The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and technologies.However,qualitative and quantitative methods have shortcomings.The ...The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and technologies.However,qualitative and quantitative methods have shortcomings.The former involve high costs and many limitations,while the latter lack expert experience.Intelligent knowledge management emphasizes human–machine integration,which combines the advantages of expert experience and data mining.Thus,we proposed a new technology foresight method based on intelligent knowledge management.This method constructs a technological online platform to increase the number of participating experts.A secondary mining is performed on the results of patent analysis and bibliometrics.Thus,forward-looking,innovative,and disruptive areas and relevant experts must be discovered through the following comprehensive process:Topic acquisition→topic delivery→topic monitoring→topic guidance→topic reclamation→topic sorting→topic evolution→topic conforming→expert recommendation.展开更多
Based on the sticking point of the low intelligence of the existing management decision system,this paper puts forward the idea of enriching and refining the knowledge of the system and endowing it with the ability to...Based on the sticking point of the low intelligence of the existing management decision system,this paper puts forward the idea of enriching and refining the knowledge of the system and endowing it with the ability to learn by means of adopting three types of heterogeneous knowledge representation and knowledge management measures.At length,this paper outlines the basic framework of an intelligence system for the sake of management decision problem.展开更多
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
Despite of the fact that knowledge management has become the focus of current literature and management practice, the core process of knowledge has. not been identified. After comparing the pl：ocess of knowledge move...Despite of the fact that knowledge management has become the focus of current literature and management practice, the core process of knowledge has. not been identified. After comparing the pl：ocess of knowledge movement and that of fermenting, we put forward a new model-knowledge fermenting model. In this paper, we thoroughly analyze the element of knowledge fermenting model, and show how knowledge increase is realized through that model.展开更多
It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in...It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.展开更多
Introduction: Visceral leishmaniasis is a severe form of leishmaniasis that affects about 90,000 people annually worldwide. It is mainly transmitted by Leishmania donovani and infantum, which can cause damage to inter...Introduction: Visceral leishmaniasis is a severe form of leishmaniasis that affects about 90,000 people annually worldwide. It is mainly transmitted by Leishmania donovani and infantum, which can cause damage to internal organs, such as the spleen, liver, and bone marrow. If left untreated, severe cases can be fatal, as the disease can lead to severe secondary diseases, mycological and bacterial infections, and hemorrhages. Nutritional deficiencies and concurrent infections increase the incidence of visceral leishmaniasis and the likelihood of lethality. There is limited information about the relationship between the disease and nutrition in endemic areas in Kenya. Objective: The study was to analyze the association of nutritional supplements with the nutritional status and treatment outcomes of visceral leishmaniasis among children aged 5 - 12 years in Baringo and West Counties in Kenya. Methods: A quasi-experimental study design adopting quantitative data collection method was used in this study. A total of 204 children aged 5 - 12 years were included in the study. Data on nutritional status and treatment outcomes for VL was collected using a questionnaire and consent form. Pre- and post-intervention assessments were conducted to compare BMI, fever, spleen size, splenic aspirate after treatment, and the presence of PKDL in a 3-month follow-up. The data was analyzed using R statistical software with descriptive and inferential statistics, including chi-square tests and t-tests. The impact of treatment was estimated using the difference-in-difference method to compare changes in outcomes over time between the intervention and comparison groups. Results: The baseline characteristics assessed in this study were socio-demographics (age, gender, marital status, education and religion), vitamins (A, B, C, D) and minerals (zinc, Iron and Iodine). The results showed that the mean age was 8.72, children aged 5 - 9 years were 64.7%, and those aged 10 - 12 years were 35.3% in the intervention and comparison groups. There were more males than females in the study (53.9% in the intervention and 52.9% in the comparison group respectively). All the children in the study were from a Christian background, were underweight, had enlarged spleen, and were positive for VL by Splenic Aspirate. Those who presented with fever were 88.2% (88% in both intervention and comparison groups). Most children had lower levels of zinc, iron, vitamins A, B12, and D at baseline (54.9%, 91.2%, 54.4%, 57.8%, and 58.8% respectively). The majority (93.1%) were deficient in vitamin C (90.2% in the intervention and 96.1% in the comparison group). Conclusion: According to the study findings, the effect of administering micronutrients is significant at 5% significance level with the intervention having a positive effect. The administration of the nutritional supplement led on average to an increase of the minerals, vitamins and BMI levels in the body.展开更多
Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint...Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.展开更多
