Search Results (14,994)

This review critically evaluates current PA6 recycling technologies, with a specific focus on caprolactam-oriented chemical recycling pathways, including hydrolysis, pyrolysis, glycolysis, ammonolysis, hydrothermal treatment, ionic-liquid-assisted depolymerization, and microwave-assisted processes. Reported caprolactam yields vary significantly depending on reaction conditions and catalyst systems, ranging from below 60 wt% in conventional hydrolysis to above 90 wt% under optimized catalytic, hydrothermal, or microwave-assisted conditions. Among these approaches, microwave-assisted hydrolysis and catalytic depolymerization have emerged as particularly promising, offering substantially reduced reaction times (minutes rather than hours), improved energy efficiency, and high monomer selectivity at moderate temperatures (typically 200–350 °C). This review integrates kinetic modeling approaches, analytical methods for monitoring depolymerization, and downstream separation considerations that govern monomer purity and recyclability. Key challenges, including energy demand, feedstock contamination, scalability, and economic competitiveness, are critically discussed in relation to industrial implementation. Overall, hydrolysis-based and microwave-assisted chemical recycling routes are the most viable pathways for closed-loop recycling of PA6. Future progress will rely on integrated reaction–separation–repolymerization designs, catalyst optimization, and process intensification to enable sustainable and industrially relevant PA6 circularity. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

Sentiment analysis of online game reviews has increasingly become important in understanding player experiences and supporting data-driven game development. However, research in this domain has continuously faced two unresolved challenges: (1) the extreme imbalance between positive and negative feedback, and (2) the inefficiency of existing feature-weighting schemes in capturing sentiment signals embedded in informal gaming discourses. Prior works demonstrated that negative feedback—though a few in number are highly influential—usually contain richer emotional content and longer textual structures; yet, prevailing classification models often perform poorly for these minorities (i.e., negative feedback). Numerous studies explored multimodal imbalance issues, class imbalance in cross-lingual ABSA (Aspect-Based Sentiment Analysis), reinforcement-learning-based architectures for imbalanced extraction tasks, and oversampling strategies like SMOTE (Synthetic Minority Over-sampling Technique) variants. Few investigations specifically addressed imbalanced sentiment classification in the contexts of online game reviews, where user-generated content exhibits unique lexical, structural, and emotional characteristics. To address these gaps, this study integrated TF-IDF (Term Frequency-Inverse Document Frequency), VADER (Valence Aware Dictionary and Sentiment Reasoner) lexicon features, and IGM (Inverse Gravity Moment) weightings with advanced oversampling methods such as ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) and Borderline-SMOTE to improve the detection of minority sentiment classes. Ensemble models, including XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient-Boosting Machine), were further employed to enhance the robustness of imbalance. Using a large-scale dataset of Steam game reviews, the proposed framework demonstrated substantial improvement in identifying negative sentiments, addressing a critical limitation in the existing computational game-analysis literature, and advancing the modeling for detecting the emotion-rich but imbalance-prone user feedback. Full article

Background: Undernutrition among children under five years remains a major public health challenge, particularly in low- and middle-income countries and rural communities where poverty, food insecurity, and limited access to health services persist. Maternal and caregiver perceptions play a critical role in shaping feeding practices and health-seeking behaviours that influence child nutritional outcomes. Objective: This study explored mothers’ and caregivers’ perceptions of factors contributing to undernutrition among children under five years in a rural community of Ngqeleni, Eastern Cape, South Africa. Methods: A qualitative descriptive study was conducted at a primary healthcare clinic in the Ngqeleni sub-district. Purposive sampling was used to recruit mothers and caregivers of children under five years. Data were collected through seven in-depth interviews and three focus group discussions involving a total of 25 participants. Interviews were conducted using a semi-structured guide and analyzed thematically. Results: Five major themes emerged: caregivers’ perceptions of nutrition, household food insecurity and unemployment, limited dietary diversity, culturally influenced feeding practices, and gaps in practical nutrition knowledge. Caregivers demonstrated concern for child nutrition but described constrained feeding choices shaped by poverty, reliance on social grants, environmental challenges, and limited access to diverse foods. Environmental challenges such as drought and lack of piped water further limited food production. Limited nutrition knowledge and reliance on informal information sources contributed to suboptimal feeding practices. Conclusions: Undernutrition in this rural setting is shaped by a complex interaction of economic hardship, environmental constraints, and limited caregiver knowledge. Community-based nutrition education, strengthened primary healthcare counselling, and multisectoral interventions addressing poverty, water access, and food security are essential to improve child nutrition outcomes. Full article

