Search Results (17,383)

Decoding short-window electroencephalography (EEG) signals is critical for low-latency brain–computer interfaces (BCIs), yet current models struggle to extract robust features under high cross-subject variability and low signal-to-noise ratios. To address this, we propose a spatiotemporal decoding framework integrating dynamic weighted permutation entropy (DWPE) with a hybrid neural network. We introduce DWPE to quantify nonlinear dynamic complexity while retaining amplitude information. These features are subsequently processed by a cascaded convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architecture with spatial attention, enabling the simultaneous extraction of topological patterns and temporal dependencies. The framework was evaluated on three public motor imagery datasets (hBCI, BCI Competition IV-2a, and IV-2b) using a fixed 3 s window. Empirical results demonstrate that our approach achieves an average accuracy of 84.35% and an AUC of 0.8821 on the hBCI dataset, significantly outperforming current representative recent baselines (p < 0.01). Ablation studies confirm that integrating DWPE yields a 3.89% accuracy improvement over the spatial–temporal backbone alone. With a single-sample inference time of 20.94 ms and an estimated total decision latency of approximately 3.02 s under the 3 s window setting, the proposed method provides a favorable balance between decoding accuracy and computational efficiency for short-window and near-online BCI applications. Full article

Background/Objectives: Telemedicine offers significant potential to improve the quality and accessibility of geriatric care, particularly in resource-constrained settings. However, its effective implementation depends largely on healthcare professionals’ acceptance and willingness to use such systems. Drawing on an extended Technology Acceptance Model (TAM), this study examines the determinants of doctors’ and nurses’ intentions to adopt telemedicine for elderly care in Algeria, with particular emphasis on self-efficacy and institutional support. Methods: This cross-sectional study employed a structured questionnaire administered to 130 healthcare professionals, including physicians and nurses, in Algeria. Hierarchical multiple regression analysis was conducted to test the proposed hypotheses and assess the incremental explanatory power of the extended model. Results: The extended TAM accounted for 48.7% of the variance in intention to use telemedicine. Institutional support (β = 0.432, p < 0.001) and self-efficacy (β = 0.264, p = 0.001) emerged as the strongest predictors. Perceived ease of use (β = 0.178, p = 0.038) and perceived usefulness (β = 0.139, p = 0.021) also had significant positive effects. The inclusion of self-efficacy and institutional support increased the model’s explanatory power by 23.5%. Conclusions: The findings highlight the critical role of organizational support mechanisms, digital competencies, and system usability in fostering telemedicine adoption among healthcare professionals. The study provides practical implications for policymakers and healthcare institutions, emphasizing the need for targeted training programs, supportive infrastructure, and institutional policies that enhance confidence and facilitate the integration of telemedicine into clinical workflows. Full article

Deploying medical visual question answering (VQA) systems over wireless networks introduces a fundamental challenge: channel-induced image degradation may corrupt the visual representations extracted by large vision-language models (VLMs), leading to unreliable diagnostic decisions. We investigate the robustness of frozen LLaVA-1.6, BLIP-2, and BioViL-T hidden-state features under additive white Gaussian noise (AWGN), Rayleigh fading, and six combined JPEG-compression-plus-channel conditions (quality factors $q\in \{20,50,70\}$) across signal-to-noise ratios (SNRs) from $-5$ to $+20$ dB. A lightweight MLP classifier is trained exclusively on clean features and evaluated on channel-degraded features, enabling controlled analysis of representation robustness without retraining. We introduce the Feature Robustness Score (FRS), defined as the difference between cosine similarity and normalized L2 drift of clean versus degraded features, together with a validation-set FRS threshold analysis as a label-free retraining criterion. A wavelet sub-band energy analysis further characterizes the spectral distribution of channel-induced feature drift. Experiments on PathVQA and VQA-RAD reveal four key findings: (1) LLaVA-1.6 features maintain cosine similarity above $0.98$ across all eight channel conditions and all SNR levels, with statistically significant MLP gains at every tested point ($p<0.05$, McNemar’s test); (2) BLIP-2 and BioViL-T features are less stable but still support consistent MLP improvements, with BioViL-T performing competitively on VQA-RAD, suggesting domain alignment matters; (3) JPEG compression quality ($q=20,50,70$) has negligible impact on feature drift, establishing VLM features as JPEG quality-invariant; and (4) wavelet analysis confirms that channel noise primarily affects high-frequency detail bands while preserving low-frequency semantic content. Full article

