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Asphalt mixture recycled technology (Al-Saffar et al. 2021) is an efficient method for achieving asphalt pavement sustainability. The optimum asphalt content of recycled asphalt mixture is first determined by the Marshall test in this paper. Second, Materials Studio software was used to simulate the interface effect between three different asphalts and aggregate, and the adhesion between asphalt and aggregate was characterised based on the interface energy. The mesoscopic structural characteristics of three asphalt mixtures were then analysed using digital image processing technology. Finally, through three macro experiments, the influence of old asphalt on the mechanical properties of recycled asphalt mixture was studied from a macro perspective. At the microscopic level, the results show that the presence of the old asphalt reduces the adhesion between the asphalt and the aggregate. At the mesoscopic level, the presence of old asphalt reduces the porosity of the asphalt mixture. At the macroscopic level, the addition of old asphalt improves the high temperature and water stability of recycled asphalt mixture while decreasing the low temperature crack resistance. The microscopic and mechanical properties of old asphalt on recycled asphalt mixture are explained on three levels, and new ideas for research on recycled asphalt mixtures are provided. [ABSTRACT FROM AUTHOR]

Cement is one of the main sources of negative environmental impact of concrete. One way to reduce this impact of concrete could be through the reduction of the cement content. This could be achieved by replacing a part of cement with mineral additives such as fly ash, blast-furnace slag, silica fume, metakaolin (MK), etc. in concrete. In this paper, the effect of curing temperature on MK blended cements is investigated. Isothermal calorimetry and compressive strength tests are performed on mortars prepared with various replacement levels of cement by MK (5%, 15% and 25%) and cured at different temperatures (10°C, 20°C and 40°C). Further, water porosity test and microscopic observations were carried out to confirm the findings. Results showed that the pozzolanic activity of MK is conditioned by the curing temperature and its levels of replacement. [ABSTRACT FROM AUTHOR]

A science, business, or law that is basing its validity on the level ofp-values,tstatistics and other tests of statistical significance is looking less and less relevant and more and more unethical. Today’s economist uses a lot of wit putting a clever index of opportunity cost into his models; but then, like the amnesiac, he fails to see opportunity cost in statistical estimates he makes of those same models. Medicine, psychology, pharmacology and other fields are similarly damaged by this fundamental error of science, keeping bad treatments on the market and good ones out. A few small changes to the style of the published research paper using statistical methods can bring large beneficial effects to more than academic research papers. It is suggested that misuse of statistical significance be added to the definition of scientific misconduct currently enforced by the NIH, NSF, Office of Research Integrity and others. [ABSTRACT FROM AUTHOR]

This note first describes the details of an interesting incident of plagiarism where the author of a previously published paper was sent a verbatim copy of that paper for his review for possible publication in another journal. It then offers a few insights into the possible reasons underlying such blatant plagiarism attempts, and provides caveats against them. [ABSTRACT FROM AUTHOR]

Radiographic images of paper are used to study the watermarks and the pattern of chain and laid lines originating from the sieve used in the production of the paper. X-rays with low penetrative power are used to obtain images with sufficient contrast as a result of differences in thickness/density of the paper. This article explains the improvement in contrast which can he obtained by lowering the X-ray tube potential, which reduces the penetrative power of the beam. Lowering the tube potential is limited by the attenuation of the radiation in air, which ultimately becomes so strong that exposure times become Unacceptably long. By replacing the air with helium, this attenuation of the X-ray beam can be reduced. A container for helium was developed which had an airtight fit to the exit window of the X-ray tube and has an exit window of a very thin layer of polyethylene. The effect of the technique was investigated by measuring the first and second half value layer (HVL) the beam expressed in millimetres of high density polyethylene (HDPE). [ABSTRACT FROM AUTHOR]

Publications in Renal Failure in Science Citation Index Expanded (SCI-EXPANDED) between 1992 and 2021 were analyzed. Six publication indicators: total, independent, collaborative, first author, corresponding author, and single author publications as well as their related citation indicators, were used to compare performances of countries, institutes, and authors. Comparison of the highly cited papers and journal's impact factor (IF) contributors was discussed. In addition, the main research topics in the journal were presented. Results show that China published the most total articles and reviews, as well as the first-author papers and corresponding-author papers in the journal. The Chang Gung Memorial Hospital in Taiwan ranked the top in five publication indicators: total, single-institution, inter-institutionally collaborative, first author, and corresponding-author papers. A low percentage of productive authors emerged as a journal IF contributor. Similarly, only a limited relationship between highly cited papers and IF contributing papers was found. Publications related to hemodialysis, chronic kidney disease, and acute kidney injury were the most popular topic, while meta-analysis was new focus in the last decade in the journal. [ABSTRACT FROM AUTHOR]

Under the mobile computing environment, a large number of convenient mobile terminals and extensive mobile network application services have been produced. The technology has been used in college sports teaching to optimize the management of sports teaching. In this context, this paper studies the application of the association rule algorithm and cluster analysis in basketball teaching, which will effectively promote the practice and application of the association rule algorithm and cluster analysis in college PE teaching in China. This paper studies basketball teaching in colleges and universities based on the applied explainable association rule algorithm and cluster analysis. It is concluded that the p value of positioning shooting in the basic basketball technology test between the experimental class and the control class before the experiment is 0.883, which is greater than 0.06, indicating that there is no significant difference between the experimental class and the control class before the experiment. The p value of round-trip straight dribble in the whole field is 0.735, which is greater than 0.07, indicating that there is no significant difference between the experimental class and the control class before the experiment. This teaching mode plays a significant role in cultivating beginners' learning interest and enthusiasm, preliminarily mastering movement skills and establishing solid technical concepts. With the complex emotional experience of the association rule algorithm, it is of great significance for teachers to grasp students' various emotional experiences in learning, cultivate students' team consciousness by cluster analysis, guide and dredge their negative emotions, and develop students' sense of cooperation, team spirit and democratic spirit, personal responsibility and personality. [ABSTRACT FROM AUTHOR]

