Search Results (103)

Urban bus operations under signalized traffic conditions are characterized by frequent stop-and-start behaviors which significantly degrade fuel economy, especially for fuel cell buses (FCB). In this paper, a collaborative optimization method is proposed that combines speed planning and energy management for FCB in this situation. The method calculates the target speed of FCB using traffic light phase information and the remaining signal time. With an intelligent driving model, the vehicle can adjust its speed in advance when approaching intersections so it can pass through intersections without stopping. At the same time, a learning-based energy management strategy is used to reasonably share power between the fuel cell and the battery. The results indicate that the method proposed in this paper reduces hydrogen consumption by approximately 11.3% compared to the standard method. Full article

Background/Objectives: The global prevalence of overweight and obesity among young adults has doubled since 1975, primarily due to unhealthy dietary habits and sedentary lifestyles. Understanding dietary patterns (DPs) in this population is essential for designing effective prevention strategies. This study aimed to characterize the dietary patterns and diet quality of university students and to examine their physical activity and associated health risks. Methods: A convenience sample of 136 participants (77.9% females, 22.1% males) was recruited. Data on clinical history, lifestyle behaviors, and physical activity were collected using a structured questionnaire. Dietary intake was assessed using a food frequency questionnaire and three 24-h dietary recalls. Intake was analyzed by food groups, total energy, and macronutrient and fiber composition. Principal component analysis was applied to identify DPs. Results: Three major DPs were identified: Ultra-Processed Foods, Variety Foods, and Traditional Mixed Mexican. Overall, participants showed low consumption of fiber, legumes, and nuts, coupled with high intake of animal-based foods. The mean daily energy intake was 2278 kcal for men and 2008 kcal for women. Although participants demonstrated higher adherence to the Traditional Mixed Mexican pattern, a strong tendency toward the Ultra-Processed Foods pattern was observed, which is linked to an increased risk of chronic diseases and poor nutritional outcomes. Conclusions: The findings highlight the urgent need for targeted dietary interventions among university students. Strategies should emphasize increased intake of fiber-rich plant foods, moderation of protein consumption, and reduction in refined carbohydrates and added sugars to promote healthier dietary habits and prevent chronic disease development. Full article

This study focuses on an 800 kV Filter Circuit Breaker (FCB) equipped with phase-controlled closing. The real-time health status of the circuit breaker was evaluated by analyzing the factors influencing its condition based on field-operation data. Key performance parameters, including mechanical characteristics, switching performance, and insulation properties, were employed for the assessment. Furthermore, field operation data are preprocessed to establish a comprehensive database that integrates data from C2-class margin tests, phase-controlled switching tests, and phase-controlled closing factory tests. A combined subjective–objective weighting method is applied to assign weights to health impact factors, and an improved fuzzy comprehensive evaluation method incorporating field operation data is proposed. Based on this evaluation, different operation and maintenance strategies were formulated. The results demonstrate that the proposed method enables a comprehensive and accurate assessment of the health status of FCBs, offering a reliable framework for optimizing condition-based maintenance in practical engineering applications. Full article

