Search Results (276)

Background: Sleep disturbance is highly prevalent in dementia syndromes and increasingly viewed as a correlate of disease expression, not just ageing. This study investigated associations between subjective sleep quality, cognitive performance, and structural MRI markers in a dementia syndromes sample, comparing Alzheimer’s disease (AD) and non-AD groups, with exploratory inclusion of objective sleep and nocturnal blood pressure in a sub-sample. Methods: Observational cross-sectional design with 128 memory clinic patients (41 AD, 87 non-AD). Subjective sleep quality assessed via Pittsburgh Sleep Quality Index (PSQI). Cognitive measures: Mini-Mental State Examination (MMSE) for global cognition, Symbol Digit Modalities Test (SDMT) for processing speed, Trail Making Tests (TMT-A/B), and CLOX-1/2 for executive function. MRI markers: Scheltens scale (medial temporal atrophy), Fazekas scale (white matter hyperintensities). An exploratory sub-sample (N = 24) included additional nocturnal and daytime blood pressure monitoring; these data were analyzed descriptively and are reported as hypothesis-generating only. Analyses: group comparisons, Spearman correlations, hierarchical and logistic regression models in the full sample, and descriptive analyses with Spearman correlations in the exploratory sub-sample. Results: The AD group reported poorer sleep quality (higher PSQI) and worse cognitive performance across domains compared with the non-AD group (p < 0.001). Higher PSQI scores were associated with poorer cognitive outcomes, particularly executive function and processing speed, after adjustment for demographics and structural MRI markers (e.g., β = −0.181 to −0.425 for MMSE/SDMT). In the exploratory sub-sample (N = 24), PSQI was correlated with SDMT (ρ = −0.653) and TMT-A (ρ = 0.788). Conclusions: Subjective sleep quality was associated with cognitive performance in individuals with dementia syndromes after accounting for structural MRI markers. These findings suggest that subjective sleep disturbance may represent a complementary clinical dimension warranting further longitudinal research, including evaluation of whether sleep-focused interventions may offer clinical benefits. Full article

Conventional black-box models for electric vehicle (EV) battery State-of-Health (SOH) prediction achieve high accuracy but lack interpretability, limiting their practical deployment in Battery Management Systems (BMSs). To circumvent these limitations, this study proposes a novel Grey-Box Residual-Driven Framework (GBRDF) that synergizes Deep Symbolic Regression (DSR) with a residual-learning BiLSTM network with two contributions: (1) the DSR component derives explicit, interpretable mathematical expressions governing global degradation trajectories based on electrochemical features, and (2) the BiLSTM network models the residual errors to capture high-frequency nonlinearities and complex sequential dependencies not addressed by the symbolic baseline. By fusing the physics-informed transparency of DSR with the data-driven refinement of BiLSTM, the GBRDF significantly enhances forecasting precision. Experimental validation across four independent EV datasets shows that the GBRDF achieves the highest coefficient of determination (R²) of 0.982, and the lowest mean absolute error (MAE) of 0.1398 and root mean square error (RMSE) of 0.3176, significantly outperforming existing methods. Furthermore, the DSR-derived SOH equation shows that battery degradation is primarily driven by high voltage exposure and charging time, with mathematical transformations reflecting how degradation accelerates initially then slows, matching real-world aging patterns where voltage stress dominates over temperature and usage variations. Full article

Using hedonic consumption theory (HCT) and a niche marketing strategy as analytical frameworks, our study examines consumer behavior in the luxury flower market, a swiftly growing segment of the global luxury goods industry. Adopting a nonexperimental, cross-sectional survey design, we collected primary data from 392 individuals from affluent households (defined as those with annual incomes exceeding $75,000, per standard demographic criteria) via purposive stratified sampling. Our questionnaire, which was reviewed by experts and tested in a pilot study, examined demographics, buying preferences, sustainability awareness, and hedonic motivations. The main findings show that most clients are well-educated women with substantial wealth. They care most about sensory, emotional, and symbolic qualities. Chi-square tests and logistic regressions robustly supported three hypotheses, gender disparities in appreciation, educational and sustainability awareness, and income influences on quality and variety emphasis, with descriptive evidence aligning with two further hypotheses regarding perceived supply shortages and sustainability preferences. The preferred places to buy include nurseries and high-end florists, suggesting opportunities for SMEs. Our study offers initial evidence supporting the application of HCT to perishable luxury floriculture among younger, educated, affluent consumers in North America. It underscores the hedonic appeal heightened by ephemerality and the potential influence of sustainability as a guilt-free enhancement, while indicating opportunities for niche marketing strategies through customization and sustainable sourcing. Our findings indicate opportunities for businesses aiming to reach comparable younger, educated, affluent demographics to fulfill unmet demand through sustainable sourcing, unique varieties, and customized experiences, which align with the SDGs. We conclude with a future research agenda. Full article

