Search Results (87)

Pelvic organ prolapse (POP) affects millions of women around the world, with age-standardized prevalence rates of 2769 per 100,000 women in 2021. Although it greatly affects quality of life (QoL), only 18–50% of women experiencing this issue seek medical attention, largely due to a lack of knowledge, misunderstandings about the condition, and obstacles to accessing healthcare. This narrative review explores the progression of POP management towards a focus on patient-centered care, highlighting the importance of personalized treatment strategies that prioritize patient-reported outcomes (PROs) over solely anatomical factors. The approach to treatment has transitioned from being centered on anatomy to focusing on the patient, emphasizing the relief of symptoms and enhancement in QoL. Existing research indicates that monitoring without intervention is advisable for asymptomatic patients, as long-term studies have revealed that up to 40% of women experience stable or improved prolapse over a period up to 60 months. Pessary treatment has a fitting success rate above 90% and a treatment persistence rate of 60%, providing an effective non-surgical option for management. The approach to selecting surgical treatments has progressed to prioritize sufficient apical support as a key factor for achieving lasting results. For primary POP, native tissue repair (NTR) is now recommended as the first-line surgical option. Mesh-augmented repairs are used only in certain high-risk situations, whereas sacrocolpopexy offers the best anatomical stability for particular cases, such as those involving post-hysterectomy prolapse and recurrences. Contemporary POP management involves personalized, patient-focused decision-making that emphasizes addressing symptom severity and functional objectives rather than solely aiming for anatomical precision. The evidence suggests that NTR should be the primary surgical approach, while other procedures should be reserved for specially chosen patients. Success should primarily be evaluated based on PROs instead of anatomical factors, ensuring that treatments align with each patient’s preferences and expectations while reducing complications. Full article

Accurate detection of fetal distress from cardiotocography (CTG) is clinically critical but remains subjective and error-prone. In this research, we present a leakage-safe Calibrated Global Logit Fusion (CGLF) framework that couples TabNet’s sparse, attention-based feature selection with XGBoost’s gradient-boosted rules and fuses their class probabilities through global logit blending followed by per-class vector temperature calibration. Class imbalance is addressed with SMOTE–Tomek for TabNet and one XGBoost stream (XGB–A), and class-weighted training for a second stream (XGB–B). To prevent information leakage, all preprocessing, resampling, and weighting are fitted only on the training split within each outer fold. Out-of-fold (OOF) predictions from the outer-train split are then used to optimize blend weights and fit calibration parameters, which are subsequently applied once to the corresponding held-out outer-test fold. Our calibration-guided logit fusion (CGLF) matches top-tier discrimination on the public Fetal Health dataset while producing more reliable probability estimates than strong standalone baselines. Under nested cross-validation, CGLF delivers comparable AUROC and overall accuracy to the best tree-based model, with visibly improved calibration and slightly lower balanced accuracy in some splits. We also provide interpretability and overfitting checks via TabNet sparsity, feature stability analysis, and sufficiency (k95) curves. Finally, threshold tuning under a balanced-accuracy floor preserves sensitivity to pathological cases, aligning operating points with risk-aware obstetric decision support. Overall, CGLF is a calibration-centric, leakage-controlled CTG pipeline that is interpretable and suited to threshold-based clinical deployment. Full article

This study provides the first comprehensive multiscale evaluation of raw coconut fibers as biosorbents for crude oil removal, encompassing laboratory adsorption tests, mesoscale hydrodynamic simulations, and field trials in marine environments. Fibers were characterized by SEM, FTIR, XRD, XPS, and chemical composition analysis (NREL method), confirming their lignocellulosic nature, high lignin content, and functional groups favorable for hydrocarbon adsorption. At the microscale, a 2⁵⁻¹ fractional factorial design evaluated the influence of dosage, concentration, contact time, temperature, and pH, followed by kinetic and equilibrium model fitting and regeneration tests. Dosage, concentration, and contact time were the most significant factors, while low sensitivity to salinity highlighted the material’s robustness under marine conditions. Adsorption followed pseudo-second-order kinetics, with an equilibrium adsorption capacity of 4.18 ± 0.19 g/g, and it was best described by the Langmuir isotherm, indicating chemisorption and monolayer formation. Mechanical regeneration by centrifugation allowed for reuse for up to five cycles without chemical reagents, aligning with circular economy principles. In mesoscale and field applications, fibers maintained structural integrity, buoyancy, and adsorption efficiency. These results provide strong technical support for the practical use of raw coconut fibers in oil spill response, offering a renewable, accessible, and cost-effective solution for scalable applications in coastal and marine environments. Full article

