Search Results (1,758)

Wearable sensing technologies are increasingly used to assess neuromuscular function during daily-life activities. This study presents and evaluates a multisensor wearable system integrating a textile-based surface Electromyography (sEMG) sleeve and a pressure-sensing insole for monitoring Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) activation during gait. Eleven healthy adults performed overground walking trials while synchronised sEMG and plantar pressure signals were collected and processed using a dedicated algorithm for detecting activation intervals across gait cycles. All participants completed the walking protocol without discomfort, and the system provided stable recordings suitable for further analysis. The detected activation patterns showed one to four bursts per gait cycle, with consistent TA activity in terminal swing and GL activity in mid- to terminal stance. Additional short bursts were observed in early stance, pre-swing, and mid-stance depending on the pattern. The area under the sEMG envelope and the temporal features of each burst exhibited both inter- and intra-subject variability, consistent with known physiological modulation of gait-related muscle activity. The results demonstrate the feasibility of the proposed multisensor system for characterising muscle activation during walking. Its comfort, signal quality, and ease of integration encourage further applications in clinical gait assessment and remote monitoring. Future work will focus on system optimisation, simplified donning procedures, and validation in larger cohorts and populations with gait impairments. Full article

Traditional carriers play a major role in wastewater treatment worldwide due to their reliability, ease of production, well-established analytical methods, and strong treatment performance. Recent studies indicate that polyvinyl-alcohol-based microcarriers may surpass conventional media, as their smaller size, higher porosity, and increased specific surface area enable them to retain substantially more biomass within reactors. However, their practical application remains limited because fewer analytical methods and studies exist for these materials, largely due to their small dimensions and heat sensitivity, and their behaviour under industrial conditions—including their kinetics—has yet to be fully characterised and validated. This study aims to address these gaps by reviewing existing biomass measurement standards and highlighting their limitations when applied to microcarriers and by proposing alternative experimental approaches better suited for evaluating biomass on such sensitive yet high-capacity carriers. We present a set of experimental methods (still subject to further refinement) that demonstrate reliable performance with these materials, and to validate our approach, we quantified biomass in both in vitro systems and containerised-scale technologies, reaching up to 14 kg/m³ during winter and 8.7 kg/m³ in spring. Laboratory-scale experiments showed that both heterotrophic and autotrophic cultures can achieve high biomass levels of up to 21 kg/m³ and 16 kg/m³, respectively. Heterotrophs exhibited lower growth inhibition under shear stress, while autotrophs displayed a distinct shear-force niche around 0.09 µN within the reactor. Full article

The circular economy offers effective strategies to support the transition from fossil fuels to renewable energy. However, research at the nexus of the circular economy and energy transition remains fragmented across disciplines and lacks a systematic and integrative overview of its intellectual structure and thematic evolution. To address this gap, this study conducts a large-scale bibliometric analysis of 2977 journal articles published between 2008 and 2025 to examine the development, knowledge structure, and global distribution of this field. Performance analysis and scientific mapping are employed to evaluate research output, subject areas, thematic structures, intellectual foundations, journal dissemination, and international collaborations. The results indicate that the circular economy–energy transition nexus is a rapidly growing and multidisciplinary field. It is anchored by conceptual and policy-oriented works and complemented by applied studies on waste management, bioenergy, and decarbonization technologies that directly relate to energy production, conversion, and system efficiency. The geographical distribution shows a multi-pillar but uneven research landscape, with Europe and China emerging as leading contributors, while other regions remain comparatively underrepresented, shaped by regional priorities and collaborative networks. The study highlights emerging research gaps and future directions, offering insights into how circular economy strategies such as resource circularity and waste-to-energy applications can contribute to sustainable and equitable energy transitions and inform future energy-focused research agendas in the context of low-carbon transformation. Full article

