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During the past few decades, social sciences and humanities has been involved in numerous methodological and theoretical debates that is described as the paradigms war between positivists, interpretativists and critics. In the 1970s or theoretical turn period, first constructivist interpretive and critical approaches emerged then hybrid approaches entered into this field. Critical realism is a modern approach that tries to combine paradigms and make a new definition of reality, knowledge, values, and research. This paper intends to overview the three dominant paradigms in social sciences and humanities in order to analyse methodological and theoretical innovations of critical realism.

Due to new advancements in communication technologies and globalization, pathology of communicative interactions between religious organizations is a fundamental requirement of today's society. Lack of adequate attention to this issue will bring about a weak value- norm system, religious alienation and finally promote unusual behavioral patterns in our society. The present paper is concerned with organization of mosque as one of the main headquarters in strengthening the religious behavior and its interactions with other related organizations dealing with existing challenges and problems in this field.

The LLC resonant converter is a widely used DC/DC converter that offers the benefit of enabling soft switching compared to classical DC/DC converters. However, traditional PI control strategy based on a linear model has drawbacks such as slow dynamic response and poor anti-interference performance. To overcome the shortage, a passivitybased control strategy based on the Euler–Lagrange (EL) model is proposed in this paper to improve the dynamic performance of the half-bridge LLC resonant converter. In addition, the stability of the system based on the proposed strategy is analyzed and verified. Further, the effectiveness and performance of the proposed strategy is verified in the simulation by comparing with the traditional PI controller. Finally, a prototype was built to verify the dynamic performance of the LLC resonant converter based on the proposed control strategy.

This study aims to develop a new data-driven methodology for identifying suitable influencers for a brand using data from social media. The increasing presence of such figures in these communication channels makes it challenging to select consistent and influential influencers for a specific audience. This paper introduces an innovative approach to defining these figures based on the analysis of relationships within the brand's network. Specifically, this methodology will be applied to the case study of a brand named “Anemonia”. The approach relies on the sequential application of various steps, including the use of tools such as Social Network Analysis (SNA) centrality, Sentiment Analysis (SA), and Analytical Hierarchical Process (AHP). Through the application of this methodology, the brand has been able to identify influencers consistent with its aesthetics and vision.

ABSTRACTThis paper addresses yield loss due to red deer (Cervus elaphus) grazing on permanent grasslands on three organic farms in SE Slovenia over two consecutive years. Deer graze on permanent grasslands throughout the year, but the resulting crop loss varies during the growing season. Accounting for three mowings, the quantitative yield loss due to deer grazing totalled 48–52%, or 3.9–4.3 t/ha. Deer had the highest consumption capacity in the spring (first mowing: yield loss of 1.7–1.9 t of air-dried hay/ha), and towards the end of the growing season, consumption decreased (third mowing: loss of 0.9 t/ha). A floristic inventory indicated low species diversity and production in permanent grasslands in SE Slovenia. The crude protein content in unprotected plots was generally greater than that in protected plots, but due to a large quantitative yield loss, the yield of crude protein and net energy for lactation on heavily grazed land were much lower than those on protected land in all mowings. The average additional cost per unit of heavily grazed area ranged from 182 to 344 EUR/ha, and the differences are the result of the different intensities of game grazing in individual locations and grassland production capacities.

ABSTRACTFuture wireless systems integrating communication and sensing will require handling an enormous amount of data with extremely low latency. Intense video streaming data traffic, pervasive-augmented and virtual reality services, advanced haptic–tactile interaction and holography-type communications, in fact, reveal a trend towards experience-based networks and multi-sense media, which require seamless connectivity and ultra-high capacity. Using current 5G technologies to take mentioned challenges, might not represent the desired solution and a completely new paradigm is needed. Recently, to support this change, scientists have been focusing on the concept of Smart Electromagnetic Environments (SEE), based on deploying a number of smart nodes, whose electromagnetic response needs to be finely adapted to the changing operative conditions. In this framework, a pivotal role is played by reconfigurable intelligent metasurfaces, allowing the implementation of smart skins, reflecting intelligent surfaces, smart repeaters, and intelligent antennas. This paper aims at offering an insightful review of the role of intelligent metasurfaces in advancing the concept of SEE. Different application domains will be illustrated: advanced signal processing at the physical layer, a new generation of smart antennas whose intelligence is enabled by the physical layer, and practical implementation of reflecting intelligent surfaces. With an in-depth analysis of these areas, this review sheds light on how intelligent metasurfaces drive innovation in wireless communication shaping the future of SEEs.

Research issues around tourism development in small town South Africa have generated a growing scholarship. With the announcement in May 2023 by the World Health Organisation of the end of the COVID-19 pandemic the paper reflects both on the pandemic experiences for tourism in South African small towns and of relevant initiatives for revival at the beginning of the post COVID-19 era. Specifically, the aim in this paper to extend the literature on tourism in small town South Africa by focusing on the context of small town recovery from COVID-19 analysing the example of the Overstrand, Africa’s first UNESCO Creative City of Gastronomy. The study shows that under certain circumstances amenity-rich small town localities can be resilient to external shocks such as the pandemic. For the resilience and recovery of small-town tourism destinations it is argued that place-based responses are critical.