Neural machine translation (NMT) is applied to generate a more reliable and accurate translation practice as the most cutting-edge technology. In recent years, NMT has achieved gratifying results. However, the main obstacle for market-oriented NMT application systems appears to suffer from weak translation quality that fails to meet users' needs. This paper focuses on the machine translation of political documents and implements six dominant NMT application systems in the market to evaluate their translation quality. The evaluation process further employs both BLEU and NIST technical evaluation algorithms and re-verifies the results with the manual evaluation method called the "Score Ranking System" to compare the performances of the six NMTs in Chinese-English translations of political documents. Through diagnosis and evaluation of the problems and errors in NMTs, the paper eventually proposes the "Cue Lexicon+" model to remedy prominent problems. Besides, the "NMT+ Lexicon Intelligent Translation Assistant" soft is developed and the "Cue Lexicon+" is integrated into the NMT application systems to further improve the translation quality, providing a reference and research basis to increase the performance and update the NMT application systems. [ABSTRACT FROM AUTHOR]

Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user's interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users' interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set. [ABSTRACT FROM AUTHOR]

In order to improve the effect of visual communication art design, this paper combines virtual reality technology to analyze the quantitative impact of visual communication art design. The paper aims to address the need for more effective visual communication art design by proposing a new design system incorporating virtual reality technology. Moreover, this paper elaborates two implementations of semi-covered virtual reality graphics through pictures and texts: by translating the turntable or offsetting the detector. In addition to this, this paper expounds the semi-covered virtual reality graphics realized by the translation stage. By interpolating the projection data onto a virtual detector, the half-covering virtual reality graphics realized by the translation stage can theoretically be equivalent to the half-covering virtual reality graphics realized by offsetting the virtual detector. It uses semi-covered virtual reality graphics achieved through the translation stage as the main methodological approach to achieve this goal. It conducts experimental research to evaluate the system's effectiveness. The experimental research shows that the visual communication design system based on virtual reality technology proposed in this paper has good design performance and can effectively improve the effect of visual communication art design. [ABSTRACT FROM AUTHOR]

In order to improve the decision-making effect of regional economic development, this paper analyzes the decision-making model of regional economic development in combination with the engineering management process, and proposes a density-based outlier detection algorithm. Moreover, this paper analyzes the process and characteristics of the LOF algorithm, adopts the ZH-tree index structure based on the space filling curve, and improves it appropriately. The clustering feature can help to quickly search for neighbors of objects, and the hierarchical structure can perform effective spatial pruning to reduce unnecessary computation. The experimental research shows that the decision-making model of regional economic development based on the intelligent analysis of project management proposed in this paper can effectively improve the decision-making effect of regional economy. [ABSTRACT FROM AUTHOR]

This research paper aims to enhance the operation of visual communication systems by incorporating computer image processing technology. The study investigates the potential applications of visual communication systems by utilizing imaging equipment to capture distorted fringe images of height-modulated objects from different angles. Phase information closely associated with the object is then extracted from the images and utilized to obtain the object's three-dimensional surface topography following calibration. The paper applies multi-frequency phase-shift profilometry and Fourier transforms profilometry to construct further a visual communication system based on computer graphics processing technology. The article contributes to visual communication systems by proposing a novel approach that incorporates computer image processing technology. This approach enhances the performance of visual communication systems, resulting in a more effective means of communication. The study's experimental analysis demonstrates computer image processing technology's superiority and ability to enhance the visual communication system's application. The study's key findings indicate that computer image processing technology can effectively enhance the effectiveness of visual communication systems. The incorporation of imaging equipment and phase extraction methods leads to the production of accurate three-dimensional surface topography data. Furthermore, multi-frequency phase-shift profilometry and Fourier transform profilometry aid in constructing a visual communication system based on computer graphics processing technology, which leads to an overall enhancement of the visual communication system's application. [ABSTRACT FROM AUTHOR]

To improve the teaching effect of the Chinese language and literature majors, this paper combines artificial intelligence technology and performance to build a teaching system for the Chinese language and literature majors. Based on the existing coordination algorithms, this paper proposes a classifier-based fast calculation method for successive breaking of active power flow, which provides high-quality active power information for the static safety check and control of successive breaking at a small computational cost. Moreover, this paper improves the operation effect of the teaching system, increases the calculation speed, and adopts the NR method for algorithm-sensitive faults to ensure the reliability of the power flow results. The proposed teaching system was tested in a simulated teaching environment to evaluate its effectiveness. The testing results showed that the system significantly improved student performance, engagement, and satisfaction. However, some challenges and limitations were also encountered during the testing process, such as the need for sufficient computing power and the difficulty in accommodating individual learning styles. The findings of this paper suggest that the proposed teaching system has the potential to enhance the learning outcomes of Chinese language and literature students. However, further testing and optimization are needed to address the challenges and limitations encountered in the testing process. [ABSTRACT FROM AUTHOR]

