Reliable and safe operation of batteries is increasingly challenged by diverse operating conditions and stringent demands for system resilience.Artificial intelligence(AI)has emerged as a transformative enabler of bat...Reliable and safe operation of batteries is increasingly challenged by diverse operating conditions and stringent demands for system resilience.Artificial intelligence(AI)has emerged as a transformative enabler of battery health management,offering capabilities beyond traditional models.This review provides a structured synthesis of recent progress in AI-enabled diagnostics.Advances in state estimationincluding state of health(SOH)and remaining useful life(RUL)-are first examined,with methodological breakthroughs identified across diverse task formulations.The evolution of AI architectures is then traced,from conventional neural networks to attention-based Transformers,physics-informed models,and federated learning,with particular attention to emerging paradigms such as foundation models,neuro-symbolic reasoning,and quantum machine learning that promise improved robustness and interpretability.To bridge laboratory innovation with deployment,a domain-adaptive four-stage data pipeline has emerged as a promising framework for real-world BMS signals-spanning operational segmentation,multi-scale denoising,degradation-aware feature engineering,and structured sample construction-designed to enhance generalization under heterogeneous and noisy conditions.Looking forward,a technological roadmap is outlined that integrates edge AI,digital twins,AIOps,quantum computing,wireless sensing,and self-repair systems.Collectively,these innovations transform batteries from passive energy reservoirs into intelligent cyber-physical agents endowed with perception,autonomous decision-making,and resilient fault response-paving the way toward truly battery-centric autonomous energy systems.展开更多
Heavy metal contamination is a global issue caused by anthropogenic activities leading to severe negative effects on the environment and human health.To address this problem,bioremediation strategies utilizing plants ...Heavy metal contamination is a global issue caused by anthropogenic activities leading to severe negative effects on the environment and human health.To address this problem,bioremediation strategies utilizing plants such as Typha latifolia and their symbiotic fungi have been adopted to remediate contaminated areas and mitigate the harmful effects of these pollutants.In this study,the endophytic fungus Neosartorya fischeri was isolated from the roots of T.latifolia plants growing in heavy metal-contaminated sites.N.fischeri colonized the epidermis and root cortex and showed high tolerance to toxic concentrations of silver(Ag)(1 mg/kg),copper(Cu)(60 mg/kg)and cadmium(Cd)(8 mg/kg).N.fischeri removed 8.7%±0.5%Cd from the medium,biosorbed 15.24±0.2 mg/kg into its biomass,and enhanced the tolerance and bioaccumulation of Cd(184.18±1.14 mg/kg)in plant roots.Moreover,N.fischeri produces siderophores,volatile compounds and solubilizes phosphates,which improve plant fitness.This was evidenced by a 28%increase in photosynthetic pigments in T.latifolia plants colonized with N.fischeri.Additionally,N.fischeri inhibits the growth of important phytopathogens from the Fusarium genus.These findings highlight the important role of N.fischeri in enhancing the fitness and resilience of T.latifolia in hostile environments,demonstrating the potential of N.fischeri-T.latifolia association for the bioremediation of contaminated sites.展开更多
Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their...Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.展开更多
Federated learning is a decentralized model training paradigm with significant potential.However,the quality of Federated Network’s client updates can vary due to non-IID data distributions,leading to suboptimal glob...Federated learning is a decentralized model training paradigm with significant potential.However,the quality of Federated Network’s client updates can vary due to non-IID data distributions,leading to suboptimal global models.To address this issue,we propose a novel client selection strategy called FedPA(Performance-Based Federated Averaging).This proposed model selectively aggregates client updates based on a predefined performance threshold.Only clients whose local models achieve an F1 score of 70%or higher after training are included in the aggregation process.Clients below this threshold receive the updated global model but do not contribute their parameters.In this way,the low-performance clients are still in the process of learning and,after some rounds,will be able to contribute.If no client meets the performance threshold in a given round,the system falls back to standard FedAvg aggregation.This ensures the global model continues to improve even when most clients perform poorly.We evaluate FedPA on a subset of the MURA dataset for abnormality detection in radiographs of four bone types.Compared to baseline federated learning algorithms such as Federated Averaging(FedAvg),Federated Proximal(FedProx),Federated Stochastic Gradient Descent(FedSGD),and Federated Batch Normalization(FedBN),FedPA consistently ranks first or second across key performance metrics,particularly in accuracy,F1 score,and recall.Moreover,FedPA demonstrates notable efficiency,achieving the lowest average round time(≈2270 s)and minimal memory usage(≈645.58 MB),all without relying on GPU resources.These results highlight FedPA’s effectiveness in improving global model quality while reducing computational overhead,positioning it as a promising approach for real-world federated learning applications in resource-constrained environments.展开更多