Introduction: Optimal systemic treatment for recurrent or advanced endometrial cancer (EC) remains uncertain, particularly in the second-line setting. While first-line chemotherapy with paclitaxel and carboplatin is widely used, its efficacy is limited. Recent evidence suggests that adding immune checkpoint inhibitors (ICIs) to chemotherapy (ChT) can improve progression-free survival (PFS). In addition, hormonal therapies (such as progestins and aromatase inhibitors), targeted therapies and ICIs used alone or in combination are emerging as potential treatment options. Objectives: Our objective was to systematically evaluate the efficacy and safety of systemic therapies beyond standard ChT for patients with recurrent or advanced EC, focusing on progression-free survival (PFS), overall survival (OS), and treatment-related adverse events (TRAEs). Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs), prospective studies, and retrospective studies up to June 2024 on PubMed, Embase, and CENTRAL. Studies evaluating systemic therapies—chemotherapy, hormonal therapy, targeted agents, and ICIs—in recurrent or advanced EC were included. Primary outcomes were PFS and OS; secondary outcomes included grade ≥ 3 treatment-related adverse events. Risk of bias was assessed using the Cochrane RoB 2 and MINORS tools, and GRADE was applied to evaluate the certainty of evidence. Results: Five RCTs evaluated the addition of ICI to conventional ChT. In mismatch repair-deficient (MMRd) patients, the addition of ICI significantly improved PFS and OS compared to the ChT-only group. In mismatch repair-deficient (MMRd) patients, the median PFS (mPFS) was 8.39 months in the ChT group and 22.73 months in the ICI group (HR: 0.34, p < 0.001). OS results were 26.48 and 41.36 months, respectively. In mismatch repair-proficient (MMRp) patients, mPFS was 9.4 months in the ChT group and 10.18 months at the ICI group (HR: 0.72, p = 0.002). OS was 27.37 and 27.79 months, respectively. We conducted several exploratory single-arm subgroup analyses and multiple individual patient data (IPD) meta-analyses across several clinically relevant subgroups, including patients treated with progestins, lenvatinib plus pembrolizumab, PI3K/AKT/mTOR inhibitor monotherapy, and PI3K/AKT/mTOR inhibitors in combination with aromatase inhibitors. Detailed descriptive analyses were performed for each subgroup. Conclusions: Combining chemotherapy with ICIs appears to show the most favorable survival outcomes, especially in MMRd tumors. Taking into account the methodological limitations inherent in the elaboration of lower-level evidence and IPD, hormonal and targeted therapies might be considered viable options, with efficacy and safety profiles dependent on tumor biology. Better stratification of the EC patient cohort is warranted. Full article

A set S of vertices in a graph G is a total dominating set if every vertex of G is adjacent to (totally dominated by) some vertex of S. The number of vertices in S that totally dominate a vertex v of G is denoted by ${\sigma }_S\left(v\right)$. A total dominating set S is a $pt$-dominating set (or $pt$-dominating set) if ${\sigma }_S\left(u\right)\ne {\sigma }_S\left(v\right)$ for every two adjacent vertices u and v of G. The $pt$-domination number ${\gamma }_{pt}\left(G\right)$ of a graph G is the minimum cardinality of a $pt$-dominating set in G. Two well-known classes of highly symmetric graphs are investigated here, namely fans and wheels. For $n\ge 2$, the fan $F_n$ is the join $P_n\vee K_1$ of the path $P_n$ of order n and the complete graph $K_1$ of order 1, while for $n\ge 3$, the wheel $W_n$ is the join $C_n\vee K_1$ of the cycle $C_n$ of order n and $K_1$. For a positive integer t, the general fan $F_{n,t}$ is the graph $P_n\vee {\overline{K}}_t$ and the general wheel $W_{n,t}$ is the graph $C_n\vee {\overline{K}}_t$ where ${\overline{K}}_t$ is the empty graph of order t. All fans, wheels, general fans and general wheels are determined that possess $pt$-dominating sets. Furthermore, $pt$-domination numbers of all these graphs are determined as well. Full article

