Search Results (1,877)

Immunity plays critical roles in cutaneous melanoma. We investigated the association between the pro-inflammatory interleukin-1β gene (IL-1B) rs16944 (-511 C > T) promoter single nucleotide polymorphism (SNP) and cutaneous melanoma susceptibility and clinical features. This observational case–control study included 133 patients with cutaneous melanoma and 900 healthy controls from Northeastern Italy. The rs16944 C > T polymorphism was determined by genomic DNA restriction fragment analysis. The IL-1B rs16944 C allele (OR = 1.44, p = 0.014) and CC genotype (OR = 1.48, p = 0.037) were associated with modestly higher odds of melanoma. Among melanoma patients, the CC genotype was associated with Stage I disease (OR = 2.45, p = 0.016) and Breslow thickness ≤ 0.75 mm (OR = 2.27, p = 0.049), whereas it was less frequent in Stage IV melanoma (OR = 0.37, p = 0.029). The CT genotype was associated with Stage IV melanoma (OR = 3.03, p = 0.014) and with lower-limb (OR = 2.45, p = 0.038) and lower-extremity (OR = 3.09, p = 0.005) melanoma. These divergent associations are exploratory and do not establish disease trajectory or a functional effect of rs16944; mechanistic interpretation remains uncertain because IL-1β expression or activity was not measured in this cohort. To our knowledge, this is the first report of an association between a genetic polymorphism and lower-limb/lower-extremity melanoma. These exploratory findings require confirmation in independent cohorts. Full article

In a closed-inventory large language model (LLM) system such as Online-CADCOM, which recommends engineering tools from a verified inventory, we measure inventory non-compliance, that is, a mention-level event in which the model recommends a tool not present in the verified inventory. We use this inventory-relative sense of hallucination throughout: an out-of-inventory mention may be a fabricated tool or a real commercial tool absent from the curated inventory, so the metric reports inventory non-compliance rather than factual fabrication. We evaluate a three-mechanism mitigation stack consisting of database-grounded context injection, fixed vocabulary constraints, and enforced JavaScript Object Notation (JSON) output across three commercial LLM providers (OpenAI, Anthropic, Google), two model generations, and two output modes (standard and reasoning), totaling 6912 Application Programming Interface (API) calls over 12 configurations. Under a recall-equalized detector adopted as the primary metric, the inventory non-compliance rate, which we denote the hallucination rate (HR) following common usage, decreases from roughly 69–80% to 4–13% under the full architecture. The cross-provider average is similar across the two generations tested (8.5% Generation 1 (Gen1), 6.9% Generation 2 (Gen2)), although per-provider directions diverge. We also examine the C3 configuration, in which only JSON output enforcement is active without grounding. A naive detector reports a large hallucination increase over the unconstrained baseline (+10.1 percentage points (pp) Gen1, +15.1 pp Gen2), but we show this gap is largely a detection-format artifact: structured JSON fields make out-of-inventory tools easy to extract, whereas the same real tools are frequently missed in free text. Under a recall-equalized detector the gap narrows to +2.6 pp (Gen1) and +4.8 pp (Gen2) and remains statistically significant only for two current-generation models, indicating a small, current-generation effect rather than a universal one. Reasoning-mode models provide no statistically significant improvement under architectural constraints. A frequency-weighted audit shows that the majority of remaining out-of-inventory mentions correspond to real engineering tools absent from the platform’s inventory. Under the full architecture, roughly half of responses (pooled $P_{\mathrm{any}}\approx 49.5\%$) still contain at least one such mention, indicating that handling unseen tools remains an open challenge for closed-inventory recommendation systems. Our evidence comes from a single engineering platform with four related electronic-design and power-electronics domains, so the findings characterize this setting rather than recommendation domains in general. Full article

This study characterised the profiles of 16 polycyclic aromatic hydrocarbons (PAHs) in the indoor air of hairdressing, beauty, and nail salons and assessed the associated incremental cancer risk (ILCR). A distinctly divergent exposure pattern, particular to the nature of the service, was exhibited: light PAHs, notably naphthalene, prevailed in nail salons, while in hairdressing salons, the profile was thermally conditioned, with a preponderance of polycyclic compounds such as fluoranthene, pyrene, and benzo(g,h,i)perylene. The most severe, carcinogenic profile (including benzo(a)anthracene and dibenzo(a,h)anthracene) and the highest individual ILCR value (3.3 × 10⁻⁶) were observed in a beauty salon, though nail salons also exhibited elevated risk levels (1.9 × 10⁻⁶). In all premises, however, ILCR remained below the 10⁻⁵ threshold. The findings are consistent with earlier reports indicating that beauty salons are complex microenvironments with multiple concurrent emission sources, with significant contributions from cosmetic products and thermal processes in determining pollutant concentrations. The findings underscore the necessity for preventive measures customised to the nature of the salon, such as product substitution and enhanced general and local ventilation. Moreover, they highlight the imperative for further research to be conducted into personal exposure over extended time frames. Full article

