Search Results (29,105)

This study describes the development of an electrochemical sensor for quantitatively measuring acetaminophen (ACOP) in drug tablets. The sensor design is based on the modification of glassy carbon electrode (GCE) using zinc oxide nanoparticles (ZnONPs) embedded in a naturally occurring clay matrix (Sa) functionalized with phenylalanine (Phe). To ensure that the ZnONPs are homogeneously dispersed on the clay surface, the nanocomposite was synthesized using an impregnation approach and low-temperature heat treatment. The amino acid promotes specific interactions with ACOP through hydrogen bonding and π-π stacking, acting as both a stabilizing agent and a molecular recognition moiety. FTIR, UV-Vis, XRD, and FESEM/EDX mapping were employed to fully characterize the developed material (ZnONPs-Sa/Phe). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for the electrochemical determination of ACOP using the modified electrode GCE/ZnONPs-Sa/Phe. Parameters susceptible to affecting the sensitivity of the developed sensor were optimized, revealing that 5 µL of the suspension ZnONPs-Sa/Phe immobilized on GCE was ideal for the sensing of ACOP in a phosphate buffer solution at pH 2.0. The calibration curve obtained by plotting peak current intensity against ACOP concentration exhibited linear behavior within the concentration range between 0.02 µM and 0.28 µM, enabling determination of the limits of detection (LOD) and quantitation (LOQ) at 8.54 × 10⁻⁹ M and 2.84 × 10⁻⁸ M, respectively. The reproducibility, stability, and selectivity of the sensor were evaluated, followed by its application to the nano-sensing of ACOP in Africure and Doliprane tablets, yielding satisfactory results. The simplicity, affordability, and high analytical sensitivity of the developed sensor make this sensing platform a promising tool for pharmaceutical quality control applications. Full article

Background/Objectives: Financial toxicity (FT) is increasingly recognised as a critical dimension of the cancer care continuum, reflecting both objective financial burden and subjective financial distress arising from cancer-related care. Head and neck cancers (HNC) may be particularly vulnerable to FT because treatment often involves multimodal care, functional morbidity, prolonged rehabilitation, and disruption to employment. This scoping review mapped and synthesised the literature on FT in a focused subset of head and neck cancers (HNC), namely malignancies of the oral cavity, oropharynx, nasopharynx, sinonasal tract, and major and minor salivary glands. Methods: A scoping review was conducted in accordance with the methodological guidance of the Joanna Briggs Institute for scoping reviews to identify and synthesise studies addressing FT in the selected HNC subsites. Searches were undertaken in MEDLINE, Embase, Scopus, Web of Science, CINAHL, EconLit, and Global Index Medicus for English-language studies published between 1 January 2015 and 1 January 2025. The search window was restricted to this period to capture the more contemporary evolution of FT as a distinct research construct in oncology. Eligible studies included adult patients and reported patient-level FT outcomes, including direct costs, indirect costs, out-of-pocket expenditure, financial hardship, financial distress, employment disruption, or related economic strain. Findings were synthesised narratively and organised thematically. Results: Twenty-five studies published between 2015 and 2025 were included. The evidence base was dominated by cross-sectional and retrospective designs, with limited prospective follow-up and very little intervention-focused research. FT was conceptualised heterogeneously across studies, spanning direct expenditure, indirect and non-medical costs, subjective financial distress, and coping-related consequences. Questionnaire-based approaches were used in 13 studies, but only a smaller subset employed FT-specific instruments such as COST. Across the literature, FT was most commonly associated with lower income, weaker financial protection, employment disruption, rural residence in some settings, and more intensive treatment. Reported downstream associations included poorer quality of life, psychological distress, care alteration, and work-related burden, although evidence for treatment delay or survival effects was more limited and should be interpreted cautiously. Conclusions: In this focused HNC subset, FT appears multidimensional, socially patterned, and clinically relevant. However, the literature remains methodologically fragmented, with inconsistent measurement and sparse longitudinal evidence. Future work should prioritise validated and tumour-specific assessment strategies, prospective study designs, and evaluation of mitigation interventions that address both direct and indirect burden across the cancer continuum. Full article