In deep foundation pit risk assessment, expert ratings are often aggregated without preserving the dispersion of individual opinions, yet such dispersion directly reflects the reliability of the assessment. To address this shortcoming, this study integrates dual probabilistic linguistic term sets (DPLTS), entropy theory, and the best–worst method (BWM). In this DPLTS framework, the membership set L(p) encodes the central tendency of expert ratings (the assessed risk level), while the non-membership set U(q) encodes the dispersion of ratings, serving as a proxy for expert disagreement—a source of uncertainty that is as critical as the risk level itself for decision-making. The least common multiple expansion method standardizes information length. Secondary indicator weights are determined using fuzzy entropy and cross-entropy, while primary indicator weights are derived via BWM, forming a combined subjective-objective weighting model. Hierarchical aggregation yields the overall risk expectation value. A case study assesses the project as Level III (moderate) risk, with a low variance of 0.0503 indicating strong expert consensus. The risk expectation varies by less than 4% under different entropy measures, confirming robustness. Comparative analysis with fuzzy comprehensive evaluation and CRITIC–Grey system methods shows consistent results, with all three identifying excavation and support as key risk indicators. The proposed method provides not only a reliable risk level but also a quantitative measure of expert agreement, offering enhanced support for targeted risk management. Full article

Falls represent a severe public health issue and a primary cause of impairment across both geriatric and adults clinical populations, underscoring the importance of early and tailored prevention. Research evidence shows that technology-assisted rehabilitation may enhance balance and reduce fall risk; however, long-term real-world evidence remains limited. This retrospective study evaluated the impact of personalized balance training by hunova^® robotic platform on fall risk in a heterogeneous clinical cohort (n = 355; mean age 58.24 ± 19.63 years) comprising neurological and orthopedic conditions. Fall risk was assessed using the Silver Index, a sensor-based robotic posturographic score to predict 12-month fall probability at baseline (T0), after 6 weeks (T1), and at 6 months (T6). Robotic rehabilitation was provided to 162 patients (TREAT-G), while 193 served as a control group (NOTREAT-G). TREAT-G demonstrated a significant reduction in fall risk over time (p = 0.036), with improvements from T0–T1 (p < 0.001) and T0–T6 (p = 0.009). Domain-specific analysis showed significant increases in limits of stability, sit-to-stand, and gait speed (p < 0.001). Significant time × group interactions were observed for limits of stability (p = 0.004) and sit-to-stand (p < 0.001). Based on the occurrence of falls in the previous six months, the sample was then divided into ‘Fallers’ (at least one fall) and ‘No-Fallers’ (no falls). Subgroup analysis revealed significant improvements in ‘Fallers’ (p = 0.016) and ‘No-Fallers’ (p = 0.034) following treatment in contrast to untreated patients—both ‘Fallers’ and ‘No-Fallers’. These results indicate that tailored robot-assisted therapy with hunova^® significantly reduces risk of fall and improves long-term dynamic balance. This study supports the clinical integration of robotic assessment and therapy as a robust tool for proactive fall prevention in diverse clinical settings. Full article

