Search Results (23,753)

Background/Objectives: Sports tourism, particularly international running events such as half marathons and marathons, has rapidly grown due to rising health consciousness and active lifestyles. Runners competing abroad face unique nutritional challenges that extend beyond physiological needs, including adaptation to local food cultures and psychosocial factors. This study aims to explore the nutritional difficulties encountered by international runners during competitions abroad, using participants of the Poznan Half Marathon 2025 as a case example. Methods: A qualitative research design was employed, involving semi-structured in-depth interviews with 12 international runners from the United Kingdom, Germany, and Ukraine. Participants had at least two years of experience competing internationally. Results: Four primary categories of nutritional challenges emerged: (1) quality and availability of food, (2) adaptation to local eating habits and physiological impacts, (3) hydration and access to appropriate fluids, and (4) logistical factors and the interactions between psychological stress, physical well-being, and nutritional choices. These factors influenced runners’ preparation, race-day performance, and recovery, highlighting the complexity of managing nutrition in unfamiliar environments. Conclusions: Nutritional challenges for international runners are multidimensional, requiring flexible and culturally sensitive nutritional strategies. Although these findings offer useful insights, they are based on a small, specific sample and should be generalized with caution. Further research is necessary to explore the broader applicability of the findings and their relevance to diverse athletic populations and contexts. Full article

This study employs advanced numerical simulation to investigate the influence of shelf temperature on the freeze-drying kinetics and product quality of Cordyceps militaris. Emphasis is placed on the glass transition and structural collapse mechanisms during the primary drying stage. A detailed computational model was developed to predict temperature profiles, glass transition temperature, collapse temperature, and moisture distribution under varying process conditions. Simulation results indicate that maintaining the shelf temperature below 10 °C minimizes the risk of structural collapse and volume shrinkage while improving drying efficiency and product stability. Based on the model, an optimal freeze-drying protocol is proposed: shelf heating at 0 °C, condenser plate at −32 °C, and chamber pressure at 35 Pa. Experimental validation confirmed the feasibility of this regime, yielding a shrinkage of 9.52%, a color difference (ΔE) of 4.86, water activity of 0.364 ± 0.018, and a rehydration ratio of 55.14 ± 0.789%. Key bioactive compounds, including adenosine and cordycepin, were well preserved. These findings underscore the critical role of simulation in process design and optimization, contributing to the development of efficient and high-quality freeze-dried functional food products. Full article

Land–sea segmentation is a fundamental task in remote sensing image analysis, and plays a vital role in dynamic coastline monitoring. The complex morphology and blurred boundaries of coastlines in remote sensing imagery make fast and accurate segmentation challenging. Recent deep learning approaches lack the ability to model spatial continuity effectively, thereby limiting a comprehensive understanding of coastline features in remote sensing imagery. To address this issue, we have developed VMMT-Net, a novel dual-branch semantic segmentation framework. By constructing a parallel heterogeneous dual-branch encoder, VMMT-Net integrates the complementary strengths of the Mix Transformer and the Visual State Space Model, enabling comprehensive modeling of local details, global semantics, and spatial continuity. We design a Cross-Branch Fusion Module to facilitate deep feature interaction and collaborative representation across branches, and implement a customized decoder module that enhances the integration of multiscale features and improves boundary refinement of coastlines. Extensive experiments conducted on two benchmark remote sensing datasets, GF-HNCD and BSD, demonstrate that the proposed VMMT-Net outperforms existing state-of-the-art methods in both quantitative metrics and visual quality. Specifically, the model achieves mean F1-scores of 98.48% (GF-HNCD) and 98.53% (BSD) and mean intersection-over-union values of 97.02% (GF-HNCD) and 97.11% (BSD). The model maintains reasonable computational complexity, with only 28.24 M parameters and 25.21 GFLOPs, striking a favorable balance between accuracy and efficiency. These results indicate the strong generalization ability and practical applicability of VMMT-Net in real-world remote sensing segmentation tasks. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

The amount of protein and fat in raw milk determines its quality, value in the marketplace, and related payment to suppliers. Technicians use expensive specialized laboratory equipment to measure milk quality in specialized laboratories. The continuous quality monitoring of the milk supply in the supplier’s tanks enables the production of higher quality products, better milk supply chain optimization, and reduced milk waste. This paper presents an inexpensive AI-powered IoT sensor that continuously measures the protein and fat in the raw milk in the tanks of dairy farms, pickup trucks, and intermediate storage depots across any milk supply chain. The proposed sensor consists of an in-tank IoT device and related software components that run on any IoT platform. The in-tank IoT device quality incorporates a low-cost spectrometer and a microcontroller that can send milk supply measurements to any IoT platform via NB-IoT. The in-tank IoT device of the milk quality sensor is housed in a food-safe polypropylene container that allows its deployment in any milk tank. The IoT software component of the milk quality sensors uses a specialized machine learning (ML) algorithm to translate the spectrometry measurements into milk fat and protein measurements. The paper presents the design of an in-tank IoT sensor and the corresponding IoT software translation of the spectrometry measurements to protein and fat measurements. Moreover, it includes an experimental milk quality sensor evaluation that shows that sensor accuracy is ±0.14% for fat and ±0.07% for protein. Full article

