Search Results (21,300)

Background: Cancer, type 2 diabetes mellitus (T2DM), and psychiatric comorbidities such as depression and anxiety frequently coexist, with shared mechanisms involving systemic inflammation and neuroinflammatory pathways. Understanding these interactions is critical for improving multidisciplinary oncological care. Methods: We conducted a monocentric cross-sectional study (n = 174). Beyond descriptive and univariate analyses, we fitted multivariable models: linear regressions (HADS-Anxiety/Depression) with robust HC3 errors and the predictors ECOG, T2DM, age, sex, and residence, and logistic regression for ECOG ≥ 3. We assessed collinearity and model fit, and performed sensitivity checks. Results: Psychiatric comorbidity was present in 58% of patients, while more than 80% of those with available HADS data (n = 136) exceeded the clinical threshold for anxiety or depression. No significant differences in ECOG status were observed between patients with and without T2DM (mean ECOG 2.5 in both groups). Higher ECOG remained positively associated with both HADS-Depression (adjusted β = 2.77, 95% CI −1.03–6.57, p = 0.149) and HADS-Anxiety (β = 1.62, 95% CI −2.76–6.00, p = 0.468), although not statistically significantly. T2DM showed no independent association with either outcome (Depression β = −2.91, p = 0.130; Anxiety β = −0.80, p = 0.595). In logistic regression, T2DM was not significantly associated with ECOG ≥ 3 (aOR = 3.58, 95% CI 0.23–56.66, p = 0.365). Conclusions: The psychiatric burden is high among Romanian cancer patients, irrespective of T2DM status, and strongly associated with functional decline. These findings support the relevance of a neuroinflammatory framework linking somatic comorbidities and psychological distress. Routine psychiatric screening, early intervention, and integration of psycho-oncology into multidisciplinary care are recommended. Future prospective studies should incorporate inflammatory biomarkers to better define underlying mechanisms. Full article

Background/Objectives: This study analyzes how Health and Fitness Awareness influences quality of life (QOL), mediated by Health Behavior Action and Safety Behavior Practice, within the Social Determinants of Health (SDH) framework. Methods: Accordingly, a multi-group structural equation modeling (SEM) analysis was conducted on the data obtained from 6601 respondents selected from the 2024 National Sports for All Survey, jointly administered by the Ministry of Culture, Sports and Tourism and Korea Sports Promotion Foundation. Nationally representative survey data was collected across metropolitan, mid-sized, and rural areas in South Korea. The analysis further examined whether the structural pathways differed by regional size. Outcome measures included path coefficients and latent mean differences among Health and Fitness Awareness, Health Behavior Action, Safety Behavior Practice, and Improvement in QOL. Results: The analysis revealed that Health and Fitness Awareness significantly influenced both Health Behavior Action and Safety Behavior Practice; these, in turn, had positive effects on Improvement in QOL. Moreover, the structural pathways differed by region: whereas Health Behavior Action played a more significant mediating role in large cities, Safety Behavior Practice was more prominent in mid-sized ones. Conclusions: These findings are expected to provide a theoretical and policy-based foundation for region-specific health promotion strategy development and health equity advancement. Full article

Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs (Small- and medium-sized enterprise), and links announcement reactions to subsequent operating outcomes. Using an event study and multivariate regressions on 155 M&A announcements by KOSDAQ-listed SMEs (Korean Securities Dealers Automated Quotations) (2016–2020), we find that smaller acquirers earn significantly higher announcement-period cumulative abnormal returns (CAR)—i.e., smaller firm size is positively associated with superior market-adjusted performance around M&A events. Although acquisitions of privately held targets and diversifying deals show higher unadjusted means, their effects become statistically insignificant once firm fundamentals and size are controlled for. To connect M&A strategy with business-model sustainability, we operationalize sustainability as the alignment between short-term market expectations (CAR) and realized operating performance over 1–2 years, measured by return on operating cash flow (ROCF); medium-term checks indicate that the short-run “size effect” attenuates, underscoring the role of execution and scale in longer-run outcomes. Overall, the evidence highlights the primacy of firm-specific fundamentals, strategic fit, and integration capacity in guiding M&A decisions that advance both near-term performance and longer-term resilience. The Korean SME setting—marked by concentrated ownership, resource constraints, and a chaebol-influenced market and policy environment—provides a stringent context for these tests. Full article