Domestic tourism has garnered much attention recently as a result of the COVID-19 pandemic, being identified as a key solution to the post-pandemic recovery. The worth of domestic tourism prior to the pandemic, however, despite its tremendous economic value, has largely been overshadowed in scholarship and amongst many governments in lieu of the more glamorous international tourism. Notwithstanding, the current emphasis on domestic tourism, a gap still exists in the literature on specific sub-segments of the domestic tourist market and understanding their profiles and perceptions in order to address issues related to growing such markets and thus contributing to the overarching growth of domestic tourism and strengthening its role in the recovery of the tourism sector. This paper thus seeks to unpack the profile of 297 domestic road trip tourists surveyed from the Gauteng province of South Africa. The results outline the socio-demographic characteristics in terms of age, race, income and education levels, and their travel preferences and behaviour. Findings reveal facets linked to the importance of youth tourists, the need to address access to travel, availability of products and facilities geared to the needs of domestic tourists, the conditions of roads and the ease of travel, as well as issues related to financial access to tourism through pricing and affordability. The study provides a springboard to better understand the domestic road trip market by uncovering key socio-demographic components of this lesser studied market in the context of South Africa and provides recommendations for addressing the issues at hand.

ABSTRACTBackground Pseudomonas aeruginosa (P. aeruginosa) is widely distributed in air, soil, and water, human respiratory tract, intestinal tract, and skin. It can induce bloodstream infection, urinary tract infection, gastrointestinal tract infection, respiratory tract infection, etc. Conventional bacterial isolation, culture, and identification are time-consuming, and many false negative results, which cannot meet the needs of precise clinical diagnosis，and proper treatment. This study aims to develop a rapid isothermal amplification assay of Pseudomonas aeruginosa.Methods Specific primers were designed according to the National Center for Biotechnology Information (NCBI) database based on the highly conserved sequence of Pseudomonas aeruginosa virulence factor gene lecA, and a recombinase polymerase amplification (RPA) detection method was established. The sensitivity and specificity were calculated, as well as the collection and processing of clinical samples.Results The thermostatic amplification technique for Pseudomonas aeruginosa established in this paper allows nucleic acid detection within 10 minutes without cross-amplification with other bacterial strains. 27 P. aeruginosa infections were accurately detected in 300 clinical samples.Conclusion The rapid detection system based on thermostatic amplification had shown high sensitivity and specificity in this study, indicated that this method can effectively assist clinical bacterial detection.

ABSTRACTUrban footpaths are the main outdoor activity spaces for pedestrians. It is traditionally believed that the air temperature of a pedestrian walkway under tree shade is lower. However, from the perspective of vertical air temperature profiles, there is little difference in air temperatures between a shaded pedestrian walkway and one under unshaded sunlight. This paper adopts a human-centred approach to investigate the three-dimensional spatial distribution of air temperatures in a pedestrian walkway in Taipei City under tree shade. It analyzes the air temperature reduction caused by attenuation of ground reflected long-wave radiation due to shade, and the effect of shade on environmental ventilation. It was found that air temperatures varied along pedestrian walkway height and direction. The measured data shows that the main difference in air temperature between shaded areas and areas exposed to sunlight is in the 0–0.5 m height range, and there is little difference in air temperature between the 0.5 m-2 m height range.Higher shades led to poorer ventilation and were more likely to cause local overheating. This suggests the need to assess urban pedestrian thermal comfort from a three-dimensional perspective, considering not just planar shades but also vertical shading and ventilation.

ABSTRACTIn large-scale debris flow susceptibility assessments, there is often excessive manual intervention, low efficiency, and inadequate model accuracy. To address these issues, this paper integrates multiple data sources and proposes a Multi-channel and Multi-scale Residual Network (MMRNet) for automatic extraction of gully features. Firstly, MMRNet employs a multi-scale feature fusion module to capture both local and global features of gullies, enhancing the model’s feature representation capabilities. It then uses an improved residual structure to fuse shallow features, compress features, and improve assessment efficiency. Additionally, channel rearrangement techniques are used to enhance feature flow. Finally, susceptibility prediction is made based on the similarity between the gully under evaluation and gullies where debris flows have occurred. The natural breakpoint method is used to classify susceptibility results into five levels. Experimental results show that the very high susceptibility zones for debris flows are mainly concentrated in areas with abundant river systems along the Nujiang River, covering 61.68% of the entire study area, with a debris flow proportion of 98.78%. The MMRNet model achieves an accuracy (ACC) of 81.6% and an area under the curve (AUC) of 0.8320, indicating that this model is a high-performance method for debris flow susceptibility assessment.

