Search Results (2,548)

This study proposes a theoretical–empirical framework for analyzing political regimes based on a structural analogy between electoral behavior and spin-glass systems in statistical physics. Society is modeled as a system of interacting agents (voters) influenced by both interpersonal interactions and external factors such as media and institutions, formalized through a social Hamiltonian. By introducing a partition function and free energy, political regimes are interpreted as distinct macroscopic phases governed by four effective macro-parameters: external field, conformism, interaction heterogeneity, and inverse social temperature. Democratic societies correspond to a multistable regime characterized by sensitivity to initial conditions and replica symmetry breaking (RSB), reflecting the coexistence of competing social configurations. Authoritarian regimes, in contrast, arise when a strong unidirectional external field, high conformism, and low effective social temperature stabilize a single dominant macroscopic state, producing a regime analogous to replica symmetry (RS). A central result of the model is the distinction between the predictability of macroscopic outcomes and structural social multistability, as well as between natural and externally imposed homogenization of collective behavior. To illustrate the empirical relevance of the framework, the model is applied to the transition from the Weimar Republic to the National Socialist regime (1919–1933), using aggregated electoral data to construct proxy indicators for the effective parameters governing social interactions. The proposed approach enables structural identification of early signals of authoritarian transition through changes in the parameters of social dynamics. Full article

Accurate characterization of inter-vehicle spacing is fundamental for safety assessment and sustainable operation of road networks, particularly on two-lane rural roads where monitoring infrastructure is limited. Unlike temporal headways, vehicle spacing directly reflects physical vehicle interactions and roadway occupancy, making it a more appropriate variable for evaluating collision risk and operational efficiency. This study develops a probabilistic framework for modeling vehicle spacing based on the statistical isomorphism between Event Flows and Linear Fields of Random Points. Using a calibrated microscopic simulation model, spacing distributions are generated for unidirectional traffic over flow rates from 100 to 1300 veh/h. A Pearson Type III distribution is shown to consistently reproduce the observed asymmetry, kurtosis, and non-zero minimum spacing across traffic regimes. Distribution parameters are estimated via maximum likelihood and validated using a heuristic Kolmogorov–Smirnov procedure suitable for large samples. Results demonstrate systematic relationships between spacing distribution parameters and macroscopic traffic variables, enabling estimation of the probability of unsafe spacing conditions from commonly available traffic data. The proposed framework supports sustainability-oriented traffic management by providing a quantitative basis for safety evaluation and operational control without requiring extensive sensing infrastructure. Full article

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI framework that improves hyperthyroid-related abnormality detection by simultaneously analyzing facial images of both the eye and neck based on pattern clinical knowledge. The multi-modal framework mimics a biological visual mechanism by using a dual-pathway architecture that concurrently processes foveal-like details of the eyes and neck. It integrates these high-resolution visual embeddings with quantitative morphological measurements to simulate a clinician’s ability to fuse observation with physical assessment. The proposed system employs a multi-faceted decision-making process derived from three distinct data components: two from frontal face analysis and one from neck region analysis. Specifically, eye regions extracted from facial images are preprocessed using the YOLOv11s model. The proposed system leverages a dual-pathway processing architecture to extract comprehensive diagnostic features. For the eye dataset, the framework utilizes a face mesh-based eye landmark (FMEL) to extract both eye regions and perform eyes unfold processing. These regions are subsequently analyzed by the proposed sclera map unwrapping engine (SMUE) to derive quantitative sclera metrics from both the left and right eyes. To optimize classification, a dual-branch architecture is employed by integrating CNN visual embeddings with SMUE-derived statistical features through a feature fusion layer. Simultaneously, the neck processing path executes the neck region of interest (ROI) prediction {upper, lower} to segment critical regions for goiter assessment via the proposed neck $\mu -\sigma$ ensemble thresholding (NSET) algorithm. The experimental results demonstrate that the proposed algorithm for eye analysis achieved a mean average precision (mAP50) of 96.4%, with a specific mAP50 of 98.6% for the hyperthyroid class. Regarding quantitative scleral measurement, the SMUE process revealed distinct morphological differences, with the experimental data group exhibiting consistently higher pixel distances across the reference points compared with the normal group. Furthermore, the proposed NSET algorithm yielded the highest performance for swollen neck classification with an mAP50 of 92.0%, significantly outperforming the baseline deep learning models while maintaining lower computational complexity. Full article

