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Advances in probability distributions are important for modelling complex data across fields such as actuarial science, environmental science, biomedical science, economics, finance, and insurance. Classical distributions often have limitations when dealing with highly skewed data, heavy tails, or unusual failure patterns. To address these challenges, this study introduces the Sine Inverse Lomax Burr III distribution, a new flexible model that combines the tail behaviour of the Burr III distribution with the skewness-control properties of the sine inverse transformation. Statistical properties, including quantiles, moments, moment generating functions, and order statistics, are derived. Some risk measures, including the value at risk, tail value at risk, and tail variance, are derived and studied. Parameter estimation is performed using five different estimation techniques: maximum likelihood estimation, least squares, weighted least squares, percentile matching, and Anderson–Darling. The usefulness of the proposed model is demonstrated using monthly tax revenue data. The results show that the SILBIII distribution performs better than the competing distributions. The proposed model is an alternative model suitable for modeling data in finance, actuarial, and related fields. Full article

Due to its extensive industrial applications and high market prices, as well as low mining yield, the recovery of rhodium from various secondary resources is becoming increasingly urgent for addressing its supply issues. Generally, rhodium is extracted last from the leaching solutions containing other platinum group metals and base metals. The lengthy processing flow led to the inevitable yield loss of rhodium. Compared to the conventional extraction process, selective preferential separation and extraction of rhodium are of great significance for achieving its high economic value and efficient recovery. However, selective preferential separation and extraction of rhodium have to face many difficulties, such as its kinetically inert properties, being prone to hydration and hydrolysis reactions, etc. This paper reviews various promising improvements and new technologies for selective preferential separation and extraction of rhodium from mixed metal solutions, including precipitation, liquid–liquid extraction, adsorption and other emerging technologies. The advantages and disadvantages of those reported technologies were evaluated. It is pointed out that the selective preferential adsorption of rhodium based on molecular recognition and ion imprinting is a promising rhodium recovery technology, which is economical and consistent with the concept of green chemistry. Full article

In this study, we examine the persistence of gendered inequality in unpaid domestic work among employed individuals during Thailand’s 2004–2014 structural transformation, a period shaped by major reforms in education and healthcare. We provide new evidence from middle-income Southeast Asia, where gender norms remain strong but empirical evidence is still limited, especially for marriage and labor market segregation. Methodologically, we use repeated cross-sectional data from Thailand’s Time Use Survey (N = 57,555) and pooled OLS models with survey-year fixed effects under alternative sample definitions. Our results reveal a large and persistent gender gap across all specifications. Marriage is associated with substantially higher amounts of unpaid domestic work for women, while labor market segregation displays gendered dynamics: employment in female-dominated industries and female household headship are associated with lower domestic work burdens, whereas employment in male-dominated industries shows no robust association with women’s unpaid domestic work time. Although the raw gender gap narrowed over the ten-year period, the adjusted gap widened after accounting for individual, employment, and household characteristics, suggesting that compositional improvements among women masked a deepening relative domestic burden. These findings suggest that economic development alone may not automatically reduce gender inequality within households, with important implications for labor markets and social policies in developing economies. Full article

Selecting the right energy storage system (ESS) for grid integration is a high-stakes decision involving conflicting technical, economic, environmental, and risk criteria under deep uncertainty. The existing fuzzy multi-criteria decision-making (MCDM) methods either fail to capture neutral or abstaining expert judgments or treat evaluation criteria as independent, which is an unrealistic assumption in complex engineering decisions. To address both limitations simultaneously, this study develops four new aggregation operators by extending the Bonferroni mean (BM) into the q-rung picture fuzzy sets (q-RPFSs) framework: the q-RPFBM-based, q-RPFWBM-based, q-RPFGBM-based, and q-RPFWGBM-based operators. Unlike the existing q-RPFS operator families (Dombi, Frank, Fermatean, Yager, Maclaurin), which aggregate criteria independently, BM-based operators explicitly model pairwise interactions among criteria with a structurally distinct aggregation logic that is especially critical when criteria such as cost, risk, reliability, and environmental impact are mutually correlated. The theoretical validity of the operators is confirmed through proofs of idempotency, monotonicity, and boundedness. Applied to a comprehensive ESS selection problem for Türkiye (covering nine alternatives across nineteen sub-criteria and five main criteria, including an explicit risk dimension), the framework consistently identifies pumped hydro storage as the optimal choice. Sensitivity analyses under varying q, s, and t parameters, as well as perturbed criterion weights, confirm the robustness of this ranking. The proposed framework offers energy planners and decision-makers a principled and transparent tool for evaluating ESS under high uncertainty and criterion interdependence. Full article

