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This document discusses the societal impact of applying artificial intelligence (AI) to traffic and transportation. Sustainable transport is considered crucial for sustainable cities and communities, and AI has the potential to significantly influence daily life in urban areas. The document highlights the interdisciplinary challenges of understanding and coordinating transportation systems, which involve various players with conflicting goals and constraints. It also presents a selection of innovative solutions related to traffic management, including machine learning, reinforcement learning, and optimization approaches. The document concludes by expressing gratitude to the authors, reviewers, and committee members involved in the creation of the special issue. [Extracted from the article]

BACKGROUND: Data about performance measures (PM) in patients with ST segment Elevation Myocardial Infarction (STEMI) in low- and middle-income countries is really scarce. One of the reasons is the lack of appropriate measures for these scenarios where coronary intervention is not the standard treatment. OBJECTIVE: This study aimed to develop a set of PM and quality markers for patients with STEMI in these countries. METHODS: Two investigators systematically reviewed existing guidelines and scientific literature to identify potential PM by referring to documents searched through PubMed from 2010 through 2019, using terms "Myocardial Infarction", "STEMI", "quality indicator", and "performance measure". A modified Delphi technique, involving multidisciplinary panel interview, was used. A 15-member multidisciplinary expert panel individually rated each potential indicator on a scale of 1 (lowest) to 5 (highest) during three rounds. All indicators that received a median score ≥4.5, in final round without significant disagreement were included as PM. RESULTS: Through the consensus-building process, 84 potential indicators were found, of which 10 were proposed as performance measures and 2 as quality metrics, as follows: Pre-Hospital Electrocardiogram; Patients with reperfusion therapy; Pre-hospital Reperfusion; Ischemic time less than 120 minutes; System delay time less than 90 minutes; In-hospital Mortality; Complete in-hospital Treatment; Complete in-hospital Treatment in patients with Heart Failure; 30 day-Re-admissions; 30 day-mortality; Patients with in-hospital stress test performed; and, Patients included in rehabilitation programs. CONCLUSION: This document provides the official set of PM of attention in ST segment Elevation Myocardial Infarction of the Cuban Society of Cardiology and Cuban National Group of Cardiology. [ABSTRACT FROM AUTHOR]

BACKGROUND: Workplace inspections are applied to facilitate the adherence to the occupational health and safety regulations. The Iranian Ministry of Health introduced a new software system for tablets to inspect workplaces. OBJECTIVES: The aim of this study was to take measurements of the usability, mental workload, and mood of inspectors. METHODS: Inspectors used both pen-and-paper and tablet methods to inspect the automotive industry in a mixed-method procedure. The NASA-TLX score, QUIS score, I-PANAS (SF) situation, inspection time, and number of errors were collected throughout the procedure. The differences were investigated using a paired sample and the Wilcoxon signed ranks test. RESULTS: In terms of efficacy, using the tablet resulted in lower error rates, but it took longer to complete the inspection task (P < 0.001). Participants perceived a lower workload when inspecting with a tablet rather than the traditional method. (Mental Demand: p < 0.002, Performance: p < 0.009, Effort: p < 0.012, TLX: p < 0.002 based on various subcomponents of NASA-TLX). The newly introduced system's usability was insufficient. CONCLUSION: Although the use of tablets has improved safety inspections, ergonomic redesign of the system and consideration of a user-centered approach, as well as inspector training, can make the system more likely to succeed. [ABSTRACT FROM AUTHOR]

Background: Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach. Objective: To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials. Methods: As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory. Results: An extensive inventory of OM was created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages. Conclusion: We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges. [ABSTRACT FROM AUTHOR]

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model. [ABSTRACT FROM AUTHOR]

BACKGROUND: External focus isometric exercises using a paper balloon can change trunk muscle activation in the chest squeeze; however, it is unknown whether this method affects muscle activities in conventional exercises. OBJECTIVE: To check variations of trunk muscle activity during front plank (static task) and shoulder press (dynamic task) both with and without instruction to avoid crushing an object. METHODS: Twenty-six healthy adult males aged 19–49 were recruited. Ten trunk muscle activities were measured using surface electromyography during a front plank and dynamic shoulder press exercises, both with and without external-focus instruction. RESULTS: Adding the external-focus using the paper balloon to the front plank significantly activated 8 out of the 10 muscles. In the downward shoulder press, 5 out of 10 muscles with 50% 1 RM, 2 out of 10 muscles with 100% 1 RM were significantly activated. CONCLUSIONS: Adding external-focus instruction using paper balloon increases trunk muscles in front plank and shoulder press while possibly improving trunk stability. Novel exercises using paper balloon may efficiently activate specific muscles without external loading thus possibly reducing the stress on the involved joints during exercise. [ABSTRACT FROM AUTHOR]