Background/Objectives: Clomiphene citrate (CC) is widely used off-label for male infertility despite limited evidence and inconsistent guideline recommendations. Although previous studies suggest variability in clinical practice, real-world data on prescribing patterns, patient selection, monitoring, and treatment success definitions remain limited. This study assessed CC prescribing patterns among urologists and identified factors associated with its use. Methods: A national, cross-sectional, web-based survey was conducted among urologists in Türkiye between November and December 2025. Of 1558 invited participants, 421 responded (27.0%), and 402 were included in the final analysis. The questionnaire was based on European Association of Urology and American Urological Association guidelines, refined through expert consensus, and pilot-tested. Multivariable logistic regression identified factors independently associated with CC use. Results: CC was used by 39.3% of respondents and was independently associated with private practice (odds ratio [OR] = 2.90, p < 0.001), greater professional experience (OR = 2.18, p = 0.002), and higher infertility case volume (OR = 2.27, p = 0.001). Substantial heterogeneity was observed in patient selection, dosing, monitoring, and success definitions. Treatment goals and perceived success definitions most frequently focused on laboratory-based endpoints, including semen parameters and testosterone levels, which were more frequently selected than pregnancy-related endpoints. However, spontaneous pregnancy was also commonly reported as a perceived success definition, whereas live birth was not separately assessed. An apparent indication paradox was observed for hypogonadotropic hypogonadism, which may reflect differing interpretations of functional versus irreversible hypogonadotropic states, and 31.6% of clinicians reported not routinely providing risk counseling. Conclusions: CC prescribing for male infertility remains heterogeneous among responding urologists and was associated with clinician experience, practice setting, and infertility case volume rather than standardized protocols. The predominance of laboratory-based endpoints, together with the frequent inclusion of spontaneous pregnancy as a perceived success definition and the absence of separate live-birth assessment, underscores the need for clearer terminology, standardized prescribing frameworks, structured risk counseling, and future studies incorporating clinically meaningful reproductive endpoints. Full article

Background/Objectives: To develop and validate a preoperative nomogram incorporating clinical and ultrasonographic features for the non-invasive differentiation of invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) of the breast. Methods: Preoperative clinical information and ultrasonographic features of patients with pathologically confirmed IDC and ILC were retrospectively collected. A total of 803 patients (600 with IDC and 203 with ILC) were enrolled and randomly allocated to training and validation sets in an 8:2 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors for differentiation. These predictors were subsequently incorporated into a nomogram and a corresponding weight plot. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), while calibration was evaluated using calibration curves. Clinical net benefit was determined through decision curve analysis (DCA). Results: Significant differences were noted between IDC and ILC in multiple clinical and ultrasonographic characteristics (p < 0.05). Multivariate logistic regression analysis in the training set identified lesion margin, shape, depth, menopausal status, palpability, lesion classification, and internal echo as independent predictors of ILC. Notably, our constructed nomogram exhibited favorable predictive performance, calibration, and clinical utility in both the training and validation sets. Conclusions: A nomogram incorporating seven independent predictors, namely lesion margin, shape, depth, menopausal status, palpability, lesion classification, and internal echo, was developed and validated. This nomogram enables individualized and quantitative prediction of the preoperative probability of ILC and may serve as a non-invasive adjunct to support surgical decision-making. Full article

Background/Objectives: Undergraduate nursing students consider bioscience courses essential to their education and clinical practice, yet they often find them challenging. This study explored the associations among achievement goal orientations, perceived stress, and self-esteem to examine factors associated with bioscience learning in nursing education. Methods: A quantitative cross-sectional study was conducted with undergraduate nursing students enrolled in the first-year courses “Biology–Clinical Biochemistry” and “Genetics”. Data were gathered using the Perceived Stress Scale-14 (PSS14); the Achievement Goal Questionnaire-Revised (AGQ-R), divided into four subscales, each representing a goal type (mastery-approach or AGQ MA, mastery-avoidance or AGQ MAV, performance-approach or AGQ PA, performance-avoidance or AGQ PAV); and the Rosenberg Self-Esteem Scale (RSES). Univariate and multiple regression analyses were conducted using SPSS v26.0, with significance set at p < 0.05. Results: Perceived stress was negatively associated with self-esteem in both Biology–Clinical Biochemistry and Genetics (p < 0.001). The assessment of potential links between quantitative variables and the study topic using univariate analysis showed an association of course category with the PSS14 score (p= 0.008). This finding remained significant in the regression analysis (p = 0.004), which also identified an effect of gender on the same scale (p = 0.029). Multiple regression further revealed associations between AGQ MA and the subject of study (p = 0.047), AGQ MAV and gender (p = 0.001), AGQ PAV and gender (p = 0.016), and RSES Total and type of secondary education (p = 0.007). Conclusions: Psychological factors interact dynamically with the demands of bioscience education within tertiary nursing curricula, varying according to demographic and academic traits. Full article

