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Despite widespread adoption and maturity, paper persistence endures in many Electronic Health Record (EHR) systems, particularly for complex workflows involving multiple steps from different stakeholders separated in time. In our health system, Latent Tuberculosis Infection (LTBI) testing was one such workflow where a Tuberculin Skin Test (TST) must be administered and then correctly read 48-72 hours later and documented. This paper discusses a low-resource workflow analysis and clinical decision support approach to replace a paper workflow and garner the benefits of the EHR for clearer documentation and retrieval of LTBI results. Our approach resulted in a significant increase in completed TST documentation, 57% (24/42) to 95% (18/19), P < 0.003. Human-centered design practices such as work system analysis and formative usability testing are feasible with limited resources and improve the likelihood of success of electronic workflows by designing solutions that fit existing clinical workflows and automating processes wherever possible.

Digital Health Twins (DHTs) hold immense potential to revolutionize healthcare by providing personalized virtual models of individual patients and their health conditions. This position paper discusses about the needs to overcoming the challenges in standardization and information models for data integration, collection, and visualization for realizing the full potential with digital twin technology. By addressing these challenges, digital health twins can enable better decision-making, enhance patient care, and contribute to the advancement of personalized healthcare solutions.

The verdict of the UK Supreme Court in the case of Bellman versus Boojum-Snark Integrated Care Trust (2027) will have profound implications for medical practice, medical education, and medical research, as well as the regulation of medicine and allied healthcare fields. Major changes will result from the definition of person-centred care built into the expanded definition of informed and preference-based consent central to the judgment made in favour of Bellman's negligence claim. (For the avoidance of doubt this is a vision paper.).

In the context of global economic integration and Industry 4.0, digital manufacturing has become crucial. As one of the economic cores of China, the digitization process of the manufacturing industry in the Yangtze River Delta is particularly critical to the overall economic growth. Based on the theory of Industry 4.0 and digital manufacturing, this study deeply analyzes the current digital development of the manufacturing industry in the Yangtze River Delta. More importantly, this paper successfully constructs a fuzzy control model to quantitatively evaluate and guide the process of digital transformation of manufacturing industry in this region. The empirical results of the model reveal how key factors such as capital, talent, technology and data security affect the digitization process, and provide concrete and operational transformation strategies for the Yangtze River Delta region. In addition, combined with industrial advantages, policy support, technological progress and market demand, this paper predicts the digital development prospects of the manufacturing industry in the Yangtze River Delta. Overall, the study not only provides in-depth insights on the digitization of manufacturing in the Yangtze River Delta, but also provides practical guidance for actual operation, which has high theoretical and practical value. [ABSTRACT FROM AUTHOR]

Background: Telehealth and mHealth apps become increasingly popular in health professions such as physiotherapy calling for increased awareness on functionality, privacy, and data security., Objectives: This work presents a functionality, privacy, and data-security evaluation of four telehealth services commonly used in physiotherapy., Methods: We examined functionality and features, data protection, privacy implementations and data-security with a questionnaire and performed an in-depth investigation of the services., Results: Privacy and security relevant findings such as use of outdated webservers, problems with certificate renewal as well as questionable GDPR compliance were reported., Conclusion: Due to the privacy and security relevant findings in this analysis it can be concluded that there is a need for improvement in design, development, operation as well as regulation of telehealth apps and services.

Background: There is no molecular test for Alzheimer's disease (AD) using self-collected samples, nor is there a definitive molecular test for AD. We demonstrate an accurate and potentially definitive TempO-Seq® gene expression test for AD using fingerstick blood spotted and dried on filter paper, a sample that can be collected in any doctor's office or can be self-collected. Objective: Demonstrate the feasibility of developing an accurate test for the classification of persons with AD from a minimally invasive sample of fingerstick blood spotted on filter paper which can be obtained in any doctor's office or self-collected to address health disparities. Methods: Fingerstick blood samples from patients clinically diagnosed with AD, Parkinson's disease (PD), or asymptomatic controls were spotted onto filter paper in the doctor's office, dried, and shipped to BioSpyder for testing. Three independent patient cohorts were used for training/retraining and testing/retesting AD and PD classification algorithms. Results: After initially identifying a 770 gene classification signature, a minimum set of 68 genes was identified providing classification test areas under the ROC curve of 0.9 for classifying patients as having AD, and 0.94 for classifying patients as having PD. Conclusions: These data demonstrate the potential to develop a screening and/or definitive, minimally invasive, molecular diagnostic test for AD and PD using dried fingerstick blood spot samples that are collected in a doctor's office or clinic, or self-collected, and thus, can address health disparities. Whether the test can classify patients with AD earlier then possible with cognitive testing remains to be determined. [ABSTRACT FROM AUTHOR]

