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In the paper industry, numerous studies have explored means of optimizing order allocation and cutting trim loss. However, enterprises may not adopt the resulting solutions because some widths of the inventory exceed or are less than those required for acceptable scheduling. To ensure that the results better suit the actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate trim loss and inventory distribution data. Differential analysis is used to reduce data filtering and the information is valuable for decision making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss. [ABSTRACT FROM AUTHOR]

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology. [ABSTRACT FROM AUTHOR]

Patient-reported outcomes are an important tool in clinical research. In the setting of cancer treatments, benefit of therapy is essentially characterised by improvement of survival as well as quality of life ( QoL). A standardised instrument to assess QoL is the standardised QoL questionnaire of the European Organisation for Research and Treatment ( EORTC QLQ- C30 questionnaire). QoL instruments provide data on different aspects (domains) of the framework of QoL. Using these questionnaires in studies provides data on how a treatment affects QoL in a group of patients. The goal of our concept is to individualise QoL and to use validated instruments in order to integrate patients' perspectives and aims into treatment assessment, planning and control. We propose to use the domains of the EORTC QLQ- C30 and to ask the patient to determine which objectives besides survival are relevant for him and should be achieved by treatment. These individual goals can be used in a process of shared decision-making to choose and monitor treatment. In clinical studies, this approach would allow to recruit more patients who would most probably benefit from the therapy. In addition, supportive data could be gathered in correlation to treatment goals and actual benefits. [ABSTRACT FROM AUTHOR]

Low energy consumption and green transformation of automobile product portfolio is the trend of the times. Automotive manufacturers make product portfolio decisions by setting multiple criteria such as fuel consumption, sales, and volume. It is also important to take into account the symbiotic interaction effects between automotive products. In order to achieve the above research objectives, this paper constructs the Lotka–Volterra MCGP model to make multicriteria decisions on automobile product portfolio design from the perspective of energy sustainability with BMW Brilliance is taken as an example to illustrate the process of using the multicriteria model. The empirical analysis successively measures product growth using the logistic model, analyzes the symbiotic relationship of product portfolios using the Lotka–Volterra model, and finally performs multicriteria evaluation using the MCGP model. In order to verify the reliability of the model, this paper verifies the robustness of the model from the perspectives of parameter dynamics, system boundaries, and model scalability. The results of empirical analysis and robustness analysis show that the Lotka–Volterra MCGP model proposed in this paper is applicable to the multicriteria decision-making of automobile product portfolio design from the perspective of energy sustainability. [ABSTRACT FROM AUTHOR]

This integrative review aims to evaluate the experiences of health and social care practitioners with regard to how they exercise professional curiosity in child protection practice. Professional curiosity gained significant currency following the Munro Review of Child Protection (2010) in England, as a means of seeking clarity on what is happening within a family. However, a recurrent finding from child safeguarding practice reviews is that practitioners continue to struggle to exercise curiosity. This is evident within both the United Kingdom and international literature, although descriptors for the concept may differ. This study attempted to identify facilitators and barriers to applying professional curiosity to provide a greater understanding of this theoretical concept. Title and abstract review of 1428 articles identified from databases and 11 from other sources resulted in 52 papers for full‐text review. The quality of each article was appraised using the Critical Appraisal Skills Programme tool for qualitative studies, the Mixed Methods Appraisal Tool (MMAT) for quantitative/mixed method studies and the Joanna Briggs framework for theoretical/opinion papers. Key findings were recorded in the Summary Table of Literature Reviewed. Data extracts were thematically analysed. Twenty‐four papers predominantly from the UK, but also from Australia, Italy, Sweden and USA formed the data set. Overarching themes that emerged from the thematic analysis included: noticing dissonance, emitting curiosity, constructing meaning, facilitators, individual professional challenges, organisational and macro‐level influences and conceptual development. This review demonstrated that professional curiosity is multifaceted and involves a whole system approach, from empowered, knowledgeable and competent frontline practitioners to creative, innovative and empathic organisations, that value staff contributions and place the child's best interests at the forefront of service development. Recommendations are made for practice and further research. [ABSTRACT FROM AUTHOR]

The single‐valued neutrosophic set (SVNS) is a more common platform for representing the degree of truth, indeterminacy, and falsity memberships. Indeterminacy is an important aspect of SVNS, and the score function (SF) is important in ranking alternatives in decision‐making scenarios. When the score and accuracy values for different SVNNs were equal, existing SFs were unable to rank the alternatives. To address the limitations, this paper introduces the perfect score function (PSF) as a solution for ranking single‐valued neutrosophic numbers (SVNNs) without any errors and presents the concepts of strong and weak SVNSs. This article has emphasized the drawbacks of the existing SF in SVNNs. Additionally, we propose an optimized weighted geometric operator (OWGO) for SVNNs, and its properties such as idempotency, boundedness, monotonicity, and commutativity are explored. Furthermore, the decision‐making approach has been depicted based on the proposed score function and OWGO to select the finest faculty for a university. [ABSTRACT FROM AUTHOR]

