In the dynamic landscape of mobile technology, where a myriad of options burgeons, compounded by fluctuating features, diverse price points, and a plethora of specifications, the task of selecting the optimum mobile phone becomes formidable for consumers. This complexity is further exacerbated by the intrinsic ambiguity and uncertainty characterizing consumer preferences. Addressed herein is the deployment of fuzzy hypersoft sets (FHSS) in conjunction with machine learning techniques to forge a decision support system (DSS) that refines the mobile phone selection process. The proposed framework harnesses the synergy between FHSS and machine learning to navigate the multifaceted nature of consumer choices and the attributes of the available alternatives, thereby offering a structured approach aimed at maximizing consumer satisfaction while accommodating various determinants. The integration of FHSS is pivotal in managing the inherent ambiguity and uncertainty of consumer preferences, providing a comprehensive decision-making apparatus amidst a plethora of choices. The elucidation of this study encompasses an easy-to-navigate framework, buttressed by sophisticated Python codes and algorithms, to ameliorate the selection process. This methodology engenders a personalized and engaging avenue for mobile phone selection in an ever-evolving technological epoch. The fidelity to professional terminologies and their consistent application throughout this discourse, as well as in subsequent sections of the study, underscores the meticulous approach adopted to ensure clarity and precision. This study contributes to the extant literature by offering a novel framework that melds the principles of fuzzy set (FS) theory with advanced computational techniques, thereby facilitating a nuanced decision-making process in the realm of mobile phone selection.
In general, a stable and strong system shouldn't have an overly sensitive/dependent response to inputs (unless consciously and planned desired), as this would reduce efficiency. As in other techniques, approaches, and methodologies, if the results are excessively affected when the input parameters change in MCDM methods, this situation is identified with sensitivity analyses. Oversensitivity is generally accepted as a problem in the MCDM (Multi-Criteria Decision Making) methodology family, which has more than 200 members according to the current literature. The MCDM family is not just a weight coefficient-sensitive methodology. MCDM types can also be sensitive to many different calculation parameters such as data type, normalization, fundamental equation, threshold value, preference function, etc. Many studies to understand the degree of sensitivity simply monitor whether the ranking position of the best alternative changes. However, this is incomplete for understanding the nature of sensitivity, and more evidence is undoubtedly needed to gain insight into this matter. Observing the holistic change of all alternatives compared to a single alternative provides the researcher with more reliable and generalizing evidence, information, or assumptions about the degree of sensitivity of the system. In this study, we assigned a fixed reference point to measure sensitivity with a more robust approach. Thus, we took the distance to the fixed point as a base reference while observing the changeable MCDM results. We calculated sensitivity to normalization, not just sensitivity to weight coefficients. In addition, past MCDM studies accept existing data as the only criterion in sensitivity analysis and make generalizations easily. To show that the model proposed in this study is not a coincidence, in addition to the graphics card selection problem, an exploratory validation was performed for another problem with a different set of data, alternatives, and criteria. We comparatively measured sensitivity using the relationship between MCDM-based performance and the static reference point. We statistically measured the sensitivity with four types of weighting methods and 7 types of normalization techniques with the PROBID method. The striking result, confirmed by 56 different MCDM ranking findings, was this: In general, if the sensitivity of an MCDM method is high, the relationship of that MCDM method to a fixed reference point is low. On the other hand, if the sensitivity is low, a high correlation with the reference point is produced. In short, uncontrolled hypersensitivity disrupts not only the ranking but also external relations, as expected.