Traditionally, Electronic Medical Records (EMR) have been designed to mimic paper records. Organizing and presenting medical information along the lines that evolved for non-digital records over the decades, reduced change management for medical users, but failed to make use of the potential of organizing digital data. We proposed a method to create clinical dashboards to increase the usability of information in the medical records. Official clinical guidelines were studied by a working group, including dashboard target users. Necessary clinical concepts contained in the medical records were identified according to the clinical context and finally, dedicated technical tools with standard terminologies were used to represent categories of information. We used this method to generate and implement a dashboard for sepsis. The dashboard was found to be appropriate and easy to use by the target users.

The 2023 edition of the AIxIA Conference, held in Rome, brought together a large number of researchers and practitioners to discuss the most recent and important advancements in Artificial Intelligence (AI). The conference featured 19 workshops, organized by 77 experts, attracting 248 submissions and resulting in 16 proceedings. This special issue presents extended versions of selected papers initially showcased at these workshops. Each paper underwent rigorous review and represents a diverse array of topics, reflecting the multifaceted nature of the Italian AI community. The topics covered include ethical foundations to symbiotic AI, symbolic knowledge extraction from black-box models, creative influence prediction using graph theory, AI approaches to multidimensional poverty prediction, an assessment of AI-based supports for informal caregivers, deep learning-based EEG denoising, AI-assisted board-game-based learning, large language models for assessment and feedback in higher education, geometric reasoning in the Traveling Salesperson Problem, defeasible reasoning in weighted knowledge bases, and conditional computation in neural networks. These contributions demonstrate the innovative and interdisciplinary research within the AI community, offering valuable insights and advancing the field. [ABSTRACT FROM AUTHOR]

The aim of the work is to assess the environmental impacts of Ultra High Frequency RFID tags. Through a Life Cycle Assessment approach, two case studies have been investigated, namely a standard plastic and a paper-based tags. Primary data on tags' components, manufacturing and transportation were collected, while secondary data for the raw materials processing and tags' end of life were retrieved. The Recipe Midpoint method was used to evaluate the impacts. Results show that, for both tags, the greatest contributions to global warming, terrestrial acidification, mineral and fossil resource scarcity are due to raw material extraction (more than 50%) and manufacturing phase (30–50%), which resulted impactful also on the ionizing radiation (70%). Interestingly, the paper tag allows to save up to 23% of the greenhouse gas emissions and decreases the impact on the above-mentioned categories, resulting the eco-friendly option. The conclusion of the work contributes to update the scientific literature, still poor in RFID environmental evaluations, and are useful for researchers interested in comparing the traditional handling systems' impacts to the RFID scenario. Furthermore, the outcomes will be used as input for subsequent research, aimed at developing a tool to measure the return on the environment of RFID deployments. [ABSTRACT FROM AUTHOR]

The text is an introduction to the selected papers from the HES-2023 conference, which focuses on the application of electromagnetic fields for materials processing. The conference was postponed by a year due to the pandemic and the war in Ukraine. The main developments in electroheat involve a multi-physics approach to modeling and simulation, with a focus on coupling different physical domains. The use of AI-based methods, such as digital twins and machine learning, in electrothermal systems is a current topic of research. The conference also covered topics such as induction heating, induction hardening, and inverse problems in induction heating. Overall, 52 papers were presented at the conference, and the 10 selected papers cover the main topics discussed. [Extracted from the article]

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers' decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer's opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product's features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer's opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. [ABSTRACT FROM AUTHOR]

To improve the effect of immersive animation design, this paper combines digital media technology (DT) to establish an immersive animation design system and analyzes the media digital signal data processing algorithm. According to the advantages and disadvantages of the FHT algorithm and probabilistic algorithm, this paper proposes the FHT-SLM algorithm and the FHT-IPTS algorithm. Moreover, this paper analyzes the basic principle of TPWC transform and M-TPWC and the CO-OFDM system of cascaded FHT algorithm and M-TPWC algorithm. Finally, this paper simulates the CO-OFDM simulation system built by Matlab2018.a and Optisystem. Through the experimental analysis results, the reliability of the algorithm and the system in this paper is verified, and the design effect of immersive animation is effectively improved. [ABSTRACT FROM AUTHOR]