Coral reef ecosystems of the Egyptian Red Sea face escalating pressure from compounding climate-driven warming and intensifying coastal development, dual stressors whose spatial expression and governance implications remained insufficiently characterized prior to the designation of the Great Fringing Reef Marine Protected Area under Prime Ministerial Decree No. 4419/2025. This study develops an integrated multi-decadal framework combining 32 years of sea surface temperature and surface current velocity records, two decades of satellite-derived chlorophyll-a observations, and four decades of satellite-derived coral reef extent mapping across five monitored coastal sites. Basin-wide SST warming of ≈1.15 °C is spatially heterogeneous, with the northern coastal sector warming nearly 25% above the southern sector rate. Surface circulation diverges structurally at Ras Banas: acceleration in the north is consistent with enhanced advective dilution of anthropogenic inputs, whereas consistent deceleration in the south (mean −0.03 m/s) is associated with an increased ecological footprint of wadi-derived sediment and nutrient discharge. Sharply localized chlorophyll-a anomalies, reaching 0.97 mg/m³ at Hurghada and spatially congruent with documented development chronologies, are consistent with anthropogenic disruption of oligotrophic water quality as an important contributor to reef degradation at northern sites. Field evidence from 2026 repeat surveys, interpreted alongside documented severe regional bleaching affecting southern Egyptian reefs during 2024, suggests that climate-driven thermal stress constitutes a parallel and active pressure, particularly in the southern sector where thermal and anthropogenic stressors appear to have converged during the period of most acute reef contraction. Landsat-derived losses of 24.1–50.2% across all monitored sites since 1985, including a 33.3% contraction at Halaib–Shalateen between 2022 and 2025 alone, reflect the cumulative outcome of these interacting pressures. These findings support the ecological necessity of the 2025 MPA while identifying spatially differentiated, dual-stressor governance priorities essential to translating its policy commitment into measurable conservation outcomes. Full article

Accelerated urbanisation and associated land-use conversion are reshaping the composition and functions of terrestrial ecosystems globally. In Southeast Asia, ecological change is increasingly mediated not only by demographic urbanisation but also by urban expansion, peri-urban development, and the conversion of agricultural, coastal, and forest land into built-up surfaces. This study integrates multi-source geographical information from 2014 to 2024 to examine 351 provincial-level units in 11 Southeast Asian nations. To describe the spatial-material dimension of urbanisation and ecological conditions, two indices were created: the Composite Nighttime Light Index (CNLI), used as a proxy for urban expansion and built-up development intensity, and the Improved Remote Sensing Ecological Index (IRSEI), which is tailored to tropical coastal locations. The development of human-environment interactions was measured using the Coupling Coordination Degree (CCD) model. Pathways of synergy and trade-off were found using an incremental four-quadrant framework, and nonlinear causes of spatial differentiation were investigated using Spearman correlation and the Optimal Parameter-based Geographical Detector (OPGD). Uncertainty was addressed through data-quality masking, annual compositing, consistent index-construction rules, and cautious interpretation of CCD and driver results as relative provincial-scale patterns. The regional mean CCD rose from 0.250 to 0.314 during the decade, showing a slow improvement; nevertheless, most places still have low to moderate levels of coordination. There is clear pathway divergence, with 38.7% of locations enduring trade-offs where built-up development happens at the price of ecological quality and 58.4% of regions seeing synergistic improvement. The coupling pattern is primarily driven by built-up area expansion, with multiple factors jointly producing strong nonlinear enhancement effects. Climate conditions and forest disturbance further strengthen these effects. This study extends beyond single-country analyses by situating remote-sensing coupling results within land-use transition, peri-urbanisation, urban–rural linkage, and regional-governance perspectives. It provides quantitative evidence to support differentiated policy strategies in rapidly urbanising places. Full article

