With rapid urbanization, issues such as blind planning, disorder, and inefficiency in urban construction and land use have become increasingly prominent. To address these challenges, this study proposes a comprehensive suitability evaluation framework for urban construction land, using Zhengzhou City as a case study. The evaluation system incorporates five dimensions: topography, transportation, location, current land use status, and soil clay content. A hybrid weighting method, combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM), was employed to determine indicator weights. The research indicates that the suitability of the construction land can be classified into four categories: highly suitable, moderately suitable, critically suitable, and unsuitable. Among them, the highly suitable area accounted for 6.907% (502.71 km2), the moderately suitable area accounted for 81.668% (5943.54 km2), the critically suitable area accounted for 11.422% (830.98 km2), and the unsuitable area only accounted for 0.003% (0.18 km2). The results show that most areas in Zhengzhou City are highly suitable or moderately suitable for construction land, while Gongyi and Dengfeng, due to their complex terrain and long distances from the city center, are mostly in the critically suitable or unsuitable construction land. This evaluation result is in good agreement with the actual situation and can offer valuable insights for sustainable urban development.

This paper proposes a novel dynamical systems approach to model and optimize Takaful insurance operations, contributing to the growing body of research in Islamic finance. To the best of our knowledge, this is the first study to formalize the interactions between the three core components of Takaful—participants, claims, and the mutual fund—within a continuous-time dynamical framework. The model integrates key operational parameters such as enrollment and attrition rates, claim frequency, contribution levels, and profit-and-loss sharing mechanisms. We first establish the mathematical well-posedness of the system, proving existence, uniqueness, positivity, and boundedness of solutions, followed by a stability analysis of equilibrium points supported by numerical simulations. Building on this foundation, we formulate a multi-objective optimization problem to address the strategic goals of Takaful operators: maximizing participant retention, minimizing claim incidence, and ensuring fund stability. The problem consists in determining the optimal values for the attrition rate, claim occurrence rate, and average contribution, subject to realistic operational constraints. We solve this problem using an integrated NSGA-II and entropy weighting approach, enabling robust trade-off analysis between conflicting objectives. The proposed methodology offers practitioners a quantitative decision-support tool for enhancing membership strategies and risk management while maintaining financial sustainability in accordance with Sharia-compliant principles.

Purpose This study aims to examine the impact of financial inclusion through Islamic banking and fintech-driven digital financial services on sustainable energy access (SEA) in the Middle East and North African (MENA) region. Despite the region’s abundant energy resources, persistent challenges of energy poverty remain. This study explores how complementary financial mechanisms can address financing gaps in clean energy initiatives. Design/methodology/approach This study explores the influence of Islamic banking and fintech on energy poverty in the MENA region. Using the entropy weight approach, the authors uniquely construct composite SEA indices for individual MENA countries and ascertain how these SEAs are influenced by Islamic banking (FI1) and fintech-driven financial inclusion (FI2). Findings The findings reveal an average SEA value of 0.25, indicating that MENA countries face energy poverty. The analysis shows the influence of banking infrastructure and Islamic financial services on SEA in the Islamic MENA region, highlighting the complexity of the issue. From an aggregate view, both aspects of financial infrastructure improve SEA. The results show that unemployment, trade openness, urbanization and inflation significantly predict SEA. Originality/value The findings apply to the Islamic MENA region because of the larger sample of countries than the single-country studies primarily found in the relevant literature. The findings underscore the complementary roles of Islamic banking and fintech-based financial inclusion in addressing energy poverty, presenting essential implications for policymakers and governments in the MENA region.

