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Background. Until recently, information behaviour scholars have mainly focused on the workplace or educational settings, and defined information as either a tool for problem solving or a means to satisfy needs. This, however, only represents a partial picture of a larger horizon. In some arenas of life, such as serious leisure, people do not seek information to necessarily solve a problem or satisfy an urgent need. They look for information to enjoy a hobby or participate in an entirely voluntary activity. Objectives. The paper introduces transcendental information as a subjective and contextual concept to provide insights on lesser explored corners of the information behaviour scholarship. This concept is compared with existing theories and concepts in the information behaviour area. Method. This conceptual paper is based on a critical literature review of human information behaviour, and reflects on some key concepts in the field, including the nature of information, information needs, information seeking and sharing. The paper also provides a selective literature review of the serious leisure perspective to contextualise the analysis. Results. It is found that transcendental information usually has an aesthetic and intellectual essence. It may be expressed in various imaginative forms and can appear in different non-textual and embedded formats. Moreover, it can generate joyful and inspiring impacts. The paper refers to serious leisure as an exemplary setting to contextualise transcendental information within a relevant and well-established theoretical framework. [ABSTRACT FROM AUTHOR]

This study aimed to describe the environmental problem solving ability, analyze differences in the environmental problem solving ability when viewed from gender and school differences. To test the environmental problem solving ability in students, a test instrument that refered to the Sanjaya's indicator was developed. The test items were developed by researchers and validated by material and language experts. After revising the results of expert validation, the test of the environmental problem solvng ability was tested. The trial results obtained a test instrument that was valid and reliable. Quantitative data were obtained from the answers of 136 students to the problem solving ability test. The results showed that: 1) the environmental problem solving ability in students was still low, 2) there is no relationship between gender differences and the environ- mental problem solvng ability. 3) there is no relationship between school differences and problem solving abilities. Further researchers are suggested to facilitate students through teaching materials in the implementation of learning and increase student activity in learning activities in order to improve the environ- mental problem solving ability. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

These instructions give you guidelines for preparing the papers for Annals of the University of Craiova, Economic Sciences Series. These instructions are also formatted as a sample for your paper layout. No fulfillment of these instructions determines the Editorial Board to reject the paper. [ABSTRACT FROM AUTHOR]

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1] . They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. [ABSTRACT FROM AUTHOR]

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [Copyright &y& Elsevier]

In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]

Academic networks express the academic relationships among authors, papers and conferences, which offer perspectives and tools to rethink and solve traditional problems in the academic field. Among them, conference ranking is a problem which normally produced by collecting information from different sources and considering many factors. Not like most of the existed ranking methods that based on subjective evaluation, in this paper, we develop a Conference Ranking method based on Academic Network Embedding (CRANE) algorithm to simultaneously rank authors and conferences which improving the accuracy of ranking by embedding the Academic Network. The CRANE uses both conference features like internationality, citations and co-authors, and structure in Academic Network to evaluate the ranking of certain conferences. The theoretical analysis and convergence analysis were provided in this paper. Experiments on datasets of the Digital Bibliography & Library Project (DBLP) and AMiner academic platforms also be conducted. The experimental results show that the CRANE algorithm is superior to other algorithms by comparing with the public academic ranking like the China Computer Federation (CCF) ranking. [ABSTRACT FROM AUTHOR]

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the "whole-electric-bicycle annotation" method, which could help with the development of electric bicycle detection applications in lifts. [ABSTRACT FROM AUTHOR]

Achieving a balance between convergence and diversity and their mutual enhancement is a complex task in the process of algorithm improvement. This is crucial because it is directly related to the effectiveness of the algorithm in obtaining accurate and uniformly distributed Pareto frontiers. Although significant progress has been made in particle swarm algorithms, exploring new approaches is necessary. In this paper, we construct a projection plane (projection line in 2D) based on the extreme values of the non-dominated solutions, select a set of uniform reference points on the projection plane, and then project the non-dominated solutions onto the constructed projection plane to form projection points. The reference points and projection points on the projection plane are thus utilized to guide the updating of the population as well as the maintenance of the external archive, a strategy that enhances the algorithm's global exploration and local exploitation capabilities. Secondly, we aggregate the target values of particles into a single scalar value and combine the idea of particle fusion to design a scheme for the particle selection of individual optimal particles. This paper further improves the algorithm's overall performance by using the information between populations to select individual optimal particles. Lastly, it is evaluated against a number of multi-objective algorithms that are currently in use and perform well on 22 test problems. The findings demonstrate that the algorithm this paper proposes performs better when solving multi-objective problems. [ABSTRACT FROM AUTHOR]