Background: Sex differences in cognitive impairment in Parkinson’s disease (PD) have been reported, with several studies suggesting greater cognitive decline in males. However, evidence on long-term trajectories across specific cognitive domains is limited, as most prior work is cross-sectional. Aim: To investigate sex-specific longitudinal changes across multiple cognitive domains over 9 years in idiopathic PD, compared to age- and sex-matched controls. Methods: We analyzed data from the prospective ParkWest study, including n = 190 persons with PD (PwPs) and n = 202 controls. Regularly assessed cognitive domains included semantic fluency, processing speed, cognitive control, memory, and visuospatial ability. Linear mixed-effect models including a three-way interaction between time, disease status, and sex, to test whether sex-specific cognitive change in PD differed from that observed in controls, were estimated. Models were adjusted for age, years of education, baseline depression, and UPDRS part II. Results: Male PwPs showed faster decline than female PwPs in most cognitive measures, resulting in gradually increasing sex differences over 9 years. However, compared with controls, the differential sex-specific decline was statistically significant only for semantic fluency (p = 0.013) and processing speed (p = 0.027 and p = 0.035). These findings remained significant after covariate adjustment. Conclusions: PD was associated with increasing sex differences in semantic fluency and processing speed, likely driven by faster decline in males. Other domains did not show sex-specific divergence over time, suggesting that sex-specific vulnerabilities in cognitive trajectories are domain-specific rather than generalized among PwPs. Full article

Moment condition models are popular in statistics and econometrics, as they provide a powerful and flexible framework for estimation. However, estimation procedures based on these models can be sensitive to misspecification or the presence of outliers in the data. In the present paper, we introduce a class of robust estimators for moment condition models, representing robust alternatives to minimum empirical divergence estimators. The estimators are constructed by using truncated orthogonality functions and minimizing divergences in dual form, allowing to limit the impact of outliers or model deviations. We give the expressions of the influence functions of the estimators and prove their robustness. We also prove that the estimators are consistent. These theoretical results together with numerical examples, based on Monte Carlo simulations, show that extreme observations do not disproportionately affect the final estimates. Full article

Creative thinking has become an increasingly important competence in education, yet less is known about how it may be supported in human–AI collaboration as large language models (LLMs) are increasingly employed as partners in learning and problem solving. This study examined whether and how cognitive support and social interaction in AI-assisted collaboration were associated with divergent and convergent thinking. A total of 92 undergraduate students completed creative problem-solving tasks with GPT-4.0. Data were collected from task outputs and self-report measures, and the hypothesized associations were tested using structural equation modeling, with indirect effects examined as supplementary analyses. Results showed that in human–AI collaboration, social interaction rather than cognitive support was associated with more positive affective experiences, and this affective pathway was linked specifically to convergent thinking rather than divergent thinking. These findings suggest that associations between AI-assisted collaboration and creative thinking may depend less on perceived cognitive support alone than on the extent to which collaboration is experienced as socially engaging and affectively positive, particularly for convergent thinking. Full article

Driven by the human demand for tea beverages, tea production has expanded worldwide. Tea production in China, the world’s largest tea producer, is constrained by Dendrothrips minowai and Matsumurasca (Matsumurasca) onukii. Various management measures for controlling these pests have been developed, but their implementation requires knowledge of the pest distribution, which is currently insufficient. Therefore, precise management of these pests is a major challenge. Using optimized MaxEnt models for the distributions of the two pests across the current and future timeframes, we predicted the overlap of their suitable habitats. The central and southern provinces of China were identified as the primary suitable habitats of both pests at the current time. The suitable habitats will diverge in the future, with D. minowai habitats declining by 29.70–61.90% and M. onukii habitats increasing by 8.05–43.62%. These results demonstrate species-specific responses to climate change. Despite a decrease in overlap areas, the current and future overlap areas consistently coincide with some major tea-growing areas such as Guizhou, Yunnan, Hunan, and Fujian. The predicted overlap areas can aid the identification of priority areas, optimization of resource allocation, and dynamic adjustment of management measures, improving the precision and efficiency of managing the two pests. Full article