BACKGROUND: In todays' society, the rapid development of the wine industry and the improvement of peoples' living standards make people pay more and more attention to wine grape quality. OBJECTIVE: To evaluate the wine grape varieties in Northeast Chinas' grape growing regions for better wine grape quality, we evaluated the quality of different varieties of wine grapes. METHODS: The grape varieties 'Hassan' 'Zuoshaner' 'Beibinghong' 'Zuoyouhong' 'Beta' 'Shuanghong' 'Zijingganlu' 'Cabernet Sauvignon' and 'Syrah' were planted in the grape growing area of Jilin, Northeast China, were used as the subjects of this study. The grape berries were analyzed and tested for morphological indicators, basic physicochemical indicators, color, and phenolic and aromatic composition. RESULTS: According to lab results, 'Hassan' contained the highest amount of total phenolics; 'Zuoyouhong' had the highest solids and total sugar content; 'Shuanghong' had the most elevated total acid and anthocyanin content; 'Zijngganlu' had the highest tannin content and acid fixation ratio; Seventy-one volatile compounds were detected in nine grape varieties. CONCLUSIONS: Each of the nine grape varieties has a distinctive flavor, and because of this, grape processing products with regional flavors can be created. The same offer valuable data for future scientific grape resource collection, conservation, and exploitation. [ABSTRACT FROM AUTHOR]

In this paper, the mechanical properties of nano-silica modified insulating paper under the combined action of mechanical vibration and temperature conditions are studied. Unmodified and nano-silica modified cellulose insulating paper with 2 wt% and 4 wt% were prepared, respectively, and a series of mechanical-thermal synergy experiments were carried out. With the same mechanical stress and temperature, and with the same aging duration of 144 h (6d), the tensile strength of modified insulating paper with 4 wt% nano-silica, increased 0.99 kN/m and 0.55 kN/m, respectively, compared with those of the unmodified and the 2 wt% nano-silica modified insulating paper. The experiments indicate that the nano-silica modification can effectively improve the mechanical properties of insulating paper. In this work, the modified mechanism of nano-silica is analyzed from the interface effect of modified polymer and the quantum effect of the modified polymer interface two aspects. It is shown that the interface formed in the modified insulating paper can transfer the mechanical stress acted on the insulating paper and prevent the cracks formed in the aging process of the test sample from further expansion, while the quantum effect discretizes the electron energy level, which can restrict the motion of the molecular chain segment to some extent. The conclusion can be used for reference to improve the performance of insulating paper. [ABSTRACT FROM AUTHOR]

For many, Covid-19 is a short-term, mildly debilitating disease. But some people are still struggling with monthly symptoms with persistent inflammation, chronic pain and shortness of breath. The situation of "long-term cowardice" has become so debilitating that it is now common for some to say that they are tired even if they walk a short distance. So far, the focus has been on saving lives from the plague. But now there are growing concerns about people facing the long-term consequences of the COVID epidemic. The fundamental question, with the uncertainty of whether those with chronic goiter, or all those affected, will fully recover is raised. In this paper a smart monitoring model was proposed to keep monitoring the COVID patient's health conditions. The smart method keep on watching the different changes reflected in the body conditions and ensure the changes in the database. In case any emergency is raised, then these smart monitoring tools inform the information to the doctors. This can very much helpful for the patients to communicate with the doctors. [ABSTRACT FROM AUTHOR]

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]

The Clinical Hemorheology & Microcirculation journal has established the A.L. Copley Best Paper Prize to recognize the best article published each year. The prize is named after Alfred Lewin Copley, the journal's founding editor. The winning paper for 2022 is titled "COVID-19 hemodynamic and thrombotic effect on the eye microcirculation after hospitalization: A quantitative case-control study" by Aristotle Koutsiaris and colleagues. The study investigates the impact of COVID-19 on the eye microcirculation of patients who received thromboprophylaxis after hospital discharge. The findings suggest that COVID-19 significantly reduces blood velocity and increases microthrombosis in the eye, potentially contributing to long COVID and indicating the presence of an unknown coagulation factor. [Extracted from the article]

Toe function is characterised by the strength and dexterity of toe motion. However, previous studies have mostly focused on the importance of toe strength. This study aimed to investigate the relationships between flexion strength and dexterity of the toes and physical performance. 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. 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. 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]

With the rapid development of information technology, the field of education is undergoing a profound change, in which intelligent hybrid learning and virtual reality technology are increasingly valued. This study proposes an intelligent hybrid learning method based on virtual reality for student performance improvement. This paper reveals the limitations of traditional learning methods in meeting the needs of modern education, and expounds the theoretical basis of intelligent hybrid learning and virtual reality technology. This paper collects and processes a large amount of learning data, based on which a new model of student learning performance prediction is established. The verification results of the model show that the model in this study has excellent performance in predicting students' learning performance. This paper gives some suggestions for future educational practice and research. In general, this study provides a new learning method for the field of education and has important reference value for education reform and teaching practice. [ABSTRACT FROM AUTHOR]