Climate change necessitates direct measures in horticultural crop production, including the adoption of sustainable agronomic practices, such as the use of biostimulants and the inclusion of alternative species in agroecosystems. The aim of the present study was to evaluate the effect of two biostimulant formulations, one based on beneficial bacteria and fungi and the other based on seaweed extracts, on the growth, nutritional value, and bioactive properties of three wild edible species, namely, Sonchus oleraceus, Cichorium spinosum, and Scolymus hispanicus, grown in a greenhouse under optimal conditions. Our results indicate that biostimulant application had a variable effect on crop performance depending on the biostimulant formulation and species, with Bactiva showing a clear beneficial effect on the fresh weight, number of leaves, and leaf area of S. oleraceus (increased by 63.2%, 32.4%, and 51.1%, respectively, compared to the control), while seaweed extracts mostly improved the crop performance of S. hispanicus and the number of leaves and the Soil Plant Analysis Development (SPAD) index of C. spinosum (increased by 1.1% and 24.8%, respectively, compared to the control). Moreover, Bactiva significantly increased the leaf protein content of all the studied species (increased by 2.1%, 5.2%, and 6.9% for S. oleraceus, C. spinosum, and S. hispanicus, respectively, compared to the control), whereas a varied response was observed for the rest of the macronutrients, depending on the species and biostimulant. Similarly, the macromineral content (N, P, and K) increased for the application of Bactiva and/or seaweed extracts in S. oleraceus (increased by 2.1%, 22.4%, and 14.0% for N, P and K, respectively, compared to the control) and C. spinosum (increased by 5.2%, 19.3%, and 14.7% for N, P, and K, respectively, compared to the control) leaves, while for S. hispanicus leaves, only N and K increased for Bactiva (increase by 7.0% and 17.9% for N and K, respectively, compared to the control). Finally, the use of the studied biostimulants had a varied effect on the polyphenol content of the three species, and the antioxidant activity also varied among the three assays implemented. In conclusion, the use of biostimulants on these underexplored species showed promising results in terms of crop performance and chemical composition/. However, considering that the plants were subjected to optimal conditions, further research is needed to reveal the stress-mitigating effects of these biostimulant formulations for their integration as a sustainable agronomic tool for the commercial exploitation of wild edible greens. Full article

To investigate the deformation behavior of corrugated steel pipe arch bridges subjected to differential foundation settlement, this study examines a ten-span continuous corrugated steel pipe arch bridge as the engineering background. A one-year field monitoring program was conducted to record the settlement of each span, and the spatial distribution pattern, annual cumulative settlement, and settlement growth rate were evaluated. Numerical analyses were then performed to compare the deformation response of the bridge under ideal foundation conditions, differential foundation settlement, and vehicle loading. Based on the numerical results, the effectiveness of a concrete lining installed inside the corrugated steel pipe was further assessed. The results show that the settlement of the side spans is significantly larger than that of the middle spans due to the differential foundation settlement in the mining area. The maximum annual cumulative settlement at the side span (span 2) reaches 21.66 mm, which is approximately 4.1 times that of the middle span (span 6). During the monitoring period, the settlement growth rate was high in the early stage (1~3 months), reaching up to 30 percent, and gradually stabilized to about 10 percent per month in the later stage. Compared with the ideal foundation condition, differential settlement increases the pipe stress by a factor of 3.4 and amplifies the deformation by a factor of 9.1. Vehicle loading has a pronounced effect on the deformation of the pipe crown, increasing the settlement by approximately 9 percent, while its influence on the pipe invert is relatively small, with an increase of about 4 percent. Installing a 100 mm thick concrete lining inside the corrugated steel pipe has limited influence on the overall load-carrying behavior but reduces the deformation by 10~20 percent. This reinforcement method is suitable for applications in existing bridges. Full article

