Search Results (11,720)

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes’ technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination. Full article

Rural tourism agglomerations are increasingly viewed as catalysts for diversified regional growth, integrated rural revitalization, and improved farmer prosperity. However, most studies focus on urban and developed regions, leaving spatial patterns and evolutionary mechanisms in underdeveloped rural areas poorly understood. This study takes Jilin Province, an economically lagging region, as an example, measuring rural tourism agglomeration using spatial analysis methods including the Gini coefficient, nearest-neighbor index, Ripley’s K function, kernel density, and buffer analysis. Results show that agglomeration is significant and strengthening over time, with clear regional variations. All types of rural tourism products exhibit an “increase followed by decrease” pattern across spatial scales, evolving from isolated “nodes” to continuous “areas”. Agglomeration is subject to triple constraints from natural, economic, and social dimensions. This study suggests that high-quality rural tourism development should leverage point–axis spillover from flagship scenic areas, promote surface expansion of characteristic villages and towns, and strengthen network connectivity through roads and talent-information channels. Full article

Background/Objectives: The concurrent circulation of SARS-CoV-2 with influenza A and B viruses and respiratory syncytial virus (RSV) represents a new diagnostic challenge in the post-COVID-19 area, especially considering that these infections have overlapping clinical presentations but different approaches to treatment and management. Multiplexed molecular testing on point-of-care platforms that focus on the simultaneous detection of multiple respiratory viruses in a single tube constitutes a useful approach for diagnosis of respiratory infections in decentralized clinical settings. This study evaluated the analytical performances of the VitaSIRO solo™ SARS-CoV-2/Flu/RSV Assay performed on the VitaSIRO solo™ Instrument (Credo Diagnostics Biomedical Pte. Ltd., Singapore, Republic of Singapore). Methods: With a view to accreditation, the criteria of the 2022-revised EN ISO 15189:2022 norma were applied for the retrospective on-site verification of method using anonymized respiratory specimens collected during the last 2024–2025 autumn–winter season in France. Results: Usability and satisfaction were comparable to current reference point-of-care platforms, such as the Cepheid GeneXpert^® Xpress System (Cepheid Diagnostics, Sunnyvale, CA, USA). Repeatability and reproducibility (2.34–4.49% and 2.78–5.71%, respectively) demonstrated a high level of precision. The platform exhibited a low invalid rate (2.9%), with most resolving on retesting. Analytical performance on 301 clinical samples showed high overall sensitivities: 94.8% for SARS-CoV-2 (Ct ≤ 33), 95.8% for influenza A and B viruses, 95.2% for RSV, and 95.4% for all viruses. Specificities were consistently high (99.2–100.0%). False negatives (2.6%) were predominantly associated with high Ct values. Agreement with the comparator reference NeuMoDx™ Flu A-B/RSV/SARS-CoV-2 Vantage Assay (Qiagen GmbH, Hilden, Germany) was almost perfect (Cohen’s κ 0.939–0.974), and a total of 91.1%, 94.8%, and 100.0% of Ct values were within the 95% limits of agreement for the detection of SARS-CoV-2, influenza A and B viruses, and RSV, respectively, by Bland–Altman analyses. Passing–Bablok regression analyses demonstrated good Ct values correlation between VitaSIRO solo™ and NeuMoDx™ assays, with a slight, non-significant, positive bias for the VitaSIRO solo™ assay (mean absolute bias +0.509 to +0.898). Conclusions: These findings support VitaSIRO solo™ Instrument as a user-friendly and reliable point-of-care platform for the rapid detection and differentiation of SARS-CoV-2, influenza A and B viruses, and RSV responding to the EN ISO 15189:2022 criteria for accreditation to be implemented in hospital or decentralized settings. Full article

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article

Castles situated in rural areas occupy a distinctive position within the European heritage landscape, serving economic, residential and symbolic functions. While the great urban royal residences have benefited from constant attention, conservation and valorisation in Central and Eastern Europe, castles in rural areas have often been subjected to systematic neglect. The objective of this study is to analyse three castles (Bulci, Căpâlnaș and Petriș) in the Lower Mureș Valley in Romania, with a view to observing how these symbols of a fragmented past have been marked by historical ruptures, regime changes and marginalisation policies, and the current potential of these castles to be transformed from derelict spaces into spaces of local importance. Should our research contribute to the study of the transformation of derelict spaces of historical castles into invigorating spaces, from a methodological point of view the following three steps were taken: (1) an assessment of the state of conservation and the factors that led to the degradation of these noble domains was carried out; (2) research was conducted on the perception of stakeholders regarding how castles can become a generator of local development; and (3) an analysis of the development proposals around these castles from stakeholders was performed. The study is based on 35 semi-structured interviews conducted with stakeholders and residents of the castle communities analysed, and on a thematic content analysis of these interviews. The findings of the research suggest a state of conservation that is insecure, and the perceptions of stakeholders indicate a necessity for institutional intervention and public–private partnerships. There is also a conviction that attracting large-scale investors is essential for the revitalisation of these monuments. The responses indicate a genuine concern for the future of the castles. The proposals for the development of the castles are oriented towards their utilisation in cultural tourist circuits. Full article

