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Copyright of Studies in Conservation is the property of Taylor & Francis Ltd 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.)

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]

Background and purpose: Diversity within the physical therapy profession lags in comparison to the United States population. As the profession strives to diversify, faculty must pay attention to diversity, equity, inclusion, and belonging (DEI-B) in curricular approaches, including classroom materials, instruction, and assessment. With critical application, students from equity-deserving groups (EDGs) can provide unique perspectives to faculty about curricular approaches. Case study purposes were to 1) enable students from EDGs to partner with faculty, as student pedagogical consultants (SPCs), in 2 courses in a Doctor of Physical Therapy program to provide feedback on DEI-B efforts related to curricular approaches and 2) describe the outcomes of SPCs experience. Case description: Eight female students from EDGs partnered with 3 White, female, faculty members in 2 courses: pediatrics and neurorehabilitation. Two SPCs teams observed the classrooms, met with faculty, and administered two-minute papers to classmates to gather feedback on DEI-B curricular approaches. Faculty and student SPCs wrote reflective papers, postproject, documenting their experiences. Outcomes: Themes informed a conceptual framework describing SPCs: 1) motivation for engaging in partnership; 2) creation of a pedagogical partnership space to promote dialogue and problem-solve barriers to DEI-B; 3) deeper understanding of teaching; 4) transfer of learning from the SPC experience to future work locations; and 5) faculty modification of teaching. Discussion and conclusion: Students raised awareness regarding the pain of exclusion and provided suggestions for modifying curricular approaches to consider DEI-B. Curriculum redesign using innovative strategies can meet the contemporary needs of students from EDGs. [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]

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]

Based on strategic management principles, it is reasonable to assume that lower-level units within the organizational structure, such as business and functional levels, will be carrying out the strategies. However, such organizational units should be considered not only as decision implementers, but also as strategists and program developers for the areas of activity in which they operate. Managers at the functional level solve problems related to setting goals and objectives in areas such as marketing, resource allocation, finance, production, and accounting. There is a lack of research on functional strategies, emphasizing the need for additional studies and publications. The goal of this paper is to outline significant elements of functional strategies, with a particular emphasis on their distinctiveness in comparison to other strategic aspects of management in small and medium-sized enterprises (SMEs). Additionally, the paper aims to explore the feasibility of implementing such strategies within these enterprises. The study seeks to accomplish its objectives through the utilization of theoretical deduction, in conjunction with the analysis of data derived from a concise survey of SMEs and pertinent practical illustrations. From a scientific perspective, the study's findings are predominantly positive rather than normative-regulatory in nature. [ABSTRACT FROM AUTHOR]

• Crime scene investigation training and education has to address specific problem solving and psychomotor skills. • AR technology could improve current training and education approaches when used as a supplementary tool. • Potential challenges and considerations are highlighted that are to be explored before implementing AR technology. The role of the crime scene investigator is complex, and investigators need to be able to conduct multiple frequently performed tasks. Appropriate training and education are critical to impart the crime scene investigator with the necessary capabilities. For a range of reasons, including the COVID-19 pandemic and the need for multi-disciplinary capabilities, training and education requirements have evolved in recent times to be more flexible and accessible. The skills of a crime scene investigator can be broadly categorised into two types of fundamental skills: decision-making and psychomotor skills. Both these skills need to be taught and assessed within training and education programs. The most common approach to impart these skills is by incorporating a crime scene simulation facility; however, not everyone has access to these facilities. Furthermore, crime scene staff often undertake refresher courses and are required to complete proficiency assessments. Conducting these activities in a dedicated crime scene simulation facility is time consuming and costly. Virtual tools have been developed in recent years to address this, but these tools only assess decision-making skills and not psychomotor skills. This paper argues that the implementation of augmented reality (AR) technology should be considered in crime scene investigator training and university education, because it can provide significant advantages when paired with conventional methods of training and education. When appropriately managed, AR can provide an avenue of training where both decision-making and psychomotor skills can be addressed simultaneously, while providing a more flexible and accessible approach. The implementation of AR has the potential to significantly improve the standards of teaching, resulting in better equipped crime scene investigators. The paper will explore how AR has the potential to improve accessibility of training, increase safety, enhance the student experience, enhance collaboration and feedback through connectivity and potentially reduce cost. This paper will also provide an insight into what would need to be considered before implementing AR technology into crime scene investigation training and education alongside current approaches. It is argued that the inclusion of AR into the current training and education arsenal provides significant benefits that are worthy of exploration. [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 summarizes five critical aspects of problem-solving competency for engineers in medicine, including the balance of depth and breadth, research capability, ideation skills, teamwork, and communication skills. Furthermore, the paper outlines the imperatives for enhancing undergraduate engineering education to cultivate problem-solving competency. An interdisciplinary approach to education in medical engineering can cultivate students to develop a holistic view of the field and equip them with a broad range of skills for problem-solving. [ABSTRACT FROM AUTHOR]

