Search Results (29,662)

Migration has become an inescapable reality affecting South African families, extending its impact far beyond the immigrant to those staying behind. The geographical separation of parents from their adult children and grandchildren significantly alters family dynamics, creating logistical and emotional challenges. Participants in this study reveal a deeply felt need to physically reconnect with their loved ones, emphasizing the emotional solace derived from in-person interactions. The enduring parent-child bond motivates family members to find meaningful ways to maintain their connections across vast distances and differing time zones. Transnational visits serve as a crucial lifeline, enabling parents to experience their children’s new environments and strengthen bonds with their grandchildren. This article draws upon ongoing qualitative research exploring the lived experiences of South African parents with emigrant children and grandchildren, focusing on the barriers that hinder these transnational visits. It focusses on parents’ unique experiences travelling to visit their emigrant children, rather than return visits. While they are essential for sustaining familial bonds, visits are deeply layered experiences, shaped by financial constraints, the logistical complexities of long-distance travel and the emotional weight of farewells. These factors have the potential to render visits infrequent and emotionally complex. Full article

In traditional studies on grassland degradation drivers, researchers often lacked the flexibility to selectively consider driving factors and quantitatively depict their contributions. Interpretable machine learning offers a solution to these challenges. This study focuses on Inner Mongolia, China, incorporating four categories and sixteen specific driving factors, and employing four machine learning techniques (Logistic Regression, Random Forest, XGBoost, and LightGBM) to investigate regional grassland changes. Using the SHAP approach, contributions of driving factors were quantitatively analyzed. The findings reveal the following: (1) Between 2015 and 2020, Inner Mongolia experienced significant grassland degradation, with an affected area reaching 12.12 thousand square kilometers. (2) Among the machine learning models tested, the LightGBM model exhibited superior prediction accuracy (0.89), capability (0.9), and stability (0.76). (3) Key factors driving grassland changes in Inner Mongolia include variations in rural population, livestock numbers, average temperatures during the growth season, peak temperatures, and proximity to roads. (4) In eastern and western Inner Mongolia, changes in rural population (31.4%) are the primary degradation drivers; in the central region, livestock number changes (41.1%) dominate; and in the southeast, climate changes (19.3%) are paramount. This work exemplifies the robust utility of interpretable machine learning in predicting grassland degradation and offers insights for policymakers and similar ecological regions. Full article

Background: The minimally invasive approach, performed via ministernotomy, is now often preferred for isolated aortic valve replacement (AVR). However, its benefits in patients with prior cardiac surgery remain unclear. This article compares traditional and minimally invasive surgery for isolated aortic valve replacement in reoperative cases. Methods: A retrospective analysis of 382 patients who underwent reoperative AVR between January 2010 and June 2024 divided them into two groups: 309 patients (80.1%) had a traditional full sternotomy, while 73 patients (19.1%) had minimally invasive AVR via upper ministernotomy. Results: Significant differences were noted between the groups. The full sternotomy group had a higher logistic EuroSCORE (SMD = 0.203), more patients with active endocarditis (SMD = 0.312), and a higher pacemaker rate. To minimize bias, inverse probability of treatment weighting (IPTW) was used. The minimally invasive group had shorter aortic cross-clamp (50 vs. 65 min, p < 0.001) and cardiopulmonary bypass times (62 vs. 85 min, p < 0.001), shorter intensive care unit (ICU) stays (p < 0.001), lower rates of acute renal failure (p = 0.001), and less blood loss (p < 0.001), but similar transfusion needs. Early mortality was higher in the full sternotomy group (4.5% vs. 1.6%, p = 0.025). Conclusions: Minimally invasive aortic valve reoperation via upper “J” sternotomy is as safe as full sternotomy. Patients experienced lower rates of acute renal failure and less postoperative bleeding, contributing to a safer recovery with decreased hospital mortality. Full article

