Search Results (2,232)

This paper examines how artificial intelligence can support sustainability assessment in cultural industries, using national film industries as a test case. The Film Industry Sustainability Index (FISI) is introduced as a composite indicator covering cultural diversity, economic resilience, and Sustainable Development Goal (SDG) alignment for 42 national economies from 2005 to 2023. Knowledge-Enhanced Mamba (KE-Mamba), a selective state-space forecasting model, is then proposed to combine annual panel indicators with country-level film-industry knowledge graph (KG) embeddings and large language model (LLM)-derived screenplay-oriented narrative proxies from film synopses. To reduce factual errors in title-level narrative scoring, the LLM is anchored to verified United Nations Educational, Scientific and Cultural Organization (UNESCO) records and the European Audiovisual Observatory’s LUMIERE film-admissions database using rank-one model editing (ROME). On the 2020–2023 held-out test period, KE-Mamba achieves a composite FISI mean absolute error (MAE) of 0.0389, a mean absolute percentage error (MAPE) of 5.61%, and an $R^2$ of 0.934, outperforming autoregressive integrated moving average (ARIMA), tree-based, long short-term memory (LSTM), and base Mamba baselines. Additional robustness checks using a pre-pandemic split, two-way fixed-effects panel regression, alternative FISI weighting schemes, KG embedding ablations, and human validation of LLM narrative scores support the reliability of the proposed framework. Policy simulations are interpreted as model-based projected associations rather than causal estimates. The results show that knowledge-enhanced sequence models can provide transparent forecasting support for sustainable cultural-industry policy. Full article

In the ongoing data-rich era, intelligent cognition is playing an increasingly important role in advancing remote sensing applications. However, traditional intelligent methods for remote sensing processing no longer fully meet the growing demands of this era and still suffer from several limitations, such as passive data-dependent processing, predefined-task execution, and lack of closed-loop optimization. As a customized GeoAI innovation for remote sensing, Remote Sensing Agent has entered an early stage of research explosion. This paper focuses on its paradigm-shifting role in reshaping remote sensing information processing, clarifies the “4+1” core characteristics including multimodal spatial perception, goal-driven spatial mission planning, geoscientific knowledge reference, geospatial workflow execution, and feedback loop. It elaborates the threefold reshaping of remote sensing information processing from initiation mode, execution mode, and evaluation criterion, namely shifting from passive data processing to active task-driven, from predefined-task processing to multi-agent collaboration, and from result-oriented output to full-process closed-loop optimization. Future prospects of Remote Sensing Agent in geoscientific knowledge base optimization, multi-agent collaboration efficiency, and complex-scenario adaptability are discussed. This paper provides targeted and forward-looking perspectives for intelligent innovation research in remote sensing. Full article

Addressing bidirectional section disruptions in urban rail transit, this paper proposes a fault recovery strategy that explicitly incorporates the constraints imposed by train operation intersections (i.e., turn-back stations). To achieve this goal, this study develops a resilience-oriented recovery framework that captures operational dependencies associated with turn-back sections, compares recovery outcomes under three designed failure scenarios with and without train operation adjustment, and evaluates how variations in the number of repair teams affect resilience loss, recovery time, and repair priorities. Using the Chengdu Metro network as a case study, the results show that, compared with strategies that do not consider turn-back operation adjustment, the proposed method reduces resilience loss by 17.9%, 16.0%, and 38.6% across the three scenarios. The results also indicate that increasing the number of repair teams shortens total recovery time and reduces resilience loss, although the marginal improvement gradually decreases. For example, in Scenario 1, resilience loss decreases from 2.3% to 0.7%, while total recovery time is reduced from 13.4 to 3.4. The main contribution of this study is the integration of turn-back-section dependency and repair-team constraints into a unified resilience-based recovery framework, which may serve as a reference for post-disruption recovery planning in urban rail transit systems. Full article

