Search Results (4,302)

Geopolitical risk has become an increasingly important source of financial uncertainty, with implications for asset prices, financial institutions, capital flows, and financial stability. This review examines how geopolitical risk can be incorporated into financial risk management by synthesizing the literature on measurement, transmission channels, and risk governance. It discusses major approaches to measuring geopolitical risk, including news-based indices, country-specific indicators, broader uncertainty measures, and firm- or industry-level exposure measures. It then reviews how geopolitical risk is transmitted through asset pricing, volatility, tail risk, sovereign and funding channels, commodities, banking stability, and systemic spillovers. A central argument of the review is that geopolitical risk should not be treated as a standalone uncertainty variable. Rather, it is a cross-cutting risk driver that can affect market risk, credit risk, liquidity risk, counterparty credit risk, operational and legal risk, model risk, and systemic risk at the same time. Building on this synthesis, the paper proposes an integrative framework that links geopolitical risk indicators to exposure mapping, scenario translation, stress quantification, and governance action. The review highlights counterparty credit risk, collateral stress, reverse stress testing, and scenario governance as important areas for future research. Full article

Rhenium (Re) is a scarce technology-critical transition metal used in high-temperature superalloys, catalysts and other advanced materials, yet its occurrence in terrestrial biota and potential relevance to environmental exposure remains poorly characterised. This study investigated total Re in wild mushroom fruiting bodies collected from selected green spaces in Leicester and Leicestershire, United Kingdom, using inductively coupled plasma mass spectrometry (ICP–MS) and censoring-aware statistical analysis. The analytical dataset comprised 157 records. After exclusion of four unmatched tissue-only records, 153 analytical portions were reconstructed into 121 fruiting-body-level biological units, avoiding pseudoreplication from separately analysed cap and stem tissues. The analysis incorporated procedural digestion blanks, sample-specific dry-weight analytical limits and interval censoring. Under the primary analytical scenario, 73/121 units (60.3%) were non-detects, 21 (17.4%) were detected below the operational lower limit of quantification (LLOQ), seven (5.8%) were partially quantified and 20 (16.5%) were fully quantified. Overall, 48/121 units (39.7%) contained at least one Re signal above the operational detection threshold, whereas 27/121 (22.3%) contained at least one quantitatively valid component. Fully quantified concentrations ranged from 3.91 to 163.70 ng g⁻¹ dry weight. The complete dataset comprised 73 left-censored, 28 interval-censored and 20 exact observations. An interval-censored lognormal model estimated a population median of 1.055 ng g⁻¹ dry weight (95% bootstrap confidence interval: 0.672–1.571), with all 1000 bootstrap refits converging successfully. Classification was identical for all 59 units directly comparable under the primary and stricter same-batch procedural-background definitions. These findings extend the limited evidence for Re occurrence in wild fungal fruiting bodies and should be interpreted as environmental-occurrence data rather than as evidence of either adverse risk or safety. Because total elemental Re does not resolve chemical form, gastrointestinal bioaccessibility or internal dose, the measured concentrations do not support quantitative estimates of dietary exposure or human-health risk. Full article

Background/Objectives: Treatment for pancreatic ductal adenocarcinoma (PDAC) depends on early detection, yet most present at an inoperable stage. Focal pancreatic parenchymal atrophy (FPPA) is a prodromal imaging feature lacking objective quantification. We evaluated whether longitudinal, AI-measured pancreatic volume (PV) change is associated with subsequent PDAC. Methods: In this retrospective case–control study, we analyzed longitudinal CT from 40 PDAC patients and 61 controls, each with ≥5 years of pre-index imaging. PV was measured by AI segmentation and compared as a subject-specific rate of change, with robustness assessed via multivariable, mixed-effects, and sensitivity analyses. Results: PDAC patients showed more rapid PV decline than matched controls (5-year reduction 9.71 vs. 1.23 mL; p = 0.013), persisting after excluding diabetes and adjustment. A mixed-effects model confirmed faster decline, consistent across truncation windows but absent near diagnosis. Adding the marker to a basic covariate model raised the area under the curve (AUC) from 0.679 to 0.823. Exploratory transcriptomic profiling of microdissected FPPA identified 131 concordantly altered genes across PanIN- and invasive-PDAC-adjacent comparisons, with an immune-related signal present adjacent to non-invasive PanIN and larger adjacent to invasive PDAC. Conclusions: Patients who later developed PDAC showed greater long-term PV decline than controls. Exploratory tissue profiling raises the hypothesis that the same process underlying FPPA may also contribute to PV loss. Because the imaging and molecular analyses only partially overlapped and included no within-patient imaging–transcriptomic correlation, the proposed tissue-level correlate remains an unproven hypothesis requiring same-patient validation. This marker requires validation before use as a risk-enrichment signal, not a screening test. Full article

