This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducte...This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducted on 184 nurses working in intensive care units(ICUs).The study data were collected through demographic information,Nursing Informatics Competency Assessment Tool(NICAT),and information literacy skills for EBP questionnaires.The intensive care nurses received competent and low-moderate levels for the total scores of NI competency and information literacy skills,respectively.They received a moderate score for the use of different information resources but a low score for information searching skills,different search features,and knowledge about search operators,and only 31.5%of the nurses selected the most appropriate statement.NI competency and related subscales had a significant direct bidirectional correlation with information literacy skills for EBP and its subscales(P<0.05).Nurses require a high level of NI competency and information literacy for EBP to obtain up-to-date information and provide better care and decision-making.Health planners and policymakers should develop interventions to enhance NI competency and information literacy skills among nurses and motivate them to use EBP in clinical settings.展开更多
Polymeric materials,known for their lightweight and strength,are widely used today.However,their non-biodegradable nature poses significant environmental challenges.This research aimed to develop biodegradable films f...Polymeric materials,known for their lightweight and strength,are widely used today.However,their non-biodegradable nature poses significant environmental challenges.This research aimed to develop biodegradable films from fruits and vegetables,using alginate as a binding agent.Using a completely randomized design,seven experimental sets were prepared with carrots,Kimju guava,and Namwa banana peel fibers as the primary materials and alginate as the secondary material at three levels:1.2,1.8,and 2.4 by weight.The solution technique was employed to create the samples.Upon testing mechanical and physical properties,experimental set 3,consisting of 60%guava and 1.8%alginate,emerged as the optimal ratio.This combination exhibited favorable physical properties,including a thickness of 0.26±0.02 mm,meeting the standards for food packaging films.Additionally,the tensile strength was 0.50±0.01 N/m²,and the elongation at break was 55.60±0.44%.Regarding chemical properties,the moisture content of 5.64±0.03%fell within the acceptable range for dried food.Furthermore,a 30-day soil burial test revealed that the sample from experimental set 3 exhibited the highest degradation rate.In conclusion,these findings suggest that guava can be a promising raw material for producing biodegradable plastics suitable for packaging applications.展开更多
This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the C...This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the CID card from the background.Next,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector.Finally,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together.The length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label sequence.This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences.We may directly learn the proposed model from a sequence of words without detailed annotations.We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques.展开更多
In modem society information has become one of the most important resources, playing a crucial role in everyday life. As a result, information technology （IT） is now an indispensable tool in the business world, whic...In modem society information has become one of the most important resources, playing a crucial role in everyday life. As a result, information technology （IT） is now an indispensable tool in the business world, which has a direct impact on company competitiveness and performance. With all the changes in business environment there is also a dramatic shift in the way the management functions. In order to efficiently perform their tasks and thus rise up to the challenges of the information society, managers need not only to master appropriate IT skills, but also to understand the processes launched by the information revolution. The aim of this paper was to determine the extent to which Croatian managers are prepared for the information society. In addition to the data regarding the level of IT equipment at managers＇ disposal, the paper presents the results on computer usage, self-assessment of IT competencies, and the level of acceptance of modern technologies. Although satisfactory attainment has been recorded in some areas, the results of the analysis reveal that additional effort should be devoted to further improve computer and information literacy of Croatian managers.展开更多
Objective:To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to 2020.Methods:Random forest regression was used to analyse the independent variables ...Objective:To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to 2020.Methods:Random forest regression was used to analyse the independent variables contributing to malaria incidence.Environmental conditions were extracted from remotely sensed data,including vegetation,land temperature,soil moisture,precipitation,and elevation.In contrast,the social aspects of poverty were obtained from government statistical reports.Results:From 2016 to 2020,the contribution of each environmental and social aspect of poverty to malaria incidence fluctuated annually.Generally,the top three essential variables were people aged 15 years and above,experiencing poverty(variable importance/VI=32.0%),people experiencing poverty who work in the agricultural sector(VI=14.4%),and precipitation(VI=9.8%).It was followed by people experiencing poverty who are unemployed(VI=9.2%),land temperature(VI=5.2%),people experiencing poverty who have low education(VI=8.0%),soil moisture(VI=7.4%),elevation(VI=6.0%),and vegetation(VI=3.8%).Conclusions:Poverty and variables related to climate have become the crucial determinants of malaria in Indonesia.The government must strengthen malaria surveillance through climate change mitigation and adaptation programs and accelerate poverty alleviation programs to support malaria elimination.展开更多
