Search Results (12,128)

The use of remote sensing technologies for mapping forest fires has experienced significant growth in recent decades, driven by advancements in remote sensors, processing platforms, and artificial intelligence algorithms. This study presents a review of 192 scientific articles published between 1990 and 2024, selected using PRISMA criteria from the Scopus database. Trends in the use of active and passive sensors, spectral indices, software, and processing platforms as well as machine learning and deep learning approaches are analyzed. Bibliometric analysis reveals a concentration of publications in Northern Hemisphere countries such as the United States, Spain, and China as well as in Brazil in the Southern Hemisphere, with sustained growth since 2015. Additionally, the publishers, journals, and authors with the highest scientific output are identified. The normalized burn ratio (NBR) and the normalized difference vegetation index (NDVI) were the most frequently used indices in fire mapping, while random forest (RF) and convolutional neural networks (CNN) were prominent among the applied algorithms. Finally, the main technological and methodological limitations as well as emerging opportunities to enhance fire detection, monitoring, and prediction in various regions are discussed. This review provides a foundation for future research in remote sensing applied to fire management. Full article

Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological and pathological conditions. While many studies focus on custom MCL designs tailored to specific applications, few have systematically reviewed their use in device testing, and none have assessed their broader utility across diverse biomedical domains. This comprehensive review categorizes MCL designs into three types: mechanical, computational, and hybrid. Applications are classified into four major areas: Cardiovascular Devices Testing, Clinical Training and Education, Hemodynamics and Blood Flow Studies, and Disease Modeling. Most existing MCLs are complex, highly specialized, and difficult to reproduce, highlighting the need for simplified, standardized, and programmable hybrid systems. Improved validation and waveform fidelity—particularly through incorporation of the dicrotic notch and other waveform parameters—are critical for advancing MCL reliability. Furthermore, integration of machine learning and artificial intelligence holds significant promise for enhancing waveform analysis, diagnostics, predictive modeling, and personalized care. In conclusion, the development of MCLs should prioritize standardization, simplification, and broader accessibility to expand their impact across biomedical research and clinical translation. Full article

Background: Undercover police work is a psychologically high-risk profession that exposes officers to chronic stress, identity conflicts, and moral dilemmas. The aim of the present review is to evaluate the psychological consequences associated with undercover police work, focusing on specific psychopathological risk factors. Methods: A systematic search was conducted in PubMed, PsycINFO, Web of Science, and Scopus databases. Studies were conducted in the United States, the United Kingdom, New Zealand, and Canada. The present systematic review analyzed data from 380 current undercover operatives, 372 former UCOs, 578 officers without undercover experience, and 60 pre-operational agents. Results: From an initial pool of 365 records, 10 studies were identified, of which 6 met the inclusion criteria. The most frequently reported psychological risk factors included anxiety, hypervigilance, identity issues, dissociative symptoms, and substance misuse. These were assessed using validated self-report instruments (e.g., SCL-90), structured interviews, and clinical evaluations. Long-term consequences were more prominent post-deployment, particularly among former UCOs. Conclusions: Undercover work is associated with an elevated risk of mental health problems, especially after the end of operations. Future research should focus on standardizing assessment tools and identifying protective factors. The findings support the development of targeted interventions such as pre-deployment psychological screening, ongoing monitoring, and structured reintegration programs to safeguard UCOs’ well-being. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

The ship waterjet propulsion system is a crucial power unit for high-performance vessels, and the operational state of its core component, the waterjet pump, is directly related to navigation safety and mission reliability. To enhance the intelligence and accuracy of pump fault diagnosis, this paper proposes a novel diagnostic framework that integrates a supervised autoencoder (SAE) with a large language model (LLM). This framework first employs an SAE to perform task-oriented feature learning on raw vibration signals collected from the pump’s guide vane casing. By jointly optimizing reconstruction and classification losses, the SAE extracts deep features that both represent the original signal information and exhibit high discriminability for different fault classes. Subsequently, the extracted feature vectors are converted into text sequences and fed into an LLM. Leveraging the powerful sequential information processing and generalization capabilities of LLM, end-to-end fault classification is achieved through parameter-efficient fine-tuning. This approach aims to avoid the traditional dependence on manually extracted time-domain and frequency-domain features, instead guiding the feature extraction process via supervised learning to make it more task-specific. To validate the effectiveness of the proposed method, we compare it with a baseline approach that uses manually extracted features. In two experimental scenarios, direct diagnosis with full data and transfer diagnosis under limited-data, cross-condition settings, the proposed method significantly outperforms the baseline in diagnostic accuracy. It demonstrates excellent performance in automated feature extraction, diagnostic precision, and small-sample data adaptability, offering new insights for the application of large-model techniques in critical equipment health management. Full article