Background: Nirsevimab was indicated in a population level for all infants < 6 months during the 2023–2024 season in Andalusia (southern Spain). Our aim was to analyse the effect of nirsevimab in the reduction in complications in infants hospitalised for RSV bronchiolitis. Methods: A retrospective observational cohort study was conducted in nine relevant hospitals from all provinces of Andalusia, a region with over 9 million inhabitants. The study sample included 222 children, divided into two groups: infants administered with nirsevimab for passive immunisation (exposure) and infants not administered with nirsevimab. Clinical outcomes were analysed, including the use of respiratory support, the need for admission to paediatric intensive care unit (PICU), and duration of hospitalisation. Bivariate analyses were performed, and multivariable logistic regression models were designed to calculate adjusted odds ratios (ORa), and Cox regression models to calculate adjusted hazard ratios (HRa). Results: Bivariate analysis showed an association between passive immunisation with nirsevimab and a lower frequency of numerous outcomes. After adjustment for relevant covariates, multivariable models showed that the exposure (nirsevimab) reduced nasal cannula use by 64% (13–85%), invasive or non-invasive mechanical ventilation by 48% (1–73%), PICU admission by 54% (14–75%), length of hospitalisation by 30% (8–47%), and length of nasal cannula by 31% (7–49%). A higher risk of co-infection was observed in those immunised (aOR = 3.42, 95%CI: 1.52–7.68). Conclusions: Passive immunisation with nirsevimab may decrease the severity of RSV bronchiolitis in infants requiring hospitalisation, thus contributing tertiary prevention that extends beyond the prevention of RSV infection. Full article

Fish waste (FW) serves as an underutilized resource in agriculture, especially among small-scale processors. The trimmings and skins generated during the manufacturing of smoked salmon often end up in landfills due to insufficient quantities and logistical challenges to promote upcycling. An additional consideration is the high fat and mineral content in the smoked Atlantic salmon (Salmo salar) used for this study. We tested the feasibility of technology that small-scale processors can adapt to upcycle smoked salmon by-products into fish meal (FM) and fish oil (FO). A two-phase decanter centrifuge was optimized by manipulating acceleration, differential speed, flow rate, weir disc diameter, sample temperature, and the number of decanter runs. FW, processed through the decanter three times, produced FM with the least fat content compared to other trials. The optimized FM contained 74.09% protein, 8.56% fat, 15.41% ash, and 0.20% salt. FO production involved running a 9:1 water-to-by-product dilution through a three-phase clarifier centrifuge, followed by batch centrifugation and storage. Proximate, amino acid, and fatty acid profiles of the produced FM and FO aligned with industry standards. This study highlights a potentially sustainable solution for small-scale processors to transform FW into high-quality FM and FO, reducing waste and supporting sustainable resource recovery. Full article

In response to the increasing volume of network traffic and the growing sophistication of cyber threats, this study examines the use of deep learning-based intrusion detection systems (IDSs) in large-scale network environments. Traditional IDS face challenges such as high false positive rates, complex feature engineering, and class imbalances in datasets, all of which impede accurate threat detection. To overcome these limitations, we implement various deep learning models, including multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM), alongside traditional machine learning algorithms such as logistic regression, naive Bayes, random forest, K-nearest neighbors, and decision trees. A significant contribution of this study is the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance, enhancing the representativeness of the learning process. Additionally, we conduct a comprehensive performance comparison of the models, incorporating correlation-based feature selection and hyperparameter tuning to maximize detection accuracy. Our results indicate that deep learning models, particularly CNN and LSTM, outperform traditional machine learning approaches in cyber threat detection, achieving accuracy rates of 98%. However, random forest achieves the highest accuracy at 99.9%, demonstrating its effectiveness in structured intrusion detection tasks. Moreover, we evaluate computational efficiency and practical deployment considerations, discussing trade-offs between accuracy and resource consumption. These findings highlight the potential of deep learning-based IDS for large-scale network security applications while addressing key challenges such as interpretability and computational overhead. The study provides actionable insights for selecting the most suitable IDS models based on specific network environments and security requirements. Full article

