Search Results (22,513)

Vegetation, as a fundamental component of terrestrial ecosystems, plays a pivotal role in the flux of water, heat, and nutrients between the lithosphere, biosphere, and atmosphere. Assessing the impacts of climate change and human activities on vegetation dynamics is essential for maintaining the health and stability of fragile ecosystems, such as the Yarlung Zangbo River (YZR) basin of the Tibetan Plateau, the highest-elevation river basin in the world. Vegetation responses to climate change are inherently asymmetric, characterized by distinct temporal effects. However, these temporal effects remain poorly understood, particularly in high-altitude ecosystems. Here, we examine the spatiotemporal changes in leaf area index (LAI) and four climatic factors—air temperature, precipitation, potential evapotranspiration, and solar radiation—in the YZR basin over the period 2000–2019. We further explore the time-lag and time-accumulation impacts of these climatic factors on LAI dynamics and apply an enhanced residual trend analysis to disentangle the relative contributions of climate change and human activities. Results indicated that (1) a modest increase in annual LAI at a rate of 0.02 m² m⁻² dec⁻¹ was detected across the YZR basin. Spatially, LAI increased in 66% of vegetated areas, with significant increases (p < 0.05) in 10% of the basin. (2) Temperature, precipitation, and potential evapotranspiration exhibited minimal time-lag (<0.5 months) but pronounced notable time-accumulation effects on LAI variations, with accumulation periods ranging from 1 to 2 months. In contrast, solar radiation demonstrated significant time-lag impacts, with an average lag period of 2.4 months, while its accumulation effects were relatively weaker. (3) Climate change and human activities contributed 0.023 ± 0.092 and –0.005 ± 0.109 m² m⁻² dec⁻¹ to LAI changes, respectively, accounting for 60% and 40% on the observed variability. Spatially, climate change accounted for 85% of the changes in LAI in the upper YZR basin, while vegetation dynamics in the lower basin was primarily driven by human activities, contributing 63%. In the middle basin, vegetation dynamics were influenced by the combined effects of climate change and human activities. Our findings deepen insights into the drivers of vegetation dynamics and provide critical guidance for formulating adaptive management strategies in alpine ecosystems. Full article

Vessel traffic is an important source of global greenhouse gas emissions. The carbon emissions from ships in the canal network are directly linked to the environmental performance of China’s inland waterway transportation, contributing to the achievement of global carbon reduction goals. Therefore, systematically assessing the carbon emission reduction levels of ships in canal networks is essential to provide a robust foundation for developing more scientific and feasible emission reduction strategies. To address the limitations of current evaluations—which often focus on a single dimension and lack an objective, quantitative representation of the mechanisms driving carbon emission and their synergistic effects—this study took a comprehensive approach. First, considering the factors influencing ship carbon emissions and emission reduction strategies, an evaluation index system was developed. This system included 6 first-level indexes and 22 s-level indexes, covering aspects such as energy utilization, technical equipment, and economic benefits. Second, a novel combination of methods was used to construct an evaluation model. Qualitative weights, determined through the interval binary semantic method, were integrated with quantitative weights calculated using the CRITIC method. These were then combined and assigned using a game-theory-based comprehensive assignment method. The resulting evaluation model, built upon the theory of matter-element topology, represents a significant methodological innovation. Finally, the evaluation method was applied to the empirical analysis of ships operating in Jiangsu section of the Beijing–Hangzhou Grand Canal. This application demonstrated the model’s specificity and feasibility. The study’s findings provide valuable insights for improving carbon emission reduction levels for inland ships and advancing the sustainable development of the shipping industry. Full article