The quality of living environments for older adults represents a critical component of social sustainability in an ageing society. Among various housing options, nursing homes are the most institutionalised form of elderly care and demand special attention regarding architectural design. This paper explores the impact of architectural and open space features on social sustainability in living environments for older adults. A comparative analysis of three Slovenian nursing homes is presented. The first two, built in the post-war period, were based on elevated architectural criteria inspired by Swedish human-centred housing design. The third was completed in 2021 in the post-COVID-19 era, which revealed the vulnerability of such housing typologies. An integrated methodological framework was developed by applying two complementary assessment tools: the Safe and Connected, developed by the authors to evaluate the spatial, health, and social resilience of nursing home environments, and the Well-being and Integration, addressing the role of open space with a particular emphasis on the identification of older adults with their environment and the facilitation of social resilience. Both tools evaluate indicators linked to the social dimension of quality of life for older adults. The results show a gradual improvement in architectural quality from the 1960s to the 1980s, followed by a partial decline in the contemporary case. The Swedish example scored highest across both tools. In contrast, the newest Slovenian facility scored surprisingly low in social integration, highlighting critical gaps in current nursing home design. This study demonstrates the value of applying interdisciplinary, tool-based evaluations in identifying design strategies that foster resilient and inclusive LTC environments, and the proposed framework may serve as a decision-making aid for architects, planners, and policymakers. This research highlights the importance of reintroducing human-oriented design principles to support socially sustainable nursing home environments. Full article

(1) Background: The provision of monofunctional or inadequately distributed services in urban park green spaces often constrains residents’ opportunities and diversity for outdoor activities, particularly limiting access and participation for specific age groups or activity preferences. However, functional nodes with temporal and spatial flexibility demonstrate high-quality characteristics of resilient and shared services through integrated development. Accurately identifying user demand provides a solid basis for optimizing the functional configuration of urban parks. (2) Methods: This study took the old city area of Zhengzhou, Henan Province, China, as a case study. By collecting and integrating various types of data, such as geographic spatial data, field investigation data, and behavioral observations, we developed a population demand quantification method and a modular analysis approach for park service functions. This framework enabled correlation analysis between diverse user needs and park services. The study further classified and combined park functions into modular units, quantifying their elastic and shared service capabilities—namely, the adaptive flexibility and shared utilization capacity of park services. Additionally, we established a demand-responsive evaluation system for identifying and diagnosing problem areas in park services based on multi-source data. (3) Results: The demand response index and diagnostic results indicate that the supply of fitness facilities—particularly equipment-based installations—is insufficient within the old urban district of Zhengzhou. Among the three user groups—children, young and middle-aged adults, and the elderly—the elderly population exhibited the lowest demand response index, revealing a significant gap in meeting their specific needs. (4) Conclusions: Based on the research findings, a three-tier optimization strategy is proposed: A. improve green space connectivity to expand the service coverage of parks; B. implement multifunctional overlay and coordinated integration in spatial design based on site characteristics and demand diagnostics; and C. increase the total supply of facilities to enhance spatial efficiency in parks. By integrating the demand assessment data and diagnostic results, this approach enabled a data-driven reorganization of service types and targeted allocation of resources within existing park infrastructure, offering a practical tool and reference for the planning of urban outdoor activity spaces. Full article

Air quality monitoring systems based on Internet of Things (IoT) technology are critical for addressing environmental and public health challenges, but their energy efficiency poses a significant challenge to their autonomous and scalable deployment. This study investigates strategies to enhance the energy efficiency of IoT-based air quality monitoring systems. A comprehensive analysis of sensor types, data transmission protocols, and system architectures was conducted, focusing on their energy consumption. An energy-efficient system was designed using the Smart Air sensor, Zigbee gateway, and Mini UPS, with its performance evaluated through daily energy consumption, backup operation time, and annual energy use. An integrated efficiency index (IEI) was introduced to compare sensor models based on functionality, energy efficiency, and cost. The proposed system achieves a daily energy consumption of 72 W·h, supports up to 10 h of autonomous operation during outages, and consumes 26.28 kW·h annually. The IEI analysis identified the Ajax LifeQuality as the most energy-efficient sensor, while Smart Air offers a cost-effective alternative with broader functionality. The proposed architecture and IEI provide a scalable and sustainable framework for IoT air quality monitoring, with potential applications in smart cities and residential settings. Future research should explore renewable energy integration and predictive energy management. Full article

