Search Results (1,310)

Managing the transition risks of carbon-intensive firms is essential for reconciling climate governance with the stable operation of the real economy; nevertheless, existing scholarship has yet to fully elucidate how climate policy uncertainty contributes to the formation of these risks. In this paper, we develop a firm-specific measure of climate policy uncertainty exposure by integrating China’s aggregate climate policy uncertainty index with climate-risk-related textual data retrieved from listed companies’ annual reports. Drawing on a panel dataset of A-share listed companies in nine carbon-intensive sectors over 2010–2023, we employ a partial-linear double/debiased machine-learning methodology to investigate how climate policy uncertainty exposure influences multidimensional firm transition risk. Our baseline estimations indicate that greater climate policy uncertainty exposure is associated with a statistically significant rise in transition risk among high-carbon firms, with the preferred model producing a coefficient estimate of 0.0243. These findings remain robust to an array of sensitivity checks and endogeneity-correction procedures. Mechanism analysis provides evidence consistent with four potential channels involving weaker intra-industry competition, lower corporate risk-taking, tighter financing constraints, and higher agency costs. Heterogeneity examinations reveal that the detrimental impact is particularly evident among larger enterprises, high-technology companies, and firms characterized by comparatively lower pollution levels. Further analysis based on conditional average treatment effects and best linear predictors reveals that media supervision and the presence of long-term institutional investors substantially reduce the extent to which climate policy uncertainty translates into firm transition risk. This study provides firm-level empirical evidence elucidating how climate policy uncertainty shapes multidimensional transition risk in the low-carbon transformation of high-carbon industries. Full article

Large mineral flotation workshops in severe cold regions represent special industrial indoor environments characterized by the coexistence of limited ventilation and intense heat release. Such conditions generate pronounced spatial thermal stratification and localized heat accumulation within the workshop, leading to uneven worker thermal exposure and increased thermal discomfort and heat stress risk. However, conventional thermal comfort models were primarily developed for ordinary buildings with relatively stable thermal environments. Their applicability to large industrial workshops remains insufficiently validated. Nine representative monitoring points were arranged in the summer operating areas of the workshop, and thermal comfort surveys were conducted among 35 workers who had adapted to the local climate and working environment. The predicted mean vote (PMV) model was used as the baseline assessment framework, while an adaptive predicted mean vote (aPMV) model was further calibrated using field-based thermal sensation information. Monte Carlo simulation was employed to evaluate uncertainty propagation under field-data constraints, and Sobol sensitivity analysis was conducted to identify the dominant factors affecting thermal comfort predictions. The results demonstrated that the conventional PMV model exhibited a clear warm prediction bias under the investigated industrial conditions. After adaptive correction, the deviation from the field-based TSV was reduced by 82.93%, indicating improved agreement with workers’ actual thermal perception. Sensitivity analysis identified metabolic rate as the dominant contributor to aPMV output variance, with first-order and total-effect Sobol indices of 0.530 and 0.535. The proposed framework provides a scenario-specific approach for thermal comfort assessment in the investigated flotation workshop and offers preliminary methodological references for similar large-scale flotation workshops. Full article

Background: Fragrance-free policies are increasingly adopted to improve indoor air quality and reduce fragrance exposure linked to adverse health outcomes and accessibility barriers. This scoping review mapped the evidence on fragrance-free policies across sectors. Methods: Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we searched five peer-reviewed databases and grey literature sources. Documents describing formal scent-free or fragrance-free policies or guidance in workplaces, healthcare, educational, or public settings were included. Data were charted on policy characteristics, enforcement mechanisms, implementation supports, and governance features. Results: Sixty-three documents were included. Findings revealed substantial variability in terminology, scope, and enforcement. Many policies relied on voluntary compliance and awareness-based strategies, with limited integration of structural supports such as procurement controls, staff training, and evaluation frameworks. Enforcement mechanisms were predominantly reactive, responsibilities were inconsistently defined, and consequences for non-compliance were limited. Although many policies referenced multiple chemical sensitivity or fragrance sensitivity, accommodation pathways and accountability structures were frequently lacking. Four key themes indicate that many fragrance-free policies function as accommodation tools rather than integrated environmental health interventions. Conclusions: Strengthening definitional clarity, institutional accountability, and structural integration may enhance effectiveness and support more consistent, equitable, and evidence-informed approaches to reducing fragrance-related exposures in indoor environments. Full article