More than 70% of cancer patients receive radiotherapy during their treatment, with consequent various side effects on normal cells due to high ionizing radiation doses despite tumor shrinkage. To date, many radioprotectors and radiosensitizers have been investigated in preclinical studies, but their use has been hampered by the high toxicity to normal cells or poor tumor radiosensitization effects. Genistein is a naturally occurring isoflavone found in soy products. It selectively sensitizes tumor cells to radiation while protecting normal cells from radiation-induced damage, thus improving the efficacy of radiotherapy and consequent therapeutic outcomes while reducing adverse effects. Genistein protects normal cells by its potent antioxidant effect that reduces oxidative stress and mitigates radiation-induced apoptosis and inflammation. Conversely, genistein increases the radiosensitivity of tumor cells through specific mechanisms such as the inhibition of DNA repair, the arrest of the cell cycle in the G₂/M phase, the generation of reactive oxygen species (ROS), and the modulation of apoptosis. These effects increase the cytotoxicity of radiation. Preclinical studies demonstrated genistein efficacy in various cancer models, such as breast, prostate, and lung cancer. Despite limited clinical studies, the existing evidence supports the potential of genistein in improving the therapeutic effect of radiotherapy. Future research should focus on dosage optimization and administration, the exploration of combination therapies, and long-term clinical trials to establish genistein benefits in clinical settings. Hence, the unique ability of genistein to improve the radiosensitivity of tumor cells while protecting normal cells could be a promising strategy to improve the efficacy and safety of radiotherapy. Full article

Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking. Full article

Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described. In this way, this hybrid approach leverages the detection of mode mixing based on the analysis of intrinsic mode functions through Empirical Mode Decomposition to determine the number of components to be estimated and thus provide greater information for Variational Mode Decomposition. The computational time and dependency on a predefined number of modes are significantly reduced by providing crucial information about the approximate number of vibratory components, enabling a more precise estimation with Variational Mode Decomposition. This hybrid strategy is employed to compute unknown natural frequencies of vibrating systems using output measurement signals. The algorithm for this hybrid strategy is presented, along with a comparison to conventional techniques such as Empirical Mode Decomposition, Variational Mode Decomposition, and the Fast Fourier Transform. Through several case studies involving multi-degree-of-freedom vibrating systems, the superior and satisfactory performance of the hybrid method is demonstrated. Additionally, the advantages of the hybrid approach in terms of computational efficiency and accuracy in signal decomposition are highlighted. Full article

Background: Streptococcus sanguinis is a leading cause of infective endocarditis (IE), which causes diverse clinical symptoms and even death. Recurrence after treatment is a crucial problem in IE, possibly caused by the presence of “persister” cells, a small bacterial population that can survive antimicrobials. In this study, the residual risk for penicillin G (PCG) and gentamicin (GM), used for treating IE, to induce Streptococcus sanguinis persisters, was investigated. Methods: The bactericidal effects of PCG and GM on S. sanguinis were evaluated. Furthermore, we confirmed whether the S. sanguinis that survived following combination treatment with PCG and GM were persisters. The bactericidal effect of the combination of PCG and GM against S. sanguinis was measured after the addition of glucose or arginine. Results: Following 48 h of treatment with PCG (1600 μg/mL) and GM (64 μg/mL), S. sanguinis survived, albeit with a low bacterial count, indicating the presence of persisters. The addition of glucose or arginine to PCG and GM increased the bactericidal effect on residual persister cells and reduced the number of persister cells. Moreover, the addition of glucose at concentrations of 10 mg/mL or higher was substantially effective in achieving sterilization. Conclusions: Our findings demonstrate that persisters of S. sanguinis that survive antimicrobial treatment may make the treatment of IE challenging, and that combining antimicrobial treatment with glucose is effective for eliminating persisters of S. sanguinis. Taken together, these findings may facilitate the development of novel therapeutic strategies against IE caused by oral streptococcal infection. Full article