Over the years, intelligent wheelchairs have been developed to fulfill the needs of wheelchair users by gathering information from the environment to make autonomous decisions. This paper proposes an intelligent object manipulation algorithm that leverages users' intentions, past experiences, and preferences. To gather the necessary information for object placement, a human study involving 20 healthy participants was conducted. The study included two situation‐based object placement scenarios and object placement when objects were given in pairs. Building on the authors' previously developed electromyography‐based wheelchair navigation algorithm, a semiautonomous object manipulation algorithm was developed. This new algorithm was tested in the CoppeliaSim simulation environment by replicating the tasks performed in the human study. The object arrangements in the simulation were then compared to the results from the human study. The findings indicate that the implemented object manipulation algorithm successfully replicated the outcomes observed in the human study. The application of this controller is expected to significantly enhance accessibility for individuals with impaired motor functionalities in addition to their ambulatory impairments. This development represents a significant step forward in creating user‐friendly and adaptive assistive technologies for wheelchair users. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the relevance of early childhood education to occupational psychotherapy using a virtual-assisted affective interaction approach. Starting from the educational theory of interactive cognitive psychology, the theoretical basis for parent-child picture book education for interactive learning is explored, as well as the theoretical development after the introduction of AR technology. Firstly, the analysis of young children's emotions involves massive image processing, and the use of cloud computing architecture leads to high latency, while young children's safety is a latency-sensitive service. Secondly, face recognition accuracy based on static images is not high due to problems such as inconspicuous facial features of toddlers and low-quality kindergarten surveillance videos. In this paper, a face identity correction model based on location features is proposed and the superiority of the model is demonstrated through experiments. Finally, this paper analyzes and mines the emotional data of young children. The level of kindergarten teachers' awareness of early childhood mental health education generally showed an upward trend as their titles rose, and there were significant differences in the seven dimensions of early childhood mental health, the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, and effects; significant differences existed between teachers of different kindergarten natures, and there were significant differences in the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, effects, and mental health education for young children. Therefore, this paper proposes a face identity correction model based on position information, which considers both the correlation between pixel values in the spatial domain and the correlation between frames in the temporal domain. This paper has developed an emotion analysis system for kindergartens and put it into use in kindergartens to meet the needs of monitoring the safety of young children and evaluating early childhood education and has received good feedback from users, demonstrating the effectiveness of the system. [ABSTRACT FROM AUTHOR]

With the sustained and rapid development of China's economy, the business environment of enterprises is becoming more and more complicated. Coupled with the increasing international competitive pressure of enterprises, the management of enterprises needs more attention. The emergence of DSS provides an opportunity for enterprise management intelligent decision-making. Based on the in-depth study of RF algorithm, this paper proposes a new intelligent decision-making model for enterprise management, aiming at the shortcomings of traditional decision-making systems. In this paper, the decision-making process of the system for decision support is given. Through the quantification and evaluation of various data by subsystems, the best decision-making scheme with strong operability is provided for enterprises. And this model can effectively solve the problem of heterogeneity, duplication and clutter of data in traditional database, and avoid the loss of historical data. Through simulation, the accuracy of this system can reach 96%, which is about 17% higher than other systems. It has certain practicability and feasibility. It is hoped that the research in this paper will play an important role and service support for enterprise intelligent decision-making and further promote the development of intelligent enterprise management decision-making system in China. [ABSTRACT FROM AUTHOR]

This short overview paper points out the striking similarity between decision under uncertainty and multicriteria decision making problems, two areas which have been developed in an almost completely independent way until now. This pertains both to additive and non-additive (including qualitative) approaches existing for the two decision paradigms. This leads to an emphasis on the remarkable formal equivalence between postulates underlying these approaches (like between the “sure-thing principle” and mutual preferential independence of criteria). This analogy is exploited by surveying classical results as well as very recent advances. This unified view should be fruitful for a better understanding of the postulates underlying the approaches, for cross-fertilization, and for adapting artificial intelligence uncertainty representation frameworks to preference modelling. © 2000 John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Decision-making is an important component of autonomous driving perception, decision-making, planning, and control pipeline, which undertakes the task of how the ego vehicle makes high-level decision-making behaviors (such as lane change and car following) after sensing the environmental state, and then these high-level decision-making behaviors can be transmitted to the downstream planning and control module for specific low-level action execution. Based on the method of deep reinforcement learning (specifically, Deep Q network (DQN) and its variants), an integrated lateral and longitudinal decision-making model for autonomous driving is proposed in a multilane highway environment with both autonomous driving vehicle (ADV) and manual driving vehicle (MDV). The classic MOBIL and IDM models are used for the lateral and longitudinal decisions of MDV (i.e., lane changing and car following), while the lateral and longitudinal decisions of ADV are dominated by deep reinforcement learning models. In addition, this paper also uses the nonlinear kinematic bicycle model and two-point visual control model to realize the low-level control of both MDV and ADV. By setting a reasonable state, action, and reward function, this paper has carried out a large number of simulation experiments on the proposed autonomous driving decision-making model based on deep reinforcement learning in a three-lane road environment. The results show that under such scenario setting conditions, the deep reinforcement learning-based model proposed in this paper performs well in autonomous driving safety and travel efficiency. At the same time, when compared with the classical rule-based decision-making model (MOBIL&IDM), it is found that the model proposed in this paper can significantly achieve better results in episode rewards after stable training. In addition, through a large number of hyper-parameter tuning experiments, the performance of DQN, DDQN, and dueling DQN models, which are also deep reinforcement learning-based decision-making models, under different hyper-parametric configurations is compared and analyzed, which can provide a valuable reference for the specific scenario application of these models. [ABSTRACT FROM AUTHOR]

People with dementia and their care partners report a lack of support, treatment, and information, fragmented services, and a lack of inclusion in decisions about their care. Care planning may address these issues; however, there is scarce literature on the process or benefits of care planning for people with dementia. This review describes the literature on care planning for community-dwelling people with dementia and their care partners. A systematic scoping methodology was followed to identify the research questions, identify relevant documents, select relevant documents, chart the data, and collate, summarise, and report the results. 31 full-text documents published between 2010 and May 2020 were identified and reviewed. Seven were guidelines, seven were expert opinion pieces, 11 were intervention studies, and six were descriptive studies. The topics and process of care planning varied depending on the service context (e.g., memory clinic, home care, and primary care). Care planning was presented as a component of case management in 15 papers. Six of the 11 intervention studies reported positive outcomes, one showed no improvement, and one did not evaluate outcomes for people with dementia or their care partners. Of the six with positive outcomes, four evaluated care planning in the context of care management. There is limited evidence that care planning alone improves outcomes for people with dementia and their care partners. It is unclear whether it may have benefits when combined with care management. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the colour optimal matching model in art design through the method of Bayesian decision-making and the colour optimal matching model designed and applied to practice. Although Bayesian conditional theory constructs a representation theorem for causal decision theory, eliminating the formal differences between evidence decision theory and causal decision theory and reflecting their common intrinsic form. However, it still does not resolve the conflict between causal and evidential decision theories, but only translates it into a different interpretation of state parsing, and the choice of interpretation still comes from people's different intuitive understanding of rationality. To address the problem that the colour information of the target image in the traditional recolouring method easily interferes with the recolouring process and affects the colour effect of the resulting image, a new method of recolouring based on the centralization constraint is proposed in the paper. A new method of colour transfer based on random walk image segmentation is proposed. First, an improved random walk image segmentation method is introduced to segment the reference image and the content image to obtain a more reasonable segmentation region, which can enhance the hierarchy of the resulting image. Second, the proposed colour transfer strategy performs feature matching in the corresponding region to achieve colour transfer. Finally, the structure-preserving filter is introduced to further optimize the resulting image to effectively improve the visual effect of the resulting image. Extensive experimental results show that the proposed method can achieve significantly better-quality results than the colour clustering-based colour transfer method. The experimental analysis shows that the newly designed comprehensive objective evaluation index of colour transfer in the paper can effectively solve the problem of the one-sidedness of a single evaluation index and can achieve highly consistent evaluation results with subjective evaluation. [ABSTRACT FROM AUTHOR]