Energy dissipation in stepped weirs depends on the complex interaction between geometry, flow regime, and surface aeration. The research proposes a dimensionless empirical model (RE3T) to predict the overall energy dissipation in three-section stepped weirs with variable slopes. The formulation integrates dimensional analysis based on the Vaschy–Buckingham theorem, controlled physical experimentation, and three-dimensional numerical simulations using CFD employing the RANS–SST turbulence model implemented in ANSYS CFX. Eighteen numerical simulations were performed covering seven geometric configurations and four hydraulic inlet conditions, covering slug, transitional, and skimming flow regimes. The CFD model was previously validated by comparison with a physical scale model, obtaining a discrepancy of only 0.38% in relative energy dissipation. The validated dataset was then used to calibrate an empirical multiplicative correlation composed of eight dimensionless groups associated with sectional slopes, number of steps, overall geometric ratio, and upstream Froude number. The proposed model achieved a coefficient of determination R² = 0.81, with relative errors generally less than 1% and a maximum deviation of 2.34%. The statistical indicators (RMSE, MAE, and bias) confirm the absence of significant systematic trends within the defined domain of validity. The results show that the Froude number and the slopes of the sections are the variables with the greatest influence on overall dissipation. The RE3T formulation is a physically consistent and computationally efficient predictive tool for the design and analysis of stepped weirs with variable slopes, extending the scope of traditional correlations developed for uniform slopes. Full article

This study presents a quantitative, scenario-based framework for analyzing humanity’s potential progression along the Kardashev scale, with emphasis on the transition to Type I (planetary-scale) and Type II (stellar-scale) civilization status. Using humanity as an empirical reference case, we integrate four coupled dimensions of civilizational development: energy utilization, information processing capacity, large-scale construction mass, and population dynamics, modeled through historical data, empirical trends, and physically motivated growth constraints. Energy availability is characterized using global energy production records and insolation statistics for potentially habitable exoplanets, explicitly acknowledging observational biases toward cooler host stars. Information processing growth is constrained by thermodynamic limits and observed trends in global data generation, while construction mass and population evolution are described using exponential and logistic growth models, respectively. These components are combined into a composite Civilization Development Index (CDI), a weighted logarithmic metric designed to track multi-scale civilizational advancement and tested through sensitivity analyses. Under optimistic assumptions of uninterrupted technological growth and absence of civilization-scale catastrophes, the framework suggests that humanity could reach Type I civilization status on the order of the 23rd century, while Type II status represents a substantially longer-term outcome extending into the third millennium or beyond. These timescales should be interpreted as lower bounds, as catastrophic events, sociopolitical constraints, or resource bottlenecks could significantly delay or prevent such transitions. By explicitly delineating assumptions, uncertainties, and physical constraints, this work provides a structured baseline for studies of long-term civilizational trajectories and the factors governing the emergence or absence of advanced technological civilizations. Full article