Global urbanization is driving a rising demand for sand and gravel, which has intensified riverbed mining. This threatens fluvial stability, flood safety, and ecological integrity. Although previous studies have documented localized geomorphic and hydrological impacts, systematic assessments that integrate long-term incision trends, embankment stability mechanisms, and resource optimization under multiple objectives remain limited. In this study, we investigate the Wengang section of the Fu River (Jiangxi, China), a sediment-deficient river reach subjected to decades of intensive mining. Through the application of hydrosediment analysis, hydrodynamic modeling, geotechnical–hydrological–mechanical (GHM) simulations, and a dynamic optimization model, the sustained impacts of mining are quantified, and science-based management strategies are proposed. The results indicate that extensive excavation has resulted in irreversible riverbed incision, with a net volume increase of 12.97 × 10⁶ m³ between 2003 and 2023, far exceeding the natural sediment deposition volume (0.853 × 10⁶ m³). Although the overall longitudinal profile remains stable, localized flow velocities in the primary mining area are increased by 0.22–0.39 m/s. A GHM analysis identifies a critical safe distance of 13–14 m between pit edge and embankment toe and demonstrates that wide-shallow pit morphology is associated with reduced stability risk compared to narrow-deep pits. Based on these constraints, an economic optimization model incorporating flood safety and market demand is developed, yielding an optimal extraction plan for 2024–2028 with a total volume of 4.4848 million tons and an estimated revenue of 50.03 million USD. This study provides an integrated framework for assessing mining impacts and offers actionable strategies to support sustainable sediment management in vulnerable river systems. Full article

Objective: This study aimed to design and clinically evaluate a bespoke cosmetic formulation tailored to individual skin characteristics and user preferences, focusing on hydration and barrier recovery in mature, therapy-affected skin. In addition, this study aimed to explore the feasibility and short-term outcomes of a structured, biometry-driven personalization approach applied within a single-subject case study design. Materials and Methods: A personalized dermato-cosmetic formulation incorporating melatonin, astaxanthin, low-molecular-weight hyaluronic acid, allantoin, yarrow oil (Achillea millefolium), lecithin, cholesterol, and arginine was developed based on objective biophysical assessment of the skin. A clinical case evaluation was conducted in a male subject over 55 years of age (Fitzpatrick phototype III) presenting persistent xerosis and dehydration following completed oncologic therapy. Quantitative skin biometry was performed at baseline and after 28 days of daily application, assessing hydration at six anatomical sites, sebum secretion, pigmentation and erythema indices, elasticity, and stratum corneum turnover and scaling. Results: After 28 days, sebum secretion increased by more than 100%, indicating partial restoration of the lipid barrier. Hyperpigmented areas decreased from 7.2% to 2.3%, while skin elasticity improved from 25% to 44%. A reduction of 8% in the erythema index suggested decreased vascular reactivity. Hydration levels improved consistently across all evaluated sites, and epidermal renewal was enhanced, as evidenced by reduced scaling and smoother skin surface. The melanin index remained stable throughout the study period. Conclusions: This pilot evaluation shows that bespoke cosmetic formulations, customized to individual skin biometry and preferences, can yield measurable improvements in hydration, barrier repair, elasticity, pigmentation uniformity, and epidermal renewal within 28 days, even in skin compromised by previous oncologic therapy. Given the single-subject nature of this pilot evaluation, these findings cannot be generalized to broader populations but rather highlight the importance of personalization and objective skin assessment in guiding individualized dermato-cosmetic formulation strategies. Personalized dermato-cosmetology using objective biophysical assessment may be a promising future strategy for effective, consumer-centered skincare. Full article

The performance degradation of concrete structures in underground water sumps within the Ordos mining area has become increasingly prominent due to environmental factors, particularly the sulfate-induced dry–wet cycles. These conditions lead to the development of cracks, spalling, and structural instability, which poses significant safety risks. This issue must be addressed with consideration of the regional hydrogeological characteristics and the current requirements for safe, sustainable, and environmentally responsible coal mining practices. The study investigates the concrete employed in the underground central water reservoir of Bulianta Coal Mine in the Ordos mining area. A novel approach is proposed for developing sulfate-resistant concrete capable of withstanding dry–wet cyclic conditions in underground environments through the utilization of coal gangue sourced from the same mining operation. Considering concrete performance, cost-effectiveness, and coal gangue utilization, a laboratory mix optimization study was conducted and the optimal mixture proportion was determined to be a 60% gangue content, a 30% fly ash content, a water–binder ratio of 0.38, which produced concrete with a compressive strength of 31 MPa. Sulfate resistance tests were conducted on the optimal mixture of dry–wet cycle-resistant concrete. The effect of different dry–wet cycle counts on the compressive strength of the coal gangue concrete was investigated, and the evolution patterns of the ascending segment shape coefficient a and descending segment shape coefficient b under sulfate-induced dry–wet cycling were analyzed. Combining the Guo Zhenhai concrete constitutive model, a concrete constitutive model suitable for the dry–wet cycle conditions of sulfate was established. Based on the proposed constitutive model, the uniaxial compressive mechanical behavior of coal gangue concrete subjected to sulfate attack was investigated through numerical simulations using the Abaqus (2020) software. The simulation results are basically consistent with the laboratory results, which proves the applicability of the constitutive model and confirms the performance of the optimal proportioning scheme for preparing sulfate-resistant dry–wet cycle concrete using coal gangue from underground mines. This study provides a new type of concrete for similar underground conditions in this mining area and offers a new approach for the comprehensive utilization of coal gangue. Full article