ABSTRACTAmidst the rapid urbanisation, the ecological environment of plateau lake basins faces significant challenges. To better coordinate the relationship between urbanisation development and ecological environment construction, this paper conducts an analysis and assessment of the ecological resilience and urbanisation coupling coordination in Dali Prefecture based on socio-geographical spatial data. Initially, an assessment model for ecological resilience is established using resilience theory. Subsequently, the urbanisation development level is evaluated based on panel data. Finally, a coupling coordination model is employed to calculate the coupling coordination between the two aspects. The results indicate: (1) a rapid increase in basin urbanisation from 2010 to 2019, particularly between 2010 and 2015; (2) a continuous expansion of low-value ecological resilience areas across the entire basin, with a predominance of townships experiencing fluctuating decreases or stagnation, leading to an overall negative trend in ecological resilience indices; (3) evident spatial and temporal disparities in coupling coordination between the two systems in the basin’s villages and towns, primarily reflected in differences in coupling coordination types, with a few towns consistently exhibiting environmental lag. This study introduces a customised ecological resilience model for the Erhai Basin, enhancing comprehension of urbanisation-ecological resilience dynamics in plateau lake basins.

ABSTRACTC-terminal binding protein 1 (CtBP1) is a critical transcriptional corepressor of many tumor suppressor genes and plays diverse roles in the progression of cancers. The transcriptional repression function of CtBP1 is mediated by recruiting histone-modifying enzymes, such as histone deacetylases and histone methyltransferases, to target genes by binding with DNA-interacting factors. Several post-translational modifications of CtBP1 have been identified, including ubiquitination, phosphorylation, and SUMOylation. This paper reports that CtBP1 is conjugated by ISG15. Endogenous CtBP1 was modified by ISG15 after interferon-α treatment in HeLa cells. The ISGylation process of CtBP1 was regulated by deISGylation enzyme USP18 and ISG15 E3 ligase EFP. Interestingly, CtBP1 ISGylation affected the binding affinity between CtBP1 and some components of CtBP1-associated transcriptional complexes. HDAC1 and LSD1 bound more efficiently to ISG15-conjugated CtBP1 than non-conjugated CtBP1. On the other hand, binding between CtBP1 and HDAC4 was unaffected by ISG15 modification. Furthermore, ISG15 modification enhanced the transcriptional repression activity of CtBP1 on several target genes related to EMT and apoptosis. These findings suggest that the ISG15 modification of CtBP1 modulates the function and activity of CtBP1 and that CtBP1 ISGylation may provide a new insight for CtBP1-mediated cancers.

AbstractBackground Six Homo naledi early juveniles were recovered from U.W. 101 (Dinaledi Chamber), U.W. 102 (Lesedi Chamber), and U.W. 110 in the Rising Star cave system.Aim This paper develops the information for the H. naledi early juvenile life stage, as defined by a combination of deciduous and permanent dentition, and the eruption of the first permanent molar.Subjects and methods The growing number of young individuals recovered from the Rising Star cave system allows us to gain a better understanding of their variation, or lack thereof, and provides a basis to estimate broad ranges for age at death of the individuals. The individuals are identified and described through craniodental remains and spatial associations.Results and conclusion Our results show that the teeth are remarkably consistent across the localities in their metric and non-metric traits, and our analyses refine previous estimations on dental eruptions with the first permanent molar erupting first in the sequence among permanent teeth.

AbstractThe World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.

In the case of a physical education and sport teacher, the way in which he or she orients his or her mindset can contribute significantly to communicating and working effectively with students and can positively influence the outcomes of the educational process. The aim of this paper is to conduct a comparative analysis of students’ performance on the Y-Balance test, evaluating and comparing their results to identify and understand differences and similarities in functional ability and balance. The objectives of the study: to identify and analyze student balance levels between the experimental group (who played weekly basketball) and the control group (who followed the regular school curriculum); to analyze differences in performance on the Y Balance test between the two groups of students to determine if playing weekly basketball had a significant impact on balance compared to the control group; To differentiate the results of the Y Balance test in the group that practiced basketball during the experimental period (September – December 2023) and the control group that followed the standard school curriculum, in order to highlight possible changes in balance; To analyze and interpret the balance results obtained, providing an understanding of how regular basketball practice can influence students’ balance.

ABSTRACTThis paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.

ABSTRACTIn this paper, a system for automatic word segmentation of Chinese criminal judgment documents is proposed. The system uses a hybrid model composed of fine-tuned BERT (Bidirectional Encoder Representations from Transformers), BiLSTM (Bidirectional Long Short Term Memory) and CRF (Conditional Random Field) for named entity recognition, and introduces a custom dictionary that includes common professional terms in Chinese criminal trial documents, as well as a rule system based on judicial system and litigation procedure related regulations, to further improve the accuracy of word segmentation. BERT uses a deep bidirectional Transformer encoder to pre-train general language representations from large-scale unlabeled text corpora. BiLSTM uses two LSTM networks, one for the forward direction and one for the backward direction, to capture the context from both sides of the input sequence. CRF uses a set of features and weights to define a log-linear distribution over the output sequence. Experimental results show that the proposed system has significantly improved word segmentation accuracy compared to the current commonly used Chinese word segmentation models. In the results of the segmentation of the test data, the F1 scores for jieba, THULAC and the segmentation system proposed in this paper are 85.59%, 87.94% and 94.82%, respectively.