This paper aims to improve the level of social credit system and the accuracy and efficiency of bank users' credit scoring by using business intelligence technology based on deep neural network (DNN). Firstly, based on the theory of personal credit evaluation factors, a comprehensive credit evaluation factor system is constructed, taking into account social and economic background, consumption habits, behavior patterns and other factors. Meanwhile, back propagation neural network (BPNN) theory is introduced as the core method of modeling to cope with the nonlinear relationship in the credit scoring task and the demand of large-scale data processing. Secondly, by analyzing the operation process of BPNN in detail, the specific application in credit scoring model is emphasized. Finally, on the basis of theory and operation, this paper implements a credit scoring model for bank users based on BPNN theory. The experimental results show that the model realized in this paper can automatically discover the key attributes and internal rules in the sampled data, and adjust the weight and threshold of the network by modifying the parameters and network structure to meet the expected requirements. The accuracy of the credit score of the predicted sample data reaches 99.5%, and the prediction error is very small, which has a good prediction effect. This paper provides a feasible solution for business intelligence and DNN in the field of credit scoring, and also provides strong empirical support for improving the level of social credit system. [ABSTRACT FROM AUTHOR]

In order to improve the security and performance of the oral English instant translation model, this paper optimizes the instant translation model through the Internet of Things (IoT) security technology and deep learning technology. In this paper, the real-time translation model based on deep learning and IoT technology is analyzed in detail to show the application of these two technologies in the real-time translation model, and the related information security issues are discussed. Meanwhile, this paper proposes a method combining deep learning network and IoT technology to further improve the security of instant translation model. The experimental results show that under the optimized model, the parameter upload time is 60 seconds, the aggregation calculation time is 6.5 seconds, and the authentication time is 7.5 seconds. Moreover, the average recognition accuracy of the optimized model reaches 93.1%, and it is superior to the traditional machine translation method in accuracy and real-time, which has wide practical value and application prospects. Therefore, the research has certain reference significance for improving the security of the English corpus oral instant translation model. [ABSTRACT FROM AUTHOR]

The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio's value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios. [ABSTRACT FROM AUTHOR]

Word2vec is often used in text sentiment analysis to generate word vector, which maps the same word into the same vector. Although Word2vec plays a very good effect in the initial model training task, it still cannot solve the problems of polysemy and new use of old words, which leads to inaccurate extracted features and affects the final classification results. In this paper, BERT model was used to vectorize the review text of tourist attractions, and fusion attention mechanism and long and short-term memory model were used to extract the emotional features of the text for classification at the feature extraction layer. The emotional accuracy of the model proposed in this paper reached 95.79% in the review text of tourist attractions. [ABSTRACT FROM AUTHOR]

This paper proposes a detection method for countering strategic attacks in zero-boundary trusted networks. In a normal network, malicious nodes are only a minority; therefore, this paper employs a simple game-theoretic approach to suppress the occurrence of malicious events. Firstly, the paper introduces a behavior-based event inference method to detect malicious events, wherein nodes reference the inference results of other nodes to form composite reports. Subsequently, the paper introduces a simple game, allowing malicious nodes to choose not to falsify reports under disadvantaged scenarios, reaching a Bayesian equilibrium with normal nodes, thereby reducing the incidence of malicious events. This method demonstrates significant effectiveness in conventional networks where malicious nodes constitute a minority. [ABSTRACT FROM AUTHOR]

This original paper, based on data from the Erasmus+Blurred Lives Project, presents a new multi-dimensional categorisation model to describe negative online experiences, including forms of cyberbullying, based on a study of internet usage among over N = 2,500 adolescents with lower socio-economic status (SES) backgrounds across five European countries. The paper first sets out the rationale for the development of a new coding system, before describing the current study and nature of the survey data collected. There follows a description of the development of the new system and the series of reliability checks undertaken by the research team (N= 11, from 5 countries) and of the refinements made to the categories and codes. The resulting coding system is presented with consideration of the strengths and limitations, and description of two early pilot studies which have successfully adopted the new system. [ABSTRACT FROM AUTHOR]

In this paper, the development of action and event detectors over the past three decades is summarized. The detectors are divided into 2D detectors, 3D detectors and deep learning detectors according to whether they contain spatial information and whether they use deep learning. This paper briefly introduces the typical detectors of the different types mentioned above, and explains the advantages, disadvantages and characteristics respectively, and compares them. Comparing traditional feature detection methods with ones based on deep learning, we found that the method of first detecting microscopic details such as point, line, surface angle, etc., and then performing action and event recognition is no longer the mainstream of current research. Due to the strong generalization ability, end-to-end action and event recognition methods based on deep learning perform better than traditional methods. Finally, this paper proposes three research directions for action recognition and event recognition based on feature detectors. [ABSTRACT FROM AUTHOR]

