Search Results (14,641)

Constructing efficient conductive networks is essential to overcome the intrinsically low electronic conductivity of LiFePO₄ cathodes. Previous studies have demonstrated that different conductive agents possess distinct electrical conduction mechanisms. The synergistic integration of multiple types of conductive agents can achieve more favorable conductive performance. Nevertheless, most relevant studies are still limited to binary conductive systems, and the synergistic mechanism among various conductive agents has not been systematically investigated and deeply analyzed. In this work, a multidimensional ternary conductive system composed of Super P carbon black (SP), graphene (GN), and carbon nanotubes (CNTs) was systematically optimized to regulate electron and ion transport pathways. By adjusting the relative proportions of SP, GN, and CNTs, the evolution of conductive network structure and its impact on electrochemical performance were investigated, and the optimized composition (SP/GN/CNTs = 50/15/35, denoted as S5GC37) was identified. The results reveal that the multidimensional conductive framework formed by S5GC37 effectively integrates short-range ion diffusion with long-range electron transport, leading to reduced polarization, suppressed surface oxidation, and enhanced charge transport kinetics. As a result, the LiFePO₄ electrode with S5GC37 delivers an initial discharge capacity of 164.8 mAh·g⁻¹ and maintains 151.9 mAh·g⁻¹ after 200 cycles at 1C. Even at 3C, a capacity retention of 83.2% is achieved after 200 cycles, demonstrating excellent rate capability and cycling stability. These findings highlight the importance of multidimensional conductive network design for high-performance LiFePO₄ batteries. Full article

In this work, we assess the reliability of a new Bhatnagar–Gross–Krook (BGK)-type model of the linearized Boltzmann equation for binary gas mixtures by investigating the propagation of high-frequency sound waves in microchannels. In order to take into account the different gas–wall interaction properties experienced by the mixture components, we solve the kinetic equations assuming Maxwell boundary conditions, with different accommodation coefficients for the two species. Unlike other BGK models existing in the literature, the newly proposed model can describe general intermolecular forces. Therefore, in order to test this ability, we specialize our computations to mixtures with two components of very different masses (disparate-mass gas mixtures like He-Xe), since, in this case, the intermolecular forces play a more significant role compared to mixtures with species of similar masses. Then, we compare the results with those obtained by the McCormack model, which has been shown to correctly reproduce many experimental data. Full article

Background/Objectives: Midwifery workforce sustainability faces critical challenges including high burnout and turnover rates threating the service quality and the maternal health outcomes. While self-leadership and self-compassion represent promising psychological resources, their roles relative to organizational factors remain underexplored. This study examined associations between self-leadership, self-compassion, and workforce outcomes (job satisfaction, turnover intention, performance) among Turkish midwives. Methods: A cross-sectional survey was conducted with 346 midwives working in diverse healthcare settings across Turkey from May 2021 to April 2022. Data were collected through an online self-report questionnaire using validated scales for self-leadership and self-compassion as well as measures of job satisfaction, turnover intention, and job performance, and including demographic and organizational items. Descriptive statistics, one-way ANOVA (with Eta-squared [η²] calculated to determine effect size), and correlation analyses were conducted, followed by hierarchical multiple regression and binary logistic regression to examine predictive relationships, with organizational factors entered before psychological resources. Results: Self-leadership and self-compassion demonstrated a moderate positive correlation (r = 0.342, p < 0.01). Self-leadership strongly predicted job performance (OR = 2.497, p = 0.001), particularly through natural reward strategies emphasizing intrinsic motivation (OR = 1.970, p < 0.001). However, neither psychological resource significantly predicted job satisfaction or turnover intention when organizational factors were included. Work schedule, healthcare setting, professional position, and income emerged as primary predictors of satisfaction and retention. Work experience predicted increased psychological distress (OR = 1.073, p = 0.003). Conclusions: Psychological resources demonstrate domain-specific effects on workforce outcomes in midwifery: self-leadership strategies strongly enhance job performance, whereas job satisfaction and turnover intention are influenced primarily by organizational conditions. These findings highlight the need for multi-level strategies to support the sustainability of the midwifery workforce. Full article

