Search Results (2,239)

Thyroid diseases are very common endocrine diseases that afflict millions of people around the world and need proper and timely diagnosis to ensure proper treatment. Although machine learning and hybrid metaheuristic methods have advanced, current models have high computation costs, low interpretability, and low probability calibration, which limit their use in clinical settings. In this research, a new DFSel-FT (Differentiable Feature Selection and an FT-Transformer) system is suggested, which combines DFSel-FT to allow one to diagnose thyroid disease effectively and interpretably. It employs Concrete (Gumbel-Softmax) gates to select the features end-to-end to make sure that only the most relevant clinical attributes are carried through the training. A Transformer-based architecture is then used to process the chosen features to learn intricate interdependencies. The model is trained with class-balanced focal loss and temperature scaling to better enhance calibration. Experimental evaluation on the UCI Thyroid Disease Dataset (22,632 samples) showed that the proposed model achieved 97.85% accuracy, 97.65% Macro-F1, and 98.10% AUC-OVR, showing competitive performance compared with traditional machine learning models, modern tabular deep learning baselines, and hybrid metaheuristic methods. Other indicators of robustness and reliability include MCC (0.955), Cohen Kappa (0.951), and small calibration error (ECE = 0.021). SHAP and LIME explainability analysis reveals clinically relevant features that include TSH, TT4, and T3. The proposed framework provides a balanced integration of predictive performance, interpretability, and probability calibration, making it a promising benchmark-level framework for interpretable and calibrated thyroid disease classification, requiring external clinical validation before real-world deployment. Full article

IDH-wildtype glioblastoma remains the most aggressive primary brain tumor, with a median overall survival (OS) of 14–16 months despite maximal treatment. A small subset of patients, however, survive beyond 30 months, suggesting distinct underlying biological features. The aim of this pilot study was to explore whether selected molecular alterations detectable by FISH show differing distribution patterns between patients with prolonged and poor survival in IDH-wildtype glioblastoma. We retrospectively analyzed 20 patients with newly diagnosed primary IDH-wildtype glioblastoma who underwent gross-total resection followed by standard radiotherapy and temozolomide treatment between 2016 and 2022. Patients were categorized into two predefined groups according to survival outcomes: long-term survivors (OS > 30 months) and short-term survivors (OS < 10 months). Fluorescence in situ hybridization (FISH) was used to evaluate alterations in ATRX, BRAF, and PIK3CA. MGMT promoter methylation, EGFR amplification, and TERT promoter mutation status were obtained from routine diagnostic reports. Because survival groups were intentionally pre-selected as extreme phenotypes, time-to-event analysis was not appropriate. Therefore, statistical comparisons were performed using Fisher’s exact test and multivariable logistic regression with long-term versus short-term survival as a binary outcome. Short-term survivors had a significantly higher median age (57.5 vs. 46.5 years, p = 0.043) and a higher rate of EGFR amplification (100% vs. 50%, p = 0.033). Strikingly, combined BRAF and PIK3CA alterations (predominantly polysomy) were detected in 8 out of 10 (80%) long-term survivors, compared to 0 out of 10 (0%) short-term survivors (p = 0.0007). In multivariable logistic regression adjusted for age and MGMT promoter methylation, BRAF/PIK3CA alteration remained strongly associated with long-term survival, though the effect size was mathematically inflated due to perfect separation (0 events in Group B). BRAF and PIK3CA copy number alterations were observed exclusively in long-term survivors in this small exploratory cohort, suggesting a possible association with prolonged survival. However, given the limited sample size, the selection of extreme survival groups, and the predominance of chromosomal polysomy detected by FISH, these findings should be interpreted as hypothesis-generating only. Further validation in larger cohorts using high-resolution genomic methods is warranted. Full article