This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization (DP-DZOGT) algorithm for solving optimization problems of decentralized systems, where the gradient information of the function is unknown. To address the challenge of unknown gradient information, a one-point zeroth-order gradient estimator (OPZOGE) is constructed, which can estimate the gradient based on the function value and guide the update of decision variables. Additionally, to prevent privacy leakage of agents, random noise is introduced into both the state and the gradient of the agents, which effectively enhances the level of privacy protection. The linear convergence of the proposed DP-DZOGT under a fixed step size can be guaranteed. Moreover, it has been applied to the fields of smart grid (SG) and decentralized federated learning (DFL). Finally, the effectiveness of the algorithm is validated through three numerical simulations. • This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization algorithm. • The proposed algorithm not only protects data privacy but also effectively improves convergence speed. • This paper explores the practical applications of DP-DZOGT in the domains of smart grids and decentralized federated learning. [ABSTRACT FROM AUTHOR]

The circumferential non-uniform tip clearance (CNTC) due to casing out-ofroundness adversely affects the turbine aerodynamic performance due to machining and assembly errors, thermal deformation, and improper active clearance control (ACC), etc. Moreover, the asymmetric computational domain caused by casing out-of-roundness presents difficulties for conventional numerical techniques that consider rotational periodicity. Since previous traditional methods using split computational domains have the disadvantages of high interpolation error and high time cost, an efficient fast-moving mesh (FMM) method based on an algebraic approach is proposed in this paper. This method is first validated by using a single-stage turbine with elliptical casing. The results show that the FMM has the advantages of high accuracy, high efficiency, and easy operation, which helps to solve the CNTC problem quickly in scientific research or engineering applications. Then, the effects of CNTC induced by the elliptical casing on the flow field and aerodynamic performance are investigated by using an in-house code that integrates the FMM method. Finally, the effect of stator row interference on the aerodynamic performance in the turbine stage with an elliptical casing is demonstrated. The results show that different types of elliptical casings have a significant effect on the aerodynamic performance. However, the variation law is not consistent (decreasing by 0.538% or increasing by 0.212%). Importantly, the novel finding of this paper is that this discrepancy is jointly determined by the interaction of multiple secondary flows (passage vortex, scraping vortex, etc.) at different spans, not just related to the variation of the tip leakage vortex (TLV) with tip size. Furthermore, this study is the first to indicate that the stator row interference can mitigate the extent of performance degradation due to elliptical casings by suppressing the development of secondary flows. These results may provide theoretical support for blade tip gap design and can also serve as a reasonable reference for the effective application of ACC in engineering. Finally, low-order harmonic components with high amplitudes are also innovatively found in the rotor row with a CNTC. These components may cause low-engine-order (LEO) resonances that endanger the safe operation of engines. [ABSTRACT FROM AUTHOR]

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. [ABSTRACT FROM AUTHOR]

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm to solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold algorithm is proposed, which is AHCSMA, to solve these problems efficiently. Specifically, there are three innovations in this paper: (i) new self-adaptive Cauchy mutation operator is developed to improve the mutation ability of the population; (ii) the crossover rate balance mechanism is proposed to make up for the neglected relationship between individuals and crossover rates. Then the differential vector information between the dominant individual and other individuals in the population is highly utilized to increase the evolution speed of the algorithm; (iii) self-adaptive restart hybrid opposition learning is designed to alleviate the situation where the algorithm falls into local optimality. To verify the competitive of AHCSMA, UAV path planning problems, engineering problems, nonlinear parameter extraction of photovoltaic model problems and parameter identification problems of highly nonlinear infinite impulse response are used to test the ability of the AHCSMA, accumulation more than 50 algorithms are used as comparison algorithms, and results report that AHCSMA is extremely competitive and performs better when optimizing these real-life problems. [ABSTRACT FROM AUTHOR]