Urban green spaces are increasingly recognized as important habitats for pollinators, and wildflower seed mixes marketed as pollinator-friendly are gaining popularity, though their actual conservation value remains poorly understood. This study provides the first systematic screening of commercially available seed mixes in Portugal, evaluating their taxonomic composition, origin, life cycle traits, and potential to support pollinator communities. A total of 229 seed mixes were identified. Although these have a predominance of native species (median 86%), the taxonomic diversity was limited, with 91% of mixes comprising species from only one or two families, predominantly Poaceae and Fabaceae, potentially restricting the range of floral resources available to pollinators. Only 21 seed mixes met the criteria for being pollinator-friendly, based on a three-step decision tree prioritizing native species, extended flowering periods, and visual diversity. These showed the highest percentage of native species (median 87%) and a greater representation of flowering plants. However, 76% of all mixes still included at least one non-native species, although none is considered invasive. Perennial species dominated all seed mix types, indicating the potential for the long-term persistence of wildflower meadows in urban spaces. Despite their promise, the ecological quality and transparency of the seed mix composition remain inconsistent, with limited certification or information on species origin. This highlights the need for clearer labeling, regulatory guidance, and ecologically informed formulations. Seed mixes, if properly designed and implemented, represent a largely untapped yet cost-effective tool for enhancing the pollinator habitats and biodiversity within urban landscapes. Full article

As part of its modernisation and economic diversification policies, Saudi Arabia is building smart, sustainable cities intended to improve quality of life and meet environmental goals. However, involving the public in urban planning remains complex, with traditional methods often proving expensive, time-consuming, and inaccessible to many groups. Integrating artificial intelligence (AI) into public participation may help to address these limitations. This study explores whether Saudi residents are ready to engage with AI-driven tools in urban planning, how they prefer to interact with them, and what ethical concerns may arise. Using a quantitative, survey-based approach, the study collected data from 232 Saudi residents using non-probability stratified sampling. The survey assessed demographic influences on AI readiness, preferred engagement methods, and perceptions of ethical risks. The results showed a strong willingness among participants (200 respondents, 86%)—especially younger and university-educated respondents—to engage through AI platforms. Visual tools such as image and video analysis were the most preferred (96 respondents, 41%), while chatbots were less favoured (16 respondents, 17%). However, concerns were raised about privacy (76 respondents, 33%), bias (52 respondents, 22%), and over-reliance on technology (84 respondents, 36%). By exploring the intersection of generative AI and participatory urban governance, this study contributes directly to the discourse on inclusive smart city development. The research also offers insights into how AI-driven public engagement tools can be integrated into urban planning workflows to enhance the design, governance, and performance of the built environment. The findings suggest that AI has the potential to improve inclusivity and responsiveness in urban planning, but that its success depends on public trust, ethical safeguards, and the thoughtful design of accessible, user-friendly engagement platforms. Full article

In Network-on-Chip (NoC) research, the task mapping problem has attracted considerable attention as a core issue influencing system performance. As an NP-hard problem, it remains challenging, and existing algorithms exhibit limitations in both mapping quality and computational efficiency. To address this, a method named ET (Enhanced Coati Optimization Algorithm) is proposed, which leverages the nature-inspired Coati Optimization Algorithm (COA) for task mapping. An incremental hill-climbing strategy is integrated to improve local search capabilities, and a dynamic mechanism for adjusting the exploration–exploitation ratio is designed to better balance global and local searches. Additionally, an initial mapping strategy based on spectral clustering is introduced, which utilizes inter-task communication strength to cluster tasks, thereby improving the quality of the initial population. To evaluate the effectiveness of the proposed algorithm, the performance of the ET algorithm is compared and analyzed against various existing algorithms in terms of communication cost, energy consumption, and latency, using both real benchmark task maps and randomly generated task maps. Experimental results demonstrate that the ET algorithm consistently outperforms the compared algorithms across all performance metrics, thereby confirming its superiority in addressing the NoC task mapping problem. Full article