Objective: Village Health Volunteers (VHVs) are vital to Thailand’s primary healthcare, yet many face high risks for non-communicable diseases (NCDs). This preliminary study aimed to implement health empowerment theory-based personalized health promotion for individuals in the NCD-risk group. Methods: The preliminary mixed-methods study implemented a 6-month empowerment-based health promotion program for 21 VHV leaders (mean age 62.43 ± 7.28 years) at risk for NCDs. The intervention integrated laboratory data, behavioral and qualitative focus-group insights, and quantitative anthropometric data obtained via bioelectrical impedance analysis (BIA). Results: Participants’ exercise adequacy significantly improved after the intervention, increasing from 8.3% to 61.9% (p = 0.03). BIA revealed a physiological shift toward improved energy homeostasis, including decreased body weight, reduced visceral fat area, and increased muscle hydration. While biochemical markers did not reach statistical significance, clinically favorable downward trends were observed in median HbA1c (8.0% to 7.3%) and LDL cholesterol (141.8 to 119.0 mg/dL), alongside stable renal and liver function. Qualitative thematic analysis identified four primary domains of impact: sustainability and systemic advocacy, personal transformation, broad competence acquisition, and enhanced social capital. Participants reported a marked increase in self-efficacy, transitioning from inactive beneficiaries to active health advocates. This change was largely driven by mastery experiences, such as visible improvements in body composition and functional health literacy. Conclusions: The empowerment program significantly improved physical activity and body composition while fostering the social capital and health literacy necessary for community leadership, suggesting that personal health mastery is a critical precursor to effective systemic advocacy and long-term sustainability in community-led health programs. Full article

Hydrogen is a carbon-free energy carrier that can support decarbonization of the energy and transport systems. Its usage as a fuel in internal combustion engines can abate the pollutants and CO₂ emissions but also presents various challenges. Among these, the formation of under-expanded jets requires proper injector design and accurate control of the injection process. CFD can accelerate the development of hydrogen engine technologies towards market readiness. Low-dissipative density-based schemes are essential to accurately describe the complex flow structures, that affect mixture formation in under-expanded injections. In the present work, the AUSM scheme was implemented in the OpenFOAM library, and successfully used to simulate an experimental hydrogen-into-nitrogen injection. The numerical method, validated against experimental Schlieren images, was compared with the Kurganov–Noelle–Petrova scheme implemented in the current density-based OpenFOAM solver. The numerical results highlighted the reduced dissipation of the AUSM scheme, leading to improved jet penetration and gas mixing. The investigation demonstrated the superior performance of the AUSM scheme, suggesting it as an alternative OpenFOAM solver. Nevertheless, the study identified areas for improvement and critical issues associated with this type of simulations. Full article

Background: Schizophrenia in adolescence disrupts neurodevelopment and long-term functioning. While symptom reduction remains a primary treatment goal, quality of life (QoL) represents a critical, patient-centered outcome. Pharmacogenetic variability, particularly in CYP2D6 metabolism of second-generation antipsychotics, may influence tolerability and subjective well-being beyond symptom control. Materials and Methods: Forty-seven adolescents (aged 14–18 years) diagnosed with schizophrenia (DSM-5) were followed in routine clinical care. CYP2D6 genotyping classified patients as normal metabolizers (NM, n = 27) or reduced-function metabolizers (RFM, including intermediate/poor, n = 20). Symptom severity was assessed with PANSS, QoL was assessed with the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q), and adverse effects (hyperprolactinemia, extrapyramidal symptoms, sedation, metabolic changes) were monitored. Non-parametric tests and multiple linear regression were applied. Results: At 12 months, RFM patients showed significantly higher PANSS scores, markedly more adverse reactions (95% vs. 48.1%), and lower PQ-LES-Q total and domain scores (all p < 0.0001) compared to NM patients. A regression analysis identified the metabolizer status (β = −0.410, p = 0.001), extrapyramidal symptoms (β = −0.248, p = 0.003), sedation (β = −0.193, p = 0.029), and hyperprolactinemia (β = −0.190, p = 0.012) as independent predictors of a reduced QoL, explaining 84% of the variance. The residual symptom severity was not independently associated. Conclusions: In adolescent schizophrenia, the CYP2D6-reduced metabolizer status is the strongest independent predictor of long-term QoL impairment, associated primarily through a substantially higher burden of treatment-related adverse effects (metabolic, endocrine, neurological, and sedative) rather than through persistence of psychotic symptoms alone. These findings support early pharmacogenetic testing to guide individualized dosing and improve tolerability and patient-reported outcomes. Full article

