Search Results (8,152)

A variable-to-variable (V2V) length code parses a source sequence into phrases of variable length and maps each phrase to a binary codeword of, generally, a different random length. After encoding n phrases, the realized compression ratio $R_n={\Lambda }_n/{\Sigma }_n$—total codeword length over total source-symbol count—is the finite-sample counterpart of the code’s asymptotic rate $\rho$, to which it converges only as $n\to \infty$. This paper first derives exact formulas for all integer moments of $R_n$ for a given discrete memoryless source (DMS). Specifically, we obtain a closed-form formula for every moment $\mathbb{E}\left\{R_n^k\right\}$ as a one-dimensional integral involving only single-phrase moment generating functions of the pair $(L,\ell )$—the phrase length, in source symbols, and codeword length, in bits. From these moments we derive an Edgeworth approximation to the cumulative distribution function (CDF) of $R_n$ that is substantially more accurate than the central limit theorem (CLT) approximation. Using the Laplace method of integration, we also derive explicit closed-form formulas for the bias constant $C={lim}_{n\to \infty }n(\mathbb{E}\left\{R_n\right\}-\rho )$ and for the variance constant ${lim}_{n\to \infty }n·\mathrm{Var}\left\{R_n\right\}$. The analysis extends to Markov sources via state-indexed matrices with a redundancy formula obtained in closed form. On the coding-theoretic side, we cast V2V length codes as finite-state encoders and apply a generalized Kraft inequality for a compression-rate lower bound, and give a structural decomposition of the bias coefficient that separates cleanly across variable-to-fixed (V2F) length codes, fixed-to-variable (F2V) length codes, and V2V length codes. Applied to the Khodak code of Bugeaud, Drmota, and Szpankowski, this decomposition shows that its improved performance is reflected in its smaller bias constant. Full article

Background: Diabetes mellitus affects over 500 million people worldwide, yet many machine learning prediction models remain difficult to interpret, limiting their clinical applicability. This study proposes an explainable machine learning framework integrating the Marine Predators Algorithm (MPA) for hyperparameter optimization with SHAP-based explainability to diabetes prediction. Methods: Logistic Regression (LR), Random Forest (RF), and MPA-optimized XGBoost were evaluated using a publicly available Kaggle diabetes dataset of approximately 100,000 records. Statistical significance was assessed using Wilcoxon signed-rank tests with Bonferroni correction for fold-wise cross-validation results, while McNemar’s and DeLong’s tests were employed for paired comparison of independent test-set predictions and ROC-AUC values, respectively. Performance was assessed using accuracy, precision, recall, F1-score, specificity, ROC-AUC, and Brier score. SHAP was used to provide global and local model explanations. Results: The MPA-optimized XGBoost model achieved the highest performance, with 96.72% accuracy, 97.70% precision, 95.70% recall, 96.71% F1-score, and 99.56% ROC-AUC, significantly outperforming LR and RF (p < 0.001). The model demonstrated good calibration with a Brier score of 0.0262. SHAP analysis identified HbA1c level, blood glucose level, and age as the most influential predictors, while interaction analysis indicated a synergistic relationship between HbA1c and blood glucose. Conclusions: The proposed framework demonstrated strong predictive performance and interpretable model behavior on the publicly available diabetes dataset used in this study. These findings indicate the potential of MPA-based optimization combined with SHAP explainability for supporting transparent machine learning research in diabetes prediction. However, additional external validation using independent clinical datasets is required before considering the framework for clinical decision support or real-world deployment. Full article