Background: Periodontal charting remains the gold standard for periodontal diagnosis but is time-consuming, operator-dependent, and not always feasible in routine clinical practice. In contrast, panoramic radiographs are widely available and increasingly amenable to automated analysis through artificial intelligence (AI). Recent AI systems have demonstrated high accuracy in the detection and quantification of periodontal bone loss and in radiographic disease staging. Objective: To review current evidence on AI applications in periodontal radiology and explore whether routinely acquired panoramic radiographs may support the development of AI-assisted radiographic periodontal charting. Methods: This narrative review summarizes recent evidence regarding AI-based periodontal and peri-implant radiographic assessment. The literature was identified through searches of PubMed, Scopus, and Google Scholar using combinations of terms related to artificial intelligence, periodontal disease, radiographic diagnosis, panoramic radiography, and peri-implantitis, with emphasis on studies relevant to AI-assisted periodontal assessment. Results: Recent deep learning architectures have demonstrated high diagnostic performance for periodontal bone loss detection, quantification, and disease staging. Current AI systems are capable of identifying radiographic manifestations of periodontal destruction and localising key anatomical landmarks, including the cemento-enamel junction and the alveolar bone crest. However, they remain unable to assess clinical parameters such as probing depth, bleeding on probing, suppuration, and tooth mobility. The review highlights the distinction between Clinical Attachment Level (CAL), Clinical Attachment Loss (CALoss), and radiographic measures of periodontal destruction. In this context, the concept of Radiographic Attachment Loss (RAL) is proposed as the radiographically measurable distance between the cemento-enamel junction and the alveolar bone crest, providing a conceptual estimate of periodontal support loss. Discussion: The principal novelty of this review is the proposal of AI-assisted radiographic periodontal charting as a clinically oriented framework for translating AI-derived radiographic information into meaningful periodontal assessment. Rather than replacing conventional periodontal charting, this approach aims to transform routinely acquired panoramic radiographs into a source of structured periodontal information that may support screening, risk stratification, longitudinal monitoring, and clinical decision-making. Conclusions: Artificial intelligence is unlikely to replace conventional periodontal examination. Nevertheless, AI-assisted radiographic periodontal charting may represent a realistic intermediate step between routine radiographic interpretation and comprehensive digital periodontal assessment. Future research should focus on validating AI-derived radiographic biomarkers, particularly Radiographic Attachment Loss (RAL), determining their clinical utility, and evaluating their potential contribution to periodontal screening, longitudinal monitoring and population-based oral health assessment. Full article

Intelligent navigation of mobile robots in unknown environments has become a key enabling technology for service and logistics applications. However, in unstructured scenarios, perception noise and environmental uncertainty can significantly degrade system performance, making it a major challenge to balance safety and efficiency. In this work, we propose a Human–Machine Shared Control method with Model Predictive Control constraints (HMSC). HMSC establishes a confidence-driven human–machine shared control mechanism that maximizes collaborative efficiency by dynamically assessing the reliability of agent decisions to regulate control weights. Simultaneously, the method introduces a composite confidence evaluation model which, by fusing epistemic uncertainty with geometric feasibility from lightweight LiDAR measurements, achieves a robust quantification of policy risk. To ensure safe execution, we develop a multi-trajectory prediction mechanism which, after validating kinematic constraints, minimally intervenes to safely adjust control commands. We conducted Gazebo-based simulation experiments on obstacle avoidance and target navigation using a LiDAR-equipped mobile robot model and validated the rationality of the confidence model. The results demonstrate that the proposed shared control strategy, which combines confidence assessment with deterministic safety boundaries, significantly improves the success rate and robustness of the system in uncertain environments. Full article