This study examines how the organizational work climate shapes the effectiveness of supervision on employee performance. While traditional management theory assumes supervision universally enhances productivity, we observe a puzzling paradox: facing identical tasks and wage systems, some firms rely heavily on hierarchical supervision while others thrive with minimal oversight. Through a four-month field experiment across two Chinese agricultural enterprises (5851 observations), we test whether the supervision’s effectiveness depends on the alignment between leadership practices and organizational climate. In formal management firms (FMFs) characterized by hierarchical governance and arm’s-length employment relationships, directive supervision significantly reduces task completion times by 0.126 standard deviations, equivalent to approximately 4.3 s or 2.8% of the average completion time, with this effect remaining stable throughout the workday. Conversely, in network-embedded firms (NEFs) operating through trust-based relational contracts and social norms, identical supervisory practices yield no performance gains, as informal social control mechanisms already ensure high effort levels, rendering formal supervision redundant. These findings challenge the “best practices” paradigm in strategic HRM, demonstrating that HR success requires a careful alignment between leadership approaches and the organizational climate—an effective HR strategy is not about implementing standardized practices but about achieving a strategic fit between supervisory leadership styles and existing work climates. This climate–leadership partnership is essential for optimizing both employee performance and organizational success. Full article

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article

The growing interest in sustainable materials has encouraged the valorization of agro-industrial byproducts for pharmaceutical, nutraceutical, and food applications. This study evaluated yellow pitaya peel powder, obtained via convective and refractance window drying, as a diluent in immediate-release quercetin tablets. The powders were characterized by physicochemical, granulometric, morphological, and surface properties, and compared with conventional excipients, including partially pregelatinized corn starch and spray-dried lactose monohydrate. Refractance window drying improved solubility, flowability, and structural integrity, while convective drying produced finer, more porous particles with lower water activity. Tablets formulated with both powders showed adequate hardness, low friability, and disintegration times under five minutes. All systems achieved complete quercetin release. Kinetic modeling revealed anomalous, matrix-regulated transport, with Weibull and Modified Hill models providing the best fit. Based on these results, pitaya peel powder could be considered a suitable diluent for the development of immediate-release tablets, offering functional performance aligned with sustainable formulation strategies. Full article

Phosphates are key pollutants involved in the eutrophication of water bodies, creating the need for efficient and low-cost strategies for their removal in order to meet environmental quality standards. This study presents a comparative thermodynamic evaluation of phosphate ion adsorption from aqueous solutions using two sustainable and readily available materials: autoclaved aerated concrete (AAC) and pumice stone (PS). Batch experiments were conducted under acidic (pH 3) and alkaline (pH 9) conditions to determine equilibrium adsorption capacities, and kinetic experiments were carried out for the best-performing adsorbent. Adsorption data were fitted to the Langmuir and the Freundlich isotherm models, while kinetic data were evaluated using pseudo-first-order and pseudo-second-order models. The Freundlich model showed the best correlation (R² = 0.90 − 0.97), indicating the heterogeneous nature of the adsorbent surfaces, whereas the Langmuir parameters suggested monolayer adsorption, with maximum capacities of 1006.69 mg/kg for PS and 859.20 mg/kg for AAC at pH 3. Kinetic results confirmed a pseudo-second-order behavior, indicating chemisorption as the main mechanism and the rate-limiting step in the adsorption process. To the best of our knowledge, this is the first study to compare the thermodynamic performance of AAC and PS for phosphate removal under identical experimental conditions. The findings demonstrate the potential of both materials as efficient, low-cost, and thermodynamically favorable adsorbents. Furthermore, the use of AAC, an industrial by-product, and PS, a naturally abundant volcanic material, supports resource recovery and waste valorization, aligning with the principles of the circular economy and sustainable water management. Full article

Optical coordinate measurement is a universal technique that aligns with the rapid development of industrial technologies and new materials. Nevertheless, can this technique be consistently effective when applied to the precise measurement of all types of materials? As shown in this article, an analysis of optical measurement systems reveals that some materials cause difficulties during the scanning process. This article details the matting process, resulting, as demonstrated, in lower measurement uncertainty values compared to the pre-matting state, and identifies materials for which applying a matting spray significantly improves the measurement quality. The authors propose a classification of materials into easy-to-scan and hard-to-scan groups, along with specific procedures to improve measurements, especially for the latter. Tests were conducted in an accredited Laboratory of Coordinate Metrology using an articulated arm with a laser probe. Measured objects included spheres made of ceramic, tungsten carbide (including a matte finish), aluminum oxide, titanium nitride-coated steel, and photopolymer resin, with reference diameters established by a high-precision Leitz PMM 12106 coordinate measuring machine. Diameters were determined from point clouds obtained via optical measurements using the best-fit method, both before and after matting. Color measurements using a spectrocolorimeter supplemented this study to assess the effect of matting on surface color. The results revealed correlations between the material type and measurement accuracy. Full article

