Search Results (33,124)

Background: Sensors and mass spectrometry (MS) are frequently used in combination for food safety and quality assessment, yet their functional integration lacks a formal methodological framework. This review categorizes the synergies between these technologies into distinct Relational Connections. Methodology: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 155 original research articles published between 2015 and 2025 were systematically analyzed. Records were identified via the Scopus database within the food science domain. Experimental meta-data, including extraction protocols, instrumental configurations (ionization source, mass analyzer, cost tier), and chemometric strategies, were extracted to identify core methodological patterns. Statistical associations were quantified using chi-squared tests with Cramer’s V effect sizes. Results: Five Relational Connections were identified: (1) MS as reference for sensor validation (25.2%); (2) MS-sensor correlative analysis (10.3%); (3) MS quantifying data to train predictive sensor models (6.5%); (4) MS identifying targets for sensor detection (7.1%); and (5) MS enabling sensor classification models (51.0%). Technology pairing is governed by a three-level hierarchy: analyte polarity determines the ionization source (V = 0.69), required precision determines the mass analyzer (V = 0.64), and cost/availability constraints shape the practical integration strategy. Gas Chromatography (GC)-MS is predominantly coupled with Electronic Noses for volatile profiling (86% of classification studies), while Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) pairs with biosensors for contaminant analysis (74% of reference validation studies). Systematic analysis of the full pairing matrix reveals that 75% of theoretically possible MS-sensor combinations remain unexplored or underrepresented, identifying both technical boundaries and innovation frontiers. Discussion: The findings clarify the strategic logic behind technology pairings, demonstrating that MS provides the quantitative molecular data required for sensor training. The hierarchical decision framework and identification of underexplored pairings provide an evidence-based guide for designing future integrated food analysis systems. Full article

Background: Quality-of-life (QoL) assessment has become an essential component of outcome evaluation in oral and maxillofacial surgery (OMFS), particularly in interventions with functional implications for breathing, sleep, and oro-facial performance. Existing instruments often lack specificity for the postoperative OMFS population. This study aimed to develop and psychometrically validate the OMFS-QoL-18 questionnaire, a condition-oriented patient-reported outcome measure designed for postoperative assessment. Methods: A cross-sectional validation study was conducted on 226 adult patients evaluated 6–12 months after orthognathic or function-oriented OMFS procedures. Internal consistency was assessed using Cronbach’s alpha, and reproducibility using the intraclass correlation coefficient (ICC) based on a two-way random-effects model with absolute agreement. The internal structure of the instrument was explored through an exploratory dimensionality analysis using Principal Component Analysis (PCA), including Kaiser–Meyer–Olkin (KMO) testing and Bartlett’s test of sphericity. Descriptive statistics were calculated for item and domain scores. Results: The OMFS-QoL-18 demonstrated good internal consistency (Cronbach’s α = 0.789; standardized α = 0.783) and satisfactory reproducibility (ICC = 0.81; 95% CI: 0.74–0.87). The exploratory dimensionality analysis suggested a multidimensional structure, with five components explaining 67.1% of the total variance. Item clustering was broadly consistent with the predefined conceptual domains, including respiratory comfort, sleep quality, daytime function, oro-maxillofacial function, and global satisfaction. Given the use of PCA as a component-based method, these findings are interpreted as preliminary evidence of dimensional organization rather than confirmation of latent constructs. Conclusions: The OMFS-QoL-18 demonstrated good internal consistency and preliminary evidence of a coherent factor structure. These findings support its use as a promising condition-specific instrument, pending further validation studies. Further multicenter and longitudinal validation studies are warranted to confirm structural stability and responsiveness over time. Full article

Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a critical review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil–structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role that LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future. Lastly, this paper highlights the different LLM capabilities which can be used to empower geotechnical engineers. Full article

University data governance is an essential requirement for the informatization of universities and holds significant importance in advancing the modernization of university governance systems and governance capabilities. This study focuses on the data governance policies released by “Double First-Class” universities in China since 2015. Based on policy text mining and the PMC index model, the paper developed an evaluation system for university data governance policies consisting of 9 primary indicators and 43 secondary indicators and conducted quantitative assessment. The results indicate that the policies are of good quality overall, with 25% rated as excellent, 66.1% as good, and 8.9% as moderate. Many universities have made significant progress in formulating data governance policies. However, there is still considerable room for improvement. For example, while the policy objectives are clearly defined, certain aspects require further refinement; the stakeholder involvement is relatively narrow, lacking diversity; and the mix of policy instruments is imbalanced. To address these issues, it is recommended that policies be optimized by balancing regulatory priorities, establishing a multi-stakeholder collaborative governance framework, and rationalizing the policy instruments mix. Full article