Most unoccupied aerial systems (UAS) research in cetacean monitoring has shown that drones can acquire high-resolution imagery, but the conditions under which such imagery becomes auditable ecological evidence remain less clearly defined. A structured narrative synthesis (evidence map) was conducted using 242 Web of Science Core Collection records retained from a final export dated 23 June 2026, with search transparency supported by index specification, sentinel-paper recall checking, and predefined evidence and validation maturity coding. Five application domains were identified: morphometrics and health assessment (79 records, 32.6%), behavioral monitoring (76, 31.4%), individual identification (34, 14.0%), abundance and distribution monitoring (33, 13.6%), and multimodal or operational applications (20, 8.3%). Across these domains, the field is shifting from opportunistic visual documentation toward calibrated photogrammetry, computer-vision-assisted detection and re-identification, behavior quantification, and targeted health or molecular sampling. However, ecological inference remains constrained by availability bias, perception bias, group-size error, measurement uncertainty, algorithmic-transfer bias, and disturbance-induced bias. UAS monitoring is therefore evaluated as an observation-to-inference workflow linking platform design, sensor geometry, image preprocessing, annotation, model validation, and uncertainty propagation to management-relevant cetacean evidence. Full article

Background: Intracranial arterial calcification (IAC), commonly detected on CT/CTA during ischemic stroke work-up, may reflect atherosclerotic burden relevant to recurrence. We evaluated whether CTA-detected IAC was associated with subsequent ischemic cerebral events. Methods: This retrospective single-center cohort included patients with ischemic stroke or transient ischemic attack admitted to a tertiary stroke unit between 2010 and 2023. IAC was assessed on index-event CTA using volumetric quantification and the Babiarz score. Time to first recurrent ischemic cerebrovascular event was analyzed using multivariable Cox regression. Robustness was evaluated through adjustment for stroke etiology and CTA acquisition parameters, penalized regression, bootstrap internal validation, and competing-risk analysis accounting for death. Results: Among 556 patients (median age, 71.5 years [IQR, 62.3–78.6]; 354 male), 60 (10.8%) experienced recurrence during a median follow-up of 3.5 years (IQR, 1.8–7.5). Median IAC volume was higher in patients with recurrence than in those without recurrence (45.0 mm³ [IQR, 14.1–143.2] vs. 18.3 mm³ [IQR, 0–105.6]; p = 0.01). A one-unit increase in log₁₀(IAC + 1), corresponding to a tenfold increase in IAC + 1, was associated with a higher recurrence hazard (HR, 1.63; 95% CI, 1.18–2.24; p = 0.003). The model had a C-index of 0.727, and the association remained consistent across sensitivity analyses. Babiarz scores correlated strongly with volumetric IAC measurements in the intracranial carotid and vertebral arteries (ρ ≥ 0.93; p < 0.01). Conclusions: Greater CTA-derived IAC burden was associated with a higher hazard of recurrent ischemic cerebrovascular events. Quantitative IAC assessment warrants further investigation as a potential imaging marker of recurrence risk, although external validation is required before clinical implementation. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for airflow measurements because they enable sensing where traditional instrumentation is difficult to deploy. However, most existing approaches rely on hovering or predefined trajectories, which can introduce aerodynamic disturbances and limit operation in confined environments. This study investigates a semi-Lagrangian measurement concept for miniaturized UAVs in which drift is permitted and explicitly accounted for in the reconstruction. Experiments were conducted using a Crazyflie 2.1+ quadrotor (≈35 g total mass, 92 mm footprint) equipped with a differential pressure sensor to measure relative airflow. A stereo-vision system measured the UAV ground-relative motion. Wind velocity was reconstructed by combining the UAV drift velocity with the relative airflow. Laboratory experiments over 2–$7\mathrm{m}/\mathrm{s}$ show that the combined reconstruction improves the wind estimate compared with drift velocity alone. The propagated standard uncertainty is approximately $0.88$–$1.28\mathrm{m}/\mathrm{s}$, and the proposed platform reduces UAV mass by nearly a factor of 20 compared with previously reported setups. However, signal filtering and the time-averaged reference field limit the assessment of instantaneous measurement accuracy. These results demonstrate the feasibility of semi-Lagrangian airflow sensing with very small UAVs while identifying aerodynamic interference and the onboard pressure measurement as the main limitations. Full article