Acne vulgaris is a common chronic inflammatory skin condition. Conventional acne treatments are often limited by adverse effects, driving interest in alternative therapies. This study explored the multifunctional bioactivities of a lyophilized cell-free supernatant (LCFS) derived from Lacticaseibacillus paracasei T0901, isolated from fermented palm sap, with a focus on its antimicrobial, antibiofilm, and antimelanogenic potential for dermatological applications. Antimicrobial activity was evaluated using agar well diffusion and broth microdilution assays against acne-associated pathogens, while antibiofilm effects were quantified via crystal violet staining. Antimelanogenic activity was assessed in α-melanocyte-stimulating hormone (α-MSH)-stimulated B16F10 melanoma cells by measuring melanin content and tyrosinase activity. Whole-genome sequencing was performed to identify genes linked to observed bioactivities, and molecular docking was used to predict metabolite–protein interactions. The LCFS exhibited strong inhibitory activity against acne-associated bacteria, with inhibition zones of C. acnes (10.67 ± 0.58 mm), S. epidermidis (21.00 ± 0.00 mm), and S. aureus (20.00 ± 0.00 mm), and a minimum inhibitory concentration of 25 mg/mL. Biofilm formation was significantly reduced by 62.98 ± 3.54%. In α-MSH-stimulated B16F10 cells, LCFS treatment (10 mg/mL) significantly decreased melanin content (73.23 ± 2.36%) and intracellular tyrosinase activity (68.19 ± 6.29%) relative to control. Genomic analysis revealed antioxidant-related genes (sodA, trxB, nox), pigmentation regulators (mco, fcbD), and buk (butyrate kinase), supporting the observed bioactivities. Molecular docking further demonstrated strong binding affinities of LCFS-derived metabolites to tyrosinase and MITF, suggesting modulation of melanogenic pathways. Collectively, these results indicate that L. paracasei T0901 produces safe postbiotic compounds with potent antimicrobial, antibiofilm, and antimelanogenic activities, highlighting its promise as a multifunctional ingredient in probiotic-based skincare formulations. Full article

Anthropogenic pressures and climate change have increasingly affected biodiversity and ecosystem services, particularly in freshwater ecosystems, which are among the most sensitive and vulnerable environments. Citizen science has emerged as a promising approach to expand ecological knowledge and strengthen biomonitoring efforts, mitigating the limitations of conventional research in scale, cost, and speed. This study presents a global bibliometric analysis of citizen science applied to freshwater biomonitoring using aquatic insects. A total of 153 articles indexed in Scopus and Web of Science, published between 2002 and 2025, were analyzed. Results reveal a marked increase in publications since 2010, concentrated mainly in the Global North, especially the United States (37.51%) and Germany (14.42%). The most frequent taxa were Odonata (25.58%) and Diptera (25.19%), with studies focusing primarily on species (70.59%) level, and adult stage (69%). Participants were mainly from the general public (70%) and naturalists (12%), predominantly under contributory models (98%). Reported challenges involved taxonomic limitations (28%) and citizen science engagement (28%). Despite these constraints, the findings highlight the growing relevance of citizen science as a complementary tool for aquatic biomonitoring, emphasizing the need for inclusive approaches, taxonomic training, and participatory strategies in biodiversity conservation. Full article

A new species of Encotyllabe Diesing, 1850 (Monopisthocotylea: Capsalidae), Encotyllabe tantaliani n. sp., is described from the pharyngeal plates of the Lorna drum, Sciaena deliciosa (Tschudi, 1846) (Eupercaria: Sciaenidae), collected from two localities along the Peruvian coast. This new species was originally proposed as E. callaoensis Tantaleán, 1974, in an unpublished doctoral thesis, and is herein recognized as a nomen nudum under the International Code of Zoological Nomenclature (ICZN). Encotyllabe tantaliani n. sp. is distinguished from all known congeners by the following combination of morphological features: (1) an anteriorly tapering body proper, (2) slightly lobed testes markedly larger than the ovary, (3) vitelline follicles beginning at the level of the male copulatory organ (MCO) and absent from the regions of the reproductive organs, (4) a genital pore positioned posterolateral to the pharynx, and (5) an oblong-shaped MCO. Phylogenetic analysis based on cox1 sequence places E. tantaliani n. sp. in a clade with Encotyllabe percussa Morales-Ávila, Jufaili & Ogawa, 2024, a parasite of Lethrinus nebulosus (Forsskål, 1775) (Eupercaria: Lethrinidae) from the Arabian Gulf. Pairwise genetic distances support the distinctiveness of the new species from its closest congeners. Encotyllabe tantaliani n. sp. represents the first species of the genus described from a host belonging to the Sciaenidae host. Full article