In this paper, machine learning techniques and risk factor analyses are applied to a marine hazard potential map of the Taiwan Strait. The waters surrounding Taiwan are characterized by dense maritime traffic, including commercial cargo transportation and fishing operations. Marine accidents caused by severe weather conditions are frequently reported, leading to irreversible loss of life and property. To mitigate these risks, this study utilizes the XGBoost machine learning model in conjunction with oceanic parameters and historical accident statistics to map the risk potential distribution of maritime accidents across the Taiwan Strait on a monthly basis. To address the challenge of limited historical accident data, this research employs a TVAE (Tabular Variational Autoencoder) to generate synthetic maritime accident data. The quality of such synthetic data is evaluated by comparing the similarity of probability distributions between the original and synthetic datasets. The resulting risk potential maps indicate that risk levels are significantly higher during the winter and lower during the summer. Furthermore, the SHAP (SHapley Additive exPlanations) model is applied to analyze key risk factors, identifying wave height as the primary driver, followed by meridional (north–south) wind speed and the primary spatial modes of wave height. These findings are validated using the National Ocean Database and Sharing System (NODASS) data, providing a comprehensive explanation of the underlying physical mechanisms. This study has successfully utilized the XGBoost machine learning model together with the TVAE generative technique to develop monthly marine hazard potential distribution maps for the Taiwan Strait. The novel research flowchart employed in this study can be applied to many other marine problems. Full article

Transmembrane pressure (TMP) is a central state variable in haemodialysis (HD) and haemodiafiltration (HDF), governing ultrafiltration dynamics, convective transport, and membrane performance. Although dialysis devices specify high maximum allowable pressure limits derived from in vitro testing and mechanical safety margins, the effective operating pressure space encountered under routine clinical conditions remains insufficiently quantified from a systems engineering perspective. In this study, aggregated real-world minimum–maximum TMP intervals collected from four geographically distributed dialysis centres were used to anchor a model-based characterisation of operational pressure ranges. To enable reproducible modelling and numerical exploration, Gaussian-based synthetic datasets were constructed from empirically observed pressure intervals while incorporating physiological and operational constraints. Across all centres, HD exhibited stable and narrowly distributed TMP values (typically 20–60 mmHg), whereas HDF operated within higher but well-defined pressure regimes (approximately 120–260 mmHg). Values above 300 mmHg were rare, and pressures exceeding 400 mmHg were not observed under routine conditions. Statistical tail modelling, extreme value theory, and unsupervised anomaly detection consistently identified such extreme pressures as structurally incompatible with the learned operational state space. These results provide quantitative engineering bounds for TMP that may be directly integrated into reduced-order models, control design, and digital twin development for dialysis systems. By constraining modelling environments to empirically supported pressure regimes, the proposed framework enhances numerical stability, prevents non-physical extrapolation, and supports physiologically realistic data-driven applications in biomedical engineering. Full article

The accurate prediction of gas turbine output power is critical for flexible scheduling and shipboard microgrid resilience. However, purely data-driven models suffer from poor generalization and physical inconsistency in complex marine environments, especially under unseen operation conditions. This paper proposes a condition-adaptive physics-informed long short-term memory (CAPI-LSTM) framework to ensure physical consistency across the full operation envelope. In the proposed framework, an MLP-based condition-adaptive regulator is developed to dynamically adjust the compressor air flow rate within the embedded physics-informed loss function. The proposed CAPI-LSTM model is verified using the operation data from an LM2500+ gas turbine. The comparison results demonstrate the superiority of the proposed method over traditional architectures. The CAPI-LSTM model achieves the lowest root mean square error of 0.177 MW, and its error distribution is the most concentrated near zero among all compared models. The robustness of the CAPI-LSTM model is further verified under the unseen operation conditions. The CAPI-LSTM still maintains excellent generalization capability compared to both purely data-driven models and standard physics-informed models, with an average error of only 0.218 MW and a narrow interquartile range of [0.058, 0.363]. The paired t-test results confirm that the improvement of the CAPI-LSTM model is statistically significant. The CAPI-LSTM model achieves competitive computational efficiency despite the integration of the physics-informed loss function with a condition-adaptive regulator. Furthermore, the CAPI-LSTM model achieves superior performance in noise immunity and transferability to other types of gas turbines. In summary, the proposed CAPI-LSTM model provides an effective and practical solution for marine gas turbine output power prediction. Full article