Software requirements are the basis of software design,test and maintenance. The trend that future software should be intelligent and distributed and the changing software environment challenge the Object-Oriented met...Software requirements are the basis of software design,test and maintenance. The trend that future software should be intelligent and distributed and the changing software environment challenge the Object-Oriented methodology that is a primary technology to build software system currently. GoalOriented requirements analysis is discussed in detail in this paper. Goals are more powerful logical mechanism to identify,organize and justify software requirements than objects. This methodology provides convincing supports to build high quality software system in the future.展开更多
In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge s...In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge sharing and remanufacturing engineering management are highlighted. It is noticeable that a great deal of knowledge transfer and sharing activities, which can improve the performance of remanufacturing engineering management constantly, are involved in remanufacturing engineering.展开更多
In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-con...In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.展开更多
Writing is an important part of language learning and is considered the best approach to demonstrate the comprehensive language skills of students.Manually grading student essays is a time-consuming task;however,it is...Writing is an important part of language learning and is considered the best approach to demonstrate the comprehensive language skills of students.Manually grading student essays is a time-consuming task;however,it is necessary.An automated essay scoring system can not only greatly improve the efficiency of essay scoring,but also provide more objective score.Therefore,many researchers have been exploring automated essay scoring techniques and tools.However,the technique of scoring Chinese essays is still limited,and its accuracy needs to be enhanced further.To improve the accuracy of the scoring model for a Chinese essay,we propose an automated scoring approach based on a deep learning model and validate its effect by conducting two comparison experiments.The experimental results indicate that the accuracy of the proposed model is significantly higher than that of multiple linear regression(MLR),which was commonly used in the past.The three accuracy rates of the proposed model are comparable to those of the novice teacher.The root mean square error(RMSE)of the proposed model is slightly lower than that of the novice teacher,and the correlation coefficient of the proposed model is also significantly higher than that of the novice teacher.Besides,when the predicted scores are not very low or very high,the two predicted models are as good as a novice teacher.However,when the predicted score is very high or very low,the results should be treated with caution.展开更多
The Nigeria National Response Management Information System (NNRIMS), developed in 2004 as a framework for monitoring and evaluating the country’s response to HIV, does not function at an optimum level due to several...The Nigeria National Response Management Information System (NNRIMS), developed in 2004 as a framework for monitoring and evaluating the country’s response to HIV, does not function at an optimum level due to several challenges, including a confusing proliferation of vertical reporting systems, competition among sectors, and the nascent nature of the monitoring and evaluation (M&E) sub-systems within many institutions. An assessment of the existing M&E system was conducted to verify whether the system has the capacities to provide essential data for monitoring the epidemic and identifying critical programming gaps. Nigeria’s National Agency for the Control of AIDS (NACA) used an organizing framework for a national HIV M&E system developed by UNAIDS, to assess the strengths and weaknesses of the NNRIMS to generate data for evidence-based decisionmaking. The participatory approach used during an assessment workshop ensured that the process was country-led and -owned to build consensus and local capacity, and that it encouraged adoption of a single national-level multisectoral HIV M&E system. The assessment found an operable M&E system at the national level but a much weaker system at the state and local levels and across seven other sectors. There are multiple data collection and reporting tools at the facility level that lead to vertical reporting systems, which increases the burden of reporting at lower levels, especially by service providers. Human resources are being developed, but problems remain with the quantity and quality of staff. Data use, though evident at the national level, is still very weak among five of the seven sectors assessed. The assessment results have been used to develop a national costed M&E workplan to which all stakeholders contributed in a coordinated response to strengthen the system.展开更多
There are various types of pyramid schemes that have inflicted or are inflicting losses on many people in the world.We propose a pyramid scheme model which has the principal characters of many pyramid schemes that hav...There are various types of pyramid schemes that have inflicted or are inflicting losses on many people in the world.We propose a pyramid scheme model which has the principal characters of many pyramid schemes that have appeared in recent years:promising high returns,rewarding the participants for recruiting the next generation of participants,and the organizer takes all of the money away when they find that the money from the new participants is not enough to pay the previous participants interest and rewards.We assume that the pyramid scheme is carried out in the tree network,Erd?s–Réney(ER)random network,Strogatz–Watts(SW)small-world network,or Barabasi–Albert(BA)scale-free network.We then give the analytical results of the generations that the pyramid scheme can last in these cases.We also use our model to analyze a pyramid scheme in the real world and we find that the connections between participants in the pyramid scheme may constitute a SW small-world network.展开更多