Information-Centric Networking (ICN) typically utilizes DRAM (Dynamic Random Access Memory) to build in-network cache components due to its high data transfer rate and low latency. However, DRAM faces significant limitations in terms of cost and capacity, making it challenging to meet the growing demands for cache scalability required by increasing Internet traffic. Combining high-speed but expensive memory (e.g., DRAM) with large-capacity, low-cost storage (e.g., SSD) to construct a hierarchical cache architecture has emerged as an effective solution to this problem. However, how to perform efficient cache management in such architectures to realize the expected cache performance remains challenging. This paper proposes a cache management scheme for hierarchical cache architectures in ICN, which introduces a differentiated replica replacement policy to accommodate the varying request access patterns at different cache layers, thereby enhancing overall cache performance. Additionally, a probabilistic insertion-based SSD cache admission filtering mechanism is designed to control the SSD write load, addressing the issue of balancing SSD lifespan and space utilization. Extensive simulation results demonstrate that the proposed scheme exhibits superior cache performance and lower SSD write load under various workloads and replica placement strategies, highlighting its broad applicability to different application scenarios. Additionally, it maintains stable performance improvements across different cache capacity settings, further reflecting its good scalability. Full article

Background/Objectives: This single-center analysis evaluated the number of potential candidates for endocrine-based oral maintenance therapy in a real-world setting, focusing on three therapeutic agents, namely, olaparib, abemaciclib, and ribociclib, for patients with hormone receptor-positive HER2-negative early breast cancer. Methods: All breast cancer cases from the past 10 years (n = 3230) that underwent treatment at the certified Breast Cancer Center of the Department of Gynecology and Obstetrics, University Hospital Schleswig-Holstein, Lübeck Campus, were analyzed. Results: Of a total of 2038 patients with HR+ HER2− eBC, 685 patients (33.6%) qualified for one or more of the three agents—olaparib, abemaciclib, and ribociclib. Of these 685 patients, 523 patients (76.4%) had node-positive and 162 (23.6%) node-negative disease. Moreover, 368 patients (18.1% of a total of 2038 patients with HR+ HER2− eBC) were eligible exclusively for ribociclib, including all node-negative patients. A total of 141 patients (6.9%) met the criteria for all three agents. In contrast, 1353 patients (66.4%) had no indication for combined endocrine therapy. Conclusions: To our knowledge, this is the largest analysis addressing all three therapeutic strategies for combined endocrine therapy. The broad indication criteria of the NATALEE study may increase clinic workloads due to more frequent physician/patient interactions. It also remains unclear how therapy recommendations will influence actual treatment, as increased visits and potential side effects could affect patient compliance and adherence. Full article

This scoping review aims to understand the cell-based meat production process, including the regulations, potential hazards, and critical points of this production. This review includes studies on cultured meat production processes, health hazards, and regulatory guidelines, excluding those without hazard analysis, incomplete texts, or studies published before 2013. The search was performed in eight electronic databases (MEDLINE, Web of Science, Embase, Cochrane Library, Scopus, LILACS, and Google Scholar) using MeSH terms and adaptations for each database. The search for local studies on regulations and guideline documents was complemented by a manual search on the websites of governments and regulatory agencies from different regions (e.g., FDA, FAO, EFSA, USDA, Health Canada, EC, EU, ANVISA/Brazil, MAPA/Brazil, FSANZ, and SFA). This step involved reading full texts to confirm eligibility and extract key data, including author, year, country, study design, objectives, results, cultured meat protocols, health hazards, and hazard control measures, followed by data analysis. A comprehensive search of the databases yielded 1185 studies and 46 regulatory or guidance documents. After removing duplicate studies and applying eligibility criteria to titles, abstracts and full texts, 35 studies and 45 regulatory or guidance documents were included. The cultured meat production protocols are well-established, highlighting potential hazards and critical control points. Although guidance documents and regulations are limited, they are expanding globally. The development and commercialization of cultured meat require clear, and up-to-date regulations and supervision, which are being studied and formulated by regulatory agencies worldwide. Cultured meat production presents some potential hazards (chemical, biological, and physical) that require food safety considerations: (i) genetic stability of cells/cell lines; (ii) microbiological hazards related to cell lines; (iii) exposure to substances used in the production process; (iv) toxicity and allergenicity of the product or its component for the population; (v) post-harvest microbiological contamination; (vi) chemical contamination/residue levels; and (vii) nutritional aspects/risks. Currently, no standardized testing approach exists for cultured meat. However, effective hazard and safety assessment strategies, such as HACCP combined with best practices, should be implemented throughout the production process. Full article