Wine production is one of the most important agricultural activities worldwide, and generates significant amounts of organic by-products, particularly grape pomace. Traditionally, this was seen as waste, but currently, this residue has been reanalyzed from the perspective of the principles of the bioeconomy and circular economy, demonstrating its potential as a rich source of bioactive compounds with great potential for valorization. Its heterogeneous composition accumulates a variety of polyphenols, dietary fibers, flavonoids, phenolic acids, and other secondary metabolites that confer important biological properties, including antioxidant, anti-inflammatory, and antimicrobial activities. The chemical composition of grape pomace varies substantially according to variety, winemaking method, and extraction conditions, directly impacting its potential application. Extraction methods have progressed from traditional procedures to more advanced techniques such as ultrasound, supercritical fluids, and natural solvents, enabling the selective separation of high-value compounds. This review provides a comprehensive and critical overview of grape pomace valorization, emphasising its composition, green extraction and current industrial applications. In addition, regulatory frameworks and sustainability strategies supporting the integration of grape pomace into value-added production chains are discussed. Overall, grape pomace valorization supports waste reduction and the production of new functional products that balance economic efficiency and environmental responsibility. Full article

Background: This study evaluates the return on investment (ROI) in new transport equipment using a purpose-built graphical user interface (GUI), addressing whether acquiring additional vehicles for peak demand periods is economically viable compared to optimizing the existing fleet. The research focuses on agricultural product transportation—specifically potatoes—across four key routes. Methods: A system dynamics (SD) methodology was applied, combining simulation and data analysis through a GUI that enabled the adjustment of key variables, including operating costs, yields, and transportation expenses. Results: The analysis revealed notable differences in costs and profitability across the studied routes. Variables such as diesel costs and fuel efficiency proved particularly influential on outcomes. The GUI demonstrated clear value as a visualization tool, enhancing comprehension of simulated scenarios and supporting strategic decision-making. Conclusions: Investing in new transport equipment can be profitable under specific operational and economic conditions, providing a solid foundation for expansion and optimization decisions. Beyond its immediate operational contribution, the study offers a replicable profitability analysis model applicable to future projects within the company. Full article

The emergence of artificial intelligence (AI) tools, particularly generative models, in the last five years has fundamentally transformed the framework and methodologies of learning in higher education. Students are integrating AI for producing new ideas, assisted and personalized search, academic writing, advanced data analysis, and personalized learning. For this reason, an update of the theoretical and conceptual framework regarding the adoption of technologies in the educational environment is required. Based on traditional Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) models, we propose a new Partial Least Squares Structural Equation Modeling (PLS-SEM) model developed for the context of AI in higher education. The novelty of the model lies in the integration of the mediating relationship through trust (trust in AI outputs, TAIO) between perceived academic integrity risk (PAIR) and behavioral intention to use (BI), while anchoring perceived learning utility (PUL) and perceived effort expectancy (PEE) in AI literacy-specific self-efficacy (AILSE). The model is tested using a sample of 339 higher education students from economics and computer science specializations and validated using the R environment and the SEMinR package as specific software tools. Our proposed research hypotheses consider six reflective latent constructs and a mediating relationship, which we analyze using validated PLS-SEM techniques. All items included in the model constructs are formulated for use in university educational contexts and are adapted to specific AI tools for learning in the university environment. Full article