Background: Distribution systems often face the dual challenge of delivering products to customers and retrieving damaged items for repair, especially when the service center is separate from the depot. An optimized solution to this logistics problem produces benefits in terms of costs, greenhouse gas emissions, and disposal reduction. Methods: This research proposes a Capacitated Vehicle Routing Problem with Service Center (CVRPwSC) model to determine optimal routes involving customers, the depot, and the service center. AMPL-Gurobi was used to solve the model on adapted instances and new instances developed for the CVRPwSC. Additionally, a Variable Neighborhood Search (VNS) algorithm was implemented and compared with AMPL-Gurobi. Results: The model was applied to a real-world case study, achieving a 40% reduction in fuel costs, a reduction from 5 to 3 routes, and a sustainable logistics operations model with potential reductions of greenhouse gas emissions and item disposals. Conclusions: The main contribution of the proposal is a minimum-cost routing model integrating item returns for repair with customer deliveries, while the limitation is the exclusion of scenarios where return items exceed vehicle capacity. Finally, future research will enhance the CVRPwSC model by incorporating additional constraints and decision variables to address such scenarios. Full article

In the digital era, cybersecurity is integral for preserving national security, digital privacy, and social sustainability. This research emphasizes the role of non-functional equirements (NFRs) in developing secure software systems that enhance societal wellbeing by ensuring data protection, user privacy, and system robustness. Specifically, this study introduces a proof-of-concept approach by leveraging machine learning (ML) models to classify NFRs and identify security-related issues early in the software development lifecycle. Two experiments were conducted to assess the effectiveness of different models for binary and multi-class classification tasks. In Experiment 1, BERT-based models and artificial neural networks (ANNs) were fine-tuned to classify NFRs into security and non-security categories using a dataset of 803 statements. BERT-based models outperformed ANNs, achieving higher accuracy, precision, recall, and ROC-AUC scores, with hyperparameter tuning further enhancing the results. Experiment 2 assessed logistic regression (LR), a support vector machine (SVM), and XGBoost for the multi-class classification of security-related NFRs into seven categories. The SVM and XGBoost showed strong performance, achieving high precision and recall in specific categories. The findings demonstrate the effectiveness of advanced ML models in automating NFR classification, improving software security, and supporting social sustainability. Future work will explore hybrid approaches to enhance scalability and accuracy. Full article

Abstract: Background/Objectives: Single-port access (SPA) laparoscopic surgery has gained popularity due to its cosmetic benefits and reduced postoperative pain. However, concerns persist regarding the increased risk of adhesions due to the larger umbilical incision. This study aims to contribute to personalized medicine by evaluating the effectiveness of applying an anti-adhesive agent (Guardix SG^®, HanmiPharmaceutical Co., Ltd., Seoul, Korea) at the umbilical incision and identifying patient-specific risk factors for adhesion formation in SPA laparoscopic surgeries. Methods: In this randomized prospective study, 55 female patients with benign gynecological conditions were enrolled. Participants were randomly assigned to either an intervention group, which received the anti-adhesive agent at both the surgical and umbilical sites, or a control group, which received it only at the surgical site. Participants returned for outpatient visits 1–3 months post-surgery to assess incision site complications, including adhesions. Results: The overall adhesion rate was 10.9%, with 13.3% in the control group and 8% in the intervention group, though the difference was not statistically significant (p = 0.678). Infection rates were 6.7% in the control group and 4% in the intervention group; however, there was no significant difference in complications. Logistic regression identified pre-existing adhesions as a significant risk factor (p = 0.0379; OR = 6.909). Conclusions: Although the adhesion barrier showed a trend toward reducing umbilical adhesions, the difference was not statistically significant. The application of the adhesion barrier did not influence incision site complications, confirming its safety. Our findings highlight the need for personalized approaches to adhesion prevention, considering individual patient characteristics and risk factors. Further larger studies are necessary to explore adhesion prevention in a more personalized manner for individual patients in this context. Full article