Ambiguity Resolution (AR) is regarded as an effective technique for enhancing positioning accuracy and reducing convergence time in Precise Point Positioning (PPP). However, the Wide-Lane Fractional Cycle Bias (WL FCB) and Narrow-Lane Fractional Cycle Bias (NL FCB) needed for AR are generated from network solutions based on numerous globally distributed stations, leading to considerable computational load and processing time. A prediction model for FCB is proposed using the Genetic Algorithm Optimized Backpropagation Neural Network (GA-BPNN), and high-precision predictions of WL and NL FCB for Day of Year (DOY) 321 in 2023 are successfully achieved. Comparisons with iGMAS products show that predicted WL FCB deviations are within 0.01 cycles, and predicted NL FCB over 12 h deviates within 0.1 cycles (excluding satellite C20). The performance of three PPP schemes, Float, Fixed (based on FCB from iGMAS), and BP-Fixed (based on FCB predicted by GA-BPNN), is compared through experiments. For GPS + BDS-3, the accuracies of the BP-Fixed scheme are 0.0034 m, 0.0039 m, and 0.0100 m in the east, north, and up directions, respectively. The ambiguity fixed rates reach 98.62% for BP-Fixed. These outcomes confirm that the positioning performance using the predicted FCB of GA-BPNN is highly consistent with that using FCB products. Full article

To address circularity and resource recovery in modern structural applications, industry is seeking materials that are sustainable and lightweight. Although natural fiber-reinforced composites offer sustainability advantages, their mechanical properties remain inferior to those of synthetic fiber systems, limiting practical deployment. Flax fibers were selected as reinforcement due to their high specific stiffness, biodegradability, and wide availability. This study implements a three-level strategy to enhance the flexural performance of flax fiber-reinforced composites: at the process level, curing under distinct heating rates to promote a more uniform polymer network; at the material level, incorporation of a carbonaceous additive derived from fuel–oil furnace waste to strengthen interfacial adhesion; and at the structural level, adoption of a sandwich configuration with a recycled PET core to increase section bending inertia. Specimens were fabricated via vacuum-assisted resin transfer molding (VARTM) and tested using a three-point bending method. Mechanical testing shows clear improvements in flexural performance, with the sandwich architecture yielding the highest values and increasing flexural strength by up to 4.52× relative to the other conditions. For the curing series, FTIR indicates greater reaction extent, evidenced by lower intensities of the epoxide ring at 915 cm⁻¹ and glycidyl/oxirane band near 972 cm⁻¹, together with a more pronounced C–O–C stretching region, consistent with the higher flexural response. While SEM observations revealed interfacial debonding at 5% FCB, a hybrid mechanism with crack deflection appeared at 10%. This transition created tortuous crack paths, consistent with the higher flexural strength and modulus at 10% FCB. A distinctive feature of this work is the integration of three reinforcement strategies—controlled curing, waste-derived carbon additive, and recycled PET sandwich design. This integration not only enhances the performance of natural fiber composites but also emphasizes sustainability by valorizing recycled and waste-derived resources, thereby supporting the development of greener composite materials. Full article

Target detection is essential in intelligent transportation and autonomous control of unmanned aerial vehicles (UAVs), with single-stage detection algorithms used widely due to their speed. However, these algorithms face limitations in detecting small targets, especially in aerial photography from unmanned aerial vehicles (UAVs), where small targets are often occluded, multi-scale semantic information is easily lost, and there is a trade-off between real-time processing and computational resources. Existing algorithms struggle to effectively extract multi-dimensional features and deep semantic information from images and to balance detection accuracy with model complexity. To address these limitations, we developed FFG-YOLO, a lightweight small-target detection method for UAVs based on YOLOv8. FFG-YOLO incorporates three modules: a feature enhancement block (FEB), a feature concat block (FCB), and a global context awareness block (GCAB). These modules strengthen feature extraction from small targets, resolve semantic bias in multi-scale feature fusion, and help differentiate small targets from complex backgrounds. We also improved the positioning accuracy of small targets using the Wasserstein distance loss function. Experiments showed that FFG-YOLO outperformed other algorithms, including YOLOv8n, in small-target detection due to its lightweight nature, meeting the stringent real-time performance and deployment requirements of UAVs. Full article