Human resource management was of great significance for enterprises to formulate development strategies and improve competitiveness. With the rapid development of new technologies such as knowledge economy and big data, the traditional human resource performance appraisal model had certain limitations and was not objective, which had been difficult to adapt to the increasingly highly integrated development needs of modern enterprises. Therefore, this paper used big data analysis and new technical means to put forward the study of human resource performance evaluation model based on Bayesian network, aiming to provide some theoretical reference for modern enterprise human resource management. First of all, it summarized the related concepts of performance appraisal and its important position in enterprise management, analyzed the characteristics of human resources performance appraisal indicators and their mutual relations, and expounded the common methods and characteristics needed to build a performance appraisal system. Secondly, it summarized the relevant theory of Bayesian network and its advantages in solving complex random problems, and proposed a performance appraisal model based on Bayesian network for the needs of enterprise human resources management. Finally, the balanced scorecard method was used to build a performance appraisal system, and the performance appraisal model proposed in this paper was tested by experiments, and compared with the traditional method. The results showed that the performance appraisal model proposed in this paper had significant advantages and could be better applied to the performance appraisal management of enterprise human resources. The research of this paper can not only provide theoretical guidance for further research on enterprise human resource allocation and management, but also provide reference for performance appraisal in other fields. [ABSTRACT FROM AUTHOR]

In order to improve the practical effect of clothing design, this paper applies the relevant theories of behavioral psychology to the design of fashion clothing, and improves the algorithm through the intelligent psychological identification algorithm to construct a quantitative identification system of behavioral psychology to help designers design more comprehensively. At the system level, the overall structure of the entire behavioral psychological ECG signal acquisition system is planned, and the Multisim software is used to simulate and verify whether each module of the front-end processing circuit achieves the expected effect according to the calculated parameters. In addition, on the basis of verifying the reliability of quantitative identification of behavioral psychology, the construction of intelligent clothing design system is carried out. Finally, this paper combines the simulation test and evaluation test to verify the effect of the system in this paper. Through the experimental research, it is verified that the clothing design system based on behavioral psychology proposed in this paper can effectively improve the clothing design effect and user experience. [ABSTRACT FROM AUTHOR]

In recent years, the rapid progress of mobile internet technology has revolutionized how data is transmitted and shared wirelessly, making it possible to deliver information in real-time to users anywhere. This technological advancement has created new research opportunities, particularly in teaching quality evaluation in colleges and universities using mobile applications. Teaching quality is a crucial evaluation index at the university level, and accurately assessing it is a complex task due to numerous influencing factors. Therefore, there is a need to develop a model that can effectively evaluate the teaching quality to address this challenge. To this end, this paper proposes a teaching quality evaluation model based on data mining algorithms that consist of four parts: evaluation model, evaluation method, evaluation procedure, and practice optimization. This model aims to improve the accuracy of teaching quality evaluation in colleges and universities. Moreover, this study suggests that virtual simulation experiment systems can positively affect the effectiveness of teaching environmental courses. The paper recommends incorporating virtual simulation experiments into the curriculum to enhance students' learning experience. In conclusion, the progress in mobile internet technology has opened up new research opportunities, and data mining algorithms can improve the accuracy of teaching quality evaluation. Additionally, integrating virtual simulation experiments in environmental course teaching can enhance students' learning experience. [ABSTRACT FROM AUTHOR]

Taekwondo is a martial art emphasizing defense, protection, and moral and spiritual values. Evaluating the effectiveness and quality of Taekwondo sports training is a complex task that requires the consideration of various factors, such as the learners' skills, physical condition, and psychological state. Traditional methods for evaluating sports training effectiveness and quality have limitations, such as being subjective, time-consuming, and not considering individual differences. This paper proposes a new approach to evaluating the effectiveness and quality of Taekwondo sports training using optimization algorithms. Specifically, three optimization algorithms, TLBO, DSLTLBO, and GWO, are compared and analyzed for their suitability at different levels. These algorithms can automatically adjust the sports training plan based on individual differences and learner feedback, which can address the limitations of traditional evaluation methods. Validity statistics and moral/spiritual level assessment are used to evaluate the training results and process quality. The results indicate that different optimization algorithms are appropriate for learners at different levels. Diversified algorithm forms are recommended for a comprehensive evaluation of the sports plan. This paper provides insights into optimizing Taekwondo sports training and highlights the importance of cultivating moral and spiritual values through this martial art. The proposed approach can potentially improve the effectiveness and quality of Taekwondo sports training and can be extended to other sports or physical activities. [ABSTRACT FROM AUTHOR]

In order to improve the effect of enterprise credit risk assessment, this paper combines the improved fuzzy neural network to construct the enterprise credit risk assessment model. The various jamming patterns that can threaten the credit data transmission system include various blocking jamming and tracking jamming. Moreover, this paper analyzes the bit error performance of the credit data transmission system against various interferences, and obtains the bit error curves against various interferences through program simulation. In addition, this paper builds a variable-speed credit data transmission system based on Simulink, analyzes the key technologies in variable-speed credit data transmission, and simulates the anti-interference performance of variable-speed credit data transmission technology. The simulation study verifies that the enterprise credit risk assessment model proposed in this paper has good risk assessment effect and risk response strategy effect. [ABSTRACT FROM AUTHOR]

Reversible lanes play an important role in alleviating urban traffic congestion and improving the utilization of road resources. However, most of the reversible lanes currently implemented are timed and fixed, and there is little research on the safety of reversible lanes without isolation facilities. In view of the existing problems of reversible lanes, this paper proposed a real-time dynamic reversible lane safety control model. The influencing factors of reversible lane safety control are analyzed first, and then determine model parameters of the no-entry zone and the buffer zone in the conflict area. Moreover, model parameters of emptying, exit, and recovery zones in the operation process were determined as well. Additionally, this paper discussed the length of reversible lanes and the timing of signals. The surrogate safety assessment model (SSAM) is adopted to analyze vehicle trajectory data for traffic simulation under different traffic flows. The experiment results show that the proposed reversible lane safety control scheme can keep the vehicle's traffic conflict rate below 5%, which can effectively ensure the safe operation of the reversible lane. [ABSTRACT FROM AUTHOR]