Aiming at the issues of low recognition accuracy and high model computational complexity for power quality disturbances (PQDs) in strong-noise environments, this paper proposes a novel lightweight PQD-recognition method that integrates a hybrid architecture of variational mode decomposition (VMD), convolutional neural network (CNN), and transformer. Firstly, a hybrid optimization algorithm named the monkey–genetic hybrid optimization algorithm (MGHOA) is proposed to optimize VMD parameters for denoising disturbance signals, thereby enhancing recognition accuracy in noisy environments. Secondly, to fully extract disturbance signal features and reduce the computational complexity of the model, a lightweight CNN–transformer model is designed. Depthwise separable convolution (DSC) is employed to extract local features and the multi-head attention mechanism of transformer is utilized to mine the long-distance dependence and global features, thereby enhancing the feature representation. Thirdly, a multitask joint-learning method is proposed to collaboratively optimize classification accuracy and temporal localization tasks, enhancing the discrimination of similar disturbances. Additionally, a dual-pooling global feature fusion strategy is designed to further enhance the model’s ability to discriminate complex disturbances. Comparative experiments on 16 typical PQD types demonstrate that the proposed method achieves excellent performance in recognition accuracy, model robustness, and computational efficiency. The integration of the MGHOA–VMD module improves recognition accuracy by 1.08%, while the multitask joint-learning method contributes an additional 0.55% improvement. When achieving recognition accuracy comparable to complex models, the training time of the proposed method is 36.51% of that required by DeepCNN and merely 5.90% of that required by bidirectional long short-term memory (BiLSTM), with a 31.22% reduction in parameter scale. This work provides a novel solution for intelligent power quality disturbance recognition. Full article

Knowledge graphs play a vital role in tasks such as recommendation systems, question-answering systems, and information retrieval. However, during practical construction, they commonly suffer from structural incompleteness and sparse relationships, which limit reasoning performance and downstream applications. Existing methods typically focus solely on either structural modeling or semantic modeling: embedding models relying solely on graph structures struggle to leverage textual information about entities and relationships. In contrast, semantic approaches relying solely on pre-trained language models struggle to accurately capture complex relationship patterns. To address this challenge, this paper proposes SeSKGC, a semantic–structural fusion knowledge graph completion model. At the semantic level, the model employs the DeBERTa pre-trained language model to encode entity and relation text. It incorporates a neighbor text augmentation mechanism to introduce local semantic context and enhance representation quality. At the structural level, it adopts complex-space rotation to model relationships using a RotatE-like approach, and aggregates local topological information through relative position attention to capture complex relationship patterns. At the scoring stage, the model employs a weighted fusion strategy to combine semantic and structural scores and utilizes InfoNCE contrastive loss for joint optimization. Experiments conducted on WN18RR and FB15k-237 datasets demonstrate that SeSKGC achieves overall superior performance on metrics including MRR and Hits@N compared to multiple representative baseline methods. Ablation studies and parameter sensitivity analysis of fusion weight $\lambda$ further reveal that the semantic encoding and structural modeling modules exhibit distinct complementary roles, while the weighted fusion design in the scoring layer plays a crucial role in enhancing model performance and stability. Full article