Studies have discovered that the virtual financial system performs a advantageous role in selling monetary improvement. in the meantime, the digital industrialization and industrial digitalization have given upward thrust to new demands and deliver modes of intake. it's miles important to investigate the function of intake enlargement inside the effect of digital economic system on monetary improvement. based totally on Indian provincial panel information, we first applied the entropy weight approach to assemble the digital economy index and the monetary improvement index. And in this basis, it become demonstrated that the digital economic system can undoubtedly sell the improvement of the inverted Ufashioned structural financial system. Then we one at a time used the mediation and the edge impact models to investigate the role of intake growth in empowering development within the digital economic system. regional heterogeneity turned into similarly taken into account. The effects dedicate that client demand and the virtual financial system have a partial or entire mediating impact. The promoting of improvement through the virtual economic system may be affected by the brink of consumption expansion, which is manifested inside the marginal incremental impact due to the increase of intake deliver. at the contrary, the increase of client call for has caused the inverted U-shape of the digital financial system to promote monetary development. in the heterogeneity evaluation, the edge effect additionally various substantially. This studies enriches the theoretical achievements and exhibits the impact of intake expansion at the virtual economy affecting the improvement, which might also provide sure reference for other international locations and areas

Purpose This study aims to investigate the coupling coordination relationship between new infrastructure construction (NIC) and high-quality development (HQD) from the perspective of urban agglomerations, analyse its spatial-temporal evolution characteristics and reveal the underlying driving mechanisms. Design/methodology/approach This study focuses on the Shandong Peninsula urban agglomeration. By creating an indicator system for NIC and HQD, the study explores the coupling coordination degree (CCD) by employing the entropy weight approach and CCD model. Furthermore, it analyses the spatial-temporal evolution characteristics and influencing factors by using nonparametric kernel density estimation, the Dagum Gini coefficient and the Tobit panel regression model. Findings 1) Over time, the developmental levels of NIC and HQD within urban agglomerations and their CCD have shown a steady upward trend. (2) In terms of spatial distribution, the CCD between NIC and HQD within urban agglomerations exhibits a “core-periphery” structure. (3) The CCD of NIC and HQD is influenced by various factors. Originality/value Current research mainly emphasises national and provincial levels, exhibiting a paucity of studies centred on urban agglomerations. Most studies emphasise the effect of NIC on specific fields, indirectly exploring its role in HQD. However, a systematic understanding of the overall development trends of both is lacking. This study provides quantitative support for policies aimed at building a modern NIC system and advancing HQD collaboratively within urban agglomerations.

The sustained growth of Chengdu's urban tourism economy has brought the relationship between tourism and ecological environment into sharp focus, becoming a key issue in both scholarly research and public policy discourse. In order to promote the positive interaction between the two, this study constructs a set of index system to evaluate the coupled and coordinated development of tourism economy and ecological environment based on the theory of ecological economics, coupled and coordinated theory and sustainable development theory, and based on the actual development background of Chengdu city. This study employed the entropy weighting approach to quantitatively measure the development status of the tourism economy and ecological environment in Chengdu during the period from 2013 to 2022. Subsequently, a coupling coordination model was utilized to examine their interrelationship and the level of coordinated development. The findings indicate that Chengdu's tourism economy and ecological environment have experienced continuous improvement in their comprehensive benefit index over the past decade. This development trajectory can be categorized into three distinct phases: a period of rapid growth, a phase of fluctuation and adjustment, and a stage of relative stability. The observed coupling and coordination levels between the tourism sector and ecological environment demonstrate clear phase characteristics, underscoring the presence of a tightly interwoven and interactive relationship. Specifically, economic indicators such as tourism value-added exert a positive influence on the ecological environment, whereas certain ecological indicators impose constraints on the further enhancement of the coupling and coordination level. These findings offer theoretical support and policy-oriented recommendations for advancing the integrated development of Chengdu's tourism economy and ecological environment.

Domain adaptation is a key approach to ensure that artificial intelligence models maintain reliable performance when facing distributional shifts between training (source) and testing (target) domains. However, existing methods often struggle to simultaneously preserve domain-invariant representations and discriminative class structures, particularly in the presence of complex covariate shifts and noisy pseudo-labels in the target domain. In this work, we introduce Conditional Rényi α-Entropy Domain Adaptation, named CREDA, a novel deep learning framework for domain adaptation that integrates kernel-based conditional alignment with a differentiable, matrix-based formulation of Rényi’s quadratic entropy. The proposed method comprises three main components: (i) a deep feature extractor that learns domain-invariant representations from labeled source and unlabeled target data; (ii) an entropy-weighted approach that down-weights low-confidence pseudo-labels, enhancing stability in uncertain regions; and (iii) a class-conditional alignment loss, formulated as a Rényi-based entropy kernel estimator, that enforces semantic consistency in the latent space. We validate CREDA on standard benchmark datasets for image classification, including Digits, ImageCLEF-DA, and Office-31, showing competitive performance against both classical and deep learning-based approaches. Furthermore, we employ nonlinear dimensionality reduction and class activation maps visualizations to provide interpretability, revealing meaningful alignment in feature space and offering insights into the relevance of individual samples and attributes. Experimental results confirm that CREDA improves cross-domain generalization while promoting accuracy, robustness, and interpretability.