The rotational speed of the hydraulic turbine generator set will change with the load change of the power system, therefore, the speed control system of the unit needs to adjust its operating parameters in time according to the load change. The conventional PID control algorithm is widely used in the current hydraulic turbine speed control system, but it is unable to self-tune the parameters, which makes it difficult to achieve the desired control effect. In order to solve the above problems, this paper combines the neural network and fuzzy control to optimize the PID control parameters, and through the Matlab simulation experiments, it is verified that the control described in this paper can make the hydraulic turbine speed control system have satisfactory control effects in different operating conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a computational framework based on the SAPEVO-M method to enhance decision-making processes in complex scenarios. By integrating technological advancements with the SAPEVO-M methodology, the framework facilitates a more efficient, accurate analysis based on multi-criteria decision-making problems. The paper presents a literature background of the methodology and its axiomatic structure, highlighting its capability for decision-making for a group of stakeholders, thus offering favorable improvements as a technological aid for decision-making analysis. The results demonstrate the potential of the framework, providing decision-makers with robust and reliable insights, and making it an invaluable tool in fields requiring complex decision analysis. This study not only contributes a novel and free computational approach to the decision-making literature but also paves the way for future research and development, applying the axiomatic methodology for problem-solving in diverse areas of science, searching for increase and transparency concerning decision-making processes worldwide. [ABSTRACT FROM AUTHOR]

Sign language is an important way for deaf-mutes to communicate, and sign language recognition plays an important role in solving the communication problems between able-bodied people and deaf-mutes. In order to help them communicate better with others and gradually switch to universal sign language, this paper proposes a design of Uyghur sign language recognition system based on the improved YOLOv7. Through the self-made 2568 Uyghur sign language datasets, a total of 35 categories such as 'time', 'you', 'morning', etc. were involved. In terms of network improvement, the backbone network is improved, and the improved convolution structure of SimAM attention + PConv is used; Second, replace the EIoU loss function. The sign language recognition model used in this paper has achieved significant improvement in Uyghur sign language recognition, which fully demonstrates its potential for in-depth research and wide application. [ABSTRACT FROM AUTHOR]

The rotational speed of the hydraulic turbine generator set will change with the load change of the power system, therefore, the speed control system of the unit needs to adjust its operating parameters in time according to the load change. The conventional PID control algorithm is widely used in the current hydraulic turbine speed control system, but it is unable to self-tune the parameters, which makes it difficult to achieve the desired control effect. In order to solve the above problems, this paper combines the neural network and fuzzy control to optimize the PID control parameters, and through the Matlab simulation experiments, it is verified that the control described in this paper can make the hydraulic turbine speed control system have satisfactory control effects in different operating conditions. [ABSTRACT FROM AUTHOR]

This paper aims to contribute to both theory and practice by describing current problem-solving practices, proposed by various authors, as part of the quality management process of companies in the automotive industry. The paper, based on proposals from different authors, also includes a set of quality tools that can be used in the different steps of the problem-solving process. The results show that there is general agreement on best problem-solving practices, emphasizing root-cause analysis and the development and implementation of corrective actions, which are included in all the proposals analyzed. The analysis also shows that many quality tools can be used in the different steps of this process. In addition, the paper proposes an initial framework developed by the authors that can be used by organizations to improve their problem-solving practices. [ABSTRACT FROM AUTHOR]

This paper is devoted to the tracking control for an uncertain robotic system with both output constraint and dead-zone input. Remarkably, the distinctive characters of the system are reflected by system uncertainties and output constraint. First, more serious uncertainties are involved since unknown nonlinear dynamic matrices, external disturbance and the dead-zone input (see unknown slopes and break points therein) are simultaneously considered, but those of the related literature are not. Second, weaker conditions on the output constraint are allowed since the constraint functions considered are only first but not more order continuously differentiable while any their time derivatives are not necessarily available for feedback. This leads to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel control framework is proposed based on time-varying feedback which overcomes the serious system uncertainties while relaxes the conditions on output constraints. Specifically, a state transformation with a time-varying gain is first introduced to derive a new system. Then, by using the traditional backstepping method with the introduction of the time-varying gain in the estimations of some uncertain terms, a time-varying feedback controller is explicitly designed, which ensures that all the states of the resulting closed-loop system are bounded while system output asymptotically tracks the reference signal without any violation of the output constraint. Finally, simulation results for two practical examples are provided to validate the effectiveness of the proposed theoretical results, and moreover, a comparison with PID method is given to show the superiority of the proposed method on tracking accuracy and robustness. • More serious uncertainties are involved in the system under investigation than those of the related literature. • Weaker conditions are allowed on the output constraint than those of the related literature. • A novel time-varying control scheme is proposed by introducing a time-varying gain in control design. [ABSTRACT FROM AUTHOR]