In order to solve the problems of inconsistent accounting and untimely accounting information, this paper proposes a research method of intelligent optimization of financial sharing path based on big data of accounting. This paper analyzes the current working situation of the financial sharing service center of large- and medium-sized enterprises and expounds the reasons for the blockchain. The author embeds the blockchain technology in the financial sharing service center, with a view to optimizing the design of the current FSSC's functions in terms of the scope of daily business work, the effectiveness of accounting information processing, and the utilization of financial analysis decisions, and finally analyzes the corporate effect of applying the financial sharing architecture based on blockchain technology. The results show that compared with the financial sharing score under the traditional mode, the financial sharing score under the blockchain technology is higher, and the former is 69.675 points, and the latter is 80.6340 points. From this, we can conclude that financial sharing under blockchain technology has significant advantages. This framework realizes the effective allocation of enterprise information resources through finance to achieve real industry finance integration. [ABSTRACT FROM AUTHOR]

3D printing or additive manufacturing (AM) is considered to be the most important technology among the emerging technologies. 3D printing technology is considered as an alternative to the conventional manufacturer machine traditionally used in the manufacturing sector. 3D printing technology is generally classified into seven types. Each type of 3D printing technology has its separate own uniqueness (i.e., operation, material usage, and no wastage). The price of a manufactured item includes all its costs. The most important of these is to take into account the price of the machine being manufactured and the features of the machine. Moreover, the price of the product produced in AM will depend on all the costs required to produce it. Then, it is possible to reduce the cost of the product by choosing the AMM that has significant features and the right price. Therefore, this paper aims to solve a decision-making problem from the AMM selection by using one of the multicriteria decision-making (MCDM) tools, i.e., analytical hierarchy process (AHP). This paper outcome is meant to meet the expectation of end-users. As an initial step, the Micro, Small, and Medium Enterprise (MSME) company gets quotations from some AM companies to choose a type of AM machine known FDM for its structure product and doll product. The first step is to select the most appropriate machines based on cost, size/volume, extruder type, and weight of the machine. Criteria for AHP are derived from decision-makers. Also, in AHP, the pair-wise matrix is obtained from the decision-makers by answering the standard Saaty's scale criteria questions. In this paper, such a selection method is explored. The outcome of this paper may vary depending on the expectations of the decision-makers. The end of this paper helps to choose the AMM with the right price and features to suit the decision-makers. [ABSTRACT FROM AUTHOR]

Occupying the same crucial position as land resources, ecological resources originate from the marine system nourish various aspects of human welfare and the world economy. It is significant to apply efficient decision-making approaches, which assist with the achievements of sustainable development and environmental protection. This paper aims to review decision-making approaches in marine management and present a comprehensive bibliometric study. To achieve this goal, bibliometric analysis is used to analyze the current status and emerging trends in this field, including the cluster network, the collaboration network, and burst detection. The results show that the number of publications in this field has increased rapidly, and it has become an increasing interdisciplinary research subject. Besides, marine conservation is the largest clusterand the USA is the most productive country in marine management research and the marine policy and NOAA are the most productive journal and institution, respectively. Furthermore, "Diversity," "Management," and "Population" are the most influential keywords, and "People" and "Public participation" are the emerging trends in decision-making and marine management. Our work clarifies where we are now in terms of marine management and decision-making and how do they serve each other to achieve sustainable development. More importantly, some deep insights could be given that motivate researchers to learn the connections and collaborations between natural science and social study, which is our effort to deal with the social-ecological obstacle as well. [ABSTRACT FROM AUTHOR]

With the growing development of the era of big data, data acquisition and analysis have become hot spots, and Python-based crawler technology is one of the most widely used tools in data analysis work at present. In this paper, we apply Python crawler key technology to acquire data of movie list and hot movies on Cat's Eye movie network, analyze data based on Python development environment Spyder, use the Numpy system to store and process large data, Chinese Jieba word separation tool to crawl data for word separation text processing, Snownlp library to process text sentiment, and finally by the word cloud map and web dynamic map display information such as viewers' emotional tendency and movie rating statistics, and provide decision support for users' movie viewing. [ABSTRACT FROM AUTHOR]

The postmanagement of an enterprise is to rationally and scientifically arrange the talents who meet the postrequirements and are suitable for the development of the enterprise in their suitable posts through testing, selection, and employment. The job recruitment information published on the recruitment website is the data that best reflects the market demand for data analysis talents. However, as online recruitment information is mostly presented in the form of text, this paper explores the information contained in the recruitment information through text mining technology, which has reference value for data analysis job seekers. Based on text mining and multicriteria decision-making, this paper studies job matching. The technical jobs under text mining are mainly distributed in the Internet industry, and the number of recruits accounts for 66.45%. Other industries have less demand for technical data analysis, all of which are below 15%. However, the market of business data analysis is scattered and extensive, and education still occupies the largest demand provider, accounting for 30.53%. However, real estate and media are closely followed, accounting for 24.28% and 22.34%, respectively. Besides these three industries, compared with technical talents, service industries will also need business data analysis talents more. In the stage of data collection, through the data analysis of the recruitment website, the job recruitment information is crawled, as well as other job information such as job salary, job qualifications, work experience requirements, and the number of recruits. This paper carries out the data collection stage and crawls the job recruitment information through the data analysis of the recruitment website. And, other position information, such as position salary, position qualification, and work experience requirements, as well as the number of recruits. The research of this paper can help the data released by the recruitment website to analyze the recruitment information and quickly reflect the market demand data for data analysis talents. [ABSTRACT FROM AUTHOR]