I applaud Adekeye AP and the journal for publishing‘Smoking of Carica papaya in Nigeria:The rationale,the public health effects and policies for intervention’.It is important to draw attention to newly recognised us...I applaud Adekeye AP and the journal for publishing‘Smoking of Carica papaya in Nigeria:The rationale,the public health effects and policies for intervention’.It is important to draw attention to newly recognised uses of substances which might alter perception,mood,and/or behaviour.展开更多
The introduction of the digital renminbi(eCNY)by the People’s Bank of China serves as a means for the central bank to effectively comprehend macroeconomic dynamics and enhance payment infrastructure within the domest...The introduction of the digital renminbi(eCNY)by the People’s Bank of China serves as a means for the central bank to effectively comprehend macroeconomic dynamics and enhance payment infrastructure within the domestic market.Among the pioneering digital currencies,the eCNY is at the forefront of technological research and development,pilot implementation,and the establishment of a robust system.Thus,employing the unified theory of acceptance and use of technology,this study aims to explore the factors shaping the adoption of the eCNY and to determine the mediating effects of intention toward the eCNY and the moderating role of age and gender among various relationships.A cross-sectional survey methodology was deployed to collect data from pilot communities situated within the Yangtze River Delta,Pearl River Delta,and Beijing–Tianjin–Hebei regions.The empirical analysis comprised 809 valid online questionnaires,and the examination was conducted through structural equation modeling employing the partial least squares technique,ultimately subjecting the conceptual model to a comprehensive assessment.The results for intention to use the eCNY indicate that performance expectancy,effort expectancy,social influence,and perceived government policy have significant effects.Facilitating conditions and intentions toward the eCNY positively influenced its actual use.According to the findings of this study,age and sex did not moderate the effect of each hypothesis on the intention to use in the research model.This study breaks new ground by investigating the adoption of the eCNY,a novel form of currency,highlighting its multifaceted nature and providing empirical evidence for a comprehensive model encompassing psychological,social,and contextual factors.This study employs social surveys to identify obstacles in the process of promoting the widespread adoption of the eCNY and offers suggestions to the central bank and government to increase user enthusiasm and decrease user perceptions of risk,thereby promoting its widespread adoption.展开更多
Objective:This study aims to determine the effectiveness of giving a combination of Fe tablets and beetroot juice in increasing hemoglobin(Hb)levels of pregnant women with anemia in the Mataram City area.Methods:This ...Objective:This study aims to determine the effectiveness of giving a combination of Fe tablets and beetroot juice in increasing hemoglobin(Hb)levels of pregnant women with anemia in the Mataram City area.Methods:This study was designed with quasi-experimental design with pre-test and post-test with control design.The location of this study was conducted in the city of Mataram on pregnant women with anemia.The sample of this study was pregnant women with mild anemia based on inclusion and exclusion criteria,divided into 2 groups:a control group and a treatment group of 15 respondents each,bringing the total respondents to 30 people.Analysis of Hb level measurement results was carried out using the independent sample t-test.Results:The results obtained in the treatment group(combination of beet juice and Fe tablets)were the mean pre-test of 9.93 mg/dL and post-test of 11.90 mg/dL(P-value=0.000),which means there is effectiveness in increasing hemoglobin levels while in the control group.Comparison of increased Hb levels of the control group and significantly different treatments marked by a P value of 0.001.Conclusions:the combination of Fe tablets and beetroot juice is effective in increasing Hb levels of pregnant women with anemia in the Mataram City area.展开更多
Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top caus...Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.展开更多