Precarious and informal employment has been increasingly recognized as a key social determinant of health, particularly in countries of the Global South. In Chile, despite relatively strong labor institutions, informal employment remains widespread, yet contemporary evidence on its health implications is limited. This study examines the association between the absence of a written employment contract, used as an indicator of labor informality, and multiple health and well-being outcomes among employed adults in Chile. A cross-sectional analysis was conducted using data from the nationally representative 2022–2023 National Health and Sexuality Survey (ENSEX), restricted to the urban employed population (n = 5193). Informal employment was defined by self-reported absence of a written contract. Health outcomes included perceived general health, quality of life, physician-diagnosed conditions, and recent anxiety–depressive symptoms assessed with the PHQ-4. Weighted descriptive analyses and logistic regression models were estimated, accounting for the complex survey design and adjusting for sex, age, and educational level. Approximately 12.8% of employed individuals reported not having a written contract. Contract absence was associated with higher odds of anxiety–depressive symptoms and lower odds of reporting good quality of life after adjustment. Associations with general health and chronic physical conditions were weaker and not statistically significant. These findings suggest that contractual informality is particularly linked to reduced psychological well-being and quality of life, highlighting the relevance of informal employment as a public health concern beyond traditional disease outcomes. Full article

With the considerable number of low-dose CT examinations performed in lung cancer screening, variations in participant positioning, scan direction, or localiser angle are likely to occur in practice. These variations are known to affect automatic tube current modulation (ATCM) operation, yet organ-specific dose implications across CT models remain unknown. Therefore, this simulation study systematically characterised the effect of the aforementioned variations. Using the Alderson RANDO phantom, ATCM profiles were established on CT scanners from four major vendors (GE, Siemens, Canon, Philips) after introducing vertical and lateral mispositioning, craniocaudal and caudocranial scan directions, and varying localiser projection angles. Additionally, off-centre positioning and scan direction changes preceded by either a single posteroanterior (PA) or dual (PA+lateral) localiser were evaluated. Doses to the lungs, heart, thyroid, liver, and breasts were calculated from Monte Carlo simulations of each setup for 32 patient-specific voxel models. The results demonstrate statistically significant and scanner-dependent dose variations. PA localisers generally produced the highest organ doses. However, on the Philips system, organ dose increases of at least 50% were observed after the lateral projection angle. GE and Siemens scanners showed pronounced dose increases following downward mispositioning with a single PA localiser (18–50% and 5–25%, respectively), an effect largely mitigated by adding a lateral localiser. Canon and Philips scanners exhibited generally stable ATCM behaviour after vertical off-centring, although Canon showed notable dose increases upon lateral mispositioning, with dose increases up to 37.5% and 34% after a single PA or dual localiser, respectively. Variations in scan direction displayed highly model- and organ-dependent effects. Dose deviations were largely mitigated after dual localisers for the GE, Canon, and Philips scanner types. Here, organ dose differences were within an absolute range of 10%, indicating that a change in scan direction preceded by a dual localiser can reduce extreme dose deviations. Remarkably, no significant difference was observed solely for the Siemens scanner when combined with a dual localiser, as lung, heart, breast, and liver doses remained significantly (between 20 and 35%) lower when scanning craniocaudally, whereas the thyroid dose in this setup remained considerably higher (up to 20% mean increase). Ultimately, findings indicate that seemingly minor protocol deviations can lead to significant underestimation of anticipated organ-specific doses associated with lung cancer screening. Scanner-specific optimisation, supported by medical physics expertise, is therefore essential. Full article