The investment decision-making behavior of investors refers to the capital purchase behavior of investors in order to achieve a certain purpose or goal. As an individual decision-maker, investors will be affected by various factors when making investment decisions, and the influencing factors may be caused by the outside or generated by themselves. The focus of this paper is to analyze the impact of investors' past returns on investors' decision-making based on multisource heterogeneous data. Multisource heterogeneous data can show the results of investors' behavior evaluation at multiscale and multilevel. Using conceptual deduction and empirical research methods, based on the analysis of investors' behavior, the financial risk prediction results are proposed. Financial risk prediction mainly analyzes the impact of investment returns on investors' decision-making from two aspects: individual returns and overall returns. The measurement indicators of investment decisions use investors' transaction frequency and asset size. [ABSTRACT FROM AUTHOR]

Machine breakdowns (MB) often occur, resulting in changes in the production layout and the material flow and causing a surge in production cost. Obviously, it is imperative to consider MB in workshop layout. Scholars mainly focus on the double row layout problem in an ideal nonfailure condition while scarcely considering MB mentioned above in the layout field. This paper proposes an enhanced multiobjective double row layout model considering MB (MDRLP-MB) to fill the research gaps. That model adds redundancy to reduce the losses caused by machine failure based on the prediction of breakdowns. In MDRLP-MB, machines with the same machine type have the same size and failure rate. Besides, this paper redefines the biobjectives of material handling costs (MHC) and the utilization ratio based on the feature of MDRLP-MB. To reduce the complexity of the problem, a decision rule is developed to reduce the alternative machine types. Furthermore, it revises the NSGA-II to solve the MDRLP-MB effectively. Results of numerical experiments and application cases show MDRLP-MB can obtain the optimal set of Pareto solutions in the case of increasing different numbers of redundancy considering MB to provide a more refined layout scheme for decision making. [ABSTRACT FROM AUTHOR]

In the past 3 decades, the emerging technology in manufacturing sector other than conventional manufacturing is additive manufacturing (AM). The additive manufacturing principle was completely born from the research gaps of conventional manufacturing concepts such that the elimination of wastage and produce the component layer upon layer. Every new technology obtains the sustainability that may consider in the supply chain management principle of the raw material to finished products and decision-making techniques are to reach the consumer or customer as properly and periodically but these things are addressed challenges in additive manufacturing. This review paper aims to narrate the common research gaps on additive manufacturing, the structure of the supply chain management on AM, MCDM tools/techniques, AM application in different fields, challenges of selection of additive manufacturing, AM processes, and its application. The traditional method of literature review has been used for this review. The outcome of this review has helped the new researcher's decision making on the AM various problems as easily and the new researchers may give the experimental solution for explored research gaps. [ABSTRACT FROM AUTHOR]

Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML-based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL-based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI-based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in-depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs. [ABSTRACT FROM AUTHOR]

Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we construct a model of convolutional neural network speech emotion algorithm, analyze the classroom identified by the neural network with a certain degree of confidence together with the school used in the dataset, find the characteristics and rules of teachers' control of classroom emotion nowadays using big data, find the parts of classroom emotion, and design a classroom emotion recognition model based on convolutional neural network speech emotion algorithm according to these characteristics. This paper will investigate the factors and patterns of teachers' emotional control in the classroom. In this paper, the existing neural network is adapted and improved, and some preprocessing is performed on the current dataset to train the network. The network used in this paper is a combination of convolutional neural network (CNN) and recurrent neural network (RNN), which takes advantage of both CNN for feature extraction and RNN for memory capability in the sequence model. This network has a good effect on both object labeling and speech recognition. For the problem of extracting emotion features of whole-sentence speech, we propose an attention mechanism-based emotion recognition algorithm for variable-length speech and design a spatiotemporal attention module for the speech emotion algorithm and a convolutional channel attention module for the CNN network to reduce the contribution of the spatiotemporal data of the speech emotion algorithm and the unimportant parts of the CNN convolutional channel feature data in the subsequent recognition by the attention mechanism. In turn, the weight of core key data and features is increased to improve the model recognition accuracy. [ABSTRACT FROM AUTHOR]

Cultural and creative design (CCD) plays an important role in the transmission and development of culture, and this paper takes the design of campus cultural and creative products (CCCPs) in the context of new media communication as an example to study. The rapid development of new media has been accompanied by the full development of a wide range of communication media. With the application of Internet technology in cultural development, it has led to better design of CCCPs. However, research on CCCPs reveals that the homogenization of products is relatively serious and the quality of cultural and creative products is too low. In order to select high-quality cultural and creative products, this paper proposes a method for evaluating and selecting CCD solutions based on grey correlation multicriteria decision analysis. By using a combination of grey correlation analysis and multicriteria decision making, a comparative study of various CCCPs is conducted and specific selection strategies are given, providing a reference for effectively grasping the scientific design of CCCPs in the context of the times. [ABSTRACT FROM AUTHOR]

Family's academic cognition influences the family's academic concept, rearing fashion, and academic participation. It is no longer solely associated to kid's bodily and intellectual development; however, additionally associated to household concord and social progress. With the development of the times, the complicated traits of training proceed to pose new challenges to parents. Exploring the composition and operation mechanism of family training decision-making cognition is envisioned to stop up the key to promote parents' orderly coaching participation and home university cooperation. However, the associated lookup of usual cognitive mannequin has terrible steadiness and prediction charge in focus results. This paper constructs a cognitive model of family training decision-making principally based totally on neural network. Through the assessment of relevant data, they have an effect of the cognitive model of family coaching decision-making mainly based totally on neural neighborhood evaluated from the accuracy, root suggest rectangular error RMSE, and AUC curve. The experimental effects exhibit that the prediction accuracy of the cognitive mannequin of household training decision-making primarily based on neural community is 15% greater than that of the standard model, and the cognitive balance of the mannequin is 8.2%. This paper demonstrates the effectiveness, feasibility, and practicability of the mannequin in realistic teaching. [ABSTRACT FROM AUTHOR]