ABSTRACTBreast cancer is a serious disease, and therefore early detection is crucial for successful treatment and patient management. Unfortunately, globally, the number of breast cancer cases is increasing due to various multifaceted factors. It is currently one of the leading causes of cancer deaths in women, worldwide. Cancerous cells in the breast can form lumps that impact the patient’s health, and even seemingly harmless tumors could be fatal if undiagnosed early enough. Fortunately, artificial intelligence techniques have proven effective in detecting diseases, and doctors can therefore use them to effectively and accurately diagnose breast cancer early. This paper explores the use of genetic algorithms, ant colony optimization, and Hybrid Hopfield Neural Network-E2SAT (HHNN-E2SAT) models, for breast cancer prediction. The HHNN-E2SAT models outperform standard algorithms like the Random Forest and Support Vector Machines, achieving over 98% on all performance metrics (i.e. Accuracy, F1-score, Sensitivity, Specificity, and Precision).

ABSTRACTIn human-agent collectives, humans and agents need to work collaboratively and agree on collective decisions. However, ensuring that agents responsibly make decisions is a complex task, especially when encountering dilemmas where the choices available to agents are not unambiguously preferred over another. Therefore, methodologies that allow the certification of such systems are urgently needed. In this paper, we propose a novel engineering methodology based on formal model checking as a step toward providing evidence for the certification of responsible and explainable decision making within human-agent collectives. Our approach, which is based on the MCMAS model checker, verifies the decision-making behavior against the logical formulae specified to guarantee safety and controllability, and address ethical concerns. We propose the use of counterexample traces and simulation results to provide a judgment and an explanation to the AI engineer as to the reasons actions may be refused or allowed. To demonstrate the practical feasibility of our approach, we evaluate it using the real-world problem of human-UAV (unmanned aerial vehicle) teaming in dynamic and uncertain environments.

ABSTRACTScholars often recommend incorporating context into the design of an explainable artificial intelligence (XAI) model in order to ensure successful real-world adoption. However, contemporary literature has so far failed to delve into the detail of what constitutes context. This paper addresses that gap by firstly providing normative and XAI-specific definitions of key concepts, thereby establishing common ground upon which further discourse can be built. Second, far from pulling apart the body of literature to argue that one element of context is more important than another, this paper advocates a more holistic perspective which unites the recent discourse. Using a thematic review, this paper establishes that the four concepts of setting, audience, goals and ethics (SAGE) are widely recognized as key tools in the design of operational XAI solutions. Moreover, when brought together they can be employed as a scaffold to create a user-centric XAI real-world solution.

ABSTRACTNatural Language Processing (NLP) is a technology that permits computers to recognize human languages. Words are the fundamental unit of analysis in deep-level grammatical and semantic analysis. The main goal of NLP is typically word segmentation. Since the machine learning techniques cannot be directly applied to the practical issue of significant structural disparities between various data modalities in a multi-modal context. In this paper, English Vocabulary Learning Aid System Using Digital Twin Wasserstein Generative Adversarial Network Optimized using Jelly Fish Optimization Algorithm is proposed. The problematic of multiple modal data heterogeneity is handled by the feature extraction of Parameterized Local Maximum Synchro squeezing Transform and extract the features such as Phonetic features, sentence length, word embedding’s, part of speech tags, word frequencies, N-grams. Then, the Digital twin Wasserstein generative adversarial network classifies the English vocabulary to easy words, intermediate words, and difficult words. The performance of the proposed EVLS-DtwinWGAN-NLP approach attains 3.101%, 7.12%, 7.73% higher accuracy, 24.13%, 13.04%, 29.51% lower computation Time and 2.292%, 5.365%, 1.551% higher AUC compared with existing methods like Feature extraction and analysis of natural language processing for deep learning English language (EVLS-BiLSTM-NLP), State of art for semantic analysis of natural language processing (EVLS-SA-NLP) respectively.

ABSTRACTThe predictive analysis of solar flux distribution on the receiver surface is critical in optimizing the concentration processes of concentrating solar power (CSP) plants. Due to the difficulties of directly measuring the solar flux distribution of the heliostat field, tracking the Moon and measuring the lunar concentration ratio distribution become a promising option. However, many factors affect the flux distribution of a heliostat field. To obtain an accurate predictive model for the solar flux distribution, we propose a deep-learning method using conditional generative adversarial networks (cGAN) and lunar concentration images. The method can take account of tracking errors of individual heliostats, defects of reflecting surfaces, as well as atmospheric attenuation effects, and has the potential to give a reliable prediction of solar flux distribution. Mathematical relations between the solar flux distribution and the solar concentration ratio distribution are discussed in the paper. Experiments have been designed and carried out with an ordinary heliostat at the Beijing Badaling solar concentrating power station. Experimental results show that the AI-generated solar concentration ratio distributions are very close to the actual solar concentration ratio distributions, demonstrating the feasibility of AI models for the prediction of solar flux distribution.