Speaker variability is an important source of speech variations which makes continuous speech recognition a difficult task.Adapting automatic speech recognition(ASR) models to the speaker variations is a well-known st...Speaker variability is an important source of speech variations which makes continuous speech recognition a difficult task.Adapting automatic speech recognition(ASR) models to the speaker variations is a well-known strategy to cope with the challenge.Almost all such techniques focus on developing adaptation solutions within the acoustic models of the ASR systems.Although variations of the acoustic features constitute an important portion of the inter-speaker variations,they do not cover variations at the phonetic level.Phonetic variations are known to form an important part of variations which are influenced by both micro-segmental and suprasegmental factors.Inter-speaker phonetic variations are influenced by the structure and anatomy of a speaker's articulatory system and also his/her speaking style which is driven by many speaker background characteristics such as accent,gender,age,socioeconomic and educational class.The effect of inter-speaker variations in the feature space may cause explicit phone recognition errors.These errors can be compensated later by having appropriate pronunciation variants for the lexicon entries which consider likely phone misclassifications besides pronunciation.In this paper,we introduce speaker adaptive dynamic pronunciation models,which generate different lexicons for various speaker clusters and different ranges of speech rate.The models are hybrids of speaker adapted contextual rules and dynamic generalized decision trees,which take into account word phonological structures,rate of speech,unigram probabilities and stress to generate pronunciation variants of words.Employing the set of speaker adapted dynamic lexicons in a Farsi(Persian) continuous speech recognition task results in word error rate reductions of as much as 10.1% in a speaker-dependent scenario and 7.4% in a speaker-independent scenario.展开更多
Artificial stock market simulation based on agent is an important means to study financial market.Based on the assumption that the investors are composed of a main fund,small trend and contrarian investors characteriz...Artificial stock market simulation based on agent is an important means to study financial market.Based on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes.Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main fund.Our work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.展开更多
Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on t...Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.展开更多
Time‐varying matrix inversion is an important field of matrix research,and lots of research achievements have been obtained.In the process of solving time‐varying matrix inversion,disturbances inevitably exist,thus,...Time‐varying matrix inversion is an important field of matrix research,and lots of research achievements have been obtained.In the process of solving time‐varying matrix inversion,disturbances inevitably exist,thus,a model that can suppress disturbance while solving the problem is required.In this paper,an advanced continuous‐time recurrent neural network(RNN)model based on a double integral RNN design formula is pro-posed for solving continuous time‐varying matrix inversion,which has incomparable disturbance‐suppression property.For digital hardware applications,the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas.As a result of theoretical analysis,it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance,and they are excellent for suppressing different disturbances.Finally,inspiring experiments,including two numerical experiments and a practical experiment,are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression.展开更多
Despite the fact that the sale of tobacco to minors is illegal in Ontario, youth are still able to purchase tobacco. This study aims to determine the geographic variations of underage tobacco sales at the neighborhood...Despite the fact that the sale of tobacco to minors is illegal in Ontario, youth are still able to purchase tobacco. This study aims to determine the geographic variations of underage tobacco sales at the neighborhood level within the Windsor-Essex County Health Unit. Data were collected on all inspections of tobacco retail stores from 2007 to 2011 in the Windsor-Essex County Health Unit. Data were split into season 1 (September-February) and season 2 (March-August) to assess a possible seasonal effect. Relative risks were calculated for each dissemination area (DA) by modeling the risks in a hierarchical Bayesian fashion, incorporating appropriate random effects terms for both spatially correlated and uncorrelated random errors with adjustments for neighborhood income. The association between violation rate and proximity to a school was assessed through a buffer analysis. Elliptical analysis detected a significant cluster of high risk DAs in season 1 in Windsor (p-value = 0.022) but no significant cluster in season 2. Some DAs exhibited higher relative risks of tobacco sales to minors, however after adjusting the model for neighborhood income no excess risk was observed. The results of the buffer analysis showed that in season 1 there was a significantly higher probability (p-value = 0.045) of tobacco vendors located closer to schools to sell tobacco to minors. This analysis demonstrates the utility of a systematic approach to identifying neighborhoods with higher risks of tobacco sales to minors. The insights provided by this exploratory, ecologic study are valuable for program planning and directing tobacco enforcement efforts to high risk areas.展开更多
基金The work is supported by the National Natural Science Foundation of China(Grant Nos.71471169,91546201 and 71071151).
