Search Results (2,963)

Background: Groin pain is a complex and multifactorial condition commonly observed in athletes, often impairing performance and quality of life. While conservative treatments are the first-line approach, the variability in intervention protocols and inconsistent evidence necessitate a comprehensive synthesis of current knowledge. Methods: This narrative review analyzed the available literature on conservative management of groin pain in athletes. A systematic search was conducted across the MEDLINE, Cochrane CENTRAL, Scopus, PEDro, and Web of Science databases. Studies focusing on pain reduction, functional recovery, return-to-sport outcomes, and prevention strategies were included. Findings were synthesized to evaluate the efficacy of conservative interventions and identify gaps in the evidence. Results: Conservative treatments, particularly active rehabilitation and multimodal therapy, demonstrated significant efficacy in reducing pain (50–80%) and improving function, as measured by tools such as the HAGOS score. Return-to-sport rates ranged from 70% to 90%, depending on intervention type and adherence. Screening tools, including the adductor squeeze test, were effective in predicting and preventing groin injuries. However, variability in methodologies, small sample sizes, and a lack of long-term follow-up limited the generalizability of the findings. Conclusions: Conservative management remains a cornerstone for treating groin pain in athletes, offering effective outcomes for pain reduction, functional recovery, and injury prevention. However, standardized protocols and high-quality research are needed to enhance clinical guidance and optimize patient outcomes. Full article

Ubiquitous connectivity is envisioned through the integration of terrestrial (TNs) and non-terrestrial networks (NTNs). However, NTNs face multiple routing and Quality of Service (QoS) provisioning challenges due to the mobility of network nodes. Distributed Software-Defined Networking (SDN) combined with Multi-Agent Deep Reinforcement Learning (MADRL) is widely used to introduce programmability and intelligent Traffic Engineering (TE) in TNs, yet applying DRL to NTNs is hindered by frequently changing state sizes, model scalability, and coordination issues. This paper introduces 3DQR, a novel TE framework that combines hierarchical multi-controller SDN, hierarchical MADRL based on Graph Neural Networks (GNNs), and network topology predictions for QoS path provisioning, effective load distribution, and flow rejection minimisation in future 3D networks. To enhance SDN scalability, introduced are metrics and path operations abstractions to facilitate domain agents coordination by the global agent. To the best of the authors’ knowledge, 3DQR is the first routing scheme to integrate MADRL and GNNs for optimising centralised routing and path allocation in SDN-based 3D mobile networks. The evaluations show up to a 14% reduction in flow rejection rate, a 50% improvement in traffic distribution, and effective QoS class prioritisation compared to baseline techniques. 3DQR also exhibits strong transfer capabilities, giving consistent performance gains in previously unseen environments. Full article

The Paris Agreement (PA), an authoritative political document on emissions reduction and low-carbon initiatives, requires the transportation sector to take decisive action toward achieving low-carbon objectives. This study uses CiteSpace to conduct a bibliometric analysis of 746 transportation sector low-carbon (TSLC) research articles published since the PA. The analysis reveals that China, the United States, and the United Kingdom are the leading contributors, with Tsinghua University being the most prolific institution. Sustainability, the Journal of Cleaner Production, and Transportation Research Part D are the most influential in terms of publication volume. This study reviews recent studies of TSLC from the perspective of renewable energy and technology applications, the evolution of intelligent transport systems, policy support, and public participation. Then, an in-depth interpretation of the potential impacts of low-carbon policies on the circulation of transport commodities, the energy system, the transportation system, and socioeconomic development is conducted. Finally, a knowledge map is presented, illustrating pathways for achieving TSLC targets under the guidance of the PA, laying a foundation for future research and policy efforts in sustainable transport. Full article

Municipal solid waste management (MSWM) is an advantageous subject for implementing circular economy (CE) strategies. In this context, the waste generation and waste collection steps of MSWM in the third largest Greek city (Patras), in western Greece, were evaluated according to the proposed CE indicators. Public opinion and knowledge on CE in MSWM were also evaluated in a small sample of citizens from the Municipality of Patras (207 individuals) through a questionnaire survey. Results showed that (a) the CE performance indicators objectively assessed circularity in MSWM; (b) Patras fared better than Greece and EU in some indicators [waste generation (kg per capita × year), food waste generation (kg per capita × year)] and worse in others [food waste composting (% w/w), WEEE recycling (kg per capita × year)]; (c) citizens have not adopted CE practices in their waste management; and (d) there is a clear reluctance to change practices in older individuals. Based on these results, some recommendations for improvement were made. These results may aid in delineating existing conditions in MSWM in large eastern Mediterranean cities and contribute to the transition toward a reduction in waste disposal and an increase in material reuse. Full article