This paper investigates the robust cooperative output regulation problem for heterogeneous lower triangular nonlinear multi-agent systems with an unknown exosystem over jointly connected switching networks. The problem has been studied for the exactly known exosystem over switching networks. However, the existing result for the unknown exosystem is still limited to the static networks. To ensure that all followers acquire the reference trajectory generated by the unknown exosystem through the jointly connected switching networks, by combining a set of auxiliary filtering variables and fixed-time stability theory, an adaptive distributed observer is designed. On the basis of the adaptive distributed observer and the distributed internal model approach, we propose a distributed controller under several standard assumptions to solve the problem. Compared with the similar work subject to the static networks, the controller in this paper is applicable to the more general communication network while weakening the assumptions of the controlled system. • A new distributed observer is designed for relatively general exosystem and networks. • We transform an unknown exosystem into a known exosystem in a fixed time. • We avoided the additional assumptions of articles studying similar exosystems. • We extended similar articles to more general networks and controlled objects. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

Computer programming courses are known to challenge many students, especially in their first programming courses. Some topics in computer programming are more challenging than others due to the complexity of the logic underlying the concepts. One such topic that is considered challenging is recursive programming or recursion. Recursion as a teaching topic has long been a point of discussion, especially in relation to how it can be taught effectively. This paper delves into the topic of recursive programming and strategies applied to teach it recursively in a four-year institution. It examines the factors that make recursion challenging to learn and some strategies to teach recursion. The strategy pursued here stems from previous studies using data recursion and functional programming, which uses recursion by default. The data structure of 'list' from the LISP programming language, which is apt to data recursion, is implemented in Python and used for solving problems. The effectiveness of the strategy is assessed. Results indicate that the use and understanding of recursive programming can be enhanced by using recursive data structures and having the students use them in more practical applications. This hopefully will encourage other instructors to use the same/similar strategies to teach recursion more effectively. [ABSTRACT FROM AUTHOR]

Since information seeking behavior is a problem-solving process and includes different steps, users are influenced by their reasoning and thinking style during searching and making decisions at each stage of information seeking process, and they may repeat, change the search term, or perform a search many times until reaching the goal. Therefore, identifying users’ reasoning types at each stage of information seeking process increases awareness about their mental challenges and the reasoning process that occur in their minds. Therefore, the purpose of writing this paper is to investigate, theoretically explain the role of reasoning in information seeking process in general and compare the position of the three intellectual approaches of mental logic, mental models and reasoning strategies and in information seeking process in particular. In this research, the role of three thinking approaches (mental logic, mental models, and reasoning strategies) in information seeking process was compared and based on that, the relative advantage of reasoning strategies in comparison with other two approaches were explained. The examination of different intellectual approaches shows that it is not possible to solve the problem by relying on one approach in information seeking process, because the approach of mental logic and the use of logical rules can only be used in the stage of formulating different pieces of information or the stage of evaluating the results, while the examination of the mental models approach shows the application of this approach both in the stage of expressing the need, defining and understanding the problem, and in the stage of evaluating the results. In the meantime, the approach of reasoning strategies indicates that considering some reasoning strategies are based on logical rules and some of them are based on mental models. Therefore, this approach is used in each of the stages of information seeking process. The comparison of three thinking approaches (mental logic, mental models and reasoning strategies) showed that in the reasoning strategies approach, a wide range of strategies can be used to perform reasoning tasks and tasks that are not at the same level in terms of the application of logical rules. In other words, the intellectual approach of reasoning methods includes rule-based approaches (mental logic) or mental models in problem solving. In addition, in this approach, the syntactic and semantic structure as well as the content and context of the problem are taken into consideration. Therfore, compared to the other two approaches, the reasoning strategies approach can be considered as a suitable alternative for explaining and advancing information seeking process. [ABSTRACT FROM AUTHOR]