High-frequency transformer (HFT) noise is a pivotal indicator of equipment performance. To conduct a comprehensive evaluation, this study systematically performed testing and evaluation on the noise generated by a 70 kW HFT under no-load conditions. Acoustic data were collected using acoustic sensors and a head-and-torso simulator, followed by an analysis of noise characteristics focusing on the impacts of voltage levels and operating frequencies. A multi-dimensional evaluation of HFT noise was carried out using sound quality parameters to unravel its intrinsic attributes under electrical parameter excitation. The key findings are as follows: HFT noise exhibits steady-state time-domain behavior and distinct tonal frequency-domain features; the dominant frequency is twice the operating frequency, with prominent harmonics. The noise intensity increases with the voltage levels (~47.0 dB (A) at 200 V to ~72.0 dB (A) at 750 V at 5 kHz) but decreases with the operating frequencies (~82.0 dB (A) at 4 kHz to ~47.0 dB (A) at 10 kHz at 750 V). This study establishes correlations between the electrical parameters and sound quality metrics; the loudness, sharpness, tone-to-noise ratio and prominence ratio are sensitive to the electrical parameters of HFT. Single-frequency noise from HFT exhibits remarkable perceptual salience, exacerbating the perceived annoyance. Thus, HFT design should prioritize reducing single-frequency noise to alleviate such issues. Full article

In people-centered urban planning, enhancing the well-being of residents and tourists is one of the core objectives. Tourist emotion serves not only as a key indicator of the tourism experience but also indirectly reflects the quality of a city’s public spaces and built environment. In recent years, user-generated content has provided abundant data for understanding human emotional responses in urban environments, while deep learning models offer new technological pathways for extracting spatial–emotional associations from such data. However, existing research lacks a systematic evaluation of emotion analysis models from an urban spatial perspective and their application to uncover the relationship between emotional distribution and spatial characteristics in specific urban contexts. Based on a dataset of 9419 manually annotated travel reviews from Harbin, this study developed a multi-level evaluation framework and conducted a systematic comparison of seven emotion analysis models. This study then screened for the optimal model combinations based on two dimensions—spatial location and emotion polarity—to create a model matching matrix for mapping Harbin’s emotion map. Subsequently, a regression analysis was performed to examine the relationship between emotions and built environment elements. The results show that the ERNIE model demonstrated the best overall performance. Road density, green space density, and accommodation facility density were positively correlated with emotion, while POI diversity showed a negative correlation. This study demonstrates that emotion analysis technology can serve as a valuable analytical tool for identifying spatial patterns of sentiment, thereby offering empirical support for optimizing spatial design parameters and advancing a more people-centered approach to urban development. Full article

Diet quality, nutrition knowledge, and psychosomatic literacy—defined as the understanding of the interactions between diet, gut microbiota, and mental well-being—may shape weight-related behaviours in youth. This study used a cross-sectional design to integrate these domains with digital information pathways in Central–Eastern Europe. This study assessed diet quality, nutrition, and psychosomatic knowledge, supplement use, and health-information sources among Polish adolescents and young adults, with emphasis on age-related differences and the role of social media. A cross-sectional, anonymous online survey (October 2025–January 2026) was conducted in Poland (final analytical sample: n = 478; adolescents 15–19 years vs. young adults 20–30 years). Of 591 individuals who accessed the survey, 478 were included in the final analytical sample. Diet quality was estimated from FFQ data using KomPAN-derived indices (pHDI-10, nHDI-14, DQI). Nutrition knowledge (0–25 points), psychosomatic/gut–brain indicators, supplementation, and information sources were analysed using χ²/Fisher tests and Mann–Whitney U tests with effect sizes. The primary outcomes measured were dietary supplement use and excess body weight (BMI ≥ 25 kg/m²). Multivariable logistic regression examined predictors of supplement use and BMI ≥ 25 kg/m². Overall diet quality was low to moderate, with limited intake of whole grains, legumes, and fish, and common nutrition misconceptions. Social media was the most frequently indicated source of diet/supplement information and was independently associated with more frequent supplement use (OR = 2.29; 95% CI: 1.43–3.64). Adolescents reported lower whole-grain intake and more misconceptions than young adults. Predictors of BMI ≥ 25 kg/m² included male sex (OR = 2.46; 95% CI: 1.46–4.15), lower education, and lower nutrition knowledge, while age showed a non-linear positive association with excess body weight. Polish adolescents and young adults show gaps between declared pro-health attitudes and actual diet quality/competencies. Social media reliance appears particularly linked to product-oriented behaviours (supplementation). Prevention should strengthen nutrition and food safety education, digital health literacy, and professional guidance on supplementation, especially in adolescents. Our findings suggest that social media is a primary driver for dietary supplementation among Polish youth, more so than objective nutrition knowledge. While diet quality is linked to weight status, the relationship is complex. These results may inform future public health interventions targeting digital health literacy to promote balanced nutrition and safe supplementation practices. Full article