Background: Rome IV criteria promote a symptom-based (“positive”) diagnosis of pediatric disorders of gut–brain interaction (DGBIs). In clinical practice, however, organic gastrointestinal diseases may mimic DGBIs and lead to diagnostic revision after further evaluation. We aimed to quantify the diagnostic stability of an initial Rome IV-oriented functional diagnosis in a tertiary pediatric outpatient setting and to identify symptom phenotypes associated with a higher likelihood of later organic reclassification. Methods: We performed a single-center retrospective cohort study (2014–14 May 2021) based on outpatient chart review. Eligible patients were children and adolescents aged 0–18 years with an initial Rome IV-oriented functional diagnosis. Diagnostic reassessment was based on follow-up data, available laboratory and instrumental investigations, and/or response to exclusion therapies. Final diagnoses after reassessment were categorized as functional only, organic, or mixed. Groups were compared using Pearson’s chi-square test. Results: The cohort included 220 males (50.0%) and 220 females (50.0%), with a mean age of 8.86 ± 4.65 years. After reassessment, 343/440 (77.95%) remained functional, 73/440 (16.59%) were reclassified as organic, and 24/440 (5.45%) were classified as mixed. Final diagnosis differed by GI tract involvement (p = 0.001) and by symptom cluster (p = 0.001). Upper GI/dyspepsia-spectrum presentations showed the highest organic yield (27.03%), followed by lower abdominal pain/IBS-spectrum presentations (19.61%). Diarrhea and vomiting/cyclic vomiting each showed 16.67% organic diagnoses (mixed: 10.0% and 7.14%, respectively), whereas constipation showed the greatest diagnostic stability (98.89% functional; 1.11% organic). Functional confirmation rates were similar before and during the pandemic (77.71% vs. 78.70%; p = 0.756). Monthly case volume was higher in 2020–2021 (6.29 vs. 4.61 cases/month). Conclusions: In this tertiary cohort, about one in six children initially diagnosed with a functional disorder were later found to have an organic disease, and an additional 5% had mixed organic–functional presentations. Diagnostic revision was associated with presenting phenotype, with the highest organic yield observed in dyspepsia/upper GI presentations and the lowest in constipation. These findings support symptom-stratified evaluation and follow-up alongside Rome IV criteria. Full article

Against the backdrop of China’s “dual carbon” goals and rising global uncertainties, new energy technology innovation plays a critical role in advancing low-carbon transitions and ensuring energy security. However, existing studies mainly focus on single policy instruments, with limited attention to the causal effects of comprehensive, location-based policies. This study treats the establishment of National Green Industrial Parks (GIPs) as a quasi-natural experiment and employs a multi-period difference-in-differences (DID) approach based on panel data from 289 Chinese cities over 2008–2023. The results show that GIPs significantly increase local new energy innovation by approximately 19.1%, and this effect remains robust across multiple tests. Mechanism analysis indicates that fiscal support, green innovation, and industrial agglomeration are the main driving channels. Heterogeneity analysis further reveals stronger effects in the biomass (ρ = 0.243, p < 0.01) and wind energy (ρ = 0.179, p < 0.01) sectors, as well as in cities located southeast of the Hu Huanyong Line, with higher fiscal expenditure, and in non-resource-based cities. These findings provide empirical evidence for optimizing industrial park policies and promoting energy transition through localized policy diffusion. Full article