Objective:To determine the overall and pooled prevalence of Leishmania(L.)infantum in sandfly vectors in Iran.Methods:The present research conducted a systematic review and meta-analysis and searched regional database...Objective:To determine the overall and pooled prevalence of Leishmania(L.)infantum in sandfly vectors in Iran.Methods:The present research conducted a systematic review and meta-analysis and searched regional databases such as PubMed,Scopus,Web of Science(WoS),Embase,PAHO Iris,LILACS,WHO Iris,and local databases named:SID,Magiran,Civilica,and also grey literatures.The current research included studies that were conducted in Iran and examined L.infantum in different sandfly vectors.The studies’quality assessment/risk of bias assessment was evaluated by the Joanna Briggs Institute Critical Appraisal Checklist for prevalence data studies,and the data were analyzed by Stata 14 software.In addition,we examined 22 primary studies to estimate the overall prevalence of L.infantum among various vectors of visceral leishmaniasis.Results:According to the meta-analysis,the pooled prevalence of Phlebotomus(Ph.)tobbi,Ph.alexandri,Ph.kandelaki,Ph.perfiliewi,Ph.major,Ph.keshishiani were 5.34%,4.36%,2.23%,1.79%,4.37%and 1.18%.Ph.tobbi has the highest infection rate(25.00%)of L.infantum among the sandfly vectors.Conclusions:Visceral leishmaniasis is widespread in Fars,Ardebil,and East-Azerbaijan provinces,which are the most important endemic regions in Iran.展开更多
Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her e...Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.展开更多
Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important r...Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.展开更多
Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors ca...Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for SPF.Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches.This study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches.First,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features.Then,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock prices.The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual information.The complex structure enables this network to capture more stochasticity within the stock price.The method does not change when applied to stock data or Forex data.Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error.展开更多
Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the p...Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product.展开更多
Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scal...Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.展开更多
Taking Harare metropolitan province in Zimbabwe as an example, we classified Landsat imagery (1984, 2002, 2008 and 2013) by using support vector machines (SVMs) and analyzed built-up and non-built-up changes. The over...Taking Harare metropolitan province in Zimbabwe as an example, we classified Landsat imagery (1984, 2002, 2008 and 2013) by using support vector machines (SVMs) and analyzed built-up and non-built-up changes. The overall classification accuracy for the four dates ranged from 89% to 95%, while the overall kappa varied from 86% to 93%. The results demonstrate that SVMs provide a cost-effective technique for mapping urban land use/cover by using mediumresolution satellite images such as Landsat. Based on land use/cover maps for 1984, 2002, 2008 and 2013, along with change analyses, built-up areas increased from 12.6% to 36.3% of the total land area, while non-built-up cover decreased from 87.3% to 63.4% between 1984 and 2013. The results revealed an urban growth process characterized by infill, extension and leapfrog developments. Given the dearth of spatial urban growth information in Harare metropolitan province, the land use/cover maps are valuable products that provide a synoptic view of built-up and non-built-up areas. Therefore, the land use/cover change maps could potentially assist decision-makers with up-to-date built-up and non-built-up information in order to guide strategic implementation of sustainable urban land use planning in Harare metropolitan province.展开更多
Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time...Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.展开更多
Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt bat...Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.展开更多
We examined the neural correlates of the statistical learning of orthographic-semantic connections in Chinese adult learners.Visual event-related potentials(ERPs) were recorded while participants were exposed to a seq...We examined the neural correlates of the statistical learning of orthographic-semantic connections in Chinese adult learners.Visual event-related potentials(ERPs) were recorded while participants were exposed to a sequence of artificial logographic characters containing semantic radicals carrying low,moderate,or high levels of semantic consistency.The behavioral results showed that the mean accuracy of participants’ recognition of previously exposed characters was 63.1% that was significantly above chance level(50%),indicating the statistical learning of the regularities of semantic radicals.The ERP data revealed a temporal sequence of the neural process of statistical learning of orthographic-semantic connections,and different brain indexes were found to be associated with this processing,i.e.,a clear N170-P200-N400 pattern.For N170,the larger negative amplitudes were evoked by the high and moderate consistency than the low consistency.For P200,the mean amplitudes elicited by the moderate and low consistency were larger than the high consistency.In contrast,a larger N400 amplitude was observed in the low than moderate and high consistency;and more negative amplitude was elicited by the moderate than high consistency.We propose that the initial potential shifts(N170 and P200) may reflect orthographic or graphic form identification,while the later component(N400) may be associated with semantic information analysis.展开更多