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

Aquatic ecosystems are essential for biodiversity and ecosystem services, but anthropogenic pressures threaten them. In this context, citizen science has emerged as an innovative strategy for biodiversity conservation and environmental monitoring. We conducted a scientometric analysis to identify patterns and gaps in the scientific literature on citizen science in aquatic ecology. We analyzed 185 articles published between 2003 and 2024 on the Web of Science and Scopus databases, with the highest number of publications on the topic (15.14%) in 2023. The United States, Australia, and the United Kingdom were the most productive and frequently studied countries. Studies focused on marine ecosystems (50.28%), while freshwater environments, such as rivers (12.99%), remain under-represented. Taxonomic groups such as fish (30.64%) and aquatic mammals (13.87%) were most commonly studied. The focus of monitoring was on ecology and species conservation. The projects adopted a contributory model of citizen engagement (92.97%), with a predominance of urban citizens (60.51%). Participants were trained through online platforms (25.75%) and in-person courses (21.56%), while communication methods involved sharing photos and videos (38.77%) and online uploads (23.79%). Therefore, expanding studies on freshwater ecosystems and rural and traditional communities, and integrating different levels of citizen participation, is essential. Full article

The SARS-CoV-2 main protease (Mpro), essential for viral replication, remains a prime target for antiviral drug design against COVID-19 and related coronaviruses. In this study, we present a systematic investigation into the molecular determinants of Mpro inhibition using an integrated approach combining large-scale data mining, cheminformatics, and quantum chemical calculations. A curated dataset comprising 963 high-resolution structures of Mpro–ligand complexes—348 covalent and 615 non-covalent inhibitors—was mined from the Protein Data Bank. Cheminformatics analysis revealed distinct physicochemical profiles for each inhibitor class: covalent inhibitors tend to exhibit higher hydrogen bonding capacity and sp³ character, while non-covalent inhibitors are enriched in aromatic rings and exhibit greater aromaticity and lipophilicity. A novel descriptor, Weighted Hydrogen Bond Count (WHBC), normalized for molecular size, revealed a notable inverse correlation with aromatic ring count, suggesting a compensatory relationship between hydrogen bonding and π-mediated interactions. To elucidate the energetic underpinnings of molecular recognition, 40 representative inhibitors (20 covalent, 20 non-covalent) were selected based on principal component analysis and aromatic ring content. Quantum mechanical calculations at the double-hybrid B2PLYP/def2-QZVP level quantified non-bonded interaction energies, revealing that covalent inhibitors derive binding strength primarily through hydrogen bonding (~63.8%), whereas non-covalent inhibitors depend predominantly on π–π stacking and CH–π interactions (~62.8%). Representative binding pocket analyses further substantiate these findings: the covalent inhibitor F2F-2020198-00X exhibited strong hydrogen bonds with residues such as Glu166 and His163, while the non-covalent inhibitor EDG-MED-10fcb19e-1 engaged in extensive π-mediated interactions with residues like His41, Met49, and Met165. The distinct interaction patterns led to the establishment of pharmacophore models, highlighting key recognition motifs for both covalent and non-covalent inhibitors. Our findings underscore the critical role of aromaticity and non-bonded π interactions in driving binding affinity, complementing or, in some cases, substituting for hydrogen bonding, and offer a robust framework for the rational design of next-generation Mpro inhibitors with improved selectivity and resistance profiles. Full article