Background: Intracranial hypertension is a critical complication of acute intracerebral hemorrhage (ICH), contributing to high early mortality and poor functional outcomes. Invasive intracranial pressure (ICP) monitoring remains the gold standard but carries procedural risks and is resource-intensive. This study evaluated the diagnostic and prognostic utility of optic nerve sheath diameter (ONSD) ultrasonography and transcranial Doppler (TCD)-derived pulsatility index (PI) as non-invasive ICP surrogates in patients with severe ICH. Methods: A prospective observational study was conducted in 42 patients with acute ICH who underwent concurrent invasive ICP monitoring and serial ONSD/PI measurements at 10 time points (T0–T9) between October 2021 and August 2024. Diagnostic performance was assessed using measurement-level receiver operating characteristic (ROC) curve analysis. Exploratory early mortality prediction was evaluated using random forest machine learning models incorporating ONSD, PI, age, and sex. Results: A total of 274 paired ONSD–PI–ICP measurements were obtained. Both ONSD and PI showed moderate positive correlations with invasive ICP (rho = 0.49 and 0.43, respectively; p < 0.001). ONSD demonstrated superior diagnostic accuracy for detecting ICP ≥ 20 mmHg (AUC = 0.83; optimal threshold: 5.88 mm; sensitivity: 81%; specificity: 82%) compared to PI (AUC = 0.75). In exploratory random forest analyses, the combined ONSD–PI model showed high apparent discrimination for elevated ICP detection (AUC = 0.98), while the model incorporating ONSD, PI, age, and sex showed promising but potentially optimistic discrimination for early mortality prediction (AUC = 0.95). These machine learning results should be interpreted cautiously because of the small sample size, repeated-measurement structure, measurement-level data partitioning, and limited number of early deaths. Conclusions: ONSD ultrasonography and TCD-derived PI showed promising performance as non-invasive ICP markers in severe acute ICH. However, because of the small sample size, repeated-measurement design, measurement-level analyses, and exploratory nature of the machine learning models, these findings require validation in larger external cohorts before routine clinical implementation. Full article

Background/Objectives: How the histologically benign tier of lung preinvasive lesions—atypical adenomatous hyperplasia (AAH) and squamous dysplasia (SD)—relates genomically to its paired carcinoma is unclear. To identify early versus late events, we compared synchronous preinvasive and invasive lesions from the same patient. Methods: Whole-exome sequencing was performed on 33 FFPE samples from 11 patients (7 AAH–lung adenocarcinoma [LUAD] and 4 SD–squamous cell carcinoma [SqCC] pairs, with paired normal lung). FFPE artefacts were mitigated by paired-normal subtraction, panel-of-normals filtering, and orthogonal caller cross-validation. Cancer-panel variants were classified as cancer-only, shared, or preinvasive-only. Results: Only ∼10% of cancer-panel variants were shared between paired lesions (∼50% carcinoma-only, ∼40% preinvasive-only), indicating that benign AAH/SD do not broadly mirror the paired carcinoma. Within this small shared fraction, the early-driver pattern diverged between tracks: AAH–LUAD pairs tended to share EGFR alterations, whereas the four SD–SqCC pairs featured MET-pathway alterations without any shared EGFR events. TP53 and most other canonical drivers were carcinoma-confined (within-cohort contrast direction-consistent but non-significant); three patients lacked any canonical driver despite substantial mutational burden. Conclusions: In this pilot cohort, benign AAH and SD were genomically largely distinct from their paired carcinomas, sharing only a small set of key drivers whose identity diverged between glandular and squamous tracks. This suggests that benign-appearing AAH/SD differ from the more advanced AIS/MIA precursors not only histologically but also at the genomic level. These hypothesis-generating findings require confirmation in larger, multi-omic cohorts. Full article

Modular construction advances sustainability and is reshaping designer competencies, making workforce development critical to industry transition. Existing competency models rely mainly on expert interviews and Delphi methods, offering limited quantitative evidence on role-specific labor-market demands, causal relationships among competencies, or experience-based perceptual differences. This study presents a preliminary, data-driven competency-mapping study for modular construction designers by integrating BERTopic, Ward clustering, CVR, Bayesian BWM, and Fuzzy DEMATEL. Applied to 243 job postings from six countries, the text-mining stage identifies a candidate competency structure of 3 domains, 9 categories, and 36 performance statements. This candidate structure was then examined through an exploratory survey of 30 Korean respondents. The results suggest that Codes and Compliance represents the most clearly recognized high-consensus competency area within this local validation sample, whereas Modular Construction shows an indicative experience-related divergence in perceived causal position. Given the small and uneven subgroup sample and the formative state of Korea’s modular construction industry, the findings should be interpreted as preliminary evidence rather than as a validated competency framework or a confirmed expert–novice model. The study contributes a reproducible mixed-method workflow, a candidate competency map, and an illustrative maturity prototype for future validation and refinement. Full article