We introduce the Probabilistic Multimodal Consistency Index (PMCI), a prototype measure of probabilistic semantic agreement used to quantify how much two modalities share in terms of mutually compatible affective evidence. PMCI does not model feature fusion or emotion classification directly; instead, each modality is mapped to a different probability distribution over learnable latent affective-agreement anchors, and the agreement of those distributions is quantified via the Jensen–Shannon divergence. Consequently, we propose PMCI not as a substitute for a discriminative model of pair matching, but rather as an auxiliary diagnostic index of cross-modal affective agreement. Experiments were conducted on pose-based facial and body keypoint sequences obtained from the RAVDESS and MELD datasets. In the updated RAVDESS setup, we extended the cache of preprocessed keypoint files to include all 24 actors, and we sampled four distinct pose-derived 12-frame crops per source video. This resulted in a total of 11,520 pose-derived windows, with the actor label determining the train/validation/test splits for the RAVDESS dataset. MELD contained 402 filtered pose-derived windows and served as an auxiliary in-the-wild validation setting with additional noise. When applying the updated RAVDESS standard pair-matching, DirectCosine_K0 achieved ROC–AUC = 0.943 and PR–AUC = 0.917, demonstrating that it is indeed the best exact pair-matching baseline and that PMCI-based configurations are not as accurate as DirectCosine_K0 when performing common exact face–body pair discrimination. Under this protocol, PMCI_K8, PMCI_K16, and PMCI_K32 produced ROC–AUC scores of 0.851, 0.805, and 0.887, respectively. Running a one-window-per-source-video experiment with 100 repetitions yielded similar results in the following order: DirectCosine_K0 = 0.942 plus-minus 0.009, PMCI_K16 = 0.807 plus-minus 0.016, and PMCI_K32 = 0.893 plus-minus 0.012, which shows that the results obtained were not the result of only repeated temporal crops. In the DirectCosine-mined hard negative setting, PMCI produced modest diagnostic separation above chance, where Direct+PMCI_K32 yielded ROC–AUC = 0.582. In a suite of shuffle control, permutation control, model initialization control, temperature control, anchor usage control, control latency, and pose perturbation control experiments, the diagnostic stability of PMCI was tested, confirming that PMCI is sensitive to both pose-estimation accuracy and domain shift. Finally, PMCI must be interpreted as a probabilistic diagnostic index of cross-modal affective agreement that is based on pose, not as a general-purpose emotion-recognition system. Full article

Environmental, social, and governance (ESG) ratings are now central to how firms’ sustainability is assessed, yet leading providers disagree sharply, injecting measurement error and uncertainty into any rating-based decision. This study proposes and applies a transparent, fully reproducible alternative for the environmental pillar: an ordinal, sector-based carbon-exposure measure constructed solely from firms’ publicly observable industry classifications, free of vendor scoring choices and reconstructable at zero cost. The measure captures structural, between-sector carbon exposure; it is not a firm-level environmental-performance score and does not use reported emissions. Using 3067 firm-year observations on 1049 listed non-financial firms that carry an observed sector classification across six developed markets (the United States, the United Kingdom, Australia, France, Italy, and Spain) over fiscal 2021–2025 (with a small number of early fiscal-2026 period-ends), we assess the measure’s informativeness by estimating its association with accounting profitability in high-dimensional fixed-effects models. Descriptively, more carbon-intensive sectors are less profitable (a monotone brown tilt), but this association is not robust: once firm controls and country-by-year fixed effects are absorbed, carbon exposure is not significantly related to return on assets, return on equity, or net margin, and there is no evidence of a concave (inverted-U) pattern. The measure does recover a real operating difference, higher asset turnover among carbon-intensive sectors, but this is offset by margins and does not translate into a profitability premium. A within-firm design detects no differential effect of mandatory non-financial reporting. The contribution is a transparent, rule-based environmental measure that sidesteps rating divergence; the honestly reported null on profitability cautions that apparent effects are sensitive to sample construction and sectoral composition, and positions the measure as a complement to, rather than a substitute for, proprietary ESG ratings. Full article

Urban systems exhibit multifractal scaling behaviors arising from their hierarchical and heterogeneous spatial organization. Yet the Green View Index (GVI), a widely used indicator of street-level green visibility, is conventionally measured as a static value, overlooking how green exposure is reorganized across scales, which may bias greening evaluations and planning decisions. This study proposes an “X-minute Greenery” framework to examine GVI as a scale-dependent spatial process. Street view imagery and walking isochrone data from Hong Kong and Shenzhen, China, are integrated to measure GVI across 5, 10, 15, and 20 min walking ranges, while a multifractal-inspired approach is developed to characterize its scale-dependent variation structure. Empirical findings show that (1) GVI variation is not random but associated with urban governance and spatial morphology; (2) a staged pattern of variation is identified, with a GVI level around 0.2 marking an approximate shift in configurations of greening dynamics; (3) even under similar initial GVI conditions, areas can still follow divergent variation patterns across walking scales, exposing latent spatial inequalities aligned with institutional and morphological factors. By framing urban green visibility as a scale-aware complex-systems phenomenon, this study adapts multifractal reasoning to street-level greenery, challenges the “equal green, equal policies” assumption, and supports context-sensitive planning interventions. Full article