Research has shown the importance of helping students, especially those with mild-to-moderate learning disabilities, to offload information during problem-solving. When students can get their thoughts onto paper, number line strategies can help them develop a firm foundation in mathematical problem-solving while understanding the relationships between mathematical operations. These strategies are helpful for the development of addition, subtraction, multiplication, division, and later, fractional mathematics. In this article, we describe the progression of number lines as a supportive strategy for elementary students and those with developmental delays in mathematics to improve mathematical understanding. This strategy is based on students being able to show their work and think about what they have written on paper or how they have used manipulatives. [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]

In the application of interval type-2 (IT2) Takagi–Sugeno–Kang (TSK) fuzzy logic systems (FLSs), the center-of-sets (COS) type-reduction (TR) is more advantageous than the centroid TR. This paper proposes three types of discrete non-iterative algorithms to solve the problem of COS TR in IT2 TSK FLSs. Multiple simulation experiments are carried out for the IT2 TSK FLSs with different fuzzy rule numbers. Experimental results show that the computational efficiencies of the three discrete non-iterative algorithms are better than that of Karnik–Mendel (KM) algorithms, which provides latent value for the application of type-2 FLSs. [ABSTRACT FROM AUTHOR]

A formation and containment control problem is discussed for the Nonlinear Multi-Autonomous Ship Systems (NM-ASSs) with uncertainties and disturbances based on the Interval Type-2 (IT-2) Takagi-Sugeno Fuzzy Model (T-SFM) in this paper. A different formation control scheme is provided by using the state feedback controller of leader ships. Because of this feature, information communication between leader ships, which are farthest from each other in formation and containment problems, isn't required. However, the analysis problem in the IT-2 fuzzy containment controller design method is caused by the leader's formation controller. A design concept for the unknown leader's input of linear multi-agent systems is successfully extended to solve the problem by the expression of IT-2 T-SFM. Nevertheless, the analysis process will become conservative while the agent number or fuzzy rule number is increased. Thus, a relaxed analysis method is also considered for the containment controller design. Additionally, the passive performance constraint is combined into the IT-2 fuzzy formation controller design method to dissipate the disturbance effect and improve the control performance. Finally, two examples are provided to illustrate the advantage of the proposed IT-2 fuzzy controller design method in the formation and containment control problem of NM-ASSs. [ABSTRACT FROM AUTHOR]

Using the Apocynaceae as an example, the present paper demonstrates how the new tool Rhakhis highlights taxonomic problems in a large vascular plant family and, at the same time, invites specialists in the family to solve these problems with the aim of producing an up‐to‐date taxonomy. This taxonomy can then be used to estimate species and genus numbers, thus approaching the goal of completing our picture of the diversity surrounding us and providing a solid basis for science and conservation. The most recent estimates for genus and species number in the Apocynaceae are provided. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