To address the demands of large-scale production in the photovoltaic industry for laminators with a small footprint, low energy consumption, and high encapsulation quality, this paper presents research on the structural design, simulation optimization, and performance validation of a multi-layer laminator for photovoltaic modules. Different from existing single-layer or double-layer structures, this paper proposes for the first time an eight-layer, three-stage overall scheme, develops modular lamination units, completes the design of core systems, and achieves multi-chamber coordination. Simulation validation was conducted on the temperature uniformity of the heating plates and the thermo-mechanical coupling under vacuum conditions. A prototype, model HCDL2743DSiT, was developed and subjected to a 30-day production trial. The results show that the equipment reaches a vacuum degree of 92 Pa within 100 s and drops to 38 Pa within 120 s; the temperature uniformity error of the heating plates is ±1.3 °C; the maximum positioning deviation of the transmission is ±2.8 mm. All core indicators meet the design requirements, and the module encapsulation pass rate reaches 99.9%. At the same production rate, the footprint is reduced by approximately 72% compared with that of a traditional double-layer laminator, achieving dual optimization of space utilization and energy consumption and providing technical equipment support for the high-efficiency encapsulation of photovoltaic modules. Full article

Public participation in planning, though a foundational democratic principle, faces persistent implementation challenges across diverse planning systems. This paper examines participatory planning practice in Ireland and Serbia—two countries representing distinct planning traditions (discretionary and conformance-based, respectively) yet confronting shared structural pressures. Through comparative analysis of four local land use planning instruments (the Development Plan and Local Area Plan in Ireland; the Municipal Spatial Plan and General Regulation Plan in Serbia), the study investigates how institutional design and legislative frameworks shape the depth and quality of participatory practice. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across two planning scales (county/municipal and local/sub-municipal). A four-dimensional analytical framework—informing, consultation, collaboration, and monitoring—guides the systematic comparison of participatory mechanisms across the selected cases. Findings reveal that, while both systems remain predominantly at the informing and consultation levels, critical differences emerge in how participation is structured and documented in institutional practice. Ireland’s discretionary system enables multi-channel information dissemination, feedback-oriented consultation, and non-statutory collaborative experimentation beyond legal minimums. Serbia’s conformance-based system confines participation largely to statutory procedures, with objection-based consultation and limited collaborative mechanisms, though distinctive features, such as the public hearing session, provide direct opportunities for deliberation absent in the Irish context. The study contributes to European comparative planning scholarship by demonstrating that participatory depth is shaped less by the formal existence of legal provisions than by the interplay between institutional design, procedural arrangements, transparency, and responsiveness. Full article

Artificial intelligence (AI) has become increasingly embedded in technology-enhanced learning environments, where peer assessment now serves both instructional and analytic purposes. Beyond allocating feedback and grades, it also produces data that is later interpreted through learning analytics systems. In practice, visible indicators such as students’ fairness perceptions and the degree of agreement among peer raters are often treated as signs that the assessment process is functioning effectively. However, these indicators do not necessarily correspond to grading validity. Students may regard a peer assessment process as fair even when peer-generated ratings remain weakly aligned with expert judgement. This study, therefore, examines whether the socio-technical configurations associated with high perceived fairness in a digitally mediated peer assessment environment also correspond to criterion-referenced grading validity. Data were collected from 215 undergraduate students enrolled in an Artificial Intelligence Foundations course over two consecutive semesters at a university in Taiwan, with instructor ratings serving as an external expert reference within the course context, rather than as a universal ground truth. Because anonymity conditions and semester were fully confounded in the study design, differences linked to anonymity should not be interpreted as isolated causal effects. A two-stage fuzzy-set Qualitative Comparative Analysis (fsQCA) was used. In the first stage, three equifinal configurations associated with high perceived fairness were identified. In the second stage, these configurations were examined against four grading objectivity outcomes: peer–instructor alignment, peer convergence, familiarity bias, and leniency bias. The findings show that fairness perception and grading validity are only partially aligned. Configurations anchored in explicit criterion transparency consistently supported both experiential legitimacy and evaluative accuracy. By contrast, one configuration was associated with high peer convergence while remaining weakly aligned with instructor standards, a pattern described here as false objectivity; this context-dependent configurational finding warrants further investigation across other settings. The study contributes to research on digitally enhanced assessment and learning analytics by showing that fairness perception, peer convergence, and grading validity should be treated as analytically distinct dimensions of assessment quality. Full article

