Search Results (132)

(1) Background: Rapid population aging in China intensifies physical and mental health challenges, including negative emotions and social barriers. Physical activity (PA) fosters resilience, adaptability, and successful aging through emotional and social benefits. This study examines the relationship between square-dancing exercise and psychological capital (PsyCap) in middle-aged and elderly individuals using cross-validation, subgroup analysis, and a cross-sequential design. (2) Methods: A cross-sectional study with 5714 participants employed a serial mediation model. Online questionnaires assessed square-dancing exercise, cognitive reappraisal, prosocial behavior tendencies, PsyCap, and interpersonal relationships. Statistical analyses were conducted using SPSS 27.0 and Mplus 8.3, incorporating correlation analysis, structural equation modeling, and subgroup comparisons. (3) Results: (a) Cognitive reappraisal and prosocial behavior mediated the link between square-dancing and PsyCap through three pathways; (b) model stability was confirmed across two random subsamples; (c) cross-group differences emerged in age and interpersonal relationships. Compared with secondary data, this study further validated PsyCap’s stability over six months post-pandemic. (4) Conclusions: The study, based on China’s largest square-dancing sample, establishes a robust serial mediation model. The findings strengthen theoretical foundations for PA-based interventions promoting psychological resilience in aging populations, highlighting structured exercise’s role in mental and social well-being. Full article

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

Background: Blackberry seed oil (BSO), obtained from Rubus spp. Xavante cultivar via supercritical CO₂ extraction, contains bioactive lipids and antioxidants, but its cosmetic application is limited by poor solubility and stability. Nanoencapsulation with poly(ε-caprolactone) (PCL) can overcome these limitations. Methods: BSO was characterized by Ultra-High-Performance Liquid Chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry and incorporated into PCL nanocapsules (NCBSO) using the preformed polymer deposition method. Physicochemical properties, stability (at 4 °C, room temperature, and 37 °C for 90 days), cytotoxicity, and collagen production were assessed in human fibroblasts. Additionally, a predictive in silico analysis using PASS Online, Molinspiration, and SEA platforms was performed to identify the bioactivities of major BSO compounds related to collagen synthesis, antioxidant potential, and anti-aging effects. Results: NCBSO showed a nanometric size of ~267 nm, low polydispersity (PDI < 0.2), negative zeta potential (−28 mV), and spherical morphology confirmed by FE-SEM. The dispersion remained stable across all tested temperatures, preserving pH and colloidal properties. In particular, BSO and NCBSO at 100 µg.mL⁻¹ significantly enhanced in vitro collagen production by 170% and 200%, respectively, compared to untreated cells (p < 0.01). Superior bioactivity was observed for NCBSO. The in silico results support the role of key compounds in promoting collagen biosynthesis and protecting skin structure. No cytotoxic effects were achieved. Conclusions: The nanoencapsulation of BSO into PCL nanocapsules ensured formulation stability and potentiated collagen production. These findings support the potential of NCBSO as a promising candidate for future development as a collagen-boosting cosmeceutical. Full article

Driven by the environmental benefits associated with reduced greenhouse gas emissions, oil companies have intensified research efforts into reassessing the strategies used to meet the electrical demands of offshore production platforms. Among the various alternatives available, the deployment of onshore–offshore interconnections via High-Voltage Direct Current (HVDC) transmission systems has emerged as a promising solution, offering both economic and operational advantages. In addition to reliably meeting the electrical demand of offshore facilities, this approach enables enhanced operational flexibility due to the advanced control and regulation capabilities inherent to HVDC converter stations. Based on the use of interconnection through an HVDC link, aiming to evaluate the operation of the electrical system as a whole, this study focuses on evaluating dynamic events using the PSCAD software version 5.0.2 to analyze the direct online starting of a large induction motor and the sudden loss of a local synchronous generating unit. The simulation results are then analyzed to assess the effectiveness of both Grid-Following (GFL) and Grid-Forming (GFM) control strategies for the converters, while the synchronous generators are evaluated under both voltage regulation and constant power factor control operation, with a particular focus on system stability and restoration of normal operating conditions in the sequence of events. Full article