The paper presents an original method for controlling a surface-electromyography-driven (sEMG) prosthesis. A context-dependent recognition system is proposed in which the same class of sEMG signals may have a different interpretation, depending on the context. This allowed the repertoire of performed movements to be increased. The proposed structure of the context-dependent recognition system includes unambiguously defined decision sequences covering the overall action of the prosthesis, i.e. the so-called boxes. Because the boxes are mutually isolated environments, each box has its own interpretation of the recognition result, as well as a separate local-recognition-task-focused classifier. Due to the freedom to assign contextual meanings to classes of biosignals, the construction procedure of the classifier can be optimised in terms of the local classification quality in a given box or the classification quality of the entire system. In the paper, two optimisation problems are formulated, differing in the adopted constraints on optimisation variables, with the methods of solving the problems based on an exhaustive search and an evolutionary algorithm, being developed. Experimental studies were conducted using signals from 1 able-bodied person with simulation of amputation and 10 volunteers with transradial amputations. The study compared the classical recognition system and the context-dependent system for various classifier models. An unusual testing strategy was adopted in the research, taking into account the specificity of the considered recognition task, with two original quality measures resulting from this scheme then being applied. The results obtained confirm the hypothesis that the application of the context-dependent classifier led to an improvement in classification quality. [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]

In this paper, we propose a new approach to efficiently plan trajectories for industrial robots, where total trajectory time is reduced while limiting the robot end-effector acceleration to a safe threshold, which guarantees an object to be transported safely without damage or spills. We also propose to solve the optimization problem in two steps by utilizing both the quadratic programming algorithm and sparse sequential quadratic programming method. The proposed two-step procedure empirically proves that computing time of solving the optimization problem is significantly reduced. Effectiveness of the proposed approach was verified in Drake, where the obtained results are highly promising. [ABSTRACT FROM AUTHOR]

Nearly 30 years after the end of apartheid in South Africa, the education system has yet to provide learners with an adequate school experience. With school closures during the pandemic, the urgency to provide a quality education in the townships became even more dire. In collaboration with the leadership of Charles Duna Primary School in Gqeberha, South Africa, Partnerships for Educational Excellence and Research (PEER) International reestablished a partnership to design a flexible implementation of methods and curricula to build numeracy repertoires. Professional development efforts leverage evidence-based best teaching and learning strategies included in the Morningside Model of Generative Instruction (Johnson et al., 2020). Weekly videoconferences target the development of basic computation skill acquisition and math problem solving skills. The dramatically different environment and available resources in township schools is a prevailing consideration in this transfer of instructional technology. Our project started early in 2022 and continues with optimism and determination. The purpose of this paper is to describe our constructional approach (Goldiamond, 1974/2002 Behavior and Social Issues, 11(2), 108-197) when invited to improve classroom conditions for teachers and learners. [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]

The paper’s purpose is to analyze the relationship between leadership and decision-making for the sustainable development of organizations. Both components are considered very important processes in organizational management. Leadership and decision-making effectiveness are directly related to the leadership style applied by organizational leaders. For research purposes, the study has adopted a combined methodology, using secondary and tertiary data that are compared with primary data collected in place from the managers/leaders in different organizations in Kosovo. As a tool for the data collection, a questionnaire was drafted and as well we did use semi-structured interviews in several cases. Based on research, the results have shown that the leadership styles with the lowest applicability are autocratic and laissez-faire style, simultaneously these two styles have influenced low decision-making effectiveness, while the most applied leadership style is liberal style followed by democratic style. The application of liberal and democratic style has influenced the increase of decision-making effectiveness in organizations. Also, it has been proven that there is a positive relationship between staff participation in organizational processes, such as problem solving and decision-making, and decision-making effectiveness. [ABSTRACT FROM AUTHOR]

Armor technologists' improvement of protection systems led to the design of complex systems. Given the risk factor on human life, increasing requirements on the ballistic resistance evaluation are imposed. Consequently, an increased effort is dedicated to estimating the perforation probability curve as a function of the bullet impact velocity. The main limitation of methods that fits a normal law to perforation velocities is their purely statistical character. A Brownian-based approach that couples the system response variability and physics was proposed using the Chi-square and Kolmogorov-Smirnov criterion function for model parameters estimation. One major limitation of this inference approach is the large experimental database required for its execution. The contribution of this paper is the introduction of the maximum likelihood inference for parameters estimation of the Brownian-based approach. The agreement between the obtained results and the experimental ones confirms the appropriateness of the likelihood inference to solve the studied problem. Moreover, the estimations uncertainty was analyzed and compared to the existing method ones. It was observed that the proposed model reduces the confidence intervals on key velocity estimations. Accordingly, the present work encourages the adoption of this proposed methodology in a laboratory context with a restrained sample size. [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]