This Data Descriptor presents a georeferenced dataset of electromagnetic-field exposure measurements collected in Colombia between 2 November 2023 and 30 June 2024. The records originate from the Sistema de Monitoreo de Campos of the Agencia Nacional del Espectro (ANE), which uses isotropic probes to monitor broadband radiofrequency electromagnetic fields from 100 kHz to 8 GHz and reports six-minute averages of incident power density in W/m². The comma-separated value file contains 1,286,346 timestamped records and seven source variables, including Well-Known Text (WKT) point geometry. The principal 2024 subset comprises 995,238 records from 24 monitoring stations. We provide a reproducible workflow for timestamp and coordinate parsing, structural and numeric validation, station-level aggregation, and spatial sensitivity analysis using inverse distance weighting with the mean, median, and 95th percentile. The dataset does not provide frequency-resolved measurements, calibration certificates, or measurement-uncertainty budgets; these omissions limit the use of the public file for formal regulatory-compliance assessment. The accompanying repository includes validated station-level summaries, descriptive tables, figures, and reproducible R code. The package supports environmental monitoring, geospatial analysis, methodological comparison, and reproducible reuse of electromagnetic-field exposure data. Full article

Background and Objectives: Topical diclofenac is recommended over oral non-steroidal anti-inflammatory drugs (NSAIDs) for osteoarthritis, particularly in older adults and those with comorbidities, on the assumption that low systemic absorption limits renal effects. Whether this presumed safety extends to patients already at increased risk of kidney injury has not been systematically evaluated. This review assessed the evidence on renal outcomes of topical diclofenac in adults. Materials and Methods: We conducted a systematic review (PROSPERO CRD420261393454) of studies reporting renal outcomes after topical diclofenac exposure. PubMed, Embase, Web of Science, and Scopus were searched from inception to 19 April 2026. Studies were stratified a priori into increased-renal-risk and general populations and synthesised separately, without pooling. Risk of bias was assessed with RoB 2, ROBINS-I, and JBI tools, certainty with GRADE, and synthesis followed the SWiM framework. Results: Eighteen studies, contributing data from more than 500,000 participants, were included; 14 underwent primary risk-of-bias appraisal (three at low, three at serious or high risk). In general populations, topical diclofenac produced little to no change in serum creatinine or creatinine clearance and smaller renal effects than oral diclofenac, providing high-certainty evidence of a favourable profile. In increased-renal-risk populations, one adjusted cohort reported higher acute kidney injury (AKI) risk among topical NSAID users, although exposure was predominantly to non-diclofenac agents; within the same cohort, topical NSAIDs carried lower risk than systemic NSAIDs. No study evaluated early renal injury biomarkers; certainty was moderate owing to indirectness. Conclusions: Relative to oral diclofenac, topical diclofenac shows a favourable renal safety profile, with high-certainty evidence in general populations. In adults at increased renal risk, moderate-certainty evidence derived from predominantly non-diclofenac exposure over short observation windows cannot exclude a modest excess of any-stage AKI; renal risk therefore appears reduced rather than absent. Diclofenac-specific studies in chronic kidney disease using sensitive biomarkers and longer follow-up are needed. Full article