Ornithine transcarbamylase （OTC） deficiency is an X-linked trait that accounts for nearly half of all inherited disorders of the urea cycle. OTC is one of the enzymes common to both the urea cycle and the bacterial ...Ornithine transcarbamylase （OTC） deficiency is an X-linked trait that accounts for nearly half of all inherited disorders of the urea cycle. OTC is one of the enzymes common to both the urea cycle and the bacterial arginine biosynthesis pathway; however, the role of OTC has changed over evolution. For animals with a urea cycle, defects in OTC can trigger hyperammonemic episodes that can lead to brain damage and death. This is the fifth mutation update for human OTC with previous updates reported in 1993, 1995, 2002, and 2006. In the 2006 update, 341 mutations were reported. This current update contains 417 disease-causing mutations, and also is the first report of this series to incorporate information about natural variation of the OTC gene in the general population through examination of publicly available genomic data and examination of phenotype/genotype correlations from patients participating in the Urea Cycle Disorders Consortium Longitudinal Study and the first to evaluate the suitability of systematic computational approaches to predict severity of disease associated with different types of OTC mutations.展开更多
Chronic infection with the hepatitis B virus(HBV) is the leading risk factor for the development of hepatocellular carcinoma(HCC). With nearly 750000 deaths yearly, hepatocellular carcinoma is the second highest cause...Chronic infection with the hepatitis B virus(HBV) is the leading risk factor for the development of hepatocellular carcinoma(HCC). With nearly 750000 deaths yearly, hepatocellular carcinoma is the second highest cause of cancer-related death in the world. Unfortunately, the molecular mechanisms that contribute to the development of HBV-associated HCC remain incompletely understood. Recently, micro RNAs(mi RNAs), a family of small non-coding RNAs that play a role primarily in post-transcriptional gene regulation, have been recognized as important regulators of cellular homeostasis, and altered regulation of mi RNA expression has been suggested to play a significant role in virus-associated diseases and the development of many cancers. With this in mind, many groups have begun to investigate the relationship between mi RNAs and HBV replication and HBV-associated disease. Multiple findings suggest that some mi RNAs, such as mi R-122, and mi R-125 and mi R-199 family members, are playing a role in HBV replication and HBV-associated disease, including the development of HBV-associated HCC. In this review, we discuss the current state of our understanding of the relationship between HBV and mi RNAs, including how HBV affects cellular mi RNAs, how these mi RNAs impact HBV replication, and the relationship between HBV-mediated mi RNA regulation and HCC development. We also address the impact of challenges in studying HBV, such as the lack of an effective model system for infectivity and a reliance on transformed cell lines, on our understanding of the relationship between HBV and mi RNAs, and proposepotential applications of mi RNA-related techniques that could enhance our understanding of the role mi RNAs play in HBV replication and HBV-associated disease, ultimately leading to new therapeutic options and improved patient outcomes.展开更多
In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medica...In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.展开更多
In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new tes...In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans.展开更多
Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 fra...Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity.展开更多
In this paper,we provide a new approach to data encryption using generalized inverses.Encryption is based on the implementation of weighted Moore–Penrose inverse A y MNenxmT over the nx8 constant matrix.The square He...In this paper,we provide a new approach to data encryption using generalized inverses.Encryption is based on the implementation of weighted Moore–Penrose inverse A y MNenxmT over the nx8 constant matrix.The square Hermitian positive definite matrix N8x8 p is the key.The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge.We have provided NIST(National Institute of Standards and Technology)quality assurance tests for a random generated Hermitian matrix(a total of 10 different tests and additional analysis with approximate entropy and random digression).In the additional testing of the quality of the random matrix generated,we can conclude that the results of our analysis satisfy the defined strict requirements.This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications.In the experimental part of this paper,we give a comparison of encryption methods between machine learning methods.Machine learning algorithms could be compared by achieved results of classification concentrating on classes.In a comparative analysis,we give results of classifying of advanced encryption standard(AES)algorithm and proposed encryption method based on Moore–Penrose inverse.展开更多
Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research metho...Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.展开更多