The principal difficulty in studying the physico-mechanical and filtration-capacity properties of coals and host rocks under laboratory conditions using core samples lies in reproducing natural thermodynamic conditions characteristic of in situ depths. To address this issue, specialized equipment and methodologies for transferring measurement results are employed, including the Hoek–Brown failure criterion, the structural weakening coefficient, and the development of thermodynamic models. The reliability and accuracy of such measurements are determined by the degree of conformity between the adopted laboratory conditions and natural in situ conditions, the number of samples representing different lithological varieties, and the adequacy of sampling procedures ensuring representativeness. Particular challenges arise when sampling cleated and fractured coals formed under natural stress–strain conditions and contain methane, which significantly influences their physical properties. These difficulties are especially pronounced in prepared-for-mining high-gas-content coal seams of the Karaganda Basin at depths of approximately 700 m, where obtaining representative samples is technically complicated. Reliable values of the physico-mechanical properties of the coal–rock mass are essential for geomechanical calculations aimed at ensuring safe mining of high-gas-content seams through risk assessment of geodynamic phenomena, particularly in zones of geological disturbances, floor heave, and roof collapse. In this context, the use of a comprehensive suite of geophysical logging data from exploration boreholes makes it possible to obtain continuous, high-precision information on physico-mechanical and filtration-capacity properties. These methods are particularly important for characterizing the coal–rock mass in operating mines, since the natural state of host rocks and prepared coal seams is altered due to stress relief caused by mine workings, preliminary degasification measures, and hydraulic fracturing. The problem addressed is the need for reliable assessment of rock and coal seam parameters under natural thermodynamic stress–strain conditions, taking into account lithological composition, structural heterogeneity, fracture development, stratigraphic differentiation, and gas saturation. The aim of this study is to ensure efficient and safe coal extraction based on geomechanical calculations utilizing physico-mechanical and filtration-capacity properties of host rocks and gas-bearing coal seams, whether prepared for mining or not yet extracted. The research methods are based on an integrated complex of geophysical logging of exploration wells, specialized software tools, and statistical processing techniques to identify patterns in physico-mechanical and filtration-capacity properties of host rocks and coal seams under natural stress–strain conditions, as well as to determine the nature of changes in these properties within coal seams and roof and floor rocks in prepared mining areas. The physico-mechanical and filtration-capacity properties of host rocks and coals from the Lenin and Kazakhstanskaya mines were determined. Regularities governing the application of these parameters to coals of different formations and depths were established; fracture orientations and characteristics were evaluated; and relationships between changes in coal seam parameters and gas content were identified. A comprehensive methodological framework for studying the physical and capacity properties of the coal–rock mass under natural thermodynamic conditions has been developed. Its primary application is the investigation of coal seams prepared for mining to support geomechanical calculations for efficient and safe coal extraction, the implementation of degasification measures for high-gas-content seams, and the assessment of gas-dynamic risks based on the character of variations in physical parameters. Full article

Background: Housing is a social determinant of health. In 2015, a lottery assigned low-income families from a waitlist to a new subsidized building (NSB) in Chinatown, Boston, MA. In 2019–2020, we explored associations between housing status (NSB or being on waitlist) and self-rated physical and mental health; household food insecurity (FI); weekly consumption of fruits/vegetables (FV), weekly consumption of soda, and monthly consumption of fast food. Methods: Surveys were sent to NSB (n = 95) and waitlist (n = 2498) households. Logistic and linear regressions explored associations between housing status and outcomes of interest. Models were adjusted for age, sex, Asian background, household size, education, income, employment and distance to the closest food store. Results: A total of 138 respondents completed the survey (NSB = 36, waitlist = 102). Groups were demographically similar. In terms of self-reported health, most respondents reported good/better physical health (Waitlist: 62%, NSB: 60%) and good/better mental health (Waitlist: 68%, NSB: 74%). FI was prevalent among both waitlist households (63%) and NSB households (56%). FV intake was similar among NSB households (13.5 times/week) compared to waitlist households (12.8 times/week). The NSB group reported similar soda consumption (1.7 times/week) compared to the waitlist group (2.3 times/week), along with similar fast-food consumption (NSB: 2.7 times/month, Waitlist: 3.7 times/month). We found no statistically significant associations between housing status and outcomes of interest after adjusting for covariates. Conclusions: In this small sample, outcomes were not significantly different between groups. Future studies should explore mechanisms through which NSB residence affects nutrition and health, particularly in minority populations. Full article