Background: Liver transplant recipients are highly susceptible to infectious complications due to surgical invasiveness and immunosuppressive therapy, and post-transplant bloodstream infection is associated with substantial morbidity and mortality. Although several prediction models for bloodstream infection have been proposed, most focus on emergency department or general ward populations and rely on black-box approaches. This limits their applicability and clinical interpretability in liver transplant settings. Therefore, this study aimed to develop predictive models for post-transplant bloodstream infection using preoperative and perioperative clinical data and to derive an interpretable risk equation through symbolic regression. Methods: We conducted a retrospective observational study including 245 adult liver transplant recipients treated at a single tertiary center. Clinical and laboratory variables were extracted from electronic medical records and analyzed using standard statistical methods. For prediction tasks, multiple conventional machine learning models were developed and compared with a symbolic regression-based model. Predictive performance and model interpretability were evaluated using discrimination metrics and Shapley Additive Explanations. Results: Post-transplant bloodstream infection occurred in 82 patients (33.4%). In the test set, conventional machine learning models showed modest discriminative performance (area under the curve, 0.53–0.64). The symbolic regression model achieved comparable discrimination (area under the curve, 0.63) while providing transparent, threshold-based risk equations. While conventional models primarily relied on laboratory variables, symbolic regression additionally identified perioperative clinical factors and viral serologic markers as important predictors. Discussion: Although overall predictive performance was modest, symbolic regression highlighted viral serologic markers as potential indicators of immunologic vulnerability, extending beyond standard laboratory predictors. Conclusions: This interpretability-focused approach may inform future risk stratification models incorporating richer perioperative data. Full article

Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement amount. This study introduces a hybrid computational process that integrates metaheuristic optimization with symbolic regression to generate direct analytical equations for the estimation of the required CFRP area. First, a comprehensive database containing 1300 optimal strengthening scenarios was generated using the Jaya optimization algorithm under the constraints specified in ACI 440.7R and ACI 530. The resulting dataset was subsequently processed through symbolic regression using the PySR platform to identify explicit mathematical relationships between structural parameters and the optimum CFRP area. Most traditional machine learning approaches operate as black-box predictors. In contrast, the proposed approach generates interpretable closed-form expressions that can be used directly in engineering calculations. Two models were derived from the Pareto-optimal solution set. The first model is a simplified equation emphasizing algebraic simplicity. The second model prioritizes prediction accuracy. The simplified formulation achieved a coefficient of determination of approximately 0.992. The accuracy-focused model achieved a value above 0.997 with very low prediction errors. Validation studies with independent test samples showed that the obtained equations are reliable. The average error for the simplified model is below 4%, and for the high-accuracy model, it is approximately 2%. The results demonstrate that combining the optimization-generated datasets with symbolic regression makes it possible to obtain transparent design equations. These equations eliminate iterative design processes and provide a fast and reliable estimation tool for CFRP strengthening of masonry walls. Full article

Environmental, social, and governance (ESG) engagement has been identified as a strategic priority for firms. However, its impact on green innovation (GIN) remains contested. Indeed, the propensity for climate risk to shape the effectiveness of ESG-driven GIN is underexplored. This study investigates how ESG performance (ESGPerf) influences GIN and examines the moderating effect of climate physical risk within the Saudi setting over 2002–2024. Results from fixed-effects and two-stage least squares (2SLS) regressions applied to 460 firm-year observations show that ESGPerf promotes GIN, while climate risk independently stimulates innovation and dampens ESGPerf’s positive effect on GIN. These findings suggest that environmental uncertainty shifts firms’ resource allocation between long-term innovation and short-term adaptation, demonstrating that the strategic value of ESG investments is contingent on risk contexts and underscores ESG commitment as a potential strategic capability rather than mere symbolic compliance. These findings are insensitive to rigorous robustness checks, including alternative variables’ measures and estimation techniques. Full article