Background: Metabolic and alcohol-associated liver disease (MetALD) has been associated with an increased risk of cancer. However, it remains unclear whether combinations of favorable, intermediate, and adverse lifestyle behaviors differentiate cancer risk within MetALD. Methods: We analyzed 25,712 participants with MetALD using UK Biobank. Smoking, moderate-to-vigorous physical activity (MVPA), and diet were classified into favorable, intermediate, and adverse categories. Favorable and adverse behavior counts ranged from 0 to 3, and a unified lifestyle profile was constructed. Cox proportional hazards models were used to analyze adjusted hazard ratios (aHRs) and 95% confidence interval (CI) for incidence of all cancers. Results: During 296,965 person-years of follow-up, 4416 incident cancers occurred. Compared with no favorable behaviors, those having two favorable behaviors showed significance 0.92 (95% CI, 0.84–1.00). Compared with no adverse behaviors, the aHRs were 1.06 (95% CI, 1.00–1.13), 1.15 (95% CI, 1.04–1.27), and 1.11 (95% CI, 0.84–1.47) for one, two, and three adverse behaviors, respectively (p for trend = 0.003). The mixed/intermediate profile was associated with higher cancer risk than the favorable profile (aHR, 1.09; 95% CI, 1.02–1.16), whereas the adverse-profile estimate was not statistically significant. Among individual lifestyle behaviors, current smoking was associated with a higher cancer risk than never smoking (aHR, 1.20; 95% CI, 1.09–1.32; p < 0.001). Conclusions: Among participants with MetALD, favorable and adverse behavior counts showed opposite adjusted trends in cancer risk. Current smoking showed the strongest association with cancer risk among the individual behaviors. Full article

Traditional food products are essential for gastronomic tourism, yet their integration into hospitality is limited by supply chain challenges and a significant attitude-behavior gap. This study identifies perceptual factors, operational barriers, and socio-professional predictors influencing traditional food procurement. The research was conducted on a sample of 307 hospitality managers in Vojvodina, Serbia. Data was systematically analyzed through examination of manager perceptions and procurement decisions. Results reveal that although managers highly rate sociocultural and ecological impacts, these factors were not statistically significant predictors of actual procurement in the estimated model, suggesting the presence of a perceptual–operational gap. Instead, professional characteristics drive decisions. Formal education in hospitality and tourism, combined with 6 to 10 years of professional experience, significantly increases the decision to source traditional products. Among non-procuring managers, operational barriers such as inconsistent quality and delivery issues outweigh economic factors as perceived obstacles to procurement. To address these challenges, this study conceptualizes the Perceptual–Operational Gap (POG) as a diagnostic and integrative analytical framework that receives preliminary empirical support in this study. This three-level framework maps product characteristics, managerial perceptions, and procurement outcomes. The model provides a diagnostic tool enabling practitioners to identify attitude-behavior gaps, understand operational barriers, and recognize which manager groups require targeted attention. Based on these insights, decision-makers can design targeted interventions. The POG model moves focus from cultural promotion to practical professional approaches, offering a replicable solution for integrating gastronomic heritage in destinations worldwide. Full article

Animal personalities, reflecting consistent individual behavioral differences, are increasingly recognized as key factors influencing fitness. However, their role in the reproductive success of endangered ungulates remains poorly understood. In this study, 17 personality traits, including “Clumsy”, “Dominant”, “Vigilant”, “Affiliative” and so on, were assessed in 123 adult female captive Alpine musk deer (Moschus chrysogaster) using a scale-based personality rating method. We explored the relationships between these personality traits and reproductive fitness indicators, including litter size, calving success, and fawn mortality. Our exploratory analyses revealed that “Clumsy” scores were significantly higher in females that experienced calving failure (miscarriage or dystocia) compared to those that successfully gave birth (p = 0.013, Cohen’s d = 0.88), indicating a large effect size. However, no traits were correlated with litter size. For fawn mortality, although initial correlation analyses suggested associations with “Vigilant” and “Affiliative”, these were not supported when a Poisson regression model—appropriate for count data—was applied with litter size as an offset (model p = 0.092; all predictors p > 0.05). These findings suggest that the previously observed associations between maternal personality and fawn mortality may have been influenced by the use of an inappropriate statistical model. Overall, our study highlights the importance of using distribution-appropriate models for count-based fitness data and provides exploratory, hypothesis-generating evidence that, while “Clumsy” may be robustly associated with calving success (large effect size), personality–fitness relationships in this species require further validation with larger samples and across different captive populations. Full article