With the comprehensive arrival of the fifth generation mobile communication technology (5G) era, the Internet of Things (IoT) has developed rapidly and is widely used in various industries. As one of the modern transportation signs, highways play an irreplaceable role in social and economic development. The research on Intelligent Traffic Systems (ITS) on highways has always been a hot topic. With the continuous improvement of the highway network and the increasing mileage, the efficiency of the existing highway ITS in processing information and solving problems is relatively low. Introducing new technologies to achieve breakthroughs has become a top priority. Edge computing is widely used in the Internet of Things technology because of its low latency and high response speed. Based on the characteristics of edge computing technology, this paper conducted in-depth research on expressway ITS, and analyzed the specific functions of intelligent system through IoT technology and edge computing technology. Through analysis and experiment, it is concluded that in the experimental highway area, the ITS using edge computing technology has slightly improved compared with the traditional ITS in all aspects. The response speed of the monitoring system has increased by 4.9 %, the congestion rate has decreased by 11.27 %, the congestion duration has decreased by 37.3 %, and the accident rate has decreased by 7.63 %. The edge computing application of expressway ITS based on IoT technology has improved the safety and comfort of residents' travel. It not only improves the intelligence level of intelligent transportation systems, but also enhances the safety and comfort of residents' travel, meeting the travel needs of modern people. [ABSTRACT FROM AUTHOR]

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301 respectively, which is much higher than Google translation. A model of machine translation evaluation based on neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation. [ABSTRACT FROM AUTHOR]

In order to solve the problems of security and efficiency in Chinese teaching and management, this paper uses the intelligent technology of block chain to achieve this goal. First of all, this project intends to use block chain technology for distributed storage to solve the problems of data tampering and embezzlement in the traditional system. Secondly, through the design of intelligent contract to standardize the management of system operation, enhance the real-time and fairness of the interactive process. In addition, the distributed authentication technology is adopted to ensure the integrity of the user's identity and the clarity of the information. Finally, the safety evaluation 8.68 and the efficiency evaluation 8.65 are given, which proves that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Industry 4.0 technologies have become one of the main enablers for implementing Circular Economy e↵orts, creating smart and connected products, processes, and systems that can optimize resource usage, reduce waste, and improve efficiency, with a significant environmental impact. Blockchain is a technology that was initially centered on authentication and security applications. Currently, its utility has extended to solving multiple problems across industries. This paper explores the di↵erent applications of the Blockchain related to the circular economy. A systematic literature review method was applied to analyze the main objectives, advantages, disadvantages, opportunities, and challenges of implementing Blockchain in various industrial sectors. Results show a growing trend in applications with favorable impacts, especially in sectors like Manufacturing, Energy, and Automotive. [ABSTRACT FROM AUTHOR]

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations. [ABSTRACT FROM AUTHOR]

In today's manufacturing environment, companies are facing new challenges brought by advances in technology which are reflected in the output of their processes or products delivered to the customers. At the same time, customer expectations have changed over time, and the modern customer needs a fast solution when the product received does not meet its requirements. Having this background in mind, how can a traditional problem-solving model tackle a modern issue? This question has been the starting point for developing the Synchronous Model for structured problem solving and further propose its digitalization and AI (artificial intelligence) usage for ensuring operational excellence, enhancing customer satisfaction, and maintaining competitiveness in this rapidly evolving market landscape. The model was created from a comparative analysis between seven most known and used structured problem-solving models within manufacturing companies where all the advantages and disadvantages were collected. Within this paper, a digitalized platform is proposed, and its features are validated through a case study on a manufacturing company, struggling with answer time for customer complaints, where a decreasing trend was observed after using the Synchronous model for addressing complaints, proving its efficiency. [ABSTRACT FROM AUTHOR]

Cultivating the ability to solve complex and contradictory problems is one of the important goals of engineering education. This paper introduces extenics into the training of cultivating college students' problem-solving skills, streamlines the process of solving contradictory problems, and develops a systematic and patterned extenics model for cultivating college students' problem-solving abilities. This study verified the validity of the methodology through comparative case studies and experiments. The training model of contradictory problems extraction and resolution has implications for pedagogical innovations in other specialized courses. [ABSTRACT FROM AUTHOR]