The Bell regression model (BRM) is a statistical model that is often used in the analysis of count data that exhibits overdispersion. In this study, we propose a Bayesian analysis of the BRM and offer a new perspective on its application. Specifically, we introduce a G-prior distribution for Bayesian inference in BRM, in addition to a flat-normal prior distribution. To compare the performance of the proposed prior distributions, we conduct a simulation study and demonstrate that the G-prior distribution provides superior estimation results for the BRM. Furthermore, we apply the methodology to real data and compare the BRM to the Poisson and negative binomial regression model using various model selection criteria. Our results provide valuable insights into the use of Bayesian methods for estimation and inference of the BRM and highlight the importance of considering the choice of prior distribution in the analysis of count data. Full article

Biomechanical modeling of the cornea is crucial for understanding the progression of some ocular diseases and optimizing surgical treatments. However, hyperelastic non-linear material models, such as those used for corneal tissue, often yield highly variable parameter sets in the scientific literature, influenced by factors like the chosen optimization intervals and differences between tensile and inflation test curve optimization, both of which are addressed in this study. This variability complicates the understanding of corneal mechanical properties. In this research, the aim is to optimize and calibrate the key parameters of the corneal material model, particularly focusing on $c_1$, $c_2$, $k_1$ and $k_2$, using the Holzapfel–Gasser–Ogden (HGO) hyperelastic model, and a novel methodology is proposed that separately estimates the isotropic and anisotropic components in a stepwise manner, addressing the issue of multiple parameter sets fitting experimental curves similarly. This approach helps to standardize corneal material models and improve the reliability of parameter estimations. Moreover, accurate biomechanical characterization within this framework contributes not only to clinical applications but also to biomimetics, inspiring the design of artificial corneal substitutes and bioengineered materials. Full article

The interference fit connection with slug rivets is widely used in aircraft assembly, and an appropriate interference value is vital for aircraft structural integrity. This study proposed a prediction–optimization framework that a deep neural network (DNN) surrogate was trained on a parametric finite element dataset to regress four interference measurements (G1–G4), and the trained DNN was embedded into a genetic algorithm (GA) to search process parameters that meet prescribed target interference. An orthogonal design with range analysis was employed to rank factor importance and provide interpretable trends, while finite element model (FEM) re-runs were used for validation. Compared with support vector regression, random-forest regression, and Bayesian regression, the DNN demonstrated superior fitting accuracy and a more favorable error distribution on held-out data. GA solutions obtained using the DNN surrogate achieved the target interference with a maximum relative deviation of 9.75%, confirming the effectiveness of the proposed workflow for rapid, physics-consistent interference control. The contributions of the study were as follows: (i) an end-to-end, quick-response, reproducible FEM→DNN→GA pipeline for slug-rivet interference; (ii) quantitative factor ranking with mechanistic interpretation; and (iii) minute-scale parameter optimization suitable for engineering deployment. Full article

Fast and precise monitoring of soil organic matter (SOM) during maize growth periods is crucial for real-time assessment of soil quality. However, the big challenge we usually face is that many agricultural soils are covered by crops or snow, and the bare soil period is short, which makes reliable SOM prediction complex and difficult. In this study, an unmanned aerial vehicle (UAV) was utilized to acquire multi-temporal hyperspectral images of maize across the key growth stages at the field scale. The auxiliary predictors, such as spectral indices (I), field management (F), plant characteristics (V), and soil properties (S), were also introduced. We used stepwise multiple linear regression, partial least squares regression (PLSR), random forest (RF) regression, and XGBoost regression models for SOM prediction, and the results show the following: (1) Multi-temporal remote sensing information combined with multi-source predictors and their combinations can accurately estimate SOM content across the key growth periods. The best-fitting model depended on the types of models and predictors selected. With the I + F + V + S predictor combination, the best SOM prediction was achieved by using the XGBoost model (R² = 0.72, RMSE = 0.27%, nRMSE = 0.16%) in the R3 stage. (2) The relative importance of soil properties, spectral indices, plant characteristics, and field management was 55.36%, 26.09%, 9.69%, and 8.86%, respectively, for the multiple periods combination. Here, this approach can overcome the impact of the crop cover condition by using multi-temporal UAV hyperspectral images combined with valuable auxiliary variables. This study can also improve the field-scale farmland soil properties assessment and mapping accuracy, which will aid in soil carbon sequestration and soil management. Full article