In this paper, we investigated a new heroin–cocaine epidemic model which incorporates spatial heterogeneity and nonlinear incidence rate. The main project of this paper is to explore the threshold dynamics in terms of the basic reproduction number $ \mathcal {R}_0 $ R 0 , which was defined by applying the next-generation operator. The threshold type results shown that if $ \mathcal {R}_0 \lt 1 $ R 0 < 1 , then the drug-free steady state is globally asymptotically stable. If $ \mathcal {R}_0 \gt 1 $ R 0 > 1 , then heroin–cocaine spread is uniformly persistent. Furthermore, the globally asymptotic stability of the drug-free steady state has been established for the critical case of $ \mathcal {R}_0=1 $ R 0 = 1 by analysing the local asymptotic stability and global attractivity. [ABSTRACT FROM AUTHOR]

Path-breaking transformative change is needed in human-environment relations to move towards more sustainable development trajectories at local, national and global scales. Crises may trigger transformative change and learning in the short and in the long term. However, in particular, a short-term response to crises may also be reactive, strengthening established unsustainable practices and further perpetuating vulnerability and inequality rather than supporting transformative change towards a more sustainable path. To understand the nature and response to a crisis in the context of sustainability transformations, this paper elaborates on the following aspects of a crisis: What are the characteristics of a crisis? What and who shapes the narrative(s) of a crisis? What and who shapes the nature of the response to a crisis? Do responses to crises trigger higher levels of learning? Conceptual synthesis is complemented with an exploratory comparative analysis of the Cape Town water crisis and of the COVID-19 pandemic in South Africa. To this end the paper analyzes the interplay between mobilizing individual, collective and relational agency and navigating and transforming power relations to challenge and profit from already weakened unsustainable structures. This approach proves to be promising to understand the role of crises in catalysing and supporting transformative learning to eventually replace unsustainable structures. ● During and immediately after crises, it is important to identify opportunities for policy change to address persistent governance failures. ● To support transformative change towards sustainability, governments typically should adopt a network governance style and act more as a convenor for deliberative processes in the later phase of the response to a crisis. ● Formation of innovation platforms bringing together actors from different levels and different roles (e.g. pioneering innovators, investors, scientists, policymakers, regulators) could support the scaling up of local initiatives and innovative approaches that have been developed during crises. [ABSTRACT FROM AUTHOR]

Despite increasing efforts by research and policy to approach sustainability, human impact on nonhuman nature is intensifying the current social-ecological crisis. To foster sustainability transformation, there is a need to re-think qualities of human-nature connections which calls for relational discourses that provide alternatives to the predominance of mindsets postulating a human-nature divide. Against this backdrop, this conceptual paper introduces 'human-nature resonance' as a relational account that provides system, target, and transformation knowledge for sustainability transformation. The paper argues that the social-ecological crisis has one of its root causes in mute human-nature relations. On this basis, it is illustrated how the social-ecological crisis is only slightly affecting the behaviours of Western societies, which are subsequently failing to establish responsive human-nature relations. Considering that mute relations are fostered by making the world constantly available, the non-affective human-nature relation can be traced back to a lack of material and moral boundaries of nonhuman nature perceived as a lifeless object of infinite availability. For strengthening human-nature resonance, the paper calls for the vision of human-nature partnership neglecting hierarchical human-nature relations. To strengthen the human-nature partnership, nature will speak with an own voice by assigning her legal personhood, agency, and soulfulness. Furthermore, human self-efficacy needs to be strengthened to listen to nature by nourishing internal relational capacities such as compassion and self-worth. Future work on human-nature resonance can integrate basic and applied inter- and transdisciplinary research which links natural and social sciences, Western and Indigenous ontologies, and the scientific world of logos and transcendental wisdom. [ABSTRACT FROM AUTHOR]

In recent years, the global environmental science-policy interface has come to include a greater variety of knowledge. Social scientists have joined natural scientists at the policy table, and Indigenous and local knowledge is being taken ever more seriously. But this pluralisation raises political, normative, and epistemic challenges for environmental expert organisations, including with respect to how knowledge is managed, how it is judged to be valid, how it is made policy-relevant, and how it is presented to policy-makers and decision-takers. Based on an interview study of experts involved in the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), we identify three broad approaches to these challenges: the integrationist logic, which seeks to combine all knowledge into a single ontology; the parallelist, which looks for similarities and connections between irreconcilable ontologies; and the pragmatist, which strives to apply knowledge when and where it will have the greatest positive impact. Rather than champion any one of these approaches, the paper explores their origins and how they negotiate paralyses to the timeliness of work. In avoiding ultimate formalisation of how value and knowledge pluralism are to be handled, IPBES allow more contextually sensitive practices to come to the fore. The paper concludes by discussing implications for environmental expertise more broadly. [ABSTRACT FROM AUTHOR]

Sustainability-focused research networks and communities of practice have emerged as a key response and strategy to build capacity and knowledge to support transformation towards more sustainable, just and equitable futures. This paper synthesises insights from the development of a community of practice on social-ecological systems (SES) research in southern Africa over the past decade, linked to the international Programme on Ecosystem Change and Society (PECS). This community consists of a network of researchers who carry out place-based SES research in the southern African region. They interact through various cross-cutting working groups and also host a variety of public colloquia and student and practitioner training events. Known as the Southern African Program on Ecosystem Change and Society (SAPECS), its core objectives are to: (1) derive new approaches and empirical insights on SES dynamics in the southern African context; (2) have a tangible impact by mainstreaming knowledge into policy and practice; and (3) grow the community of practice engaged in SES research and governance, including researchers, students and practitioners. This paper reflects on experiences in building the SAPECS community, with the aim of supporting the development of similar networks elsewhere in the world, particularly in the Global South. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy. [ABSTRACT FROM AUTHOR]