Current research focussed on the assessment of metal machining process parameters and on the development of adaptive control, shows that machine performance, work-piece and tool material selections, tool life, quality...Current research focussed on the assessment of metal machining process parameters and on the development of adaptive control, shows that machine performance, work-piece and tool material selections, tool life, quality of machined surfaces, the geometry of cutting tool edges, and cutting conditions are closely related to the cutting forces. This information is of great interest to cutting tool manufactures and users alike. Over the years there have been significant developments and improvements in the equipment used to monitor such forces. In 1930 mechanical gauges were replaced by resistance strain gauges, and some 30 years later compact air gauge dynamometers were invented. Since this time intensive research has continued being directed to- wards developing new approaches to cutting force measurement. The Kistler Company, well-known manufacturer of acceleration and piezoelectrical dynamometers, has worked in this field for more than three decades, and developed very sensitive devices. While leading manufacturing research laboratories are often equipped with this technology, classical electrical strain gauges and other dynamometers of individual designs are still commonly used in industry. The present paper presents data obtained using different techniques of force measurement in metal machining processes. In particular, areas of uncertainties, illustrated through results concerning the turning process, are analysed, leading to an appraisal of the current status of these measurements and their significance.展开更多
Background: The department of defense's field manual(FM) 3-11 is among the military's field manuals for preparing for, reacting to and recovering from chemical, biological, radiological and nuclear attacks. Si...Background: The department of defense's field manual(FM) 3-11 is among the military's field manuals for preparing for, reacting to and recovering from chemical, biological, radiological and nuclear attacks. Since post 9-11, U.S. military service members have been deployed in the global war on terrorism. This study attempted to determine the effectiveness of the FM 3-11 in detecting, deterring or preventing a human-borne with bioagent(HBBA) terrorist breach at an entry control point(ECP).Methods: This time-specific, cross-sectional study disseminated a validated survey tool with Cronbach's α>0.82 to respondents who have had antiterrorism training and combat ECP experience. The return rate was greater than 75.0%; however, many of the respondents failed to meet the inclusion criteria. Consequently, only 26 questionnaires were included in the sample.Results: The results revealed that while over 60.0% of the respondents either strongly agreed or agreed that biointelligence, the deployment of biodetectors and the use of biowarning systems could be effective in preventing an ECP breach by a terrorist with a bioagent, the use of protective equipment and immunization to decontaminate service members or other tactics, techniques and procedures(TTPs) would never prevent a breach. A large percentage of respondents claimed that soldiers at the ECP lacked the devices or the knowledge to detect an HBBA at an ECP, and 72.0% suggested modifying current ECP TTPs to include education, training and equipment for security personnel at military base ECPs.Conclusion: If obtained from appropriate sources and communicated to the personnel at the ECP in an effective or timely manner, the possible effectiveness of certain TTPs in the FM 3-11, specifically FM 3-11.86(intelligence), might increase.展开更多
The surface quality of solid wood is very important for its effective response in manufacturing processes. The effects of feed rate, cutting depth and rake angle on surface roughness and power consumption were investi...The surface quality of solid wood is very important for its effective response in manufacturing processes. The effects of feed rate, cutting depth and rake angle on surface roughness and power consumption were investigated and modeled. Neuro-fuzzy methodology was applied and shown that it could be useful, reliable and an effective tool for modeling the surface roughness of wood.Thus, the results of the present research can be successfully applied in the wood industry to reduce time, energy and high experimental costs.展开更多
Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest ...Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest and buying potentials resulted in a number of forgeries manufactured and introduced to the open antique market.The items such as early silver candlesticks dictate a very high price,for many high quality fakes show very good appearances and matching similarities with originals.Such copies are traditionally manufactured by casting using the original items as patterns.Small details and variances in design features,position and shape of hallmarks,including the final surface quality are usual features to distinguish the fakes from the originals.This paper presents results of a study conducted on several silver candlesticks,including two artefacts bearing features of those produced in the mid 18th century,one original Italian candelabrum from Fascist era,and small candlesticks made in the early 20th century.Also,the paper presents some interesting contemporary coins-replicas of many those produced in different countries.The coins were offered for sale by unscrupulous dealers via auctions and e-bays.Finally the main results and findings from this study are discussed from a manufacturing point of view,such as fabrication technology,surface quality and hallmarks,which will help the collectors,dealers and investors to detect and avoid forgeries.展开更多