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation. Full article

Background: Lumbar Interbody Fusion (LIF) is a surgical procedure aimed at addressing a range of pathological conditions affecting the structure and function of the spine. Patient-Specific Interbody Cages (PSICs) are an emerging technology that are used in LIF; however, there is a lack of clinical outcome data, making it difficult to assess the potential risks, benefits, and value of PSICs. The purpose of this present study is to contribute data to the field on this new emerging technology. The aims were to investigate Quality of Life (QoL), pain, and the complications of PSICs in LIF. To provide a comparative cohort, we performed a systematic review of patient-reported outcomes of conventional fusion techniques. Methods: This study reports on a multi-surgeon, multi-centre clinical trial of patients with lumbar degenerative disc disease, necessitating discectomy and fusion. All patients underwent LIF procedures with 3D-printed PSICs. Pain Visual Analogue Scale (VAS) and QoL (EQ-5D) scores were collected pre-operatively and at 6m, 12m, and 24m post-operatively. For comparative purposes, we performed a systematic review of the VAS scores from traditional LIF cages and analysed the Australian Spine Registry QoL data. Results: The literature search yielded 4272 publications. The studies were subdivided into four groups based on the interbody device type. All the groups demonstrated improvements in the VAS (for back pain) scores post-operatively. In total, 78 patients (109 instrumented levels) underwent LIF procedures with 3DP PSICs. There were three reoperations (3.8%) and no revisions of any PSIC. The mean VAS scores improved significantly (p < 0.01) from 7.85 (1.50 SD) pre-operatively to 2.03 (2.13 SD) at 24 months post-operatively. The mean QoL index scores improved significantly (p < 0.01) from a pre-operative 0.257 (0.332 SD) to 0.815 (0.208 SD) at 24 months. Conclusions: The systematic review indicated that device fixation to the interbody space was associated with lower VAS scores. The results from the investigational cohort suggest that PSICs may represent a new progression in implant design for spinal fusion, with an associated clinical benefit for LIF. Full article

Passenger safety remains a primary goal in vehicle engineering, requiring the development of advanced passive safety systems to reduce injuries during collisions. Impact attenuators (particularly for race cars) are a crucial component for the safety of the driver. The impact of the impact attenuator (IA) is demonstrated by the behavior of a seat-belted dummy in a frontal collision with a rigid wall. The aim of this paper is to confirm the qualities of water as a damping agent in the manufacturing of the IA. To reach a conclusion, a theoretical model is used and experimental tests are performed. Once the loads operating on the dummy have been identified, it is confirmed that they fall within the range that the existing requirements recommend. The car is viewed as a structure with a seat-belt-fastened dummy and an impact attenuator. Research is being conducted on a new water-based impact attenuator technology. A frontal collision of the car–dummy assembly was taken into consideration when analyzing the dummy’s behavior in accordance with the criteria. A simulation program was used to calculate the accelerations at various points on the mannequin’s body as well as the force that manifested on the seat belts. So, the good qualities of IAs using water are revealed and support designers in their efforts to obtain better shock behavior. In the simulation, the variation of internal energy accumulated by the vehicle, displacements and velocities of various points on the chassis, as well as the accelerations of the vehicle and the occupant were determined. In the experiment, the vehicle velocities for both test cases were established and used in the simulation, and the accelerations of the vehicle and dummy were measured. The assessment was carried out by comparing experimental and simulation data, focusing on acceleration values recorded on both the dummy and the vehicle. Evaluation criteria such as HIC and ThAC were applied to determine the severity of the impact and the effectiveness of the proposed water-based attenuator. Full article

Objective: The present review aims to compare the oral microbial profile (OMP) of patients undergoing fixed orthodontic therapy (OT) versus clear aligner therapy (CAT) for the treatment of malocclusions. Methods: Clinical studies were included. Case-reports/-series, letters to the editor, reviews, perspectives, and expert opinions were excluded. Indexed databases (MEDLINE/PubMed, Embase, Scopus, and Web of Science) were searched up to the end point of May 2025, without time and language barriers. The study was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias (RoB) and quality of evidence were assessed. Results: Three randomized clinical trials (RCTs) and seven non-RCTs were included. In all RCTs and five non-RCTs, OMP was assessed using subgingival plaque samples. Periodontopathogenic bacteria and Gram-negative anaerobic microbes were more often identified in patients undergoing fixed OT than CAT. The biofilm mass was higher in patients undergoing fixed OT than CAT. In two RCTs, periodontopathogenic bacteria were dominant among patients undergoing fixed OT than CAT. All RCTs and two non-RCTs had a high RoB. The certainty of evidence was “moderate” in 70% of the studies. Conclusions: Due to a high RoB, variability in study designs, and lack of power analysis, direct comparisons remain limited. Full article