Through the digitalisation of Civil Trials and the implementation of the Telematic Civil Process framework, the Italian Ministry of Justice has amassed a wealth of data covering all facets of modern Trials. While data availability has surged, the focus now lies in actively analysing this data to optimise Trials and curtail their duration. This paper, with a strong emphasis on its outcomes, delves into the analysis of data from the Court of Livorno. It seeks to pinpoint specific events within the Trial workflow that significantly extend Trial durations. Notably, Domain Experts have identified a set of events, intending to validate their pivotal role in recognising critical Trials. Leveraging Machine Learning techniques, the paper evaluates multiple binary classifiers to proactively identify potentially critical Trials, empowering Judges to mitigate future issues. The study has yielded a remarkable 80% accuracy rate in predicting Trials exceeding acceptable duration thresholds. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

This paper focuses on exploring and comparing different machine learning algorithms in the context of diabetes management. The aim is to understand their characteristics, mathematical foundations, and practical implications specifically for predicting blood glucose levels. The study provides an overview of the algorithms, with a particular emphasis on deep learning techniques such as Long Short-Term Memory Networks. Efficiency is a crucial factor in practical machine learning applications, especially in the context of diabetes management. Therefore, the paper investigates the trade-off between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance. By analysing these algorithms, the research uncovers their distinct behaviours and highlights their dissimilarities, even when their analytical underpinnings may appear similar. [ABSTRACT FROM AUTHOR]

This paper proposes a particle-based nonlinear elastic object simulation technique for virtual surgery. Particle-based techniques are used to model and simulate nonlinear elastic objects, such as the skin and internal organs. This enables the simulation to consider various factors, such as location, direction, and depth, when making incisions in the organs. However, the issue with this method is that it can only simulate precisely cut tissue during incision. Objects with elasticity, such as tissue, require the generation of complex debris during incision. This paper proposes a particle-based elastic object simulation technique to model the debris from torn tissue when making an incision in the organs. It can predict where the tissue will tear based on the maximum shear stress (MSS) theory and Tresca's yield criterion when the force applied to the tissue exceeds the maximum stress. Newly generated particles at the predicted location are remeshed with nearby particles. We verified the superiority of our proposed method over traditional particle-based methods by accurately representing more complex debris and comparing the results of incisions in the same area of the body. This allows various incision types, such as stab wounds and lacerations, to be simulated. [ABSTRACT FROM AUTHOR]

This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP). Firstly, MoCoUTRL employs multi-granularity semantic contrastive learning objectives, enabling a more comprehensive understanding of the semantic features of samples. Secondly, MoCoUTRL uses a dynamic dictionary to act as the approximately ground-truth representation for each token, providing the pseudo labels for token-level contrastive learning. The MoCoUTRL can extend the use of pre-trained language models (PLM) and even large-scale language models (LLM) into a plug-and-play semantic feature extractor that can fuel multiple downstream tasks. Experimental results on several publicly available datasets and further theoretical analysis validate the effectiveness and interpretability of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Crowdsourcing takes advantage of human intelligence to solve complex problems that computers cannot handle. People actively participate in computational tasks for rewards, especially those that are relatively simple for humans but challenging for computers. However, most traditional crowdsourcing systems must rely on a central organisation to handle the work associated with crowdsourcing. This may lead to privacy and security issues such as information leakage, single point of attack and unfair judgement. Meanwhile, the high service fees of existing central crowdsourcing platforms will reduce people's motivation to participate in crowdsourcing tasks. Consequently, PrivBCS is proposed in this paper, a blockchain-based decentralised crowdsourcing system that does not rely on third-party agencies and requires only low transaction fees. In this paper, PrivBCS protects the confidentiality of crowdsourcing data and the identity privacy of participants by cryptographic means, such as Paillier, El-Gamal and CPABE. It selects the most suitable worker to perform tasks by setting access control policies on task data and matching workers' attributes while ensuring the anonymity of workers' identity. Considering that the data volume of crowdsourcing tasks may be large and there is a certain upper limit to the block capacity of the blockchain, an on-chain and off-chain collaborative storage scheme is proposed, which can increase the throughput of the blockchain system and improve its scalability to a certain extent. In PrivBCS, we implement the transaction process of crowdsourcing tasks through smart contracts. The experimental results and security analysis in this paper demonstrate the effectiveness, efficiency, and feasibility of PrivBCS. [ABSTRACT FROM AUTHOR]

Image super-resolution reconstruction is one of the methods to improve resolution by learning the inherent features and attributes of images. However, the existing super-resolution models have some problems, such as missing details, distorted natural texture, blurred details and too smooth after image reconstruction. To solve the above problems, this paper proposes a Multi-scale Dual-Attention based Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features and obtain multi-scale feature information. This paper also designs the channel and spatial attention block (CSAB), which is combined with the enhanced residual dense block (ERDB) to extract multi-level depth feature information and enhance feature reuse. In addition, the multi-scale feature information extracted under the three-branch path is fused with global features, and sub-pixel convolution is used to restore the high-resolution image. The experimental results show that the objective evaluation index of MARDGAN on multiple benchmark datasets is higher than other methods, and the subjective visual effect is better. This model can effectively use the original image information to restore the super-resolution image with clearer details and stronger authenticity. [ABSTRACT FROM AUTHOR]

The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network. Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact. Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models. [ABSTRACT FROM AUTHOR]

Microservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of computing microservice systems, while solving the problem of latency in computing the best combination of services with the maximum QoS objective function value, this paper proposes a microservice combination approach based on the QoS model and a CGWO algorithm for optimisation computation for this model. The experimental results verify that the error rate of the method is only 0.528% on the non-functional combination optimisation problem, and the computational efficiency of the algorithm increases by 97.29% when the complexity of the problem search space increases, while CGWO improves 65.97% and 81.25% respectively in the accuracy of optimisation compared to the prototype of the algorithm (GWO), and has a stable optimisation performance, aspect. It proves that the research in this paper has a high advantage in automatically searching for the best QoS for the microservice combination problem. [ABSTRACT FROM AUTHOR]

Object detection technology is a popular research direction which is widely used in areas such as autonomous driving and medical diagnosis. At this stage mobile devices often have limited storage resources to deploy large object detection networks and need to meet real-time requirements. This paper proposes a lightweight and efficient object detection model based on YOLOv4, first using the lightweight network GhostNet to extract image features and reduce the number of parameters and computation of the backbone structure; then combining AFmodule and Meta-ACON activation function to enhance the feature extraction capability of the backbone network, which strengthen the mode's ability to capture image spatial feature information; this paper also designs the RL-PAFPN feature fusion structure is with the Reslayer module to further improve the model's ability to extract and fuse image features. By comparing other mainstream object detection models, the YOLOv4-Ghost-AMR network in this paper has less computation and fewer parameters, and the accuracy of the model reaches 86.83%, which is suitable for deployment in mobile devices with limited storage. The model proposed in this paper can be applied to medical, traffic and fault detection fields, which changes the traditional manual detection method and saves manpower and time costs, achieving high precision real-time object detection. [ABSTRACT FROM AUTHOR]

The autonomous driving algorithm studied in this paper makes a ground vehicle capable of sensing its environment via visual images and moving safely with little or no human input. Due to the limitation of the computing power of end side devices, the autonomous driving algorithm should adopt a lightweight model and have high performance. Conditional imitation learning has been proved an efficient and promising policy for autonomous driving and other applications on end side devices due to its high performance and offline characteristics. In driving scenarios, the images captured in different weathers have different styles, which are influenced by various interference factors, such as illumination, raindrops, etc. These interference factors bring challenges to the perception ability of deep models, thus affecting the decision-making process in autonomous driving. The first contribution of this paper is to investigate the performance gap of driving models under different weather conditions. Following the investigation, we utilise StarGAN-V2 to translate images from source domains into the target clear sunset domain. Based on the images translated by StarGAN-V2, we propose Conditional Imitation Learning with ResNet backbone named Star-CILRS. The proposed method is able to convert an image to multiple styles using only one single model, making our method easier to deploy on end side devices. Visualization results show that Star-CILRS can eliminate some environmental interference factors. Our method outperforms other methods and the success rate values in different tasks are 98%, 74%, and 22%, respectively. [ABSTRACT FROM AUTHOR]

The global biodiversity crisis and constantly growing human impact on the natural environment call for more evidence where conservation actions are the most urgent. As the agricultural lands are under increased pressure of growing human food needs here the literature survey on farmland bird conservation (FBC) was conducted to reveal geographical distribution in research intensity, the topics addressed and to identify potential drivers for the worldwide scientific effort. The Scopus database search has revealed 2290 papers dedicated to FBC from the period 1990-2020. The distribution was spatially uneven with most papers published in Western Europe and North America. Scientific collaboration was also spatially biased in favor of countries located close to each other and having high scientific capacity. The analysis also revealed 139 terms representing main topics raised in FBC research. The number of FBC papers per country was positively correlated with Gross Domestic Production (GDP), the total number of scientific papers, and number of threatened species, and negatively with the GDP from agricultural production, whereas it was not related to area of agricultural lands or bird species richness. Spatial and thematic biases in studies of farmland birds may have important consequences since uneven scientific evidence constrains development of proper conservation solutions and limits their implementation. We conclude that research on FBC should be globally coordinated and flexible enough to undertake burning conservation problems adjusted to regional differences in agriculture, socio-economy, and bird diversity. [ABSTRACT FROM AUTHOR]

The paper is an attempt at applying Samir Amin's lens in the analysis of socio-economic development in Africa. Social and economic development in Africa has been substandard, largely because of the economic system followed and because effective structural transformation has not taken place – Samir Amin's works explained what needed to be done to transform Africa (and the broader global south). It is in this context that the paper posits that post-colonial Africa has had to contend with disruptive socio-economic and political realities instituted by European colonialism, slave trade and inappropriate integration of Africa to the so-called global economy. The fundamental explanation for the poor socio-economic development in Africa is global capitalism, and one of the possible solutions is in Samir Amin's delinking proposal as well as the restructuring of the African economies. [ABSTRACT FROM AUTHOR]

The paper is nested within the decolonial theme by repositioning Patrice Lumumba's decolonial thought within the radical writings of Samir Amin. As seen in his famous independence speech, Patrice Lumumba is arguably one of the African decolonial giants who championed the remembering of the black bodies by challenging their dismembering facilitated and sustained by the grand construction of the human by Euro-modernity. Rereading his decolonial ideas through repositioning it in Amin's works comes at an opportune time given the recent burial of his last known remains – a tooth that was removed from his body as a trophy by a Belgian officer who participated in his brutal murder in the 1960s. It is within this context that this paper seeks to underscore the logic and legitimacy of Lumumba's decolonial thinking by repositioning it in the works of Amin. By doing so, we aim to contribute to all non-European discourses aimed at decolonial self-reconstruction and self-definition, such as Afrocentricty. [ABSTRACT FROM AUTHOR]