Background/Objectives: Acute ischemic stroke (AIS) is a leading cause of disability and mortality worldwide. Despite advances in interventions such as thrombolysis (TL) and mechanical thrombectomy (MT), current treatment protocols remain largely standardized, focusing on general eligibility rather than individual patient characteristics. To address this gap, we introduce the Stroke-SCORE (Simplified Clinical Outcome Risk Evaluation), a predictive tool designed to personalize AIS management by providing data-driven, individualized recommendations to optimize treatment strategies and improve patient outcomes. Methods: The Stroke-SCORE was derived using retrospective data from 793 AIS patients admitted to the University of Pécs (February 2023–September 2024). Logistic regression analysis identified age, National Institutes of Health Stroke Scale (NIHSS) score at admission, and pre-morbid modified Rankin Scale (pre-mRS) score as key predictors of unfavorable outcomes at 90 days (defined as modified Rankin Scale [mRS] score > 2). Based on these predictors, a simplified risk score was developed to stratify patients into low-, moderate-, and high-risk groups, guiding treatment decisions on TL, MT, combination therapy (TL + MT), or standard care (SC). Internal validation was performed to assess the model’s predictive performance via receiver operating characteristic (ROC) analysis and isotonic regression calibration with bootstrapping. Results: The Stroke-SCORE was moderately positively correlated with a 90-day mRS score > 2 (odds ratio [OR] = 0.70, 95% confidence interval [CI]: 0.58–0.83, p < 0.001), with an area under the curve (AUC) of 0.86, a sensitivity and specificity of 79% and 81%, respectively, and an overall accuracy of 80%. Simulations indicated that personalized treatment guided by the Stroke-SCORE significantly reduced unfavorable outcomes. Conclusions: The Stroke-SCORE demonstrates strong predictive performance as a practical, data-driven approach for personalizing AIS treatment decisions. In the future, external, multicenter prospective validation is needed to confirm its applicability in real-world settings. Full article

Objectives: This study investigated the inflammatory responses of fibroblast-like synoviocytes (FLS) isolated from osteoarthritis (OA) patients, stimulated with lipopolysaccharide (LPS) and interleukin-6 (IL-6). Both experimental and synthetic data were utilised to investigate the variability in IL-6 and myeloperoxidase (MPO) production and its implications for OA pathogenesis. Methods: Synovial biopsies were obtained from OA patients undergoing joint replacement surgery. FLS were isolated, cultured, and stimulated with varying concentrations of LPS and IL-6. The production of IL-6 and MPO was measured using enzyme-linked immunosorbent assays (ELISA). Synthetic data generation techniques expanded the dataset to support comprehensive statistical analyses. Results: The patterns of inflammatory responses revealed distinct patient subgroups, highlighting individual variability. The integration of synthetic data with experimental observations validated their reliability and demonstrated dose-dependent differences in IL-6 and MPO production across patients. Conclusions: The results highlighted the importance of patient-specific factors in OA inflammation and demonstrated the utility of combining experimental and synthetic data to model individual variability. The results support the development of personalised treatment strategies in OA. Future research should include larger patient datasets and an exploration of molecular mechanisms underlying these responses. Full article

Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the geographical location, road conditions, and environment. The data are divided into accident-related categories, weather-related categories, and road- and environment-related categories. The machine learning method is improved through integration for the accident level prediction. In the experiment, effective preprocessing measures were taken for problems such as data imbalance, missing values, the encoding of categorical variables, and the standardization of numerical features. The unbalanced distribution of “Severity” was improved through under-sampling and over-sampling techniques. Firstly, we adopted a multi-stage fusion strategy. A multi-layer perceptron (MLP) was used for the preliminary prediction, and then its result was combined with the original features to form a new feature. Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. The analysis results show that the improved machine learning model is significantly superior to a single model in the overall performance. The “MLP + random forest” model performs well in evaluation indicators such as the accuracy, recall rate, and F1 value. The accuracy rate is as high as 94%. In the prediction of different traffic accident severity levels (minor, moderate, and severe), the improved machine learning model also generally shows better performance and stability. The research results of this study have broad prospects in the field of intelligent driving. It can realize real-time accident prediction and early warnings, and provide decision support for drivers and autonomous driving systems. The research also provides a scientific basis for traffic planning and management departments to improve driving conditions and reduce the probability and losses of traffic accidents. Full article

