Search Results (28,418)

Growing global migration flows highlight the importance of understanding their economic impact. While many studies have explored how immigration affects host countries’ macroeconomic indicators, the results are mixed. In this work, we develop a meta-analysis to investigate the effect of immigration on the economic performance of the host country, focusing on key indicators such as economic growth, productivity, unemployment, and innovation. The results indicate that, on average, immigration has a positive and statistically significant impact on economic performance. The effect varies based on immigrant and host country characteristics, including qualifications, age, and economic development level. Additionally, differences in methodological approaches across studies contribute to the observed heterogeneity in findings. These findings underscore the importance of further research on the economic effects of immigration and offer implications for both policy and future research. Full article

The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detection. In this context, balancing classification accuracy with computational efficiency is key to enabling practical, real-world deployments. Traditional payload-based and packet inspection methods are based on the identification of relevant patterns and fields in the packet content. However, such methods are nowadays limited by the rise of encrypted communications. To this end, the research community has turned its attention to statistical analysis and Machine Learning (ML). In particular, Convolutional Neural Networks (CNNs) are gaining momentum in the research community for ML-based NTC leveraging statistical analysis of flow characteristics. Therefore, this paper addresses CNN-based NTC in the presence of encrypted communications generated by the rising Quick UDP Internet Connections (QUIC) protocol. Different models are presented, and their performance is assessed to show the trade-off between classification accuracy and CNN complexity. In particular, our results show that even simple and low-complexity CNN architectures can achieve almost 92% accuracy with a very low-complexity architecture when compared to baseline architectures documented in the existing literature. Full article

Background: Sarcopenic obesity, characterized by reduced skeletal muscle mass and excess adiposity, is strongly associated with chronic pain and functional decline in older adults. Objective: This prospective controlled trial without randomization investigated the effects of a structured, three-phase rehabilitation program on physical performance, pain, and sarcopenia-specific quality of life in elderly patients with sarcopenic obesity and chronic pain. Methods: In this study, 82 participants were enrolled and allocated to a study group (SG, n = 40), receiving supervised rehabilitation, nutritional counseling, and supplementation, or to a control group (CG, n = 42), which did not receive rehabilitation. The final analysis included 35 patients in SG and 36 in CG. Outcomes were assessed at baseline and six months using the Sarcopenia Quality of Life Questionnaire (SarQoL), Short Physical Performance Battery (SPPB), Numeric Rating Scale (NRS), and Pressure Pain Threshold (PPT). Results: The SG showed significant improvements in all outcomes: SarQoL increased from 57.02 to 63.98, SPPB increased from 7.14 to 8.4, PPT increased from 69.31 to 78.05, and NRS decreased from 6.94 to 4.65 (all p < 0.001). The CG showed no significant changes. Conclusions: The implementation of a structured, three-phase rehabilitation program resulted in clinically and statistically significant improvements in physical performance, pain perception, and sarcopenia-related quality of life in older adults with sarcopenic obesity and chronic pain. Full article

Background: Limited data are available regarding the mechanical performance of 3D-printed fixed partial dentures (FPDs) fabricated from different materials and connector geometries. The purpose of this in vitro study was to evaluate the influence of connector size and material type on the fracture resistance of three-unit posterior FPDs fabricated with two commercially available 3D-printable dental resins. Methods: A standardized metal model with two cylindrical abutments was used to design three-unit FPDs. A total of sixty samples were produced, considering three connector sizes (3 × 3 mm, 4 × 4 mm, and 5 × 5 mm) and two different resins: Temp Print (GC Corp., Tokyo, Japan) and V-Print c&b temp (Voco GmbH, Cuxhaven, Germany) (n = 10). Specimens were fabricated with a DLP printer (Asiga MAX UV), post-processed per manufacturer recommendations, and tested for fracture resistance under occlusal loading using a universal testing machine. Data were analyzed using nonparametric tests (Mann–Whitney U and Kruskal–Wallis; α = 0.05). Results: Significant differences were found between material and connector size groups (p < 0.001). Temp Print (GC Corp., Tokyo, Japan) demonstrated higher mean fracture loads (792.34 ± 578.36 N) compared to V-Print c&b temp (Voco GmbH, Cuxhaven, Germany) (359.74 ± 131.64 N), with statistically significant differences at 4 × 4 and 5 × 5 mm connectors. Fracture strength proportionally increased with connector size. FPDs with 5 × 5 mm connectors showed the highest resistance, reaching values above 1500 N. Conclusions: Both connector geometry and material composition significantly affected the fracture resistance of 3D-printed FPDs. Larger connector dimensions and the use of Temp Print (GC Corp., Tokyo, Japan) resin enhanced mechanical performance. Full article

Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected. Full article

Cross-validation and open research on future electric grids, particularly in their stability modeling and dynamic performance, can greatly benefit from high-fidelity, publicly available test cases, since access to dynamic response models of actual grid models is often limited due to legitimate security concerns. This paper presents a methodology for designing and validating the dynamic performance of large, IBR-heavy synthetic grids, that is, realistic but fictitious test cases. The methodology offers a comprehensive framework for creating dynamic models for both synchronous generators (SGs) and inverter-based resources (IBRs), focusing on realism, controllability, and flexibility. For realistic dynamic performance, the parameters in each dynamic model are sampled based on statistical data from benchmark actual grids, considering power system dynamics such as frequency and voltage control, as well as oscillation response. The paper introduces system-wide governor design, which improves the controllability of parameters in dynamic models, resulting in a more realistic frequency response. As an example, multiple case studies on a 2000-bus Texas synthetic grid are shown; these represent realistic dynamic performance under different transmission conditions in terms of frequency, voltage control, and oscillation response. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

Optical networks serve as the backbone of modern communication, requiring statistical analysis and modeling to optimize performance, reliability, and scalability. This review paper explores statistical methodologies for analyzing network characteristics, dimensioning, parameter estimation, and cost prediction of optical networks, and provides a generalized framework based on the idea of convex areas, and link length and shortest path length distributions. Accurate dimensioning and cost estimation are crucial for optical network planning, especially during early-stage design, network upgrades, and optimization. However, detailed information is often unavailable or too complex to compute. Basic parameters like coverage area and node count, along with statistical insights such as distribution patterns and moments, aid in determining the appropriate modulation schemes, compensation techniques, repeater placement, and in estimating the fiber length. Statistical models also help predict link lengths and shortest path lengths, ensuring efficiency in design. Probability distributions, stochastic processes, and machine learning improve network optimization and fault prediction. Metrics like bit error rate, quality of service, and spectral efficiency can be statistically assessed to enhance data transmission. This paper provides a review on statistical analysis and modeling of optical networks, which supports intelligent optical network management, dimensioning of optical networks, performance prediction, and estimation of important optical network parameters with partial information. Full article

Background and Objectives: Accurate noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) remains a clinical challenge. This study aimed to assess the dignostic performance of apparent diffusion coefficient (ADC) values from diffusion-weighted MRI in distinguishing between HCC and ICC, with histological confirmation as the gold standard. Materials and Methods: A retrospective analysis was performed on 61 patients (41 HCC, 20 ICC) who underwent liver MRI and percutaneous biopsy between 2019 and 2024. ADC values were measured from diffusion-weighted sequences (b-values of 0, 500, and 1000 s/mm²), and regions of interest were placed over solid tumor areas. Statistical analyses included t-tests, one-way ANOVA, and ROC curve analysis. Results: Mean ADC values did not differ significantly between HCC (1.09 ± 0.19 × 10⁻³ mm²/s) and ICC (1.08 ± 0.11 × 10⁻³ mm²/s). ROC analysis showed poor discriminative ability (AUC = 0.520; p = 0.806). In HCC, ADC values decreased with lower differentiation grades (p = 0.008, η² = 0.224). No significant trend was observed in ICC (p = 0.410, η² = 0.100). Immunohistochemical markers such as CK-7, Glypican 3, and TTF-1 showed significant diagnostic value between tumor subtypes. Conclusions: ADC values have limited utility for distinguishing HCC from ICC but may aid in HCC grading. Immunohistochemistry remains essential for accurate diagnosis, especially in poorly differentiated tumors. Further studies with larger cohorts are recommended to improve noninvasive diagnostic protocols. Full article