The purpose of this research is to address the challenges faced by visually impaired individuals, particularly in handling household appliances independently. With approximately 285 million visually impaired individuals worldwide, technological solutions are crucial to enhancing their accessibility and independence. This paper introduces a Smart Assistance System designed to empower visually impaired individuals to interact with household appliances in real-time without assistance.In this study, three Convolutional Neural Network (CNN) algorithms are compared to develop the system. The evaluation metrics include accuracy, precision, recall, F1 score, and hamming loss on validation images. The performance comparison reveals that the custom architecture CNN, MobileNetv2, and YOLO models achieve F1 scores of 0.43, 0.63, and 0.24, respectively. To enhance object detection and classification, the paper suggests implementing bounding box buttons categorization using YOLOv8, which demonstrates superior performance with a 95% classification accuracy on testing images of home appliance buttons.They face similar difficult while in public and accessing public property. Expanding upon the proposed system’s capabilities, the paper introduces the concept of panic button detection and activation in a bus environment tailored for blind individuals. This system relies on various factors such as the number of people onboard, heart rate monitoring, and the detection of distress signals or SOS sounds emitted by the user. By integrating advanced sensing technologies and intelligent algorithms, this panic button detection system aims to provide prompt assistance and ensure the safety of visually impaired passengers in public transportation settings. [ABSTRACT FROM AUTHOR]

Traditional methods have the limitations of low accuracy and inconvenient operation in analyzing students’ abnormal behavior. Hence, a more intuitive, flexible, and user-friendly visualization tool is needed to help better understand students’ behavior data. In this study a visual analysis and interactive interface of students’ abnormal behavior based on a clustering algorithm were examined and designed. Firstly, this paper discusses the development of traditional methods for analyzing students’ abnormal behavior and visualization technology and discusses its limitations. Then, the K-means clustering algorithm is selected as the solution to find potential abnormal patterns and groups from students’ behaviors. By collecting a large number of students’ behavior data and preprocessing them to extract relevant features, a K-means clustering algorithm is applied to cluster the data and obtain the clustering results of students’ abnormal behaviors. To visually display the clustering results and help users analyze students’ abnormal behaviors, a visual analysis method and an interactive interface are designed to present the clustering results to users. The interactive functions are provided, such as screening, zooming in and out, and correlation analysis, to support users’ in-depth exploration and analysis of data. Finally, the experimental evaluation is carried out, and the effectiveness and practicability of the proposed method are verified by using big data to obtain real student behavior data. The experimental results show that this method can accurately detect and visualize students’ abnormal behaviors and provide intuitive analysis results. This paper makes full use of the advantages of big data to understand students’ behavior patterns more comprehensively and provides a new solution for students’ management and behavior analysis in the field of education. Future research can further expand and improve this method to adapt to more complex students’ behavior data and needs. [ABSTRACT FROM AUTHOR]

In this paper, a scheme for image data security is designed by using Bifid cipher and Arnold map. The conventional 2D-Bifid cipher is changed to handle the encryption and decryption of color images. Further, a block-based method is realized using Arnold map to encrypt and decrypt the image data of square as well as non-square sizes. The proposed scheme is constructed on a widely adopted cryptographic framework, i.e., substitution-permutation design, where Bifid cipher-based scheme acts as a substitution layer and Arnold map-based scheme acts as a diffusion layer. The designed scheme has a huge key space with key sensitivity not only to accurate keys but also to their accurate orders. The practicability and performance of the designed scheme is validated by conducting a detailed analysis along with a comparative study. This paper contributes to a simple, efficient and secure image encryption method, which outperforms to the related works. [ABSTRACT FROM AUTHOR]

In this paper we propose the idea that compliance of a producer of official statistics to the basic principles of data protection in the preparation of a survey, while imposing additional burden, complexity or even restrictions to the process, can actually prove to be an effective tool to ensure and demonstrate compliance with the Fundamental Principles of Official Statistics and the European Statistics Code of Practice. To show the validity of this idea we utilize the Greek Population and Housing Census of 2021, conducted in 2021 by the Hellenic Statistical Authority (ELSTAT), as a representative example of statistical survey, which was built from the beginning with a primary look to the basic data protection principles of the General Data Protection Regulation of the European Union (GDPR). While preparing the Census we came across the intrinsic interplay that exists between principles of the GDPR and the Fundamental Principles and the Code. In the paper we analyze this interplay as we came across it in the Census and show that it can be used as a tool to strengthen the implementation of basic principles of official statistics in all statistical production processes. [ABSTRACT FROM AUTHOR]