The solubility values of polyphenolic compounds in different extraction solvents are crucial for their recovery from natural matrices. Hansen solubility parameters (HSPs) stand out as a predictive tool for evaluating solute-solvent affinity and thus rational solvent selection for extraction processes. In this study, HSPs of trans-resveratrol and hesperetin were calculated using a semi-empirical method to assess the capability to predict the solubility behavior of both polyphenols in organic binary solvent mixtures. Experimental solubility of both polyphenols was determined in up to 21 monosolvents at 298.15 K and 0.1 MPa and used to classify them to iteratively calculate HSPs. Calculated HSPs were compared and discussed with literature values in terms of molecular interactions, demonstrating a fair agreement. Solubility of trans-resveratrol and hesperetin was then determined in methanol + MEK, ethanol + MEK, methanol + MiBK, ethanol + MiBK, and methanol + ethanol binary solvent mixtures. trans-Resveratrol achieved higher mole fraction solubility than hesperetin in all binary mixtures across the whole molar fraction range except in methanol + MiBK. Both compounds exhibited enhanced solubility in all alcohols + ketone binary mixtures, attributed to synergistic solvent effects. HSP analysis revealed a minimum Hansen distance between solute and solvent mixtures at compositions corresponding to the solubility maximum in synergistic systems. Additionally, calculated HSPs proved to effectively estimate the concentration at which this phenomenon occurs in all tested systems, reaching a robust correlation between maximum solubility and minimum Hansen distance. Overall, insights from this study underscore the effectiveness of experimentally derived HSPs in predicting the solubility behavior of polyphenols and seek to provide valuable guidance on solvent selection strategies for the recovery of bioactive compounds. Full article

Distinguishing Active from Inactive Tuberculosis (TB) on Chest X-rays presents a clinical challenge due to overlapping radiological signs. This study introduces Vision Mamba CGSM, a deep learning framework integrating a State Space Model (SSM) backbone with a Context-Guided Slot Mixing (CGSM) module. The SSM captures global anatomical context, while the CGSM module isolates subtle pathological features by applying localized spatial attention. We validated the model using a hierarchical diagnostic scheme covering Normal, Pneumonia, Active TB, and Inactive TB. Experimental evaluations demonstrate an accuracy of 92.96% and a Youden Index of 79.55% on the independent test set. In the specific binary classification of Active vs. Inactive TB, the model recorded a specificity of 97.04%, outperforming standard baseline architectures including ResNet152 and ViT-B. Additional validations on external datasets confirm the consistent generalization of the proposed feature extraction mechanism. Full article

This comparative study investigates binder-free binary WC-TiC and ternary WC-TiC-TaC carbide ceramics as alternatives to cobalt-bonded hard materials. Equimolar compositions were processed via high-energy ball milling (HEBM) and consolidated by spark plasma sintering (SPS) at 1700–2100 °C. X-ray diffraction analysis (XRD) revealed fundamentally different homogenization kinetics: the ternary system achieved a complete single-phase structure at 2000 °C, 100 °C earlier than the binary system. This acceleration correlates with finer initial particle size (2–5 μm vs. 3–10 μm) and near-stoichiometric TaC, facilitating interdiffusion. Lattice parameter evolution confirmed the formation of (W,Ti)C and (W,Ti,Ta)C substitutional solid solutions. Mechanical characterization showed contrasting behaviors: binary WC-TiC exhibits maximum hardness at 1900 °C (1793 HV30, fracture toughness 5.07 MPa·m^1/2), while ternary WC-TiC-TaC peaks at 1700–1800 °C (1947–1782 HV30) with higher toughness (max 5.42 MPa·m^1/2). Optimal processing windows with acceptable property uniformity are 1800–1900 °C (binary) and 1700–1900 °C (ternary). The binary system offers superior toughness and stability; the ternary system enables faster processing and higher initial hardness, defining distinct application domains. Full article