Accurate full-body motion reconstruction from sparse sensors is crucial for VR/AR applications but remains challenging due to the under-constrained nature of limited observations and the computational constraints of mobile platforms. This paper presents LGSIK-Poser, a unified and lightweight framework that supports real-time motion reconstruction from heterogeneous sensor configurations, including head-mounted displays, handheld controllers, and up to three optional inertial measurement units, without requiring reconfiguration across scenarios. The model integrates temporally grouped LSTM modeling, anatomically structured graph-based reasoning, and region-specific inverse kinematics refinement to enhance end-effector accuracy and structural consistency. Personalized body shape is estimated using user-specific anthropometric priors within the SMPL model, a widely adopted parametric representation of human shape and pose. Experiments on the AMASS benchmark demonstrate that LGSIK-Poser achieves state-of-the-art accuracy with up to 48% improvement in hand localization, while reducing model size by 60% and latency by 22% compared to HMD-Poser. The system runs at 63.65 FPS with only 3.74 M parameters, highlighting its suitability for real-time immersive applications. Full article

In the digital age, online teacher professional development (TPD) has become a key strategy for enhancing instructional quality and ensuring equitable access to continuous learning. This research compares and analyzes Chinese online teacher professional development (TPD) with the United States over a period of ten years, from 2014 to 2024. This study uses a mixed-methods approach based on policy documents, structured surveys, and interviews to investigate how governance regimes influence TPD outcomes for fair education. Both countries experienced a massive expansion of web-based TPD access and engagement, with participation rates over 75% and effectiveness scores over 4.3 by 2024. China focused on fast scaling by way of centralized mandates and investments in infrastructure, while the United States emphasized gradual expansion through decentralized, locally appropriate models. Most indicators had converged by the end of the period, even with these different approaches. Yet, qualitative evidence reveals persisting gaps in functional access and contextual appropriateness, especially in rural settings. Equality frameworks with attention to teacher agency, policy implementation, and digital usability must supplant weak access metrics. A hybrid paradigm presents itself as an attractive means toward building equitable and productive digital TPD environments through the symbiotic integration of China’s successful scalability and the United States’ professional autonomy. Full article

Cassava (Manihot esculenta Crantz) is essential for global food security, especially in tropical regions. As an important genetic resource, its genetics plays a key role in crop breeding, enabling the development of more productive and pest- and disease-resistant varieties. Scientometrics, which quantitatively analyzes the production and impact of scientific research, is crucial for understanding trends in cassava genetics. This study aimed to apply bibliometric methods to conduct a scientific mapping analysis based on yearly publication trends, paper classification, author productivity, journal impact factor, keywords occurrences, and omic approaches to investigate the application of genetics to the species from 1960 to 2022. From the quantitative data analyzed, 3246 articles were retrieved from the Web of Science platform, of which 654 met the inclusion criteria. A significant increase in scientific production was observed from 1993, peaking in 2018. The first article focused on genetics was published in 1969. Among the most relevant journals, Euphytica stood out with 36 articles, followed by Genetics and Molecular Research (n = 30) and Frontiers in Plant Science (n = 25). Brazil leads in the number of papers on cassava genetics (n = 143), followed by China (n = 110) and the United States (n = 75). The analysis of major methodologies (n = 185) reveals a diversified panorama during the study period. Morpho-agronomic descriptors persisted from 1978 to 2022; however, microsatellite markers were the most widely used, with 102 records. Genomics was addressed in 87 articles, and transcriptomics in 65. By clarifying the current landscape, this study supports cassava conservation and breeding, assists in public policy formulation, and guides future research in the field. Full article