Tumour development is not only an independent event of genetic mutation and overgrowth of tumour cells but is the result of a synergistic interaction between a malignant tumour and its surrounding tumour stromal microenvironment. In this paper, we address the shortcomings of current tumour therapy by focussing on the tumour itself and the surrounding microenvironment to achieve a two-pronged targeting model. In this paper, a dual-targeting, pH/reactive oxygen species (ROS) sensitive nano-drug delivery system for tumour cells and CAFs was designed. A hyaluronic acid (HA) with CD44 receptor targeting on the surface of tumour cells was selected as the main carrier material, and a dipeptide Z-glycine-proline (ZGP) with specific targeting of fibroblast activating protein (FAP) on the surface of CAFs was modified on HA to achieve precise targeting of CAFs, open the physical barrier of tumour cells and improve the deep penetration effect of the tumour, while introducing thioketone bond and ketone condensation bond to take advantage of the highly reactive ROS and low pH microenvironment at the tumour site to achieve chemical bond breaking of nano micelles encapsulating paclitaxel (PTX), drug release, and thus drug aggregation at the tumour site and improved bioavailability of the drug. [ABSTRACT FROM AUTHOR]

To achieve the goal of carbon reduction, China has been piloting the Carbon Emission Trading System (CETS) since 2013. As the economy faces a downward trend, it is significant to explore the impact of CETS on business development. There is still debate in academia about whether this policy can boost the level of business development. This paper, based on all A-share data of listed companies in China from 2009 to 2018, uses the Difference in Differences (DID) model to verify the impact of CETS on the input of capital and labor factors and the level of technology in enterprises and discusses the industry heterogeneity of this impact in detail. Placebo tests, propensity score matching, and triple differences ensure the robustness of the conclusions. In further research, this paper decomposes the policy effect of CETS. It regresses the impact of carbon quota prices and carbon market trading scale on business development. The final conclusion is that CETS has a positive impact on the input of capital and labor in enterprises and a negative impact on the level of technology. After distinguishing industries, this conclusion shows differences according to different characteristics of high emissions and low emissions. In addition, the increase in carbon quota prices hinders business development, while the scale of carbon market trading shows an inverted "U" relationship with business development. The article provides meaningful policy references for China and countries in the early stages of CETS construction. [ABSTRACT FROM AUTHOR]

Transdisciplinary research has gained traction globally for mapping pathways for sustainable urbanisation. The involvement of local communities is believed to central to knowledge co-production needed to address sustainability challenges. But working with local communities can be challenging. This paper is structured around the authors' personal reflections on undertaking transdisciplinary household energy conservation projects in Ghana and South Africa. The paper reflects on the assumptions and challenges of doing transdisciplinary research in urban Africa and suggests some guidelines for consideration when doing transdisciplinary projects. An inductive analysis of our reflections showed five key challenges: lack of integration, divergent problem identification, tensions in knowledge co-production, asymmetrical power relations, and distrust and managing expectations. We draw on our collective experiences to develop guidelines for conducting transdisciplinary research in urban Africa. These guidelines are not prescriptive but can be useful for a growing and broader audience interested in undertaking transdisciplinary research. [ABSTRACT FROM AUTHOR]

Globally, cities have become hubs for economic activity, productivity, and important platforms for achieving sustainable development goals. The potential of urban agriculture (UA) in improving urban local economies and urban micro-climate has been acknowledged in the literature. The study looked at how the concepts of UA, local economic development (LED), and climate change can be treated in unison. Based on the review of credible published papers on the various concepts of UA, LED, climate change and nature-based solutions (NBS), a proposed conceptual framework was developed in this paper showing the linkages. The paper established that UA could boost LED, build resilient urban settlements, and promote social inclusiveness, but with social challenges. It is therefore recommended that policymakers make UA a crucial aspect of their agenda in the coming years to address the local economic and climate challenges now and in the future. [ABSTRACT FROM AUTHOR]

The relationship between urban density and social conditions in urban areas has received increasing attention in recent research. However, there is a lack of understanding of the dynamics between urban densification and these social conditions from a place-specific perspective, taking into account the institutional, socio-cultural, and contextual complexities. This paper seeks to enhance this understanding by unpacking the relationship between soft densification and place attachment in Tehran, Iran. The paper develops a framework for studying 'soft densification' as a process of incremental place change by prioritising local knowledge. The findings suggest that soft densification impacts place attachment by disrupting the everyday functionality of place, eroding its physical characteristics, erasing some of its collective and personal memories, and altering its socio-demographic structure. The paper highlights the importance of thinking 'procedurally' and 'topologically' about urban densification and calls for incorporating local knowledge and experiences into policy planning and urban decision-making. [ABSTRACT FROM AUTHOR]

Since 2010, the Chinese government has developed several batches of low-carbon pilot provinces and cities continuously. And in the backdrop of carbon peak and carbon neutralisation recently, the carbon emission reduction policies and achievements of China have attracted more and more worldwide attention. In the field of human health, this paper investigates whether the Low-carbon Province Pilot Policy of China has positive effects on residents' health based on the micro data of China Family Panel Studies (CFPS) from 2010 to 2016. Using the time-varying difference-in-difference model to solve the endogeneity problem commonly faced in the literature, we find that Low-carbon Province Pilot Policy has a positive impact on public health through air quality improvement and the unemployment level alleviation mainly. And in urban areas, the policy effect is more obvious than that in rural regions. This paper expands the content of national low-carbon governance effects to residents' health further and contribute to China's low-carbon development. Specifically, it reminds us to focus on the dynamic adjustment and effective continuation of the policy, and also the all-round support for 'the disadvantaged' in environmental governance process. [ABSTRACT FROM AUTHOR]