With the escalating demand for automation in chemical laboratories, multi-robot systems are assuming an increasingly prominent role in chemical laboratories, particularly in the task of transporting reagents and experimental materials. In this paper, we propose a multi-robot path planning approach based on the combination of the A* algorithm and the dynamic window algorithm (DWA) for optimizing the efficiency of reagent transportation in chemical laboratories. In environments like chemical laboratories, dynamic obstacles (such as people and equipment) and transportation tasks that demand precise control render traditional path planning algorithms challenging. To address these issues, in this paper, we incorporate the cost information from the current point to the goal point into the evaluation function of the traditional A* algorithm to enhance the search efficiency and add the safety distance to extract the critical points of the paths, which are utilized as the temporary goal points of the DWA algorithm. In the DWA algorithm, a stop-and-wait mechanism and a replanning strategy are added, and a direction factor is included in the evaluation function to guarantee that the robots can adjust their paths promptly in the presence of dynamic obstacles or interference from other robots to evade potential conflicts or traps, thereby reaching the goal point smoothly. Additionally, regarding the multi-robot path conflict problem, this paper adopts a dynamic prioritization method, which dynamically adjusts the motion priority among robots in accordance with real-time environmental changes, reducing the occurrence of path conflicts. The experimental results highlight that this approach effectively tackles the path planning challenge in multi-robot collaborative transportation tasks within chemical laboratories, significantly enhancing transportation efficiency and ensuring the safe operation of the robots. Full article

Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks. This study aimed to evaluate the potential of machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at the lumbar region, to classify risk levels associated with load lifting based on the Revised NIOSH Lifting Equation. Methods: To compute postural sway parameters, the IMU captured acceleration data in both anteroposterior and mediolateral directions, aligning closely with the body’s center of mass. Eight participants undertook two scenarios, each involving twenty consecutive lifting tasks. Eight machine learning classifiers were tested utilizing two validation strategies, with the Gradient Boost Tree algorithm achieving the highest accuracy and an Area under the ROC Curve of 91.2% and 94.5%, respectively. Additionally, feature importance analysis was conducted to identify the most influential sway parameters and directions. Results: The results indicate that the combination of sway metrics and the Gradient Boost model offers a feasible approach for predicting biomechanical risks in load lifting. Conclusions: Further studies with a broader participant pool and varied lifting conditions could enhance the applicability of this method in occupational ergonomics. Full article

Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions. Methods: The study used datasets from two ethnic groups, sourced from the ISIC platform and CSMU Hospital, to develop an AI diagnostic model. Eight pre-trained models, including convolutional neural networks and vision transformers, were fine-tuned. The three best-performing models were combined into an ensemble model, which underwent multiple random experiments to ensure stability. To improve diagnostic accuracy and reduce false negatives, a two-stage classification strategy was employed: a three-class model for initial classification, followed by a binary model for secondary prediction of benign cases. Results: In the ISIC dataset, the false negative rate for malignant lesions was significantly reduced, and the number of malignant cases misclassified as benign dropped from 124 to 45. In the CSMUH dataset, false negatives for malignant cases were completely eliminated, reducing the number of misclassified malignant cases to zero, resulting in a notable improvement in diagnostic precision and a reduction in the false negative rate. Conclusions: Through the proposed method, the study demonstrated clear success in both datasets. First, a three-class AI model can assist doctors in distinguishing between melanoma patients who require urgent treatment, non-melanoma skin cancer patients who can be treated later, and benign cases that do not require intervention. Subsequently, a two-stage classification strategy effectively reduces false negatives in malignant lesions. These findings highlight the potential of AI technology in skin cancer diagnosis, particularly in resource-limited medical settings, where it could become a valuable clinical tool to improve diagnostic accuracy, reduce skin cancer mortality, and reduce healthcare costs. Full article