Optimizing manufacturing processes to reduce scrap and enhance process stability presents significant challenges, particularly when multiple conflicting objectives must be addressed concurrently. As the number of objectives increases, the complexity of the optimization task escalates. This difficulty is further intensified in online optimization scenarios, where optimal parameter settings must be delivered in real time within active production environments. In this work, we propose a reinforcement learning-based framework for the multi-objective optimization of manufacturing parameters, demonstrated through a case study on pinion gear manufacturing. The framework utilizes the Multi-Objective Maximum a Posteriori Optimization (MO-MPO) algorithm to train a reinforcement learning agent. A high-fidelity simulation of the pinion manufacturing process is constructed in Simufact, serving both data generation and validation purposes. The agent’s performance is assessed using a hold-out test set along with additional simulations of the physical process. To ensure the generalizability of the approach, further validation is performed using open-source manufacturing datasets and synthetically generated data. The results demonstrate the feasibility of the proposed method for real-time industrial deployment. Moreover, Pareto-optimality is verified via half-space analysis, emphasizing the framework’s effectiveness in managing trade-offs among competing objectives. Full article

Pulverized coal mass concentration in the primary air pipe is one of the essential parameters for promoting furnace combustion efficiency. However, attaining accurate, real-time, and online detection for pulverized coal mass concentration remains challenging due to factors such as large pipe diameter and high flow rate. This study introduces a quasi-open microwave resonant cavity sensor. The principle and model were analyzed using the perturbation method, and the design and optimization were conducted with the simulation. A prototype and its test system were constructed, and the test results demonstrated good agreement between the simulations and experiments. The simulation revealed that the resonant frequency decreased monotonically from 861 to 644 MHz as mass concentration increased within 20%~80%, resulting in a change of about 3.62 MHz/1% under static mixture. The resonant frequency showed a drop from 21 MHz to 9 MHz with an increase in mass concentration under pulverized coal flow. Prediction models were developed and validated, showing the absolute values of the relative errors to be within 4% under operational scenarios. Additionally, the impact of the sensor on pulverized coal flow was evaluated, and it was found that the sensor structure had minimal impact on the flow in terms of velocity and the distribution of continuous flow. Finally, the long-term stability was assessed by examining the wear of the antennas and barriers. With inner barriers experiencing up to 2/3d wear, the resonant frequency drift ratio remained below 1.5%, corresponding to a mass concentration deviation of less than 3.2%. Full article

The increasing penetration of renewable energy sources presents significant challenges for power system stability and operation. Accurately assessing renewable energy absorption capacity is essential to ensuring grid reliability while maximizing renewable integration. This paper proposes a security-constrained sequential production simulation (SPS) framework, which incorporates grid voltage and frequency support constraints to provide a more realistic evaluation of renewable energy absorption capability. Additionally, hierarchical clustering (HC) based on dynamic time warping (DTW) and min-max linkage is employed for temporal aggregation (TA), significantly reducing computational complexity while preserving key system characteristics. A case study on the IEEE 39-bus system, integrating wind and photovoltaic generation alongside high-voltage direct current (HVDC) transmission, demonstrates the effectiveness of the proposed approach. The results show that the security-constrained SPS successfully prevents overvoltage and frequency deviations by bringing additional conventional units online. The study also highlights that increasing grid demand, both locally and through HVDC export, enhances renewable energy absorption, though adequate grid support remains crucial. Full article

The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. Additionally, it addresses the control of dynamic constraints, such as voltage fluctuations at network nodes. A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. The algorithms were validated through simulations conducted on the IEEE 118-bus system, which included five case studies. Real datasets were used for both training and testing to enhance the algorithm’s practical relevance. The developed tool is versatile and applicable to power networks of varying sizes and load characteristics. Furthermore, the potential of RL for real-time applications was assessed, demonstrating its adaptability to online grid operations. This research represents a significant advancement in leveraging machine learning to improve the efficiency and stability of modern electrical grids. Full article

In the contemporary global educational environment, the automatic assessment of students’ online engagement has garnered widespread attention. A substantial number of studies have demonstrated that facial expressions are a crucial indicator for measuring engagement. However, due to the asymmetry inherent in facial expressions and the varying degrees of deviation of students’ faces from a camera, significant challenges have been posed to accurate emotion recognition in the online learning environment. To address these challenges, this work proposes a novel VGG-SwishNet model, which is based on the VGG-16 model and aims to enhance the recognition ability of asymmetric facial expressions, thereby improving the reliability of student engagement assessment in online education. The Swish activation function is introduced into the model due to its smoothness and self-gating mechanism. Its smoothness aids in stabilizing gradient updates during backpropagation and facilitates better handling of minor variations in input data. This enables the model to more effectively capture subtle differences and asymmetric variations in facial expressions. Additionally, the self-gating mechanism allows the function to automatically adjust its degree of nonlinearity. This helps the model to learn more effective asymmetric feature representations and mitigates the vanishing gradient problem to some extent. Subsequently, this model was applied to the assessment of engagement and provided a visualization of the results. In terms of performance, the proposed method achieved high recognition accuracy on the JAFFE, KDEF, and CK+ datasets. Specifically, under 80–20% and 10-fold cross-validation (CV) scenarios, the recognition accuracy exceeded 95%. According to the obtained results, the proposed approach demonstrates higher accuracy and robust stability. Full article