Hydrocarbon-dependent economies face a critical challenge: sustaining economic growth while reducing carbon emissions. This study examines whether structural energy transition has begun to weaken the growth–emissions relationship in four Gulf Cooperation Council (GCC) economies: Saudi Arabia, United Arab Emirates, Qatar, and Bahrain, over the period 2008–2023. The analysis integrates three complementary approaches: Tapio elasticity-based decoupling analysis, a composite Energy Transition Performance Index (ETPI), and fixed-effects panel regression. This multi-method framework distinguishes between short-term cyclical decoupling and longer-term structural transition dynamics. The results show that strong decoupling is concentrated during crisis periods (2009 and 2020), indicating that emissions reductions are often cyclical rather than structural. More consistent, though moderate, weak decoupling emerges after 2015, coinciding with gradual improvements in renewable energy adoption and carbon efficiency. However, persistent fossil fuel dependence and rising electricity demand continue to constrain bigger structural change. The ETPI reveals significant cross-country variation, with the UAE demonstrating relatively stronger transition performance. Panel regression results indicate that renewable energy expansion is associated with lower carbon intensity, but its impact remains constrained by fossil-based energy systems and demand-side pressures. Overall, the findings suggest that energy transition in GCC economies is progressing but remains partial and uneven, requiring deeper structural reforms to achieve sustained decoupling. Full article

Objective biomarkers for neurodevelopmental disorders remain an unmet clinical need. The electroretinogram (ERG), a non-invasive recording of the retinal response to light, has shown promise as a physiological marker for autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD), yet existing classification approaches based on time-domain and time–frequency features achieve limited accuracy in clinically relevant multi-group scenarios. This study introduces ERG-Graph, a novel graph signal processing (GSP) framework that transforms each ERG waveform into a weighted, undirected graph through amplitude quantization and temporal-adjacency connectivity. Nine topological and spectral features, including total load centrality, clique number, algebraic connectivity, and clustering coefficient, were extracted from each graph to characterize the structural dynamics of the signal. Using light-adapted ERG recordings from 278 participants (ASD = 77, ADHD = 43, ASD + ADHD = 21, Control = 137), we evaluated these features across binary, three-group, and four-group classification scenarios using seven machine learning classifiers with 10-fold subject-wise cross-validation. The proposed ERG-Graph features achieved balanced accuracies of 0.91 (ASD vs. control, males) and 0.88 (ADHD vs. control, females). Critically, fusing ERG-Graph with time-domain features yielded a balanced accuracy of 0.81 for three-group classification (ASD vs. ADHD vs. control), representing an 11-percentage-point improvement over the previous benchmark of 0.70. Statistical analysis confirmed significant topological differences between groups (Kruskal–Wallis, p < 0.001; Cliff’s delta: large effect sizes), and SHAP analysis revealed that graph-theoretic features dominated the top-ranked predictors. These results demonstrate that graph-based topological features capture discriminative information in the ERG waveform that is inaccessible to conventional signal analysis methods, advancing the development of objective biomarkers for neurodevelopmental disorder screening. Full article