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

Robust Arithmetic Word Problem (AWP) solving is important for applying mathematical reasoning systems in educational scenarios, where problem statements may contain changed numerical values, paraphrased descriptions, or irrelevant distracting information. Although Large Language Models (LLMs) have shown strong potential in solving AWPs, their reasoning processes may still be sensitive to surface-form variations and perturbation-induced noise. To address this issue, this paper proposes a Scene-Aware Neuro-Symbolic solver designed to improve the robustness of AWP solving under perturbations. The proposed method extends the existing scene-aware framework by introducing perturbation-oriented mechanisms at the scene, relation, and symbolic-solving levels. A Chain-of-Scene (CoS) prompting strategy first generates candidate scenes, after which goal-guided filtering retains target-related and bridge scenes while removing distractor-induced scenes. The retained scenes are then processed by the Scene-Aware Syntax-Semantics ($S^2$) method to extract explicit and implicit relations, and relation consistency checking is applied to remove locally plausible but globally irrelevant relations. Finally, the symbolic solver performs iterative equation-based reasoning over the filtered relation sets, with fallback recovery activated when standard solving does not produce a target-compatible answer. Experiments on AGG, MAWPS, and GSM8K show an average accuracy of 92.8% on clean datasets. On GSM-Perturb and AWP-Perturb, the solver achieves perturbed accuracies of 80.8% and 87.5%, with robustness drops of 8.3% and 6.8%, respectively. Ablation results show that scene filtering and relation consistency checking are the main contributors to reducing perturbation-induced errors. These findings suggest that combining LLM-based scene understanding with symbolic relation reasoning is a promising direction for improving the robustness and interpretability of AWP solvers in the evaluated perturbation settings. Full article

The European Union (EU) and national governments have set clear targets to reduce agricultural emissions, including ammonia from manure spreading practice, with regulations such as the Ambient Air Quality (AQ) and Clean Air Directives, the Common Agricultural Policy (CAP), and the Green Deal, with implication for ecosystem services and landscape planning, reflecting broader environmental sustainability objectives including those addressed by the Sustainable Development Goals (SDGs). Informative Inventory Reports (IIRs) are critical tools within the EMEP/EEA framework for monitoring long-range transboundary air pollution. They utilize three distinct methodological tiers (Tiers 1, 2, and 3) to estimate emission data across Europe. Despite the availability of Earth Observation (EO) data and products from the Copernicus Programme current estimation methods still rarely integrate EO information to produce spatially explicit estimates. This paper reviews current methodologies for estimating ammonia in IIRs and in scientific literature, including advanced methods not yet implemented in official inventories but potentially capable of supporting more spatially explicit and process-oriented estimation. A Medium Effort Methodology (MEM) is identified among those reviewed as a representative methodological pathway for integrating EO information with Tier 3 approaches. Building on this, the paper explores the association between specific EO data and Copernicus products, and input variables required by MEM, identifying opportunities and barriers for environmental monitoring with potential relevance to sustainable agriculture. Full article

This study investigates the charging behaviors, routines, and perceptions of Swedish electric vehicle (EV) users who lack access to home charging, a group that remains underrepresented in the EV adoption literature. Based on an online survey of 250 EV users—primarily located in Gothenburg—respondents were divided into two groups: those with and those without home charging availability. Nearly half of the sample (47.6%) reported not having access to charging at home. Comparative analyses, including linear regression models, were conducted to examine differences in sociodemographic characteristics, charging patterns, and perceptions of public charging. While the two groups were similar in terms of age, gender, vehicle type, charging frequency, and minimum state of charge preferences, significant differences emerged in perceived convenience, distance, and freedom to charge. Users without home charging availability reported lower access to workplace charging and evaluated public charging as less convenient and less accessible. Charging behavior in both groups was primarily goal-oriented and triggered by minimum state of charge rather than spontaneous opportunities. The findings highlight the structural disadvantages faced by users without home charging and underline the importance of adapting public charging infrastructure and policy strategies to support a broader and more equitable transition to electric mobility. Full article