Despite the increasing adoption of a syndemic approach in HIV research, few health communication campaigns have used a syndemic approach in messaging to improve health outcomes for persons living with HIV (PWH). This paper introduces a framework for practitioners and researchers developing health communication messages in support of a syndemic approach to HIV care for PWH in the United States. Grounded in insights from a review of counseling and psychosocial interventions that demonstrated significant positive effects on HIV clinical outcomes, the C4H Framework emphasizes four components: compassion, comprehensive messaging, capacity-building, and coordination. Compassion ensures that messages resonate with individuals experiencing the intertwined challenges of HIV, substance abuse, and mental health issues. Comprehensive messaging integrates a holistic view of the barriers faced by PWH. Capacity-building empowers individuals to effectively engage with and act upon health information. Coordination promotes alignment between stakeholders and resources to ensure consistent and supportive messaging. The C4H Framework bridges the gap between research and practice, offering a foundation for crafting effective communication messages that resonate with individuals facing the complex challenges inherent in HIV syndemics. Future research should explicitly test the effectiveness and acceptability of messages developed using the C4H Framework with people living with HIV. Full article

Our contemporary society is powered by fossil fuels, which results in environmental catastrophes. The combustion of these materials results in the release of CO₂, which accelerates the progression of climate change and its catastrophic consequences. The environmental repercussions of fossil fuel extraction have been highlighted through research into alternative energy sources. This inquiry uses the Tapio-Z decoupling approach to assess energy inputs and emissions. Furthermore, the fuzzy logic model is used to inspect the economic growth of the USA and China, as well as the impact of environmental factors, energy sources, and utilization, through decoupling effects from 1994 to 2023. The findings are substantiated by the individual perspectives of the environmental factors regarding decoupling, which ultimately lead to the acquisition of valuable results. We anticipate a substantial reduction in the total volume of CO₂ emissions in both the USA and China. Compared to China, the USA shows a significant increase in CO₂ emissions due to its reliance on fossil fuels. It is evident that a comprehensive transition to renewable resources and a broad range of technology is required to mitigate CO₂ emissions in high-energy zones. In their pursuit of sustainability, these two nations are making remarkable strides. The percentage change in CO₂ emissions indicates that effective changes in economic growth, energy input, and energy utilization, particularly sustainable energy, transmute energy output, as does the sustained implementation of robust environmental protection policies. The percentage change in CO₂ emissions indicates a remarkable transformation in energy input, energy consumption, and economic growth. This transition has been most visible in the areas of energy transformation, sustainability, and the maintenance of strong environmental protection measures. Full article

Visual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, this paper proposes stereo direct sparse visual–inertial odometry with efficient second-order minimization. It is entirely implemented using the direct method, which includes a depth initialization module based on visual–inertial alignment, a stereo image tracking module, and a marginalization module. Inertial measurement unit (IMU) data is first aligned with a stereo image to initialize the system effectively. Then, based on the efficient second-order minimization (ESM) algorithm, the photometric error and the inertial error are minimized to jointly optimize camera poses and sparse scene geometry. IMU information is accumulated between several frames using measurement preintegration and is inserted into the optimization as an additional constraint between keyframes. A marginalization module is added to reduce the computation complexity of the optimization and maintain the information about the previous states. The proposed system is evaluated on the KITTI visual odometry benchmark and the EuRoC dataset. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of accuracy and robustness. Full article

Background/Objectives: This study compared overall survival (OS) associated with common real-world treatment sequences in patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) in the United States. Methods: Utilizing the nationwide Flatiron Health electronic health record-derived de-identified database, adult CLL/SLL patients who initiated systemic therapy (JAN2016-NOV2023) and received at least two lines of therapy (LoTs) were analyzed. Treatment regimens were categorized based on drug class, and most frequent (n ≥ 50) sequences (first LoT followed by [→] second LoT) were compared. OS from initiation of the first LoT was compared using multivariable Cox proportional hazard models, and adjusted hazard ratios with 95% CIs were reported. Results: Among 2354 eligible patients, n = 1711 (73%) received the 16 most frequent treatment sequences. Sequencing chemoimmunotherapy (CIT) → CIT (HR: 2.29 [1.23–4.28]), anti-CD20 monoclonal antibody (anti-CD20mab) monotherapy → CIT (1.95 [1.03–3.69]), and covalent Bruton tyrosine kinase inhibitor (cBTKi) monotherapy → anti-CD20mab monotherapy (2.00 [1.07–3.74]) were associated with worse OS compared to patients treated with cBTKi monotherapy → B-cell lymphoma 2 inhibitors (BCL2i) + anti-CD20mab (reference). Conclusions: OS associated with other sequences were not significantly different from the reference sequence in adjusted analyses, suggesting a lack of evidence for the optimal standard of care for sequencing the first two LoTs in real-world settings. Future research should reassess sequencing outcomes as novel treatments become adopted into clinical practice. Full article