Chaunocephalus ferox (Rudolphi, 1795) is an intestinal trematode of the family Echinostomatidae that commonly infects waterbirds, including the Asian Openbill (Anastomus oscitans). Despite its wide geographic distribution and ecological relevance in migratory birds, molecular data for this species remain limited. This study aimed to characterize the genetic variation and phylogenetic position of C. ferox recovered from a deceased Asian Openbill in Thailand. Adult flukes were examined using nuclear (28S rDNA and 5.8S–ITS2) and mitochondrial (cytochrome c oxidase subunit 1; CO1) markers. Sequence analyses revealed low intraspecific variation among Thai isolates, with a single variable site detected in each marker. However, comparisons with sequences from Egypt, China, and Ukraine demonstrated measurable genetic divergence, suggesting possible geographic and host-associated structuring among C. ferox populations. Phylogenetic analyses based on nuclear and mitochondrial datasets consistently supported the monophyly of C. ferox within Echinostomatidae and confirmed its distinct genetic lineage among echinostomes. These findings provide baseline molecular data for a trematode parasite of migratory waterbirds, highlighting the potential role of avian hosts in the long-distance dispersal of helminths across wetland ecosystems. Broader geographic sampling is needed to better understand the population structure and evolutionary dynamics of this parasite. Full article

Dynamic consent ecosystems have become increasingly complex due to the widespread adoption of Consent Management Platforms (CMPs), multi-layer preference interfaces, asynchronous rendering architectures, and adaptive interaction workflows. Existing privacy-auditing approaches primarily rely on static interface inspection and therefore provide limited support for reconstructing and evaluating dynamic consent interactions. To address these limitations, this study proposes a computational measurement framework for the automated reconstruction and analysis of dynamic consent ecosystems. The framework integrates five computational layers for browser-based acquisition, interaction sensing, multi-layer synchronization, consent-state verification, and Adaptive Cognitive Load Dark Pattern (ACL-DP) operationalization. The proposed methodology combines asynchronous browser automation, interaction workflow reconstruction, multi-source evidence synchronization, backend consent verification, and rule-based mechanism scoring to transform complex consent interactions into reproducible quantitative representations. Evaluation across 18,665 consent ecosystems generated 59 synchronized variables spanning interface, interaction, textual, and consent-state dimensions. Workflow reconstruction successfully recovered interaction trajectories for 99.6% of observable consent environments, while backend verification identified consent mismatches in 77.6% of environments with complete frontend–backend aligned evidence, revealing substantial divergence between observable consent decisions and backend consent behavior. The ACL-DP framework operationalizes four mechanism families, Effort Engineering, Attention Engineering, Cognitive Load Amplification, and Algorithmic Adaptivity, the latter capturing observable runtime, session-dependent, context-sensitive, and backend-mediated variation in consent behavior. The results revealed persistent procedural and attentional asymmetries, recurrent hidden rejection mechanisms, and widespread frontend–backend consent inconsistencies, with Effort Engineering emerging as the dominant manipulation strategy. Validation through reproducibility analysis, sensitivity analysis, statistical uncertainty assessment, and a human benchmark of 100 independently annotated websites demonstrated high inter-run consistency and moderate-to-substantial inter-rater agreement, supporting the framework’s reliability and validity. This work provides a reproducible foundation for large-scale privacy interaction analysis and evidence-based evaluation of dynamic consent ecosystems. Full article

Purpose: This paper aims to systematically synthesize academic research published between 2020 and 2025 that investigates environmental, social, and governance (ESG) ratings and scores, with a focus on their methodologies, comparative performance, and impact on firm outcomes. Design/methodology/approach: A systematic literature review (SLR) was conducted using the Lens.org scholarly database. A structured title search retrieved 334 open access journal articles published between 2020 and May 2025 containing the terms “ESG Score”, “ESG Rating”, or “ESG Rater”. The PRISMA 2020 protocol guided the selection and screening process. Findings: The literature exhibits growing concern about the divergence among ESG ratings, the methodological opacity of rating providers, and the variable financial implications of ESG scores. Common themes include score disagreements, rating agency biases, and emerging models for standardizing ESG assessments. Originality: This review provides the most up-to-date synthesis of the ESG rating literature, focusing exclusively on articles explicitly addressing ESG ratings or scores in their titles. It contributes clarity to the fragmented ESG measurement space by organizing findings around key methodological and evaluative debates. Full article