This article examines collaboratively produced poems by displaced Lebanese and Syrian women created within a Participatory Arts-Based Research (PABR) project in Akkar, North Lebanon. The study asks how creative writing pedagogy and participatory research methods can reduce forms of ‘othering’ that may arise in top-down research on conflict and displacement. The project combined creative writing workshop practice with participatory research methods, including joint analysis workshops with NGO partners and participants. Writing prompts, group workshops, and subsequent collaborative translation resulted in reflective and creative texts drawn from lived experience. The work documents war, migration, economic hardship, and fractured social relations. Close readings show how metaphor, dialogue, and narrative fragments function as acts of self-narration rather than passive testimony. Participants describe writing as a way of thinking and coping, and several texts foreground storytelling as a relational process. The study argues that Creative Writing practice, based on the participatory tenets of the ‘workshop’ can support shared knowledge production and ethical engagement. The writings suggest that a counter-archive can emerge in which storytelling can resist victimising narratives and can instead, within a social literary practice participatory paradigm, model new forms of collaborative reflection. Full article

Play is considered the primary activity of children, and toys are their essential tools. However, the toy industry extends beyond children, constituting a significant economic sector with annual revenues exceeding one hundred billion dollars and generating substantial environmental consequences. These include resource consumption, pollution during manufacturing, energy use, consumables during operation, and waste generation at the end of the product’s life cycle. This research presents a study of the state of the art of ecodesign in the toy sector and its potential within this field. Through the analysis of the available scientific literature and the expertise of the Toy Technology Institute (AIJU), experiences from companies in the sector have been identified and classified according to the ecodesign strategy wheel. Simultaneously, a survey of industry stakeholders compared the current situation with that of 30 years ago. The results reveal perceptual progress that is uneven across dimensions, with the strongest advances in materials and production, moderate gains in distribution and end-of-life strategies, and limited improvement in product durability, while innovation in new product concepts shows the highest growth. Correlation analyses indicate that experience and professional background influence how sustainability progress is perceived. Although most improvements have been motivated by cost reduction and regulatory compliance rather than environmental awareness, recent trends reflect a growing corporate commitment to ecological innovation. For consumers, it remains essential to overcome misconceptions about eco-friendly toys, while companies must continue to invest in new materials, technologies, and design strategies that support the transition toward circular and long-lasting toy products. Full article

The global energy system is undergoing a significant transformation driven by rapid electrification, urbanization, and the emergence of new categories of electricity consumers. In particular, the increasing load density in low-voltage distribution networks within urban areas requires a reconsideration of conventional methodologies for the placement of transformer substations. Traditional planning approaches are often based on empirical service radii or static demand factors and therefore fail to adequately reflect the complexity of modern urban power systems. This study proposes a multi-objective optimization model for the optimal placement of transformer substations in 10/0.4 kV urban distribution networks. The proposed model simultaneously considers power losses, economic costs, and system reliability. In addition, the design load model is extended through the introduction of a comfort coefficient that captures additional electricity consumers typical of modern urban infrastructure, including HVAC systems, elevators, pumping systems, and electric vehicle charging stations. In contrast to traditional empirical approaches, the transformer service radius is modeled as a physical parameter determined by voltage drop limits, cable thermal constraints, and failure intensity. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Each candidate solution generated by the algorithm is validated through AC load-flow simulations performed in the DIgSILENT PowerFactory environment. The proposed methodology is evaluated using real data from a 0.48 km² urban area in the city of Tashkent. The results indicate that increasing the transformer service radius reduces capital investment costs but leads to higher power losses and longer interruption durations. According to the Pareto analysis, a service radius of approximately 300 m represents the optimal compromise between technical, economic, and reliability criteria for the studied area. The proposed methodology can serve as an effective tool for the scientifically grounded planning of urban power supply systems and for improving energy efficiency in modern distribution networks. Full article