Accurate cell and nuclei segmentation remains challenging due to the sensitivity of classical graph-cut methods to parameter tuning. While deep learning models like U-Net offer strong performance, they require large annotated datasets and substantial GPU resources. This work presents a cost-effective alternative: a genetic programming (GP) framework that jointly optimizes unary cost functions and regularization parameters for graph-cut segmentation, coupled with automatic seed selection. Evaluation is conducted under two distinct protocols: (1) oracle-guided per-image optimization, establishing upper-bound performance (mean Dice 0.822, IoU 0.733), and (2) true generalization via train/test split, where expressions learned on 50 images are applied to 50 unseen images (mean Dice 0.695, IoU 0.588). The fixed-model generalization still significantly outperforms the baseline graph cut ($+0.158$ Dice, $p<0.001$). Cross-dataset validation on MoNuSeg (H&E histopathology) achieves a Dice score of 0.823 with the fixed GP model, significantly outperforming the baseline (+0.272). This result uses a single fixed model—the best-performing expression from BBBC038 training—applied in a zero-shot manner to MoNuSeg without any retraining or domain adaptation. All 100 images showed non-negative improvement under oracle optimization in the experiments. The method requires no GPU training, runs in 550 s per image for oracle search, and offers interpretable symbolic cost functions. Code and annotations are provided to ensure reproducibility. This approach offers a practical, interpretable alternative in resource-constrained biomedical imaging settings. Full article

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study ($N=25$, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is $\beta =0.0036$ ($p=0.495$) and the Alphabet Rank coefficient is $\beta =-0.0027$ ($p=0.642$), with the results robust to all tested parameterizations ($\lambda \in [0.05,0.20]$; $p>0.50$ throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower. Full article

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). Six equations were generated for different data-availability scenarios and validated using a leave-one-tree-out (modified k-fold) approach, resulting in Ψ_stem estimates with R² values ranging from 0.67 to 0.80 and root mean square errors (RMSE) ranging from 0.11 to 0.08 MPa. Notably, SR was able to produce interpretable equations that enhance model transparency and transferability. Model robustness was further confirmed using an independent dataset from a different location. To our knowledge, this is the first application of SR for Ψ_stem estimation, offering a scalable and interpretable tool to support irrigation management in tart cherry orchards. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

The increasing accumulation of end-of-life tires has motivated the development of sustainable construction materials incorporating recycled rubber for acoustic insulation applications. This study proposes a data-driven framework for predicting the weighted airborne sound reduction index ($R_w$) of recycled rubber–polyurethane composite panels based on a limited experimental dataset. Specimens with varying granulometric composition, material density, and polyurethane adhesive dosage were evaluated in accordance with EN ISO 10140-2:2010 and EN ISO 717-1:2013. To address data scarcity, a regression-oriented SMOTE strategy was applied exclusively to the training set to preserve statistical representativeness and avoid data leakage. Test set representativeness was ensured by systematically evaluating numerous data splits and adopting the one that maximized multivariate statistical consistency. A hierarchical modeling approach was adopted, ranging from classical regression models to tree-based ensemble methods and multigene symbolic regression. Model performance was evaluated using R², RMSE, MAE, and MAPE on an independent test set. The highest accuracy and robustness were obtained using symbolic regression, with R² values close to 0.99 and minimal prediction errors. Shapley value analysis and PDP/ICE plots identified material density as the dominant predictor of $R_w$, followed by polyurethane adhesive dosage, while granulometric composition exhibited a weaker influence. The proposed framework provides an accurate and interpretable tool for the preliminary design and optimization of recycled rubber acoustic panels. Full article

Bribery remains prevalent across African countries, yet little is known about how fear of retaliation for reporting corruption interacts with national institutional contexts to shape bribery behaviour. Using Round 9 Afrobarometer data from 42,655 respondents across 39 African states, this study examines how perceptions of reporting risk combine with macro-level conditions of state capture and symbolic law to influence the likelihood of paying bribes. Hierarchical logistic regression results show that individuals who fear retaliation are significantly more likely to engage in bribery, but this relationship is conditioned by institutional strength. High levels of state capture and weak rule-of-law systems intensify the effect of retaliation risk, whereas judicial independence mitigates it. Aspects of symbolic law—judicial accountability, access to justice, and enforcement—further shape how fear translates into corrupt exchanges. The findings demonstrate that reducing bribery requires credible, protective, and functional institutions, not simply increased anti-corruption awareness. The study advances corruption research by integrating behavioural risk perceptions with multi-dimensional measures of institutional weakness, offering a cross-national explanation for when fear becomes behaviourally consequential. Full article

