Seed priming is an effective seed pretreatment technology that enhances germination and overall crop performance by optimizing seed hydration and metabolic processes before planting.Seed quality is a critical determin...Seed priming is an effective seed pretreatment technology that enhances germination and overall crop performance by optimizing seed hydration and metabolic processes before planting.Seed quality is a critical determinant of cotton(Gossypium hirsutum)crop performance,influencing germination,plant vigor,and yield.This study evaluates the effects of seed priming with potassium salts(1%and 2%KCl and K2SO4)on germination,morphological traits,and Cry1Ac gene expression in three Bt cotton cultivars(IUB-2013,NIAB-878B,FH-142)as Cry1Ac enhance the pest resistance in Bt cotton and reduce the plant’s dependence on chemical insecticides.Seeds were primed for six hours,air-dried,and sown in the field.Germination rates,plant height,number of bolls per plant,boll weight,seed cotton yield,and ginning outturn(GOT)were assessed at crop maturity.Cry1Ac gene expression was quantified to explore the influence of priming treatments on transgene activity.Results demonstrated that 1%K2SO4 priming significantly enhanced germination and yield-related traits,with Cry1Ac expression peaking in the IUB-2013 cultivar under 1%K2SO4 treatment.These findings suggest that potassium-based halopriming improves cotton seedling establishment and Bt gene expression.This study addresses the critical gaps in understanding the effects of seed halopriming on morphological traits,germination,and expression of the Cry1Ac gene in Bt cotton while providing a novel eco-friendly and cost-effective halopriming approach,offering the potential to improve cotton production.展开更多
In recent years,photo-powered energy storage devices have attracted considerable research attention due to their potential applications in smart electronics.In this review,we present a comprehensive summary of recent ...In recent years,photo-powered energy storage devices have attracted considerable research attention due to their potential applications in smart electronics.In this review,we present a comprehensive summary of recent developments in two distinct but highly promising energy storage technologies,photo-assisted metal-air batteries and photo-supercapacitors.The section on metal-air batteries primarily describes the electrochemical performance of Zn-air and Li-air systems,innovative photo-electrode designs,and mechanisms that enhance oxygen evolution and reduction reactions.A brief discussion is also provided of other metal-air systems,including Mg,Fe,and Al.In contrast,the section on photo-supercapacitors explores recent advancements in light-driven charge storage,electrode materials,and device architectures,presenting a comparative performance analysis of materials such as metal oxides,sulfides,and perovskites.Various critical challenges,including material stability,efficiency under varying light conditions,and scalability,are also thoroughly examined.Despite their different working principles,both technologies hold great potential to increase energy efficiency and sustainability through the use of photo-assisted processes.The purpose of this review is to bridge existing knowledge gaps and propose future directions for research in these emerging fields.展开更多
Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent he...Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction.展开更多
Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’...Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s edge.However,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models challenging.Additionally,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors.These factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these topics.Consequently,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using TinyML.It identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software frameworks.In the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning strategies.In the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major sectors.As a result,this article provides valuable insights for researchers,practitioners,and developers.It assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.展开更多
Perovskite oxides have shown great potential application in fuel cells due to the unique crystal structures and tunable composition as well as effective capability toward the oxygen reduction reaction(ORR),whereas the...Perovskite oxides have shown great potential application in fuel cells due to the unique crystal structures and tunable composition as well as effective capability toward the oxygen reduction reaction(ORR),whereas the investigation on the electrocatalytic performance of perovskite oxides toward the two-electron ORR to H_(2)O_(2)production remains very limited.Herein,a facile synthetic method has been developed to prepare La_(2)Sn_(2)O_(7)@La-doped ZnSnO_(3)heterostructures comprising of amorphous La_(2)Sn_(2)O_(7)and crystalline La-doped ZnSnO_(3).The optimal La_(2)Sn_(2)O_(7)@Ladoped ZnSnO_(3)heterostructures catalyst exhibits a significantly improved two-electron ORR performance to H_(2)O_(2)production with onset potential of 0.77 V and large current density of 2.51 m A.cm^(-2)at 0.1 V compared to ZnSnO_(3)(0.75 V,1.80 m A.cm^(-2),0.11 m A) as well as maintains high H_(2)O_(2)selectivity of 80%,which has been theoretically demonstrated to be contributed to the synergistic effect of amorphous La_(2)Sn_(2)O_(7)and crystalline La-doped ZnSnO_(3).Moreover,high H_(2)O_(2)yield rate of 2.9 m M.h^(-1)at 0.1 V can be achieved with a superior turnover frequency(TOF) of3.31 × 10^(-2)s^(-1)compared to the ZnSnO_(3)catalyst(2.10 × 10^(-2)s^(-1)).This work reveals the great potential of perovskite oxide as promising candidates for the environmentally friendly synthesis of hydrogen peroxide.展开更多