文摘The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and technologies.However,qualitative and quantitative methods have shortcomings.The former involve high costs and many limitations,while the latter lack expert experience.Intelligent knowledge management emphasizes human–machine integration,which combines the advantages of expert experience and data mining.Thus,we proposed a new technology foresight method based on intelligent knowledge management.This method constructs a technological online platform to increase the number of participating experts.A secondary mining is performed on the results of patent analysis and bibliometrics.Thus,forward-looking,innovative,and disruptive areas and relevant experts must be discovered through the following comprehensive process:Topic acquisition→topic delivery→topic monitoring→topic guidance→topic reclamation→topic sorting→topic evolution→topic conforming→expert recommendation.
基金The paper is supported by National Natural Science Foundation of China (No 70271002)
文摘Based on the sticking point of the low intelligence of the existing management decision system,this paper puts forward the idea of enriching and refining the knowledge of the system and endowing it with the ability to learn by means of adopting three types of heterogeneous knowledge representation and knowledge management measures.At length,this paper outlines the basic framework of an intelligence system for the sake of management decision problem.
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
基金This paper is sponsored by National Nature Science Foundation of China（NSFC）.
文摘Despite of the fact that knowledge management has become the focus of current literature and management practice, the core process of knowledge has. not been identified. After comparing the pl：ocess of knowledge movement and that of fermenting, we put forward a new model-knowledge fermenting model. In this paper, we thoroughly analyze the element of knowledge fermenting model, and show how knowledge increase is realized through that model.
文摘It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.
文摘Introduction: Visceral leishmaniasis is a severe form of leishmaniasis that affects about 90,000 people annually worldwide. It is mainly transmitted by Leishmania donovani and infantum, which can cause damage to internal organs, such as the spleen, liver, and bone marrow. If left untreated, severe cases can be fatal, as the disease can lead to severe secondary diseases, mycological and bacterial infections, and hemorrhages. Nutritional deficiencies and concurrent infections increase the incidence of visceral leishmaniasis and the likelihood of lethality. There is limited information about the relationship between the disease and nutrition in endemic areas in Kenya. Objective: The study was to analyze the association of nutritional supplements with the nutritional status and treatment outcomes of visceral leishmaniasis among children aged 5 - 12 years in Baringo and West Counties in Kenya. Methods: A quasi-experimental study design adopting quantitative data collection method was used in this study. A total of 204 children aged 5 - 12 years were included in the study. Data on nutritional status and treatment outcomes for VL was collected using a questionnaire and consent form. Pre- and post-intervention assessments were conducted to compare BMI, fever, spleen size, splenic aspirate after treatment, and the presence of PKDL in a 3-month follow-up. The data was analyzed using R statistical software with descriptive and inferential statistics, including chi-square tests and t-tests. The impact of treatment was estimated using the difference-in-difference method to compare changes in outcomes over time between the intervention and comparison groups. Results: The baseline characteristics assessed in this study were socio-demographics (age, gender, marital status, education and religion), vitamins (A, B, C, D) and minerals (zinc, Iron and Iodine). The results showed that the mean age was 8.72, children aged 5 - 9 years were 64.7%, and those aged 10 - 12 years were 35.3% in the intervention and comparison groups. There were more males than females in the study (53.9% in the intervention and 52.9% in the comparison group respectively). All the children in the study were from a Christian background, were underweight, had enlarged spleen, and were positive for VL by Splenic Aspirate. Those who presented with fever were 88.2% (88% in both intervention and comparison groups). Most children had lower levels of zinc, iron, vitamins A, B12, and D at baseline (54.9%, 91.2%, 54.4%, 57.8%, and 58.8% respectively). The majority (93.1%) were deficient in vitamin C (90.2% in the intervention and 96.1% in the comparison group). Conclusion: According to the study findings, the effect of administering micronutrients is significant at 5% significance level with the intervention having a positive effect. The administration of the nutritional supplement led on average to an increase of the minerals, vitamins and BMI levels in the body.