Purpose: Prostate cancer is one of the most common malignancies in men, yet current prognostic methods remain suboptimal. Emerging evidence indicates that microRNAs (miRNAs) play critical roles in prostate cancer progression. This study aimed to identify miRNAs associated with adverse clinical outcomes by comparing miRNA expression profiles between prostate tumors with unfavorable versus favorable prognostic features. Materials and Methods: High-throughput next-generation sequencing (NGS) was used to analyze miRNA expression in formalin-fixed, paraffin-embedded prostate cancer tissue samples. Patients were classified into favorable or unfavorable prognosis groups based on risk stratification scores, Gleason grade group, and biochemical recurrence. Differentially expressed miRNAs were identified using a fold-change threshold ≥2 and a false discovery rate (FDR) <0.05. Predicted target genes and pathway analyses were conducted to generate candidate regulatory hypotheses rather than confirm mechanistic relationships. Results: Several miRNAs were differentially expressed according to prognostic category. miR-206 was significantly downregulated in high-risk tumors compared with low-risk tumors. High-Gleason-grade tumors showed reduced expression of miR-7704 and miR-4454, while miR-25-3p and let-7f-5p were upregulated. In patients with early biochemical recurrence, miR-7704 and miR-10400-5p were downregulated relative to those with prolonged recurrence-free survival. Target prediction analysis identified CPEB3, HAND1, PTAR1, and SPRYD4 as shared candidate targets, with CPEB3 emerging as a prioritized candidate supported by consistency in external datasets rather than a confirmed molecular target. Conclusions: Distinct miRNA expression patterns correlate with prostate cancer aggressiveness and clinical outcomes. miR-206, miR-7704, miR-4454, miR-25-3p, and let-7f-5p represent candidate prognostic biomarkers. Their shared target CPEB3 should be interpreted as a prioritized candidate for future investigation. Given the very small sample size and the lack of qRT-PCR and functional validation, these findings should be considered preliminary and hypothesis-generating, requiring validation in larger independent cohorts and experimental studies. Full article

Background/Objectives: Differentiating focal nodular hyperplasia (FNH) from hepatic adenoma (HA) remains challenging, as FNH is benign whereas HA carries risks of hemorrhage and malignant transformation. This prospective single-center pilot study evaluated the diagnostic performance of three-dimensional magnetic resonance elastography (3D-MRE) using a gravitational transducer for non-invasive differentiation of FNH and HA. Methods: Thirty-three participants (23 FNH, 10 HA) underwent 3D-MRE using the gravitational transducer. Viscoelastic parameters—stiffness, shear wave speed (Cs), wave attenuation, and phase angle—were quantified for lesions and background parenchyma. $\Delta$-values were calculated by subtracting background liver measurements from lesion values. Results: FNH demonstrated significantly higher stiffness than HA (median 3.16 vs. 2.58 kPa; p = 0.02) and higher Cs (median 1.81 vs. 1.64 m/s; p = 0.001). Normalized stiffness differences ($\Delta$ stiffness) were significantly greater in FNH than HA (median 0.83 vs. 0.10 kPa; p = 0.001). Generalized additive models revealed divergent volume-dependent stiffening behaviors. In ROC analysis, $\Delta$ stiffness and $\Delta$ Cs each achieved an AUC of 0.87, indicating that single background-normalized viscoelastic parameters carry the principal diagnostic signal. An exploratory multivariable combination of $\Delta$ stiffness with patient age produced an apparent AUC of 0.93 with wide odds-ratio confidence intervals, and is presented as hypothesis-generating rather than as a clinical prediction model. Conclusions: In this pilot cohort, 3D-MRE using the gravitational transducer showed encouraging parameter-level separation between FNH and HA, with background normalization enhancing discrimination. Wave attenuation and phase angle did not differ significantly between lesion types. Given the small sample size (particularly the HA subgroup of ten patients), the mixed reference standard (histological confirmation in only 14 of 33 lesions; definitive hepatobiliary-phase MRI criteria in 19 of 33), the single-slice ROI used for lesion measurement, and the incomplete characterization of background liver parenchyma, these findings should be regarded as hypothesis-generating and require external validation in larger, multicenter cohorts before any clinical application. Full article