While national registry systems are evolving worldwide and, in some cases, replacing reliance on censuses, in countries where well-established population registers are lacking, the population and housing census remains the primary source of detailed data on the number of people, their spatial distribution, age and gender structure, living conditions, and other key socio-economic characteristics. The quality of the census findings is crucial for several reasons, including building public trust in the national statistical system. In many developing countries, conducting a Post-Enumeration Survey appears to be the only feasible way to evaluate the census results. Indeed, the lack or incompleteness of reliable demographic data from alternative sources precludes the use of other methods. This paper discusses some aspects of the feasibility of a Post-Enumeration Survey in Ethiopia. In particular, the paper reports on the main critical issues that emerged from the pilot surveys carried out in the framework of a cooperation project – funded by the Italian Agency for Development Cooperation – aimed at providing methodological support and technical assistance for the preparation of the 4th Ethiopian Population and Housing Census. [ABSTRACT FROM AUTHOR]

Deep learning has recently made great progress leading to revolutionizing image recognition, speech recognition, and natural language processing tasks that were previously challenging to make using traditional techniques. Image classification offers a lot of potential for architectural design, even though it is rarely used to uncover new techniques. It can be used to determine the client’s preferences and design a building that satisfies those preferences. The different architectural styles based on culture, region, and time are one of the main challenges for image classification in architecture. Hence, it can be challenging for untrained clients to recognize an architectural style, and sometimes some buildings are made up of various types that are difficult to classify as a single style. This paper investigates the potential of employing state-of-art cutting-edge image classification algorithms in houses classification. In addition, the paper proposes the uses of Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA) in order to enhance the performance of Vision transformer (ViT) when trained to classify house images with a small dataset, opposed to the regular ViT which requires huge dataset in order to converge. Experimentally, these techniques proved to have a positive impact on the performance of the ViT, which reached 96.85% accuracy when SPT and LSA are employed. [ABSTRACT FROM AUTHOR]

In group decision-making (GDM), when decision-makers (DMs) feel it is unfair, they may take uncooperative measures to disrupt the consensus-reaching process (CRP). On the other hand, it is difficult for the moderator to objectively determine each DM’s unit consensus cost and weight in CRP. Hence, this paper proposes data-driven robust maximum fairness consensus models (RMFCMs) to address these. First, this paper uses the robust optimization method to construct multiple uncertainty sets to describe the uncertainty of the DMs’ unit adjustment cost and proposes the RMFCMs. Subsequently, based on the DMs’ historical data, the DMs’ weights in the CRP are determined by a data-driven method based on the kernel density estimation (KDE) method. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between governments and enterprises, and the experimental results verify the rationality and robustness of the proposed consensus model. [ABSTRACT FROM AUTHOR]

Traditional fuzzy decision-making methods still have certain limitations in practical applications, such as the problem of the sum of attribute memberships and non-memberships possibly exceeding 1. Additionally, due to the attributes in real decision-making processes often not being mutually independent but rather exhibiting a certain degree of correlation, traditional fuzzy decision-making methods may not fully capture and express this complexity. To overcome these limitations, this paper proposes a new multi-attribute decision-making method addressing the problem of integrating information with correlated attributes in the generalized spherical fuzzy environment. Initially, by combining the generalized spherical fuzzy set with the Heronian averaging operator, the paper introduces the generalized spherical fuzzy weighted Heronian averaging operator and thoroughly discusses some valuable properties of both operators, providing corresponding proofs. Furthermore, the paper proposes the multi-attribute decision-making method using the generalized spherical fuzzy weighted Heronian averaging operator, enriching not only the theoretical framework of multi-attribute decision-making methods but also offering more possibilities for practical applications. Finally, the application of this method in the field of commercial bank lending decision-making will be further explored to enhance the accuracy and efficiency of credit decisions, reduce risks, and promote the healthy development of the banking industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: White spot lesions (WSLs) appear as white, opaque areas on the teeth and are caused by a demineralization of the enamel. OBJECTIVE: The purpose of this systematic review and meta-analysis was to determine whether casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) has beneficial effects on WSLs and provides remineralizing benefits that are superior to those of placebo or no treatment. METHODS: Following a thorough analysis of the literature across major online sources, 103 papers – 54 of which underwent in-depth evaluation – were found. We used inclusion and exclusion criteria to choose research articles that were appropriate for our review. RESULTS: 10 papers met this review's requirements and were subsequently included. As compared to the controls, CPP-ACP demonstrated increased efficacy in treatment of WSLs in terms of remineralisation and other parameters which included microhardness tests and enamel surface roughness. The forest plots present the results of meta-analyses comparing the effect of CPP-ACP versus control/other agents on white spot lesions (WSLs). In terms of odds ratios (OR), CPP-ACP showed a small increase in the odds of WSLs compared to control/other agents. In-vitro studies showed no significant difference in relative risk (RR) between CPP-ACP and control/other agents. Randomized control trials indicated a small increase in the risk of WSLs with CPP-ACP treatment, but the overall effect was not statistically significant. CONCLUSION: There was evidence to support the use of CPP-ACP over other agents for WSL prevention or treatment, but further research is needed to confirm these findings. [ABSTRACT FROM AUTHOR]