In cemented endoprosthetics, closed prepacked mixing systems represent the most advanced generation of cementing technology. (1) Background: The purpose of the present study is to evaluate four approved prepacked systems—Palacos^® R+G pro, SmartMix™ Cemvac GHV, Optipac^® Refobacin and Cemex^® System Genta—with a focus on practical handling and intraoperative hygiene. (2) Method: The systems were evaluated according to established standard test methods for bone cements (ISO 5833), including dough time, setting time, additional mechanical tests and the level of system closure. (3) Results: The results show that all systems are safe to use and meet the general requirements, but there are relevant differences in terms of intraoperative hygiene. The Palacos R+G pro system shows significantly shorter doughing and setting times, which helps to minimize wound exposure during surgery and thus significantly reduces the overall operating time and the risk of bacterial contamination. Two of the systems cannot be classified as completely closed “pre-packaged systems.” In two cases, the system must be temporarily opened before mixing to insert the mixing element, which may result in a temporary but clinically relevant impairment of sterility and a corresponding potential risk of contamination. (4) Conclusion: From a hygienic point of view, systems that remain completely closed throughout the entire preparation process can offer advantages in terms of infection prevention. This was the case for all systems tested. Short handling times, reduced exposure of the surgical site and a shorter overall duration of the procedure could further improve intraoperative safety and reduce the risk of contamination. In terms of intraoperative hygiene, the Palacos R+G pro system achieved the best results compared to the three other systems tested due to its rapid readiness for use and comparatively short setting time (according to ISO 5833). Cemex System Genta performed worst in this respect due to its late doughing time and setting time. Full article

In this paper, a high-precision 3D object detector—Voxel–RCNN—is adopted as the baseline detector, and an improved detector named RCAVoxel-RCNN is proposed. To address various issues present in current mainstream 3D point cloud voxelisation methods, such as the suboptimal performance of Region Proposal Networks (RPNs) in generating candidate regions and the inadequate detection of small-scale objects caused by overly deep convolutional layers in both 3D and 2D backbone networks, this paper proposes a Cascade Attention Network (CAN). The CAN is designed to progressively refine and enhance the proposed regions, thereby producing more accurate detection results. Furthermore, a 3D Residual Network is introduced, which improves the representation of small objects by reducing the number of convolutional layers while incorporating residual connections. In the Bird’s-Eye View (BEV) feature extraction network, a Residual Attention Network (RAN) is developed. This follows a similar approach to the aforementioned 3D backbone network, leveraging the spatial awareness capabilities of the BEV. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to assign dynamic weights to features, allowing the network to focus more effectively on informative features. Experimental results on the KITTI validation dataset demonstrate the effectiveness of the proposed method, with detection accuracy for cars, pedestrians, and bicycles improving by 3.34%, 10.75%, and 4.61%, respectively, under the KITTI hard level. The primary evaluation metric adopted is the 3D Average Precision (AP), computed over 40 recall positions (R40). The Intersection over IoU thresholds used are 0.7 for cars and 0.5 for both pedestrians and bicycles. Full article

In view of the problems that the track mode accuracy of the automatic steering gear on survey ships cannot meet the requirements of hydrographic survey accuracy and the workload of manual steering is large, an intelligent auxiliary decision-making algorithm based on LSTM and multiple linear regression is proposed. By learning historical track information, marine environment information, historical steering data, hull state data, etc., it provides the helm with auxiliary operation prompt information, such as the command course and its adjustment timing (time range, area), so as to reduce the number of times the helm steers. The effectiveness of the algorithm is verified through sea trials. The results show that the number of steering times is reduced by 45.5% and the number of effective measuring points is increased by 1.5% through the algorithm in this paper. This result confirms that the algorithm can improve the operational efficiency of offshore survey tasks by optimizing human–computer interaction. Full article

Olea europaea L. ‘Lavagnina’ is cultivated in the Eastern Ligurian coast (Italy), and during the pruning process a huge amount of pruning residues is produced. This by-product is generally disposed of by burning, despite still containing bioactive compounds. In particular, olive leaves are indeed rich in secondary metabolites, which can vary both in quality and quantity in relation to the cultivar considered and the area of cultivation. For this reason, we aimed to carry out a pharmacognostic study of the pruned leaves of the unexplored local cultivar ‘Lavagnina’, evaluating the possibility of reusing this by-product for new health applications. The micromorphological characterization was conducted by light and scanning electron microscopy. ‘Lavagnina’ leaf was micromorphologically similar to that of other olive cultivars; however, it differed in terms of midrib structure. Leaf extracts were obtained using solvents of increasing polarity (petroleum ether, chloroform, methanol) and the food-grade solvent, 70% ethanol. A high antioxidant activity was found only for the methanolic (ME) and hydroalcoholic (HAE) extracts, and, therefore, they were then characterized from a phytochemical point of view by LC-ESI-HR-MS. Such analysis allowed the identification of secondary metabolites belonging mainly to secoiridoids, flavonoids, and iridoids. Overall, the HAE had the highest antioxidant activity (17.3 ± 0.6 μg/mL), and it is, therefore, the best candidate for health applications related to a protective effect on a variety of inflammation-related diseases, also considering that inflammation may play a role in cancer progression. Full article