基金funded by the Independent Innovation Projects of the Hubei Longzhong Laboratory(2022ZZ-24)。
文摘Reliable and safe operation of batteries is increasingly challenged by diverse operating conditions and stringent demands for system resilience.Artificial intelligence(AI)has emerged as a transformative enabler of battery health management,offering capabilities beyond traditional models.This review provides a structured synthesis of recent progress in AI-enabled diagnostics.Advances in state estimationincluding state of health(SOH)and remaining useful life(RUL)-are first examined,with methodological breakthroughs identified across diverse task formulations.The evolution of AI architectures is then traced,from conventional neural networks to attention-based Transformers,physics-informed models,and federated learning,with particular attention to emerging paradigms such as foundation models,neuro-symbolic reasoning,and quantum machine learning that promise improved robustness and interpretability.To bridge laboratory innovation with deployment,a domain-adaptive four-stage data pipeline has emerged as a promising framework for real-world BMS signals-spanning operational segmentation,multi-scale denoising,degradation-aware feature engineering,and structured sample construction-designed to enhance generalization under heterogeneous and noisy conditions.Looking forward,a technological roadmap is outlined that integrates edge AI,digital twins,AIOps,quantum computing,wireless sensing,and self-repair systems.Collectively,these innovations transform batteries from passive energy reservoirs into intelligent cyber-physical agents endowed with perception,autonomous decision-making,and resilient fault response-paving the way toward truly battery-centric autonomous energy systems.
文摘Heavy metal contamination is a global issue caused by anthropogenic activities leading to severe negative effects on the environment and human health.To address this problem,bioremediation strategies utilizing plants such as Typha latifolia and their symbiotic fungi have been adopted to remediate contaminated areas and mitigate the harmful effects of these pollutants.In this study,the endophytic fungus Neosartorya fischeri was isolated from the roots of T.latifolia plants growing in heavy metal-contaminated sites.N.fischeri colonized the epidermis and root cortex and showed high tolerance to toxic concentrations of silver(Ag)(1 mg/kg),copper(Cu)(60 mg/kg)and cadmium(Cd)(8 mg/kg).N.fischeri removed 8.7%±0.5%Cd from the medium,biosorbed 15.24±0.2 mg/kg into its biomass,and enhanced the tolerance and bioaccumulation of Cd(184.18±1.14 mg/kg)in plant roots.Moreover,N.fischeri produces siderophores,volatile compounds and solubilizes phosphates,which improve plant fitness.This was evidenced by a 28%increase in photosynthetic pigments in T.latifolia plants colonized with N.fischeri.Additionally,N.fischeri inhibits the growth of important phytopathogens from the Fusarium genus.These findings highlight the important role of N.fischeri in enhancing the fitness and resilience of T.latifolia in hostile environments,demonstrating the potential of N.fischeri-T.latifolia association for the bioremediation of contaminated sites.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208424,52208416,52078091,and 52108399)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102).
文摘Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.
文摘Federated learning is a decentralized model training paradigm with significant potential.However,the quality of Federated Network’s client updates can vary due to non-IID data distributions,leading to suboptimal global models.To address this issue,we propose a novel client selection strategy called FedPA(Performance-Based Federated Averaging).This proposed model selectively aggregates client updates based on a predefined performance threshold.Only clients whose local models achieve an F1 score of 70%or higher after training are included in the aggregation process.Clients below this threshold receive the updated global model but do not contribute their parameters.In this way,the low-performance clients are still in the process of learning and,after some rounds,will be able to contribute.If no client meets the performance threshold in a given round,the system falls back to standard FedAvg aggregation.This ensures the global model continues to improve even when most clients perform poorly.We evaluate FedPA on a subset of the MURA dataset for abnormality detection in radiographs of four bone types.Compared to baseline federated learning algorithms such as Federated Averaging(FedAvg),Federated Proximal(FedProx),Federated Stochastic Gradient Descent(FedSGD),and Federated Batch Normalization(FedBN),FedPA consistently ranks first or second across key performance metrics,particularly in accuracy,F1 score,and recall.Moreover,FedPA demonstrates notable efficiency,achieving the lowest average round time(≈2270 s)and minimal memory usage(≈645.58 MB),all without relying on GPU resources.These results highlight FedPA’s effectiveness in improving global model quality while reducing computational overhead,positioning it as a promising approach for real-world federated learning applications in resource-constrained environments.
