Abstract: The extant body of evidence pertaining to the acceptance of cultured meat in Sub-Saharan Africa remains limited. The present study examined the determinants of intention to adopt cultured meat among a sample of young, urban, meat-eating adults in Chad (n = 290, from a recruited sample of 304). This was achieved using a cross-sectional online survey. Hierarchical OLS with HC3-robust inference was estimated across five hypothesis blocks, complemented by dominance analysis, binary-outcome sensitivity, and exploratory triangulation (Bayesian, elastic net, conditional random forest). Approximately half of the respondents expressed a willingness to try cultured meat (52.4%). The final model accounted for 30.6% of the intention variance (adjusted R² = 0.188). Following Holm's correction for multiple comparisons, the conventional-meat and knowledge blocks did not demonstrate a significant difference. The product beliefs (ΔR² = 0.056, p = 0.022), affective-risk barriers (ΔR² = 0.086, p = 0.004), and value-fit (ΔR² = 0.039, p = 0.048) were found to be significant, with affective-risk ranking first in dominance analysis (22.8%). Binary sensitivity analysis demonstrated acceptable discrimination (AUC = 0.744), although no block remained significant after correction. Exploratory analyses yielded convergent results, including notably robust Bayesian support for excluding the conventional-meat block (BF01 = 1.66 × 10^12). Sensitivity power analysis confirmed adequate power (≥ 0.80) for the significant blocks, but indicated that the conventional-meat non-significance may partly reflect limited power (estimated power = 0.47). Cultured-meat adoption intention was more strongly associated with affective-risk and value-fit appraisals than with conventional meat-purchase priorities. This suggests that acceptance strategies should prioritise risk reduction, trust-building, and perceived value. Findings should be interpreted as applying to a digitally connected, young, urban, meat-eating, predominantly tertiary-educated early-adopter-like segment (90.5% with university-level education; 72.7% residing in cities of more than 500,000 inhabitants), rather than to the general Chadian population.

Abstract: The social dimension of sustainability in building construction has long occupied an uncomfortable position in the research literature; acknowledged in theory, yet sidelined in practice. Environmental performance dominates assessment frameworks globally, while economic viability commands institutional attention. What is left, all too often, is a thin layer of social indicators that lack contextual grounding, statistical validation, or practical operationalizability, particularly in rapidly urbanizing settings across the developing world. This study confronts that gap directly. Drawing on an original mixed-methods investigation conducted across the twin cities of Rawalpindi and Islamabad, Pakistan, we develop and validate a comprehensive social sustainability assessment framework specifically tailored to building construction projects in South Asian urban environments. Employing a three-round Delphi technique with industry experts, a structured Likert-scale survey administered to 50 experienced construction professionals, and confirmatory factor analysis (CFA) using AMOS, the study identifies and statistically validates eighteen social sustainability indicators organized under five latent constructs: (i) Social Responsibility and Human Well-being, (ii) Institutional Governance and Knowledge, (iii) Stakeholder Engagement and Community Trust, (iv) Workforce Development and Labor Equity, and (v) Inclusive Design and Service Accessibility. Reliability analysis returned a Cronbach's alpha of 0.984, while the Relative Importance Index (RII) ranked project experience (SCL8, RII = 0.856), health, safety and environment at the site (SCL7, RII = 0.848), and project manager awareness (SCL12, RII = 0.828) as the most influential indicators. Kruskal-Wallis tests confirmed cross-group consensus. Crucially, the study finds that social sustainability is not merely a welfare afterthought, it is deeply interwoven with economic performance and environmental stewardship through measurable cross-pillar correlations. The resulting framework, the first of its kind validated through expert consensus and inferential statistics within the Pakistan context, offers a practical decision-support tool for project managers, urban planners, and regulatory bodies including the Pakistan Engineering Council (PEC) and the Capital Development Authority (CDA). Broader implications for South Asian and developing-country construction governance are discussed.