The second wave of COVID-19 pandemic has started globally, right now 220 countries are infected and a total of 71,351,695 confirmed cases and 1,612,372 deaths due to COVID-19 have been reported. Infection Prevention a...The second wave of COVID-19 pandemic has started globally, right now 220 countries are infected and a total of 71,351,695 confirmed cases and 1,612,372 deaths due to COVID-19 have been reported. Infection Prevention and Control (IPC) measures for COVID-19 all have proved vital in decreasing the transmission rates among the communities. <strong>Methodology:</strong> Unmatched Case-Control Study was conducted where cases were defined as “every PCR positive contact (symptomatic or asymptomatic) for any index case” similarly controls were defined as “every PCR negative contact (symptomatic or asymptomatic) for any index case who was home quarantined for 14 days based on suspicion by PDSRU team”. A simple random technique was used and 300 individuals were made part of this study. <strong>Results:</strong> The major findings of this study shows that PCR positive contacts poorly adopted certain COVID-19 IPC measures of interest in their daily life hence got infected. The odds for all the variables of interest were found to be statistically significant among cases as compared to controls like the odds for knowingly and intentionally contacted with a COVID-19 positive case was 13.7 times more among the PCR positive contacts as compare to PCR negative contacts (p = 0.00, C.I = 7.62 - 24.90), similarly, the odds of being a family member of the index COVID-19 case was 7.07 times more among the PCR positive contacts as compared to the PCR negative contacts (p = 0.00, C.I = 3.25 - 15.86). <strong>Conclusion:</strong> Before the development and availability of a vaccine, the only tools that can help prevent the spread of COVID-19 are IPC measures.展开更多
<strong>Background:</strong> COVID-19 Pandemic is still circulating within the human population and proving to be a deadlier disease with a mortality rate ranging from 0.5% to 7%. Since COVID-19 is a highl...<strong>Background:</strong> COVID-19 Pandemic is still circulating within the human population and proving to be a deadlier disease with a mortality rate ranging from 0.5% to 7%. Since COVID-19 is a highly transmissible disease;there is always a probability for its outward spread towards the general public and community from the hospitals and healthcare facilities where they come to seek treatment. <strong>Methodology:</strong> A prospective cohort study design was used, considering the limited available resources and time—a total of 200 healthcare workers (including doctors, nurses, para-medical staff, janitorial staff, reception staff & pharmacists) working in the OPDs of the two major public sector hospitals of Quetta were made part of this study. The study participants were selected using a simple random sampling technique and selection was made from the daily attendance register. The study participants from “Hospital-A” were first of all educated and trained on various COVID-19 IPC measures later on various COVID-19-IEC materials;written in simple Urdu language, were displayed clearly everywhere in the OPD. Similarly, handwashing stations along with hand sanitizers/soaps and surgical face masks were also made available free of cost for all the study participants of Hospital-A. Moreover the importance and effectiveness of COVID-19 IPC measures were continuously announced in the OPD gallery of Hospital-A, these announcements used simple wording in local languages (<em>i.e.</em>, Urdu, Pashto, Balochi and Brahvi). On the other hand, in the OPD of “Hospital-B”, no such interventions were made. The study participants of both the hospitals were followed for one month and observations like which group showed more on-job noncompliance towards various COVID-19 IPC measures were recorded. The data was recorded on daily basis (from 1<sup>st</sup> May-to-31<sup>st</sup> May 2021) after observing the study participants for compliance towards using face masks, face shields, personal protective gowns, gloves, hand sanitizers, maintaining 6 feet social distancing and implanting triage at his or her OPD counter. Any study participant with daily proper practice of at least face masks, gloves, hand sanitizer and maintaining a 6 feet social distancing SOPs during duty hours at the outdoor patients department was considered to be a compliant individual if even one of these minimum required SOPs has not practiced the study participant, he/she was classified as non-compliant individual. A