The development of metro-led urban underground public spaces (UUPSs) provides urban residents with extensive pedestrian-friendly activity areas sheltered from rain, snow, strong winds, and other extreme weather conditions. Although an increasing number of people are engaging in daily commercial and leisure activities within UUPSs, problems such as inconvenient transfer, poor visibility, and a lack of natural light, which indicate poor environmental quality, have led to an uneven distribution of user behavior, thereby reducing the efficiency of space utilization. Our aim in this study was to predict UUPS utilization rates by investigating the relationship between UUPS environmental attributes and user behavior characteristics and preferences. Six typical UUPSs in Wuhan were selected as case studies. User behavior data were collected using panoramic camera recordings, on-site observations, and space syntax methods, while spatial environmental factors were quantified. The correlation between various factors and multi-dimensional user behavior characteristics was discussed, and a Random Forest model was established to predict behavioral preferences. Our results indicate that accessibility and visibility are fundamental factors influencing user behavior characteristics, while the impact of landscape elements is relatively low. Regarding behavioral preference prediction, UUPS environmental features achieved the highest prediction accuracy for leisure behaviors, whereas the predictive performance for sports activities was lower. In this study, we reveal the influence of UUPS environmental factors on user behavior characteristics and predict preference patterns of different behaviors for space types. Focusing on the behavioral needs of space users, we provide a reference for the subsequent human-centered design of UUPSs. Full article

Introduction: This systematic review examines the association between ultraprocessed food (UPF) intake and cognitive and executive function in adults. Given the global rise in overweight and obesity and the increasing consumption of UPFs, understanding their potential impact on brain health is of growing importance. Method: A comprehensive literature search was conducted in PubMed, EBSCO, and Scopus databases following PRISMA guidelines. Fourteen studies met inclusion criteria, encompassing cross-sectional, longitudinal, and experimental designs. Risk of bias was assessed using the National Institutes of Health Quality Assessment Tool. Results: The majority of studies (78.5%) reported a significant association between higher UPF consumption and poorer cognitive outcomes, including deficits in memory, executive function, and global cognition. Longitudinal studies consistently demonstrated that increased UPF intake is linked to accelerated cognitive decline and a higher risk of mild cognitive impairment and dementia, particularly in middle-aged and older adults. In contrast, cross-sectional findings were more heterogeneous, and evidence in younger populations remains limited and inconclusive. Conclusions: Overall, the findings suggest that high UPF consumption may be a modifiable risk factor for cognitive decline. However, methodological variability and the predominance of observational studies highlight the need for further longitudinal and experimental research to clarify causal mechanisms. Full article