The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. Model performance was evaluated using key metrics, including precision, recall, accuracy, and F1-Score. The analysis also identified significant indicators of fraud, such as Accounts Receivable Turnover, Days Outstanding Accounts Receivable, Days Payables Outstanding, Logarithm of Gross Profit, Gross Profit Margin, Inventory to Sales Ratio, and Total Asset Turnover. Among the models, Random Forest emerged as the most effective algorithm, consistently outperforming others on both training and testing datasets. Logistic Regression and SVM demonstrated strong reliability, whereas KNN and Decision Tree faced overfitting challenges, limiting their practical application. These findings emphasize the critical need for enhanced fraud detection frameworks, leveraging machine learning algorithms like Random Forest to identify fraud patterns effectively. The study highlights the importance of strengthening internal controls, implementing targeted fraud detection measures, and promoting regulatory improvements to enhance transparency and financial accountability. Full article

Background: There has yet to be an agreement on which specific socioeconomic status (SES) indicator most effectively reflects disparities in children’s diets. However, children from lower SES backgrounds are particularly vulnerable, as research in other countries indicates that their diets contain fewer fruits and vegetables and more sweetened beverages. This paper aims to evaluate the associations between dietary habits and various SES indicators (education, occupation, income, and household size) among a representative sample of children in Croatia aged 7–10. Methods: Parents of children were asked to complete a questionnaire that contained indicators of their children’s dietary habits and socioeconomic status (n = 5608). Associations between SES and children’s dietary habits were assessed using logistic regression models. Results: The mother and father’s educational attainment were strongly positively associated with breakfast consumption. Children of parents with a lower educational level consumed sweetened beverages, sweet snacks, and fast food slightly more often than children in families with a higher educational background. The mother’s education was inversely associated with vegetable and cereal consumption, while the father’s education was inversely associated with fruit and bakery product consumption. Meanwhile, household income per unit had a significant influence on the consumption of soft drinks and bakery products. Household size had a significant influence solely on sweet snack consumption. Conclusions: Each SES indicator showed an independent association with at least one particular dietary habit, except for the parent’s employment status. Full article

Background/Objectives: This study investigated the prevalence of functional impairments and the effects of long COVID on long-term functional impairments in patients with severe COVID-19. Methods: We conducted a nationwide multicenter cohort study in collaboration with nine hospitals, collecting data using self-administered questionnaires from participants aged 20 years or older who were diagnosed with COVID-19, admitted to the intensive care unit (ICU) between April 2021 and September 2021, and discharged alive. Questionnaires regarding daily life, sequela, and functional impairments were mailed to patients in August 2022. The effects of long COVID on functional impairments were examined using a multivariate logistic regression analysis. Results: The survey was completed by 220 patients, with a mean of 416 days after discharge. Among respondents, 20.5% had physical impairments (n = 45), 35.0% had mental disorders (n = 77), and 42.7% had either (n = 94). Furthermore, 77.7% had long COVID (171/220), and the most common symptom was dyspnea (40.0%). The multivariate analysis showed that fatigue/malaise, upper respiratory tract symptoms, myalgia, muscle weakness, decreased concentration, sleep disorder, brain fog, and dizziness were risk factors for functional impairments at one year. Conclusions: Many patients with severe COVID-19 admitted to the ICU still suffered from post-intensive care syndrome even after one year, which manifested in combination with direct symptoms of the original disease, such as long COVID. Full article