In this paper, we propose a multilevel stochastic model for the energy consumption of public proof-of-work blockchains. The main novelty is the proposal of a closed form for the expected energy consumption in one proof of work mining round. In the case of homogeneous per-hash efficiency, this proposition shows that the expected spending is $e_0/p$ depending only on the protocol difficulty and not on the distribution of the hash power among the miners. The proposal connects three levels of analysis: a local model of mining at the node level, a semi-global model of competitive block discovery and propagation, and a global stochastic model of workload, computational capacity, network connectivity and power consumption. This leads to the above closed form energy result. The mining process is approximated locally by exponential waiting times of Bernoulli hash trials. This extends to the semi-global model where the competition among miners and the delay in the propagation lead to the wasted computation. The global layer is modeled as a set of stochastic differential equations which models the interaction between workload dynamics, capacity constraints and communication overheads. The core analysis does not need Bayesian or Markov decision components but these are recommended for modeling estimation and adaptive control. We start with preliminary simulations on the VIBES platform and find qualitative properties of the full model: the total energy cost scales roughly linearly with the size of the network, the average energy per node decreases with increasing network size, the propagation latency is the primary source of wasted computation due to stale blocks and nodes tend to operate in a capacity-depleted regime with the workload-induced degradation being substantially higher than the recovery rate. The results give a structural analysis of how the design of the protocol and the network conditions affect the energy consumption and emphasize the importance of quantitatively calibrating with empirical data from Bitcoin. Full article

Background/Objectives: Medication adherence is a critical determinant of therapeutic outcomes in type 2 diabetes mellitus (T2DM); however, its relationship with glycemic control remains inconsistent, particularly in real-world and socially vulnerable settings. This study aimed to evaluate medication adherence using multiple validated instruments, assess disease-related knowledge, and examine their relationship with glycemic control, with a focus on potential discordance between self-reported adherence and objective metabolic outcomes. Methods: A cross-sectional analytical study was conducted with 237 adults with T2DM receiving care in a primary health care (PHC) unit in the Brazilian Amazon. Medication adherence was assessed using the Almeida Adherence Scale, ARMS-12, and the Haynes–Sackett test, while disease-related knowledge was evaluated using the Batalla–Martínez questionnaire. Glycemic control was determined based on glycated hemoglobin (HbA1c) values obtained from clinical records within the previous three months. Descriptive and comparative analyses were performed. Results: The study population was predominantly female (64.1%) and aged 40–59 years (55.7%), with a high prevalence of socioeconomic vulnerability. Non-adherence was identified in 55.7% of participants using the Almeida Adherence Scale, whereas higher adherence rates were observed with ARMS-12 (91.1%) and the Haynes–Sackett test (72.2%). Inadequate disease-related knowledge was found in 77.2% of participants. Among individuals with available HbA1c data (n = 116), the mean HbA1c was 8.63% (SD = 1.65), and 81.9% presented inadequate glycemic control (HbA1c ≥ 7%). Notably, among participants classified as adherent by the ARMS-12 scale (91.1%), inadequate glycemic control was nonetheless present in 81.9% of those with available HbA1c data, illustrating the magnitude of the observed discordance between self-reported adherence and objective metabolic outcomes. Cross-tabulation of each adherence instrument against glycemic control showed no statistically significant associations (chi-square with Yates correction; ARMS-12: p = 0.631, φ = 0.045; Almeida Adherence Scale: p = 0.301, φ = 0.096; Haynes–Sackett: p = 0.800, φ = 0.024). Multivariable logistic regression (Nagelkerke R² = 0.321; AUC = 0.834) identified older age (aOR = 0.92; 95% CI: 0.87–0.96; p < 0.001) and higher income (aOR = 9.96; 95% CI: 2.05–48.32; p = 0.004) as independent predictors of glycemic outcome, while no adherence measure was independently associated with HbA1c ≥ 7%. A sensitivity analysis using HbA1c ≥ 8.0% revealed poor control in 59.5% of participants (n = 69/116). Conclusions: Despite varying levels of self-reported medication adherence, inadequate glycemic control was highly prevalent. The absence of statistically significant associations between self-reported adherence and HbA1c, combined with the high prevalence of poor glycemic control regardless of adherence status, is consistent with the hypothesis that adherence alone does not fully explain metabolic outcomes in T2DM. Given the cross-sectional design, no causal inferences can be drawn. These findings highlight the need for integrated care strategies in primary health care, including improved health literacy, structured pharmacotherapeutic follow-up, and the use of multiple adherence assessment tools to better inform clinical decision-making. Full article