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability. Full article

The corpus luteum (CL) is a transient gland that can directly influence follicular dynamics and oocyte quality. The objective of this study was to evaluate the influence of the absence or presence of a small (≤3 mm), medium (4–8 mm), or large (>8 mm) CL in slaughterhouse ovaries on in vitro embryo production. Cumulus–oocyte complexes (COCs) were collected from each group of ovaries and matured in TCM-199 medium, plus hormones and fetal bovine serum. Fertilization was performed with fresh semen from a Katahdin ram of known fertility. Embryo development was carried out in commercial sequential media for 72 and 96 h, until the blastocyst stage. The number of follicles (2–6 mm in diameter) and COCs were influenced by the presence of CL, which was higher (p < 0.05) in the Large CL group (5.51 ± 0.33 and 3.62 ± 0.27) compared to the Without CL group (4.54 ± 0.19 and 2.62 ± 0.14, respectively), with no difference between the CL sizes. Likewise, the diameter and area of the COCs were higher in the Small CL group of ovaries compared to the Without CL group. In the Large CL group of ovaries, 9% more morulae (p < 0.05) were obtained compared to the Without CL group; in the Medium CL group, 13% more blastocysts were obtained compared to the Without CL group. However, in the hatching capacity and diameter of blastocysts, no statistical difference was evident (p > 0.05). In conclusion, the presence and size of the CL in the ovaries of slaughtered sheep influence the productive efficiency of embryos in vitro under the conditions in which the present study was carried out. Full article

Background/Objectives: Despite medical advances, stigma remains a major challenge for people living with HIV (PLWH). This study examined clinical, sociodemographic, and psychological predictors of HIV-related stigma, and explored whether affective temperament moderates the impact of depression on stigma. Methods: This cross-sectional observational study included 97 PLWH attending a tertiary infectious disease unit in Rome, Italy. Participants completed a battery of validated psychometric instruments assessing depressive symptoms, anxiety, manic symptoms, mixed affective states, general psychopathology, impulsivity, and affective temperament. HIV-related stigma was evaluated using the Berger HIV Stigma Scale, which measures personalized stigma, disclosure concerns, negative self-image, and concerns with public attitudes. Descriptive statistics were used to characterize the sample. Univariate linear regressions were conducted to explore associations between clinical, psychometric, and sociodemographic variables and each stigma subdimension, as well as the total stigma score. Variables significant at p < 0.05 were included in five multivariate linear regression models. Moderation analyses were subsequently performed to assess whether affective temperaments moderated the relationship between significant psychopathological predictors and stigma. Bonferroni correction was applied where appropriate. Results: Higher depressive symptom scores are significantly associated with greater internalized stigma (B = 0.902, p = 0.006) and total stigma (B = 2.603, p = 0.008). Furthermore, moderation analyses showed that anxious temperament significantly intensified the relationship between depressive symptoms and both negative self-image (interaction term B = 0.125, p = 0.001) and total stigma (B = 0.336, p = 0.002). Conclusions: Depressive symptoms and anxious temperament are associated with HIV-related stigma. Integrating psychological screening and targeted interventions for mood and temperament vulnerabilities may help reduce stigma burden in PLWH and improve psychosocial outcomes. Full article