checklist was used to record these findings for every study participant on daily basis by trained data collectors. Lastly, all the data was analyzed using Microsoft Excel 2007 version. <strong>Results:</strong> The major findings of this study are almost in line with the set objectives, the study results are clearly showing the Risk Ratio (RR) as 0.27, indicating that the intervention group participants were only 27% as likely to develop on-job non-compliance for various COVID-19 IPC measures compare to the non-intervention group. <strong>Discussion & Conclusion:</strong> It is highly recommended that various COVID-19 specific infection prevention and control interventions like COVID-19 IPC trainings, COVID-19 IEC and BCC materials be displayed clearly everywhere in the healthcare facilities especially in the OPD department. Moreover, audio announcements made in simple wording using local languages like Urdu, Pashto, Balochi and Brahvi could really serve as constant reminder tools especially in an OPD department where every next patient in the queue could present with a different infectious bug.展开更多
A triplicate field experiment laid out in randomized complete block design was conducted to evaluate different humic acid (HA) application methods at Agricultural Research Farm, of KPK Agricultural University, Peshawa...A triplicate field experiment laid out in randomized complete block design was conducted to evaluate different humic acid (HA) application methods at Agricultural Research Farm, of KPK Agricultural University, Peshawar. Three methods of HA application: seed priming, foliar spray and soil application were included in the experiment. Humic acid application methods significantly affected pods plant-1, grains pod-1, 1000 grain weights, and grain yield whereas biological yield was not significantly affected by HA application methods. Humic acid application at the rate of 3 kg·ha-1 resulted in higher number of pods plant-1, thousand grain weights and grain yield, however it was statistically similar to the treatments where HA was soil applied at rate of 1 and 2 kg·ha-1, seed priming with 0% (water soaked), 1%, 2% HA solution and foliar spray with 0.01%, 0.05% and 0.1% of HA solution. It is concluded that HA application in all the three methods significantly enhances grain yield and yield components of mungbean.展开更多
Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brough...Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms.This phenomenon caused a state of panic among people.Different studies were conducted to stop the spread of fake news to help people cope with the situation.In this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)techniques.It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative reactions.The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment overall.The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.展开更多
Production of hydrogen(H2) and oxygen(O2) through electrocatalytic water splitting is one of the sustainable,green and pivotal ways to accomplish the ever-increasing demands for renewable energy sources,but remains a ...Production of hydrogen(H2) and oxygen(O2) through electrocatalytic water splitting is one of the sustainable,green and pivotal ways to accomplish the ever-increasing demands for renewable energy sources,but remains a big challenge because of the uphill reaction during overall water splitting.Herein,we develop high-performance non-noble metal electrocatalysts for pH-universal water splitting,based on nickel/vanadium boride(NiVB) nanoparticles/reduced graphene oxide(rGO) hybrid(NiVB/rGO)through a facile chemical reduction approach under ambient condition.By virtue of more exposure to surface active sites,superior electron transfer capability and strong electronic coupling,the asprepared NiVB/rGO heterostructure needs pretty low overpotentials of 267 and 151 mV to deliver a current density of 10 mA cm^(-2) for oxygen evolution reaction(OER) and hydrogen evolution reaction(HER)respectively,with the corresponding Tafel slope of 44 and 88 mV dec^(-1) in 1.0 M KOH.Moreover,the NiVB/rGO electrocatalysts display a promising performance in a wide-pH conditions that require low overpotential of 310,353 and 489 mV to drive a current density of 10 mA cm^(-2) for OER under 0.5 M KOH,0.05 M H2SO4 and 1.0 M phosphate buffer solution(PBS) respectively,confirming the excellent electrocata lytic performance among state-of-the-art Ni-based electrocatalysts for overall water splitting.Therefore,the interfacial tuning based on incorporation of active heterostructure may pave a new route to develop bifunctional,cost-effective and efficient electrocatalyst systems for water splitting and H2 production.展开更多
Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its i...Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.展开更多
Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process an...Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method.展开更多
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats...Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.展开更多
Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance pr...Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus（QTL） mapping, and marker-assisted selection（MAS） are evolving into more efficient concepts of linkage disequilibrium（LD） also called association mapping and genomic selection（GS）, respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice.展开更多
At the international level,a major effort is being made to optimizethe flow of data and information for health systems management.The studiesshow that medical and economic efficiency is strongly influenced by the leve...At the international level,a major effort is being made to optimizethe flow of data and information for health systems management.The studiesshow that medical and economic efficiency is strongly influenced by the levelof development and complexity of implementing an integrated system of epidemiological monitoring and modeling.The solution proposed and describedin this paper is addressed to all public and private institutions involved inthe fight against the COVID-19 pandemic,using recognized methods andstandards in this field.The Green-Epidemio is a platform adaptable to thespecific features of any public institution for disease management,based onopen-source software,allowing the adaptation,customization,and furtherdevelopment of“open-source”applications,according to the specificities ofthe public institution,the changes in the economic and social environment andits legal framework.The platform has a mathematical model for the spreadof COVID-19 infection depending on the location of the outbreaks so thatthe allocation of resources and the geographical limitation of certain areascan be parameterized according to the number and location of the real-timeidentified outbreaks.The social impact of the proposed solution is due to theplanned applications of information flow management,which is a first stepin improving significantly the response time and efficiency of people-operatedresponse services.Moreover,institutional interoperability influences strategicsocietal factors.展开更多
With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may f...With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may face unfavorable consequences.There are only several studies that targeted X-ray images of cardiothoracic diseases.Most of the studies only targeted a single disease,which is inadequate.Although some related studies have provided an identification framework for all classes,the results are not encouraging due to a lack of data and imbalanced data issues.This research provides a significant contribution to Generative Adversarial Network(GAN)based synthetic data and four different types of deep learning-based models that provided comparable results.The models include a ResNet-152 model with image augmentation with an accuracy of 67%,a ResNet-152 model without image augmentation with an accuracy of 62%,transfer learning with Inception-V3 with an accuracy of 68%,and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%.展开更多
In this article, we define a subclass of meromorphic multivalent Sakaguchi type functions and obtain certain sufficient conditions for functions to be in this class. The main result presented here includes a number of...In this article, we define a subclass of meromorphic multivalent Sakaguchi type functions and obtain certain sufficient conditions for functions to be in this class. The main result presented here includes a number of consequences as its special cases.展开更多
基金National Natural Science Foundation of China(3216045632360474+2 种基金32360486)grants from the Provincial Basic Research Program(Natural Science)([2020]1Z018)Provincial Key Technology R&D Program([2021]YiBan272).
文摘Seed priming is an effective seed pretreatment technology that enhances germination and overall crop performance by optimizing seed hydration and metabolic processes before planting.Seed quality is a critical determinant of cotton(Gossypium hirsutum)crop performance,influencing germination,plant vigor,and yield.This study evaluates the effects of seed priming with potassium salts(1%and 2%KCl and K2SO4)on germination,morphological traits,and Cry1Ac gene expression in three Bt cotton cultivars(IUB-2013,NIAB-878B,FH-142)as Cry1Ac enhance the pest resistance in Bt cotton and reduce the plant’s dependence on chemical insecticides.Seeds were primed for six hours,air-dried,and sown in the field.Germination rates,plant height,number of bolls per plant,boll weight,seed cotton yield,and ginning outturn(GOT)were assessed at crop maturity.Cry1Ac gene expression was quantified to explore the influence of priming treatments on transgene activity.Results demonstrated that 1%K2SO4 priming significantly enhanced germination and yield-related traits,with Cry1Ac expression peaking in the IUB-2013 cultivar under 1%K2SO4 treatment.These findings suggest that potassium-based halopriming improves cotton seedling establishment and Bt gene expression.This study addresses the critical gaps in understanding the effects of seed halopriming on morphological traits,germination,and expression of the Cry1Ac gene in Bt cotton while providing a novel eco-friendly and cost-effective halopriming approach,offering the potential to improve cotton production.
基金supported by the National Natural Science Foundation of China(Grant No.52263028)Xingdian Talent Funding Project(Year 2022,Yunnan Province,China).