Football is a popular sport in the world. Playing football can not only exercise the body and improve physical fitness but also learn some offensive and defensive skills. Football games are popular events all over the world. With the development of science and technology, virtual football games have also become popular. The virtual football game is an entertainment project that combines computer technology and virtual reality technology to analyze the attack and defense relationship in the virtual football game. It can understand the decision-making mechanism and apply the decision-making mechanism to the Internet of Things decision-making system, which will be able to make accurate and fast decisions like virtual players. This is also the content of this paper. This paper proposes a decision system based on virtual football, applies the system to the Internet of Things, and shows that the data of the system fluctuate within 1.5%, that is, the calculation accuracy of the system model is as high as 98.5%, with high calculation stability, high accuracy, and certain reliability. [ABSTRACT FROM AUTHOR]

In order to meet people's diverse, complex, and changeable living requirements and aesthetic requirements, it is necessary to rationalize the design of indoor three-dimensional space. The optimization of the indoor environmental space layout is affected by the energy consumption and rationality of functional areas, resulting in indoor environmental space The performance of the layout optimization system deteriorates. In order to improve the performance of the indoor environment spatial layout and meet the living needs of more people, this paper proposes an optimal design of a spatial layout based on multi-intelligence decision-making. The research results of the article show that (1) the average confidence and the average recognition time of logo set B are both smaller than those of logo set A , indicating that logo set B is easier to be recognized by users in the same commercial building environment and can be more quickly recognized. In terms of discrimination, the time used to correct the misrecognition of identification set B is shorter than that of identification set A , which means that the different types of identifications in identification set B are easier to distinguish. (2) The spatial positioning algorithm proposed in this paper has good reliability and practicability, high positioning accuracy, and small error for indoor spatial coordinate positioning. Compared with the traditional method, the projection method of this paper has a significantly smaller error, and the maximum error is only 0.272, which makes the indoor space design more rational. (3) Comparing the energy consumption coefficients of the three spatial layout optimization systems, it can be seen that the indoor environmental spatial layout optimization system based on multi-intelligence decision-making has the lowest energy consumption when optimizing the indoor environmental spatial layout, because the system is in the design process. The indoor environment space layout model is designed, which effectively reduces the energy consumption of the indoor environment space layout optimization. When the indoor environment space layout optimization system based on multi-intelligence decision-making is used to optimize the indoor environment space layout, the average optimization accuracy is 98.32%, while the average indoor environment space layout optimization accuracy of the space layout optimization system with curved shading space layout optimization is 42.2% %, the layout optimization of binocular stereo vision space. When optimizing the indoor space layout, the average optimization accuracy is 70.87%. [ABSTRACT FROM AUTHOR]

This paper adopts virtual reality technology to conduct in-depth research and analysis on sports rehabilitation training, designs a corresponding sports rehabilitation training program, and applies it to practice. An AR algorithm based on dynamic target tracking under VSLAM is proposed. The algorithm can effectively reject dynamic targets in static scenes while ensuring that the virtual objects registered based on dynamic template target tracking are still in the world coordinate system of VSLAM. To facilitate patients' hand function rehabilitation training, this paper uses OpenPose for 2D gesture pose recognition, combines camera pose information and depth information provided by VSLAM to map key points of the hand into the world coordinate system, and then completes the interaction by collision detection algorithm. The virtual interaction module is implemented in this paper to meet the demand for multiuser off-site interaction in virtual training. This paper uses the Unity3D software and Photon Server server to create a VR virtual scene and design a user interaction mechanism to realize a system that supports multiple users to train together online, which effectively extends the application scope of the VR training system. The module utilizes Unity3D's VR development capabilities to develop VR virtual basketball gym scenes and single-player offline interaction mechanisms such as virtual user single shooter and shooter; then, Photon Server is used to design and implement a multiuser remote login system and a multiuser interpass mechanism, thus achieving the effect of multiplayer remote online interaction in the same VR space. Finally, the proposed module was validated, and the results proved the effectiveness of the sports rehabilitation training program. [ABSTRACT FROM AUTHOR]

There is no well-built theory on credit risk measurement and decision analysis for financial big data, and an effective and scientific evaluation system for them has not been formed. A review of them can contribute to grasping the abovementioned topics, understanding current issues, analyzing research problems, mastering research challenges, and predicting future research directions. Besides, this paper points out four research directions of credit risk measurement and decision analysis for financial big data. Moreover, this paper can provide some guidance directions and insights for practitioners, researchers, financial institutions, and government departments who have an interest in complex decision-making in big data. [ABSTRACT FROM AUTHOR]

When decision-makers' judgments are uncertain, they often express their opinions using grey linguistic variables. Once used, the data often retains its grey nature throughout all subsequent decision-making iterations. Multicriteria decision-making (MCDM) is a tool used when making complicated decisions and in circumstances where several criteria require evaluation to choose the most desirable option. Grey data serves as the basis for several MCDM methods. This paper compares two MCDM methods, Grey-Linear-Programming (GLP) and Grey-Best-Worst-Method (GBWM), in terms of the weights of decision criteria and their rankings. Moreover, Grey-The Technique for Order of Preference by Similarity to Ideal Solution (GTOPSIS) was used to rank the weights of the two methods. Study findings demonstrated that GBWM requires more mathematical calculations than GLP, based on linear programming's classic simplex method. On the other hand, when GTOPSIS follows GLP, the alternative rank does not change compared to when GTOPSIS followed GBWM. For the applications used in this comparison, GLP procedure is considered simpler than GBWM procedure. [ABSTRACT FROM AUTHOR]