Background/Objectives: Traumatic brain injury (TBI) is increasingly understood as a chronic condition, but the role of early post-injury lifestyle behaviors in later cardiometabolic risk remains unclear. We examined whether lifestyle behaviors reported 1 year after injury were associated with the accumulation of common cardiometabolic risk factors by 5 years in the Traumatic Brain Injury Model Systems (TBIMS) National Database. Methods: This retrospective cohort secondary analysis included adults with followed 1-year and 5-year interviews, complete 1-year data on four behaviors, and the complete ascertainment of hypertension, diabetes or high blood sugar, and high cholesterol at both waves. The exposure was a favorable lifestyle count based on not smoking, non-heavy alcohol use, non-obese body mass index, and sports or exercise at least 10 times per month. The primary endpoint was the incident accumulation of at least two new common cardiometabolic conditions between years 1 and 5. The analytic cohort was an observed-data subset defined by follow-up retention, complete behavior data, paired outcome ascertainment, and baseline at-risk status rather than a random sample of all TBIMS participants. Results: Among 10,057 linked participants with followed interviews at both waves, 9593 were adults, 3182 had complete four-behavior exposure data, 689 had complete cardiometabolic ascertainment, and 581 formed the primary at-risk observed-data cohort. The primary endpoint occurred in 39 participants (6.7%). Each additional favorable behavior was associated with lower odds of the primary endpoint in the adjusted model (odds ratio [OR], 0.63; 95% confidence interval [CI], 0.41–0.98; p = 0.040). The results were similar after adjustment for the 1-year Functional Independence Measure cognitive score and in Firth logistic regression. Because the final cohort was selected and the number of primary events was small, the estimates should be interpreted as exploratory and may not generalize to the broader TBI population. Conclusions: More favorable 1-year lifestyle profiles were associated with lower 5-year cardiometabolic risk factor accumulation after TBI. These findings support prevention-oriented follow-up but do not establish causality or validate a prognostic score. Full article

Designing ultra-high-performance concrete (UHPC) mixtures requires balancing multiple, often conflicting, performance criteria, particularly mechanical strength and rheological behavior. However, the limited availability of publicly accessible datasets containing synchronized multi-property measurements, together with cross-source heterogeneity, poses a major challenge for robust data-driven modeling under small-sample conditions. To address this issue, this study proposes an integrated framework combining cross-source data harmonization, Variational Autoencoder (VAE)-based latent-space augmentation, multi-output deep learning, interpretability analysis, and Genetic Algorithm (GA)-driven inverse design. A dataset comprising 139 valid UHPC records was curated from 22 peer-reviewed studies and expanded to 2780 samples through VAE-based augmentation. Using the augmented dataset, a multi-output deep neural network was developed to jointly predict compressive strength, flexural strength, yield stress, and plastic viscosity. On the independent test set, the model achieved R² values of 0.8601, 0.9212, 0.8464, and 0.6603, respectively. Comparative benchmarks and augmentation ablation analyses further showed that VAE-based augmentation consistently improved predictive performance and generalization, especially under small-sample conditions. SHAP and partial dependence analyses identified curing age, steel fiber content, water-to-binder ratio, and superplasticizer dosage as the dominant factors governing UHPC performance. Finally, the trained surrogate model was coupled with a GA for multi-objective inverse optimization, and experimental validation of three candidate mixtures confirmed good agreement between predicted and measured values. This study provides a transparent and engineering-oriented methodology for the integrated prediction, interpretation, and optimization of UHPC mixtures. Full article