Former explorations have revealed that laser shock peening can enhance the integrity of the carbide surface. This study aims to examine the effect of laser shock parameters on WC-Co cemented carbide surface integrity and a comparative analysis of carbide protrusion. An experimental design is conducted to quantify the impact of relevant process parameters. Surface roughness and residual stress have been employed to assess the surface integrity parameters. Additionally, the TOPSIS entropy weight approach has been utilized to analyze the comprehensive evaluation of the surface integrity by the coupling effect of multiple laser parameters. The results indicate that the laser parameters substantially affect the surface quality and are the determining factor of the laser power density. The constrained layer protects the carbide surface well and gives it better surface integrity. The laser power density directly influences the thermal effect of the material, making the WC particles progressively more prominent and increasing the surface roughness. At the same time, the surface roughness and residual stresses coupled with multiple parameters are analyzed using the TOPSIS entropy weight approach. The results highlight that the relatively best WC-Co carbide strengthening effect is achieved during laser processing, with laser power density of 3.98 J/cm2, scanning speed of 400 mm/s, and impact number of 2 times.

Wildlife protection is a critical issue in today’s world, yet determining the most effective human interventions remains challenging. To enhance wildlife conservation planning, this paper establishes a mathematical model based on the TOPSIS method with an entropy weight approach. By analyzing data from 15 organizations, we found that WWF achieved a standardized score of 0.3741, indicating the most significant contribution to wildlife protection. Additionally, the Spearman correlation coefficient was used to assess the interactions among various factors and conduct a correlation analysis of different indicators. Finally, the correlation coefficient between protected areas and protected species was compared with WWF’s findings. It was observed that some discrepancies existed between the conclusions derived from the Spearman correlation analysis and actual outcomes. This analysis highlights the complexities of wildlife protection and the importance of precise data-driven approaches.

The synergistic advancement of industrial and innovation ecosystems plays a crucial role in bolstering regional economic competitiveness and fostering sustainable growth, which contributes to achieving superior development outcomes. This research examines Guangxi's development trajectory during the 2013-2022 period by establishing a comprehensive assessment framework for chain integration. The methodology incorporates three principal analytical tools: the entropy weighting approach for objective measurement, coupled system modeling for interaction evaluation, and coordination degree assessment for developmental alignment. Furthermore, constraint factor analysis identifies key impediments to integration. Empirical findings demonstrate that while Guangxi's dual-chain convergence exhibits progressive improvement, the current integration maturity remains at an initial stage. Diagnostic results highlight several primary constraining elements, including regional technology diffusion capacity, innovation output commercialization efficiency, spatial concentration of manufacturing enterprises, industry-academia-research partnership intensity, and cluster-based economic productivity. These evidence-based insights offer valuable theoretical foundations and practical guidance for optimizing chain integration strategies in regional development contexts.

Agro-tourism has been developing rapidly in China and globally, but agro-tourism development is deeply affected by the frequency of extreme weather in the region and other constraints on tourism development. Based on the above viewpoints, this paper synthesizes the frequency of extreme weather and the key factors of regional tourism development, and discusses the development of agritourism layout in mainland China. Firstly, based on ARIMA time series model to construct the risk assessment system of agro-tourism industry affected by extreme weather, to predict the boundary value of the risk of damage to agro-tourism industry caused by natural disasters, to provide reference for the development of agro-tourism industry. Secondly, the Spearman-entropy weighting method model is used to determine the weight of each influencing factor by combining the six key factors restricting the development of agro-tourism industry. Finally, the Gaussian mixed clustering model combines the extreme weather risk assessment index and the weights of each influencing factor to derive the results of the assessment of the development of agro-tourism in the Chinese region, which further determines the feasibility of the development of the region, and provides inspiration and basis for the development of agro-tourism.