(1) Background: Medical education is moving from a cognition-based to a competency-based model in Germany. Traditional learning activities (LAs) are questioned. Some stakeholders criticise traditional LAs for not facilitating deep learning or operational competency transfer required in practical contexts. This qualitative study aims to take a closer look at the role of lectures in competency-based medical education from a student’s point of view. (2) Methods: Three semi-structured group interviews were held with students from the family medicine curriculum in the summer semester of 2021. Questions focused on the three lectures in this family medicine curriculum and on students’ experiences with lectures in general. One additional expert interview was held with one of the lecturers. The video-recorded interviews were transcribed verbatim and analysed using content analysis. (3) Results: Interview participants highlighted entertainment, the provision of a social and physical learning environment, and the completion of knowledge from books and educational websites as important roles of lectures. Lectures on demand were used by interviewees for time- and space-independent repetition. Lecturer-dependent qualitative differences between lectures were identified by interviewees. Important differences were the extent of interaction, as well as the enthusiasm and preparation of the lecturer. (4) Conclusions: Even though literature suggests that lectures may be a less effective learning activity, under certain circumstances, several aspects make them an essential element of modern curriculum development. By raising interest in a subject, providing a space for discussion and social interaction, interactive lectures appear to be a helpful link between knowledge acquisition and practical training of competencies. Full article

Over Over the past years, the field of Machine Learning (ML) and Deep Learning (DL) has seen strong developments both in terms of software and hardware, with the increase of specialized devices. One of the biggest challenges in this field is the inference phase, where the trained model makes predictions of unseen data. Although computationally powerful, traditional computing architectures face limitations in efficiently managing requests, especially from an energy point of view. For this reason, the need arose to find alternative hardware solutions, and among these, there are Field Programmable Gate Arrays (FPGAs): their key feature of being reconfigurable, combined with parallel processing capability, low latency and low power consumption, makes those devices uniquely suited to accelerating inference tasks. In this paper, we present a novel approach to accelerate the inference phase of a multi-layer perceptron (MLP) using BondMachine framework, an OpenSource framework for the design of hardware accelerators for FPGAs. Analysis of the latency, energy consumption, and resource usage, as well as comparisons with respect to standard architectures and other FPGA approaches, is presented, highlighting the strengths and critical points of the proposed solution. The present work represents an exploratory study to validate the proposed methodology on MLP architectures, establishing a crucial foundation for future work on scalability and the acceleration of more complex neural network models. Full article

With so many people becoming distanced from the world of agriculture, what is the best way to bridge the knowledge gap? Studies have shown that video messaging could be a key factor in lessening this gap. This study assessed the perceptions of young adults about animal agriculture and the effectiveness of emotional and cognitive videos featuring local farmers and industry experts to alter perception and build trust in the beef industry. An invitation to participate was sent to 10,000 Auburn University students, and responses were closed after 500 complete responses were received. Participants were directed to a Qualtrics (2022) survey with a 5-point Likert scale and open-ended questions. The questionnaire included opinions on animal welfare, the diet/health of red meat consumers, and environmental/sustainability aspects of the beef industry. After viewing one emotional and one cognitive video, each lasting four minutes, participants retook the survey. Data were analyzed using paired t-tests in SPSS (Version 28). Results showed participants’ views improved by 82% after watching the videos. ATLAS (Series 9) was used to code key positive and negative words in open responses. Participants reported a stronger preference for the emotional video compared to the cognitive video (190 vs. 99, p < 0.05). However, because the videos were always shown in the same order, this finding should be interpreted with caution, as order effects may have influenced participants’ responses. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

This study focuses on firefighters’ relationship with different types of change in their profession and what barriers and facilitators might contribute to how they respond. Informed by the Force Field analysis of change, interviews were conducted to better understand what specific barriers and facilitators contribute to their views on types of change and the level of influence they carried. Twenty-five interviews were conducted with firefighters from a variety of backgrounds, including different ages, genders, ranks, and experience levels for both career and volunteer firefighters. Thematic analysis identified different responses to four common rationales that helped to explain the acceptance or dismissal of changes. These were as follows: (1) openness or apprehension towards change; (2) the results of a cost–benefit analysis that considered financial and manpower limits, perceived legitimacy of the problem, and efficacy of the solution; (3) reference to past experiences with changes that had failed or succeeded; and (4) trusted messengers that respected the chain of command were preferred. These themes are applicable across multiple types of changes, including technological and cultural adaptation. However, they also reveal challenges that may emerge due to friction with firefighters’ professional identities and traditional masculine norms. The patterns identified here can help to inform future efforts to implement changes and to anticipate likely points of friction or motivation that can be leveraged. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article