ABSTRACTTitle blocks are crucial primary data on drawings necessary for the operation of modern manufacturing enterprises. However, their uniform export poses a challenge due to the heterogeneity across multiple sources and the variety of export templates. Therefore, this paper proposes an intelligent extraction method for title block information (TBI) based on fuzzy matching. This method evaluates the geometrical composition of the title block, the logical relationship between information cells, and the diversification of the title block. It establishes a weighted intersection point matrix for the title block as the topological structure model. The fuzzy matching, based on the intersection matrix points, disregards the variations in the size and position of the title block and focuses only on the style that impacts the extraction of information. The matched title blocks are categorized, and the base table of each type determines the information distribution of the title block. A hash table is then created between the description cell and the value cell of information to accumulate the TBI. The method is integrated into the AutoCAD software and presented with an interactive program interface. Finally, an example is presented to further demonstrate the stability, reliability, and efficiency of the method and the system.

ABSTRACTMoney laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature’s gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI).

ABSTRACTShort text semantic similarity is a crucial research area in nature language processing, which is used to predict the similarity between two sentences. Due to the sparsity features of short texts, words are isolated in the sentence and the correlations of words are ignored, it is very difficult to calculate the global semantic information. Based on this, short text semantic graph (STSG) model based on dependency parsing and pre-trained language models is proposed in this paper. It utilizes the syntactic information to obtain word dependency relationships and incorporate it into pre-trained language models to enhance the global semantic information of sentences. So it can address the semantic sparsity more effectively. A text semantic graph layer based on the graph attention network (GAT) is also realized, which regards word vectors as node features and word dependency as edge features. The attention mechanism of GAT can identify the importance of different word correlations and solve the word dependency modeling effectively. On the challenging short text semantic benchmark dataset MRPC, the STSG model achieves an F1-score of .946, which is further improved 2.16% over previous SOTA approaches. At the time of writing, STSG has achieved a new SOTA performance on the MRPC dataset.

ABSTRACTThis paper addresses the challenge of quality characteristics that follow an exponential distribution, which can significantly impact decision-making in various fields. Existing approaches rely on approximations to convert exponential distributions to normal distributions, upon which control charts are constructed. However, such conversions introduce errors that can lead to incorrect outcomes, particularly for highly sensitive characteristics. To address this limitation, we propose the development of control charts specifically designed for exponential characteristics, without relying on approximations. Our objective is to introduce four different schemes for constructing these control charts: a statistical scheme, an economic scheme, an economic-statistical scheme combined with Taguchi’s loss function, and an economic-statistical scheme without the application of a loss function. To determine optimal design parameter values for each scheme, we employ the artificial bee colony algorithm. Additionally, we conduct a sensitivity analysis to investigate the impact of design parameters on each proposed design. To illustrate the practical implementation of these control charts, we provide a numerical example that demonstrates their effectiveness. By addressing the limitations of existing approaches and offering novel control chart designs, this paper contributes to enhancing decision-making accuracy and reliability in scenarios involving exponentially distributed quality characteristics.

ABSTRACTThis paper proposes a new metahaeuristic algorithm named particle swarm optimization and chaotic gravitational search algorithm (PSO-CGSA) for solving the combined economic and emission dispatch (CEED) problem. First, we determine the efficiency and effectiveness measures of the algorithm and compare it with other well-known algorithms. Then, we analyze the obtained solutions using the statistical procedure proposed in the paper. The proposed procedure contains the following: (i) the behavior analysis of the algorithms when solving the CEED problem, using non-parametric tests, and (ii) the ranking of the algorithms using the PROMETHEE/GAIA multi-criteria decision-making method. The behavior analysis is performed for two cases: (i) when solving individual variants of the CEED problem (single-problem analysis) and (ii) when solving a set of CEED variants (multiple-problem analysis). The results of the applied procedure for the test system with six generators show that PSO-CGSA has (i) the best solution for each tested variant of the CEED problem; (ii) the best standard deviation, mean value, error rate, and behavior for the CEED variant with a bi-objective function that simultaneously minimizes fuel cost and emission, taking into account the valve point effect; and (iii) the best rank when solving a set of CEED variants.

ABSTRACTOne of the most important fields in current materials research is the examination of materials through the prism of topological indices. An extensive statistical investigation of topological indices relevant to the characterization of copper(II) fluoride is presented in this paper. Our goal is to use the well-understood structural features of copper(II) fluoride, a chemical that is well known for its crystalline qualities, to uncover a variety of properties inherent in its network. This work makes use of the structural information available for copper(II) fluoride. After a thorough computational investigation, a rigorous statistical analysis is conducted to determine the distributions and correlations between heat of formation and the different topological indices. The findings reveal significant patterns and trends in the copper(II) fluoride network structure, providing insights into the underlying principles governing its material behavior.