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, accounting for approximately 10% of all cancer cases. Despite the proven effectiveness of conventional screening modalities such as colonoscopy and fecal immunochemical testing (FIT), their invasive nature, high cost, and limited patient compliance hinder widespread adoption. Recent advancements in artificial intelligence (AI) and bowel sound-based signal processing have enabled non-invasive approaches for gastrointestinal diagnostics. Among these, bowel sound analysis—historically considered subjective—has reemerged as a promising biomarker using digital auscultation and machine learning. Objective: This review explores the potential of AI-powered bowel sound analytics for early detection, screening, and characterization of colorectal cancer. It aims to assess current methodologies, summarize reported performance metrics, and highlight translational opportunities and challenges in clinical implementation. Methods: A narrative review was conducted across PubMed, Scopus, Embase, and Cochrane databases using the terms colorectal cancer, bowel sounds, phonoenterography, artificial intelligence, and non-invasive diagnosis. Eligible studies involving human bowel sound-based recordings, AI-based sound analysis, or machine learning applications in gastrointestinal pathology were reviewed for study design, signal acquisition methods, AI model architecture, and diagnostic accuracy. Results: Across studies using convolutional neural networks (CNNs), gradient boosting, and transformer-based models, reported diagnostic accuracies ranged from 88% to 96%. Area under the curve (AUC) values were ≥0.83, with F1 scores between 0.71 and 0.85 for bowel sound classification. In CRC-specific frameworks such as BowelRCNN, AI models successfully differentiate abnormal bowel sound intervals and spectral patterns associated with tumor-related motility disturbances and partial obstruction. Distinct bowel sound-based signatures—such as prolonged sound-to-sound intervals and high-pitched “tinkling” proximal to lesions—demonstrate the physiological basis for CRC detection through bowel sound-based biomarkers. Conclusions: AI-driven bowel sound analysis represents an emerging, exploratory research direction rather than a validated colorectal cancer screening modality. While early studies demonstrate physiological plausibility and technical feasibility, no large-scale, CRC-specific validation studies currently establish sensitivity, specificity, PPV, or NPV for cancer detection. Accordingly, bowel sound analytics should be viewed as hypothesis-generating and potentially complementary to established screening tools, rather than a near-term alternative to validated modalities such as FIT, multitarget stool DNA testing, or colonoscopy. Full article

This article presents a case study from a Social and Health Care School in Denmark, where a rooftop garden was designed to promote student health and support nature-based teaching across subject areas. A novel aspect of the project is the formal integration of the garden into teaching, implying that its long-term impact may extend beyond the students to the end-users they will later encounter in nursing homes and hospitals nationwide. This study applies the Evidence-Based Health Design in Landscape Architecture (EBHDL) process model, encompassing evidence collection, programming, and concept design, with the University of Copenhagen acting in a consultancy role. A co-design process with students and teachers was included as a novel source of case-specific evidence. Methodologically, this is a participatory practice-based case study focusing on the full design and construction processes, combining continuous documentation with reflective analysis of ‘process insights,’ generating lessons learned from the application of the EBHDL process model. This study identifies two categories of lessons learned. First, general insights emerged concerning governance, stakeholder roles, and the critical importance of site selection, procurement, and continuity of design responsibility. Second, specific insights were gained regarding the application of the EBHDL model, including its alignment with Danish and international standardised construction phases. These insights are particularly relevant for project managers in nature-based initiatives. The results also show how the EBHDL model aligns with Danish and international standardised construction phases, offering a bridge between health design methods and established building practice. The case focuses on the EBHDL process rather than verified outcomes and demonstrates how evidence-based and participatory approaches can help structure complex design processes, facilitate stakeholder engagement, and support decision-making in institutional projects. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

