Search Results (2,702)

Background: Neonatal sepsis (NS) is a life-threatening condition in newborns, which is an infectious process with a systemic inflammatory reaction to bacterial, viral, or fungal infection acquired in the first 28 days of life. Methods: This study examines the major pathogens causing neonatal sepsis in the Gulf Cooperation Council (GCC) and their resistance patterns to antimicrobial agents. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to develop this systematic review to follow a systematic and transparent process. The comprehensive literature review was done in several national and global databases, which include PubMed, Scopus, Google Scholar, Embase, and Cochrane Library. The key words inserted in the search strategy were “neonatal sepsis,” “late-onset sepsis,” “early-onset sepsis,” and “Gulf Cooperation Council (GCC),” and the keywords of antimicrobial resistance and pathogens were used: “antimicrobial drug resistance” and “pathogens.” Only articles published from January 1983 to January 2025 were included for screening. Results: The final count of the articles that fit the inclusion criteria is 54, and 5177 neonatal sepsis cases’ data have been identified. The most common pathogens were coagulase-negative staphylococci (CoNS) and Klebsiella spp., which caused 17.4 percent (901 cases) and 15.9 percent (825 cases) of neonatal sepsis, respectively. Coagulase-negative staphylococci (CoNS) were the most resistant, especially to oxacillin and erythromycin. The most isolated pathogens among Gram-negative spp. were Klebsiella spp., which showed a resistance to ampicillin, amoxicillin, and ceftriaxone. Conclusions: The bacterial isolates had a diversity of antimicrobial resistance, stressing the necessity of continuous hospital surveillance. Sophisticated diagnostic methods and well-designed research are necessary, especially in areas characterized by high rates of neonatal mortality, to determine the prevalence of neonatal sepsis, risk factors, and clinical outcomes. Full article

Background: Meat adulteration poses serious public health, economic, and religious concerns, particularly in the Middle East and North Africa (MENA) and Gulf Cooperation Council (GCC) regions where halal authenticity is essential. While isolated studies have reported undeclared species in meat products, a comprehensive regional synthesis of prevalence, detection technologies, and regulatory responses has been lacking. Methods: This scoping review followed PRISMA-ScR guidelines. A systematic search of PubMed, Scopus, and Web of Science from database inception to 15 September 2025 was conducted using controlled vocabulary (MeSH) and free-text terms. Eligible studies included laboratory-based investigations of meat adulteration in MENA and GCC countries. Data were charted on study characteristics, adulteration types, detection methods, and regulatory context. Results: Out of 50 records screened, 35 studies were included, covering 27 MENA/GCC countries. Prevalence of adulteration varied widely, from 5% in UAE surveillance studies to 66.7% in Egyptian native sausages. Undeclared species most frequently detected were poultry, donkey, equine, pig, and dog. Molecular methods, particularly PCR and qPCR, were most widely applied, followed by ELISA and spectroscopy. Recent studies introduced biosensors, AI-assisted spectroscopy, and blockchain traceability, but adoption in regulatory practice remains limited. Conclusions: Meat adulteration in the MENA and GCC regions is localized and product-specific rather than uniformly widespread. Detection technologies are advancing, yet regulatory enforcement and halal-sensitive verification remain fragmented. Strengthening laboratory capacity, harmonizing regional standards, and investing in portable biosensors, AI-enhanced spectral tools, and blockchain-based traceability are critical for consumer trust, halal integrity, and food safety. Full article

Background: Centralization and certification mark constant processes in everyday clinical routine. Despite the continuously rising number of certified pancreatic cancer (PAC) centers in recent years, fewer than 40% of PAC resections are still performed in certified institutions nationwide. The main objective of the certification is the enhancement of patient survival. Furthermore, certification is intended to improve structural quality, multidisciplinary cooperation, and the transparency of treatment pathways. In addition, it should have a positive effect on patient satisfaction. However, it requires the substantial effort of all partners involved. We aim to illustrate both advantages and limitations of the certification process. Methods: We analyzed perioperative outcomes of patients undergoing pancreatic resection for PAC (ICD C25) before and after our center’s first certification, and benchmarked these results against national data from the German Cancer Society. In addition, we analyzed quality of life (QoL) longitudinally using the validated QLQ-C30 questionnaire administered preoperatively and at 1, 4, and 18 months postoperatively. Results: The study cohort included 47 patients treated in the three years prior to certification and 130 patients during the subsequent seven years as a certified center. The mean annual number of PAC resections increased from 15 (ranged 14–18) to 19 (ranged 10–26). In-hospital mortality, length of stay, and rate of exploration-only procedures remained unchanged. Indicators of procedural quality, such as the number of harvested lymph nodes (p = 0.1485) and the precision of histopathological assessment, improved slightly but not significantly. QoL scores generally improved after discharge in both groups; however, functional scales and symptom measures demonstrated unexpectedly inferior values following certification, possibly reflecting higher case complexity. Conclusion: Achieving and maintaining certification requires substantial and continuous effort from all disciplines involved. While major improvements in morbidity, mortality, and long-term QoL were not observed, certification ensured clearer delegation of responsibilities, standardized documentation, and structured quality control. We therefore consider the certification process valuable for promoting multidisciplinary collaboration, maintaining high treatment volumes, and ensuring transparent oncological care pathways. Full article