The inherent non-uniformity of Infrared Focal Plane Arrays (IRFPA) inevitably results in stripe noise, which severely degrades image quality and hinders downstream applications. Existing deep learning methods often struggle to strike a balance between effective denoising and the preservation of fine thermal textures. To address this issue, we propose a Bottleneck-free Channel Dual-path Aggregation Network (BCDA-Net) based on a “Perception-Reconstruction” design principle. In the perception stage, the network jointly employs the Dual-Path Channel Down-sampling (DCD) module and the Context-Guided Stripe Attention Block (CGSAB). The DCD module utilizes a channel split strategy to simultaneously extract semantic features and preserve high-frequency textures, while the CGSAB performs global context modeling on these features to precisely perceive and locate global stripe noise patterns. In the reconstruction stage, we integrate the Cascaded Dense Feature Aggregation (CDFA) module with a Bottleneck-Free Aggregation Strategy (BFAS). The CDFA utilizes the perceived information to densely aggregate features and progressively reconstruct clean image details, whereas the BFAS structurally blocks the propagation of low-resolution noise during decoding, effectively mitigating aliasing artifacts induced by deep feature upsampling. Together, these components form a complete closed loop from accurate noise perception to high-fidelity reconstruction. Extensive experiments on public and real-world datasets demonstrate that BCDA-Net maximally preserves image details while removing non-uniform stripe noise. Both objective metrics and subjective visual quality outperform existing state-of-the-art methods. Full article

The flexible job shop scheduling problem (FJSP), which simultaneously involves machine assignment and operation sequencing under multiple constraints, is a typical NP-hard combinatorial optimization problem, and efficient scheduling is of great importance for improving production efficiency and manufacturing flexibility. To address this problem, the success-history beaver behavior optimizer (SHBBO) is introduced to solve FJSP with the objective of minimizing the makespan. First, considering the discrete characteristics of FJSP, an effective encoding and decoding scheme is designed to represent operation sequences and machine assignments. Then, the adaptive success-history mechanism of SHBBO is employed to dynamically adjust the search parameters during the optimization process, enabling a better balance between global exploration and local exploitation. Meanwhile, the behavioral update strategy of SHBBO is adapted to the scheduling environment so that candidate solutions can be effectively evolved in the discrete solution space. In addition, a population updating strategy and elite-guided search mechanism are incorporated to enhance solution quality and convergence performance. Finally, extensive experiments are conducted on benchmark FJSP instances to verify the effectiveness of the proposed method. Experimental results show that SHBBO achieves the best average results on 11 out of 12 CEC2022 benchmark functions, with particularly notable improvements over the original beaver behavior optimizer (BBO) on functions such as F6 (56.69%), F5 (12.20%), and F10 (9.18%). On the BRdata benchmark instances, SHBBO obtains the best or tied-best makespan on all 10 instances, with an average percentage relative deviation (PRD) of 0, and reduces the makespan by 7.69% on MK10 and 6.25% on MK06 compared with BBO. Full article

Tomato landraces possess distinct flavors, colors, textures and aromas, making them suitable for traditional cuisine. Tomato landraces contain a wide range of genes, including those involved in fruit quality, that can be isolated and used in local breeding programs. In regions recognized as centers of origin, domestication and diversification, traditional farmers play an important role in the preservation of tomato landraces adapted to local conditions and agricultural practices, on the whole maintaining high genetic diversity. This work aimed to evaluate the effects of the crop cycle (C), genotype (G) and C × G interactions on the contents of soluble solids, reducing sugars, lycopene, total polyphenols, flavonoids, and vitamin C, as well as the pH and antioxidant activity, in fifteen tomato landraces (genotypes) undergoing phenotypic selection and a commercial tomato variety (control). All the varieties were grown in two crop cycles under uniform greenhouse management using a randomized block design with four repetitions. Fruit composition was analyzed with AOAC and spectrophotometric methods. Significant differences (p ≤ 0.01) were detected in the soluble solid content, pH, flavor and maturity indices, polyphenol and flavonoid contents, and antioxidant activity between C, G and C × G interactions. In contrast, titratable acidity, reducing sugars, lycopene and vitamin C did not differ between cycles. Coefficients of phenotypic and genotypic variation and broad-sense heritability (H²) ranged from 4.3 to 33.7, 2.0 to 19.0, and 3.2 to 63.5%, respectively. H² for bioactive compounds ranged from moderate to slightly high (16.3–38.8%). These findings, supported by laboratory analyses, suggest that genotypes under agronomic selection have potential as parents to enhance fruit quality in current and future breeding programs. Full article