Lossless image compression is vital for missions with limited data transmission bandwidth. Reducing file sizes enables faster transmission and increased scientific gains from transient events. This study compares two wavelet-based image compression algorithms, CCSDS 122.0 and JPEG 2000, used in the European Space Agency Comet Interceptor and Hera missions, respectively, in varying scenarios. The JPEG 2000 implementation is sourced from the JasPer library, whereas a custom implementation was written for CCSDS 122.0. The performance analysis for both algorithms consists of compressing simulated asteroid images in the visible and near-infrared spectral ranges. In addition, all test images were noise-filtered to study the effect of the amount of noise on both compression ratio and speed. The study finds that JPEG 2000 achieves consistently higher compression ratios and benefits from decreased noise more than CCSDS 122.0. However, CCSDS 122.0 produces comparable results faster than JPEG 2000 and is substantially less computationally complex. On the contrary, JPEG 2000 allows dynamic (entropy-permitting) reduction in the bit depth of internal data structures to 8 bits, halving the memory allocation, while CCSDS 122.0 always works in 16-bit mode. These results contribute valuable knowledge to the behavioral characteristics of both algorithms and provide insight for entities planning on using either algorithm on board planetary missions. Full article

Wheat is a crucial food crop, and low-temperature stress can severely disrupt its growth and development, ultimately leading to a substantial reduction in wheat yield. Understanding the cold-resistant genes of wheat and their action pathways is essential for revealing the cold-resistance mechanism of wheat, enhancing its yield and quality in low-temperature environments, and ensuring global food security. Rye (Secale cereale L.), on the other hand, has excellent cold resistance in comparison to some other crops. By studying the differential responses of different rye varieties to low-temperature stress at the transcriptome level, we aim to identify key genes and regulatory mechanisms related to cold tolerance. This knowledge can not only deepen our understanding of the molecular basis of rye’s cold resistance but also provide valuable insights for improving the cold tolerance of other crops through genetic breeding strategies. In this study, young leaves of two rye varieties, namely “winter” rye and “victory” rye, were used as experimental materials. Leaf samples of both types were treated at 4 °C for 0, 6, 24, and 72 h and then underwent RNA-sequencing. A total of 144,371 Unigenes were reconstituted. The Unigenes annotated in the NR, GO, KEGG, and KOG databases accounted for 79.39%, 55.98%, 59.90%, and 56.28%, respectively. A total of 3013 Unigenes were annotated as transcription factors (TFs), mainly belonging to the MYB family and the bHLH family. A total of 122,065 differentially expressed genes (DEGs) were identified and annotated in the GO pathways and KEGG pathways. For DEG analysis, 0 h 4 °C treated samples were controls. With strict criteria (p < 0.05, fold-change > 2 or <0.5, |log₂(fold-change)| > 1), 122,065 DEGs were identified and annotated in GO and KEGG pathways. Among them, the “Chloroplast thylakoid membrane” and “Chloroplast” pathways were enriched in both the “winter” rye and “victory” rye groups treated with low temperatures, but the degrees of significance were different. Compared with “victory” rye, “winter” rye has more annotated pathways such as the “hydrogen catabolic process”. Although the presence of more pathways does not directly prove a more extensive cold-resistant mechanism, these pathways are likely associated with cold tolerance. Our subsequent analysis of gene expression patterns within these pathways, as well as their relationships with known cold-resistance-related genes, suggests that they play important roles in “winter” rye’s response to low-temperature stress. For example, genes in the “hydrogen catabolic process” pathway may be involved in regulating cellular redox balance, which is crucial for maintaining cell function under cold stress. Full article

Ultrafast laser processing is a critical technology for micro- and nano-fabrication due to its ability to minimize heat-affected zones. The effects of intensity variation on the ultrafast laser ablation of fused silica were investigated to gain fundamental insights into the dynamic modulation of pulse intensity. This study revealed significant enhancement in ablation efficiency for downward ramp intensity modulation compared to the upward ramp. This effect was independent of the repetition rate ranging from 100 Hz to 1 MHz, which suggested that it originates from persistent residual effects of preceding pulses. Photoluminescence experiments indicated that the observed effect is primarily attributed to the dynamic reduction in the ablation threshold caused by the formation of defects such as non-bridging oxygen hole centers. The correlation between the sequence of intensity-modulated pulses and defect formation has been clarified. The knowledge of these correlations, combined with machine learning-based optimization methods, is useful for the optimization of the throughput and quality of ultrafast laser processing. Full article