Background: Myocardial ischemia (MI) is a major cause of morbidity and mortality worldwide and requires timely and reliable detection. Although Spin-Exchange Relaxation-Free (SERF) magnetocardiography (MCG) provides femtotesla-level sensitivity for identifying non-linear cardiac repolarization anomalies, its clinical deployment is currently impeded by the computational bottlenecks inherent to portable edge platforms. Methods: We propose a “Sensor-to-Image” Edge-AI framework that links quantum sensing with computer vision. Single-channel SERF-MCG signals from a large cohort of 2118 subjects (1135 Healthy, 983 Ischemia) were transformed into phase-space images using three distinct encoding modalities: Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). These visual representations were subsequently analyzed by a streamlined MobileNetV3-Small architecture, optimized for low-latency inference. To maximize diagnostic precision, an adaptive weighted fusion mechanism was engineered to combine the chaotic specificity captured by RP with the morphological sensitivity of GASF through a validation-optimized fixed global weighting strategy. Results: In our experiments, the fusion model achieved an Area Under the Curve (AUC) of 0.865, which was higher than the 1D-CNN baseline (AUC 0.857) and the single-modality models. Notably, the fusion strategy significantly elevated sensitivity to 88.3% while maintaining a specificity of 66.5%. Although specificity is moderate, this trade-off prioritizes high sensitivity to minimize false negatives in pre-hospital screening scenarios. The average inference time was 4.7 ms per sample on a standard CPU, suggesting suitability for real-time Point-of-Care (PoC) scenarios under further on-device validation. Conclusions: The results suggest that multi-view phase-space fusion can capture subtle spatio-temporal changes associated with ischemia. The proposed lightweight framework may support the development of portable SERF-MCG systems with embedded AI screening. Full article

Background/Objectives: Digital media play an important role in the lives of contemporary adolescents. While associated with many benefits, they also pose risks to physical health related to prolonged screen time and non-ergonomic body posture. This study analyzed the frequency of self-reported physical complaints among Polish adolescents in relation to time spent on different screen-based activities. Methods: The study included 9083 students aged 13–17 who completed an online survey in March and April 2024 in schools located in western Poland (approximately 30% of the region’s student population). Physical symptoms selected from the HBSC-SCL instrument were analyzed and supplemented with neck or shoulder pain and eye strain. Results: Longer screen time was associated with more frequent occurrence of all analyzed complaints. A 5-item index ranging from 0 to 20 points was proposed, including headache, neck or shoulder pain, eye strain, dizziness, and problems falling asleep (mean 6.56 ± 5.15). The index showed reliability at the level of α = 0.744 and good model fit according to CFA (RMSEA = 0.025). In a multivariate linear regression model (R² = 0.153), after adjusting for age, gender, place of residence, and family affluence, the variability of this index was most strongly associated with time spent on social media (β = 0.40) and browsing websites (β = 0.30). Gender-specific models were also compared. Conclusions: The results confirm the co-occurrence of physical complaints during adolescence and a significant association between their severity and screen-based activities, particularly engagement in social media. Full article

Under the “dual carbon” strategic framework, the low-carbon transition of the logistics sector—a major source of carbon emissions in the national economy—has become imperative for achieving green development. The adoption of new-energy vehicles (NEVs) represents a critical pathway for decarbonizing logistics operations. Initiated in 2009, China’s “Ten Cities, Thousand Vehicles” Demonstration Project served as a pioneering policy to accelerate NEV deployment, offering a valuable use case for reducing emissions in urban logistics. Using this initiative as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) approach and panel data from 275 Chinese prefecture-level cities (2000–2021) to evaluate the causal effect of the policy on urban logistics CO₂ emissions. The robustness of the findings is confirmed through parallel trend tests, placebo tests with reassigned treatment timing, alternative dependent variable construction, and instrumental variable estimation. Mechanism and heterogeneity analyses are further conducted to uncover underlying channels and contextual variations. The results indicate a statistically significant reduction in logistics carbon emissions in pilot cities, which remains consistent across multiple robustness checks. Mediation analysis reveals that the policy effect is partially transmitted through increased NEV stock. Moreover, the emission reduction effect is more pronounced in cities with lower logistics dependency and non-consumer-oriented economic structures, while it is weaker in consumer and highly logistics-dependent cities. These findings confirm the sustainable contribution of early NEV policies through advancing the transition to low-carbon logistics and supporting dual carbon goals, fill the empirical gap in developing countries’ freight decarbonization, and offer actionable insights for targeted regional sustainable logistics strategies. Full article