As a crucial grassland management practice, mowing plays a key role in maintaining the stability, productivity, and economic value of grassland ecosystems. The development of large-scale monitoring techniques for detecting whether mowing has occurred is of significant scientific and practical importance for improving the understanding of grassland ecosystem response mechanisms and optimizing management strategies. This study focuses on the concentrated grassland area of the Xilingol League in Inner Mongolia, restricted to the SAR-covered western sub-region. All classification accuracies reported here are obtained under spatially random train/test splits and represent an upper bound; generalization to geographically disjoint blocks remains unverified. By utilizing Sentinel-1, Sentinel-2, and Landsat-8 remote sensing images during the mowing season (August to September 2023) along with field survey data, we first applied the random forest-SHAP algorithm to select the optimal features from 70 texture features and construct a multimodal remote sensing dataset. Subsequently, we proposed the MAD-Net (Multi-Modal Attention Fusion Network with Dynamic Weighting) model to fully exploit information related to mowing identification from both optical and SAR data and conducted comparative analyses with other models. The results indicate that the CNN_LSTM_Attention model, which integrates convolutional neural networks, long short-term memory networks, and convolutional block attention modules, performed best in terms of capturing spatiotemporal variations in time series NDVI data. The U-Net model achieved the highest performance on the optimized texture dataset, while the MAD-Net model, which consists of three subnetworks that target different feature data, reached an identification accuracy of 92.59% in the SAR-covered western sub-region under a spatially random train/test split. This result represents an optimistic upper bound, as generalization to geographically independent blocks has not been evaluated. Ablation studies reveal that NDVI time series is the most informative single modality, while texture and SAR features provide complementary information; the proposed dynamic weighting module outperforms conventional fusion strategies. This study provides a new perspective for the large-scale binary classification of mown vs. non-mown grassland and effectively combines multimodal remote sensing data with deep learning models. Thus, this work not only offers a comparative basis for timely and effective identification of mowed grasslands but also provides insights for formulating optimized regional grassland management policies. Full article

Antimicrobial resistance (AMR) is a leading global cause of death, with recent World Health Organization (WHO) data revealing that one in six laboratory-confirmed bacterial infections shows resistance to at least one antibiotic treatment. This review comprehensively analyzes the AMR landscape in 2026, detailing its evolution, mechanisms, and the innovative strategies being deployed to combat it. Driven by Darwinian selection and accelerated by factors like antibiotic overuse during the Coronavirus Disease 2019 (COVID-19) pandemic (predominantly in hospitalized patients with suspected bacterial co-infection), AMR is propelled by a diverse molecular arsenal in bacteria. Key mechanisms include enzymatic drug inactivation (e.g., the diversifying β-lactamase superfamily), target site modification (e.g., mcr genes conferring colistin resistance), efflux pumps, and biofilm formation. The rapid global spread of these traits is facilitated by a dynamic “mobilome”, a network of plasmids and transposons that shuttle resistance genes between species. This crisis has sparked a major scientific mobilization. Advances include the discovery of novel antibiotic scaffolds like lariocidin and the regulatory approval of critical new antibiotic/inhibitor combinations such as sulbactam/durlobactam and aztreonam/avibactam, which target highly resistant Gram-negative bacteria. Moreover, the first-in-class antibiotic gepotidacin offers a new option for urinary tract infections. Beyond traditional drugs, the pipeline is diversifying to include phage therapy, antivirulence strategies, and artificial intelligence-guided drug discovery. This diversification is critical as it helps preserve the effectiveness of existing Medically Important Antimicrobials (MIAs), those deemed essential for human medicine, by providing alternative or adjunctive treatment options. However, scientific innovation alone is insufficient. This review argues that lasting success requires parallel progress in global policy and infrastructure. Strategic priorities beyond 2026 must include finalizing and funding updated global action plans, strengthening real-time surveillance and diagnostic capacity, especially in low-resource settings, and implementing new economic models to de-risk antibiotic development. Embedding effective antimicrobial stewardship within universal health coverage and pandemic preparedness plans is crucial. Ultimately, defeating AMR demands an unprecedented, coordinated global effort that outpaces the relentless adaptability of bacterial pathogens. Full article

Levee breaches can trigger severe flooding and substantial socioeconomic losses in flood-prone regions, making reliable risk assessment essential for targeted flood control and disaster mitigation. This study develops a comprehensive risk assessment framework that couples a 1D–2D hydrodynamic model with a multi-indicator evaluation system. First, the Integrated Flood Modeling System (IFMS) is employed to simulate flood inundation dynamics following levee failure under 50-, 100-, and 200-year return period scenarios at five representative breach locations along the middle and lower reaches of China’s Daling River. Then a multi-dimensional indicator system is built by overlaying the simulated inundation results with exposure data to quantify direct economic losses. The indicator system includes levee attributes, flood risk, typical exposed elements, and a new dimension termed the amplification effect, which characterizes the nonlinear escalation of disaster consequences and the exceedance sensitivity of the flood disaster system under extreme levee breach scenarios. The results reveal clear spatial heterogeneity in both inundation patterns and risk profiles among the five breach sites. As the return period increases from 50 to 200 years, the growth in direct economic losses consistently outpaces the expansion of inundation area. Moreover, the amplification intensity under the 200-year return period substantially exceeds that under the 100-year event at all five sites, indicating stronger nonlinear disaster escalation and lower system resilience under extreme exceedance flood conditions. Based on a weighted multi-indicator integration, Lituocun shows the highest composite risk, followed by Shenglitun, Youxicun, Yangguicun, and Xiangyangzha, with distinct risk drivers at each site underscoring the need for targeted mitigation measures. The proposed method can effectively identify weak levee sections and reveal their risk drivers, providing a scientific basis for local governments to formulate levee reinforcement plans and flood control management decisions. Full article