Digital publishing is the process of informatizing the content of traditional publishing. It not only involves the processing of information, but also includes the whole process of digital publishing enterprise management and operation. Compared with traditional publishing, digital publishing has a wider distribution channel with the advantages of more diverse forms and marketing aspects, the transition from traditional digital publishing to digital publishing has become an inevitable trend. But there are still many problems in digital publishing in our country. Including the transformation of digital copyright awareness and maintenance of digital copyright, the source and maintenance of digital publishing technology, and the scarcity of compound talent resources. In order to solve these problems, we must combine the digital publishing industry with modern information technology. This paper builds a digital market preference prediction model based on big data and fuzzy control algorithms. By analyzing and predicting each consumer's usage information, the digital consumer market preference is obtained. This research uses big data and fuzzy control algorithms to build a consumer market preference estimation model for digital publishing transformation. Through the observation of the consumer market, it can promote digital companies to make effective decisions and conduct reasonable organizational analysis, which can further improve The development process of digital publishing transformation promotes the overall development of the enterprise. Through verification, this model has high accuracy and reliability, can support the operation of actual enterprises, and plays an important role in the development of enterprises. Finally, based on the content of the article research, we put forward the following suggestions for the transformation and development of digital enterprises (1) conduct market analysis through big data and fuzzy control technology, and clarify market positioning (2) promote traditional publishing and digital publishing through big data and fuzzy control technology Integrated Development of Publishing (3) Cultivate Excellent Composite Talents for Digital Publishing Transformation. [ABSTRACT FROM AUTHOR]

The article discusses the rise of "team science" in academia, where research and academic publications are increasingly collaborative efforts. This trend has led to a significant increase in the number of multi-authored papers, with some papers having thousands of authors. However, this raises questions about the ethics of authorship and the transparency of individual contributions. The article highlights the need for clear standards and communication practices regarding authorship roles and contributions. While other disciplines have developed various methods for communicating author contributions, the journal Information Polity invites authors to be more explicit about their co-authors' inputs without imposing a single system. This promotes research ethics and transparency in the field. [Extracted from the article]

Peer review is under pressure. Without fair, transparent and efficient peer review we cannot ensure the right proposals get funded and the correct manuscripts get published. In the era of Open Access, which is driving an exponential increase in the number of submitted publications, how we carry out peer review is becoming increasingly important and how we find reviewers is coming under scrutiny. The current methods are slow and produce bias pools of reviewers. As such we need an improved way. At Prophy we have developed a state-of-the-art referee finder that can find experts to review any manuscript from any scientific field in seconds. Then through post-processing filters we can find appropriate candidate referees who are most likely to review a paper, whilst highlighting important conflicts of interest through our complex citation networks. These methods can ensure fair and independent experts who can review interdisciplinary papers from any discipline. These methods are being delivered through APIs and the editorial workflow of editors ensure the right people get access to these tools. Finally, as large-language models improve, so does Prophy and as such we will be looking to drive real innovation in this area in years to come. [ABSTRACT FROM AUTHOR]

This document is a call for a new era of artificial intelligence (AI) in the connected world, specifically focusing on the field of Web Intelligence (WI). WI has evolved over the past 20 years, with three significant stages: WI 1.0 (Wisdom Web), WI 2.0 (Wisdom Web of Thing), and WI 3.0 (Wisdom Web of Everything). The document highlights several papers published in the Web Intelligence journal that explore different aspects of WI, including its impact on a better connected world, cross-cutting areas with brain informatics, biases in recommender systems, the influence of ChatGPT on information retrieval, the role of big data in the retail industry, order pairing mechanisms in real-time ride-sharing, data replication in cloud systems, and security risks in IoT-fog architecture. The document calls for more collaboration and communication among researchers in the WI field and emphasizes the importance of global interactions for co-intelligence and co-creation. [Extracted from the article]