The spectral variation hypothesis (SVH) posits that the complexity of spectral information in remote sensing imagery can serve as a proxy for regional biodiversity. However, the relationship between spectral diversity (SD) and biodiversity differs for different environmental conditions. Previous SVH studies often overlooked these differences. We utilized species data from field surveys in Inner Mongolia and drone-derived multispectral imagery to establish a quantitative relationship between SD and biodiversity. A geographically weighted regression (GWR) model was used to describe the SD–biodiversity relationship and map the biodiversity indices in different experimental areas in Inner Mongolia, China. Spatial autocorrelation analysis revealed that both SD and biodiversity indices exhibited strong and statistically significant spatial autocorrelation in their distribution patterns. Among all spectral diversity indices, the convex hull area exhibited the best model fit with the Margalef richness index (Margalef), the coefficient of variation showed the strongest predictive performance for species richness (Richness), and the convex hull volume provided the highest explanatory power for Shannon diversity (Shannon). Predictions for Shannon achieved the lowest relative root mean square error (RRMSE = 0.17), indicating the highest predictive accuracy, whereas Richness exhibited systematic underestimation with a higher RRMSE (0.23). Compared to the commonly used linear regression model in SVH studies, the GWR model exhibited a 4.7- to 26.5-fold improvement in goodness-of-fit. Despite the relatively low R² value (≤0.59), the model yields biodiversity predictions that are broadly aligned with field observations. Our approach explicitly considers the spatial heterogeneity of the SD–biodiversity relationship. The GWR model had significantly higher fitting accuracy than the linear regression model, indicating its potential for remote sensing-based biodiversity assessments. Full article

Despite progress in inclusive education, students with Special Educational Needs (SEN) often lack valid, tailored tools for career assessment, limiting equitable transitions to adulthood and employment. Closing this gap is crucial for Sustainable Development Goal 4 (SDG 4), which calls for quality and inclusive educational opportunities. This study addresses this need by adapting and validating a 16-item Career Maturity Inventory-Form C (CMI-C) for Chinese post-secondary SEN students (n = 34) in vocational training in higher education. Rasch modeling, supported by exploratory factor analysis, indicated that a two-factor structure—‘career choice readiness’ and ‘intention to seek career consultation’—provided the best fit to the data, rather than the originally hypothesized four-factor model. The results were more consistent with a two-dimensional structure than with prior four-factor frameworks, though both were explored. Two poorly performing items were removed, resulting in a fourteen-item scale with acceptable item fit and reliability indices in this hard-to-reach group. This restructuring suggests constructs such as concern, confidence, and curiosity are closely linked in SEN populations, underscoring the value of context-sensitive assessment. The revised instrument demonstrated satisfactory model fit and internal consistency; however, convergent validity and practical utility should be interpreted cautiously given the modest sample size. While further validation in larger and more diverse samples is warranted, this study offers preliminary evidence for an adapted, inclusive assessment tool that aligns with SDG 4’s aim to promote equity and empower SEN students in educational and career pathways. Full article

This study introduces a novel reflective–formative hierarchical model specification for the classic push–pull tourist motivation construct, aligning its measurement with the theoretical distinction between intrinsic “push” drives and external “pull” attributes. Unlike the traditional reflective-reflective structuring of tourist motivation we defied the higher order factors (novelty, knowledge and facilities as formative. Using partial least squares structural equation modeling (PLS-SEM) on a purposive sample of 319 international tourists, we empirically validate the reflective–formative (reflective first-order, formative second-order) model. The reflective–formative model showed a superior fit and predictive power: it explained substantially more variance in key outcome constructs (social motives (R² = 53.60) and self-actualization (R² = 23.10)) than the traditional reflective–reflective specification (social motives (R² = 49.30) and self-actualization (R² = 21.70)), which is consistent with best-practice guidelines for theoretically grounded models. In contrast, the incorrectly specified reflective–reflective model showed stronger effects between unrelated constructs, supporting concerns that choosing the wrong type of measurement model can lead to incorrect conclusions. By reconciling the push–pull theory with measurement design, this work’s main contributions are a theoretically justified reflective–formative model for tourist motivation, and evidence of its empirical benefits. These findings highlight a methodological innovation in motivation modeling and underscore that modeling push–pull motives formatively yields more accurate insights for theory and practice. Full article