ABSTRACTSmart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

ABSTRACTPresent economy is largely dependent on the precise forecasting of the business avenues using the stock market data. As the stock market data falls under the category of big data, the task of handling becomes complex due to the presence of a large number of investment choices. In this paper, investigations have been carried out on the stock market data analysis using various density-based clustering approaches. For experimentation purpose, the stock market data from Quandl stock market was used. It was observed that the effectiveness of Dynamic Quantum clustering approach were better. This is because it has better adopting capability according of changing patterns of the stock market data. Similarly performances of other density-based clustering approaches like Weighted Adaptive Mean Shift Clustering, DBSCAN and Expectation Maximization and also partitive clustering methods such as k-means, k-medoids and fuzzy c means were also experimented on the same stock market data. The performance of all the approaches was tested in terms of standard measures. It was found that in majority of the cases, Dynamic Quantum clustering outperforms the other density-based clustering approaches. The algorithms were also subjected to paired t-tests which also confirmed the statistical significance of the results obtained.

ABSTRACT3D moment invariants are important tools for 3D image feature representation. In this paper, we introduced a novel approach for constructing 3D moment invariants using Gaussian geometric moments. Our proposed method demonstrated invariance under translation, rotation, and scale transformations. The numerical experiments validate the invariance and robustness of the proposed method, comparing it with traditional 3D geometric moments and revealing superior performance in the presence of noise and transformations. Additionally, the method is applied to content-based 3D image retrieval, exhibiting promising results through Minkowski distance-based retrieval on the Princeton Shape Benchmark (PSB) database.

ABSTRACTIn the rapidly advancing realms of computer vision and artificial intelligence, the quest for human-like intelligence is escalating. Central to this pursuit is visual perception, with the human eye as a paragon of efficiency in the natural world. Recent research has prominently embraced the emulation of the human eye’s visual system in computer vision. This paper introduces a pioneering approach, the visually-aware biomimetic network (VBNet), composed of a dual-branch parallel architecture: a Transformer branch emulating the peripheral retina for global feature dependencies and a CNN branch resembling the macular region for local details. Furthermore, it employs feature converter modules (CFC and TFC) to enhance information fusion between the branches. Empirical results highlight VBNet’s superiority over RegNet and PVT in ImageNet classification and competitive performance in MSCOCO object detection and instance segmentation. The dual-branch design, akin to the human visual system, enables simultaneous focus on local and global features, offering fresh perspectives for future research in the field of computer vision and artificial intelligence.

ABSTRACTWith the development of advertising technology, especially the emergence of generative artificial intelligence, the generation and editing of advertising content has become more convenient and efficient. However, this also brings new security risks to digital advertising. For example, in order to generate personalized advertising content, AI can collect user behavior data without the user’s knowledge and automatically generate ads that are beneficial to advertisers. In addition, the main issue regarding outdoor advertising is whether the outdoor advertising platform can deliver the advertising content required by the advertiser at the designated place and time according to the actual requirements of the customer. Combining blockchain and edge computing technology, this paper builds a security-enhanced outdoor advertising platform. The decentralized, non-tamperable and traceable features of blockchain technology provide the advertising platform with credibility between users and advertisers, as well as credibility between advertisers and the advertising platform. At the same time, the terminal controller with edge processing capabilities improves the efficiency of advertising and enhances the task balance between the server and the playback side. Experimental results show that the platform can effectively manage user identities and maintain advertising plans. It not only improves data security and credibility, but also has good performance.

In our present paper, we have employed the Atangana–Baleanu derivative in the multi-layered Navier–Stokes condition to analyse the nature of the flow. Actually, the stream, the impacts and modifications caused by the operator and the mathematical technique approach are the main subjects of our attention. In this exploratory work, the Laplace transform strategy and the Atangana–Baleanu fractional operator were combined. Due to the Atangana–Baleanu operator's convergence in many engineering and technological issues, it is employed. We have also carefully evaluated the presence and uniqueness of the outcome. To visualize the variations in the flow, we have presented the graphs of the stream's velocity components in each direction during the study.

The outbreak of COVID-19 causes a serious threat to human health and life around the world and puts enormous pressure on the healthcare system. The lockdown policy has effectively reduced the number of cases and suppressed the spread of the COVID-19 epidemic, but it also requires high social and economic costs. In this paper, we aim to use feedback control to help decision-makers establish lockdown policies, which can effectively constrain the spread of the COVID-19 pandemic with minimal financial loss. The time-dependent susceptible-infected-removed (SIR) model is used for the dynamics of the COVID-19 pandemic. The feedback control is based on a modified nonlinear active disturbance rejection controller (ADRC) that includes modified nonlinear extended state and nonlinear state error feedback with parameters tuned by particle swarm optimization, and the performances are compared with a well-known proportional-–integral–-derivative (PID) controller. The final simulation results show that the modified nonlinear ADRC has better performances and is more robust against uncertainties in the parameters of the epidemiological model.

ABSTRACTRecently, degenerate harmonic numbers and degenerate hyperharmonic numbers are introduced by Kim-Kim. In this paper, we study the series involving the degenerate harmonic numbers and degenerate Stirling numbers and investigate those properties.