Background/Objectives: Population aging has been accompanied by increased institutionalization of older adults and a high prevalence of vitamin D deficiency in this group. Although the literature suggests a possible relationship between vitamin D and cognition, findings remain inconsistent, particularly in institutional settings. This cross-sectional study aimed to investigate factors associated with vitamin D deficiency in institutionalized older adults, emphasizing the role of vitamin D supplementation and length of institutionalization, as well as to evaluate the association between serum vitamin D levels, cognitive decline, and dementia. Methods: A total of 104 older adults living in different long-term care institutions (LTCFs) in the city of Pelotas, RS, Brazil, were evaluated. Sociodemographic, clinical, and nutritional data were collected via interviews and medical record review. Serum 25-hydroxyvitamin D levels were categorized according to the Institute of Medicine cutoffs (<20 ng/mL and ≥20 ng/mL). Cognitive decline was assessed using the Mini-Mental State Examination, and dementia was evaluated with the Clinical Dementia Rating scale. Analyses included bivariate tests and binary logistic regression. Results: A high prevalence of vitamin D deficiency (52.9%), cognitive decline (83.6%), and questionable or mild dementia (79.4%) was observed. In multivariate analysis, vitamin D supplementation remained independently associated with vitamin D deficiency, whereas no significant association was observed between vitamin D levels and cognitive decline or dementia. Conclusions: Vitamin D deficiency in institutionalized older adults is predominantly associated with contextual and care-related factors rather than cognitive impairment, highlighting the importance of systematic nutritional monitoring and vitamin D supplementation strategies in institutional settings. Full article

HER2-low breast cancer, also known as IHC 1+ or IHC 2+ without ERBB2 amplification, is a new concept in the biology of breast cancer that has removed the binary classification of HER2-positive or HER2-negative breast cancer. The recent introduction of antibody-drug conjugates (ADCs), such as trastuzumab deruxtecan (T-DXd), has improved therapeutic outcomes for HER2-low breast cancer by demonstrating high efficacy in HER2-low tumors through efficient payload delivery. However, differences in ADC efficacy exist among HER2-low breast cancer patients, with tumor cells showing resistance to ADCs. Recent research indicates that the tumor microenvironment (TME) plays a critical role in determining the efficacy of ADCs against tumor cells. TME creates a barrier to the delivery of ADCs to tumor cells that show resistance to ADCs. This review article aims to highlight the current understanding of the biology of HER2-low breast cancer and its response to ADCs with reference to the tumor microenvironment. Full article

Social media platforms contain rich but noisy narratives of psychological distress, creating opportunities for early mental health risk detection. However, existing datasets capture heterogeneous constructs such as suicide risk severity, depression diagnosis, and DSM-5 symptom presence, and most prior models are trained and evaluated on a single corpus, limiting their clinical alignment and cross-dataset generalizability. In this study, we fine-tune a domain-specific long-document transformer, AIMH/Mental-Longformer-base-4096, for binary mental health risk detection (risk vs. no risk) using two clinically aligned Reddit datasets: the C-SSRS Reddit corpus and the eRisk 2025 depression dataset. To handle long user histories, we introduce an LLM-based summarization pipeline that compresses posts exceeding 2000 tokens while preserving mental health-relevant information. We also conduct a seven-configuration ablation study across combinations of three corpora (C-SSRS, eRisk, and ReDSM5) to examine how dataset semantics influence model performance. On a held-out C-SSRS + eRisk test set (n = 279), the proposed model achieves a mean balanced accuracy of 0.89 ± 0.01 across five random seeds, with a best run of 0.90 and a 5.74 percentage point improvement over the strongest baseline (TF-IDF + Random Forest). The model also shows strong cross-platform generalization, achieving BA = 0.78 on the depression-reddit-cleaned dataset (n = 7731) and BA = 0.85 (ROC-AUC = 0.92) on a Twitter suicidal-intention dataset (n = 9119) without additional fine-tuning. The ablation analysis shows that although a three-dataset configuration (C-SSRS + eRisk + ReDSM5) maximizes aggregate performance, the ReDSM5 labels encode symptom presence rather than clinical risk, creating a semantic mismatch. This finding highlights the importance of label compatibility when combining heterogeneous mental health corpora. Explainability analysis using Integrated Gradients and attention visualization shows that the model focuses on clinically meaningful expressions such as therapy references, diagnosis, and hopelessness rather than isolated keywords. These results demonstrate that clinically aligned long-context transformers can provide accurate and interpretable mental health risk detection from social media while emphasizing the critical role of dataset semantics in multi-corpus training. Full article