Background: Dementia occurring before age 65 is known as young-onset dementia (YOD), with Alzheimer's disease being the most common type. YOD poses unique challenges for persons and families, impacting their working-age years and family responsibilities. Person-centered interventions and services are essential to improve their quality of life and social engagement. Objective: This study aims to synthesize non-pharmacological interventions for persons with YOD and their families to inform future targeted interventions. Methods: We conducted a systematic literature search across four databases: PubMed, PsycINFO, Scopus, and CINAHL. The included articles were carefully screened, categorized, and synthesized by following Arksey and O'Malley's five stages framework. Results: We included 20 studies reported in 24 papers, with 11 studies (14 papers) on persons with YOD and nine studies (10 papers) on families. Quantitative intervention results vary, but qualitative interviews show positive feedback. Stakeholders provided positive evaluations, stating these interventions provided a sense of normalcy, facilitated communication among families, enhanced the independence of persons with YOD, and improved the families' caregiving self-efficacy, thereby reducing care burden and psychological distress. The heterogeneity among the studies posed integration challenges. Conclusions: Interventions for YOD can improve the quality of life for both persons with YOD and their families. More extensive intervention studies are urgently needed, especially in developing countries, with a focus on family-centered and life course perspectives. In future intervention research design, a more extensive incorporation of stakeholder involvement is essential for successful implementation. Moreover, the integration of new technologies shows promise as a potential avenue for intervention advancement. [ABSTRACT FROM AUTHOR]

Ontologies are used to semantically enrich different types of information systems (IS), ensure a reasoning on their content and integrate heterogeneous IS at the semantical level. On the other hand, fuzzy theory is employed in IS for handling the uncertainty and fuzziness of their attributes, resulting in a fully fuzzy IS. As such, ontology- and fuzzy-based IS (i.e. ontology and fuzzy IS) are being developed. So, in this paper, we present a bibliometric analysis of the ontology and fuzzy IS concept to grasp its main ideas, and to increase its body of knowledge by providing a concept map for ontology and fuzzy IS. The main results obtained show that by adding ontologies and fuzzy theory to traditional ISs, they evolve into intelligent ISs capable of managing fuzzy and semantically rich (ontological) information and ensuring knowledge recognition in various fields of application. This bibliometric analysis would enable practitioners and researchers gain a comprehensive understanding of the ontology and fuzzy IS concept that they can eventually adopt for development of intelligent IS in their work. [ABSTRACT FROM AUTHOR]

Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation. [ABSTRACT FROM AUTHOR]

The patients with the nasopharyngeal cancer are required to breath through their mouth after performing the surgery. Hence, it is required to perform the breathing site classification and employs the classification results to indicate whether the patients breath correctly or not. Nevertheless, there is currently no such a medical aided tool in the market. To address this issue, this paper extracts both the mel frequency cepstral coefficients (MFCCs) based features and the gammatone frequency cepstral coefficients (GFCCs) based features as well as employs the random forest as the classifier for performing the breathing site classification. The data lasted for a few minutes acquired from 10 volunteers are employed to demonstrate the effectiveness of our proposed method. The computer numerical simulation results show that the average accuracy, the average specificity and the average sensitivity yielded by our proposed method are 95.30±2.00%, 93.27±3.87% and 97.15±1.87%, respectively. Although this paper proposes a method based on the fusion of two types of the acoustic features for classifying different breathing sites, the computer numerical simulation results show that our proposed method outperforms the common respiration or speech processing based methods. Besides, our proposed method is also compared to a series of relevant methods. It is found that our proposed method achieves the highest classification results at the majority signal to noise ratios among the state of the arts methods. [ABSTRACT FROM AUTHOR]

The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user's motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users' check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments. [ABSTRACT FROM AUTHOR]

BACKGROUND: Toe function is characterised by the strength and dexterity of toe motion. However, previous studies have mostly focused on the importance of toe strength. OBJECTIVE: This study aimed to investigate the relationships between flexion strength and dexterity of the toes and physical performance. METHODS: Twenty healthy participants were included in this study. The flexion force of each toe was measured using a digital force gauge, and the toe dexterity was evaluated using the marble pick-up and rock-paper-scissors tests. These parameters were statistically analysed in relation to physical performance, including repeated side step and balance ability, which was evaluated using centre of pressure (COP) data during single-leg standing, tiptoe standing, and single-leg drop-jumping. RESULTS: A significant correlation was found between the first toe flexion force and the total trajectory length of the COP during one-leg standing and between the time required for marble pick-up and the rock-paper-scissors score and the COP during single-leg drop-jumping. CONCLUSION: The results underscore the importance of flexion strength and dexterity of the toes in human physical performance and the necessity for the evaluation and improvement of both functions. [ABSTRACT FROM AUTHOR]