Objective: Our objective was to investigate the association between serum phosphorus levels and 28-day mortality in patients with bloodstream infection (BSI), and to explore whether this association persists after adjusting for renal function and clinical severity. Methods: This retrospective cohort study included 214 BSI patients. Patients were divided into hyperphosphatemia (≥2.2 mmol/L, n = 15) and control (<2.2 mmol/L, n = 199) groups. To address the small sample size and potential separation, multivariate Firth’s penalized likelihood regression was utilized to evaluate the association with 28-day mortality. Restricted cubic spline regression explored the continuous relationship. Fine–Gray competing risk models, 1000-resample bootstrapping, and E-value analyses were conducted to ensure the robustness of the observed associations. Results: The 28-day mortality rate was significantly higher in the hyperphosphatemia group (80.0% vs. 39.7%, p = 0.005). After adjusting for age, sex, and estimated glomerular filtration rate (eGFR), hyperphosphatemia remained significantly associated with higher observed 28-day mortality (OR = 4.46, 95% CI: 1.36–18.54, p = 0.012). This association remained robust even after further adjustment for septic shock (OR = 4.74, 95% CI: 1.30–21.64, p = 0.017). Analyzed continuously, each 0.5 mmol/L increase in serum phosphorus was associated with 34% higher odds of mortality (OR = 1.34, 95% CI: 1.07–1.74, p = 0.01). Spline analysis confirmed a nonlinear relationship with a threshold at 2.2 mmol/L. Kaplan–Meier analysis demonstrated a severity-driven survival separation in the hyperphosphatemia group (Log-rank p < 0.001). The association remained highly robust after adjusting for early discharge competing risks (sHR = 4.62, p < 0.001) and in bootstrap validation (median OR = 4.80). Conclusions: Serum phosphorus ≥ 2.2 mmol/L is associated with higher observed mortality in BSI patients, an association that remained evident after adjusting for renal function and clinical severity, including septic shock. However, given the small hyperphosphatemia subgroup (n = 15), limited statistical stability, and the potential for residual confounding, these findings should be considered hypothesis-generating rather than definitive, requiring prospective validation in larger, adequately powered cohorts. Rather than a definitive triage tool, serum phosphorus may serve as a simple, adjunctive marker for early metabolic assessment in severe infections. Full article

(1) Background: The role of baseline humoral immunization in bone regeneration remains unclear. This study assessed the relationship between baseline serological immunization, graft type, photobiomodulation (PBM), and histological outcomes after maxillary sinus floor augmentation. (2) Methods: This exploratory secondary analysis included 20 adults undergoing lateral maxillary sinus lifting. Patients were allocated according to graft type (allogeneic or xenogeneic) and postoperative protocol (with or without adjunctive PBM). Before surgery, serum samples were analyzed for anti-HLA class I, anti-HLA class II, and anti-MICA antibodies. After approximately 6 months, bone core biopsies were collected. Histological evaluation focused on inflammatory cell infiltrates (ICI). (3) Results: Baseline antibody positivity was detected in 35.0% of patients for anti-HLA class I, 55.0% for anti-HLA class II, and 45.0% for anti-MICA. Histological findings were generally favorable. ICI scores were low, with 65.0% of samples scoring 0 and 35.0% scoring 1. A nominal positive correlation was observed between anti-HLA class I NBG ratio and ICI; however, this finding did not remain statistically significant after correction for multiple comparisons. Exploratory PBM subgroup estimates were directionally different but were based on very small subgroups and should not be interpreted as evidence of effect modification. (4) Conclusions: The findings suggest a possible hypothesis-generating link between baseline humoral sensitization and mild local inflammatory infiltrates, which requires validation in larger, prospectively powered studies with predefined histological and immunological endpoints. Full article