Pesticides have been widely applied in agricultural practices over the past decades to protect crops from pests and other harmful organisms. However, their extensive use results in the contamination of soil, water, and agricultural products, posing significant risks to human and environmental health. Exposure to pesticides can lead to skin irritation, respiratory disorders, and various chronic health problems. Moreover, pesticides frequently enter surface water bodies such as rivers and lakes through agricultural runoff and leaching processes. Therefore, developing effective analytical methods for the rapid and sensitive detection of pesticides in food and water is of great importance. Electrochemical sensing techniques have shown remarkable progress in pesticide analysis due to their high sensitivity, simplicity, and potential for on-site monitoring. Two-dimensional (2D) carbon nanomaterials have emerged as efficient electrocatalysts for the precise and selective detection of pesticides, owing to their large surface area, excellent electrical conductivity, and unique structural features. In this review, we summarize recent advancements in the electrochemical detection of pesticides using 2D carbon-based materials. Comprehensive information on electrode fabrication, sensing mechanisms, analytical performance—including sensing range and limit of detection—and the versatility of 2D carbon composites for pesticide detection is provided. Challenges and future perspectives in developing highly sensitive and selective electrochemical sensing platforms are also discussed, highlighting their potential for simultaneous pesticide monitoring in food and environmental samples. Carbon-based electrochemical sensors have been the subject of many investigations, but their practical application in actual environmental and food samples is still restricted because of matrix effects, operational instability, and repeatability issues. In order to close the gap between laboratory research and real-world applications, this review critically examines sensor performance in real-sample conditions and offers innovative approaches for in situ pesticide monitoring. Full article

Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to provide a comprehensive account of the KSaOs underpinning safe civilian and commercial drone operations. Prior research demonstrates that early work drew heavily on military contexts, which may not generalize to contemporary civilian operations characterized by smaller platforms, single-pilot tasks, and diverse industry applications. Studies employing subject matter experts highlight cognitive demands in areas such as situational awareness, workload management, planning, fatigue recognition, perceptual acuity, and decision-making. Accident analyses, predominantly using the human factors accident classification system and related taxonomies, show that skill errors and preconditions for unsafe acts are the most frequent contributors to RPAS occurrences, with limited evidence of higher-level latent organizational factors in civilian contexts. Emerging research emphasizes that RPAS pilots increasingly perform data-collection tasks integral to professional workflows, requiring competencies beyond aircraft handling alone. The review identifies significant gaps in training specificity, selection processes, and taxonomy suitability, indicating opportunities for future research to refine RPAS competency frameworks and support improved operational safety. Full article

Background/Objectives: Wearable affective human–computer interaction increasingly relies on sparse-channel EEG signals to ensure comfort and practicality in real-life scenarios. However, the limited information provided by sparse-channel EEG, together with pronounced inter-subject variability, makes reliable cross-subject emotion recognition particularly challenging. Methods: To address these challenges, we propose a cross-subject emotion recognition model, termed TSCL-LwF, based on sparse-channel EEG. It combines a multi-scale convolutional network (TSCL) and an incremental learning strategy with Learning without Forgetting (LwF). Specifically, the TSCL is utilized to capture the spatio-temporal characteristics of sparse-channel EEG, which employs diverse receptive fields of convolutional networks to extract and fuse the interaction information within the local prefrontal area. The incremental learning strategy with LwF introduces a limited set of labeled target domain data and incorporates the knowledge distillation loss to retain the source domain knowledge while enabling rapid target domain adaptation. Results: Experiments on the DEAP dataset show that the proposed TSCL-LwF achieves accuracy of 77.26% for valence classification and 80.12% for arousal classification. Moreover, it also exhibits superior accuracy when evaluated on the self-collected dataset EPPVR. Conclusions: The successful implementation of cross-subject emotion recognition based on a sparse-channel EEG will facilitate the development of wearable EEG technologies with practical applications. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article