A shortage or backlog of inventory can easily occur due to the backward forecasting method typically used, which will affect the normal flow of funds in pharmacies. This paper proposes a replenishment decision model with back propagation neural network multivariate regression analysis methods. With the regular pattern between sales and individual variables, supplemented with the safety stock empirical formula, an accurate replenishment quantity can be obtained. In the case analysis, this paper takes the sales situation of a pharmacy as an example and tests the accuracy and stability of the model. The results show that the model has good prediction accuracy which can be introduced into the intelligent pharmacy system and used in the replenishment of the intelligent pharmacy to prevent overstocking or a shortage of stock, thus improving the financial situation, reducing the manpower burden of typical retail pharmacy, and helping residents buy medicines. [ABSTRACT FROM AUTHOR]

In cognitive relay networks, the cognitive user opportunistically accesses the authorized spectrum segment of the primary user and simultaneously serves as the data relay node of the primary user while sharing the spectrum resource of the primary user. This not only improves the utilization efficiency of the network spectrum resources but also improves the throughput of the primary users. However, if the primary user randomly selects the relay node, there is no guarantee for an optimal throughput. Moreover, the system power consumption may increase. In order to improve the throughput of cognitive relay network and optimize system utility, this paper proposes a cognitive relay network throughput optimization algorithm based on deep reinforcement learning. For the system model of cognitive relay networks, the Markov decision process is used to describe the channel transition probability of the system model in the paper. The algorithm proposes a cooperative wireless network cooperative relay strategy, analyzes the system outage probability under different transmission modes, and optimizes the system throughput by minimizing the outage probability. Then, the maximum utility optimization strategy based on deep reinforcement learning is proposed to maximize the system utility revenue by selecting the optimal behavior. The experimental results show that the proposed algorithm has a good effect in improving system throughput and optimizing system energy efficiency. [ABSTRACT FROM AUTHOR]

The influential stage of Internet of Things (IoT) has reformed all fields of life in general but specifically with the emergence of artificial intelligence (AI) has drawn the attention of researchers into a new paradigm of life standard. This revolution has been accepted around the globe for making life easier with the use of intelligent devices such as smart sensors, actuators, and many other devices. AI-enabled devices are more intelligent and capable of doing a specific task which saves a lot of resources and time. Different approaches are available in the existing literature to tackle diverse issues of real life based on AI and IoT systems. The role of decision-making has its own importance in the AI-enabled and IoT systems. In-depth knowledge of the existing literature is dire need of the research community to summarize the literature in effective way by which practitioners and researchers can benefit from the prevailing proofs and suggest new solutions for solving a particular problem of AI-enabled sensing and decision-making for the IoT system. To facilitate research community, the proposed study presents a systematic literature review of the existing literature, organizes the evidences in a systematic way, and then analyzes it for future research. The study reported the literature of the last 5 years based on the research questions, inclusion and exclusion criteria, and quality assessment of the selected study. Finally, derivations are drawn from the included paper for future research. [ABSTRACT FROM AUTHOR]

As the most external symbolic representation of the nation, national costumes are an integral part of the wonderful culture of the Chinese nation. Our country has many ethnic minorities and has a unique national costume culture, which provides rich resources for the art design of ethnic costumes. This paper uses artificial intelligence technology and Internet of Things technology to design a national costume element library system. In this system, users can match national costume suits according to their own preferences, and they can also transmit national culture to people through this system. After the system is designed, the system performance is optimized by interactive algorithms, and the availability of the system is verified by testing system security, stress resistance, concurrency, etc. Through the verification of the system designed in this paper, the national costumes designed by innovative technology can be copied in batches, which enhances the innovation of national costume design in our country and has high production efficiency. It is finally proved that the design results of this paper meet the design requirements. [ABSTRACT FROM AUTHOR]

In recent years, transportation, medical care, energy, and other fields have relied on a number of smart technologies such as the Internet of Things and cloud computing, and the degree of intelligence has continued to increase. As a key part of the future development of cities, education is facing tremendous pressure and challenges. How to use all kinds of smart technologies to promote the informatization of education and create a new era of smarter education is an important mission of current smart education. Decision tree classification is a frequently used technique in data mining. It has the characteristics of fast classification and high efficiency. It is not only used to analyze data but also to make predictions. Professional art education occupies an important position in college education. How to combine decision tree algorithms to develop targeted and effective professional art education and to strengthen the teaching reform and research method innovation of art design has become the current teaching work of colleges and universities. The purpose of this paper is to optimize the teaching mode of art courses in colleges and universities, improve the teaching effect of art classes, and realize the teaching goal of art courses. In this paper, art curriculum teaching experience and the reality of teaching research, through analyzing three local questionnaire surveys of college students, based on the principle of students' subjectivity from the topic content, organization form, evaluation mode of college art teaching in our student autonomy, initiative, and the role of creative mechanisms are discussed. According to the practical teaching activities of art courses conducted in this paper, the satisfaction degree of college students with art courses is up to 92%, which indicates that this experiment is still relatively successful and significant, and arouses students' subjectivity to some extent. [ABSTRACT FROM AUTHOR]

This paper considers a remanufacturing system in which an original equipment manufacturer (OEM) and an independent remanufacturer (IR) compete with each other. The paper conducts a model analysis for the optimal decisions in the remanufacturing system, taking into account the impacts of consumers' low-carbon awareness and carbon cap and trade (CCT) policy. With the studies, it is found that the effects of CCT and consumers' low-carbon awareness can be trading off sometimes on the remanufacturing system, and in this case, excessive cultivation of consumers' low-carbon awareness will weaken the beneficial effects of CCT on the remanufacturing system due to the resulted excessive consumers' willingness to pay for the remanufactured products. [ABSTRACT FROM AUTHOR]

Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure. [ABSTRACT FROM AUTHOR]

