79 million people and is associated with high morbidity and mortality.Chronic infection leads to cirrhosis in a large proportion of patients and often causes hepatocellular carcinoma(HCC)in people with cirrhosis.Of the 6 HCV genotypes(G1-G6),genotype-3 accounts for 17.9%of infections.HCV genotype-3 responds least well to directly-acting antivirals and patients with genotype-3 infection are at increased risk of HCC even if they do not have cirrhosis.AIM To systematically review and critically appraise all risk factors for HCC secondary to HCV-G3 in all settings.Consequently,we studied possible risk factors for HCC due to HCV-G3 in the literature from 1946 to 2023.METHODS This systematic review aimed to synthesise existing and published studies of risk factors for HCC secondary to HCV genotype-3 and evaluate their strengths and limitations.We searched Web of Science,Medline,EMBASE,and CENTRAL for publications reporting risk factors for HCC due to HCV genotype-3 in all settings,1946-2023.RESULTS Four thousand one hundred and forty-four records were identified from the four databases with 260 records removed as duplicates.Three thousand eight hundred and eighty-four records were screened with 3514 excluded.Three hundred and seventy-one full-texts were assessed for eligibility with seven studies included for analysis.Of the seven studies,three studies were retrospective case-control trials,two retrospective cohort studies,one a prospective cohort study and one a cross-sectional study design.All were based in hospital settings with four in Pakistan,two in South Korea and one in the United States.The total number of participants were 9621 of which 167 developed HCC(1.7%).All seven studies found cirrhosis to be a risk factor for HCC secondary to HCV genotype-3 followed by higher age(five-studies),with two studies each showing male sex,high alpha feto-protein,directly-acting antivirals treatment and achievement of sustained virologic response as risk factors for developing HCC.CONCLUSION Although,studies have shown that HCV genotype-3 infection is an independent risk factor for end-stage liver disease,HCC,and liver-related death,there is a lack of evidence for specific risk factors for HCC secondary to HCV genotype-3.Only cirrhosis and age have demonstrated an association;however,the number of studies is very small,and more research is required to investigate risk factors for HCC secondary to HCV genotype-3.
文摘Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.
基金supported by the French National Agency for radioactive waste management(ANDRA)and the National Natural Science Foundation of China(No.12202099).
文摘This paper addresses numerical analysis of thermo-hydromechanical processes in the context of deep geological disposal of radioactive waste.The emphasis is put on modeling of damaged zones induced by excavation,pore pressure and temperature changes.The theoretical background of thermo-poroelasticity for saturated porous media is first recalled.The framework for modeling initiation and evolution of induced cracks is then presented by using a variational approach of phase-field method.A specific model with two crack phase fields and considering thermo-hydromechanical interaction is proposed.A particular attention is paid on the description of shear cracks.The proposed model is implemented in the standard finite element method.An example of application is finally presented on the analysis of thermo-hydromechanical responses and cracked zones evolution around a typical disposal repository in the context of French concept for high level waste disposal.
基金the Natural Science Foundation of China(11922415,12274471)Guangdong Basic and Applied Basic Research Foundation(2022A1515011168,2019A1515011718,2019A1515011337)the Key Research and Development Program of Guangdong Province,China(2019B110209003).
文摘We report a novel double-shelled nanoboxes photocatalyst architecture with tailored interfaces that accelerate quantum efficiency for photocatalytic CO_(2) reduction reaction(CO_(2)RR)via Mo–S bridging bonds sites in S_(v)–In_(2)S_(3)@2H–MoTe_(2).The X-ray absorption near-edge structure shows that the formation of S_(v)–In_(2)S_(3)@2H–MoTe_(2) adjusts the coordination environment via interface engineering and forms Mo–S polarized sites at the interface.The interfacial dynamics and catalytic behavior are clearly revealed by ultrafast femtosecond transient absorption,time-resolved,and in situ diffuse reflectance–Infrared Fourier transform spectroscopy.A tunable electronic structure through steric interaction of Mo–S bridging bonds induces a 1.7-fold enhancement in S_(v)–In_(2)S_(3)@2H–MoTe_(2)(5)photogenerated carrier concentration relative to pristine S_(v)–In_(2)S_(3).Benefiting from lower carrier transport activation energy,an internal quantum efficiency of 94.01%at 380 nm was used for photocatalytic CO_(2)RR.This study proposes a new strategy to design photocatalyst through bridging sites to adjust the selectivity of photocatalytic CO_(2)RR.
文摘Rotaviruses are non-enveloped double-stranded RNA virus that causes acute diarrheal diseases in children(<5 years).More than 90%of the global rotavirus infection in humans was caused by Rotavirus group A.Rotavirus infection has caused more than 200000 deaths annually and predominantly occurs in the low-income countries.Rotavirus evolution is indicated by the strain dynamics or the emergence of the unprecedented strain.The major factors that drive the rotavirus evolution include the genetic shift that is caused by the reassortment mechanism,either in the intra-or the inter-genogroup.However,other factors are also known to have an impact on rotavirus evolution.This review discusses the structure and types,epidemiology,and evolution of rotaviruses.This article also reviews other supplemental factors of rotavirus evolution,such as genetic reassortment,mutation rate,glycan specificity,vaccine introduction,the host immune respo-nses,and antiviral drugs.
文摘Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation.In addition,the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy.Nevertheless,due to the lower complexity of the fusion algorithm,the efficiency of fusion methods is high in practical applications.The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information,edge contour,and overall contrast.
文摘The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.