文摘I applaud Adekeye AP and the journal for publishing‘Smoking of Carica papaya in Nigeria:The rationale,the public health effects and policies for intervention’.It is important to draw attention to newly recognised uses of substances which might alter perception,mood,and/or behaviour.
基金funded by Jiangsu Education Department(Ref.No.2022SJYB0750).
文摘The introduction of the digital renminbi(eCNY)by the People’s Bank of China serves as a means for the central bank to effectively comprehend macroeconomic dynamics and enhance payment infrastructure within the domestic market.Among the pioneering digital currencies,the eCNY is at the forefront of technological research and development,pilot implementation,and the establishment of a robust system.Thus,employing the unified theory of acceptance and use of technology,this study aims to explore the factors shaping the adoption of the eCNY and to determine the mediating effects of intention toward the eCNY and the moderating role of age and gender among various relationships.A cross-sectional survey methodology was deployed to collect data from pilot communities situated within the Yangtze River Delta,Pearl River Delta,and Beijing–Tianjin–Hebei regions.The empirical analysis comprised 809 valid online questionnaires,and the examination was conducted through structural equation modeling employing the partial least squares technique,ultimately subjecting the conceptual model to a comprehensive assessment.The results for intention to use the eCNY indicate that performance expectancy,effort expectancy,social influence,and perceived government policy have significant effects.Facilitating conditions and intentions toward the eCNY positively influenced its actual use.According to the findings of this study,age and sex did not moderate the effect of each hypothesis on the intention to use in the research model.This study breaks new ground by investigating the adoption of the eCNY,a novel form of currency,highlighting its multifaceted nature and providing empirical evidence for a comprehensive model encompassing psychological,social,and contextual factors.This study employs social surveys to identify obstacles in the process of promoting the widespread adoption of the eCNY and offers suggestions to the central bank and government to increase user enthusiasm and decrease user perceptions of risk,thereby promoting its widespread adoption.
基金supported by Muhammadiyah Research Grant (RisetMu) Batch Ⅵ (No. 1687.186/PD/I.3/D/2022)
文摘Objective:This study aims to determine the effectiveness of giving a combination of Fe tablets and beetroot juice in increasing hemoglobin(Hb)levels of pregnant women with anemia in the Mataram City area.Methods:This study was designed with quasi-experimental design with pre-test and post-test with control design.The location of this study was conducted in the city of Mataram on pregnant women with anemia.The sample of this study was pregnant women with mild anemia based on inclusion and exclusion criteria,divided into 2 groups:a control group and a treatment group of 15 respondents each,bringing the total respondents to 30 people.Analysis of Hb level measurement results was carried out using the independent sample t-test.Results:The results obtained in the treatment group(combination of beet juice and Fe tablets)were the mean pre-test of 9.93 mg/dL and post-test of 11.90 mg/dL(P-value=0.000),which means there is effectiveness in increasing hemoglobin levels while in the control group.Comparison of increased Hb levels of the control group and significantly different treatments marked by a P value of 0.001.Conclusions:the combination of Fe tablets and beetroot juice is effective in increasing Hb levels of pregnant women with anemia in the Mataram City area.
文摘Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.