Background: This study presents a sensitive method for the simultaneous determination of organic acids, flavonoids, and amino acids in Mume Fructus (MF) using ultra-performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (UPLC-QTRAP-MS/MS). Methods: Analysis was performed on a UPLC system (Shimadzu, Kyoto, Japan) equipped with a quaternary pump solvent management system, an online degasser, a triple-quadrupole mass detector, and an autosampler. An Agilent ZORBAX SB-C18 column (3.0 mm × 100 mm, 1.8 µm) was used for chromatographic analyses. The mobile phase was distributed between 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a velocity of 0.2 mL/min. The gradient evolution protocol was 0–2 min at 90–70% B; 3–7 min at 70–50% B; 7–10 min at 50–20% B; 10–14.5 min at 20–90% B; and 14.5–17 min at 10% B. Results: The method was validated for matrix effects, linearity, limits of detection/quantification, precision, repeatability, stability, and recovery of target components. It effectively determined all target compounds in 12 MF batches from different drying methods. Conclusions: Principal component analysis (PCA) of 47 active components was conducted to evaluate MF quality comprehensively. The proposed method serves as a reliable approach for assessing the consistency of MF’s quality and therapeutic efficacy. Full article

Food safety is a fundamental component of public health, economic stability, and consumer confidence. In Portugal, the National Integrated Multiannual Control Plan (NIMCP) serves as a strategic framework for ensuring food safety and compliance with European Union food regulations. However, challenges persist in policy implementation and enforcement, as well as in stakeholder engagement, which impact the effectiveness of food safety governance. This study employs a mixed-methods approach to assess stakeholder perceptions of the NIMCP, focusing on levels of compliance, barriers to its implementation, and potential improvement measures. A structured online survey was conducted with 217 stakeholders, including representatives of public institutions, private entities, associations, and consumer groups. The survey assessed the perceived importance of the NIMCP objectives and levels of compliance and identified barriers, such as a lack of communication between public entities, the dispersion of responsible agencies, and insufficient dissemination of information. The results indicate that stakeholders perceive a satisfactory level of compliance with the NIMCP objectives, especially in areas such as animal health and risk control. However, challenges persist in ensuring plant health and implementing official controls. Furthermore, stakeholders highlight systemic inefficiencies and resource constraints. The main barriers include fragmented governance structures, limited inter-agency collaboration, and insufficient professional training. Stakeholders proposed various improvement measures, emphasizing the need for better coordination, planning, and communication, including awareness campaigns for operators, the creation of an integrated IT network, and the development of training programs. The Analytical Hierarchy Process (AHP) revealed that risk control and consumer protection are top priorities for all stakeholder groups, while plant and animal health receive lower priority. The study concludes that while the NIMCP is generally perceived as effective, addressing systemic issues such as coordination, communication, and resource allocation is essential to improving food safety governance. Policymakers are encouraged to adopt a more structured and integrated approach to improve implementation of the NIMCP, ultimately strengthening public health protection and consumer confidence in the food supply chain. Full article

Objective measurement of pilot performance has long been a research challenge. This study introduces a new composite score that combines various flight metrics, along with its visual representation through an online application. Thirty general aviation pilots completed flight simulator scenarios under different Flight Rules (VFR: Visual Flight Rules vs. IFR: Instrument Flight Rules) and difficulty levels (Low vs. High). Workload was assessed using subjective and objective indicators. The composite score was developed using flight parameter compliance, approach stability, and landing quality. Workload indicators confirmed the scenario difficulties, showing significant increases under IFR compared to VFR and in High vs. Low difficulty conditions. As predicted by multiple resources theory, the composite score correlated negatively with workload, particularly in IFR conditions, demonstrating its effectiveness in assessing pilot performance. In a follow-up questionnaire, pilots rated the online application positively, highlighting its usefulness in understanding their performance and recognizing its potential for pilot training. Full article