BACKGROUND: Career Pathways initiatives in Vocational Rehabilitation (VR) have emerged as a crucial strategy for enhancing quality employment outcomes among individuals with disabilities. This paper conducts a comprehensive examination of the integration of Career Pathways within VR, emphasizing its significance in aligning vocational goals with evolving labor market demands. OBJECTIVE: Drawing from the innovative Career Pathways initiatives introduced by the Rehabilitation Services Administration (RSA), this paper addresses the RSA Career Pathways Demonstration Initiatives from 2015 to 2019 alongside the ongoing Disability Innovation Fund-Career Advancement Initiative Model Demonstration Project. The paper also discusses the strengthening of Career Pathways through the development of high-quality Individualized Plans for Employment and program evaluation. METHODS: This study utilized a modified exploratory case study approach to examine past and present career pathways initiatives in state VR agencies. Data were gathered from publicly available documentation and archival records, focusing on 12 cases from two national initiatives: the RSA Career Pathways Demonstration Initiatives (2015–2019) and the Disability Innovation Fund-Career Advancement Initiative Model Demonstration Project (2021–2026). RESULTS: The study reveals a substantial, positive impact on employment outcomes for individuals with disabilities through Career Pathways in VR programs, facilitated by the integration of adaptable pathways, individualized planning, and program evaluation. [ABSTRACT FROM AUTHOR]

The recent increasing attention to the economic and policy analysis of the food systems from international fora, public institutions and academia calls for the availability of information and data capable of informing about the interrelations across economic sectors and within value chains. The international policy agenda is pushing for a more effective application of measures at country and regional level in line with the recommendations of the 2030 Agenda and its Sustainable Development Goals, for which more systematic and integrated data about economic, social and environmental impacts of policies are requested. The Food Value Chain Domain recently published in FAOSTAT responds to this call. Its data and information shed light on the distribution of final domestic food expenditures across industries (Agriculture, Food Processing, Wholesale, Retail, Accommodations and Food Services) and primary factors (e.g.: Labour, Gross Operating Surplus) on the relative food value chain. The FAOSTAT Domain offers therefore robust and granular information on both the farm and the post-farm gate component of the Food Value Chain. The applied Global Food Dollar methodology, that FAO is contributing to upscale at global level, is based on Leontief decomposition approach on the Input-Output tables. Moreover, whenever the Input-Output table are not available, it is now possible to impute them from Supply-Use tables by applying a conversion methodology, developed by FAO in compliance with European (EUROSTAT), United Nations (UNSD) and international statistical standards as the System of National Accounts. This allows to extend the analysis to several African, Asian, and Latin American countries that produce on regular basis only Supply and Use Tables, and not Industry by industry Input Output Tables. The potential time and data coverage of the methodology is therefore significantly expanded. The aim of this paper is to describe the conceptual framework of the conversion methodology of Supply-Use Tables into Input-Output Tables of the Global Food Dollar methodology, and the potential implementation scope of these methodologies. Preliminary analytical findings of the applied methodologies are presented as well. The new methods and data presented in this paper, being based on data compliant with the International Statistical Standards, as the System of National Accounts, and therefore comparable across countries, associated to larger data availability, have the potential to effectively support food policies at international, regional and national level, as well as contribute to a decision making in line with the 2030 Agenda. [ABSTRACT FROM AUTHOR]

BACKGROUND: The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. OBJECTIVE: To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. METHODS: Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. RESULTS: In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. CONCLUSION: This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment. [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]

Gesture recognition based on wearable sensors has received extensive attention in recent years. This paper proposes a gesture recognition model (CGR_ATT) based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) fused attention mechanism to improve accuracy rate of wearable sensors. First, CNN serves as a feature extractor, learning features automatically from sensor data by performing multiple layers of convolution and pooling operations, capturing spatial features of gestures. Furthermore, a temporal modeling unit GRU is introduced to capture the temporal dynamics in gesture sequences. By controlling the information flow through gate mechanisms, it effectively handles the temporal relationships in sensor data. Finally, an attention mechanism is introduced to assign different weights to the hidden state of the GRU. By calculating the attention weights for each time period, the model automatically selects key time periods related to gesture movements. The GR-dataset proposed in this paper involves 910 sets of training parameters. The model achieves an ultimate accuracy of 97.57% . In compare with CLA-net, CLT-net, CGR, GRU, LSTM and CNN, the experimental results demonstrate that the proposed method has superior accuracy. [ABSTRACT FROM AUTHOR]

The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was >  0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing. [ABSTRACT FROM AUTHOR]