The prevalence of violence against women and children is concerning, and the initial step is to raise awareness of this issue. Certain forms of detection based techniques are not frequently regarded both socially and culturally permissible. Designing and implementing effective approaches in secondary and supplementary avoidance simultaneously depends on the characterization and assessment. Given the greater incidence of instances and mortalities resulting developing an early detection system is essential. Consequently, violence against women and children is a problem of human health of pandemic proportions. As a result, the focus of this survey is to analyze the existing methods used to identify violence in photos or films. Here, 50 research papers are reviewed and their techniques employed, dataset, evaluation metrics, and publication year are analyzed. The study reviews the potential future research areas by examining the difficulties in identifying violence against women and children in literary works for researchers to overcome in order to produce better results. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

Stance detection for user reviews on social platforms aims to classify the stance of users' reviews toward a specific topic. Existing studies focused on the internal semantic features of reviews' texts, but ignored the external knowledge associated with the review. This paper retrieves external knowledge related to the key information of each review by mapping it to a knowledge graph. Thereafter, this paper infuses the external knowledge into deep learning model for stance detection. Considering that infusing external knowledge may bring noise to the model, this paper adopts the personalized PageRank method to filter the introduced irrelevant external knowledge. Infusing external knowledge can improve the classification performance by providing background knowledge. In addition to considering the textual features of reviews when constructing the stance detection model, this paper employs a gated graph neural network (GGNN) approach to fuse the structural information between reviews to capture the interactions of reviews. The experiments show that the model improves 1.5% –6.9% in macro-average scores compared to six benchmark models in this paper. By combining the textual features and structural information of reviews and introducing external knowledge, the model effectively improves the stance detection performance. [ABSTRACT FROM AUTHOR]

An editorial presented on advancing independent health policy research in 2024 to mitigate risks and ensure safety in medicine for global health improvement. Topics include the journal's impact metrics, research contributions from various regions and specific studies addressing risk assessment, medication reconciliation and pharmacogenetics.

In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases. [ABSTRACT FROM AUTHOR]

In its regulatory enterprise to improve the conditions of data sharing and reuse, the European Union has enacted new legislation: the Data Governance Act (DGA). The DGA envisages new forms of sharing public sector data (PSD). Based on a legal analysis of the DGA and an in-depth study of data governance literature, this paper highlights what is at stake in the new regulatory framework and argues that more than the mere openness of more PSD will be necessary to ensure that the European Union policy goals are achieved, especially those concerning enhancing innovation for the common good. From this perspective, the paper argues that the public data trust model of data governance and the Responsible Research and Innovation approach offer two powerful tools for public sector data governance. In this context, this paper contributes to the debate about new data governance models and discusses tools and frameworks enabling the use of data for the common good. It also provides insights to public administration practitioners aiming to implement a framework for increased and sustainable PSD sharing. [ABSTRACT FROM AUTHOR]

System knowledge and reasoning mechanisms are essential means for intellectualization of cyber-physical systems (CPSs). As enablers of system intelligence, they make such systems able to solve application problems and to maintain their efficient operation. Normally, system intelligence has a human-created initial part and a system-produced (extending) part, called synthetic system intelligence (SSI). This position paper claims that SSI can be converted to a new industrial asset and utilized as such. Unfortunately, no overall theory of SSI exists and its conceptual framework, management strategy, and computational methodologies are still in a premature stage. This is the main reason why no significant progress has been achieved in this field, contrary to the latent potentials. This paper intends to contribute to: (i) understanding the nature and fundamentals of SSI, (ii) systematizing the elicitation and transfer of SSI, (iii) exploration of analogical approaches to utilization of SSI, and (iv) road-mapping and scenario development for the exploitation of SSI as an industrial asset. First, the state of the art is surveyed and the major findings are presented. Then, four families of analogical approaches to SSI transfer are analyzed. These are: (i) knowledge transfer based on repositories, (ii) transfer among agents, (iii) transfer of learning resources, and (iv) transfer by emerging approaches. A procedural framework is proposed that identifies the generic functionalities needed for a quasi-autonomous handling of SSI as an industrial asset. The last section casts light on some important open issues and necessary follow-up research and development activities. [ABSTRACT FROM AUTHOR]