Abstract: Advanced Driver Assistance Systems (ADAS) have demonstrated safety potential and are becoming increasingly available in the vehicle markets across the world. However, drivers’ perceptions, trust, and engagement with these systems in Thailand remain unexplored. This study therefore aimed to explore Thai drivers’ perceptions towards ADAS and investigated factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licensed drivers in Thailand. The online survey measured perceived usefulness, perceived ease of use, trust, barriers and concerns, expectations and preferences, and intention to use ADAS. Data were analyzed using Mann–Whitney U tests and Spearman’s rank correlations. Results showed that Thai drivers reported positive perceptions of usefulness and intention to use ADAS, while trust was moderate, and barriers and concerns showed variability. Trust demonstrated strong positive associations with perceived usefulness (ρ = .69), perceived ease of use (ρ = .56), and intention to use (ρ = .49). The findings highlight the important role of perceived usefulness, perceived ease of use, and trust in shaping drivers’ intent to use the system and supports the development of learning strategies to enhance ADAS usage whilst promoting utilization of these systems.

Abstract: Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge-cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k=5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.

Abstract: Microplastics (MPs) are widespread pollutants in aquatic environments, but their impacts throughout the life cycle remains of organisms are still not well understood. This systematic review integrates recent experimental results on the developmental, physiological, and neurobehavioral effects of MPs exposure on zebrafish (Danio rerio), a popular model organism for ecotoxicology research. A PRISMA-guided search using Web of Science (WoS) and Scopus as databases generated 371 articles, which was screened to 60 eligible articles. The collated results showed that MP toxicity strongly related to concentration, size, and extent of weathering or aging at various life stages of zebrafish. For developmental toxicity, a concentration-dependent yielded peer-reviewed publications assessing specific MPs properties, such as polymer identity, size, concentration, shape, and aging status. At various life stages, the toxicity of MPs was most affected by concentration, size, and aging. The developmental toxicity showed a concentration-dependent decrease in the rate of hatching, growth inhibition, and cardiac dysfunction, while, an increase in malformations, especially at concentrations of ≥100 µg/L or ≥10 mg/L has been reported. Non-monotonic and threshold effects have also been observed, the complexity of particle-based versus mass-based concentrations. Weathered and photo-aged MPs were found to exhibit higher embryotoxicity and neurodevelopmental toxicity, including changes in gene expression of neurons, decreased integrity of motor neurons, and impaired retinal development, compared with virgin MPs. Furthermore, physiological endpoints showed that oxidative imbalance was a key mechanistic process, which included changes in the activity of antioxidant enzymes (SOD, CAT, GPx), lipid peroxidation, inflammation, and disruption of tight junctions. Chronic MP exposures caused changes in the gut microbiota, hepatic metabolism, endocrine disruption, reproductive damage, thyroid function disruption, and genotoxicity in zebrafish. Neurobehavioral alterations, such as changes in locomotor activity, anxiety response, neurotransmitter homeostasis, and acetylcholinesterase function, occurred in both larvae and adults, with a potentiation effect in aged MP exposure. Previous, experimental data have also shown that zebrafish are very sensitive to MPs exposure in various biological systems, with toxicity being a function of physicochemical properties and exposure conditions. Finally, this review found major limitations for inter-study comparisons because of inconsistencies and differences in methodology related to MP concentration, simulation of MP aging, and MP dose measurements.

Abstract: We discover a synchronization admissibility boundary defined solely by the states of oscillators. The boundary is independent of structure and determines whether any two oscillators share a cluster in real time, unifying global synchronization, cluster partition, and the real-time onset of synchronization loss. This uniformity has been validated through dozens of adversarial tests. Mathematical proofs show that this boundary is mathematically equivalent to the constraint that the synchronous frequency must be a real number. This constraint is a direct corollary of a cornerstone of physics long taken for granted: all measurable physical quantities are real numbers. This equivalence reveals that the synchronous admissibility boundary (a key function) emerges directly from the principle that is logically prior to any specific structure.

Abstract: Objective fitting measures offer a means to circumvent the subjectivity of cochlea implant programming, with the stapedius reflex representing one robust predictor of the maxi-mum comfortable loudness level. With the present study, it was investigated whether long-term electromyographic measurements of the stapedius muscle using implanted electrodes are feasible. In nine sheep, myoelectrical activities were recorded intraopera-tively and synchronized with middle-ear admittance as a reference signal. For acoustic stimulation pure tones with different frequencies were used. The electrodes were placed at the stapedius muscle surface after exposing it via the retrofacial approach. Measurements were performed over a period of six months. The treated muscles were subsequently ex-cised, cut and examined histologically. Long-term electromyographic measurements were possible. No signs of atrophy were found in the muscles examined. However, the histo-logical section series showed a clear division of the muscle from proximal to distal. The ratio between tendon and muscle fibers being most pronounced in favor of the muscle fi-bers in the proximal section. The integration of an electromyography-based measurement method for the objective determination of the stapedius reflex threshold and thus, for the long-term adjustment of cochlear implants, appears fundamentally possible and could potentially enable largely autonomous fitting of the implants.