Background/Objectives: The demand for reducing iodinated contrast agent (ICA) dose in cerebral CT angiography (CTA) for aneurysm detection has increased due to concerns over patient safety and image quality. The aim of this study is to systematically review the diagnostic accuracy, image quality, and dose efficiency of optimized ICA protocols in CTA for intracranial aneurysm detection. We also aimed to distinguish between direct diagnostic studies reporting aneurysm sensitivity/specificity and indirect technical studies, emphasizing patient-specific tailoring using cardiac output and lean body weight. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, and Web of Science were searched up to 2 June 2026. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2 and the NIH tool. Due to clinical and technical heterogeneity, a narrative synthesis was performed. Results: Eleven studies comprising 716 patients were included. Low tube voltage (70–80 kVp) combined with reduced iodine load (8–30 mL, 3.2–9 gI) maintained diagnostic accuracy for aneurysms >3 mm compared to standard protocols. Iterative reconstruction and deep learning improved contrast-to-noise ratio by 23–45% at reduced dose. Technical studies showed feasibility of ultra-low-volume protocols but lacked aneurysm-specific DSA validation. Conclusions: Reduced-contrast and low-kVp CTA protocols may preserve diagnostic performance for intracranial aneurysm detection while lowering iodine exposure. However, current evidence is limited by small sample sizes and heterogeneous protocols. Prospective, aneurysm-specific validation is required before clinical implementation. Full article

Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work operationalizes how personal-data disclosures combine structurally and how these structures can be translated into auditable governance actions. This paper proposes SEM-PDPL, a computational, privacy-law-informed risk-assessment framework for modeling public social-media exposure as semantic exposure graphs and mapping graph patterns to controls aligned with the United Arab Emirates Personal Data Protection Law (PDPL) and compatible with GDPR principles. SEM-PDPL combines governance scoping; a PDPL-informed disclosure taxonomy; hybrid extraction using rule-based methods; named-entity recognition; fine-tuned BERT; and schema-constrained large language model annotation, followed by graph construction at post, platform, corpus, and persona levels. The framework is evaluated on a synthetic multi-platform corpus of 1095 posts generated for 150 personas across 290 platform accounts. Results show that, within this controlled synthetic corpus, fine-tuned BERT provides the strongest extraction performance among six evaluated methods, achieving a macro-F1 of 0.975. Graph analysis shows that exposure density increases with aggregation, rising from 0.275 at post level to 1.000 at corpus level, and from 0.859 at platform level to 0.967 at persona level. Across all graph resolutions, quasi-identifiers emerge as the dominant weighted-degree and betweenness node, indicating that ordinary location, employer, school, and demographic cues often function as bridges connecting sensitive categories such as health and biometric data to identifying information. These findings indicate that, within this controlled corpus, privacy risk in public social-media data is not only attribute-based but also structurally graph-shaped. SEM-PDPL contributes an explainable and reproducible framework for identifying exposure hubs, sensitive bridges, and aggregation risks before applying masking, minimization, exclusion, retention, or review controls. The framework does not automate legal compliance; rather, it provides evidence-based decision support for privacy-aware social-media analytics. Full article

Microplastics (MPs) have been reported in commercial beverages, but substantial differences in study design make direct comparisons difficult. This non-systematic, focused narrative review critically synthesizes a purposively selected core set of 17 particle-count studies published from 2020 to 2026 on soft drinks, beer, wine, tea, coffee, juices, energy drinks, and related beverages, while considering recent complementary thermal-analysis evidence. Data were compared for study location, beverage type, analytical method, abundance, particle size, morphology, color, polymer composition, and packaging. Fourier-transform infrared spectroscopy-based methods were most common; Raman spectroscopy, fluorescence microscopy, scanning electron microscopy, and laser direct infrared imaging were used in selected studies. Reported soft-drink concentrations ranged from approximately 0.30 particles/L to 166 ± 62 particles/100 mL (1660 ± 620 particles/L), but these values cannot support a geographic ranking because minimum particle-size thresholds, confirmation criteria, blank corrections, sample volumes, and reporting units differed. Fibers and fragments were the dominant morphologies, and polyethylene terephthalate, polyethylene, polypropylene, and polyamide were frequently identified. Findings from beverages packaged in glass and aluminum, as well as plastic, indicate that source water, ingredients, processing equipment, filtration, closures, ambient deposition, and packaging can all contribute. Current intake estimates describe potential particle ingestion rather than absorbed dose or toxicological impact. Because current data largely reflect analytical sensitivity rather than true contamination gradients, this review demonstrates that reliable cross-study exposure assessments currently remain associated with considerable uncertainty, and this uncertainty will be difficult to resolve until particle-count data are normalized to harmonized size thresholds and paired with mass-based thermal analyses. Full article