基金the support of the Kettering Family FoundationThe Urea Cycle Disorders Consortium (U54HD061221) is a part of the National Institutes of Health (NIH) Rare Disease Clinical Research Network (RDCRN)+3 种基金supported through collaboration between the Office of Rare Diseases Research (ORDR)the National Center for Advancing Translational Science (NCATS)the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)The Urea Cycle Disorders Consortium is also supported by the O’Malley Foundation, the Rotenberg Family Fund, the Dietmar-Hopp Foundation, and the Kettering Fund
文摘Ornithine transcarbamylase （OTC） deficiency is an X-linked trait that accounts for nearly half of all inherited disorders of the urea cycle. OTC is one of the enzymes common to both the urea cycle and the bacterial arginine biosynthesis pathway; however, the role of OTC has changed over evolution. For animals with a urea cycle, defects in OTC can trigger hyperammonemic episodes that can lead to brain damage and death. This is the fifth mutation update for human OTC with previous updates reported in 1993, 1995, 2002, and 2006. In the 2006 update, 341 mutations were reported. This current update contains 417 disease-causing mutations, and also is the first report of this series to incorporate information about natural variation of the OTC gene in the general population through examination of publicly available genomic data and examination of phenotype/genotype correlations from patients participating in the Urea Cycle Disorders Consortium Longitudinal Study and the first to evaluate the suitability of systematic computational approaches to predict severity of disease associated with different types of OTC mutations.
基金Supported by Pennsylvania state CURE grant,No.4100057658,[to Steel LF and Bouchard MJ(partially)]a Ruth L Kirschstein(F31)Predoctoral Fellowship,No.5F31CA171712-03,[to Lamontagne J(partially)]
文摘Chronic infection with the hepatitis B virus(HBV) is the leading risk factor for the development of hepatocellular carcinoma(HCC). With nearly 750000 deaths yearly, hepatocellular carcinoma is the second highest cause of cancer-related death in the world. Unfortunately, the molecular mechanisms that contribute to the development of HBV-associated HCC remain incompletely understood. Recently, micro RNAs(mi RNAs), a family of small non-coding RNAs that play a role primarily in post-transcriptional gene regulation, have been recognized as important regulators of cellular homeostasis, and altered regulation of mi RNA expression has been suggested to play a significant role in virus-associated diseases and the development of many cancers. With this in mind, many groups have begun to investigate the relationship between mi RNAs and HBV replication and HBV-associated disease. Multiple findings suggest that some mi RNAs, such as mi R-122, and mi R-125 and mi R-199 family members, are playing a role in HBV replication and HBV-associated disease, including the development of HBV-associated HCC. In this review, we discuss the current state of our understanding of the relationship between HBV and mi RNAs, including how HBV affects cellular mi RNAs, how these mi RNAs impact HBV replication, and the relationship between HBV-mediated mi RNA regulation and HCC development. We also address the impact of challenges in studying HBV, such as the lack of an effective model system for infectivity and a reliance on transformed cell lines, on our understanding of the relationship between HBV and mi RNAs, and proposepotential applications of mi RNA-related techniques that could enhance our understanding of the role mi RNAs play in HBV replication and HBV-associated disease, ultimately leading to new therapeutic options and improved patient outcomes.
文摘In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.
基金Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity.
基金the support of Network Communication Technology(NCT)Research Groups,FTSM,UKM in providing facilities for this research.This paper is supported under the Dana Impak Perdana UKM DIP-2018-040 and Fundamental Research Grant Scheme FRGS/1/2018/TK04/UKM/02/7.
文摘In this paper,we provide a new approach to data encryption using generalized inverses.Encryption is based on the implementation of weighted Moore–Penrose inverse A y MNenxmT over the nx8 constant matrix.The square Hermitian positive definite matrix N8x8 p is the key.The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge.We have provided NIST(National Institute of Standards and Technology)quality assurance tests for a random generated Hermitian matrix(a total of 10 different tests and additional analysis with approximate entropy and random digression).In the additional testing of the quality of the random matrix generated,we can conclude that the results of our analysis satisfy the defined strict requirements.This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications.In the experimental part of this paper,we give a comparison of encryption methods between machine learning methods.Machine learning algorithms could be compared by achieved results of classification concentrating on classes.In a comparative analysis,we give results of classifying of advanced encryption standard(AES)algorithm and proposed encryption method based on Moore–Penrose inverse.
文摘Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.