Objectives: The aim of this study was to validate the Polish version of the Somatosensory Amplification Scale (SSAS-PL) and examine its psychometric properties in clinical and non-clinical samples. Methods: The study included 1128 participants (711 healthy adults, 194 cardiac patients, 223 psychiatric patients). The analyses were categorized into exploratory and confirmatory phases. Exploratory analyses were conducted on a randomly selected sample that comprised 60% of the study participants (training sample) to estimate the reliability (Cronbach’s alpha) and factorial validity (EFA with varimax rotation). Confirmatory analyses were performed on an independent (test) sample that represented 40% of the total sample size to facilitate the cross-validation of the factor structure (CFA) and to assess the convergent and discriminant validities (using the HTMT method) in relation to health anxiety (SHAI) and psychopathological symptoms (KOFF-58). Additionally, measurement invariance was examined with respect to gender (female vs. male) and health status (healthy vs. clinical). Results: The SSAS-PL demonstrated good internal consistency (α = 0.75–0.78) after removing item 1. A one-factor structure showed the best fit and theoretical interpretability. The measurement invariance was supported across clinical groups. The SSAS-PL showed convergent validity with the measures of somatic symptoms, anxiety, and health anxiety. It demonstrated discriminant validity from other psychopathology measures. Conclusions: The SSAS-PL was a reliable and valid measure of somatosensory amplification in the Polish population. Its unidimensional structure aligned with most cross-cultural adaptations. The scale may be useful for assessing somatosensory amplification in both research and clinical settings in Poland. Further research on its utility in specific clinical populations is warranted. Full article

This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI), the Technology–Organization–Environment (TOE), and the Technology Acceptance Model (TAM) combined framework were proposed and tested using data collected from 367 higher education students, faculty members, and employees. SPSS Amos 24 was used for CB-SEM to choose the best-fitting model, which proved more efficient than traditional multiple regression analysis to examine the relationships among the proposed constructs, ensuring model fit and statistical robustness. The findings reveal that Compatibility “C”, Complexity “CX”, User Interface “UX”, Perceived Ease of Use “PEOU”, User Satisfaction “US”, Performance Expectation “PE”, Artificial intelligence “AI” introducing new tools “AINT”, AI Strategic Alignment “AIS”, Availability of Resources “AVR”, Technological Support “TS”, and Facilitating Conditions “FC” significantly impact AI adoption intentions. At the same time, Competitive Pressure “COP” and Government Regulations “GOR” do not. Demographic factors, including major and years of experience, moderated these associations, and there were large differences across educational backgrounds and experience. Full article

Objectives: To predict the survival rates of Osimertinib as first- and second-line therapy using time-to-event models based on literature data. Methods: Kaplan–Meier curves from randomized clinical trials were extracted after a systematic search of PubMed and Cochrane Library from their inception to 10 May 2023. Randomized clinical trials of Osimertinib reporting both first- and second-line overall survival (OS) and progression-free survival (PFS) in NSCLC patients with specific mutations, compared to earlier epidermal growth factor receptor (EGFR) inhibitors and chemotherapy. Kaplan–Meier curves of OS and PFS were extracted from published articles. A two-column raw dataset (time, survival probability) was extracted, and time-to-event outcomes (time, event) were derived using a graphic reconstructive algorithm. Data analysis was conducted from 1 June 2023 to 31 January 2024. Primary outcomes included OS and PFS for time-to-event modeling of Osimertinib as first- and second-line therapy. Results: The Weibull model, incorporating race as a covariate, best fit the first-line OS data. The log-logistic model best fit first-line PFS and second-line OS/PFS data. Based on these models, the predicted median OS for first-line and second-line treatment were 36.35 months (95% CI, 33.53–39.30 months) and 27.46 months (95% CI, 25.30–29.99 months), respectively. The predicted median PFS were 18.11 months (95% CI, 16.37–19.90 months) and 10.35 months (95% CI, 9.31–11.44 months), respectively. The predicted 3- and 5-year survival rates with first-line Osimertinib were 51% and 23%, respectively. Subgroup analysis revealed longer estimated 3- and 5-year survival rates for non-Asian patients compared to Asian patients (60% vs. 49% and 29% vs. 21%, respectively). Conclusions: The predicted survival rates from the time-to-event modeling align with the original clinical trial results, and an ethnic difference in Osimertinib efficacy was observed. Full article