The coexistence of cancer and severe aortic stenosis (AS) is increasing as a result of population aging and substantial improvements in cancer survival. Transcatheter aortic valve implantation (TAVI) has transformed the management of AS; however, patients with active malignancy or a history of cancer remain markedly under-represented in pivotal randomized trials. This under-representation has resulted in persistent uncertainty regarding patient selection, risk stratification, and the expected benefit of TAVI in this growing and clinically heterogeneous population. This review provides a comprehensive and contemporary synthesis of the evidence on TAVI in patients with cancer, integrating cardiovascular (CV), oncologic, and geriatric perspectives. Available data on epidemiological overlap, cancer-specific procedural challenges, and short- and long-term outcomes following TAVI are critically examined, with particular emphasis on distinctions between active cancer and cancer survivorship. Key modifiers of risk and benefit—including prior thoracic radiotherapy, competing thrombotic and bleeding risk, immunosuppression, frailty, sarcopenia, and nutritional status—are discussed in detail. Limitations of conventional surgical risk scores in oncology populations are highlighted, underscoring the need for individualized assessment beyond traditional CV metrics. Across registries and meta-analyses, TAVI is associated with high procedural success and comparable short-term outcomes in patients with and without cancer. Excess mortality observed during mid- and long-term follow-up is driven predominantly by non-CV causes related to malignancy rather than valve-related complications. Importantly, patients with cancer in remission demonstrate outcomes similar to those of non-cancer populations, whereas prognosis in active cancer is strongly influenced by disease stage, biology, and competing risks. Overall, cancer diagnosis alone should not preclude consideration of TAVI. Optimal management requires multidisciplinary, goal-oriented decision-making that integrates oncologic prognosis, functional status, and patients’ priorities. As cancer survivorship continues to expand, prospective studies, integrated risk stratification tools, and closer alignment between cardio-oncology and structural heart programs are essential to guide evidence-based and equitable care. Full article

Digital small and medium-sized enterprises (digital SMEs) in emerging market economies operate in disruption-biased environments where interruptions can quickly deteriorate operational reliability and long-term performance. Existing studies insufficiently integrate business continuity management (BCM) into capability-based performance models in the digital SME context, especially when focusing on operational rather than strategic perspectives in emerging market economies. Moreover, empirical evidence on how multiple organisational capabilities interact under disruption remains fragmented. This study therefore aims to prioritise the most influential capability-based determinants of sustainable performance in Thai digital SMEs using expert consensuses analysed via Fuzzy TOPSIS. This study adopted the following two-stage research design. Stage 1: A three-round e-Delphi panel (n = 21) refined and prioritised the most influential determinant; the expert group included SME business owners (with more than 20 years of SME management experience) and relevant specialists. The consensuses were then analysed using Fuzzy TOPSIS to rank the determinants by relative importance. Stage 2: Structural Equation Modelling (SEM) using survey data from 817 Thai digital SMEs was utilised to validate the proposed capability transmission pathways, and a strong fit was demonstrated (χ²/df = 1.672, CFI = 0.984, RMSEA = 0.029). The study findings highlight continuity-oriented routines as a practical leverage point for SME leaders and policymakers seeking resilient and sustainable performance in digital markets, and positions BCM as an actionable strategy toward achieving these goals. Full article

Sustainable higher education governance requires durable institutional mechanisms that support student engagement, faculty participation in education, and organizational coordination. Against the backdrop of China’s “Three-All Education” framework, academic culture has become a broader governance issue involving student development, faculty engagement, and organizational operation. This study aims to examine the current conditions and associated factors of academic culture governance in resource-constrained undergraduate universities. Based on survey data from three undergraduate institutions in Gansu Province, China, including 6120 valid student questionnaires and 735 valid faculty questionnaires, this study combines group difference tests with multiple regression analysis. The findings show that the sampled institutions face three main challenges: insufficient continuity in students’ academic goal commitment, limited faculty educational engagement under research-oriented incentives, and weak organizational coordination. Regression results further indicate that clarity of learning goals is significantly and positively associated with students’ learning status; perceived research orientation is significantly and negatively associated with faculty engagement in education; and information sharing and clarity of responsibility are significantly and positively associated with organizational coordination. These findings suggest that sustainable academic culture governance depends on the alignment of student goal support, faculty incentive structures, and collaborative organizational operation. Full article