Background/Objectives: The objective of this scoping review was to map and critically describe emerging speech-in-noise assessment tools developed over the last decade for the evaluation of hearing loss beyond conventional audiological measures. Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive literature search was performed in the PubMed/MEDLINE, Scopus, and Embase databases. A comprehensive review of studies describing novel or emerging SIN-based assessment tools was conducted, with a particular emphasis on those including adult participants with normal hearing and hearing loss. Results: Nine studies met the inclusion criteria and were included in the review. The identified tools cover a range of methodological innovations, including advanced digits-in-noise paradigms, antiphasic and binaural presentation modes, optimized adaptive procedures, and digital or automated testing platforms. Several studies also incorporated artificial intelligence-based approaches, such as machine learning, text-to-speech, and automatic speech recognition, to enhance test development, administration, and hearing loss classification. Across all studies, SIN measures demonstrated the ability to reliably differentiate between normal hearing listeners and individuals with hearing loss and to provide complementary information beyond pure-tone audiometry. Conclusions: Emerging speech-in-noise tools show considerable potential to improve the functional assessment of hearing loss and to support more sensitive, accessible, and scalable approaches for hearing evaluation. Further research is required to assess their clinical integration and long-term impact on hearing screening and diagnostic pathways. Full article

Background: This narrative review introduces the “From Plaque to Perfusion” framework, a clinically pragmatic approach that maps multimodality imaging technologies to critical decision points in the acute coronary syndrome (ACS) patient journey. By integrating non-invasive assessment, invasive procedural guidance, and post-event tissue characterisation, this framework provides a structured pathway for deep phenotyping of ACS. Artificial intelligence (AI) is highlighted as an essential enabling layer that enhances diagnostic precision, automates quantification, and supports scalable, data-driven care. Contemporary ACS management pathways, while effective, often leave residual clinical uncertainty. The diagnostic objective has evolved beyond confirming myocardial injury to comprehensively phenotyping the entire ACS cascade: defining the plaque substrate, identifying the culprit mechanism, and quantifying the myocardial consequence. This requires a systematic integration of advanced imaging modalities. Methods: This narrative review is based on a comprehensive literature search of major medical databases (PubMed/MEDLINE, Scopus, Embase, Google Scholar) for high-level evidence, including randomized controlled trials, meta-analyses, and international expert consensus documents published between January 2010 and February 2026. Results: The “From Plaque to Perfusion” framework consists of three core stages. First, non-invasive assessment with coronary computed tomography angiography (CCTA), fractional flow reserve (FFR-CT), and PET-CT defines plaque substrate and vascular inflammation. Second, invasive precision in the catheterization laboratory, guided by optical coherence tomography (OCT) and intravascular ultrasound (IVUS), resolves the culprit mechanism and optimizes percutaneous coronary intervention (PCI). Third, post-event tissue characterization with cardiac magnetic resonance (CMR) quantifies myocardial injury and refines prognosis. AI-driven platforms are shown to enhance each stage by automating analysis, standardizing interpretation, and providing actionable metrics for clinical decisions, including complex scenarios like Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA). Conclusions: The “From Plaque to Perfusion” framework, enabled by AI, reframes ACS imaging as an integrated, mechanism-driven pathway. This approach moves beyond isolated test interpretation toward a scalable model of precision, phenotype-led care that promises to improve diagnostic certainty and personalize patient management. Full article

Background/Objectives: Critical limits define quantitative thresholds for life-threatening diagnostic test results that require immediate clinician notification and may prompt urgent intervention to prevent adverse outcomes. This study aims to (1) characterize point-of-care (POC) critical limits for adults and newborns using a comprehensive U.S. national database, (2) identify POC instruments associated with these limits, and (3) support harmonization of point-of-care testing (POCT) practices. Methods: We gathered critical limit notification lists from 417 hospitals across all 50 states and Washington D.C., comprising university hospitals, trauma and heart centers, centers of excellence, community hospitals, and network hospitals. We extracted POC and central laboratory critical limits (at hospitals with POC), adult international normalized ratio (INR) data, and instrument usage. Results: Low and high glucose critical limits were the most frequently listed POC thresholds, with median values of 50 and 450 mg/dL, respectively, reported by 73 hospitals (17.5%). Troponin was listed by ten hospitals, specified as troponin (n = 4), troponin I (n = 5), or “troponin TnI” (n = 1). A few hospitals assigned instrument-specific critical limits for the same analyte, and 55 hospitals did not specify instrument usage for any measurand. Median differences in matched pairs of laboratory versus POC critical limits differed significantly (Wilcoxon signed-rank, p < 0.05) for low and high ionized calcium (n = 21), low hemoglobin (n = 23) and high INR critical limits for adults (n = 27) and newborns (n = 10). In some cases, matched pair analytes demonstrated identical critical limits. Conclusions: Harmonizing critical limit notification thresholds across point-of-care testing and different devices may improve consistency in clinical decision-making and patient outcomes. Despite the potential of POCT to shorten time to urgent intervention, relatively few hospitals currently include POCT critical limits on notification lists. Establishing standards, annual updating, and enforcing risk mitigation could enhance adoption and reliability. Broader inclusion and transparent sharing of POCT critical values could harmonize practices across institutions, facilitate inter-institutional collaboration, and promote more timely and reliable responses to life-threatening diagnostic results. Full article