Background/Objectives: Neurofeedback (NFB) modulates electroencephalographic activity and has been proposed to enhance cognitive performance. This study investigated the effects of repeated sensorimotor rhythm (SMR; 12–15 Hz) neurofeedback training on selective auditory attention in healthy adults aged 25–40 years, utilizing both electrophysiological and behavioral assessments. Methods: A prospective, controlled factorial design with repeated measures was employed, including a sham NFB intervention to control for placebo effects. Electrophysiological evaluation measured the amplitudes and latencies of event-related potentials (N100, N200, and P300) during an auditory Go/No-Go task. Speech-in-noise perception, which demands a higher level of selective auditory attention, was further assessed using the QuickSIN test. Results: Electrophysiological results revealed that SMR training statistically significantly reduced latencies of the N200 component at the Cz and Pz electrodes, and the P300 component at the Fz, Cz, and Pz electrodes, in a dose-dependent manner. No significant amplitude effects were observed for any ERP component. Behavioral results demonstrated a linear improvement in speech perception in noise (QuickSIN test) in the experimental group, a task that indirectly engages auditory selective attention mechanisms, as a function of training sessions, with significant group differences favoring the experimental group. These effects were maintained at the one-month follow-up. Conclusions: These findings indicate that SMR-based NFB modulates neural activity and improves selective attention, supporting its potential as an effective intervention for improving attentional performance and auditory processing in healthy populations, with effects persisting at least one month post-training. Full article

Background/Objectives: This study aimed to examine personality profiles at the intrapsychic level (“Oneself”) in adult and older patients with cancer and to compare potential differences between these age groups. Methods: In this observational, cross-sectional comparative study, 273 patients with cancer (all disease stages) were categorized into two age groups: adults (40–64 years, n = 141) and older adults (≥65 years, n = 132). Personality was assessed using the Structural Analysis of Social Behavior (SASB) Form-A. Descriptive and inferential bivariate statistical analyses, including Chi-square tests and independent-samples t-tests, were performed to evaluate differences between the two age groups. To further examine the association between age group and SASB Cluster 1, a multivariable exploratory post hoc factorial General Linear Model (factorial GLM) was conducted. Results: Comparisons between adults and older adults revealed significant differences in socio-demographic backgrounds, with older patients being more frequently widowed and having lower educational attainment (both p < 0.001). Regarding personality dimensions, a significant difference was observed exclusively in SASB Cluster 1 (Autonomy-Assertive and Separating), with higher scores among older participants (4.4 vs. 3.9; p = 0.002). However, in the adjusted factorial GLM, the main effect of age group was no longer statistically significant (p = 0.054), and no significant two-way interactions emerged, including the age-group-by-education interaction (p = 0.222). This indicates that the initial unadjusted age-group association was attenuated after accounting for selected socio-demographic variables. No significant differences were found between the two groups in the remaining SASB Clusters. Conclusions: These findings caution against overestimating the role of chronological age as an isolated variable in psycho-oncological research. Personality differences in Cluster 1 co-occur with distinct cohort-specific and socio-demographic life-course profiles. Understanding how personality relates to shifting socio-demographic circumstances across the lifespan may support the development of more tailored, context-sensitive psycho-oncological interventions. Full article

This paper introduces the Complementary Asymmetric-Odds Weibull (CAO–W) distribution, a new three-parameter distribution that improves the accuracy of modeling lifetime data with complex behavior. The proposed distribution is based on an asymmetric transformation that combines the baseline distribution with its complement, providing more flexibility when modeling a variety of hazard patterns, such as increasing, decreasing, and bathtub-shaped. Some statistical properties of the CAO–W distribution were studied including a mathematical and graphical analysis of the hazard rate function (HRF). The model parameters were estimated using four widely used estimation approaches: maximum likelihood (ML), least squares (LS), maximum product of spacings (MPS), and the Cramér-von-Mises (CVM). The efficiency of these approaches in estimating the model parameters was investigated through simulation studies under different scenarios. Moreover, the proposed distribution was applied to four real datasets, and compared to some flexible distributions to demonstrate its ability to provide a good fit for lifetime data in survival and reliability analysis applications. Finally, a change point analysis, based on the Minimum Information Criterion (MIC), is also conducted to highlight the flexibility of the proposed model. Full article

An extended Lagrangian zero-truncated Poisson (ELZTP) distribution is proposed as a new member of the Lagrangian distributions of the second kind. The model provides a flexible framework for capturing diverse dispersion patterns and tail behaviors. Key statistical properties are derived, including the probability mass function, moments, entropy, and the hazard rate function. Parameters are estimated using the maximum likelihood method, and the finite-sample behavior of the estimators is investigated through a simulation study. The effectiveness of the model is demonstrated using a real-life air-pollution dataset, highlighting the adaptability of the ELZTP distribution to environmental count data applications. Full article