文摘In recent years,photo-powered energy storage devices have attracted considerable research attention due to their potential applications in smart electronics.In this review,we present a comprehensive summary of recent developments in two distinct but highly promising energy storage technologies,photo-assisted metal-air batteries and photo-supercapacitors.The section on metal-air batteries primarily describes the electrochemical performance of Zn-air and Li-air systems,innovative photo-electrode designs,and mechanisms that enhance oxygen evolution and reduction reactions.A brief discussion is also provided of other metal-air systems,including Mg,Fe,and Al.In contrast,the section on photo-supercapacitors explores recent advancements in light-driven charge storage,electrode materials,and device architectures,presenting a comparative performance analysis of materials such as metal oxides,sulfides,and perovskites.Various critical challenges,including material stability,efficiency under varying light conditions,and scalability,are also thoroughly examined.Despite their different working principles,both technologies hold great potential to increase energy efficiency and sustainability through the use of photo-assisted processes.The purpose of this review is to bridge existing knowledge gaps and propose future directions for research in these emerging fields.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY23E040001)Fundamental Research Funding Project of Zhejiang Province,China(Project Category A,Grant No.2022YW06)National Key R&D Program of China(Grant No.2023YFF0614902).
文摘Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction.
文摘Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s edge.However,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models challenging.Additionally,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors.These factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these topics.Consequently,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using TinyML.It identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software frameworks.In the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning strategies.In the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major sectors.As a result,this article provides valuable insights for researchers,practitioners,and developers.It assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.
基金financially supported by the National Natural Science Foundation of China (No.22372057)Yunnan Fundamental Research Projects (No.202301AT070059)+2 种基金the Natural Science Foundation of Hunan Province (No.2023JJ30121)the Natural Science Foundation of Changsha (No.KQ2208259)the Fundamental Research Funds for the Central Universities (No.202044011)。
文摘Perovskite oxides have shown great potential application in fuel cells due to the unique crystal structures and tunable composition as well as effective capability toward the oxygen reduction reaction(ORR),whereas the investigation on the electrocatalytic performance of perovskite oxides toward the two-electron ORR to H_(2)O_(2)production remains very limited.Herein,a facile synthetic method has been developed to prepare La_(2)Sn_(2)O_(7)@La-doped ZnSnO_(3)heterostructures comprising of amorphous La_(2)Sn_(2)O_(7)and crystalline La-doped ZnSnO_(3).The optimal La_(2)Sn_(2)O_(7)@Ladoped ZnSnO_(3)heterostructures catalyst exhibits a significantly improved two-electron ORR performance to H_(2)O_(2)production with onset potential of 0.77 V and large current density of 2.51 m A.cm^(-2)at 0.1 V compared to ZnSnO_(3)(0.75 V,1.80 m A.cm^(-2),0.11 m A) as well as maintains high H_(2)O_(2)selectivity of 80%,which has been theoretically demonstrated to be contributed to the synergistic effect of amorphous La_(2)Sn_(2)O_(7)and crystalline La-doped ZnSnO_(3).Moreover,high H_(2)O_(2)yield rate of 2.9 m M.h^(-1)at 0.1 V can be achieved with a superior turnover frequency(TOF) of3.31 × 10^(-2)s^(-1)compared to the ZnSnO_(3)catalyst(2.10 × 10^(-2)s^(-1)).This work reveals the great potential of perovskite oxide as promising candidates for the environmentally friendly synthesis of hydrogen peroxide.
文摘The second wave of COVID-19 pandemic has started globally, right now 220 countries are infected and a total of 71,351,695 confirmed cases and 1,612,372 deaths due to COVID-19 have been reported. Infection Prevention and Control (IPC) measures for COVID-19 all have proved vital in decreasing the transmission rates among the communities. <strong>Methodology:</strong> Unmatched Case-Control Study was conducted where cases were defined as “every PCR positive contact (symptomatic or asymptomatic) for any index case” similarly controls were defined as “every PCR negative contact (symptomatic or asymptomatic) for any index case who was home quarantined for 14 days based on suspicion by PDSRU team”. A simple random technique was used and 300 individuals were made part of this study. <strong>Results:</strong> The major findings of this study shows that PCR positive contacts poorly adopted certain COVID-19 IPC measures of interest in their daily life hence got infected. The odds for all the variables of interest were found to be statistically significant among cases as compared to controls like the odds for knowingly and intentionally contacted with a COVID-19 positive case was 13.7 times more among the PCR positive contacts as compare to PCR negative contacts (p = 0.00, C.I = 7.62 - 24.90), similarly, the odds of being a family member of the index COVID-19 case was 7.07 times more among the PCR positive contacts as compared to the PCR negative contacts (p = 0.00, C.I = 3.25 - 15.86). <strong>Conclusion:</strong> Before the development and availability of a vaccine, the only tools that can help prevent the spread of COVID-19 are IPC measures.