基金partly supported by National Natural Science Foundation of China(No.71771204,72231010)the Fundamental Research Funds for the Central Universities(No.E0E48946X2).
文摘Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.
文摘Software requirements are the basis of software design,test and maintenance. The trend that future software should be intelligent and distributed and the changing software environment challenge the Object-Oriented methodology that is a primary technology to build software system currently. GoalOriented requirements analysis is discussed in detail in this paper. Goals are more powerful logical mechanism to identify,organize and justify software requirements than objects. This methodology provides convincing supports to build high quality software system in the future.
基金supported by National Natural Science Foundation of China (Grant No. 71471169 and Grant No. 71071151)
文摘In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge sharing and remanufacturing engineering management are highlighted. It is noticeable that a great deal of knowledge transfer and sharing activities, which can improve the performance of remanufacturing engineering management constantly, are involved in remanufacturing engineering.
基金supported by the West Light Foundation of the Chinese Academy of Sciences(2019-XBQNXZ-A-007)the National Natural Science Foundation of China(12071458,71731009).
文摘In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.
基金This work is supported by the National Science Foundation of China(No.61532008No.61572223)+1 种基金the National Key Research and Development Program of China(No.2017YFC0909502)the Ministry of Education of Humanities and Social Science project(No.20YJCZH046).
文摘Writing is an important part of language learning and is considered the best approach to demonstrate the comprehensive language skills of students.Manually grading student essays is a time-consuming task;however,it is necessary.An automated essay scoring system can not only greatly improve the efficiency of essay scoring,but also provide more objective score.Therefore,many researchers have been exploring automated essay scoring techniques and tools.However,the technique of scoring Chinese essays is still limited,and its accuracy needs to be enhanced further.To improve the accuracy of the scoring model for a Chinese essay,we propose an automated scoring approach based on a deep learning model and validate its effect by conducting two comparison experiments.The experimental results indicate that the accuracy of the proposed model is significantly higher than that of multiple linear regression(MLR),which was commonly used in the past.The three accuracy rates of the proposed model are comparable to those of the novice teacher.The root mean square error(RMSE)of the proposed model is slightly lower than that of the novice teacher,and the correlation coefficient of the proposed model is also significantly higher than that of the novice teacher.Besides,when the predicted scores are not very low or very high,the two predicted models are as good as a novice teacher.However,when the predicted score is very high or very low,the results should be treated with caution.
文摘The Nigeria National Response Management Information System (NNRIMS), developed in 2004 as a framework for monitoring and evaluating the country’s response to HIV, does not function at an optimum level due to several challenges, including a confusing proliferation of vertical reporting systems, competition among sectors, and the nascent nature of the monitoring and evaluation (M&E) sub-systems within many institutions. An assessment of the existing M&E system was conducted to verify whether the system has the capacities to provide essential data for monitoring the epidemic and identifying critical programming gaps. Nigeria’s National Agency for the Control of AIDS (NACA) used an organizing framework for a national HIV M&E system developed by UNAIDS, to assess the strengths and weaknesses of the NNRIMS to generate data for evidence-based decisionmaking. The participatory approach used during an assessment workshop ensured that the process was country-led and -owned to build consensus and local capacity, and that it encouraged adoption of a single national-level multisectoral HIV M&E system. The assessment found an operable M&E system at the national level but a much weaker system at the state and local levels and across seven other sectors. There are multiple data collection and reporting tools at the facility level that lead to vertical reporting systems, which increases the burden of reporting at lower levels, especially by service providers. Human resources are being developed, but problems remain with the quantity and quality of staff. Data use, though evident at the national level, is still very weak among five of the seven sectors assessed. The assessment results have been used to develop a national costed M&E workplan to which all stakeholders contributed in a coordinated response to strengthen the system.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71771204 and 91546201)
文摘There are various types of pyramid schemes that have inflicted or are inflicting losses on many people in the world.We propose a pyramid scheme model which has the principal characters of many pyramid schemes that have appeared in recent years:promising high returns,rewarding the participants for recruiting the next generation of participants,and the organizer takes all of the money away when they find that the money from the new participants is not enough to pay the previous participants interest and rewards.We assume that the pyramid scheme is carried out in the tree network,Erd?s–Réney(ER)random network,Strogatz–Watts(SW)small-world network,or Barabasi–Albert(BA)scale-free network.We then give the analytical results of the generations that the pyramid scheme can last in these cases.We also use our model to analyze a pyramid scheme in the real world and we find that the connections between participants in the pyramid scheme may constitute a SW small-world network.