Background and Objectives: Employment is a major determinant of quality of life in people with multiple sclerosis (pwMS). This multicenter cross-sectional study aimed to identify which commonly studied demographic, disease-related, clinical, cognitive, and psychological variables, alongside the presence of lower urinary tract symptoms (LUTS), predict employment status in pwMS. Materials and Methods: Seventy-eight pwMS were classified as either full-time employed (n = 41) or non-employed (n = 37). Participants underwent clinical and neuropsychological assessment including disability status (Expanded Disability Status Scale; EDSS), fatigue (Modified Fatigue Impact Scale; MFIS), information processing speed (Symbol Digit Modalities Test; SDMT), depressive symptoms (Hospital Anxiety and Depression Scale-Depression; HADS-D), and LUTS status (presence/absence), alongside demographic and disease-related variables (sex, age, education level, relationship status, and disease duration). Results: Hierarchical binary logistic regression indicated that higher information processing speed was associated with higher odds of employment (OR = 1.11, p = 0.008), whereas the presence of LUTS was associated with lower odds of employment (OR = 0.13, p = 0.026). Disability severity, fatigue, depressive symptoms, demographic characteristics, and disease duration did not contribute in the final model (p > 0.05). Conclusions: Information processing speed and urinary dysfunction were associated with employment status in pwMS. Within the present sample, the multivariable model including these variables showed good discrimination between employed and non-employed participants. The findings should be interpreted as exploratory, and they require further confirmation in independent cohorts before any potential application is considered. Full article

This paper examines the relationship between corporate carbon-reduction pledges and the subsequent adoption of renewable energy by pledging firms, and whether this relationship depends on the institutional conditions in which they operate. We propose a pressure-capacity model, highlighting two different institutional dimensions, (1) environmental policy stringency (institutional pressure) and (2) renewable energy infrastructure (institutional capacity), that may shape when firms’ symbolic pledges lead to observable change in their energy procurement behavior. We estimate random-effects logistic regression models with panel data on 552 publicly listed firms in South Korea, China, and Japan from 2002 to 2017. We find that the relationship between carbon-reduction pledges and renewable energy adoption is strengthened by both the stringency of environmental policy and the availability of renewable energy infrastructure. The marginal effects analysis indicates that the pledge effect is close to zero when institutional capacity is low. However, it increases to about 13 percentage points when policy stringency is high and 9 percentage points when renewable supply is high. The country-specific subsample analysis further uncovers that the conditional effect of institutional capacity is particularly pronounced among Japanese companies. The analysis of correlated random effects shows that these patterns remain robust even after controlling for between-firm confounding. Overall, our findings indicate that the extent to which voluntary corporate climate change commitments translate into actual green implementation depends on the regulatory and infrastructural environment in which firms operate. Full article

Background and Objectives: Interstitial lung diseases (ILDs) have been increasingly linked to neurological manifestations, including cognitive dysfunction and motor impairments, yet the prevalence and severity of these associations remain underexplored. We aimed to (1) compare the frequency of neurological symptoms between patients with and without ILD; (2) evaluate differences in cognitive and motor function scores; (3) perform subgroup analyses based on MoCA (Montreal Cognitive Assessment) scores; and (4) identify potential risk factors for neurological involvement. Methods: In this cross-sectional study, we enrolled 77 patients (40 with ILD and 37 without ILD). We recorded demographic data, smoking status, and body mass index (BMI). Neurological symptoms (tremor, diminished reflexes, paresthesia, etc.) were documented. Cognitive assessments included the MoCA and Symbol Digit Modalities Test (SDMT). Motor function was evaluated via the Berg Balance Scale (BBS), Timed Up and Go (TUG), Single-Leg Stance (SLS), and Grooved Pegboard Test (GPT). Results: Neurological symptoms were more prevalent in ILD (42.5%) than in non-ILD patients (16.2%; p = 0.003). Tremor appeared in 35% of ILD vs. 11% of non-ILD (p = 0.007). ILD patients showed lower mean SLS scores (7.2 ± 3.1 vs. 9.1 ± 3.8 s, p = 0.03) but similar TUG times (10.3 ± 2.1 vs. 9.6 ± 2.3 s, p = 0.20). MoCA scores < 26 were more common in those with ILDs (45% vs. 19%; p = 0.01). Among ILD participants, those with MoCA < 26 had significantly higher rates of tremor (51% vs. 24%, p = 0.04). Logistic regression revealed ILD diagnosis (OR = 3.12, 95% CI: 1.27–7.65, p = 0.013), older age (OR = 1.09 per year, p = 0.02), and smoking history (OR = 2.01, p = 0.05) as independent risk factors for neurological involvement. Conclusions: Our findings suggest that ILD is associated with a higher burden of neurological symptoms and subtle impairments in cognition and motor performance. Recognizing and addressing these manifestations may improve patient management, underscoring the importance of an integrative, multidisciplinary approach. Full article