The purpose of this paper is to propose a new model of asymmetry for square contingency tables with ordered categories. The new model may be appropriate for a square contingency table if it is reasonable to assume an ...The purpose of this paper is to propose a new model of asymmetry for square contingency tables with ordered categories. The new model may be appropriate for a square contingency table if it is reasonable to assume an underlying bivariate t-distribution with different marginal variances having any degrees of freedom. As the degrees of freedom becomes larger, the proposed model approaches the extended linear diagonals-parameter symmetry model, which may be appropriate for a square table if it is reasonable to assume an underlying bivariate normal distribution. The simulation study based on bivariate t-distribution is given. An example is given.展开更多
Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital ...Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.展开更多
The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic functions.The special issue publishes origi...The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic functions.The special issue publishes original papers of innovative ideas and concepts,new discoveries,and novel applications and business models relevant to the field of human-centered intelligent robots.In this special issue,modeling,intelligent control,展开更多
Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occ...Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.展开更多
The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding t...The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.展开更多
文摘This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducted on 184 nurses working in intensive care units(ICUs).The study data were collected through demographic information,Nursing Informatics Competency Assessment Tool(NICAT),and information literacy skills for EBP questionnaires.The intensive care nurses received competent and low-moderate levels for the total scores of NI competency and information literacy skills,respectively.They received a moderate score for the use of different information resources but a low score for information searching skills,different search features,and knowledge about search operators,and only 31.5%of the nurses selected the most appropriate statement.NI competency and related subscales had a significant direct bidirectional correlation with information literacy skills for EBP and its subscales(P<0.05).Nurses require a high level of NI competency and information literacy for EBP to obtain up-to-date information and provide better care and decision-making.Health planners and policymakers should develop interventions to enhance NI competency and information literacy skills among nurses and motivate them to use EBP in clinical settings.
基金funding from the Environmental Science Program for Academic Excellence and Community Research for Fiscal Year 2024,a financial resource of the Environmental Science and Technology Program,Faculty of Science,Buriram Rajabhat University.Additionally,Buriram Rajabhat University provided a publication budget.
文摘Polymeric materials,known for their lightweight and strength,are widely used today.However,their non-biodegradable nature poses significant environmental challenges.This research aimed to develop biodegradable films from fruits and vegetables,using alginate as a binding agent.Using a completely randomized design,seven experimental sets were prepared with carrots,Kimju guava,and Namwa banana peel fibers as the primary materials and alginate as the secondary material at three levels:1.2,1.8,and 2.4 by weight.The solution technique was employed to create the samples.Upon testing mechanical and physical properties,experimental set 3,consisting of 60%guava and 1.8%alginate,emerged as the optimal ratio.This combination exhibited favorable physical properties,including a thickness of 0.26±0.02 mm,meeting the standards for food packaging films.Additionally,the tensile strength was 0.50±0.01 N/m²,and the elongation at break was 55.60±0.44%.Regarding chemical properties,the moisture content of 5.64±0.03%fell within the acceptable range for dried food.Furthermore,a 30-day soil burial test revealed that the sample from experimental set 3 exhibited the highest degradation rate.In conclusion,these findings suggest that guava can be a promising raw material for producing biodegradable plastics suitable for packaging applications.
基金supported by Sai Gon University under Fund(Grant No.TD2020-11).
文摘This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the CID card from the background.Next,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector.Finally,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together.The length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label sequence.This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences.We may directly learn the proposed model from a sequence of words without detailed annotations.We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques.