Background/Objectives: Testicular cancer is one of the most common malignancies affecting young adult males. With the rise in artificial intelligence (AI) platforms, many patients seek health information online. Yet chatbot responses specific to testicular cancer remain unassessed. This study aims to evaluate the role of AI chatbots in providing patient information about testicular cancer in terms of its quality, readability and actionability. Methods: Fourteen frequently asked questions about testicular cancer were identified using Google Trends and the Cancer Council Australia website. Questions were then inputted into four different publicly accessible AI platforms: ChatSonic, Bing AI, ChatGPT 4.0 and Perplexity. Chatbot responses were recorded and evaluated using three validated instruments: DISCERN (1–5), Patient Education Materials Assessment Tool (PEMAT)-Understandability and Actionability (0–100%) and Flesch-Kincaid readability scores. Results: All platforms scored low on the DISCERN score with a median of 1 (interquartile range [IQR] 1–4). The median readability score was 34.1 (IQR 26.0–52.2), indicating a reading level suitable for college students. The median word count was 61.5 (IQR, 41.3–91.3). The overall PEMAT-Understandability was moderate (median 58.3, 50.0–66.7), whilst the PEMAT-Actionability was very poor (median 0, IQR 0–25). Conclusions: AI chatbots deliver moderately understandable information on testicular cancer, but this information is typically not actionable and is delivered at an above-average reading level. Despite this, patients may continue to use AI chatbots (AICs) to access health information. It is important that clinicians counsel patients on the benefits and downfalls of this strategy, advocating for the use of AICs as an adjunct rather than a replacement for clinician-led education. Full article

Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of nine sample categories were collected from three mining areas, with categories defined by their U concentration levels within each origin. Standard normal variate (SNV), Savitzky–Golay smoothing (SG), and their combinations (SNV-SG, SG-SNV) were applied to evaluate preprocessing effects. To address ultra-high-dimensional spectral data (49,242 points per spectrum), principal component analysis (PCA) and random forest (RF) were employed for feature engineering, integrated with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) classifiers. Hyperparameter optimization via five-fold cross-validation and Bayesian optimization enhanced accuracy and efficiency. RF-based hybrid models consistently outperformed PCA-based counterparts. Remarkably, the RF-LDA model with SNV-SG preprocessing achieved 100% classification accuracy across all test sets with a processing time of only 10.46 s, demonstrating exceptional discriminative power and computational efficiency. These findings establish that combining RF feature selection with advanced machine learning offers a robust solution for LIBS-based nuclear material classification, with significant implications for both nuclear safety and resource management. Full article

This study examines how Vietnam regulates overseas study and how this regulatory structure shapes international student mobility to South Korea. Through a qualitative analysis of key legal and policy instruments, especially Decree No. 86/2021/ND-CP, it finds that Vietnam governs overseas study through a centralized legal-administrative system that structures eligibility, student management, intermediary oversight, and return obligations. It also finds that important implementation gaps persist, particularly in relation to private intermediaries, monitoring capacity, and the gap between formal regulation and students’ actual mobility trajectories. These findings suggest that receiving countries such as South Korea should pay closer attention to the pre-departure institutional conditions that influence student mobility before arrival. The study contributes by providing a legally grounded account of how sending-state regulation operates in the Vietnamese case and why pre-departure institutional conditions matter for receiving-country contexts such as South Korea. Full article

The sustainable enhancement of regional science and technology industrial competitiveness is an important objective of artificial intelligence (AI) policy. However, how different AI policy instruments can be combined to achieve this goal remains insufficiently understood. This study aims to address this issue by identifying the configurational pathways through which combinations of AI policy instruments contribute to the sustainable enhancement of regional science and technology industrial competitiveness. Based on a policy instrument framework, we analyze AI policies issued by provincial-level governments in China and apply fuzzy-set qualitative comparative analysis (fsQCA), which is appropriate for examining the combined effects of multiple policy instruments. The results show that no single policy instrument is sufficient to produce high regional science and technology industrial competitiveness. Instead, sustained competitiveness is achieved through multiple equivalent configurations of policy instruments. Three driving pathways are identified—(supply and demand)-environmental resonance, demand-driven (supply-environmental) assurance, and supply–demand complementarity—covering five specific configurations. The variation across configurations indicates that effective AI policy mixes are contingent on regional resource endowments and development conditions. Technology R&D support, talent cultivation and collaboration, and application demonstration and promotion emerge as the most recurrent core conditions across configurations. Accordingly, local governments should develop coordinated AI policy mixes, align differentiated policy pathways with regional conditions, and prioritize technology R&D support, talent cultivation and collaboration, and application demonstration and promotion to sustain long-term regional competitiveness. Full article

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives. Full article