In engineering projects, the coordination and management of multiple objectives is an important part of project management, which has a direct impact on the realization of objectives in terms of project duration, cost, and quality. The current engineering project investment presents the characteristics of large scale, long period, and many risks, which puts forward higher requirements for the multiobjective coordinated management of the project. Therefore, the analysis and optimization of the various objectives of the engineering project is the basis for achieving multiobjective balance and coordination. The comprehensive consideration of the risks faced by the engineering project and the dynamics of the environment in which it is located can make the objective optimization more realistic. On the basis of risk identification and risk evaluation, this paper is committed to considering multiple objective factors that affect the outcome of risk decision-making to achieve the optimization of decision-making schemes and applies multiobjective genetic algorithm to the optimization of decision-making schemes, thus finding a way to optimize the decision-making scheme. This paper analyzes the research progress and status of construction project risk management decision and multiobjective evolutionary algorithm and points out the imperfections of the current research. Then it introduces the related theory of construction project risk management decision, the related terminology of multiobjective optimization problem and the method used in this paper —the principle, process, and characteristics of genetic algorithm and prepares for the following problem solving. In this paper, a mathematical model for multiobjective decision-making of engineering project risk management is established, and the multiobjective genetic algorithm is used to solve the model. Through the analysis of examples, a series of Pareto optimal solutions with good convergence and diversity are obtained, the best combination of various risk control measures is found, and three goals of risk evaluation value, management cost, and risk loss are achieved. The two models with or without risk correlation present certain differences, the model with correlation considered is more accurate and applicable than the model without correlation considered in the existing research. [ABSTRACT FROM AUTHOR]

Cross-border e-commerce logistics and transportation system is important link in the cross-border e-commerce supply chain. How to choose a fast, safe, reliable, and low-cost logistics transportation mode is an urgent problem to be solved in cross-border e-commerce logistics. In view of this, this paper first analyzes the system characteristics of cross-border e-commerce logistics transportation systems and analyzes the factors affecting the choice of transportation mode from the perspective of reliability. Then, based on the ELECTRE method system, a multiattribute decision-making method for cross-border e-commerce logistics transportation mode selection is proposed. Finally, through a data example, it is verified that the model constructed in this paper and the proposed multiattribute decision-making method can effectively help cross-border e-commerce choose logistics transportation mode according to goods. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore the attitudes of surrogacy and medical service providers toward SDM and to identify the barriers and promoters of SDM in this population. To this end, we conducted a qualitative study of surrogacy and medical service providers in the First Affiliated Hospital of Soochow University using semistructured interviews. Thirty participants (11 agents, 12 ICU physicians, and 7 ICU nurses) were interviewed. The three stakeholders showed different attitudes toward SDM. They reported barriers to SDM, including insufficient cognition of decision-makers, high expectations, negative psychological experiences, previous decision-making experiences, excessive workload, heavy financial burden, and lack of decision AIDS. They reported facilitators of SDM, including trust, effective communication, decision support, value clarification, outcome commitment, and continuous service. This study explored the different attitudes of the three stakeholders and identified various barriers and facilitators of SDM. It highlights the need to develop localised decision AIDS and to involve agents and nurses more in the decision-making process. Therefore, this paper identifies barriers and facilitators of SDM in this population. In addition, the study identified various barriers and facilitators to SDM and highlighted the need to develop localised decision AIDS and involve agents and nurses more in the decision-making process. Finally, the barriers and facilitators of SDM are established. The paper also shows that the development of localized decision AIDS and greater involvement of agents and nurses in the decision-making process are integral to good treatment outcomes. [ABSTRACT FROM AUTHOR]

In this paper, a cognitive electronic jamming decision-making method based on improved Q -learning is proposed to improve the efficiency of radar jamming decision-making. First, the method adopts the simulated annealing (SA) algorithm's Metropolis criterion to enhance the exploration strategy, balancing the contradictory relationship between exploration and utilization in the algorithm to avoid falling into local optima. At the same time, the idea of stochastic gradient descent with warm restarts (SGDR) is introduced to improve the learning rate of the algorithm, which reduces the oscillation and improves convergence speed at the later stage of the algorithm iteration. Then, a cognitive electronic jamming decision-making model is constructed, and the improved Q -learning algorithm's specific steps are given. The simulation experiment takes a multifunctional radar as an example to analyze the influence of exploration strategy and learning rate on decision-making performance. The results reveal that compared with the traditional Q -learning algorithm, the improved Q -learning algorithm proposed in this paper can fully explore and efficiently utilize and converge the results to a better solution at a faster speed. The number of iterations can be reduced to more than 50%, which proves the feasibility and effectiveness of the method applied to cognitive electronic jamming decision-making. [ABSTRACT FROM AUTHOR]

This study aims to survey the bibliometric properties of distributed decisions, decentralized decisions, and fuzzy articles published between 1995 and 2023 in the Web of Science (WoS) database. During the analysis process, the keywords "distributed decision, decentralized decision, and fuzzy" were scanned in all languages, both in the titles and the content of all publication types. As a result of the analysis, 79 articles in all fields comprised the dataset. The most used keywords in the articles were related to the distributed decision, decentralized decision, and fuzzy logic, and the most frequently cited publications were examined using the social network analysis method, which uses VOSviewer (version 1.6.19) to visualize the relationships. The study's goal on "active researchers, active journals, journal metrics, title document type, active countries, and active institutions" was to look at the words most frequently used in articles on distributed, decentralized, and fuzzy logic. The social network analysis represented the relationships between these keywords and the most frequently cited publications. The findings demonstrated a significant correlation between using these keywords in academic literature and their contribution to this field's research. These results can assist researchers in finding potential partners and keeping up with current research trends. Overall, this study offers important new perspectives on the state of research on fuzzy logic, distributed decision making, and decentralized decision making. [ABSTRACT FROM AUTHOR]

In the context of data analysis, missing data imputation is a vital issue due to the typically large scale and complexity of the datasets. It often results in a higher incidence of missing data. So, addressing missing data through the imputation technique is essential to ensure the integrity and completeness of the data. It will ultimately improve the accuracy and validity of the data analysis. The prime objective of this study is to propose an imputation model. This paper presents a method for imputing missing employee data through a combination of features and probability calculations. The study utilized employee datasets that were collected from the Kaggle along with primary data collected from RMG factories located in Chittagong. The suggested algorithm demonstrated a notable level of accuracy on the datasets, and the average accuracy for each identified technique was also quite satisfactory. This study contributes to the existing body of research on missing data imputation in big data analysis and offers practical implications for handling missing data in different datasets. Usage of this technique will enhance the accuracy of data analysis and decision-making in organizations. [ABSTRACT FROM AUTHOR]