The exploration of children's drawings as mathematical representations is a current focus in early years mathematics education research. This paper presents a qualitative analysis of 72 kindergarten to Grade 3 (5 to 8 years old) children's drawings produced during problem-solving tasks centred on multiplicative strategies. Existing frameworks for the developmental sequence of mathematical drawings and the progression of children's strategies for multiplicative situations were an interpretive lens through which to analyse the drawings. Children used pictographic and iconic drawing types to represent the "story" in the problem and the multiplicative strategies employed to solve the tasks. Exploration of the children's drawings suggested that as children's drawings become more structural, schematic in nature, it may be easier for children to show their understanding of the structural elements of multiplicative relationships. Results revealed that structural elements of multiplicative relationships were more easily seen in iconic representations; however, both pictographic and iconic drawings were useful to observe counting, additive, and multiplicative strategies when mathematical elements of the problem were visible. Additional representations attached to the drawings (e.g. numerical) were needed to confirm children's strategies when their drawings lacked structure. These findings have implications for how young children's drawings are interpreted by classroom teachers. The interpretation of these drawings suggested that some children may not yet realise how their drawings in mathematics need to shift from illustrations of the problem's story context to representing mathematical ideas and processes — which requires intentional teaching of the purpose of drawings for mathematical contexts. [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]

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]

In this paper, we propose a method of route optimization of vacant taxicab based on the abstract grid network. Firstly, an abstract grid network is built by the quadtree technology with the vacant trajectory data embedded. Secondly, the Markov decision process framework considering the sequential dependence is used to model the problem. Finally, we solve this problem by the policy iteration and compare the method by taking the GPS trajectory data in different hours of 797 taxicabs in a typical working day in Shenzhen, comparing that with the optimal strategy without considering sequential dependence, MNP, local hotspot and random walk algorithm by simulation, respectively. The results show that: The use of quadtree can reduce the sparseness of matching probability from 80.46% to 30.18%. The average revenue per unit distance has increased by 6.10%, 8.75%, 33.24% and 60.06%. The vacant driving rate has decreased by 5.33%, 6.15%, 18.86%, and 23.18%. [ABSTRACT FROM AUTHOR]

Organisations face the challenge of adapting to rapid change in a world of volatility, uncertainty, complexity, and ambiguity (VUCA). They need to seize opportunities faster than their competitors and are therefore increasingly relying on teams to tackle complex tasks and solve challenging problems. The aim of this paper is to identify team members' perceptions on the scales of Context, Morale and Norms of the Team Assessment Survey II (TAS-II) in 85 Slovak work teams - important for effective collaboration. Examination of responses to the individual items using "Heartbeat analysis" revealed relatively fewer favourable votes for all but 2 of 9 items on the Norms scale, for Conflicts and Celebration (Morale scale) and Assumptions (Context scale), in contrast to the Safeguard and Accountability (Norms scale) and Understanding (Context scale) items Proud, Cooperation and Trust from the Morale scale. We discuss the possible causes. The results provide new insights and help to understand how important organisational context, team morale and norms are for the effectiveness of teamwork, as they can promote resilience, sustainable development and competitiveness. [ABSTRACT FROM AUTHOR]

Work-based project practice was introduced to decrease the gap between academics and industry for software engineering education. For work-based project practice, the methods of inspiring students' learning motivation need to be studied more. In this paper, to inspire students' motivation better, at first, a work-based project practice framework with problem-solving is proposed; secondly, a role-driven cooperation mechanism is constructed which simulates the real development team's working mechanism; finally, a project tracking method is defined which can motivate students and evaluate the practice. Iii the past 3 years, the proposed framework, mechanism, and method have been applied in our research. It shows students' motivations are improved by facing engineering problems, taking specific roles, and tracking project status weekly, and the students are inspired better to apply software engineering knowledge to solve complex engineering problems. [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]