文摘In modem society information has become one of the most important resources, playing a crucial role in everyday life. As a result, information technology （IT） is now an indispensable tool in the business world, which has a direct impact on company competitiveness and performance. With all the changes in business environment there is also a dramatic shift in the way the management functions. In order to efficiently perform their tasks and thus rise up to the challenges of the information society, managers need not only to master appropriate IT skills, but also to understand the processes launched by the information revolution. The aim of this paper was to determine the extent to which Croatian managers are prepared for the information society. In addition to the data regarding the level of IT equipment at managers＇ disposal, the paper presents the results on computer usage, self-assessment of IT competencies, and the level of acceptance of modern technologies. Although satisfactory attainment has been recorded in some areas, the results of the analysis reveal that additional effort should be devoted to further improve computer and information literacy of Croatian managers.
文摘Objective:To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to 2020.Methods:Random forest regression was used to analyse the independent variables contributing to malaria incidence.Environmental conditions were extracted from remotely sensed data,including vegetation,land temperature,soil moisture,precipitation,and elevation.In contrast,the social aspects of poverty were obtained from government statistical reports.Results:From 2016 to 2020,the contribution of each environmental and social aspect of poverty to malaria incidence fluctuated annually.Generally,the top three essential variables were people aged 15 years and above,experiencing poverty(variable importance/VI=32.0%),people experiencing poverty who work in the agricultural sector(VI=14.4%),and precipitation(VI=9.8%).It was followed by people experiencing poverty who are unemployed(VI=9.2%),land temperature(VI=5.2%),people experiencing poverty who have low education(VI=8.0%),soil moisture(VI=7.4%),elevation(VI=6.0%),and vegetation(VI=3.8%).Conclusions:Poverty and variables related to climate have become the crucial determinants of malaria in Indonesia.The government must strengthen malaria surveillance through climate change mitigation and adaptation programs and accelerate poverty alleviation programs to support malaria elimination.
基金funded and supported by the Maragheh University of Medical Sciences(MRGUMS)(IR.MARAGHEHPHC.REC.1402.001)Maragheh,Iran.
文摘Objective:To determine the overall and pooled prevalence of Leishmania(L.)infantum in sandfly vectors in Iran.Methods:The present research conducted a systematic review and meta-analysis and searched regional databases such as PubMed,Scopus,Web of Science(WoS),Embase,PAHO Iris,LILACS,WHO Iris,and local databases named:SID,Magiran,Civilica,and also grey literatures.The current research included studies that were conducted in Iran and examined L.infantum in different sandfly vectors.The studies’quality assessment/risk of bias assessment was evaluated by the Joanna Briggs Institute Critical Appraisal Checklist for prevalence data studies,and the data were analyzed by Stata 14 software.In addition,we examined 22 primary studies to estimate the overall prevalence of L.infantum among various vectors of visceral leishmaniasis.Results:According to the meta-analysis,the pooled prevalence of Phlebotomus(Ph.)tobbi,Ph.alexandri,Ph.kandelaki,Ph.perfiliewi,Ph.major,Ph.keshishiani were 5.34%,4.36%,2.23%,1.79%,4.37%and 1.18%.Ph.tobbi has the highest infection rate(25.00%)of L.infantum among the sandfly vectors.Conclusions:Visceral leishmaniasis is widespread in Fars,Ardebil,and East-Azerbaijan provinces,which are the most important endemic regions in Iran.
基金supported by University of Macao Research Grant,China (Grant No. RG057/08-09S/VCM/FST, Grant No. UL011/09-Y1/ EME/ WPK01/FST)
文摘Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.
基金supported in part by the National Natural Science Foundation of China(61573147,91520201,61625303,61522302,61761130080)Guangzhou Research Collaborative Innovation Projects(2014Y2-00507)+2 种基金Guangdong Science and Technology Research Collaborative Innovation Projects(20138010102010,20148090901056,20158020214003)Guangdong Science and Technology Plan Project(Application Technology Research Foundation)(2015B020233006)National High-Tech Research and De-velopment Program of China(863 Program)(2015AA042303)
文摘Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.
文摘Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for SPF.Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches.This study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches.First,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features.Then,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock prices.The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual information.The complex structure enables this network to capture more stochasticity within the stock price.The method does not change when applied to stock data or Forex data.Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error.