Advancement in mobile phone (MP) technology has revolutionized the lifestyle. In recent years, we observed that MP technology had been involved in almost all aspects of life, such as communication purposes, e-commerce, mobile baking, and social media connectivity. So, it becomes a hot research topic to select the best MP that fulfills the desired feathers requirement. In this paper, the expert's familiarity with the examined objects is factored into the initial judgments under the T-spherical fuzzy sets (T-SFSs) environment. The T-SFS is the extension of the picture fuzzy (PF) set (PFS), which gives wider scope for finding the most precise options than existing fuzzy frameworks. The multiattribute decision-making (MADM) is a common and valuable method for aggregating information. For MADM, various aggregation operators (AOs) have been created over the years. The article introduces the newly proposed approach T-spherical fuzzy (T-SF) confidence level weighted averaging T − SFWA c and T-SF confidence level weighted geometric T − SFWG c . Also, some desired properties of AOs are discussed, and the T-SF entropy measure is introduced for selecting the weight criteria. A MADM framework is introduced, on the behalf of proposed operators. The proposed MADM framework is applied to solve the real-life example of consumers' preferences to show effectiveness and practicality. Lastly, the developed framework is set side by side with other prevailing approaches to demonstrate the superiority and significance of other existing AOs. [ABSTRACT FROM AUTHOR]

Aims: Nurse managers play key roles in creating and enforcing organisational hair policies and practices. This challenging paper will provoke discussion, debate and hopefully the dismantling of racist hair policies that disproportionately target black students and nurses. Background: Black people have suffered from centuries of hair racism that continues today. Unfortunately, many nurse leaders underestimate the significance of this issue, while perpetuating the injustice. Evaluation: This paper is based on research literature, media reports and authors' lived experiences regarding hair racism experienced by black people and nurses in particular. Key Issues: Nurse managers often create and police organisational hair policies and dress codes. As health services pledge to eradicate racism 'in principle', ending discriminatory hair policies offers nurse managers a practical way to make this principle a reality. Conclusions: Hair racism is real and damaging for many black nurses and has no place in a modern health service. Rather than designing and policing such structural racism, nurse managers can be instrumental in ending it. Implications for Nursing Management: Health service hair policies targeting black nurses especially are not 'neutral'. Nurse managers can challenge this institutional discrimination, demonstrating health services' commitment to ending racism in all of its guises. [ABSTRACT FROM AUTHOR]

The economies of all the countries are growing with the passage of time. In order to promote the businesses in the global markets, technology managers/technical brokers play a key role such as in the optimal allocation of scientific and technological resources. The industrialization of scientific and technological achievements is also possible due to optimal utilization of resources and a balance between exports and imports. The technology managers should be competent enough to bridge the gaps between the resource requirements and the availability of the resources required to run the businesses smoothly. This paper is using multi-criteria methods for analyzing the qualities in the technical managers which can assist the managers to run the businesses in an efficient manner to compete in the global markets. The interviews and questionnaire are used to collect the primary data. The opinion of experts is also considered. Then, SPSS 22.0 statistical analysis software is used to analyze the reliability and validity of the factors considered in the questionnaire. Then by using statistical techniques, a quantifiable competency model of technology managers is constructed. The multi-criteria AHP (analytical hierarchical process) method is used to compare the competency characteristic indexes of each level in pairs and quantitatively describe their importance of the criteria. It also assigns weights to judge the importance of each competency characteristic index. Since the evaluation of technical managers' competency will be affected by various factors as mentioned in the paper, the fuzzy comprehensive evaluation method is also used to transform the evaluation dimension to accurately evaluate the competency of the technical manager which assists to judge the effectiveness of model evaluation. The proposed method provides theoretical and practical basis for the selection, evaluation, and cultivation of technical managers. It provides accuracy in results by 94% by adopting the statistical techniques. [ABSTRACT FROM AUTHOR]

The main body of modern Chinese martial arts competition is the strategy, and fighting has just started in sports competitions. Strategy and action correspond to each other and practice as a set. Therefore, constructing the Chinese martial arts competition decision-making algorithm and perfecting the martial arts competition are intuitive and essential. The formulation of martial arts competition strategies requires scientific analysis of athletic data and more accurate predictions. Based on this observation, this paper combines the popular neural network technology to propose a novel additional momentum-elastic gradient descent. The BP neural network adapts to the learning rate. The algorithm is improved for the traditional BP neural network, such as selecting learning step length, the difficulty of determining the size, and direction of the weight, and the learning rate is not easy to control. The experimental results show that this paper's algorithm has improved both network scale and running time and can predict martial arts competition routines and formulate scientific strategies. [ABSTRACT FROM AUTHOR]

Unscheduled care (UC) refers to non-routine face-to-face care, such as accident and emergency care, out-of-hours care, or walk-in centres. Current health service policy aims to reduce its use. Unscheduled care is common in people with long-term conditions such as diabetes, asthma, chronic obstructive pulmonary disease and coronary heart disease. By reviewing qualitative research literature, we aimed to understand the breadth of psychosocial and other influences on UC use in people with long-term conditions. Few qualitative papers specifically address UC in patients in these disease groups. Therefore, our literature search also included qualitative research that explored factors potentially relevant to UC use, including attitudes to healthcare use in general. By searching Medline, Embase, Psycinfo and Cinahl from inception to 2011, we identified 42 papers, published since 1984, describing relevant original research and took a meta-ethnographic approach in reviewing them. The review was conducted between Spring 2009 and April 2011, with a further search in December 2011. Most papers reported on asthma ( n = 13) or on multiple or unspecified conditions ( n = 12). The most common methods reported were interviews ( n = 33) and focus groups ( n = 13), and analyses were generally descriptive. Theoretical and ethical background was rarely explicit, but the implicit starting point was generally the 'problem' of UC, and health-care, use in general, decontextualised from the lives of the patients using it. Patients' use of UC emerged as understandable, rational responses to pressing clinical need in situations in which patients thought it the only option. This belief reflected the value that they had learned to attach to UC versus routine care through previous experiences. For socially or economically marginalised patients, UC offered access to clinical or social care that was otherwise unavailable to them. [ABSTRACT FROM AUTHOR]