Non-invasive quantitative assessment of oral inflammatory burden could complement conventional periodontal evaluation; however, current clinical methods primarily characterize disease after structural and tissue changes have occurred. In our previously reported DePerio framework, we demonstrated the feasibility of enriching oral polymorphonuclear neutrophils (oPMNs) from saliva through their differential adhesion to a hydrophilic cornstarch (CS)-coated surface, followed by brightfield imaging and AI-assisted quantification. Building on this framework, here we present BioSA, with an emphasis on the mechanistic and quantitative characterization of this biologically inspired adhesion-based oPMN isolation process, analogous to leukocyte adhesion behavior in blood capillaries. We systematically investigate the contributions of surface physicochemical properties, oPMN surface characteristics, and physical forces governing preferential oPMN retention and epithelial-cell removal. The optimized adhesion-based isolation reduced epithelial-cell contamination by 91% while preserving >98% of oPMNs, outperforming the evaluated filtration- and poly-L-lysine (PLL)-based approaches. Following isolation, brightfield images were analyzed using an AI-assisted deep-learning model for automated oPMN quantification. Across 30 independent test days, BioSA measurements were strongly associated with those obtained using the reference HEMO method (R² ≈ 0.99; MAE ≈ 2.06 × 10⁵ cells/10 mL), while method agreement and systematic differences were further evaluated using Bland–Altman analysis. The deep-learning detector achieved 98% sensitivity, 97% precision, and an F1 score of 0.97 on a dataset containing 4617 manually annotated oPMNs. Using data-driven oPMN concentration ranges informed by prior literature, we further explored five oral inflammatory load (OIL) strata. These strata are hypothesis-generating and should not be interpreted as validated diagnostic stages or as replacements for the 2017 periodontitis staging and grading framework. Operating with standard brightfield microscopy and a cloud-based AI interface, BioSA reduces analysis time from 15 to 20 min to less than 2 min per sample. Collectively, this study extends the DePerio framework by providing mechanistic insight into biologically inspired adhesion-based oPMN isolation and demonstrates its potential for rapid, quantitative, and exploratory assessment of oral inflammatory burden. Full article

The identification and interpretation of bone surface modifications (BSM) are central to reconstructing early hominin behavior, yet recent shifts by some researchers toward metric quantification face significant epistemological and statistical challenges. This paper critically evaluates the “metric method” proposed by Keevil/Pante et al., arguing that its reliance on continuous, ratio-scale measurements is fundamentally undermined by effector variance—the inherent dimensional mismatch between experimental tools and those in the (assemblage-specific) archaeological record. I demonstrate through statistical analysis that the method’s use of quadratic discriminant analysis (QDA) is compromised by severe multicollinearity (VIF > 5), resulting in unstable models that fail to generalize to fossil contexts, as exemplified by the problematic interpretations of the Grăunceanu (Romania) assemblage. Furthermore, I deconstruct recent critiques against the application of machine learning (ML) in taphonomy, clarifying misconceptions regarding Wolpert’s “no free lunch” theorem and rebutting allegations of data leakage. I contend that ML algorithms, when properly integrated with high-resolution categorical data, provide a more robust and accurate framework for classification of taphonomic datasets. By exposing these methodological biases, I propose a new paradigm for BSM analysis that prioritizes systemic hypothesis testing, objective data generation, and empirical testing/refutation models based on original datasets over speculative arguments and strict metric quantification. Full article