基金financially supported by the Universityof Macao Research Grant (MYRG2016-00038-ICMS-QRCM &MYRG2016-00040-ICMS-QRCM)Macao Science and Technology Development Fund (FDCT) (Grant No. 103/2015/A3)the National Natural Science Foundation of China (Grant No. 61562011 )
文摘Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product.
基金This research was funded by King Mongkut’s University of Technology North Bangkok(Contract no.KMUTNB-62-KNOW-026).
文摘Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.
文摘Taking Harare metropolitan province in Zimbabwe as an example, we classified Landsat imagery (1984, 2002, 2008 and 2013) by using support vector machines (SVMs) and analyzed built-up and non-built-up changes. The overall classification accuracy for the four dates ranged from 89% to 95%, while the overall kappa varied from 86% to 93%. The results demonstrate that SVMs provide a cost-effective technique for mapping urban land use/cover by using mediumresolution satellite images such as Landsat. Based on land use/cover maps for 1984, 2002, 2008 and 2013, along with change analyses, built-up areas increased from 12.6% to 36.3% of the total land area, while non-built-up cover decreased from 87.3% to 63.4% between 1984 and 2013. The results revealed an urban growth process characterized by infill, extension and leapfrog developments. Given the dearth of spatial urban growth information in Harare metropolitan province, the land use/cover maps are valuable products that provide a synoptic view of built-up and non-built-up areas. Therefore, the land use/cover change maps could potentially assist decision-makers with up-to-date built-up and non-built-up information in order to guide strategic implementation of sustainable urban land use planning in Harare metropolitan province.
文摘Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.
文摘Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.
基金supported,in part,by the General Research Fund of the Hong Kong Government Research Grant Council(17609518)the Early Career Scheme of the Hong Kong Grants Council (28606419)the National Natural Science Foundation of China (31600903)。
文摘We examined the neural correlates of the statistical learning of orthographic-semantic connections in Chinese adult learners.Visual event-related potentials(ERPs) were recorded while participants were exposed to a sequence of artificial logographic characters containing semantic radicals carrying low,moderate,or high levels of semantic consistency.The behavioral results showed that the mean accuracy of participants’ recognition of previously exposed characters was 63.1% that was significantly above chance level(50%),indicating the statistical learning of the regularities of semantic radicals.The ERP data revealed a temporal sequence of the neural process of statistical learning of orthographic-semantic connections,and different brain indexes were found to be associated with this processing,i.e.,a clear N170-P200-N400 pattern.For N170,the larger negative amplitudes were evoked by the high and moderate consistency than the low consistency.For P200,the mean amplitudes elicited by the moderate and low consistency were larger than the high consistency.In contrast,a larger N400 amplitude was observed in the low than moderate and high consistency;and more negative amplitude was elicited by the moderate than high consistency.We propose that the initial potential shifts(N170 and P200) may reflect orthographic or graphic form identification,while the later component(N400) may be associated with semantic information analysis.
文摘The purpose of this paper is to propose a new model of asymmetry for square contingency tables with ordered categories. The new model may be appropriate for a square contingency table if it is reasonable to assume an underlying bivariate t-distribution with different marginal variances having any degrees of freedom. As the degrees of freedom becomes larger, the proposed model approaches the extended linear diagonals-parameter symmetry model, which may be appropriate for a square table if it is reasonable to assume an underlying bivariate normal distribution. The simulation study based on bivariate t-distribution is given. An example is given.
文摘Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.
文摘The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic functions.The special issue publishes original papers of innovative ideas and concepts,new discoveries,and novel applications and business models relevant to the field of human-centered intelligent robots.In this special issue,modeling,intelligent control,
基金supported in part by the National Natural Science Foundation of China(under Grant Nos.51939001,61976033,U1813203,61803064,and 61751202)Natural Foundation Guidance Plan Project of Liaoning(2019‐ZD‐0151)+2 种基金Science&Technology Innovation Funds of Dalian(under Grant No.2018J11CY022)Fundamental Research Funds for the Central Universities(under Grant No.3132019345)Dalian High‐level Talents Innovation Support Program(Young Sci-ence and Technology Star Project)(under Grant No.2021RQ067).
文摘Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.
文摘The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.