The Erguna River, a Sino-Russian transboundary river, is vital for regional ecology, but land use impacts on its water quality remain unclear. This study aimed to reveal their response relationship. Using ArcGIS/ENVI, it classified land use in 2010 and 2016 into six types, and applied Pearson correlation to 10 monitoring sections’ water quality data. Results showed land use–water quality correlations were temporally and spatially variable: correlations weakened with increasing buffer distance, with the strongest associations within 1000 m. From 2010 to 2016, grassland shifted from a positive (water-purifying) to negative (pollutant-source) impact on water quality (BOD₅ and arsenic), which was driven by area reduction and overuse; forestland transitioned from no significant effect to a positive (pollutant-intercepting) role, attributed to area expansion. Arable and construction land showed no significant correlations with water quality, due to low proportions of construction land in cross-border areas, and arable land is mostly distributed in areas far from the riverbank. This study provides critical scientific support for transboundary water resource cooperation and targeted ecological management of the Erguna River Basin. Full article

The COVID-19 pandemic exacerbated labor market inequalities, disproportionately impacting workers based on age, gender, and sector. In Turkey, the pandemic-induced economic crisis resulted in a substantial increase in unemployment, with youth (ages 15–24) encountering the most significant challenges. Young women, in particular, experienced more severe outcomes, increasing their vulnerability in the labor market. This study examined the factors contributing to the intensified challenges faced by young women during the pandemic. Using official data from the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD), and the Turkish Statistical Institute (TurkStat), comparative analyses were conducted on labor market indicators by age and gender, focusing on unemployment rates, informal employment, and sectoral distribution. This study considers both narrow and broad definitions of unemployment, including underemployment and the potential labor force. The findings indicate that young women suffered the most severe employment losses, exacerbated by their concentration in low-wage, precarious jobs and informal work, with gendered occupational segregation further intensifying these disparities. Full article

This study examines co-opetition mechanisms in China’s rapidly expanding bed and breakfast (B&B) sector, where intense competition drives operators to collaborate with rivals. A co-opetition model incorporating size classifications was tested using survey data from 500 clustered B&Bs. Data were analyzed with SPSS 26.0 and AMOS 23.0 through descriptive statistics, reliability testing, exploratory and confirmatory factor analyses, and structural equation modeling (SEM). Results show that perceived benefit (β = 0.230, p < 0.01), cooperation orientation (β = 0.223, p < 0.01), and prior experience (β = 0.232, p < 0.01) significantly drive co-opetition, whereas mutual trust and strategic fit are not significant. Co-opetition strongly enhances sustainable competitive advantage (β = 0.521, p < 0.001), indicating that strategic collaboration can mitigate homogenization in dense markets. The study contributes to co-opetition theory by (1) identifying antecedents specific to small-scale hospitality businesses, (2) challenging conventional assumptions about the role of trust, and (3) establishing empirical links between co-opetition and long-term competitiveness. Practically, the findings provide B&B operators with strategies for navigating competitive yet interdependent environments and offer policymakers evidence-based guidance to foster sustainable tourism clusters through institutional support for cooperative networks. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