Background: On 18 April 2024, the United States Department of Agriculture (USDA) published the first food package changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) in over a decade, which reduced some food benefits (juice, milk, canned fish, and infant fruits and vegetables) and offered substitutes (cash-value vouchers (CVVs) or cash-value benefits (CVBs) to redeem for fruits and vegetables, cheese, soymilk, or other dairy products). Methods: To assess the impact of the changes on the consumption and redemption of these food groups, a systematic search was conducted, identifying 23 peer-reviewed publications between 2010 and 2024. Results: They revealed significant shifts in consumption after the 2009 food package changes; e.g., a decline in 100% juice intake following reductions in juice allowances. Additionally, the review highlighted that the 2009 WIC food package revision was associated with more fruit and vegetable consumption after the increase in CVV allowance. While including milk alternatives like soy-based beverages or lactose-free milk or cheese may improve redemption rates and WIC program satisfaction, the long-term impacts of these proposed changes remain unknown. No research was identified on the consumption of canned fish. Conclusions: This review contributes to understanding the changes in redemption and consumption after the last WIC food package changes, identifies the knowledge gap about prospective impacts, and recommends that the WIC agencies implement appropriate evaluations to promote health and nutrition among vulnerable populations. Full article

Lilies, a key cash crop in Lanzhou, China, widely planted in coal-based fields, cultivated fields, and gardens, face significant yield and quality reduction due to weed infestation, which competes for essential nutrients, water, and light. To address this challenge, we propose an advanced weed detection method that combines symmetry-based convolutional neural networks with metaheuristic optimization. A dedicated weed detection dataset is constructed through extensive field investigation, data collection, and annotation. To enhance detection efficiency, we introduce an optimized YOLOv7-Tiny model, integrating dynamic pruning and knowledge distillation, which reduces computational complexity while maintaining high accuracy. Additionally, a novel Chaotic Harris Hawks Optimization (CHHO) algorithm, incorporating chaotic mapping initialization and differential evolution, is developed to fine-tune YOLOv7-Tiny parameters and activation functions. Experimental results demonstrate that the optimized YOLOv7-Tiny achieves a detection accuracy of 92.53% outperforming traditional models while maintaining efficiency. This study provides a high-performance, lightweight, and scalable solution for real-time precision weed management in lily fields, offering valuable insights for agricultural automation and smart farming applications. Full article

This article argues that indigenous knowledge is significantly resourceful for ecological sustainability, without which humanity will not survive. It addresses the intersection between the 15th United Nations Sustainable Development Goal (SGD) “Life on Land” and African indigenous knowledge systems, including the fundamental support system that can sustain the livelihoods of communities. It reconstructs the vision of ecological sustainability from the indigenous knowledge perspective by first analyzing the inadequacy of the United Nations carbon-pricing approach to reforestation and conservation in developing countries. Then, it uses the ethnosphere methodological approach, affirming the ecological ethical warrants found in indigenous epistemology and cosmology in regard to land protection in dialogue with the sustainability vision of Laudado Si’, Querida Amazonia, and Laudate Deum. This article explores indigenous knowledge’s wellspring for ecological sustainability and what it offers for a more sustainable “Life on Land”. It suggests an approach to ecological sustainability that goes beyond a market-based instrument to CO₂ reduction to embrace a view of the “sacramental universe” as essential theological input, without which sustainable “Life on Land” cannot be met. It concludes by showing how African mountain region conservancy practices are not essential in sustaining “Life on Land” not solely because they provide the earth’s freshwater but also because they contain valuable ecologically sensitive cultural and religious wisdom. Full article

In mobile edge computing networks, achieving effective load balancing across edge server nodes is essential for minimizing task processing latency. However, the lack of a priori knowledge regarding the current load state of edge nodes for user devices presents a significant challenge in multi-user, multi-edge node scenarios. This challenge is exacerbated by the inherent dynamics and uncertainty of edge node load variations. To tackle these issues, we propose a deep reinforcement learning-based approach for task offloading and resource allocation, aiming to balance the load on edge nodes while reducing the long-term average cost. Specifically, we decompose the optimization problem into two subproblems, task offloading and resource allocation. The Karush–Kuhn–Tucker (KKT) conditions are employed to derive the optimal strategy for communication bandwidth and computational resource allocation for edge nodes. We utilize Long Short-Term Memory (LSTM) networks to forecast the real-time activity of edge nodes. Additionally, we integrate deep compression techniques to expedite model convergence, facilitating faster execution on user devices. Our simulation results demonstrate that our proposed scheme achieves a 47% reduction in terms of the task drop rate, a 14% decrease in the total system cost, and a 7.6% improvement in the runtime compared to the baseline schemes. Full article

The machining scheme selection (MSS) for features is to choose the optimal machining scheme for a feature before machining. To solve the issue of excessive human subjectivity in the traditional MSS, this paper proposes a simple and easy-to-use method based on process knowledge graph retrieval and through machining scheme similarity matching. First, process knowledge is extracted using natural language processing techniques, focusing on forming ternary groups such as part–feature, feature–attribute, and scheme–resource to construct a multi-level process knowledge graph. This graph is used to retrieve the available machining schemes for the features. Based on the part property, the feature basic information and manufacturing information are used to establish a feature information model and information coding dimensionality reduction. Then, considering the influence coefficient of the process parameter and the usage coefficient of the machining scheme, an improved cosine similarity formula is designed for MSS. According to the maximum similarity, the optimal machining scheme is matched to the feature. Finally, the effectiveness of this method is verified by selecting the machining schemes for six types of hole features on a typical shell part. The results demonstrate that the recommended schemes by the proposed method closely align with the existing mature schemes. Full article