基金Project supported by the Postgraduate Award of University of SouthAustralia, Australia
文摘Current research focussed on the assessment of metal machining process parameters and on the development of adaptive control, shows that machine performance, work-piece and tool material selections, tool life, quality of machined surfaces, the geometry of cutting tool edges, and cutting conditions are closely related to the cutting forces. This information is of great interest to cutting tool manufactures and users alike. Over the years there have been significant developments and improvements in the equipment used to monitor such forces. In 1930 mechanical gauges were replaced by resistance strain gauges, and some 30 years later compact air gauge dynamometers were invented. Since this time intensive research has continued being directed to- wards developing new approaches to cutting force measurement. The Kistler Company, well-known manufacturer of acceleration and piezoelectrical dynamometers, has worked in this field for more than three decades, and developed very sensitive devices. While leading manufacturing research laboratories are often equipped with this technology, classical electrical strain gauges and other dynamometers of individual designs are still commonly used in industry. The present paper presents data obtained using different techniques of force measurement in metal machining processes. In particular, areas of uncertainties, illustrated through results concerning the turning process, are analysed, leading to an appraisal of the current status of these measurements and their significance.
文摘Background: The department of defense's field manual(FM) 3-11 is among the military's field manuals for preparing for, reacting to and recovering from chemical, biological, radiological and nuclear attacks. Since post 9-11, U.S. military service members have been deployed in the global war on terrorism. This study attempted to determine the effectiveness of the FM 3-11 in detecting, deterring or preventing a human-borne with bioagent(HBBA) terrorist breach at an entry control point(ECP).Methods: This time-specific, cross-sectional study disseminated a validated survey tool with Cronbach's α>0.82 to respondents who have had antiterrorism training and combat ECP experience. The return rate was greater than 75.0%; however, many of the respondents failed to meet the inclusion criteria. Consequently, only 26 questionnaires were included in the sample.Results: The results revealed that while over 60.0% of the respondents either strongly agreed or agreed that biointelligence, the deployment of biodetectors and the use of biowarning systems could be effective in preventing an ECP breach by a terrorist with a bioagent, the use of protective equipment and immunization to decontaminate service members or other tactics, techniques and procedures(TTPs) would never prevent a breach. A large percentage of respondents claimed that soldiers at the ECP lacked the devices or the knowledge to detect an HBBA at an ECP, and 72.0% suggested modifying current ECP TTPs to include education, training and equipment for security personnel at military base ECPs.Conclusion: If obtained from appropriate sources and communicated to the personnel at the ECP in an effective or timely manner, the possible effectiveness of certain TTPs in the FM 3-11, specifically FM 3-11.86(intelligence), might increase.
文摘The surface quality of solid wood is very important for its effective response in manufacturing processes. The effects of feed rate, cutting depth and rake angle on surface roughness and power consumption were investigated and modeled. Neuro-fuzzy methodology was applied and shown that it could be useful, reliable and an effective tool for modeling the surface roughness of wood.Thus, the results of the present research can be successfully applied in the wood industry to reduce time, energy and high experimental costs.
文摘Collecting silver artefacts has traditionally been a very popular hobby.Silver is addictive,therefore the number of potential collectors and investors appears to grow each year.Unfortunately,increases in the interest and buying potentials resulted in a number of forgeries manufactured and introduced to the open antique market.The items such as early silver candlesticks dictate a very high price,for many high quality fakes show very good appearances and matching similarities with originals.Such copies are traditionally manufactured by casting using the original items as patterns.Small details and variances in design features,position and shape of hallmarks,including the final surface quality are usual features to distinguish the fakes from the originals.This paper presents results of a study conducted on several silver candlesticks,including two artefacts bearing features of those produced in the mid 18th century,one original Italian candelabrum from Fascist era,and small candlesticks made in the early 20th century.Also,the paper presents some interesting contemporary coins-replicas of many those produced in different countries.The coins were offered for sale by unscrupulous dealers via auctions and e-bays.Finally the main results and findings from this study are discussed from a manufacturing point of view,such as fabrication technology,surface quality and hallmarks,which will help the collectors,dealers and investors to detect and avoid forgeries.