Background: Indoor air quality (IAQ) represents a key public health and accessibility concern. Improving IAQ through policies and practices that reduce exposure to fragranced products and other indoor air contaminants may enhance accessibility and support equitable participation for individuals with Multiple Chemical Sensitivity (MCS). Methods: Using a qualitative-dominant mixed-methods design, we examined IAQ accessibility barriers and explored perceived implementation and accessibility gaps in existing fragrance-free policies among sixty individuals with MCS and related IAQ exposures across ten focus groups. Qualitative and quantitative data were collected in parallel, with thematic analysis conducted in NVivo. A structured IAQ and policy survey was administered to quantify reported experiences and policy impacts. Results: Approximately 63.3% of focus group participants reported dissatisfaction with the indoor environments they encountered. Qualitative findings highlighted indoor environments as inaccessible, inadequately controlled, and associated with ongoing exposure to fragranced products as well as other indoor pollutants. These findings indicate that current approaches often fail to ensure accessibility, leaving individuals with MCS exposed and excluded from full participation in workplaces, healthcare settings, and other public spaces. Conclusions: Comprehensive fragrance-free policies incorporating source control, education, ventilation, and accountability mechanisms may reduce exposure while improving accessibility and inclusion across the built environment. Full article

Background: Multiple chemical sensitivity (MCS), a recognized disability, is a chronic, multisystem condition in which exposure to common chemical substances provokes adverse health effects and functional impairment. Although population-level data and emerging clinical research support the pathophysiological basis of MCS, individuals living with the condition frequently encounter barriers to recognition and accommodation across employment, healthcare, and housing systems. This study examines patterns of accommodation requests, refusals, and associated socioeconomic impacts among adults living with MCS in Canada, with particular attention to how institutional recognition shapes access to supports. To our knowledge, this is the first national Canadian study to examine accommodation request outcomes across both employment and housing among adults with MCS, combining quantitative and qualitative data. A national cross-sectional survey of 119 Canadian adults living with MCS was conducted between January and February 2021. Quantitative data were analyzed descriptively to assess accommodation requests, outcomes, employment status, income, and housing stability. Qualitative responses were analyzed thematically to contextualize participants’ experiences of disclosure, and accommodation outcomes. Participants were predominantly female (87%) with post-secondary education (90%). Among participants who reported being unemployed (29%), 94% attributed it primarily to MCS-related limitations. Accommodation denial was common: 85% of respondents reported requesting accommodations, and 78% of those experienced refusals. On housing conditions, 52% reported living in unsafe housing (defined as housing with the presence of mould or symptom-triggering exposures), while 17% reported being homeless (e.g., moving frequently, personal vehicle, tent). Findings should be interpreted in light of the cross-sectional design, self-reported data, and community-based recruitment, which may limit generalizability. The findings suggest that inconsistent institutional recognition of MCS can function as a barrier to accommodation, contributing to health, economic, and social inequities. Addressing these barriers requires clarified guidance on the duty to accommodate multiple chemical sensitivity, proactive scent-free and lowest-emission policies in workplaces and housing, and improved healthcare provider education to support accommodation documentation. Full article