This research presents the study of utilizing the cocoa husk biomass waste to obtain active carbon through carbonization method, followed by chemical–thermal activation. The activated carbon (CH) was characterized using BET, SEM–EDX, XPS, and Raman techniques. The obtained material showed a high specific surface area of 1661 m²·g⁻¹, and XPS confirmed the presence of oxygen-containing surface functionalities. The adsorption of reactive dye Drimaren Red K-7B by CH was studied to assess the impact of the initial concentration in water solution, temperature, and contact time. The adsorbent achieved over 90% removal within three minutes at 40 °C. The experimental data for the adsorption of Drimaren Red K-7B using CH showed a good fit with the Dubinin–Radushkevich isotherm and a pseudo-second-order kinetic model. This research offers a promising approach for advancing the circular economy through the obtaining of eco-friendly adsorbents derived from biomass waste, exhibiting high initial adsorption efficiency and rapid uptake kinetics towards reactive dye. Full article

The European Commission, through the REPowerEU plan and the “Fit for 55” package, aims to reduce fossil fuel dependence and greenhouse gas emissions by promoting electric and fuel cell hybrid electric vehicles (EV-FCHEVs). The transition to this mobility model requires energy systems that are able to provide both electricity and hydrogen while reducing the reliance of residential buildings on the national grid. This study analyses a poly-generative (PG) system composed of a Solid Oxide Fuel Cell (SOFC) fed by biomethane, a Photovoltaic (PV) system, and a Proton Exchange Membrane Electrolyser (PEME), with electric vehicles used as dynamic storage units. The assessment is based on simulation tools developed for the main components and applied to four representative seasonal days in Rende (Italy), considering different daily travel ranges of a 30-vehicle fleet. Results show that the PG system provides about 27 kW of electricity, 14.6 kW of heat, and 3.11 kg of hydrogen in winter, spring, and autumn, and about 26 kW, 14 kW, and 3.11 kg in summer; it fully covers the building’s electrical demand in summer and hot water demand in all seasons. The integration of EV batteries reduces grid dependence, improves renewable self-consumption, and allows for the continuous and efficient operation of both the SOFC and PEME, demonstrating the potential of the proposed system to support the green transition. Full article

Functional beverages enhance the nutritional value of their ingredients by increasing the levels of bioactive components, such as probiotics. To achieve consumer acceptance, functional beverages must be both palatable and nutritious. This study investigates the fermentation of quinoa and cherimoya at two temperatures (25 °C and 32 °C) using water kefir grains. The aim was to create a fermented mix that is both balanced and appealing to consumers. The response variables measured were the concentrations of lactic acid bacteria (LAB) and yeasts (CFU mL⁻¹), as well as the overall liking (OL). Ten semi-trained panellists evaluated them using a seven-point hedonic scale. All three developed models for LAB and yeast growth, and OL exhibited R² values exceeding 0.8, indicating a strong model fit and simultaneous optimisation considering the three key responses. At a temperature of 25 °C, the mass fractions of the mixes containing quinoa puree (QP) and cherimoya juice (CJ) were 0.13 and 0.87, respectively. Under optimal conditions, the LAB and yeast increased by 4.2 and 4.4 log units, respectively. Moreover, a significant 62% increase in protein levels and a notable 82% decrease in ascorbic acid were observed after 48 h of fermentation, likely caused by the Maillard reaction. Full article

The Brief Peer Conflict Scale (Brief-PCS) has been shown to be psychometrically suitable for assessing the combination of the forms and functions of aggressive behavior in adolescence. However, its validity, invariance, and utility across other age groups remains unexplored. The current study aims to evaluate the psychometric properties of the Brief-PCS in community samples of adolescents and emerging adults, and to compare self-reported aggression across these age groups and by sex. A sample of 891 individuals (58.4% female, Mage = 16.69) completed the Brief-PCS and additional measures assessing psychopathy characteristics, forms of aggression, and overall aggression. Confirmatory factor analyses supported the four-factor measurement model (i.e., proactive overt, reactive overt, proactive relational, and reactive relational aggression) as the best fit for the data. Evidence also supported the scales’ internal consistency and convergent validity. This four-factor measurement model proved to be invariant across age groups and sex. Males reported being overall more aggressive than females, and adolescents reported more aggressive behaviors than emerging adults, except for proactive relational aggression. These findings extend prior research by confirming the Brief-PCS as a psychometrically sound and developmentally invariant tool, enhancing its value for examining both theoretical and applied aspects of aggression throughout the lifespan. Full article