文摘<strong>Background:</strong> COVID-19 Pandemic is still circulating within the human population and proving to be a deadlier disease with a mortality rate ranging from 0.5% to 7%. Since COVID-19 is a highly transmissible disease;there is always a probability for its outward spread towards the general public and community from the hospitals and healthcare facilities where they come to seek treatment. <strong>Methodology:</strong> A prospective cohort study design was used, considering the limited available resources and time—a total of 200 healthcare workers (including doctors, nurses, para-medical staff, janitorial staff, reception staff & pharmacists) working in the OPDs of the two major public sector hospitals of Quetta were made part of this study. The study participants were selected using a simple random sampling technique and selection was made from the daily attendance register. The study participants from “Hospital-A” were first of all educated and trained on various COVID-19 IPC measures later on various COVID-19-IEC materials;written in simple Urdu language, were displayed clearly everywhere in the OPD. Similarly, handwashing stations along with hand sanitizers/soaps and surgical face masks were also made available free of cost for all the study participants of Hospital-A. Moreover the importance and effectiveness of COVID-19 IPC measures were continuously announced in the OPD gallery of Hospital-A, these announcements used simple wording in local languages (<em>i.e.</em>, Urdu, Pashto, Balochi and Brahvi). On the other hand, in the OPD of “Hospital-B”, no such interventions were made. The study participants of both the hospitals were followed for one month and observations like which group showed more on-job noncompliance towards various COVID-19 IPC measures were recorded. The data was recorded on daily basis (from 1<sup>st</sup> May-to-31<sup>st</sup> May 2021) after observing the study participants for compliance towards using face masks, face shields, personal protective gowns, gloves, hand sanitizers, maintaining 6 feet social distancing and implanting triage at his or her OPD counter. Any study participant with daily proper practice of at least face masks, gloves, hand sanitizer and maintaining a 6 feet social distancing SOPs during duty hours at the outdoor patients department was considered to be a compliant individual if even one of these minimum required SOPs has not practiced the study participant, he/she was classified as non-compliant individual. A checklist was used to record these findings for every study participant on daily basis by trained data collectors. Lastly, all the data was analyzed using Microsoft Excel 2007 version. <strong>Results:</strong> The major findings of this study are almost in line with the set objectives, the study results are clearly showing the Risk Ratio (RR) as 0.27, indicating that the intervention group participants were only 27% as likely to develop on-job non-compliance for various COVID-19 IPC measures compare to the non-intervention group. <strong>Discussion & Conclusion:</strong> It is highly recommended that various COVID-19 specific infection prevention and control interventions like COVID-19 IPC trainings, COVID-19 IEC and BCC materials be displayed clearly everywhere in the healthcare facilities especially in the OPD department. Moreover, audio announcements made in simple wording using local languages like Urdu, Pashto, Balochi and Brahvi could really serve as constant reminder tools especially in an OPD department where every next patient in the queue could present with a different infectious bug.
文摘A triplicate field experiment laid out in randomized complete block design was conducted to evaluate different humic acid (HA) application methods at Agricultural Research Farm, of KPK Agricultural University, Peshawar. Three methods of HA application: seed priming, foliar spray and soil application were included in the experiment. Humic acid application methods significantly affected pods plant-1, grains pod-1, 1000 grain weights, and grain yield whereas biological yield was not significantly affected by HA application methods. Humic acid application at the rate of 3 kg·ha-1 resulted in higher number of pods plant-1, thousand grain weights and grain yield, however it was statistically similar to the treatments where HA was soil applied at rate of 1 and 2 kg·ha-1, seed priming with 0% (water soaked), 1%, 2% HA solution and foliar spray with 0.01%, 0.05% and 0.1% of HA solution. It is concluded that HA application in all the three methods significantly enhances grain yield and yield components of mungbean.
文摘Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms.This phenomenon caused a state of panic among people.Different studies were conducted to stop the spread of fake news to help people cope with the situation.In this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)techniques.It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative reactions.The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment overall.The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.