Background: Celiac disease is a chronic autoimmune enteropathy whose only established treatment is a strict, lifelong gluten-free diet (GFD). Beyond physical symptoms, the disease and its dietary management affect emotional, social, and economic functioning. Objectives: This study aimed to assess health-related quality of life (HRQoL) in adult patients with celiac disease and to identify factors associated with their daily functioning and adherence to the GFD. Methods: A cross-sectional survey was conducted between February and March 2026 among 101 adults with a self-reported diagnosis of celiac disease made by a gastroenterologist, recruited through online support groups. Data were collected using an author-designed sociodemographic questionnaire, the Gastrointestinal Quality of Life Index (GIQLI), and the Celiac Dietary Adherence Test (CDAT). Analyses (Mann–Whitney U and Kruskal–Wallis tests, repeated-measures ANOVA, Spearman correlations with false-discovery-rate correction, and multivariable linear and logistic regression with prespecified sensitivity analyses) were performed in R 4.5.2, with α = 0.05. Results: The most frequently reported difficulties were eating away from home, the high cost of the GFD, and limited product availability; the most common symptoms after inadvertent gluten exposure were abdominal pain, bloating, fatigue, diarrhea, and headache. HRQoL was lowest in the emotional and physical domains. Better dietary adherence was significantly correlated with higher HRQoL across the total score and all domains except the social domain, whose weak internal consistency in this sample (α = 0.57) precludes substantive interpretation (total GIQLI: ρ = −0.50; p < 0.001), and in the multiple regression, adherence remained the strongest independent correlate of HRQoL (β = −0.36; adjusted R² = 0.41). Longer time on the diet was independently associated with better adherence (adjusted OR = 0.55 per duration category; p = 0.017). In a small vocational education subgroup (n = 8), poorer adherence and lower HRQoL were observed relative to participants with higher education, though only the HRQoL difference survived correction for multiple testing (FDR-adjusted p = 0.011 vs. 0.060 for adherence); given the size of this subgroup, this comparison is exploratory and hypothesis-generating. Higher HRQoL was also associated with milder symptoms, better-rated medical care, greater product availability, and higher household income. Conclusions: In this sample, HRQoL scores were lowest in the emotional and physical domains; because the design was cross-sectional and included no comparison group, these data characterize the distribution of HRQoL within the sample and do not establish that celiac disease reduces HRQoL. Dietary adherence was the strongest correlate of patient functioning, alongside symptom burden, self-rated quality of medical care, and product availability. Because the design is cross-sectional, these associations do not establish direction, and whether comprehensive health education and psychological support improve outcomes requires prospective and interventional evaluation. Full article

Background/Objectives: Per-capita food availability may represent a large-scale indicator of nutritional environments potentially associated with dementia risk. However, whether national dietary environments are associated with dementia burden across different exposure periods remains unclear. Methods: We linked model-derived estimates of per-capita food availability based on Food and Agriculture Organization (FAO) food supply data for 2005, 2010, and 2015 with 2018 age-standardized dementia incidence, prevalence, and mortality across 109 countries. Multivariable ordinary least-squares models were estimated with adjustment for socioeconomic, demographic, environmental, and metabolic covariates. Results: Higher per-capita food availability was positively associated with dementia outcomes in minimally adjusted models, particularly for the 2005 exposure window, although these associations were substantially attenuated after full covariate adjustment. In contrast, the 2010 exposure window showed positive associations with dementia incidence and prevalence after full adjustment using the conventional significance threshold (α = 0.05), whereas no significant associations were observed for mortality or for the 2015 exposure window. Sensitivity analyses indicated that associations varied according to exposure timing and model specification. Conclusions: Overall, these findings suggest that large-scale nutritional environments are associated with variations in dementia burden across countries, although the substantial temporal overlap among exposure measures precludes inference regarding temporally specific effects; these associations should therefore be interpreted within the broader socio-demographic and metabolic context in which national food environments are embedded. Full article