Accurate monitoring of atmospheric ammonia (NH₃) is important for air-quality assessment and sustainable agriculture, but available datasets differ in spatial resolution, temporal coverage, units, and physical meaning. This study presents a feature-based machine-learning framework with train–test leakage-controlled preprocessing to evaluate relative NH₃ classification consistency across three datasets over China: CAMS GEI, CAMS EAC4, and MEIC. Statistical descriptors were extracted for four spatial–temporal cases: Province–Year, Zone–Year, Province–Season–Year, and Zone–Season–Year. Six classifiers were evaluated using chronological testing and cross-validation. CAMS GEI provided the broadest spatial–temporal coverage, while MEIC showed comparatively stable classifier behavior. The best-performing ensemble models commonly achieved accuracies between 0.97 and 0.99 in the main province-level cases. Mean Absolute Value (MAV) was the leading feature in several province-level analyses, with a maximum reported contribution of 64.26%. These scores describe the separability of threshold-derived reference classes and should not be interpreted as an independent physical prediction of NH₃ from external atmospheric drivers. Full article

In this study, the combined effect of waste lubricating oil (WLO) and waste pine branch ash (WPBA) was investigated to improve the low-temperature fracture behavior of asphalt mixtures. To investigate the low-temperature fracture behavior of the mixtures containing 2%–6% WLO and 3%–12% WPBA, the semi-circular bending (SCB) test was conducted at −15 °C, and the experimental results were analyzed using a statistical approach. Fracture toughness (K_(IC)), total fracture energy (G_F) and its pre-peak and post-peak components, crack resistance index (CRI), flexibility index (FI), and toughness index (TI) were determined. The findings indicate that the optimal combination depends on the fracture parameter under consideration. The best fracture toughness results, K_(IC) = 24.96 MPa·mm^0.5, indicating increased resistance to crack initiation, was obtained at 4% WLO and 12% WPBA content. On the other hand, the best fracture energy results, G_F = 1.202 N/mm, reflecting an increase in energy dissipation during crack propagation, was obtained at 4% WLO and 9% WPBA content. The highest FI, CRI, and TI values were obtained with 6% WLO and 9% WPBA, corresponding to increases of approximately 66%, 69%, and 16%, respectively, compared with the reference mixture. This resulted in greater crack-initiation resistance, improved post-peak deformation capacity, and higher energy-dissipation capacity, although the greater WLO content led to a decrease in structural integrity. When the results are evaluated overall, it is understood that the use of WLO in the mixtures increases deformation capacity, while the use of WPBA controls crack propagation through its hardening and filling effects. In mixtures containing 4% WLO and 9%–12% WPBA, a balanced behavior was achieved in terms of crack initiation, crack propagation, and stiffness under low-temperature conditions. Full article

Accurate reservoir performance assessment increasingly relies on hydrological model-derived inflow series; however, the effects of hydroclimatic forcing pathways on reservoir design outcomes remain insufficiently understood. This study evaluates the sensitivity of reservoir performance to alternative forcing pathways by comparing observed inflows with SWAT+-simulated streamflow driven by ERA5-Land reanalysis data in the Cidadelhe catchment, Portugal. An integrated hydrology–reservoir modeling framework couples SWAT+ simulations with three analytical approaches: Behavior Analysis, the Modified Sequent Peak Algorithm (MSPA), and the Resilience-Regulated MSPA-2024. Results show that, despite strong hydrological model performance (NSE up to 0.83), SWAT+-simulated inflows exhibit systematic attenuation of variability, approximately 20–25%, leading to substantially lower storage capacity (up to 60–65%) and evaporation estimates relative to those derived from observed data. While volumetric reliability remains largely insensitive to forcing pathways, resilience, sustainability, and drought risk indices display pronounced method-dependent behavior. MSPA-2024 demonstrates strong robustness by maintaining near-invariant performance metrics across forcing conditions through embedded resilience constraints. These findings show that inflow statistical structure, rather than mean flow alone, controls reservoir design outcomes and that forcing-pathway consistency is critical for reliable performance assessment. The proposed SWAT+–MSPA-2024 framework supports more reliable model-based reservoir planning, particularly in data-scarce basins and climate-scenario applications. Full article