基金supported by the National Natural Science Foundation of China(Grant Nos.21771021,21822501,21720303,and 22061130206)the Beijing Municipal Natural Science Foundation(JQ20003)+5 种基金the Newton Advanced Fellowship award(NAF\R1\201285)the Fok Ying-Tong Education Foundation(Grant No.171008)the Beijing Nova Program(Grant No.xx2018115)the State Key Laboratory of Rare Earth Resources UtilizationChangchun Institute of Applied Chemistry,CAS(RERU2019005)the Fundamental Research Funds for the Central Universities and the Measurements Fund of Beijing Normal University。
文摘Production of hydrogen(H2) and oxygen(O2) through electrocatalytic water splitting is one of the sustainable,green and pivotal ways to accomplish the ever-increasing demands for renewable energy sources,but remains a big challenge because of the uphill reaction during overall water splitting.Herein,we develop high-performance non-noble metal electrocatalysts for pH-universal water splitting,based on nickel/vanadium boride(NiVB) nanoparticles/reduced graphene oxide(rGO) hybrid(NiVB/rGO)through a facile chemical reduction approach under ambient condition.By virtue of more exposure to surface active sites,superior electron transfer capability and strong electronic coupling,the asprepared NiVB/rGO heterostructure needs pretty low overpotentials of 267 and 151 mV to deliver a current density of 10 mA cm^(-2) for oxygen evolution reaction(OER) and hydrogen evolution reaction(HER)respectively,with the corresponding Tafel slope of 44 and 88 mV dec^(-1) in 1.0 M KOH.Moreover,the NiVB/rGO electrocatalysts display a promising performance in a wide-pH conditions that require low overpotential of 310,353 and 489 mV to drive a current density of 10 mA cm^(-2) for OER under 0.5 M KOH,0.05 M H2SO4 and 1.0 M phosphate buffer solution(PBS) respectively,confirming the excellent electrocata lytic performance among state-of-the-art Ni-based electrocatalysts for overall water splitting.Therefore,the interfacial tuning based on incorporation of active heterostructure may pave a new route to develop bifunctional,cost-effective and efficient electrocatalyst systems for water splitting and H2 production.
文摘Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.
文摘Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method.
文摘Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.
文摘Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus（QTL） mapping, and marker-assisted selection（MAS） are evolving into more efficient concepts of linkage disequilibrium（LD） also called association mapping and genomic selection（GS）, respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice.
基金This research received no grant funding and the APC was funded by“Stefan cel Mare”University of Suceava,Romania.
文摘At the international level,a major effort is being made to optimizethe flow of data and information for health systems management.The studiesshow that medical and economic efficiency is strongly influenced by the levelof development and complexity of implementing an integrated system of epidemiological monitoring and modeling.The solution proposed and describedin this paper is addressed to all public and private institutions involved inthe fight against the COVID-19 pandemic,using recognized methods andstandards in this field.The Green-Epidemio is a platform adaptable to thespecific features of any public institution for disease management,based onopen-source software,allowing the adaptation,customization,and furtherdevelopment of“open-source”applications,according to the specificities ofthe public institution,the changes in the economic and social environment andits legal framework.The platform has a mathematical model for the spreadof COVID-19 infection depending on the location of the outbreaks so thatthe allocation of resources and the geographical limitation of certain areascan be parameterized according to the number and location of the real-timeidentified outbreaks.The social impact of the proposed solution is due to theplanned applications of information flow management,which is a first stepin improving significantly the response time and efficiency of people-operatedresponse services.Moreover,institutional interoperability influences strategicsocietal factors.
文摘With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may face unfavorable consequences.There are only several studies that targeted X-ray images of cardiothoracic diseases.Most of the studies only targeted a single disease,which is inadequate.Although some related studies have provided an identification framework for all classes,the results are not encouraging due to a lack of data and imbalanced data issues.This research provides a significant contribution to Generative Adversarial Network(GAN)based synthetic data and four different types of deep learning-based models that provided comparable results.The models include a ResNet-152 model with image augmentation with an accuracy of 67%,a ResNet-152 model without image augmentation with an accuracy of 62%,transfer learning with Inception-V3 with an accuracy of 68%,and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%.
文摘In this article, we define a subclass of meromorphic multivalent Sakaguchi type functions and obtain certain sufficient conditions for functions to be in this class. The main result presented here includes a number of consequences as its special cases.