Background/Objectives: Urinary continence recovery after robot-assisted radical prostatectomy (RARP) follows a progressive trajectory that is often simplified into binary outcomes. Modeling continence recovery as an ordered process may better reflect post-operative functional patterns and identify clinically relevant predictors. Methods: We retrospectively analyzed 180 patients undergoing extraperitoneal single-port RARP. At 6 months, continence recovery was classified into three ordered categories: early continence, late continence, and persistent incontinence. Multivariable ordinal logistic regression was used to identify independent predictors of continence recovery. The primary model included nerve-sparing (NS) status, postoperative complications, age, and prostate volume. Sensitivity analyses were performed by sequentially replacing prostate volume with body mass index, surgical case number, or preoperative prostate-specific antigen (PSA). An interaction between NS and age group was also tested. Results: NS surgery was the factor most strongly associated with favorable continence recovery (p < 0.001), followed by absence of post-operative complications (p = 0.003). Younger age and larger prostate volume were also independently associated with improved continence recovery. Sensitivity analyses confirmed the robustness of the primary model, as replacement of prostate volume with body mass index, surgical case number, or PSA did not improve model performance and did not alter the effect of NS surgery. No significant interaction between NS and age group was observed. Conclusions: Continence recovery after extraperitoneal RARP is primarily associated with NS surgery and an uncomplicated post-operative course, with age and prostate volume providing additional refinement. Modeling continence as an ordinal outcome offers a clinically meaningful framework for evaluating functional recovery after prostatectomy. Full article

The antioxidant activity of Origanum vulgare L. and Mentha aquatica L. has been widely reported; however, interaction effects within and between different plant parts remain insufficiently characterized. This study aimed to evaluate the antioxidant behavior of methanolic extracts from leaves, flowers, and rhizomes of both species and to assess the nature of intraplant and interspecific interactions using combination analysis. Antioxidant activity was determined for individual extracts and their binary mixtures using DPPH and ABTS radical scavenging assays. Phytochemical analysis was performed by LC-MS/MS. In O. vulgare, all intraplant mixtures exhibited synergistic effects, suggesting complementary contributions of phenolic acids and flavonoids across plant organs. In contrast, M. aquatica showed more variable responses, with additive to antagonistic interactions, particularly in combinations involving rhizomes with lower phenolic content. Interspecific mixtures further demonstrated that interaction outcomes depended on the qualitative and quantitative composition of phytochemicals: leaf mixtures showed synergism, whereas flower and rhizomes mixtures tended toward antagonism. Comparable interaction trends were observed in both radical scavenging assays. These results indicate that antioxidant activity in plant mixtures is not simply additive but is strongly influenced by phytochemical composition and plant part, highlighting the importance of empirical testing when designing multicomponent plant-based antioxidant formulations. Full article

Based on data from 208 students involved in entrepreneurship studies at Tecnológico de Monterrey, Mexico, this paper examines whether prior experience with AI, expectations, gender, and age reinforce future AI use. To achieve this objective, we applied binary logistic regression with random oversampling to balance the dataset. We complemented it with additional model performance metrics, including the confusion matrix, sensitivity, specificity, and area under the ROC curve. The results show that prior experience with AI, age-related technology use, and positive expectations regarding AI are associated with a higher likelihood of reinforcing future AI use. In terms of gender, the results indicate a gender gap favoring women, who are more likely to use AI when they perceive greater utility and confidence, as well as a stronger desire to succeed. Full article