In Indonesian manufacturing, the evasion of tax obligations presents a formidable challenge, diminishing the potential tax revenues accruing to the state. Rooted in agency theory, this investigation seeks to empirically elucidate the interrelations between corporate social responsibility (CSR), profitability, leverage, capital intensity, and corporate tax aggressiveness, with an emphasis on the moderating influence of firm size. Through a causal design and quantitative analysis, this examination scrutinizes data from 66 manufacturing entities listed on the Indonesia Stock Exchange over the period 2018 to 2022. The analysis, employing panel data regression techniques, demonstrates that CSR exerts a negative influence on tax aggressiveness, whereas profitability and capital intensity are positively associated with such behavior. Leverage, however, is not found to significantly affect tax aggressiveness. Furthermore, firm size is observed to negatively moderate the relationship between CSR and tax aggressiveness while positively moderating the relationship between both profitability and capital intensity with tax aggressiveness. The moderating effect of firm size on the leverage-tax aggressiveness nexus, however, remains non-significant. These findings underscore the complex dynamics influencing tax aggressiveness and suggest a need for stringent regulatory oversight and enforcement against aggressive tax avoidance tactics deployed by manufacturing firms. Recommendations include the establishment of clearer definitions of unauthorized tax avoidance practices, the imposition of severe penalties for non-compliance, and the enhancement of international collaboration to combat tax avoidance. This study not only contributes to the scholarly discourse on tax aggressiveness but also offers pragmatic insights for policymakers aimed at curtailing practices that undermine state revenue.
In this study, the FLAC3D finite difference numerical software was employed to simulate a geotechnical engineering project, establishing scenarios with concrete and steel pipe piles for support simulation. The analysis focused on the reinforcement effects provided by different types of piles on the geotechnical project. It was found that the reinforcement effects on the soil varied significantly between the pile types. Under the support condition of concrete piles, the maximum soil settlement observed was 4.12 mm, with a differential settlement of 3.19 mm. For steel pipe piles, the maximum soil settlement was reduced to 2.38 mm, with a differential settlement of 2.19 mm, indicating a superior support effect compared to that of concrete piles. Stress concentration phenomena were observed in the piles, becoming more pronounced when pile-soil friction was considered. The substitution of concrete piles with steel pipe piles led to an intensified stress concentration phenomenon in the soil surrounding the piles. The soil undergoing support from concrete piles exhibited the largest plastic deformation, whereas soil supported by steel pipe piles showed less plastic deformation. Consequently, it is concluded that steel pipe piles provide a superior support effect over concrete piles in terms of geotechnical engineering reinforcement.
In the evolution of blockchain technology, the traditional single-chain structure has faced significant challenges, including low throughput, high latency, and limited scalability. This paper focuses on leveraging multichain sharding technology to overcome these constraints and introduces a high-performance carbon cycle supply data sharing method based on a blockchain multichain framework. The aim is to address the difficulties encountered in traditional carbon data processing. The proposed method involves partitioning a consortium chain into multiple subchains and constructing a unique “child/parent” chain architecture, enabling cross-chain data access and significantly increasing throughput. Furthermore, the scheme enhances the security and processing capacity of subchains by dynamically increasing the number of validator broadcasting nodes and implementing parallel node operations within subchains. This approach effectively solves the problems of low throughput in single-chain blockchain networks and the challenges of cross-chain data sharing, realizing more efficient and scalable blockchain applications.
Named Entity Recognition (NER), a pivotal task in information extraction, is aimed at identifying named entities of various types within text. Traditional NER methods, however, often fall short in providing sufficient semantic representation of text and preserving word order information. Addressing these challenges, a novel approach is proposed, leveraging dual Graph Neural Networks (GNNs) based on multi-feature fusion. This approach constructs a co-occurrence graph and a dependency syntax graph from text sequences, capturing textual features from a dual-graph perspective to overcome the oversight of word interdependencies. Furthermore, Bidirectional Long Short-Term Memory Networks (BiLSTMs) are utilized to encode text, addressing the issues of neglecting word order features and the difficulty in capturing contextual semantic information. Additionally, to enable the model to learn features across different subspaces and the varying degrees of information significance, a multi-head self-attention mechanism is introduced for calculating internal dependency weights within feature vectors. The proposed model achieves F1-scores of 84.85% and 96.34% on the CCKS-2019 and Resume datasets, respectively, marking improvements of 1.13 and 0.67 percentage points over baseline models. The results affirm the effectiveness of the presented method in enhancing performance on the NER task.