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision support. To address these issues, this paper constructs an integrated prediction model that combines Boruta–Lasso two-stage feature selection, grid search-optimized CatBoost, and SHAP interpretability analysis. First, the Boruta algorithm is used for rough screening of feature significance, then Lasso regression is applied for sparse fine screening, effectively eliminating redundant features and significantly mitigating multicollinearity; grid search and five-fold repeated cross-validation are employed to optimize CatBoost hyperparameters, while 10 repeated experiments with random seeds are conducted to verify model generalization robustness. SHAP is used to quantify the marginal contribution of features, revealing nonlinear associations and statistical response transition points between core features and price. This study is based on 33 publicly available real data from main combat vessels, from which 198 modeling samples were generated through interpolation-based small-sample data augmentation. The interpolated samples were only used for data augmentation and were not considered independent empirical samples. All core conclusions were validated on the 33 original real samples, and there are no missing values in the dataset. Experimental results show that the proposed model achieved the best individual results on the test set, with a coefficient of determination of R² = 0.8949, root mean square error RMSE = 0.0554, and mean absolute error MAE = 0.0476. Across 10 repeated robustness experiments, the average results were R² = 0.8828, RMSE = 0.0586, and MAE = 0.0529, with overall performance better than comparison models such as XGBoost, random forest, and standard CatBoost. Ablation experiments validated the effectiveness of the two-stage Boruta–Lasso selection strategy in improving model accuracy and stability. SHAP attribution analysis shows that full-load displacement, number of vertical missile launch cells, number of phased array radars, and combat capability are core features highly correlated with price, all showing significant nonlinear positive correlations and clear statistical response transition points. The dataset in this study has no missing values, is entirely constructed based on publicly traceable data, and does not include confidential information such as internal shipyard costs. The findings reflect statistical associations rather than causal effects. However, the sample size and ship-type coverage are limited, so the model’s applicability is somewhat constrained, and its generalization ability needs to be further verified on larger-scale, multi-ship-type independent datasets. This model combines high prediction accuracy, strong robustness, and good interpretability, providing reliable technical support for ship equipment procurement pricing demonstration, full lifecycle cost management, and scientific procurement decision-making. Full article

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration. Full article

Background: Burnout is a relevant occupational health concern in Emergency Medical Services (EMSs), with potential implications for workforce well-being, occupational health, and the sustainability of prehospital care. Although burnout has been widely studied among healthcare professionals, evidence concerning Emergency Medical Technician (EMT) students remains limited. This exploratory study aimed to estimate high burnout prevalence among EMT students and practising EMT professionals in Madrid, Spain, describe burnout dimensions in both groups, and examine sociodemographic correlates of high burnout status. Methods: A cross-sectional comparative study was conducted between March and June 2024 using a convenience sample of 85 participants: 43 EMT students and 42 practising EMT professionals. Burnout was assessed using validated Spanish versions of the Maslach Burnout Inventory: the MBI-SS for students and the MBI-HSS for professionals. Because these instruments are population-specific and rely on different norms and thresholds, between-group comparisons of raw scores were interpreted as exploratory. Descriptive analyses, between-group comparisons with effect sizes, correlation analyses, and an exploratory binary logistic regression model were performed. Results: High burnout was identified in 22 EMT students (51.2%) and 23 practising EMT professionals (54.8%), with no statistically significant between-group difference detected (p = 0.73; Cramer’s V = 0.04). Between-group comparisons of burnout dimensions showed small effect sizes for Emotional Exhaustion (Cohen’s d = 0.17), Depersonalisation (Cohen’s d = 0.24), and Personal Accomplishment (Cohen’s d = −0.26). Age was positively associated with Emotional Exhaustion (r = 0.29, p = 0.008) and Depersonalisation (r = 0.24, p = 0.028), and negatively associated with Personal Accomplishment (r = −0.26, p = 0.019). In the exploratory adjusted logistic regression model, age was associated with high burnout status (OR = 1.05; 95% CI 1.01–1.10; p = 0.017), whereas group and sex were not significant correlates. Conclusions: High burnout levels were observed in both EMT students and practising EMT professionals in this regional exploratory sample. However, the findings should be interpreted cautiously due to the cross-sectional design, convenience sampling, modest sample size, limited statistical power, and use of population-specific burnout instruments. These results suggest that burnout-related distress may be relevant across the EMT training-to-practice pathway and support the need for larger longitudinal and multicentre studies incorporating occupational, educational, and organisational variables. Full article

In this paper, a radial basis function (RBF) neural network based trajectory generation strategy is proposed to solve the online rapid generation of initial reference trajectory for low-cost hypersonic glide vehicles (HGV) under initial state perturbation. Firstly, the feasible trajectories that constitute the sample sets are offline generated by pseudospectral method according to the possible distribution of heights and velocities. Then, the sample set is randomly divided into training subset and test subset, by which the RBF neural network is trained and verified. Moreover, the input of the RBF neural network is a vector comprised by height and velocity from the initial state, whereas the output is a discrete state-control sequence which represents the trajectory from the current state to the expected final state. The simulation results validate that the proposed method has high confidence and small errors, which can improve the on-line generation efficiency of the trajectory. Full article