General-purpose polystyrene (GPPS) is widely used in packaging and insulation, although its recyclability poses environmental challenges that require circular economy strategies. The objective of this study was to evaluate the mechanical and tribological behavior of virgin GPPS (100%N), a 50% virgin–50% recycled blend (50N–50R), and 100% recycled GPPS (100%R), processed in Mexico under controlled injection molding conditions. Micro-tensile, flexural, surface roughness, and sliding wear (pin-on-disk) tests were conducted in accordance with ASTM/ANSI standards. The results show that virgin GPPS exhibited the highest strength (micro-tensile: 31.1 MPa; flexural: 87.8 MPa), while the 50N–50R blend maintained comparable tensile strength (27.5 MPa) with greater ductility, making it viable for secondary applications. Recycled GPPS exhibited a significant reduction in strength (micro-tensile: 18.6 MPa; flexural: 41.1 MPa), although with greater deformability. In tribological tests, the coefficients of friction were 0.480 (100%N), 0.128 (50N–50R), and 0.143 (100%R), all with relative errors of less than 4%, confirming statistical validity. Surface roughness analysis showed that the virgin material had the most uniform surface (Ra = 1.0 µm), while the blends exhibited greater variation (Ra ≈ 1.2 µm). In conclusion, although recycled GPPS has limitations compared to virgin material, it retains acceptable mechanical and tribological properties for non-structural applications. These findings support its potential in circular economy strategies in Mexico and provide a framework for countries with similar recycling infrastructure conditions. Full article

Background/Objectives: Breast cancer treatments, including adjuvant radiotherapy, significantly improve survival but are associated with cancer-related fatigue (CRF), a debilitating symptom that severely impacts quality of life. Previous studies often evaluate fatigue by combining radiotherapy with systemic therapies, confounding its independent association. This study aims to isolate the specific relation between exclusive radiotherapy and the longitudinal progression of fatigue in surgically treated breast cancer patients. Methods: A longitudinal study was conducted in a cohort of 70 women with breast cancer who underwent breast-conserving surgery and subsequent adjuvant radiotherapy. Cancer-related fatigue levels were evaluated across behavioral, affective, sensory, and cognitive dimensions using the Piper Fatigue Scale-Revised (PFS-R), while global health status and quality of life (QoL) were measured using the EORTC QLQ-C30 questionnaire, both before and after radiotherapy. Data were analyzed using generalized linear mixed-effects models. Results: Following radiotherapy, a marked and statistically significant increase was observed in total fatigue (1.61 ± 1.16 vs. 5.37 ± 2.76; p < 0.001) and across all specific domains (p < 0.001), accompanied by a decline in global health status (82.6 ± 20.2 to 60.1 ± 29.2). Assessment time (pre- vs. post-RT) was identified as the factor most strongly associated with an increase in fatigue. However, better baseline global health status significantly attenuated the increase in total, behavioral, affective, and sensory fatigue (time × QoL interaction, p < 0.001). Furthermore, higher physical activity levels were significantly associated with lower behavioral fatigue (p = 0.042) and showed a protective trend for sensory (p = 0.055) and total fatigue (p = 0.092). Conclusions: Radiotherapy is strongly associated with a significant increase in cancer-related fatigue scores across all dimensions. Better baseline quality of life acts as a protective factor that is associated with attenuated fatigue progression, while regular physical activity correlates with a mitigated impact, particularly within the behavioral domain, although it did not reach statistical significance across all fatigue dimensions. These findings highlight the critical need for routine clinical monitoring of fatigue and strongly support the implementation of structured, exercise-based interventions during adjuvant treatment. Full article

This paper studies how trust can be supported in exchange environments where potential responders make prior declarations about future return behavior. While prior work has separately examined communication, competition, or contract enforcement, how these mechanisms interact remains underexplored. We modify the standard trust game by introducing two potential responders who declare their intended returns before one is selected, before experimentally varying responder-side competition and contract enforcement in a 2 × 2 design. The results reveal an asymmetric relationship: competition significantly increases trust and realized trustworthiness, whereas contract enforcement alone does not generate a statistically significant increase in trust. However, when competition is combined with enforcement, the effect is substantially amplified, producing the highest levels of trust and trustworthiness. Mechanism evidence shows that competition drives higher declarations and selection incentives, while enforcement eliminates the declaration-fulfillment gap, making competitive declarations credible. These findings suggest that competition and enforcement are complements rather than substitutes—a result with implications for the design of online platforms and other markets where trust depends on prior declarations. Full article