The cold chain industry plays a pivotal role in ensuring the quality and safety of temperature-sensitive products throughout their journey from production to consumption. Central to this process is the effective monitoring of temperature fluctuations, which directly impacts product integrity. With an array of temperature monitoring devices available in the market, selecting the most suitable option becomes a critical task for organizations operating within the cold chain. This paper presents a comprehensive analysis of seven prominent temperature monitoring devices utilized in the cold chain industry. Through a systematic evaluation process, each device is rigorously assessed across six key criteria groups: price, accuracy, usability, monitoring and reporting capabilities, flexibility, and capability. A total of 23 independent metrics are considered within these criteria, providing a holistic view of each device's performance. Building upon this analysis, a robust decision support model is proposed to facilitate the selection process for organizations. The model integrates the findings from the evaluation, allowing stakeholders to make informed decisions based on their specific requirements and priorities. Notably, the Chemical Time Temperature Integrators (CTTI) emerge as the top-ranked device, demonstrating superior performance across multiple criteria. The implications of this research extend beyond device selection, offering valuable insights for enhancing cold chain efficiency and product quality. By leveraging the decision support model presented in this study, organizations can streamline their temperature monitoring processes, mitigate risks associated with temperature excursions, and ultimately optimize their cold chain operations. This study serves as a foundation for further research in the field of cold chain management, paving the way for advancements in temperature monitoring technology and strategies. Future studies may explore additional criteria or expand the analysis to include a broader range of devices, contributing to ongoing efforts aimed at improving cold chain sustainability and reliability.
This study explores the spatial accessibility of high-tech health services across municipalities on the Spanish Iberian Peninsula, focusing on the adequacy of service provision by haemodynamic facilities relative to potential demand. A comprehensive analysis utilising a Geographic Information System (GIS) was conducted to evaluate the spatial distribution of high-tech health services, employing the enhanced two-step floating catchment area (E2SFCA) method within a gravity model framework. Findings reveal a disparity in health service coverage, with peripheral municipalities in the larger Autonomous Communities exhibiting low to very low access to high-tech health services. Despite this, the majority of the population benefits from satisfactory health coverage. The study underscores the importance of improving health service accessibility in underserved areas through infrastructural enhancements or the establishment of new facilities, advocating for equitable health service distribution in line with principles of social justice. The methodology proposed herein serves as a valuable tool for health policymakers in addressing spatial inequities in health service provision. Through the lens of territorial accessibility and spatial planning, the research highlights the critical role of high-tech health infrastructure in ensuring comprehensive health coverage. The results advocate for targeted interventions to enhance health service accessibility, particularly in sparsely populated areas at the periphery of large communities, thereby contributing to the broader discourse on health equity and spatial justice in healthcare planning.
In the realm of Wireless Sensor Networks (WSNs), energy efficiency emerges as a paramount concern due to the inherent limitations in the energy capacity of sensor nodes. The extension of network lifespan is critically dependent on the strategic selection of Cluster Heads (CHs), a process that necessitates a nuanced approach to optimize communication, resource allocation, and network performance overall. This study proposes a novel methodology for CH selection, integrating Multiple Criteria Decision Making (MCDM) with the K-Means algorithm to facilitate a more discerning aggregation and forwarding of data to the network sink. Central to this approach is the application of the Einstein Weighted Averaging Aggregation (EWA) operator, which introduces a layer of sophistication in handling the uncertainties inherent in WSN deployments. The efficiency of CH selection is vital, as CHs serve as pivotal nodes within the network, their selection and operational efficiency directly influencing the network's energy consumption and data processing capabilities. By employing a meticulously designed clustering process via the K-Means algorithm and selecting CHs based on a comprehensive set of parameters, including, but not limited to, residual energy and node proximity, this methodology seeks to substantially enhance the energy efficiency of WSNs. Comparative analysis with the Low-Energy Adaptive Cluster Hierarchy (LEACH)-Fuzzy Clustering (FC) algorithm underscores the efficacy of the proposed approach, demonstrating a 15% improvement in network lifespan. This advancement not only ensures optimal utilization of limited resources but also promotes the sustainability of WSN deployments, a critical consideration for the widespread application of these networks in various fields. The findings of this study underscore the significance of adopting sophisticated, algorithmically driven strategies for CH selection, highlighting the potential for significant enhancements in WSN longevity through methodical, data-informed decision-making processes.