Single-point deformation monitoring models fail to reflect the structural integrity of the concrete gravity dams, and traditional regression methods also have shortcomings in capturing complex nonlinear relationships among variables. To solve these problems, this paper develops a data–physics-driven multi-point hybrid deformation monitoring model based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine (BOA-LightGBM). Building upon conventional single-point models, spatial coordinates are incorporated as explanatory variables to derive a multi-point deformation monitoring model that accounts for spatial correlations. Subsequently, the finite element method (FEM) is employed to simulate the hydrostatic component at each monitoring point under actual reservoir water levels. Finally, a hybrid model is constructed by integrating the derived mathematical expression, simulated hydrostatic components, and the BOA-LightGBM algorithm. A case study demonstrates that the proposed model effectively incorporates spatial deformation characteristics within dam sections and achieves satisfactory fitting and prediction accuracy compared to traditional single-point monitoring models. With further refinement and extension, the proposed modeling theory and methodology presented in this study can also provide valuable references for safety monitoring of other hydrostatic structures. Full article

Electrochemical impedance spectroscopy (EIS) is a highly versatile electrochemical technique capable of discretizing each electrochemical parameter in complex systems by employing a broad frequency spectrum. When EIS is employed in (bio)sensing applications, the electrochemical parameters are usually fitted into a relatively limited equivalent circuit model regardless of the system at hand. This work thoroughly discusses the meaning of each physical parameter in the Randles circuit, the most common equivalent circuit to model (bio)sensing systems based on EIS transduction. Additionally, it pinpoints the most suitable modifications to the Randles circuit for modern-day electrodes, where coatings of non-biological and/or biological materials can radically impact the measured impedance compared to that of unmodified electrodes. The discussion is supported by simulations that clearly exhibit the effect of each examined parameter, providing guidance for experimentalists to improve the accuracy of their work. Full article

Background/Objectives: Single-phase CT angiography (sCTA) is widely used to assess collateral circulation in acute ischemic stroke, but its static nature can lead to an underestimation of collateral flow. Our study aimed to develop and validate a direct, qualitative dynamic CTA (dCTA) collateral score based on CTP source images, without the need for post-processing software, to provide a more accurate prognostic tool. Methods: We retrospectively analyzed 112 patients with anterior circulation ischemic stroke from a prospective registry who underwent non-contrast CT, sCTA, and CTP within 8 h of onset. Collateral circulation was graded using a 4-point sCTA score and our novel 4-point dCTA score, which incorporates temporal filling patterns. We used linear regression to compare the association of both scores with CTP-derived core/hypoperfusion volumes, infarct growth, and final infarct volume. Results: The dCTA method frequently reclassified patients with poor collaterals on sCTA to good collaterals on dCTA (n = 23), while the reverse was rare (n = 5). A better collateral score was significantly associated with smaller core volume for both sCTA and dCTA, but the dCTA score demonstrated a superior model fit (R² = 0.36 vs. 0.32). Similar superior correlations for dCTA were observed for hypoperfusion, infarct growth, and final infarct volumes. Critically, only the dCTA score significantly modified the association between core volume and time since stroke onset (p for interaction = 0.04). Conclusions: A collateral score derived from CTP source images (dCTA) offers a more reliable prediction of infarct lesion sizes and progression than conventional sCTA. By incorporating temporal resolution without requiring extra software, dCTA provides a robust correlation with stroke temporal evolution and represents a readily implementable tool to enhance patient selection in acute stroke. Full article

The accurate calibration of radiochromic films is critical for high dose rate (HDR) brachytherapy dosimetry. Conventional workflows frequently rely on manually determined regions of interest (ROIs), which might increase operator variability. In this investigation, Gafchromic EBT3 films were irradiated under clinical settings at nominal doses of 0–10 Gy and evaluated using a MATLAB (R2024b)-based tool that allows for both manual and automated ROI selection. The calibration curves were modeled with a second-order polynomial and rational model, and performance was assessed using statistical measures. The study found that the rational model fits better than the polynomial model. Additionally, the automatic ROI approach outperformed the manual method in both models, resulting in higher calibration accuracy and reproducibility (R² = 0.999, RMSE = 0.118 Gy, MAE = 0.103 Gy vs. R² = 0.986, RMSE = 0.448 Gy, MAE = 0.388 Gy). Although manual ROI occasionally produced greater dose–response slopes at higher doses, it was more susceptible to operator bias and film non-uniformity. In contrast, automatic ROI reduced variability by consistently picking homogeneous sections, resulting in steady curve fitting across the entire dose range. Furthermore, a companion module transformed calibrated films into 2D false-color maps and 3D dosage surfaces, allowing for visual assessment of dose uniformity, detection of scanner-related aberrations, and quantitative verification for quality assurance. These findings demonstrate that automated ROI selection provides a more stable and reproducible foundation for film calibration in HDR brachytherapy, minimizing operator dependency while facilitating routine clinical quality assurance. Full article