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce platforms, how to better identify and filter fake reviews has become an urgent issue for the healthy development of the e-commerce industry. However, traditional fake review identification methods cannot effectively solve this problem. They are easily affected by text changes, language differences, and context, and fake reviewers may take measures to blur their behavioral characteristics, making them difficult to detect by behavior-based algorithms. Large language models can capture the contextual relationships of the entire text through self-attention mechanisms, thereby understanding the overall meaning and emotional tendency of the review, which enables them to more effectively judge the authenticity of the review. In addition, large models have strong generalization ability and a certain degree of interpretability, which also makes them suitable for research on fake review text identification. Since fake reviews have an asymmetric distribution feature, this paper uses the RoBERTa model to extract information and combine it with the behavioral features in traditional research for model training. Compared with traditional methods, the accuracy rate is improved by nearly 3%. [ABSTRACT FROM AUTHOR]

Access control has always been one of the effective methods to protect data security. However, in new computing environments such as big data, data resources have the characteristics of distributed cross-domain sharing, massive and dynamic. Traditional access control mechanisms are difficult to meet the security needs. This paper proposes CACM-MMSR to solve distributed cross-domain access control problem for massive resources. The method uses blockchain and smart contracts as a link between different security domains. A permission decision model migration method based on access control logs is designed. It can realize the migration of historical policy to solve the problems of access control heterogeneity among different security domains and the updating of the old and new policies in the same security domain. Meanwhile, a semantic reasoning-based permission decision method for unstructured text data is designed. It can achieve a flexible permission decision by similarity thresholding. Experimental results show that the proposed method can reduce the decision time cost of distributed access control to less than 28.7% of a single node. The permission decision model migration method has a high decision accuracy of 97.4%. The semantic reasoning-based permission decision method is optimal to other reference methods in vectorization and index time cost. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

The time complexity of solving the QUBO problem depends mainly on the number of logical variables in the problem. This paper focuses mainly on finding a system of equations that uniquely defines the Sbox of the AES cipher and simultaneously allows us to obtain the smallest known optimization problem in the QUBO form for the algebraic attack on the AES cipher. A novel method of searching for an efficient system of equations using linear-feedback shift registers has been presented in order to perform that task efficiently. Transformation of the AES cipher to the QUBO problem, using the identified efficient system, is presented in this paper as well. This method allows us to reduce the target QUBO problem for AES-128 by almost 500 logical variables, compared to our previous results, and allows us to perform the algebraic attack using quantum annealing four times faster. [ABSTRACT FROM AUTHOR]

This paper considers various hybrid methods for solving a system of nonlinear equations. Extensive numerical experiments confirm the research hypothesis that one particular form of hybridization of the conjugate gradient algorithm with Newton's method outperforms other hybrid strategies considered in the paper. [ABSTRACT FROM AUTHOR]

Industry 4.0 technologies have become one of the main enablers for implementing Circular Economy e↵orts, creating smart and connected products, processes, and systems that can optimize resource usage, reduce waste, and improve efficiency, with a significant environmental impact. Blockchain is a technology that was initially centered on authentication and security applications. Currently, its utility has extended to solving multiple problems across industries. This paper explores the di↵erent applications of the Blockchain related to the circular economy. A systematic literature review method was applied to analyze the main objectives, advantages, disadvantages, opportunities, and challenges of implementing Blockchain in various industrial sectors. Results show a growing trend in applications with favorable impacts, especially in sectors like Manufacturing, Energy, and Automotive. [ABSTRACT FROM AUTHOR]

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations. [ABSTRACT FROM AUTHOR]

This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain's ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students' competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks. [ABSTRACT FROM AUTHOR]

To effectively manage ships and maintain the safety of port and territorial waters, ship plate recognition is an essential technology. However, there are many different font styles in actual scenes because there is no unified format. Among them, the handwritten font is the most changeable. l'hese complex and ehangeable font styles will cause difficulties in recognizing ship plates. Furthermore, handwritten ship plates are unique in data collection, which means that it is impossible to collect enough fonts of the same style or all ship plates for training. In this paper. we propose a text recognition model architecture that simulates human learning and literacy to solve the problem of few-shot multifont, which is called learning by analysis (LBA). Humans can recognize multiple types ofcharacter through pre-train knowledge. Referring to this concept, LBA is a twin network composed of a benchmark model (BM) and an extended model (EM). BM builds a hypothesis space based on standard fonts. and then EM learns to recognize variable text based on BM through high-dimensional feature mapping and aggregation o f embedded spaces. In addition, we also propose a type change block without training, which increases the complexity of the data by making complex type changes to the text. Experiments show that the method achieves 96% accuracy on NIST. The accuracy ofship plate recognition in natural scenes is as high as 91%, which shows that our method has a robust generalizability. [ABSTRACT FROM AUTHOR]