Mycotoxin contamination remains a persistent threat to food safety in the Democratic Republic of the Congo (DRC) and neighboring countries, driven by conducive tropical agroecological conditions, inadequate post-harvest practices, and limited regulatory governance. This critical narrative review (2009–2024) synthesizes the occurrence data for major staple foods (maize, peanuts, cassava, sorghum, millet, and beans) and dairy products compiled from Google Scholar, ScienceDirect, MDPI and institutional sources. It examines the co-occurrence patterns, exposure pathways, and analytical and regulatory gaps. Warm, humid lowland environments favor Aspergillus and aflatoxins, whereas cooler, humid highland zones promote Fusarium, fumonisins, and deoxynivalenol. Across commodities, contamination intensifies along food value chains through inadequate drying, non-hermetic storage, insect damage, and prolonged handling, with processed products generally exhibiting the highest levels of mycotoxins. Regulated mycotoxins, including aflatoxins, fumonisins, trichothecenes, ochratoxins, and zearalenone, frequently exceed European Union (EU), East African Community (EAC), and Codex Alimentarius Commission (CAC) limits in staple foods. Their co-occurrence is widespread, including emerging mycotoxins such as beauvericin and enniatins, particularly in maize- and peanut-based products, raising concerns about potential additive or synergistic effects. Aflatoxin M1 in milk highlights plant–feed–animal–human transfer within a One Health framework. Despite increasing evidence, the available data remain fragmented and heterogeneous; rapid tests dominate, while few studies employ multi-mycotoxin LC-MS/MS methods. Cross-border trade between countries, such as Uganda, Tanzania, Zambia and Angola, facilitates the circulation of contaminated commodities in the absence of harmonized standards and risk-based controls. Priorities include harmonized regional surveillance, biomarker-based co-exposure assessment, cost-effectiveness evaluation of mitigation strategies, and regulatory alignment at borders. Coordinated, multisectoral action is essential to reduce chronic dietary exposure and improve food safety across the region. Full article

Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article

The soundscape serves as a critical determinant of the quality of urban wetland parks. This study employs a mixed-methods approach to comprehensively evaluate wetland soundscapes. First, field investigations combining sound level measurements and questionnaire surveys were conducted in Aixi Lake Wetland Park to analyze the spatiotemporal characteristics of the soundscape. Second, laboratory-based physiological tracking (using wearable sensors) and cognitive tests (Sustained Attention to Response Task, SART) were utilized to experimentally quantify the restorative benefits of typical soundscapes. The findings reveal that: (1) sound level indicators and sound harmonious degree in urban wetland parks exhibit significant spatiotemporal characteristics and distributional variations; (2) a marked competitive effect among biological, geophysical, and human activity sounds is observed in their spatial distribution; sound harmonious degree demonstrates significant spatial autocorrelation in both global and local models; (3) different sound sources possess varying restorative potentials, with bird song showing the highest restorative effect; the SHDs of biological and geophony, along with L_Aeq, are key factors affecting PRSS; (4) a positive correlation exists between L_Aeq and the PRSS up to 56.4 dB, beyond which PRSS declines with increasing L_Aeq; (5) at the physiological level, short-term exposure to urban wetland park soundscapes can rapidly alleviate stress, with the most pronounced restorative effects occurring within the first 60 s; and (6) in terms of attention, soundscape stimulation reduces SART response times and improves response speed, while bird song from treetops and musical sounds further decrease response errors. Full article