This study examines how investment priorities for sustainable rural development are shaped when financial, environmental, social, and institutional criteria are evaluated simultaneously. Using the Analytic Hierarchy Process (AHP), the study assesses six investment alternatives: eco-tourism, agro-tourism, renewable energy, digital tourism, sustainable agriculture, and cultural tourism. The results reveal the dominance of financial performance and risk considerations, which together account for more than two-thirds of total decision weight. Renewable energy emerges as the highest-ranked investment alternative, whereas agro-tourism and sustainable agriculture remain under-prioritized despite their environmental and social benefits. A comparative scenario analysis demonstrates that policy-oriented weighting structures substantially alter investment rankings, increasing the attractiveness of locally embedded and sustainability-oriented activities. The findings suggest a structural divergence between market-driven capital allocation and broader rural development objectives. By integrating sustainable finance and rural development within a multi-criteria decision-making framework, the study provides practical insights for investors and policymakers seeking to align investment decisions with long-term sustainability goals. Full article

Physical energy-based therapies are non-invasive adjunctive interventions that deliver mechanical, electromagnetic, light, or radiofrequency/thermal energy to tissues with the aim of reducing symptoms and improving tolerance of active rehabilitation. Osteoarthritis (OA) is a heterogeneous whole-joint disorder in which cartilage degeneration, subchondral bone remodeling, synovitis, peri-articular tissue dysfunction, neuromuscular impairment, and pain sensitization may interact to produce pain, stiffness, and activity restriction. As conservative therapy for OA, education, progressive therapeutic exercise, weight management when indicated, and self-management remain the core of care. Nevertheless, some patients cannot fully participate in exercise because of pain, fear of movement, load intolerance, comorbidity, or limited access to supervised rehabilitation. This narrative review synthesizes evidence published mainly between 2016 and 2026 for extracorporeal shock wave therapy (ESWT), photobiomodulation/low-level laser therapy (PBMT/LLLT), pulsed electromagnetic field therapy (PEMF), transfer energy capacitive and resistive/capacitive–resistive electric transfer (TECAR/CRET) therapy, body weight support and aquatic unloading strategies, and mechanosonic vibration therapies. The available literature suggests that ESWT and PBMT/LLLT may provide short- to mid-term pain and function benefits in selected patients with knee OA when parameters are aligned with evidence-supported dosing windows. PEMF and vibration therapies show promising but less consistent effects because protocols, devices, sham conditions, and populations vary. TECAR/CRET and unloading approaches are best interpreted as enabling tools that may reduce guarding, improve walking tolerance, or increase the quality of therapeutic exercise, rather than stand-alone disease-modifying treatments. Current national and society guidelines consistently prioritize exercise, education, and weight management; most of the modalities reviewed here are absent from guidelines or are supported only indirectly, which justifies cautious wording and individualized use. A practical application model is, therefore, time-limited and goal-oriented: identify the barrier to rehabilitation, select a modality with a plausible mechanism and published protocol, monitor pain and functional response, and discontinue the modality if it does not improve participation in active care. Full article

Background/Objectives: Efficacy endpoint selection in adult primary central nervous system (CNS) tumor trials remains challenging because conventional solid tumor frameworks do not adequately capture the anatomical, radiographic, and biological complexity of brain tumors. In particular, postoperative irregular residual lesions, non-enhancing tumor components, and treatment-related imaging changes complicate the interpretation of objective response and progression. This narrative review examines the current landscape of endpoint selection in adult primary CNS tumor trials and discusses strategies for standardization in the Response Assessment in Neuro-Oncology (RANO) 2.0 era from integrated regulatory science and clinical perspectives. Methods: This study was conducted as a narrative review intended to provide a regulatory science-oriented synthesis of efficacy endpoint evaluation in adult primary CNS tumor trials. The literature search primarily utilized PubMed and ClinicalTrials.gov, supplemented by major consensus guidelines, pivotal clinical trials, and regulatory documents from the major regulatory authorities, including those in the United States, Europe, and Japan, published over the past two decades. Search terms included combinations of keywords such as “brain tumor,” “glioblastoma,” “meningioma,” “Phase I,” “Phase II,” “efficacy endpoint,” and “RANO”. In addition to the literature synthesis, this review incorporates findings from our previously published empirical analyses regarding endpoint selection in Phase II glioblastoma trials, Phase II meningioma trials, and Phase I brain tumor trials. Results: Response Evaluation Criteria in Solid Tumors-based response assessment remains fundamentally limited in neuro-oncology. Across recent early-phase trials, substantial heterogeneity persists in endpoint selection and in the operational definitions of objective response rate, progression-free survival, and progression. RANO 2.0 is intended to provide a more unified and implementation-oriented framework by refining baseline definition, progression confirmation, non-enhancing lesion interpretation, and the role of minor response, while improving compatibility with contemporary molecular classification and immunotherapy-era trial design. However, important implementation challenges remain, including potential reproducibility concerns in complex imaging assessments, operational complexity, imaging standardization, and the need for independent central review. Conclusions: Standardization of endpoint strategies in adult primary CNS tumor trials should move beyond simple adoption of conventional solid tumor metrics toward a disease-specific, harmonized framework integrating imaging, clinical context, tumor biology, and regulatory interpretability. This review provides a regulatory science-oriented synthesis linking response assessment criteria, early-phase adult primary CNS tumor trial endpoint trends, empirical analyses, and emerging endpoint frameworks. RANO 2.0 represents an important step toward this goal, but it should be regarded as an evolving framework that requires continued validation, international collaboration, and implementation-focused refinement. Full article