Abstract: Background/Objectives: Two sociodemographic characteristics of the nursing workforce — formal level of education and length of professional experience — are widely assumed to shape both how often nurses report adverse events and how safe they perceive their workplace to be for patients. Empirical evidence on these associations remains uneven, however, and large multicentre data from Central and Eastern European secondary-care systems are scarce. The present study examined whether educational level and length of work experience are independently related to (a) the self-reported frequency of adverse-event reporting and (b) the perceived level of patient safety, in a national sample of nurses working in Croatian general and county hospitals. Methods: We conducted a cross-sectional, multicentre survey in 2023 covering all 22 general and county hospitals in the Republic of Croatia. A 99-item paper questionnaire — including 81 items distributed across six previously validated scales (Cronbach’s α 0.730–0.951) — was distributed proportionally to the eligible nursing workforce (N = 6,661). Of the 1,657 questionnaires distributed, 1,518 were returned fully completed (response rate 91.6%). Two outcomes were examined in parallel: self-reported frequency of adverse-event reporting in the past 12 months, and global perceived level of patient safety on the respondent’s ward. Group differences were tested with Pearson’s chi-square and Kruskal–Wallis H tests; effect sizes were assessed using the φ coefficient and Cramér’s V. The study followed the STROBE reporting guideline. Results: Educational level was associated with the frequency of adverse-event reporting (χ² = 29.873, df = 8, p < 0.001; φ = 0.14) and with safety perception (χ² = 16.084, df = 8, p = 0.041; φ = 0.10). The same monotonic gradient was confirmed by Kruskal–Wallis tests, with mean ranks rising from secondary (SSS) through bachelor (VŠS) to master’s or doctoral (VSS+DR) levels for both reporting (719.40; 772.93; 836.56; H = 15.901, p < 0.001) and safety perception (735.29; 775.89; 844.86; H = 10.539, p = 0.005). Length of total work experience was associated with reporting (χ² = 22.708, df = 12, p = 0.030; φ = 0.12; H = 9.249, p = 0.026): mean ranks were lowest for nurses with ≤ 10 years and ≥ 31 years, and highest for mid-career nurses (11–20 and 21–30 years). For safety perception, the experience gradient ran in the opposite direction — highest in nurses with ≤ 10 years (mean rank 795.08) and lowest in those with ≥ 31 years (718.17; χ² = 35.036, df = 12, p < 0.001; φ = 0.15; H = 8.517, p = 0.036). Conclusions: Educational level and length of work experience are independently related to both the reporting of adverse events and the perception of patient safety among Croatian hospital nurses, but the two characteristics operate in different ways. Higher education is associated with more reporting and more favorable safety perception, whereas longer experience is associated with more reporting at mid-career but with a less favorable view of workplace safety in late-career nurses. Investing in continuing nursing education and in mid-career retention, while remaining attentive to the deteriorating safety perception of the most experienced staff, may be more effective than redesigning reporting forms alone. The findings inform nursing leadership, continuing-education planning, and national patient-safety policy in Central and Eastern European secondary-care systems.