Background/Objectives: As safe, eco-friendly, and legally compliant solutions for the disposal of unwanted medications, drug take-back systems have attracted extensive research attention. However, there is a lack of systematic mapping of global trends, collaborative networks, research themes, and hotspots in this field. This study aimed to conduct a comprehensive bibliometric analysis and review of global academic research on drug take-back programs. Methods: Peer-reviewed research articles on drug take-back programs, published between 2005 and 2025, were retrieved from the Web of Science Core Database. Microsoft Office Excel 2019, VOSviewer (v.1.6.17), and CiteSpace (v.6.1.R3 Advanced) were used to assess publication/citation trends, countries, institutions, authors, journals, disciplines, references, and keywords. Narrative analysis was employed to synthesize data from the included articles and identify core research themes. Results: A total of 149 eligible articles with 4520 citations were included, involving 619 authors, 52 countries/regions, 310 institutions, and 95 journals. Publication/citation counts increased significantly between 2005 and 2025. The United States led in both publication output and collaborative research; Mercer University was the most influential institution, but international and cross-institutional collaboration remained limited. Environmental Sciences ranked first among disciplinary categories in drug take-back research, followed by Pharmacology/Pharmacy. Core research themes underpinning this field included stakeholders’ knowledge–attitude–practice assessment (76 articles), returned medication treatment (37 articles), intervention evaluation (25 articles), policy analysis (7 articles), and the role of drug take-back programs in mitigating environmental and public health hazards caused by medicine wastes (4 articles). Conclusions: Scholarly attention to drug take-back programs has grown steadily. Future research should prioritize cross-sectoral and international cooperation, develop and adopt evidence-based interventions to optimize the safety, sustainability, and accessibility of drug take-back systems on a global scale. Full article

To overcome the inadequacy of traditional financial metrics in appraising green infrastructure, this study develops and validates an integrated framework combining financial and sustainability indicators to more accurately predict project performance. Employing a mixed-methods design, this study synthesized metrics from expert interviews (N = 24) and literature, then collected data from 42 completed projects in Gulf Cooperation Council countries. The framework’s predictive validity was tested using a novel application of a Gradient Boosting Machine (XGBoost) model, with SHAP (SHapley Additive exPlanations) analysis ensuring model interpretability. The integrated framework yielded higher out-of-sample discriminatory performance (AUC-ROC = 0.88) than a baseline using only traditional metrics (AUC-ROC = 0.71). In SHAP analyses, RBCR and LCC contributed most to the model’s predictions, whereas NPV and IRR contributed least. These results indicate stronger predictive associations for sustainability-oriented metrics in this study’s model. Because the design is cross-sectional and predictive, all findings are associational rather than causal; residual confounding is possible. The validated, interpretable model is therefore positioned as a decision support tool that complements, rather than replaces, expert appraisal. Full article

Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inevitably reshapes urban travel patterns, posing significant challenges to the existing bus network. Understanding passenger switch behavior is key to optimizing the competition and cooperation between these two modes. However, existing methods on the switch behavior of bus passengers along the newly opened rail transit line cannot balance the predictive accuracy and model interpretability. To bridge this gap, we propose a CART (classification and regression tree) decision tree-based switch behavior model that incorporates both predictive and interpretive abilities. This paper uses the massive passenger swiping-card data before and after the opening of the rail transit to construct the switch dataset of bus passengers. Subsequently, a data-driven predictive model of passenger switch behavior was established based on a CART decision tree. The experimental findings demonstrate the superiority of the proposed method, with the CART model achieving an overall prediction accuracy of 85%, outperforming traditional logit and other machine learning benchmarks. Moreover, the analysis of factor significance reveals that ‘Transfer times needed after switch’ is the dominant feature (importance: 0.52), and the extracted decision rules provide clear insights into the decision-making mechanisms of bus passengers. Full article

In a world characterized by unpredictable change, students in Computer Science education must be deeper self-directed learners who can take ownership of their learning and transfer knowledge and skills to new contexts. This article reports on how productive failure was incorporated into an introductory coding and robotics course to enhance deeper self-directed learning. The population was 42 fourth-year pre-service teachers from two different campuses of a South African University. All students were invited to participate in the research, and 37 students consented to participate. A basic interpretative qualitative research design was followed. Guided self-reflection documents were used as data-gathering methods, and data were analyzed by applying thematic data analysis. The research concluded that productive failure, incorporated with cooperative pair programming and self-reflection, in introductory coding and robotics education, shows promising results for developing deeper self-directed learning. Furthermore, it is suggested that solvable problems should initially be introduced, because the new coding and robotics environment already contributes to the complexity of tasks. It was secondly concluded that participants’ self-reflections deepened after engaging with unsolvable problems. Follow-up research is required to determine if the transfer of knowledge and skills to new contexts occurred. Full article