This paper develops a mathematical framework that aims to control the temperature and rotational speed in the activation process of a gas turbine in an optimal way. These controls influence the deformation and the stress in the component due to centripetal loads and transient thermal stress. An optimal control problem is formulated as the minimization of maximal von Mises stress over a given time and over the whole component. To find a solution for this, we need to solve the linear thermoelasticity and the heat equations using the finite element method. The results for the optimal control as functions of the rotation speed and external gas temperature over time are computed by sequential quadratic programming, where gradients are computed using finite differences. The overall outcome reveals a significant reduction of approximately 10%, from [Formula: see text] to [Formula: see text], in von Mises stress by controlling two parameters, along with the temporal separation of physical control phenomena.

In this paper, a modified method is used to approximate the solution to the time-fractional n-dimensional Navier–Stokes equation. The modified method is the Variational Iteration Transform Method, which is implemented in the equation whose fractional order derivative is described in the Caputo sense. The proposed method's findings are presented and examined using figures. It is demonstrated that the proposed method is efficient, dependable, and simple to apply to various science and engineering applications.

The fuzzy stochastic differential equations with Markovian switching are considered. First, under the [Formula: see text] assumptions, the existence and uniqueness theorem for the aforementioned equations is given by means of stopping time techniques, discretization method, and the Gronwall-Bellman inequality. Then the boundedness of the second moment of the solutions for such equations is established. Furthermore, by using the averaging method and stochastic analysis, as well as the Bihari's inequality, this paper mainly demonstrates the asymptotic relationship between the solutions of the original equations and the corresponding averaged equations in terms of mean square and probability. Finally, the theoretical result of the averaging method is illustrated by an example.

In this paper, the existing Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiologic process model is modified for forecasting the coronavirus effect in India. The data from India was studied for weekly fatalities, weekly infected, weekly recovered, new cases, infected and recovered individuals, Reproductive Number [Formula: see text], recovery rate, death rate, and coefficient of transmission from 30 January 2020 to 31 July 2021. SARS Coronavirus 2 (SARS-CoV-2) is the Covid strain that causes Covid sickness (COVID-19), a respiratory ailment that triggered the outbreak of COVID-19 at the beginning of December 2019. We aim to provide a hybrid SIRD model for predicting the COVID-19 outbreak. In the proposed method, to improve the exploration ability of the Grey Wolf Optimizer (GWO) or to avoid stagnation in the swarm, a modified Grey Wolf Optimization Algorithm is used to optimize the initial value of Infected individuals. The modified SIRD model is further applied to get the predicted values. The data is examined on weekly basis to prevent noise. Depending on the fact, that the precise mode of transmission is highly dependent on how and when different precautions such as isolation, confinement, and other preventative measures were implemented, we put together our projections concerning satisfactory speculations based on genuine realities. The experimental results show the various trends observed in the pandemic in terms of number of peaks, increasing trend, decreasing trend, and continuous trend for infected individuals, weekly change in number of cases, weekly deaths, weekly infected, and weekly recoeverd cases of Covid-19. The proposed modified SIRD model could be a valuable tool for assessing the impact of government measures on COVID-19 outbreak.

In this paper, we study the degenerate unipoly-Dedekind sums rising from the type 2 degenerate unipoly Bernoulli functions and degenerate unipoly-Bernoulli functions of arbitrary indices, respectively.

AbstractMersenne primes, renowned for their captivating form as [Formula: see text] have intrigued mathematicians for centuries. In this paper, we embark on a captivating quest to unveil the intricate nature of Mersenne primes, seamlessly integrating methods with the Eight Levels Theorem. Initially, we extend the Eight Levels Theorem and introduce an innovative approach that harmoniously combines arithmetic and differential techniques to compute the coefficients of the polynomial expansions of [Formula: see text] in terms of binary quadratic forms. This endeavor leads us to the genesis of novel polynomial sequences as we scrutinize the coefficients within this expansion. Our research unearths previously uncharted connections between Mersenne numbers and the derivatives of specific polynomial sequences. By forging this linkage, we not only enhance our understanding of Mersenne primes but also bridge the divide between well-established sequences in number theory and differential equations. This broadens the applicability of our findings across diverse scientific domains, revealing fresh avenues for exploration in number theory and beyond. These polynomial bridges serve as conduits between these sequences, unveiling exciting prospects for future research. This interdisciplinary exploration opens up exciting possibilities for the broader implications of Mersenne primes, extending their significance beyond the realm of pure mathematics. In our paper, we also delve into the intriguing influence of the Golden Ratio constant, which unveils segments reminiscent of the beauty found in nature, adding an unexpected dimension to the world of arithmetic. In this harmonious journey, the allure of Mersenne primes resonates through the symphony of mathematical discovery.

AbstractIn a recent paper, Ishfaq Dar (2024), worked on the problem of locating the zeros of quaternion polynomials by introducing various matrix techniques. In this paper, we use those newly developed matrix methods to locate the left eigenvalues of the quaternion companion matrix of the polynomial, which in turn yield various results concerning the location of the zeros of quaternionic polynomials including extensions of the results of Rubinstein and A. Aziz to the quaternionic setting as well.