Background: People with Huntington's disease (HD) exhibit neurocognitive alterations throughout the disease, including deficits in social cognitive processes such as Theory of Mind (ToM). Objective: The aim is to identify methodologies and ToM instruments employed in HD, alongside relevant findings, within the scientific literature of the past two decades. Methods: We conducted a comprehensive search for relevant papers in the SCOPUS, PubMed, APA-PsyArticles, Web of Science, Redalyc, and SciELO databases. In the selection process, we specifically focused on studies that included individuals with a confirmed genetic status of HD and investigated ToM functioning in patients with and without motor symptoms. The systematic review followed the PRISMA protocol. Results: A total of 27 papers were selected for this systematic review, covering the period from 2003 to 2023. The findings consistently indicate that ToM is globally affected in patients with manifest motor symptoms. In individuals without motor symptoms, impairments are focused on the affective dimensions of ToM. Conclusions: Based on our analysis, affective ToM could be considered a potential biomarker for HD. Therefore, it is recommended that ToM assessment be included as part of neuropsychological evaluation protocols in clinical settings. Suchinclusion could aid in the identification of early stages of the disease and provide new opportunities for treatment, particularly with emerging drugs like antisense oligomers. The Prospero registration number for this review is CRD42020209769. [ABSTRACT FROM AUTHOR]

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid. [ABSTRACT FROM AUTHOR]

BACKGROUND: The effectiveness of the paper's bibliometric analysis and systematic assessment of change management research in administrative and technological studies may open the way for more study in this field. This study may be the first of its type, and its findings will be useful to other academics working in the subject of change management. OBJECTIVE: The goal of this literature study is to identify essential ideas that might influence change management and to lay the groundwork for future research in change management that uses bibliometric analysis. The evaluation determines the most important and frequently used terms connected with change management. METHODS: The method used in this study is a systematic review of change management publications from Web of Science. RESULTS: The most often used terms in change management research, according to the survey, were Leadership, Organizational Change, Organizational Development, Organizational Culture, Performance, Innovation, Framework, Technology, and Transformation. Change management papers were mostly published in the United States, China, Pakistan, Germany, Australia, and Finland. IMPLICATIONS: The study's findings may be used to generate articles on change management in the market discipline, notably in the domains of business and technology. [ABSTRACT FROM AUTHOR]

With the rapid economic development and high concentration of urban population, people's income level and quality of life continue to improve, resulting in more and more crowded scenes caused by people going out. Especially in urban commercial centers, transportation hubs, sports venues during important events, tourist attractions, etc., crowd gatherings occur frequently. However, accidents involving crowd gatherings in public places occur frequently, causing heavy casualties and property losses. Therefore, for crowd recognition, this paper proposes a new method to accurately estimate the number of dense crowds. In this method, a density map with accurate pedestrian locations is first generated using the focal inverse distance transform and used as ground truth labels for network training. Then, a multi-scale feature fusion algorithm based on residual network is designed, combining spatial and channel attention mechanisms to improve the accuracy and stability of crowd density estimation. In dense crowds, the phenomenon of overlapping and occlusion of people is very common and serious, making it difficult for existing pedestrian detection methods to distinguish each individual and accurately count the flow of people. To solve this problem, this paper proposes a density map-based method that uses a local maximum detection strategy and a K-nearest neighbor algorithm to convert the density map into the corresponding dense head bounding box. This method can effectively reduce the impact of occlusion and improve the accuracy of people counting. In order to further improve the estimation accuracy, a pattern recognition density peak clustering algorithm is introduced to study the clustered crowds. By treating the head bounding box as an element point, the distance between each element point is calculated, and the density of each point is calculated. Then perform clustering to find the cluster center with the highest density in each class. Finally, by comparing the density of each cluster center with the corresponding density threshold and adopting the corresponding decision-making method, the accuracy of people counting is further improved. [ABSTRACT FROM AUTHOR]

The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M. [ABSTRACT FROM AUTHOR]

How to better grasp students' learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students' self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student's learning needs, this paper proposes recommendations for developing students' learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students' learning dynamics as it generates cognitive content about students' learning and allows for the timely discovery of students' learning expectations and cutting-edge dynamics. [ABSTRACT FROM AUTHOR]