In this work, the volumetric properties of nine binary systems composed of ethyl propanoate and n-alkylcyclohexanes (from cyclohexane to decylcyclohexane) were investigated. Densities were measured at atmospheric pressure (101 kPa) over the entire composition range and at temperatures from 288.15 K to 328.15 K. A total of 525 density data points were obtained. Excess molar volumes were derived from the experimental densities and correlated using a Redlich–Kister equation, while mixture densities were modeled with the Jouyban–Acree model. All systems exhibit positive excess molar volumes over the studied temperature and composition ranges, indicating volume expansion upon mixing due to dominant repulsive interactions. The magnitude of the excess molar volume increases with increasing alkyl chain length of the branched naphthenic compound: for an equimolar mixture, V^E is about 0.65 cm³·mol⁻¹ for the methylcylohexane + ethyl propanoate mixture and reaches 0.83 cm³·mol⁻¹ for the heptylcylohexane + ethyl propanoate binary system, although a plateau tendency is observed for longer alkyl chains. Excess molar volumes increase linearly with temperature, with a more pronounced temperature effect for shorter-chain alkylcyclohexanes. The Jouyban–Acree model provides an excellent correlation of the density data, yielding average relative deviations between 0.02% and 0.04%, and allows reliable predictions within the investigated temperature range. Full article

Background: Reliable identification remains a cornerstone of forensic investigations, particularly when encountering degraded remains or suboptimal biological evidence. This study evaluates the potential of dentine metabolomics, utilizing proton nuclear magnetic resonance (¹H-NMR) spectroscopy, to detect cancer-associated metabolic signatures in dental tissues for forensic applications. Methods: Forty-four non-carious second molars were analyzed, comprising 22 samples from deceased individuals with a documented history of cancer and 22 age- and sex-matched controls. Metabolomic profiling was conducted using ¹H-NMR spectroscopy to identify and quantify dentine metabolites. Statistical evaluation included unsupervised principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), receiver operating characteristic (ROC) curve analysis, and exploratory binary logistic regression. Results: Among the 209 identified metabolites, inosinic acid and 2-ketobutyric acid were identified as the most robust discriminative biomarkers across both multivariate and univariate frameworks. The exploration within-sample predictive model achieved a Nagelkerke R² of 0.822 and an overall classification accuracy of 90.9%, with a specificity of 95.5% and a sensitivity of 86.4%. These key metabolites are fundamentally associated with purine metabolism and oxidative stress pathways frequently dysregulated in oncogenesis. Conclusions: This pilot study suggests that dentine may retain metabolomic information associated with cancer comorbidity under heterogeneous postmortem conditions. However, the findings remain exploratory and require validation in larger cohorts with standardized postmortem variables before practical forensic implementation. Full article

Speech-based depression detection is promising for objective mental health assessment. However, conventional methods relying on short-frame acoustic features often fail to capture long-term temporal and behavioral characteristics of speech essential for modeling depression-specific speaking patterns. Herein, four novel acoustic feature sets extracted from long-term speech are proposed: utterance interval feature set (UIFS), pause interval feature set (PIFS), response interval feature set (RIFS), and speech density (SD). These features explicitly characterize temporal structures and session-level speech behaviors beyond short-frame analysis. These features are combined with conventional acoustic features, including standard features extracted using openSMILE and voice level features, and evaluated using support vector machines under subject-independent conditions for the binary classification of depressed and nondepressed speakers. Incorporating the proposed features improves classification performance compared with baseline features (accuracy: 0.54 for openSMILE and 0.52 for openSMILE + voice level features). The configuration integrating all four proposed feature sets achieves an accuracy of 0.58, a precision of 0.56, a recall of 0.58, and a specificity of 0.58, indicating consistent performance gains under subject-independent and strictly controlled evaluation conditions. Thus, depression-related speech patterns can be captured by explicitly modeling temporal and behavioral speech characteristics across entire dialog sessions. This study contributes to advancing acoustic feature design for speech-based depression detection and developing clinically supportive screening and monitoring technologies. Full article