Aimed at increasingly challenging operation conditions in modern power systems, online pre-fault transient stability assessment (TSA) acts as a significant tool to detect latent stability risks and provide abundant generator-level information for preventive controls. Distinguished from “system-level” to describe terms concerning the whole system, here “generator-level” describes those concerning a generator. Due to poor topology-related expressive power, existing deep learning-based TSA methods can hardly predict generator-level stability indexes, unless they adopt the generator dynamics during and after faults by time-domain simulation (TDS) as the model input. This makes it difficult to fully leverage the speed advantages of deep learning. In this paper, we propose a generator-level TSA (GTSA) scheme based on topology-oriented graph deep learning which no longer requires time-domain simulation to provide the dynamic features. It integrates two modules to extract the network-dominated interaction trends from only the steady-state information. A sparse Edge Contraction-based Attention Pooling (ECAP) scheme is designed to dynamically simplify the network structure by feature aggregation, where the generator-specific information and key area features are kept. A Global Attention Pooling (GAP) module works to generate the interaction features among generators across the system. Hence, the constructed ECAP&GAP-GTSA scheme can not only output the system stability category but also provide the dominant generators and inter-generator oscillation severity. The performance as well as interpretability and generalization of our scheme are validated on the IEEE 39-bus system and the IEEE 300-bus system under various operation topologies and generator scales. The averaging inference time of a sample on the IEEE 39-bus system and IEEE 300-bus system is merely 1/671 and 1/149 of that of TDS, while the accuracy reaches about 99%. Full article

The continuous monitoring of radioactivity in a cabled subsea network in the North Sea Observatory was performed to test the performance of a medium-resolution underwater spectrometer, as well as to identify and to assess potential anthropogenic and/or natural hazards. The effectiveness of continuous monitoring was tested together with the operability of the underwater sensor, and quantification methods were optimized to identify the type of radioactivity as well as the activity concentration of radionuclides in the seawater. In the frame of the RADCONNECT project, a medium-resolution underwater radioactivity system named GeoMAREA was integrated into an existing cabled ocean observatory placed on Helgoland Island (COSYNA network). The system could be operated via an online mode controlled by the operational centre (AWI), as well as remotely by the end-user (HCMR). The system provided gamma-ray spectra and activity concentrations of key radionuclides that were enriched in seawater during the monitoring period. As concerns the quantification method of natural radioactivity, the average activity concentrations (in terms of the total monitoring period) of ²¹⁴Bi, ²⁰⁸Tl, ²²⁸Ac and ⁴⁰K were found to be 108 ± 30, 57 ± 14, 40 ± 5 and 9800 ± 500 Bqm⁻³, respectively. As concerns the quantification of ¹³⁷Cs, the average activity concentration in terms of the total monitoring period (although it is uncertain) was found to be 6 ± 4 Bqm⁻³. The data analysis proved that the system had a stable operation in terms of voltage stability, so all acquired spectra could be summed up efficiently in time to produce statistically optimal gamma-ray spectra for further analysis. Full article

The present study aimed to translate, culturally adapt, and present a psychometric validation for the Everyday Memory Questionnaire—Revised (EMQ-R) to the Portuguese population. The study involved 267 participants aged between 18 and 75 years (M = 39.32; SD = 14.8), recruited online. Self-report measures of anxiety and depression symptoms were administered to assess the instrument’s convergent validity. To examine the factorial structure of the measure, a two-step validation process was employed. Given the uncertainty about the optimal measurement model, the sample was randomly divided into two independent subsamples. First, a principal component analysis (PCA) was conducted to explore the factorial structure. Next, a confirmatory factor analysis (CFA) was performed to validate the identified structure. The results supported a unidimensional structure consisting of 12 items, suggesting that perceived memory difficulties are best represented as a single overarching factor. High reliability was observed for this structure (Cronbach’s alpha and McDonald’s omega values ≥ 0.90). The results also indicated that general memory complaints were moderately correlated with symptoms of anxiety and depression. Furthermore, the study highlighted the promising potential of the measure as a screening tool for detecting subjective memory complaints, with an optimal cut-off score of 16 points. Future studies should focus on validating the EMQ-R with clinical samples, exploring its discriminative ability, and examining the stability of the cut-off score across different populations and contexts. Full article