The motivation of this paper is to discuss some aspects of approach groups, recall and reformulate a characterization theorem in a self-contained form, and provide various natural examples in a new setting; in so doing, we show that every bi-invariant Menger probabilistic metric structure on a group with respect to a triangular norm ${\mathbb{T}}_m$ gives rise to an ultra approach group. Furthermore, the ultra setting is natural here because the constructions considered below are governed by maximum-type estimates arising from the triangular norm $T_m=min$ and from invariant ultra $\infty p$-metric structures. We discuss convergence approach groups and their associated approach uniform structure together with some of their relationships with approach groups. In this respect, we observe that given a convergence approach group, its induced approach uniform convergence structure is translation-invariant both on the left and on the right. Finally, we focus on the invariant pseudo-metrizability of approach groups and present several related results. Here and among others, we present an example of an invariant ∞p-metric on a group leading to an approach group. Full article

The growing expectation of citizens to deliver quality services without increasing taxes requires municipalities to adjust their cost models to remain good stewards of the voters’ finances. Cost-of-Quality (CoQ) models have traditionally been studied in relation to manufacturing processes as a method to increase profitability by reducing the life-cycle costs of the product. Municipalities have historically not been included in these studies as they operate on a semi-monopolistic basis for the services and infrastructure they maintain and have a different set of constraints and obligations from private entities. An analysis of three North Carolina municipalities (Winston-Salem, Cary, and Apex) is conducted to evaluate the Cost-of-Quality components of their water system budgets. The analysis consists of two evaluations. The initial evaluation compares the budgets of the aforementioned North Carolina municipalities with a previous study that analyzed three Texas municipalities’ water system budgets (Lubbock, San Antonio, and El Paso). The purpose of this portion of the study is to evaluate whether North Carolina Cost-of-Quality components behave like Texas municipalities. The second portion of this study evaluates the three North Carolina municipalities independently of the Texas study to see whether population size is a differentiator in how Cost-of-Quality components are divided in North Carolina. The three NC municipalities are chosen based on the Hickory Municipal Classification System (MCS). The Hickory MCS is a national classification system based on the relative population of each state and was developed for this study. The Texas municipalities that were studied had variable populations, variable locations, variable water sources, and variable water uses. The Cost-of-Quality analysis focuses on prevention costs, appraisal costs, failure costs, Total CoQ costs and opportunity costs between the North Carolina and Texas municipalities. Of the twelve comparative hypotheses, three CoQ costs are found to be significantly different with a probability level of p < 0.05. The results suggest that appraisal and failure costs are consistently impactful across the utilities in both states, but opportunity costs are not materially significantly different as in previous studies on Cost of Quality for utilities. Full article

Food safety training is a cornerstone of foodborne disease prevention in collective catering, particularly in settings serving vulnerable populations. This study aimed to assess baseline food safety knowledge and evaluate the effectiveness of a food safety training session among food handlers employed in school cafeterias and residential care facilities (RSAs) collective catering. A pre–post design was applied to 168 participants who completed a structured knowledge questionnaire before and after training. At baseline, only 31% of participants achieved a passing score. Knowledge levels were significantly associated with primary job role (p < 0.001): food preparers and managers were more likely to pass compared with food service workers involved mainly in meal distribution. In multivariate analysis, both job role and catering setting remained independently associated with test performance. Following the training session, the proportion of participants passing the test increased to 74% (p < 0.001), and differences between professional categories were reduced. These findings indicate that food safety knowledge in collective catering could vary according to occupational role and organizational context, but can be improved through training. Role-tailored, HACCP-based educational programs could be essential to strengthen compliance and protect vulnerable populations in institutional catering settings. Full article