AbstractThe (3 + 1)-dimensional generalized shallow water equation is a significant mathematical framework for analyzing the dynamic behavior of waves in ocean physics. The purpose of this article is to investigate some more generic soliton solutions of the generalized shallow water-like model in three dimensions. The investigation is conducted utilizing the sophisticated mathematical methodology known as the double variables [Formula: see text] expansion technique. With this approach, we produce new propagating wave solutions for this model in the form of hyperbolic, trigonometric, and rational functions. In addition, we offer two- and three-dimensional graphical representations to help visualize the intricate physical phenomena of the system. We have constructed many soliton solutions, such as kink shape soliton solutions, anti-bell shape solutions, single periodic solutions, singular soliton solutions, and anti-kink shape solutions for different values of the free parameters involved in the obtained solutions. These graphical representations are predicated on certain parameter selections, which facilitate the understanding of the complicated general behavior for this model. Through the presentation of new findings in the field of soliton solutions for the aforementioned equation, this paper offers fresh perspectives and highlights hitherto overlooked aspects of this fascinating mathematical challenge. The paper illuminates new results on soliton solutions with different geometrical structures for the given equation, revealing hitherto overlooked facets of this intriguing mathematical challenge.

AbstractIn this paper, we present two collocation methods utilizing subdivision schemes and Bernstein polynomials as their basis functions to solve Caputo-type fractional differential equations. These methods transform fractional differential equations into systems of linear equations, which can be solved using various suitable techniques. Notably, these methods are versatile, making them applicable to both boundary value problems and initial value problems. To demonstrate their effectiveness, we applied these methods to three test problems of fractional differential equations, including the Bagley-Torvik equation and fractional oscillation equation. Our results reveal that both methods provide a more accurate approximation to the exact solution when compared to various algorithms found in the existing literature, showcasing their efficiency, accuracy, and consistency.

AbstractThis paper aims to introduce a new class of special polynomials called q-modified Laguerre-Appell polynomials. Some definitions and concepts related to this class of polynomials, including generating function and series definition are explored. Also, certain interesting properties related to this class and some of its members are investigated. Furthermore, the class of 2D q-modified Laguerre-Appell polynomials is introduced. Finally, we present certain graphical representations related to some members of these new classes.

AbstractIn this article a deterministic model for Maize Streak Virus Disease (MSVD) using a fractional-order differential equation with the Atangana-Baleanu Caputo-type operator is developed. Focusing on the role of host-to-host transmission, the it is shown that the presence and stability of equilibria depends on maize field carrying capacity and half-saturation constant of susceptible maize. The MSVD-free equilibrium is globally asymptotically stable when the basic reproduction number is below unity. Local stability conditions for endemic equilibria are established using Lyapunov second technique and Routh-Hurwitz criteria. Matrix-based formulae are also presented for determining bifurcation and it is shown that the model exhibits forward bifurcation. Sensitivity analysis reveals the significant impact of the probability of infection between hosts on MSVD spread. A two-point Lagrange interpolation polynomial is developed for numerical solutions of the model, enabling exploration of theoretical findings and assessment of how epidemiological factors influence MSVD propagation. The paper contributes a comprehensive understanding of the disease's behavior under various conditions.

AbstractIn this paper, we delve into the analysis of the existence and stability concerning the [Formula: see text]-Mittag-Leffler-Ulam-Hyers within a particular class of coupled systems related to boundary value problems. These problems entail implicit nonlinear fractional differential equations and [Formula: see text]-generalized ϑ-Hilfer fractional derivatives. To achieve our objectives, we employ the Mönch fixed point theorem, complemented by the application of the measure of noncompactness technique and an extension of the well-established Gronwall inequality. Furthermore, we include an illustrative example to showcase the practical utility of our results. The significance of our research lies in examining a comprehensive problem involving coupled systems, which serves as a generalization encompassing all the works mentioned in the introduction. It is viewed as a logical extension and continuation within the framework of this continually evolving theory.

ABSTRACTObjective Physical inactivity and long hours being sedentary are well documented as behaviours that contribute to ill health. Such behaviours are prevalent among office workers, who are at risk of diseases like metabolic dysfunction, reduced bone mineral density and heart disease. Using a theory of planned behaviour belief-based approach, the current paper qualitatively explores the behavioural, normative and control beliefs held by sedentary office workers towards physical activity within the work environment.Method Participants included 43 full-time office workers, who self-report as having a highly sedentary job and work from either a commercial office, home office, or a combination. The study used a semi-structured interview design following the theory of planned behaviour belief elicitation guidelines, and data analysed using a theoretical thematic analysis approach.Results Participants reported advantages of stress relief, improved focus and productivity and increased positive mood with moving. Disadvantages included feelings of guilt and frustration due to work disruptions from moving more. Mixed reports for others’ approval and disapproval were identified. Office layout was a commonly identified facilitator of workplace movement, with job restrictions identified as a common barrier.Conclusions Findings highlight the importance of workplace culture, organisational support and office layout as key considerations for future interventionalists.