Abstract: Institutional shuttle fleets with fixed routes and predictable terminal parking are well suited to dedicated photovoltaic–battery (PV–BESS) charging infrastructure, yet siting and sizing are usually solved numerically without clear interpretation of the governing constraints. This study develops a closed-form active-constraint sizing rule, derived via Karush–Kuhn–Tucker (KKT) analysis under verified monotonicity of the net-present-value (NPV) objective over the feasible design region, for a 10-van electric academic shuttle fleet operating between the Huay Kaew and Doi Saket campuses of Rajamangala University of Technology Lanna, Chiang Mai, Thailand. One centralized station is compared with two distributed stations under reliability, cost, solar-fraction, autonomy, charger, budget, and rooftop-area constraints. The two-station configuration eliminates 47,600 km/year of dead-run travel and increases system NPV from USD 36,980 to USD 86,293 after the year-10 BESS replacement cost. The KKT analysis identifies two binding constraints—BESS one-day autonomy and PV rooftop area—giving 30 kWp PV and 94.85 kWh BESS per station, rounded to 100 kWh. The full transition achieves IRR = 12.9%, simple payback = 6.1 years, and 95.9% annual CO₂ reduction. Monte Carlo simulation with 5,000 scenarios yields P(NPV > 0) = 100% within the simulated scenario set, VaR_5% = USD 28,959, and CVaR_5% = USD 21,248, confirming financial robustness under the adopted uncertainty ranges.

Abstract: Large language models (LLMs) embedded in medical Patient Digital Twins (PDTs) exhibit a systemic vulnerability: they can generate fluent, narratively persuasive, yet abductively unsound clinical explanations. In this context, abductive soundness means that an explanation preserves mechanistic plausibility, temporal coherence, explicit handling of missing premises, and sensitivity to counter-evidence. This article reframes the problem as architectural rather than as a mere deficit in training data. We identify three recurrent modes of abductive failure — missing-premise neglect, weak-mechanism support, and counter-evidence discounting. They arise when local semantics, formal world ontology, and the role-specific clinical semiosphere are collapsed into a single surface flow of generation. We propose a governed abductive architecture organised around seven runtime contours and operationalise it in the MS-AGIP platform for multiple sclerosis care. The architecture separates three subsystems: an ontology-guided Research Framework, a clinician-facing Neurologist Digital Twin, and a patient-controlled Patient Digital Twin. We show how disease-specific causal templates, evidence-tiered biomarker reasoning, provenance labels, temporal-coherence checks, molecular-clinical discordance detection, and governed patient-feedback updates jointly transform plausible narrative into sound abduction. The article presents an architectural blueprint and validation protocol aligned with TRIPOD+AI and DECIDE-AI. The architectural-versus-scale distinction has direct implications for safe medical AI: the difference between a fluent and a sound clinical system lies more in architecture and governance than in model size. None of the subsystems has yet been clinically deployed.

Abstract: This research explores the aromatase-inhibitory and estrogen-agonistic/antagonistic properties of two natural naphthoquinones, α- and β-lapachone, which are known for their anticancer effects. Initial tests showed that both lapachones inhibit aromatase in the sub-micromolar range, with IC₅₀(β) = 0.78 ± 0.06 μM and IC₅₀(α) = 10.6 ± 2.4μM, similar to the steroidal aromatase inhibitor Exemestane (IC₅₀: 0.02-0.2 μM). A molecular docking study comparing these compounds with androstenedione, one of the native aromatase substrates, identified their binding sites and specific interactions with the enzyme. The Yeast Estrogen Screening assay indicated that both compounds lacked hERα-agonistic activity but exhibited antagonistic effects, similar to 4-Hydroxytamoxifen (Afimoxifene; IC₅₀ = 0.81 ± 0.65 μM). The IC₅₀ values were 0.33 ± 0.24 μM for β-lapachone and 48.3 ± 18.9 μM for α-lapachone. Overall, the study propose unexplored mechanism of action and highlights the dual role of α- and β-lapachones: inhibiting estrogen synthesis and serving as potent, selective estrogen receptor modulators, emphasizing their potential in cancer treatment, especially for hormone-dependent cancers.

Abstract: Symptomatic dermographism (SD) is the most common form of chronic inducible urticaria, typically presenting with pruritic, linear wheals that appear within minutes after stroking the skin and resolve within 30 minutes. However, not every linear urticarial eruption following friction or scratching is true SD. We present three clinical cases initially misdiagnosed or suspected as classic SD, but which after detailed evaluation proved to be different entities. The first case was an atypical follicular subtype of SD itself, with a false-negative initial FricTest. The second case was cholinergic dermographism — a rare variant of cholinergic urticaria requiring two concurrent triggers (sweating and stroking) — in a patient with hyperhidrosis. The third case was flagellate dermatitis caused by consumption of inadequately cooked Shiitake mushrooms, with lesions persisting for days and no response to antihistamines. These cases highlight that even a characteristic linear wheal pattern is not pathognomonic for SD. A thorough history, recognition of atypical morphologies, and appropriate provocation testing (including combined triggers when needed) are essential to avoid diagnostic pitfalls and initiate effective therapy.