Background/Objectives: A push–pull osmotic pump (PPOP) tablet is a controlled-release delivery system consisting of a bilayer tablet (push layer/pull (drug) layer), coated within a selectively permeable barrier membrane, and containing a laser-drilled orifice in the pull layer side of the barrier. Aqueous media permeates across the barrier membrane and into the bilayer tablet core. The push layer swells axially, creating force and acting like a piston to drive the active pharmaceutical ingredient (API) dosage through the laser-drilled orifice. The primary purpose of this study was to develop a method to quantitate swelling force generated by the PPOP push layer. Methods: A key challenge was devising measurement methodology that most closely represented continuous PPOP push-layer swelling force exerted in the axial dimension over time, so the method setup was adjusted until attaining what closely approximated PPOP push-layer swelling-force dynamics within the delivery system. After some adjustments, swelling forces were measured from PPOP push layers containing various polyethylene oxide (PEO) molecular weight (MW) grades. Results: The most representative configuration was a cylindrical holder fully submerged in water with the push-layer compact situated at the bottom of the holder. This setup constrained the push layer to swell in the axial dimension. The optimal setup also allowed the texture analyzer to continuously measure push-layer swelling force over 24 h, representing typical duration of functionality. Quantitative analysis of the swelling force vs. time profiles demonstrated comparable force generation over 24 h from the 4, 5, and 7,000,000 MW grades of PEO. Swelling force decreased in near-linear fashion, from push layers formulated with PEO 4,000,000 down to PEO 100,000. Conclusions: The optimal setup enabled discriminatory quantification of push-layer swelling force for PEO MW grades spanning from 100,000 up to 4,000,000 Daltons. Furthermore, the method demonstrated why the 4, 5, and 7,000,000 MW grades of PEO are recommended for use in the PPOP push layer, given that these three highest MW grades delivered comparable swelling force over 24 h. Finally, the method provides new quantitative insight into the push-layer swelling force that is necessary, over the duration of the dosing interval, to deliver the active dosage from the PPOP tablet. Full article

Well-facilitated farmland plays an important role in stabilizing and increasing crop yields, advancing agricultural mechanization, and improving production efficiency. Field-road quality directly affects machinery access and the transport of agricultural inputs and harvested crops. Current acceptance inspections rely mainly on field surveys and spot measurements, resulting in limited spatial coverage, low efficiency, and poor reproducibility. Therefore, a method is needed to rapidly survey entire field-road networks, extract road extents and structural indicators, and generate verifiable inspection records. Centimeter-resolution UAV imagery enables flexible and repeatable data acquisition, but deriving acceptance-oriented indicators remains challenging. Narrow field roads are readily obscured by crops and shelterbelt shadows or confused with cropland textures, resulting in blurred boundaries, discontinuities, and false detections. We developed a lightweight UAV-based framework integrating field-road segmentation and structural-indicator quantification. MobileNetV2 replaced the DeepLabv3+ backbone, while a Normalization-based Attention Module and Content-Aware ReAssembly of FEatures enhanced interference suppression and spatial reconstruction. Morphological processing, skeleton extraction, Euclidean distance transformation, and skeleton-graph analysis were then used to quantify road width and network connectivity. Across three training runs with different random seeds, the model achieved mean mIoU, mPA, and precision values of 93.34%, 96.75%, and 98.90%, respectively. The model had 6.14 million parameters and an inference speed of 17.04 FPS. After averaging five measurements from each road segment, the R² values between the predicted and manually measured widths were 0.650, 0.486, and 0.662 for asphalt, concrete, and gravel roads, respectively. The corresponding width MAEs were 0.130, 0.140, and 0.100 m. Connectivity analysis yielded an index of 1.00 in the first validation area, while gap repair increased the index from 0.4682 to 0.4795 in the second area. The framework supports efficient, quantitative, and traceable acceptance inspection of field-road infrastructure in well-facilitated farmland. Full article