Behavioural problems in dogs are a major welfare concern for both dogs and owners, which can lead to relinquishment or euthanasia. As such, it is important to find ways to minimize the risk of development or severity of problematic behaviour. Such interventions should ideally occur early in the dog’s development and need to be widely and easily accessible. One way to implement such interventions, given modern smart phone use, is via an educational application (app). Here, we conducted a cross-sectional observational study where we sought to estimate associations between use of the Zigzag dog-training app (Zigzag Pet Care Services Ltd.) and the development and severity of problematic behaviours. We focused on a subset of the survey population (n = 367) who did no training at all (n = 194) or only used the app (did not attend formal classes or in person training; n = 173). Proportional odds ordinal logistic regression models were used to estimate odds ratios and 95% confidence intervals for a range of behaviours as a function of percentage completion of the first four chapters of the app while controlling for age, sex, health, and where the puppy was from. While sample sizes were relatively small and confidence intervals wide, 19 out of 21 odds ratio point estimates were in favour of severity being lower with increasing Zigzag app completion (the remaining 2 were marginally above 1). There was no good evidence that Zigzag increased the severity of any behaviours. On the contrary, there was reasonable evidence against the null hypothesis of no association in favour of Zigzag reducing the severity of familiar aggression, house soiling, chewing, barking, escaping, and noise fear. While causal effects cannot be claimed, overall, the reported associations are favourable and suggest that further study is warranted. Full article

Background: Substance Use Disorders (SUDs) are chronic conditions characterized by high relapse rates and significant psychological, physical, and social complications. Despite the availability of traditional pharmacological and psychotherapeutic interventions, many individuals struggle to maintain abstinence. Recently, physical activity (PA) has emerged as a promising complementary intervention. This review aims to examine the existing evidence on the effects of PA in individuals with SUDs, with a particular focus on neurobiological mechanisms. Methods: A narrative review was conducted on 30 September 2024, searching relevant keywords on PubMed, Web of Science, Google Scholar, and Scopus. Randomized clinical trials, cohort studies, reviews, and meta-analyses published between 1988 and 2024 were considered. Results: Fifty studies were included. Key themes included the role of PA in inducing neuroadaptation in individuals with SUDs, which is crucial for relapse prevention and impulse control, and the effects of PA depending on the type of PA and the specific SUD. Neurobiological modifications related to PA are of particular interest in the search for potential biomarkers. Additionally, studies explored the effects of PA on cravings, mental health, and quality of life. The review overall discusses the psychological changes induced by PA during SUD rehabilitation, identifies barriers to participation in PA programs, and suggests clinical and organizational strategies to enhance adherence. Conclusions: Physical activity is a promising adjunctive therapy for the management of Substance Use Disorders. Long-time longitudinal studies and meta-analyses are needed to sustain scientific evidence of efficacy. The success of PA programs moreover depends on overcoming barriers to adherence, including physical, psychological, and logistical challenges. Full article

Objectives: Spondyloarthritis (SpA) is a chronic inflammatory arthritis that mainly affects the sacroiliac joints and spine. Despite effective biological treatments, persistent pain is common in SpA patients, potentially due to central sensitization (CS), a condition of heightened central nervous system responsiveness. The purpose of this study was to investigate the link between disease activity and CS in SpA patients on biological therapy. Patients and Methods: One hundred and twenty SpA patients with at least six months of treatment with biological agents were included in this cross-sectional study. Patients’ demographic, clinical, and functional information were collected. The assessment of CS was conducted using the Central Sensitization Inventory (CSI), whereas disease activity and quality of life were evaluated using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Ankylosing Spondylitis Disease Activity Score (ASDAS)-C-reactive protein (CRP), and Ankylosing Spondylitis Quality of Life (ASQoL). Statistical analyses included correlation assessments and logistic regression to identify predictors of CS. Results: CS (CSI ≥ 40) was present in 40.8% of patients. Disease activity was significantly higher and quality of life was lower in patients with CS. BASDAI and ASQoL scores were strongly correlated with CS (r = 0.774 and r = 0.839, respectively). Logistic regression identified ASQoL and BASDAI scores as independent predictors of CS. ROC curve analysis demonstrated that ASQoL had the highest discriminative ability for predicting CS (AUC = 0.97). Conclusions: CS is significantly associated with higher disease activity and poorer quality of life in SpA patients receiving biological therapy. Incorporating CS assessment into routine clinical practice may enhance our understanding and management of persistent symptoms in SpA, improving patient outcomes. Full article