Multi-object tracking (MOT) technology is widely applied to traffic flow monitoring, human flow- monitoring, pedestrian tracking, or tactical analysis of players on the courts. It associates the detection boxes with tracklets for each frame in the video. The challenges of MOT include long-term occlusions, missing detections, and complex scenes. Although many trackers have proposed to solve these problems, the tracking results still have room for improvement. In this paper. we propose a solution named MEDIATrack (Matching Embedding Distance & IOU Association Track), a two-stage online multi-object tracking method based on ByteTrack. We replace the Kalman Filter with the NSA Kalman Filter, introduce appearance features for track association, and design a punishment mechanism to alleviate errors in complex scenes. In addition, we remove the nonactivated strategy, and the high-score unmatched detection boxes are directly added to the tracklets. On MOI 17, we achieve 79.3 MOTA, 76.5 IDFI,and state-of-the-art performance. [ABSTRACT FROM AUTHOR]

When mainlobe interference exists in space, the traditional anti-interference methods have problems such as peak offset and the performance of sidelobe interference suppression reduction. To solve the above problems, this paper proposes a mainlobe interference suppression method based on compressive sensing and covariance matrix reconstruction. Firstly, an improved compressive sensing algorithm is proposed to accurately estimate the Direction Of Arrival of sources, and then the signal steering vectors and signal subspaces can be established. The mainlobe interference can be suppressed by establishing an oblique projection operator through signal subspaces. Meanwhile, the sidelobeinterference-noise covariance matrix can be reconstructed by the steering vectors, and then the adaptive weight vector is obtained. Simulation results show that the proposed method can form a more robust beam pattern and has better output performance. The proposed method is still effective when the desired signal exists in the received signal. [ABSTRACT FROM AUTHOR]

In this paper, the optimizations of multi-hop cooperative molecular communication (CMC) system in cylindrical anomalous-diffusive channel in three-dimensional enviroment are investigated. First, we derive the performance of bit error probability (BEP) of CMC system under decode-and-forward relay strategy. Then for achieving minimum average BEP, the optimization variables are detection thresholds at cooperative nodes and destination node, and the corresponding optimization problem is formulated. Furthermore, we use conjugate gradient (CG) algorithm to solve this optimization problem to search optimal detection thresholds. The numerical results show the optimal detection thresholds can be obtained by CG algorithm, which has good convergence behaviors with fewer iterations to achieve minimized average BEP compared with gradient decent algorithm and Bisection method which are used in molecular communication. [ABSTRACT FROM AUTHOR]

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP. [ABSTRACT FROM AUTHOR]

Chemistry is a scientific discipline that is often considered difficult because students struggle to solve contextual and open-ended problems, mainly due to a poor understanding of scientific concepts related to the problem. To fully grasp the concepts in chemistry, students must develop the ability to solve real-life problems. Problem-solving skills are applied in many aspects of life, making them crucial for the 21st century. Problem-based learning (PBL) is an effective method for improving students' problem-solving skills. This study aims to outline how the PBL approach enhances students' problem-solving skills, specifically in the context of chemistry education. The sixteen research articles reviewed in this paper were collected from well-known databases such as ERIC, SCOPUS, and Google Scholar. Systematic literature review (SLR) is the method used in this review. This review provides evidence that PBL can enhance problem-solving skills in chemistry education. All research articles have shown that PBL has a significantly positive effect on the problem-solving skills of students. Seven out of sixteen articles have provided insights into the design and framework of PBL implementation. The researcher recommends conducting more studies in all areas of chemistry to provide clear guidance on incorporating PBL. The researcher also recommends using mobile technologies to integrate PBL in a stimulating manner, such as by developing a mobile application to aid in the instructional process. Proper insights into the effectiveness of implementing PBL to enhance problem-solving skills in chemistry, as well as the integration of PBL into the instructional process, are necessary. This will aid teachers and lecturers in planning their lessons, which is why this systematic review is needed. [ABSTRACT FROM AUTHOR]