Floor live load is a governing parameter for engineering structures, and its reliable quantification is critical during both the design phase and the structural service life. Although classrooms represent a common building category, research regarding their live load characteristics remains relatively limited. Existing studies generally rely on physical sampling and on-site weighing. These methods are labor-intensive and inefficient, which limits their applicability to large-scale surveys. To address these challenges, this paper proposes a novel survey approach for classroom live loads based on group intelligence technology. This method aggregates load amplitude samples by recruiting volunteers through questionnaires. Meanwhile, the weights of indoor items are determined via an electronic inventory method. Field measurements verified the feasibility of this approach. The methodology was subsequently applied across four provinces in China. This implementation yielded an extensive dataset comprising 299 classrooms and covering a total floor area of 22,921 m². Finally, the study presents a statistical analysis and probabilistic modeling of the results. Full article

In a growing number of administrative systems, public organizations are governed through digital indicator regimes: standardized indices of compliance, integrity, and risk, published as open data and often read by regulators and intermediaries as proxies for institutional quality. Such regimes rest on the assumption that measured conformity converts into public legitimacy, yet the relationship between measured performance and public trust is empirically weak and varies across countries, and in several indicator-governed fields, compliance scores have risen and converged, while legitimacy-relevant behavior has not. This article develops dual-ledger theory to explain one mechanism behind that pattern. Drawing on institutional, quantification, accountability, signaling, and legitimacy research, we theorize an endogenous loss of informational value: when a regime succeeds in compressing cross-unit variance in compliance scores (conformity drift), the compliance signal stops differentiating units (signal collapse), and audiences shift their legitimacy judgments toward behavioral signals the regime does not measure (behavioral substitution). A legitimacy gap opens that a single-ledger regime cannot detect. We formalize the theory in four assumptions and five propositions, each with an empirical specification; derive three diagnostics computable from data that indicator-governed fields already hold; and confine the theory by explicit scope conditions to public and quasi-public organizations. Indonesia’s electoral sector, where three institutionally distinct election management bodies with separate legal mandates publish compliance, risk, and participation indices for 38 provinces, provides a fully documented 2024 baseline for both ledgers and a pre-specified forward test. Sustainable digital governance, we argue, requires two ledgers, with the coupling between them as the governance signal. Full article

Microplastics (MPs) in long-distance trains remain undercharacterized despite prolonged exposure of passengers and staff in confined environments. In this study, settled dust was collected from four distinct coach classes on an Indian railway to establish baseline MPs occurrence, morphology, polymer composition, relative load, and exposure-relevant particle intake. Samples were processed via oxidative digestion, density separation, and stereomicroscopy for particle quantification and morphological characterization. Polymer identity was assessed using micro-Raman spectroscopy, and human exposure was evaluated using estimated trip (${EDI}_{Trip}$) and annual (EAI) intake models. MPs were detected in all four sampled coach composites, with coach-level concentrations ranging from 925 to 3755 MPs/g. Among the four sampled coaches, the 1AC (first-class sleeper air-conditioned) coach had the highest measured concentration. Fibers (72%), transparent particles (61%), and polyester dominated, suggesting mixed textile and interior sources. The Relative Load Ratio (RLR) reached 4.06 in 1AC, compared to the lowest-abundance general unreserved coach (GL). Deterministic ${\mathrm{E}\mathrm{D}\mathrm{I}}_{\mathrm{T}\mathrm{r}\mathrm{i}\mathrm{p}}$ ranged from 5.78 to 388.35 particles/trip, with maximum intake observed for toddlers on full-route 1AC journeys. Monte Carlo simulations confirmed intake increases with travel duration and frequency. These findings highlight long-distance railway coaches as overlooked MPs reservoirs, emphasize the need for improved dust control, ventilation management, and lower-shedding interior materials. Full article