Carbon nanotube cement-based grouting materials currently mostly stay in the pure slurry research stage, and there is little research in the field of rock crack reinforcement combined with the engineering application background. Therefore, on the basis of existing research, it is important to expand the dispersion of carbon nanotubes in cement paste, and to carry out research on the reinforcement of fractured rocks in combination with the engineering background. This paper combines the actual needs of construction engineering to prepare carbon-based nanomaterials, and analyzes its impact on construction engineering in combination with experiments. Moreover, this paper analyzes the influence of carbon nanotubes on the macro-mechanical properties of cement-based materials, and studies the influence of modified carbon nanotubes on the macro-mechanical properties of cement-based materials. In addition, this paper explores the micromechanical properties of carbon nanotube cement materials at the nanometer scale. The research results verify the effectiveness of carbon-based nanomaterials. [ABSTRACT FROM AUTHOR]

Marine fishery is a highly mechanized sector, Industrialization and industrialization "Big agriculture. Among them, marine fishing is the main part of the fuel consumption of fishing vessels, which will produce a large amount of carbon emissions; the electricity consumption of advanced pond aquaculture and factory aquaculture in seawater will also bring high indirect emissions. It is of great practical significance to study the optimization of marine fishery industrial structure for promoting the high-quality development of marine fishery. Based on the data of marine fishery in Zhejiang Province from 2008 to 2017, this paper calculates the carbon emission efficiency of marine fishery and discusses the impact of industrial structure optimization of marine fishery on carbon emission efficiency. Based on the analysis of the research results, this paper puts forward some suggestions on optimizing the structure of marine fishery industry: increasing the technical input and personnel introduction of energy intensive industries, and developing five high-tech pillars strategically. From the perspective of regional industrial structure optimization, high-efficiency areas should enhance the ability of modern manufacturing industry to undertake international industrial transfer and form innovation clusters; the medium-efficiency areas should use industrial bases to develop characteristic leading industries and modern service industries; low-efficiency areas should develop high-tech industries in line with local resource endowments and advantageous industries, and improve the allocation efficiency of environmental protection investment expenditure. By optimizing the industrial structure of Zhejiang Province, carbon emission efficiency can be improved. Improve fishermen 's environmental awareness and management ability, vigorously develop and promote green aquaculture technology, and provide technical support for industrial transformation and upgrading. [ABSTRACT FROM AUTHOR]

As people put forward higher demand for the effect and quality of building interior decoration, the attention to building energy-saving materials has become higher and higher. In recent years, there have been many researches on green building energy-saving technology, green building evaluation and other aspects, but there are relatively few researches on applying the concept of full life cycle cost to the performance analysis of new materials for building energy conservation and environmental protection. Considering the economic performance of buildings will help to save resources in the construction industry and reduce energy consumption. The so-called full life cycle optimization of energy-saving design of green buildings is to take the concept of full life cycle optimization as the most reference, fully coordinate all elements in the portfolio project, and ensure that the full life cycle cost of the building can be greatly reduced. To realize the energy-saving optimization of green buildings, it is necessary to ensure that it runs through the whole life cycle of the building, fully combine the life cycle cost with the building function, and systematically do the optimization work. The performance analysis model of building decoration energy-saving and environmental protection new materials based on the whole life cycle proposed in this paper can be used to analyse the various properties of various new materials. The experimental results show that the maximum error of the test results is 9.6%, the minimum error is 1.3%, and the average error is only 6.43%. This shows that the model built in this paper is reliable, and the calculated results are in good agreement with the experimental results, which can be used to analyse the performance of new building decoration energy-saving and environmental protection materials. The ideas and methods of solving the model established in this paper have important guiding significance, and can be used as a powerful reference tool for studying other solving methods. [ABSTRACT FROM AUTHOR]

With the acceleration of the global modern industrialisation process and the increasingly serious environmental problems, the development of low energy consumption, high efficiency electrochemical energy conversion equipment and separation system has become a research hotspot in the scientific and industrial circles. At present, machine learning has become an important research method to explore and expand two-dimensional material family. Traditional experimental and computational methods have low fault tolerance when studying two-dimensional materials, which requires a lot of time and research and development costs. Machine learning, due to its powerful data processing capability and flexible algorithm model, can help reduce the time and cost of discovering and understanding two-dimensional materials, and can effectively predict and expand two-dimensional material systems based on data and explore their potential for experimental synthesis and application. This paper will focus on the methods of machine learning, the exploration of machine learning in 2D material design and synthesis, and the exploration of machine learning in 2D material properties and applications. Finally, this paper uses ML algorithm to test the synthesised polymer. The experimental data points and prediction data points have relatively good consistency with each other, which indicates that ML model can be used as a prediction tool to identify the undeveloped polymer for gas separation. [ABSTRACT FROM AUTHOR]

Image processing algorithms based on time-frequency analysis are frequently used in testing and researching the mechanical properties of high-temperature materials, especially in aerospace, engines, and ships. However, due to the complexity of the high temperature environment itself, there are many factors that affect the measurement accuracy, among which the existence of thermal airflow disturbance has a particularly significant impact on the digital image correlation method. This paper mainly focuses on two aspects of high temperature edge detection and digital image correlation. On the one hand, the existing algorithm is improved to make it suitable for material property detection in high temperature environment. And relevant experiments are carried out to verify the feasibility and accuracy of the algorithm. The full-field coefficient caching method for fitting initial values proposed in this paper has the premise that the accuracy is comparable to that of the reverse combined Gauss-Newton matching algorithm, but the running speed is improved by about 10%, and the matching accuracy of both is obviously higher than that of the surface fitting algorithm. [ABSTRACT FROM AUTHOR]