Human emotions are the mind’s responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. [ABSTRACT FROM AUTHOR]

The existence of a maximal ideal in a general nontrivial commutative ring is tied together with the axiom of choice. Following Berardi, Valentini and thus Krivine but using the relative interpretation of negation (that is, as “implies  0 = 1”) we show, in constructive set theory with minimal logic, how for countable rings one can do without any kind of choice and without the usual decidability assumption that the ring is strongly discrete (membership in finitely generated ideals is decidable). By a functional recursive definition we obtain a maximal ideal in the sense that the quotient ring is a residue field (every noninvertible element is zero), and with strong discreteness even a geometric field (every element is either invertible or else zero). Krull’s lemma for the related notion of prime ideal follows by passing to rings of fractions. By employing a construction variant of set-theoretic forcing due to Joyal and Tierney, we expand our treatment to arbitrary rings and establish a connection with dynamical algebra: We recover the dynamical approach to maximal ideals as a parametrized version of the celebrated double negation translation. This connection allows us to give formal a priori criteria elucidating the scope of the dynamical method. Along the way we do a case study for proofs in algebra with minimal logic, and generalize the construction to arbitrary inconsistency predicates. A partial Agda formalization is available at an accompanying repository.11 See https://github.com/iblech/constructive-maximal-ideals/. This text is a revised and extended version of the conference paper (In Revolutions and Revelations in Computability. 18th Conference on Computability in Europe (2022) Springer). The conference paper only briefly sketched the connection with dynamical algebra; did not compare this connection with other flavors of set-theoretic forcing; and sticked to the case of commutative algebra only, passing on the generalization to inconsistency predicates and well-orders.See https://github.com/iblech/constructive-maximal-ideals/. This text is a revised and extended version of the conference paper (In Revolutions and Revelations in Computability. 18th Conference on Computability in Europe (2022) Springer). The conference paper only briefly sketched the connection with dynamical algebra; did not compare this connection with other flavors of set-theoretic forcing; and sticked to the case of commutative algebra only, passing on the generalization to inconsistency predicates and well-orders. [ABSTRACT FROM AUTHOR]

Liquid hydrogen turbopumps are used in large high-performance rockets. Stable high-speed rotation is required for rocket turbopumps. The damping mechanism of the pump must suppress vibration not only in the radial direction but also in the axial direction. However, conventional damping elements using oil or rubber cannot be used due to the cryogenic temperature of liquid hydrogen. Therefore, the application of eddy current dampers to liquid hydrogen turbopumps is focused on in this paper. Although various structures of eddy current dampers have been developed, the multi-degree-of-freedom damping characteristics of dual Halbach array type eddy current dampers for liquid hydrogen turbopumps have not yet been investigated. The variation of damping characteristics with temperature has also not yet been verified. In this paper, we propose a novel dual Halbach array type eddy current damper for liquid hydrogen turbopumps. The proposed damper can generate high damping force and can be operated maintenance-free at the cryogenic temperature. The analysis results show that the damping characteristics strongly depend on temperature and that the amplitude reduction effect is greater at low temperatures. It was also found that the proposed damper has a higher damping force density than conventional dampers. [ABSTRACT FROM AUTHOR]

Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain’s ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients. The study goal is to develop an effective method to detect and segment brain tumor with high accurancy. This paper proposes a novel U-Net+⁣+ network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap. The proposed segmentation model is trained and tested on Kaggle’s LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180. This paper conducts research on brain tumor segmentation, using the U-Net+⁣+ network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180. [ABSTRACT FROM AUTHOR]

With the development of science and technology, people have higher and higher requirements for robots. The application of robots in industrial production is also increasing, and there are more applications in people’s lives. Therefore, robots must have a better ability to receive and process the external environment. Therefore, visual servo system appears. Pose estimation is a major problem in the current vision system. It has great application value in positioning and navigation, target tracking and recognition, virtual reality and motion estimation. Therefore, this paper put forward the research of robot arm pose estimation and control based on machine vision. This paper first analyzed the technology of machine vision, and then carried out experiments. The accuracy and stability of the two methods for robot arm pose estimation were compared. The experimental results showed that when the noise of Kalman’s centralized data fusion method was 1 pixel, the maximum error of the X-axis angle was only 0.55, and the average error was 0.02. In Kalman’s distributed data fusion method, the average error of X-axis displacement was 0.06, and the maximum value was 17.66. In terms of accuracy, Kalman’s centralized data fusion method was better. In terms of stability, Kalman’s centralized data fusion method was also better. However, in general, these two methods had very good results, and could accurately control the position and posture of the manipulator. [ABSTRACT FROM AUTHOR]