Relative pupillary afferent disorder (RAPD) plays a crucial role in diagnosing optic nerve dysfunction. This paper introduces an innovative equipment design with a high-speed pupil detection algorithm and a binocular independent stimulation optical path. The proposed algorithm utilizes the grayscale characteristics of the pupil region to achieve rapid and accurate pupil detection and tracking. Initially, a pupil threshold is estimated using eigenvalues, enabling the calculation of the pupil centroid. Subsequently, leveraging the unique characteristics of the pupil region, a dynamic tracking algorithm, a second-order partial derivative threshold algorithm, and a pupil diameter extraction algorithm are employed to precisely locate the centroid. By incorporating a binocular independent stimulus light path design, the algorithm overcomes limitations associated with the current measurement equipment. The experimental results demonstrate the algorithm's high robustness and fast detection speed, meeting the tracking speed requirement of 1250 frames per second for a single eye. These advancements have the potential to significantly enhance the diagnosis and assessment of optic nerve dysfunction. [ABSTRACT FROM AUTHOR]

The purpose of the Chinese similar case matching task is to compare the similarity of two case texts with a given anchor text and find out which text is more similar to the anchor text. In the area of law, it plays an important role and has been of interest to many researchers. Previous approaches have compared legal texts only at the text semantic level, without incorporating article information of law. In addition, the position correlation of words in case texts is often important, but it has not been considered in previous approaches. This paper proposes a method which extracts features from the semantic similarity level and from the level of related articles of law, respectively, to enable similarity comparisons of legal case texts. When similarity comparisons are made at the semantic similarity level, a novel capsule network method is proposed based on siamese structure that introduces the position correlation and the routing mechanism within the capsule network is improved so that deep text features between case pairs can be learned. When similarity comparisons are made at the level of related articles of law, related articles of law are selected and coded and interacted with the case text features to generate legal features. Experiment is conducted with a real-world legal text dataset, and the proposed model outperformed all baseline models, demonstrating effectiveness of the proposed model. Further, to confirm the generality of the improved capsule network proposed in the paper on long text datasets, this paper also carried out experiments on two long text datasets, demonstrating effectiveness of the improved capsule network proposed in the model. [ABSTRACT FROM AUTHOR]

With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape "hometown" score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function. [ABSTRACT FROM AUTHOR]

Previously, single classification models were mainly studied to classify human protein cell images, i.e., to identify a certain protein based on a set of different cells. However, a classifier can identify only one protein, in fact, a single cell usually consists of multiple proteins, and the proteins are not completely independent of each other. In this paper, we build a human protein cell classification model by multi-label learning. The logical relationship and distribution characteristics among the labels are analyzed to determine the different proteins contained in a set of different cells (i.e., containing multiple elements in the output space). In this paper, using human protein image data, we conducted comparison experiments on pre-trained Xception and InceptionResnet V2 to optimize the two models in terms of data augmentation, channel settings, and model structure. The results show that the Optimized InceptionResnet V2 model achieves high performance in the classification task. The final accuracy of the Optimized InceptionResnet V2 model we obtained reached 96.1%, which is a 2.82% improvement relative to that before the optimized model. [ABSTRACT FROM AUTHOR]

BACKGROUND: Work-related musculoskeletal disorders (WRMSDs) and ergonomic risk factors are very common in bakery workers. OBJECTIVE: The purpose of this study is to (1) assess the prevalence of musculoskeletal disorders among bakers because they use automated machines or traditional baking, and (2) to determine the strategies to prevent musculoskeletal disorders in bakers. METHODS: A systematic review of PubMed, Scopus, and Web of Science was conducted from the beginning to February 4, 2022, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Mesh keywords and phrases were used to execute the search strategy. Information on MSDs and ergonomic risk factors in bakery workers was collected. Two reviewers worked independently on study selection, data extraction, and paper quality ranking. RESULTS: This study identified 14 papers from seven countries. Although the prevalence of MSDs in bakery workers has been studied, only a handful of them have been studied ergonomic risk factors, and the findings have been very limited. The association between different risk factors and MSDs seemed significant compared to many other occupational diseases. The traditional bread-baking system and lack of mechanization may increase the risk of MSDs in bakery workers. CONCLUSION: WRMSDs for bakery workers have been less studied than other occupational diseases. Our systematic review found several significant relations between the factors influencing the prevalence of MSDs. This study also showed the comparison of traditional and modern cooking systems with diseases of the upper limbs, shoulders, and back pain as possible fields for future research. [ABSTRACT FROM AUTHOR]