The assessment of a firm’s economic health is critical and is the cornerstone of sound corporate control and informed investment choices. This paper ambitions to cope with the restrictions of conventional monetary evaluation strategies, which frequently hostilities to seize the dynamic and complicated nature of economic statistics. Our modern approach combines the entropy weight approach with fuzzy common sense to provide a greater nuanced and complete assessment of corporate economic situations. The entropy weight method efficiently quantifies the significance of numerous monetary signs, accordingly decreasing the subjectivity usually encountered in conventional evaluations. however, its reliance on quantitative records can occasionally oversimplify complex monetary states. Fuzzy logic, on the other hand, lets in for a qualitative interpretation of economic indicators, supplying a greater rounded perspective. with the aid of integrating these two methodologies, our method leverages the entropy weight technique to envision the significance of every economic indicator, even as fuzzy logic gives a cultured assessment of these indicators. Experimental effects confirm the superiority of this hybrid method, showcasing its potential to yield greater accurate and robust evaluations of company monetary fitness as compared to traditional methods. This provides the field of economic evaluation with an advanced but practical analytical tool specifically designed to cope with the complexity of monetary facts and to provide insightful and actionable checks.

In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors. Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. Finally, subject to constraints in the model uncertainties, equipment capacities, and energy balance, a multi-objective stochastic optimization model is formulated to minimize gas loss, steam loss, and operating costs. The entropy weight approach is then applied to filter the Pareto front solution set, selecting a final optimal solution with minimal subjectivity and preferences. Case studies using Aspen Hysys-based simulations show that optimization solutions considering model uncertainties outperform the counterparts from a standard deterministic optimization in terms of executability.

Purpose China is experiencing an economic revolution focused on reducing carbon emissions (CEs). Various technological research and development (R&D) frameworks also hasten the growth of the digital economy, which then fuels this economic revolution. Nevertheless, several correlation uncertainties in China have been observed between R&D investment and CE reduction with green economic transformation. This phenomenon is attributed to insufficient spatial impact considerations. Design/methodology/approach Therefore, this article explored the spatial impacts of the digital economy and R&D expenditures regarding environmental quality using Chinese-related panel data between 2012 and 2021. This study uses the Moran I index to test whether there is a spatial relevance between regional carbon emissions in China and assess the digital economic advancement level using the entropy weight approach. In addition, this article analyzes the direct and indirect impacts following the partial differentiation approach, and then creates an interaction term between the digital economy and R&D investment to assess the moderating effect for examining the influence of investing in R&D on reducing CO2 levels of the digital economy. Findings A positive spatial relevance between the digital economy and CEs was then highlighted from the empirical findings. The digital economy expansion also demonstrated higher local CEs while negatively impacting nearby regions. Notably, the digital economy concurrently lowered and increased local CEs in the Eastern and Central zones, respectively. Overall, a larger R&D investment directly impacted the capacity of the digital economy in decreasing the carbon emission intensity (CEI) at a regional level. An accelerated digital economy expansion and lower CEI were recorded in the Eastern zone owing to more significant R&D investments. Research limitations/implications China has gradually shifted its focus from reducing CEs to implementing “dual control of carbon” to achieve the “dual carbon” target. Future studies should then involve additional studies concerning the impact mechanism and path selection related to “dual carbon control.” Practical implications Investment in R&D plays a key role in reducing carbon emissions from the digital economy. By fostering innovation and technological advances, R&D investment activities can create more energy-efficient digital infrastructures, develop sustainable practices and optimize resource use. In addition, these R&D investments can facilitate the transition to renewable energy sources, enhance data management systems to minimize waste and promote the adoption of green technologies by businesses and consumers. As the digital economy continues to evolve, prioritizing R&D in this area is critical to achieving long-term sustainable development goals and addressing the pressing challenges of climate change. Stakeholders across industries must therefore recognize the importance of investment in research and development as a strategic approach that not only drives economic growth but also ensures environmental stewardship in an increasingly digital world. Social implications Investments in research and development not only foster innovation and technological progress, but also promote sustainable practices, which can have significant environmental benefits. In addition, they have the potential to create new jobs, improve public health through better air quality and drive economic growth in a manner consistent with climate goals. As society becomes increasingly dependent on digital solutions, it is critical to harness the power of the digital economy to achieve a more sustainable and inclusive society. Originality/value Research development investment is critical to all aspects of regulation. Research on R&D investment can provide direction to local governments in formulating digital economy policies and can be beneficial to local governments in considering regional differences in resource availability. The research and technical innovation strategies in the policies for developing the digital economy can substantially expedite carbon neutrality achievement by 2060.