Background/Objectives: To summarize the current state of knowledge regarding personalized, risk-based approaches in active surveillance (AS) for prostate cancer (PCa) and to explore the lessons learned from AS practices in other types of cancer. Methods: This mixed methods review combined a systematic review and a narrative review. The systematic review was conducted according to the Preferred Reporting Items for Systematic rviews and Meta-Analyses (PRISMA) guidelines, with searches performed in the Medline, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar databases. Only studies evaluating personalized, risk-based AS programs for PCa were included. The narrative review focused on AS approaches in other solid tumors (thyroid, breast, kidney, and bladder cancer) to contextualize the findings and highlight lessons learned. Results: After screening 3137 articles, 9 were suitable for inclusion, describing the following four unique risk-based AS tools: PRIAS, Johns Hopkins, Canary PASS, and STRATCANS. These models were developed using data from men with low-risk (Grade Group 1) disease, with little to no magnetic resonance imaging (MRI) data. They used patient information such as (repeated) prostate-specific antigen (PSA) measurements and biopsy results to predict the risk of upgrading at the next biopsy or at radical prostatectomy, or to assign a patient to a pre-defined risk category with a corresponding pre-defined follow-up (FU) regimen. Performance was moderate across models, with the area under the curve/concordance index values ranging from 0.58 to 0.85 and calibration was generally good. The PRIAS, Canary PASS, and STRATCANS models demonstrated the benefits of less burdensome biopsies, clinic visits, and MRIs during FU when used, compared to current one-size-fits-all practices. Although little is known about risk-based AS in thyroid, breast, kidney, and bladder cancer, learning from their current practices could further refine patient selection, streamline monitoring protocols, and address adoption barriers, improving AS’s overall effectiveness in PCa management. Conclusions: Personalized, risk-based AS models allow for a reduction in the FU burden for men at low risk of progression while maintaining sensitive FU visits for those at higher risk. The comparatively limited evidence and practice of risk-based AS in other cancer types highlight the advanced state of AS in PCa. Full article

A biological system can emit signals, and if these signals are correctly acquired, they can provide valuable information about the processes occurring within the system, enhancing our knowledge of the biological system. For this reason, we present a prototype system for acquiring various biopotentials using a main module that integrates amplification, high-pass filtering, band-reject filtering, and offset adjustment stages. This configuration allows for adjustable gain when working with different biopotentials and includes dedicated filtering modules for each biopotential type. We also propose a new topology for the shielded controller used in the interconnection between electrodes and the amplification stage to reduce noise introduced by the electrical network. Biopotentials acquired using the proposed topology show improved noise reduction and signal definition compared to those acquired using other topologies found in the literature. The design of the proposed system utilizes basic electronics, making it a low-cost solution. Ultimately, the system is simple, efficient, and suitable for applications requiring the acquisition of multiple types of biopotentials. Full article

Background/Objectives: Osteoarthritis (OA) is one of the most prevalent chronic conditions and significantly contributes to local and global disease burden. Common pharmaceuticals that are used to treat OA cause significant side effects, thus non-pharmaceutical bioactive alternatives have been developed that can impact OA symptoms without severe side-effects. One such alternative is the Red Algae Lithothamnion species (Litho). However, there is little mechanistic knowledge of its potential to effect OA gene expression, and a human in vitro model using commercially available cell lines to test its effectiveness has yet to be developed. Methods: Human osteoblast (hFOB 1.19. CRL-11372) and chondrocyte (C28/I2) cell lines were co-cultured indirectly using transwells. IL1-β was used to induce an inflammatory state and gene expression profiles following treatment were the primary outcome. Conclusions: Results indicated that the model was physiologically relevant, remained viable over at least seven days, untreated or following induction of an inflammatory state while maintaining hFOB 1.19. and C28/I2 cell phenotypic characteristics. Following treatment, Litho reduced the expression of inflammatory and pain associated genes, most notably IL-1β, IL-6, PTGS2 (COX-2) and C1qTNF2 (CTRP2). Confirmatory analysis with droplet digital PCR (ddPCR) revealed that Il-1β induced a significant reduction in C1qTNF2 at 7 days which was ameliorated with Litho treatment. These data present a novel and replicable co-culture model of inflammatory OA that can be used to investigate bioactive nutraceuticals. For the first time, this model demonstrated a reduction in C1qTNF2 expression that was mitigated by Red Algae Lithothamnion species. Full article