Background: Postoperative pain is an unpleasant experience for the patient and impairs postoperative functional outcomes. The current literature on the influence of preoperative predictors on postoperative pain outcomes remains limited. This study aimed to identify sociodemographic, clinical, psychological, and temperamental predictors of postoperative pain in patients undergoing surgery for degenerative disc disease (DDD). Methods: Eighty-one adults with DDD, qualified for neurosurgical intervention, were enrolled. All patients underwent neurological and psychiatric evaluations, as well as preoperative pain assessments using the Visual Analogue Scale (VAS) and the West Haven-Yale Multidimensional Pain Inventory (WHYMPI). Psychological assessments included the Perceived Stress Scale, Hospital Anxiety and Depression Scale, Somatic Symptom Scale, temperament, and personality inventories (e.g., FCB-TI, NEO-FFI), and cognitive tests (Trail Making Test, Digit Span Test). Postoperative pain was re-evaluated with the VAS 12 weeks after surgery. Data were analyzed using univariate and multivariate statistical methods. Results: Univariate analyses revealed significant differences between the defined groups regarding lack of improvement of pain 12 weeks after surgery compared to preoperative VAS, systolic blood pressure, and four scales from the WHYMPI. However, stepwise logistic regression identified only preoperative VAS score as an independent predictor of postoperative pain improvement. Receiver Operating Characteristic analysis and Youden’s index indicated a preoperative VAS cut-off score of 6 as the most predictive. Conclusions: A VAS score of 6 or more before surgery independently predicts the absence of chronic pain 12 weeks postoperatively for patients without neurological deficits. Moreover, given the complexity of this topic, further prospective, randomized controlled research is essential. Full article

Introduction: Asthma and chronic obstructive pulmonary disease (COPD) are chronic respiratory illnesses frequently accompanied by anxiety and depression. These psychological symptoms often go undetected due to their overlap with somatic complaints. According to the regulatory theory of temperament (RTT), biologically based temperament traits may influence emotional responses to chronic illness. This study examined whether RTT-defined temperament traits predict depression and anxiety severity in patients with asthma and/or COPD. Material and Methods: The study included 210 adult patients with asthma and/or COPD recruited from a university hospital and pulmonology clinics. Individuals with a prior history of mental illness were excluded. Participants completed three validated questionnaires: the Formal Characteristics of Behavior–Temperament Inventory (FCB-TI), the Beck Depression Inventory (BDI), and the State–Trait Anxiety Inventory (STAI). Additional demographic and clinical data were collected. Multiple linear regression was used to assess the predictive value of six temperament traits for depression, state anxiety, and trait anxiety. A significance threshold of α = 0.05 was used in all statistical tests. Results: Temperament structure significantly predicted all three mental health outcomes: depression (R² = 0.37), state anxiety (R² = 0.45), and trait anxiety (R² = 0.35). Briskness negatively correlated with all outcomes, while emotional reactivity showed a positive correlation. No significant associations were found for the remaining four traits. Socioeconomic and lifestyle factors were not significant predictors. Conclusions: Temperament traits, particularly briskness and emotional reactivity, significantly influence depression and anxiety severity in asthma and COPD. Temperament assessment may serve as a low-cost, telemedicine-compatible tool to identify at-risk patients and support integrated, personalized care. Full article

Amperometric biosensors (ABSs) and enzymatic biofuel cells (BFCs) share several fundamental principles in their functionality, despite serving different primary purposes. Both devices rely on biorecognition, redox reactions, electron transfer (ET), and advanced electrode materials, including innovative nanomaterials (NMs). ABSs and BFCs, utilizing microbial oxidoreductases in combination with electroactive NMs, are both efficient and cost-effective. In the current study, several laboratory prototypes of BFCs have been developed with bioanodes based on yeast flavocytochrome b₂ (Fcb₂) and alcohol oxidase (AO), and a cathode based on fungal laccase. For the first time, BFCs have been developed featuring anodes based on Fcb₂ co-immobilized with redox NMs on a glassy carbon electrode (GCE), and cathode-utilizing laccase combined with gold–cerium–platinum nanoparticles (nAuCePt). The most effective lactate BFC, which contains gold–hexacyanoferrate (AuHCF), exhibited a specific power density of 1.8 µW/cm². A series of BFCs were developed with an AO-containing anode and a laccase/nAuCePt/GCE cathode. The optimal configuration featured a bioanode architecture of AO/nCoPtCu/GCE, achieving a specific power density of 3.2 µW/cm². The constructed BFCs were tested using lactate-containing food product samples as fuels. Full article