Poly(vinyl alcohol) (PVA)/montmorillonite (MMT)/Ti₃C₂T_x (MXene) nanocomposite membranes (PVA/MMT/MXene) were developed and evaluated in terms of their mechanical properties, mesoporosity, and adsorption performance toward Pb²⁺ ions and methylene blue (MB). The incorporation of MMT and MXene resulted in a strong synergistic reinforcement, increasing the ultimate tensile strength from 10 to 20 MPa, the Young’s modulus from 14.7 to 29.5 MPa, and reducing the swelling ratio from 2.0 to 1.1 g·g⁻¹. BJH porosimetry revealed a refined and interconnected mesoporous structure, with the cumulative pore volume increasing from 0.134 to 0.448 cm³·g⁻¹. In adsorption experiments (mono-solute systems, 25 °C), the ternary membrane achieved high uptake capacities of 55 mg·g⁻¹ for Pb²⁺ and 80 mg·g⁻¹ for MB, outperforming binary PVA/MMT and neat PVA. Statistical–physics modeling provided microscopic descriptors consistent with the experimental isotherms: Pb²⁺ adsorption follows a monolayer regime (n ≈ 1), whereas MB exhibits multilayer behavior (n > 1) with a higher site density (Nm ≈ 1.6 mmol·g⁻¹). These results demonstrate that the hybrid 2D–2D architecture of MMT and MXene significantly enhances the structural robustness, pore accessibility, and adsorption efficiency of PVA-based membranes, highlighting their potential for efficient removal of metal ions and dyes from aqueous media. Full article

Natural disasters disrupt maritime operations, yet their environmental consequences remain underexplored. This study quantifies CO₂ emission changes following the February 2023 İskenderun Bay earthquakes (7.6 Mwg and 7.5 Mwg) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO₂ emissions during the acute phase (t = 11.79, p < 0.001, Cohen’s d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 h). Counterfactual analysis estimated 27,574 tonnes of excess CO₂ emissions directly attributable to earthquake disruption. Network analysis showed 23.8% reduction in edge density during the acute phase. The graph neural network (GNN) emission prediction model achieved R² = 0.985 (baseline) and R² = 0.997 (recovery) in predicting emission patterns, while acute phase showed predictability collapse (R² = −1.591). These findings demonstrate that seismic events generate sustainability-relevant externalities beyond immediate physical damage, and that quantifying disruption-driven excess emissions supports sustainability-oriented port resilience planning and more robust maritime emission accounting (e.g., under the EU MRV framework). Full article

Wellness tourism represents a significant growth sector within the global tourism industry; however, empirical research examining development determinants in resource-constrained, emerging African destinations remains limited. This study investigates the structural relationships among infrastructure development, community involvement, marketing and promotion, and visitor expectations/service quality in advancing wellness tourism at Allelobad Hot Spring in Ethiopia’s Afar Region. Using a quantitative methodology, structured questionnaires were administered to 210 respondents (visitors, local community members, and tourism stakeholders), resulting in 186 valid responses. Data were analyzed through Confirmatory Factor Analysis (CFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrate that all four determinants exert statistically significant positive effects on wellness tourism development (p < 0.001), with visitor expectations and service quality emerging as the strongest predictor (β = 0.35), followed by infrastructure development (β = 0.32), marketing and promotion (β = 0.30), and community involvement (β = 0.27). The structural model explains 68% of the variance in wellness tourism development, indicating substantial explanatory power. These findings underscore that sustainable wellness tourism growth in emerging destinations necessitates integrated, multidimensional strategies that simultaneously address physical infrastructure, stakeholder engagement, strategic positioning, and experiential excellence, rather than isolated sector-specific interventions. Full article