HPMC, regulating slurry properties, is widely used in cement-based materials. Research on the application of HPMC in gangue slurry is still in its early stages. Moreover, the interactive effects of various factors on gangue slurry performance have not been thoroughly investigated. The work examined the effects of slurry concentration (X₁), maximum gangue particle size (X₂), and HPMC dosage (X₃) on slurry performance using response surface methodology (RSM). The microstructure of the slurry was characterized via scanning electron microscopy (SEM) and polarized light microscopy (PLM), while low-field nuclear magnetic resonance (LF-NMR) was employed to analyze water distribution. Additionally, industrial field tests were conducted. The results are presented below. (1) X₁ and X₃ exhibited a negative correlation with layering degree and slump flow, while X₂ showed a positive correlation. Slurry concentration had the greatest impact on slurry performance, followed by maximum particle size and HPMC dosage. HPMC significantly improved slurry stability, imposing the minimum negative influence on fluidity. Interaction terms X₁X₂ and X₁X₃ significantly affected layering degree and slump flow, while X₂X₃ significantly affected layering degree instead of slump flow. (2) Derived from the RSM, the statistical models for layering degree and slump flow define the optimal slurry mix proportions. The gangue gradation index ranged from 0.40 to 0.428, with different gradations requiring specific slurry concentration and HPMC dosages. (3) HPMC promoted the formation of a 3D floc network structure of fine particles through adsorption-bridging effects. The spatial supporting effect of the floc network inhibited the sedimentation of coarse particles, which enhanced the stability of the slurry. Meanwhile, HPMC only converted a small amount of free water into floc water, which had a minimal impact on fluidity. HPMC addition achieved the synergistic optimization of slurry stability and fluidity. (4) Field industrial trials confirmed that HPMC-optimized gangue slurry demonstrated significant improvements in both stability and flowability. The optimized slurry achieved blockage-free pipeline transportation, with a maximum spreading radius exceeding 60 m in the goaf and a maximum single-borehole backfilling volume of 2200 m³. Full article

According to the World Health Organization (WHO), a healthy lifestyle is defined as a way of living that lowers the risk of becoming seriously ill or dying prematurely. Physical activity, as a well-known contributor to overall health, plays a vital role in supporting such a lifestyle. Exercise induces complex molecular responses that mediate both acute metabolic stress and long-term physiological adaptations. FGF21 (fibroblast growth factor 21) and GDF15 (growth differentiation factor 15) are recognized as metabolic stress markers, while their receptors play critical roles in cellular signaling. However, the differential gene expression patterns of these molecules in trained and untrained individuals following exhaustive exercise remain poorly understood. This study aimed to examine the transcriptional and protein-level responses in trained and untrained individuals performed a treadmill maximal exercise test to voluntary exhaustion. Blood samples were collected at six time points (pre-exercise, immediately post-exercise, and 0.5 h, 6 h, 24 h, and 48 h post-exercise). Gene expression of FGF21, GDF15, FGFR1 (fibroblast growth factor receptors), FGFR3, FGFR4, KLB (β-klotho), and GFRAL (glial cell line-derived neurotrophic factor receptor alpha-like) was analyzed using RT-qPCR, while plasma protein levels of FGF21 and GDF15 were quantified via ELISA. The results obtained were statistically analyzed by using Shapiro–Wilk, Mann–Whitney U, and Wilcoxon tests in Statistica 13 software. Untrained individuals demonstrated significant post-exercise upregulation of FGFR3, FGFR4, KLB, and GFRAL. FGF21 and GDF15 protein levels were consistently lower in trained individuals (p < 0.01), with no significant correlations between gene and protein expression. Trained individuals showed more stable expression of genes, while untrained individuals exhibited transient upregulation of genes after exercise. Full article

Background/Objectives: Hospitals are environments where care-related infections (HAIs) can occur, including those caused by resistant microorganisms. In addition, inappropriate use of antibiotics contributes to the development of antimicrobial resistance (AMR), a serious public health challenge. As part of the “Choosing Wisely—Italy” initiative, this study complements a previous publication on hand hygiene compliance in an intensive care unit (ICU) by analyzing antibiotic consumption over the same period and comparing it with the previous two years. Methods: A nine-month observational study was carried out from January to September 2018 in the ICU of a university hospital in Salerno province. Antibiotic order forms from the observation period were compared with those from the same months in 2016 and 2017. Glove consumption and costs were also analyzed over the three-year period. Statistical analysis was performed using ORIGIN* and EXCEL* software. Results: Overall antibiotic consumption during the observational period aligned with national averages reported in the National Plan to Combat Antimicrobial Resistance (PNCAR). Conclusions: These findings suggest that the presence of regular external monitoring may positively influence antibiotic use and hygiene behavior. Further research is needed to assess the long-term impact of observational interventions on clinical practice and AMR containment. Full article