Abstract: Large language models (LLMs) are advancing intelligent writing systems from local text continuation and language polishing toward long-form structured text generation. However, directly generating full-length academic paper drafts remains challenging due to unclear research objectives, unstable discourse structures, insufficient long-text coherence, and the lack of explicit quality control mechanisms. To address this long-form structured generation task, we propose MetricDraft, a metric-driven framework for academic paper draft generation. The framework organizes the drafting process as a closed-loop pipeline comprising research ideation clarification, structural anchoring, section-by-section generation, quality assessment, and feedback-driven revision. Its key components include adversarial research ideation clarification, staged structural anchoring, the PRISM structured metric system, progressive context injection with section-type-aware guided generation (PCI+STAGG), and a metric-feedback-driven generation–evaluation co-optimization mechanism. Experimental results demonstrate that MetricDraft achieves significantly higher composite quality scores compared to one-shot generation, summary-based context passing, and context-accumulation-only baselines, with differences reaching statistical significance. Furthermore, PRISM exhibits moderate-to-high positive correlations with expert ratings, providing preliminary evidence that it can serve as an auxiliary evaluation reference for draft quality diagnosis and iterative revision. This work reformulates academic writing as an adjustable, assessable, and iteratively optimizable long-form structured text generation problem, offering methodological insights for human–AI collaborative writing and intelligent text generation system design.

Abstract: Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability.

Abstract: The role of human leukocyte antigen F (HLA-F) at the maternal-fetal interface (MFI) during viral infection and its regulation by interferon signaling remains poorly understood. Here, we investigated HLA-F expression and regulation in first-trimester trophoblast cells following activation of the type I interferon pathway and viral infection. We demonstrate that HLA-F is significantly upregulated at both mRNA and protein levels in response to Poly(I:C) and IFN-β in a dose- and time-dependent manner, suggesting its regulation as an interferon-stimulated gene (ISG). Zika virus (ZIKV) infection similarly induced HLA-F upregulation over time. In contrast, HSV-2 infection downregulated HLA-F mRNA while maintaining steady protein levels, indicative of virus-specific regulatory mechanisms. Moreover, we identified a soluble HLA-F secreted following Poly(I:C) stimulation. These findings reveal that HLA-F is dynamically regulated in trophoblasts during viral challenge and type I IFN signaling activation, supporting its broader immunomodulatory role in antiviral defense and immune tolerance at the MFI.

Abstract: Cape Coast is a prominent tourism destination in Ghana, distinguished by its historical landmarks, coastal ecosystems, and cultural heritage. Yet the city faces mounting threats from environmental hazards such as coastal erosion, flooding, extreme heat, and lagoon degradation, which directly compromise the sustainability of its tourism sector. Guided by the Sustainable Tourism Development Theory (STDT) and the Tourism Resilience and Adaptation Theory (TRAT), this study investigates the impacts of these hazards on tourism development, the effectiveness of current disaster risk reduction (DRR) strategies, and the roles of key stakeholders in building sectoral resilience. Using a qualitative research design, data were collected through in-depth interviews with eighteen stakeholders comprising four policymakers, six community leaders, five tourism business operators, and three representatives from non-governmental organisations, alongside documentary analysis of four institutional reports. The study contributes to the literature by demonstrating that fragmented, reactive DRR strategies and weak stakeholder coordination undermine Cape Coast’s tourism resilience, and by showing how urban natural assets, a dimension largely neglected in existing tourism-DRR scholarship, are central to both hazard exposure and adaptive capacity. The findings call for integrated, ecosystem-based DRR frameworks that align governance mechanisms with sustainable tourism imperatives.