Amid growing global attention to corporate sustainability and responsible investment, the disclosure of Sustainable Development Goals (SDGs) has emerged as an important component of non-financial reporting. However, the extent to which SDG disclosure contributes to firm value remains underexplored, particularly in emerging markets. This study examines the association between SDG disclosure in corporate reports and firm value among 660 publicly listed companies across four Southeast Asian countries: Indonesia, Malaysia, Thailand, and Singapore. SDG disclosure is measured using 17 SDG indicators derived from the Refinitiv database and should be interpreted as a measure of disclosure breadth rather than disclosure quality or depth. The analysis begins with descriptive statistics to illustrate the distribution of key variables, followed by ANOVA to assess differences in SDG disclosure across countries and industries. Hypothesis testing is then conducted using multiple regression analysis with robust standard errors, with firm value proxied by price-to-book value (PBV). Several robustness checks are performed, including winsorised regression, year-by-year regressions, and regression models incorporating country and industry dummy variables. The results indicate that SDG disclosure is positively associated with firm value, although the relationship is interpreted as correlational rather than causal because of the short observation period and potential endogeneity. The findings also show that SDG disclosure is unevenly distributed across goals and countries, with SDG 8 and SDG 13 receiving the highest attention, while SDG 2 and SDG 14 remain among the least disclosed. These results highlight the importance of sustainability transparency in shaping market valuation and underscore the need for more balanced, comparable, and quality-oriented sustainability reporting frameworks across the region. Full article

Climate change represents one of the defining global health challenges of the 21st century, with far-reaching implications for population health, health systems, and health equity. The acceleration of environmental change, evidenced by record-breaking global temperatures, extreme weather events, and ecological degradation, poses a direct threat to achieving Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lives and promote well-being for all. This manuscript presents a narrative review and policy analysis of the intersection of climate change and global public health in light of the outcomes of the 2025 United Nations Climate Change Conference (COP30) in Belém, Brazil. Drawing on peer-reviewed literature, major institutional reports, and relevant policy documents, we explore how climate change exacerbates communicable and non-communicable diseases, undermines health system resilience, and disproportionately affects vulnerable populations worldwide. Particular attention is given to heat-related morbidity, infectious disease expansion, air pollution, food and water insecurity, displacement, gender inequities, antimicrobial resistance, and mental health impacts. The paper highlights the significance of the Belém Health Action Plan (BHAP), which is treated here as a COP30-associated action framework that places health more centrally within climate policy discussions. However, major challenges remain, including its voluntary orientation, the absence of dedicated financing mechanisms within the framework itself, and limited clarity on accountability arrangements, as identified through our synthesis of the available policy and evidence base. We argue that achieving SDG 3 is no longer feasible without integrating climate adaptation and mitigation into health systems and policies, and that progress will depend on translating global commitments into context-specific country strategies, governance arrangements, and implementation pathways. Full article