Unmanned aerial vehicle (UAV) accidents pose growing challenges to public safety and airspace management. This study develops an open-data-driven hierarchical Bayesian network (BN) with a Leaky Noisy-OR mechanism to analyse factors associated with consequence severity among recorded UAV accidents, and extends it to a Bayesian decision network for response simulation. Using 633 public accident records and matched meteorological data, 14 binary risk-factor nodes spanning human, machine, environmental, and management dimensions were constructed. Stratified five-fold cross-validation yielded a mean validation F1 score of 0.922 and an AUC of 0.784. Backward inference ranked airspace exposure, wind, and operation error highest under severe-consequence conditioning, whereas sensitivity analysis identified wind, bad weather history, and operation error as the most influential root-node parameters. Under the assumed directed acyclic graph (DAG), the bad weather history→weather→environment→risk state path had the highest average edge-influence score (0.853). Under the baseline safety-priority assumptions, the reroute strategy was preferred, yielding the highest expected utility (31.967) and reducing the model-estimated post-decision high-risk probability from 81% to 45%. Alternative preference settings ranked the adjust strategy first. The framework integrates open-data severity analysis with assumption-explicit response simulation. Full article

Persistent racial and ethnic disparities in type 2 diabetes mellitus (T2DM) are incompletely explained by genetic susceptibility, obesity, lifestyle, and socioeconomic factors, suggesting that additional biological mechanisms may link environmental exposures to metabolic disease risk. Increasing evidence indicates that the exposome, encompassing environmental exposures across the life course, may influence long-term metabolic health through molecular mechanisms that integrate environmental signals with host biology. This Review synthesizes epidemiological, experimental, and mechanistic evidence on the gut microbiota–host epigenetic axis and its potential role in linking environmental exposures to population differences in T2DM susceptibility. Gut microbial dysbiosis alters the production and metabolism of short-chain fatty acids, bile acids, and tryptophan-derived metabolites. These microbial metabolites can modulate host signaling and epigenetic processes, including DNA methylation, histone modifications, and non-coding RNA activity, which may influence the expression of genes involved in glucose homeostasis, inflammation, and insulin sensitivity. Conversely, host epigenetic programs may influence intestinal barrier integrity and immune responses, thereby potentially affecting the gut microbial ecosystem, highlighting the reciprocal nature of host–microbiota interactions. We further examine how diet, psychosocial stress, environmental pollutants, and socioeconomic conditions may shape the microbiota–epigenetic axis across diverse populations, providing a framework for understanding differences in metabolic susceptibility without attributing disparities to intrinsic biological variation. However, direct human evidence linking the gut microbiota–host epigenetic axis to racial and ethnic disparities in T2DM risk remains limited, and much of the current framework is based on mechanistic studies, animal models, and associative human data rather than direct causal evidence. Further research in diverse human populations is needed to validate these pathways and establish their contribution to T2DM disparities. This framework may inform biomarker discovery, stratification, precision nutrition, and microbiome-targeted interventions and generate hypotheses for equitable prevention and personalized management of T2DM across diverse populations. Full article

High-speed and high-precision laser interferometric surface-form analysis must balance acquisition speed, phase accuracy, spatial resolution, and systematic-error control. With interferogram acquisition, phase extraction, and phase unwrapping as the main thread, this article reviews high-speed measurement approaches, including two-frame phase shifting, simultaneous phase shifting, and spatial carrier, and discusses the application of deep learning to phase recovery. Unified numerical simulations are used to compare the error characteristics of representative two-frame phase-shifting and spatial-carrier algorithms. The analysis shows that two-frame phase shifting can shorten the acquisition sequence but is relatively sensitive to noise, the phase-shift value, and preprocessing; simultaneous phase shifting can acquire multiple phase-shift states in a single exposure; and spatial carrier enables single-frame demodulation but is constrained by spatial bandwidth and retrace error. Deep learning provides new approaches to phase extraction, phase unwrapping, and end-to-end recovery, but still depends on consistency between the training data and actual measurement conditions. This article aims to clarify the implementation principles and applicable conditions of different technical approaches and to provide a reference for method selection in dynamic testing and in-line surface-form measurement. Full article