Abstract: This study examines how digital income diversification, measured by the non-interest income ratio (NII), affects bank performance and risk in emerging Asian markets. Drawing on panel data from 44 banks across China (36) and Thailand (8) over 2022-2025, the analysis employs fixed-effects regressions, mediation analysis, and subsample testing to unpack the performance implications of digital transformation. Results indicate that NII exerts a statistically significant positive effect on bank profitability (ROA and ROE), with no corresponding increase in risk exposure as measured by Z-score. The relationship is markedly stronger among large banks, consistent with scale advantages in technology infrastructure, network effects, and regulatory compliance cost amortization. Cost efficiency does not mediate the NII-performance nexus, suggesting that revenue-side mechanisms dominate in this context. Cross-country comparisons reveal stable but modest effects in China's mature digital ecosystem against larger but less precise coefficients in Thailand's early-stage transition. These findings challenge the Western-centric complexity-risk narrative and highlight institutional boundary conditions governing digital banking outcomes in emerging markets.

Abstract: This study examines how ESG performance, innovation performance, and policy support relate to organizational resilience in China’s real estate industry. Drawing on the Resource-Based View, Institutional Theory, and Configurational Theory, the study conceptualizes organizational resilience through recovery and resistance capacities. Using panel data from 80 Chinese A-share listed real estate firms during 2015–2024 (800 firm-year observations), the study applies fixed-effects regression, robustness tests, and heterogeneity analyses. The findings show that ESG performance positively influences accounting-based recovery, particularly return on equity, but negatively affects market-based recovery, reflected in Tobin’s Q in the baseline models. Additional analysis reveals a U-shaped relationship between ESG performance and Tobin’s Q, suggesting that initial market valuation penalties may decline as ESG engagement deepens. Innovation performance shows limited baseline effects but becomes more relevant in alternative specifications related to recovery and leverage. Policy support demonstrates limited direct effects, indicating a more conditional role. Overall, organizational resilience is shaped by heterogeneous interactions among ESG, innovation, and policy-related factors.

Abstract: Accurate prediction of significant wave height (SWH) is essential for fisheries management, coastal socio-economic activities, and marine ecological conservation. In recent years, deep learning-based bias correction has shown considerable potential for improving numerical wave forecasts. However, many existing approaches are still constrained by limited receptive fields and often struggle to capture long-range spatiotemporal dependencies in wave forecast errors. To deal with this issue, we adapt and improve a video prediction framework, namely the Vision Mamba Recurrent Neural Network (VMRNN), to model and correct the spatiotemporal patterns of SWH prediction biases. Comprehensive evaluations show that the multi-channel VMRNN achieves consistently high predictive accuracy across different forecast lead times and sea-state conditions. When validated against reanalysis data, the proposed model reduces the root mean square error (RMSE) of WAVEWATCH III forecasts by 28.2%, 26.1%, and 24.7% at lead times of 24, 48, and 72 hours, respectively. It also preserves the spatial structure of SWH fields quite well, with the spatial structural similarity index remaining as high as 0.945 even at the 72-hour lead time. Regional assessments over high-wave areas further indicate that VMRNN can effectively reduce both the mean error and the systematic overestimation commonly found in numerical wave models. Additional validation using in-situ buoy observations confirms that the model has a robust ability to correct systematic positive biases, especially for wave heights ranging from 0.5 m to 2 m. Taken together, these results suggest that VMRNN has strong spatiotemporal modeling capability and can serve as a promising post-processing framework for improving operational physics-based wave forecasting systems.

Abstract: This paper addresses the robust trajectory tracking problem of an Unmanned Aerial Vehicle (UAV) equipped with a 2-DOF manipulator, designed for fast aerial manipulation of varying payloads. To overcome the high computational cost and adaptability limitations of traditional model-based controllers, this work introduces a novel hybrid gain-scheduling framework that shifts the computational complexity to the pre-flight phase. The approach utilizes an approximate inverse dynamics linearization, based on fixed nominal models, which transforms the complex nonlinear system into a simple linear plant with bounded, structured uncertainties. The entire configuration space, including manipulator states and a range of payload properties, is partitioned into dynamically similar regions using K-Means clustering. For each local region, a dedicated robust PD controller is designed using a multi-objective Genetic Algorithm (GA). This framework also successfully implements a gain interpolation technique to mitigate the potential for abrupt control actions. Simulation results validate the controller’s ability to maintain high-precision tracking during fast maneuvers and payload switching, confirming the robustness and adaptability of the offline-tuned design.