Low-light image enhancement (LLIE) enables to serve high-level vision tasks and improve their efficiency. Retinex-based methods have well been recognized as a representative technique for LLIE, but they still suffer from inflexible regularization terms in decomposing illumination and reflectance. In this paper, we propose a new weighted fractional-order variational model based on the Retinex model. First, the constructed weighted fractional-order variational model estimates piecewise smoothed and weakly pixel-shifted illumination by aware structures and textures. Then, to solve this problem accurately, we chose a semi-decoupled approach and an alternating minimization method. Finally, the designed multi-illumination fusion method accurately enhances the structure-rich dark regions of the image through well-exposedness and local entropy weights, while realizing adaptive enhancement based on a naturalness-preserving parameter estimation algorithm. The results of subjective and objective experiments on several challenging low-light datasets demonstrate that our proposed method shows better competitiveness in enhancing low-light images compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed. [ABSTRACT FROM AUTHOR]

Purpose -- The paper aims to contribute to the knowledge of the problem-solution and the creation of a successful idea of the restaurant business development (in particular, through applying of the separation principles). Methodology/Design/Approach -- A case study is conducted as a research strategy to test propositions expressing a necessary condition to investigate the possibility of applying the separation principles to solve a problem. The restaurant industry is defined as the area to which the theory is applied and from which cases are selected for testing. Findings -- The application of separation principles as a methodological tool contributes to finding an effective solution to problems of the restaurant business development. Originality of the research -- This paper represents the analysis of how some restaurateurs have overcome the COVID-pandemic problems. A model of the invention process is proposed, which helps practitioners to find an effective solution for the development of the restaurant business under crisis conditions and to implement the successful idea in time. The results of the study show the possibility of improving the created idea and its development. Two ways of implementing this process were identified: alternate application of the principles of separation and unification of different parameters. [ABSTRACT FROM AUTHOR]

One important aspect of human behavior understanding is the recognition and monitoring of daily activities. An accurate activity recognition system can improve the quality of life in many key areas. The multi-metric feature extraction and DeepForest classifier designed in this paper effectively had solved the problems of incomprehensive feature extraction and insufficient classifier accuracy. First, this paper extracts a total of 450 dimensional feature vectors using mean, variance, maximum, skewness, minimum, kurtosis, regression of independent variables and sample entropy as feature indicators, so that the features of the original vectors can be presented comprehensively. The dimensionality of the feature set is too large, so PCA is used to reduce the dimensionality of the high-dimensional feature vector. After studying the relationship between the dimensionality reduction and the cumulative contribution rate, it is concluded that the cumulative contribution rate reaches 90% when the dimensionality is 15, which can retain the original features better. Then DeepForest was used as the classifier, and the reduced-dimensional feature set was used as the sample set to divide the training set and test set by 3:1. The model was tested on the test set after training, and the classification accuracy of the test set was 98.202%. Six classifiers, GaussianNB, SVM, K-NN, XGBoost, RandomForest and DecisionTree, were selected as the control experimental group, of which only the RandomForest model reached 97%, while the rest of the control models did not achieve 95% effect, indicating that DeepForest was more accurate in classifying human activities. Next, the generalisation ability of the DeepForest classifier was assessed using Monte Carlo Cross Validation (MCCV), K-fold and its confusion matrix. The MCCV validation used 50% of the data as the training set and 30% of the data as the test set, and set the number of splits to 10, resulting in a mean accuracy of 98.227%. The size of K in the K-fold validation was determined to be 8 based on the number of people conducting the human activity experiment, and the final mean accuracy value obtained was 97.790%. The combined confusion matrix from the K-fold validation (which aggregates the confusion matrix for each classification result) was calculated, and the results showed that the highest accuracy reached 100% for A9 and A14 classifications, where A5 and A10, A15 and A10, A12 and A5 were more likely to be confused, with the highest error rate for A5 classification, which was 4.167%, and the rest of the activity classifications were better. [ABSTRACT FROM AUTHOR]