PurposeIn many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.Design/methodology/approachThe proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.FindingsUsing the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.Research limitations/implicationsFirst of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.Originality/valueThis article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Background/Objectives: In regions of Africa with a high prevalence of malaria, pregnant women in their first or second trimester should be administered intermittent preventive treatment in pregnancy (IPTp). However, infants may contract malaria despite the IPTp therapy that their mothers have received. The objective of the present investigation was to assess the symptoms and various treatments for neonatal malaria. Methods: Entropy weight and TOPSIS were used to achieve the study goal. The TOPSIS multi-attribute decision-making system was used to assess newborn malaria symptoms and select the optimal treatment, even for mothers receiving IPTp medication during pregnancy. The entropy weight approach calculated TOPSIS attribute weights. The present research used UNICEF data for 14 African nations in 2023. Results: The results indicated that neonates whose mothers received IPTp therapy ultimately contracted malaria, with diarrhea being the primary symptom. It is important to note that health providers administer a combination of zinc and oral rehydration solution (ORS) to infants as the most effective treatment for malaria symptoms, thereby abandoning the first-line treatment for malaria, artemisinin-based combination therapy (ACT). Conclusions: The most effective treatment for neonatal malaria is a combination of zinc and ORS, although less than half of children in Africa have access to ORS. Therefore, the findings of this study may encourage African countries to prioritize co-pack therapy in their procurement and supply, healthcare provider training, and expenditures. This therapy will also help alleviate the symptoms of malaria in neonates.

Crop productivity relies on the quantity and quality of irrigation water, with groundwater quality playing a major role. Exploitation of groundwater for irrigation is slowly increasing in central districts of Odisha. A study was carried out in these areas to assess the groundwater quality for irrigation for sustainable use of the groundwater resources. This was done by developing a Water Quality Index (WQI) for eight parameters. The groundwater quality was assessed using a framework that utilised the entropy weight approach and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This framework was employed to determine the relative proximity of each solution to the ideal solution. The TOPSIS range is divided into three categories: low quality (less than 0.1), medium quality (0.1–0.5), and good quality (0.5–0.9). The low-quality category has a percentage of 6.45%, categorized as poor quality and unfit for drinking and irrigation uses; the medium-quality category has 52.07%, and the good quality category has 41.47% of the area falls under the very good category. These parameters are essential when considering water usage for agriculture, especially in regions where farmers use fertilizers and pesticides extensively to enhance crop production.

A comprehensive evaluation index system for the integrated advancement of finance in science and technology, alongside the manufacturing sector, has been established. Furthermore, the entropy weight approach has been employed to assess both the development indicators of technology-driven finance and the integration metrics of the manufacturing industry across the three provinces and one municipality within the Yangtze River Delta region from 2012 to 2021. This study employs the fixed effects model to analyze the influence of science and technology financing on the integrated development of the manufacturing industry in this region, and it further examines the role of production costs and innovation capabilities in the relationship between technology financing and manufacturing integration. Research has shown that science and technology finance significantly enhances the integrated development of the manufacturing industry in the Yangtze River Delta, with production costs and innovation capabilities acting as intermediary factors. However, in the high-level areas of manufacturing integration development, the mediating effect of production cost and innovation ability is not significant.

Analyzing the transportation infrastructure–rural industry integration relationship in China

Selection of urban flood shelter locations based on risk assessment