Amid intensifying climate crises, widening inequalities, and geopolitical volatility, spatial economic resilience (SER) has become critical for regions facing systemic uncertainty. Traditional land-intensive productivity models prove increasingly untenable as spatial resources become finite and development space constrained. China’s new quality productivity (NQP) has emerged as a strategic response emphasizing innovation-driven structural renewal and territorial coordination. Conceptually, NQP is positioned as a SER-oriented strategy prioritizing adaptability, recoverability, and transformability. However, its actual associations remains theoretically overlooked and empirically untested, with existing research viewing it narrowly as technological upgrading while neglecting institutional dimensions, spatial dependencies, and multi-scalar heterogeneities. This study explores how NQP relates to SER from a spatio-temporal perspective: (1) How do the technological and institutional dimensions of NQP relate to SER? (2) What are the spatial patterns of NQP-SER associations across multi-scale locations? Employing XGBoost-SHAP, spatial generalized difference-in-differences, and Geographical Gaussian Process Regression across provincial, city, and enterprise scales in China, we find that NQP’s two dimensions relate to SER very differently. The technological–industrial dimension is the strongest predictor of SER at the provincial scale, exhibiting threshold-type, non-linear associations, while its predictive salience attenuates at the city and enterprise scales, where industrial structure and firm-specific fundamentals are more strongly associated with resilience. The institutional dimension, by contrast, is not positively associated with above-expectation resilience: once common shocks and provincial heterogeneity are absorbed, higher institutional policy intensity is negatively associated with SER, both within provinces and across neighbouring provinces. Spatially, provincial associations rely on coordination and interregional spillovers, while city associations concentrate in nodal clusters where the strength of association depends on capability–context alignment. The findings provide practical theoretical and analytical guidance for tailored policy-making in structurally diverse Global South facing ongoing uncertainty. Full article

This paper examines how value-added tax (VAT) reforms affected recorded point-of-sale (POS) spending in Saudi Arabia’s restaurant, café, and food service sector during a period of rapid payment digitalization. Two policy shocks are analyzed: the introduction of a 5% VAT in January 2018 and the increase to 15% in July 2020. Using monthly official POS data from January 2016 to January 2024, the study applies an interrupted time-series framework. Baseline estimates are obtained using Generalized Least Squares (GLS) with AR (1) correction. In contrast, seasonal SARIMAX and Error Correction Model (ECM) specifications are used as robustness checks and to distinguish short-run from long-run dynamics. Controls include food and beverage price indices, headline inflation, and COVID-19 disruptions. Results show statistically significant positive level shifts in recorded POS sales after both VAT reforms, with larger measured effects after the 2020 increase. However, the evidence suggests that these changes primarily reflect formalization of transactions, migration toward electronic payments, improved reporting compliance, and intertemporal expenditure timing rather than persistent growth in real demand. Post-reform trend coefficients indicate gradual normalization in subsequent months. ECM estimates suggest that approximately 56% of short-run disequilibrium is corrected within one month. Findings are robust across alternative specifications. The paper contributes new evidence from the Gulf region by showing that retail transaction indicators may overstate real consumption responses when tax reforms coincide with rapid financial digitalization. From a sustainability perspective, the findings highlight the role of digital financial systems and modern tax administration in improving economic transparency, strengthening fiscal sustainability, enhancing formal-sector integration, and supporting the institutional transformation objectives of Saudi Vision 2030. The results imply that fiscal-policy evaluations should jointly account for tax administration reforms and changes in payment technology. Full article