Rabies remains a significant global public health concern, causing an estimated 59,000–69,000 human fatalities annually. Despite being entirely preventable through vaccination, rabies continues to impose substantial economic burdens worldwide. This study presents a scoping review of the economic research on rabies to determine overlaps and gaps in knowledge and inform future research strategies. We selected 150 studies (1973–2024) to analyze. The review categorizes the literature based on geographic distribution, species focus, and type of study. Findings indicate that economic studies are disproportionately concentrated in developed countries, such as the United States and parts of Europe, where rabies risk is low, while high-risk regions, particularly in Africa and Asia, remain underrepresented. Most studies focus on dog-mediated rabies, reflecting its dominant role in human transmission, while fewer studies assess the economic impacts of wildlife and livestock-mediated rabies. Case studies and modeling approaches dominate the literature, whereas cost–benefit and cost–effectiveness analyses—critical for informing resource allocation—are limited. The review highlights the need for more economic evaluations in rabies-endemic regions, expanded research on non-dog reservoirs, and broader use of economic methods. Addressing these gaps will be crucial for optimizing rabies control and supporting global initiatives to eliminate dog-mediated rabies by 2030. Full article

Blast-induced airblast poses a significant environmental and operational issue for surface mining, affecting safety, regulatory adherence, and the well-being of surrounding communities. Despite advancements in machine learning methods for predicting airblast, present studies neglect essential geomechanical characteristics, specifically rock mass strength (RMS), which is vital for energy transmission and pressure-wave attenuation. This paper presents a capuchin search algorithm-optimized multilayer perceptron (CapSA-MLP) that incorporates RMS, hole depth (HD), maximum charge per delay (MCPD), monitoring distance (D), total explosive mass (TEM), and number of holes (NH). Blast datasets from a granite quarry were utilized to train and test the model in comparison to benchmark approaches, such as particle swarm optimized artificial neural network (PSO-ANN), multivariate regression analysis (MVRA), and the United States Bureau of Mines (USBM) equation. CapSA-MLP outperformed PSO-ANN (RMSE = 1.120, R² = 0.904 compared to RMSE = 1.284, R² = 0.846), whereas MVRA and USBM exhibited lower accuracy. Sensitivity analysis indicated RMS as the main input factor. This study is the first to use CapSA-MLP with RMS for airblast prediction. The findings illustrate the significance of metaheuristic optimization in developing adaptable, generalizable models for various rock types, thereby improving blast design and environmental management in mining activities. Full article

The agony of medical negligence for all involved is well documented. Health practitioners involved in harm events are described in the literature as “second victims”. Injured patients report that clinical negligence litigation is traumatic, slow, expensive, and does not meet their needs. Clinical negligence lawyers have complained that healthcare injury cases are so complex and expensive that many firms do not accept these cases. This article uses a qualitative case study research design to analyse two cases from the United States of America (US) to explore the promise of an alternative resolution process: the communication-and-resolution program (CRP). CRPs involve the hospital disclosing the healthcare injury, investigating and explaining what happened, apologising and, sometimes, offering compensation to injured patients and families. In the US, CRPs have not replaced tort law. The two case studies analysed in this article offer a rare insight into the accounts of those who have experienced clinical negligence and an alternative non-litigation approach. The case study approach delves into the detail, providing an in-depth glimpse into the complexity of healthcare injuries in their real-life context. The case studies provide valuable lessons for reshaping resolution processes to better meet injured patients’ needs. Full article