文摘Speaker variability is an important source of speech variations which makes continuous speech recognition a difficult task.Adapting automatic speech recognition(ASR) models to the speaker variations is a well-known strategy to cope with the challenge.Almost all such techniques focus on developing adaptation solutions within the acoustic models of the ASR systems.Although variations of the acoustic features constitute an important portion of the inter-speaker variations,they do not cover variations at the phonetic level.Phonetic variations are known to form an important part of variations which are influenced by both micro-segmental and suprasegmental factors.Inter-speaker phonetic variations are influenced by the structure and anatomy of a speaker's articulatory system and also his/her speaking style which is driven by many speaker background characteristics such as accent,gender,age,socioeconomic and educational class.The effect of inter-speaker variations in the feature space may cause explicit phone recognition errors.These errors can be compensated later by having appropriate pronunciation variants for the lexicon entries which consider likely phone misclassifications besides pronunciation.In this paper,we introduce speaker adaptive dynamic pronunciation models,which generate different lexicons for various speaker clusters and different ranges of speech rate.The models are hybrids of speaker adapted contextual rules and dynamic generalized decision trees,which take into account word phonological structures,rate of speech,unigram probabilities and stress to generate pronunciation variants of words.Employing the set of speaker adapted dynamic lexicons in a Farsi(Persian) continuous speech recognition task results in word error rate reductions of as much as 10.1% in a speaker-dependent scenario and 7.4% in a speaker-independent scenario.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71932008 and 91546201).
文摘Artificial stock market simulation based on agent is an important means to study financial market.Based on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes.Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main fund.Our work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.
文摘Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.
文摘Time‐varying matrix inversion is an important field of matrix research,and lots of research achievements have been obtained.In the process of solving time‐varying matrix inversion,disturbances inevitably exist,thus,a model that can suppress disturbance while solving the problem is required.In this paper,an advanced continuous‐time recurrent neural network(RNN)model based on a double integral RNN design formula is pro-posed for solving continuous time‐varying matrix inversion,which has incomparable disturbance‐suppression property.For digital hardware applications,the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas.As a result of theoretical analysis,it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance,and they are excellent for suppressing different disturbances.Finally,inspiring experiments,including two numerical experiments and a practical experiment,are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression.
文摘Despite the fact that the sale of tobacco to minors is illegal in Ontario, youth are still able to purchase tobacco. This study aims to determine the geographic variations of underage tobacco sales at the neighborhood level within the Windsor-Essex County Health Unit. Data were collected on all inspections of tobacco retail stores from 2007 to 2011 in the Windsor-Essex County Health Unit. Data were split into season 1 (September-February) and season 2 (March-August) to assess a possible seasonal effect. Relative risks were calculated for each dissemination area (DA) by modeling the risks in a hierarchical Bayesian fashion, incorporating appropriate random effects terms for both spatially correlated and uncorrelated random errors with adjustments for neighborhood income. The association between violation rate and proximity to a school was assessed through a buffer analysis. Elliptical analysis detected a significant cluster of high risk DAs in season 1 in Windsor (p-value = 0.022) but no significant cluster in season 2. Some DAs exhibited higher relative risks of tobacco sales to minors, however after adjusting the model for neighborhood income no excess risk was observed. The results of the buffer analysis showed that in season 1 there was a significantly higher probability (p-value = 0.045) of tobacco vendors located closer to schools to sell tobacco to minors. This analysis demonstrates the utility of a systematic approach to identifying neighborhoods with higher risks of tobacco sales to minors. The insights provided by this exploratory, ecologic study are valuable for program planning and directing tobacco enforcement efforts to high risk areas.