In order to improve the remanufacturing efficiency of scrap mechanical parts and comprehensively detect their surface fault status, this paper proposes a color three-dimensional reconstruction method of scrap mechanical parts based on an improved semi-global matching (SGM) algorithm. In experiments, this method demonstrated significant performance advantages in dealing with complex mechanical component structures and large illumination interference environments. Experimental results show that the three-dimensional color model reconstructed by this method has clear texture and small dimensional error, and is suitable for online analysis of surface fault information of scrap mechanical parts in actual production lines. Through quantitative analysis, compared with the traditional SGM method, the method in this paper improves the structural similarity index (SSIM) by an average of 19.8% and reduces the mean square error (MSE) by an average of 33.1%. [ABSTRACT FROM AUTHOR]

Long-term evolution in wireless broadband communication aims to provide secure communication for users and a high data rate for a fourth-generation network. Even though the fourth-generation network provides security, some loopholes lead to several attacks on the fourth-generation network attacks. The denial-of-service attack occurs when the user communicates with a rogue base station, and the radio base station in fourth-generation long-term evolution networks ensures that the user is attached to the rogue node assigned network. The location leak attack occurs when the packets are sniffed to find any user’s location using its temporary mobile subscriber identity. Prevention of rogue base station and location leak attacks helps the system achieve secure communication between the participating entities. Earlier works in long-term evolution mobility management do not address preventing attacks such as denial-of-service, rogue base stations and location leaks and suffer from computational costs while providing security features. Hence, the present paper addresses the vulnerability of these attacks. It also investigates how these attacks occur and exposes communication in the fourth-generation network. To mitigate these vulnerabilities, the paper proposes a novel authentication scheme. The proposed scheme is simulated using Network Simulator 3, and the security analysis of the proposed scheme is shown using AVISPA –a security tool. Numerical analysis demonstrates that the proposed scheme significantly reduces communication overhead and computational costs associated with the fourth-generation long-term evolution authentication mechanism. [ABSTRACT FROM AUTHOR]

This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach. [ABSTRACT FROM AUTHOR]

The correspondence between the decision space and the objective space is often many-to-one in multi-objective optimization problems. Therefore, a class of problems with such mapping relationships is defined as a MMOPs. For these problems, how to ensure the final solution converges to each Pareto solution set and guarantees the diversity of the algorithm is an urgent problem. The method of the paper with opposition-based strategy, a multimodal multi-objective optimization algorithm, is proposed. The algorithm proposed is called MMODE_OP, which is framed by a differential evolutionary algorithm, and opposition-based learning is applied to the initialization phase and generation-hopping phase to filter out the more promising individuals in the population for iteration to enhance the global search capability and the diversity of population. In addition, different Gaussian perturbation strategies are adopted with iteration to achieve the search of the neighborhood, which can further not only improve the quality of the Pareto solution set but also enable the convergence of the Pareto solution set quickly. This method improves the algorithm’s local and global search ability, and enables multiple the Pareto solution set and improving the convergence. In the meantime, adaptive scaling factors and crossover factors are designed in this paper to enhance the improved search capability. Finally, the experiment results of MMODE_OP and other excellent algorithms on 13 test problems corroborate the proposed methods have superior performance. [ABSTRACT FROM AUTHOR]

With explosive growth of industrial big data, workshop scheduling faces problems such as high complexity, multi-dimensionality and low stability. Recent years, the wide application of deep learning provides new idea for scheduling problem. In this paper, a hybrid deep convolution network and differential evolution algorithm is proposed to solve the non-permutation flow shop scheduling problem with the goal of minimizing total completion time. Mining relationship between job attributes and process priority by deep convolutional network is core idea of this method. In this paper, differential evolution algorithm is used to obtain the data set for deep learning, and neighborhood search algorithm is used to optimize scheduling solution. Additionally, a method combining k-means algorithm and data statistics is proposed, which provides a reasonable way for priority division. The experimental results show that this method can greatly improve scheduling efficiency. [ABSTRACT FROM AUTHOR]

Predictors of macroeconomic indicators rely primarily on traditional data sourced from National Statistical Offices. However, new data sources made available from recent technological advancements, namely data from online activities, have the potential to bring about fresh perspectives on monitoring economic activities and enhance the accuracy of forecasting. This paper reviews the literature on predicting macroeconomic indicators, such as the gross domestic product, unemployment rate, consumer price index or private consumption, based on online activity data sourced from Google Trends, Twitter (rebranded to X) and mobile devices. Based on a systematic search of publications indexed on the Web of Science and Scopus databases, the analysis of a final set of 56 publications covers the publication history of the data sources, the methods used to model the data and the predictive accuracy of information from such data sources. The paper also discusses the limitations and challenges of using online activity data for macroeconomic predictions. The review concludes that online activity data can be a valuable source of information for predicting macroeconomic indicators. However, one must consider certain limitations and challenges to improve the models’ accuracy and reliability. [ABSTRACT FROM AUTHOR]