To understand the mechanism of innovative work behaviour (IWB) in China’s higher education. With a total of 495 valid responses from six universities in China, this study utilised Amos26 for data analysis. The structural equation model indicates that organisational politics (OP) significantly influences academics’ knowledge sharing behaviour (KSB) (β = -0.220, p < 0.000) and IWB (β = -0.126, p < 0.005). The mediating effect of knowledge sharing is confirmed (β = -0.193, p < 0.003). This study confirms the detrimental effect of OP on KSB and IWB within Chinese high education institutions. Consequently, to foster innovation among academics, management should consider controlling OP within the organisational environment. Standardising the supervision and management of executive power, ensuring that administrative power operates transparently. Additionally, delineating between OP and non-OP behaviours will mitigate the negative impact of OP on innovation.
The escalating migration from rural to urban locales necessitates an augmented demand for the workforce, local utility services, and mechanization to sustain a balance conducive to public health. This investigation delineates the pivotal role of human resources in executing daily operations required for the upkeep of public green and asphalted areas within Doboj, Bosnia and Herzegovina. It is posited that teamwork and the requisite competencies of the workforce are integral to the utility company’s efficacy and the establishment of conditions requisite for addressing business tasks delineated on weekly and monthly schedules. A cohort of 20 personnel, tasked with the aforementioned responsibilities, was segmented into three categories, predicated upon their skills and capability to fulfil the designated tasks within specified temporal bounds. A novel hybrid Multi-Criteria Decision-Making (MCDM) model, integrating Improved fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA) with Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), was employed to appraise employees across the designated categories. Decision-makers articulated five criteria, which were quantified via the IMF SWARA methodology. Subsequently, the appraisal of worker categories through three discrete models was undertaken employing the MARCOS technique. Outcomes for each category were individually derived and subjected to verification tests, revealing that criterion significance markedly influences human resource ranking. This study underscores the crucial intersection between environmental stewardship and human resource management, advocating for a systematic approach to urban maintenance that leverages MCDM techniques to optimize workforce performance.
In recent years, many researchers have studied some complex phenomena in the world from a new angle, and the subject of complexity science was born. In the field of complexity research, the study of social economics is very common. Because of the characteristics of financial market and its position in social economy, it is very important in complexity research. Based on complex network theory and vector autoregressive model (VAR) study of the stock market fluctuation and discusses the conduction effect between stock volatility in the industry on the influence of the correlation of share price volatility, aims to better understand the operating mechanism of the stock market, the more effective controlling the market development direction, to promote the healthy development of China's financial markets. In this paper, the rise and fall of stocks in 11 industries in the CSI 300 industry index are selected as variables, and the data from January 1, 2024, to March 1, 2024, are selected as research samples. Granger test and vector autoregressive model are used to calculate the conduction benefits between different industries. The complex network is constructed with industry as node and stock conduction direction as edge. Based on the analysis, we can find that in the CSI 300 industry index, there are several industries as the influence center of volatility, and their stock price fluctuations will affect the market of related industries, thus affecting the whole body. Based on the market situation of related industries in recent years, the correlation of fluctuations of different stocks in the stock market can be explained, and the influence of industry factors on stock price fluctuations can be deeply explored.
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