Integrated optical filters are fundamental and indispensable components of silicon photonics, which enhance the data throughput of high-demand communication networks. Grating-assisted filters have been widely used due to the merits they offer: flat top, low crosstalk, and no FSR. In this paper, we report an inverse-designed narrow-band silicon Bragg grating filter that unites lateral-misalignment apodization with cooperative particle swarm optimization (CPSO). The initial coupling-coefficient profile of the filter is first yielded by a layer-peeling algorithm (LPA). Subsequently, the final structure is designed by CPSO to approach the desired spectral response. The filter is fabricated on a 220 nm silicon-on-insulator platform. The measured results exhibit 3.39 nm bandwidth, 19.34 dB side lobe suppression ratio (SLSR), and 1.75 dB insertion loss. The proposed design method effectively solves the problem of excessively high side lobes in uniform gratings and LPA-designed gratings when designing narrow-bandwidth filters. Full article

As an important part of the circular economy, recycling old garments not only lessens resource waste, but also offers significant social benefits and environmental conservation. Taking Hefei City, Anhui Province, China, as a case, this study adopted the innovative Planned Behavior Theory (TPB) model and introduced innovative variable community promotion as the moderating variable to analyze the influencing factors of residents’ used clothing recycling behavior. It was found that residents’ attitudes, perceived behavioral control, and subjective norms were key factors influencing their intention to recycle used clothes. Community promotion activities play a positive role in improving residents’ perceived behavior control. However, there is also an interaction between community promotion and perceived behavior control, indicating that the effect of community promotion is affected by residents’ perceived behavior control level. This shows that the publicity and promotion of the community will improve residents’ enthusiasm for recycling old clothes, but if the publicity or promotion is too strong, it may lead to a decline in residents’ enthusiasm. The results show that improving residents’ environmental awareness, simplifying the recycling process, utilizing social influence, rationally planning community promotion activities, policy support and incentive measures, and establishing multi-party cooperation mechanisms are effective ways to promote the recycling of used clothing and resources. Through these measures, we can better promote the recycling of used clothing, realize the rational development, utilization, and protection of resources, and contribute to the realization of green and high-quality development. However, this study is limited to the research and investigation in Hefei, Anhui Province, and most of the respondents have a certain educational background, so the universal applicability of the data may not be significant. Full article

Deep learning has been broadly applied in many fields and has greatly improved efficiency compared to traditional approaches. However, it cannot resolve issues well when there are a lack of training samples, or in some varying cases, it cannot give a clear output. Human beings and machines that work in a collaborative and equal mode to address complicated streaming data-driven tasks can achieve higher accuracy and clearer explanations. A novel framework is proposed which integrates human intelligence and machine intelligent computing, taking advantage of both strengths to work out complex tasks. Human beings are responsible for the highly decisive aspects of the task and provide empirical feedback to the model, whereas the machines undertake the repetitive computing aspects of the task. The framework will be executed in a flexible way through interactive human–machine cooperation mode, while it will be more robust for some hard samples recognition. We tested the framework using video anomaly detection, person re-identification, and sound event detection application scenarios, and we found that the human–machine collaborative learning mechanism obtained much better accuracy. After fusing human knowledge with deep learning processing, the final decision making is confirmed. In addition, we conducted abundant experiments to verify the effectiveness of the framework and obtained the competitive performance at the cost of a small amount of human intervention. The approach is a new form of machine learning, especially in dynamic and untrustworthy conditions. Full article

People are exposed to pesticides daily through food, drinking water, and the environment, both in urban and rural settings. These chemicals, while offering economic and agricultural benefits through pest control and increased productivity, may pose a growing risk to human health and ecosystem biodiversity. While the European regulatory framework offers a robust foundation for risk assessment, significant limitations persist, especially in addressing cumulative exposure, low-dose effects, and chemical mixtures. This review focuses on selected scientific and regulatory challenges by reviewing recent European Food Safety Authority (EFSA) conclusions, Organization for Economic Co-operation and Development (OECD) test guidelines updates, and current European legislative approaches. Particular attention is given to the regulation of endocrine-disrupting and reprotoxic substances, highlighting progress and remaining gaps in implementation. A brief mention will also be made of immuno-toxic substances, for which no specific hazard class has yet been established. Building on official reports and peer-reviewed literature, this review provides a structured evaluation of the scientific and regulatory landscape, including underexplored issues like the transition to animal-free toxicology and integration of biomonitoring with health data. The goal is to propose realistic, evidence-based improvements to current frameworks using integrated, interdisciplinary approaches that connect toxicology, policy, and implementation science. A shift to a holistic, systems-based, and precautionary paradigm is vital to address emerging challenges and ensure strong protection of health and environment, as well as supporting the needs of the agricultural sector. Full article