Search Results (1,765)

In Ecuador, the transmission of dengue has steadily increased in recent decades, particularly in coastal cities like Manta, where the conditions are favorable for the proliferation of the Aedes aegypti mosquito. The objective of this study was to model the spatial distribution of dengue transmission risk in Manta, a coastal city in Ecuador with consistently high incidence rates. A total of 148 georeferenced dengue cases from 2018 to 2021 were collected, and environmental and socioeconomic variables were incorporated into a maximum entropy model (MaxEnt). Additionally, climate and social zoning were performed using a multi-criteria model in TerrSet. The MaxEnt model demonstrated excellent predictive ability (training AUC = 0.916; test AUC = 0.876) and identified population density, sewer system access, and distance to rivers as the primary predictors. Three high-risk clusters were identified in the southern, northwestern, and northeastern parts of the city, while the coastal strip showed lower suitability due to low rainfall and vegetation. These findings reveal the strong spatial heterogeneity of dengue risk at the neighborhood level and provide operational information for targeted interventions. This approach can support more efficient surveillance, resource allocation, and community action in coastal urban areas affected by vector-borne diseases. Full article

Introduction: Primary glomerulopathies are immune-driven kidney diseases. IgA nephropathy (IgAN) and membranoproliferative glomerulonephritis (MPGN) are prevalent entities with a risk of chronic progression. Immune checkpoints, such as PD-1/PD-L1, CTLA-4/CD86, and CD200R/CD200, regulate activation and tolerance in T, B, and NK cells, and also exist in soluble forms, reflecting systemic immune balance. Objective: To compare immune checkpoint profiles in IgAN and MPGN versus healthy volunteers (HV) through surface expression, soluble serum levels, and PBMC transcripts, with attention to sex-related differences and diagnostic value assessed by ROC curves. Materials and Methods: Ninety age-matched subjects were studied: IgAN (n = 30), MPGN (n = 30), HV (n = 30). Flow cytometry evaluated checkpoint expression on CD4+/CD8+ T cells, CD19+ B cells, and NK cells. ELISA quantified sPD-1, sPD-L1, sCTLA-4, sCD86, sCD200, sCD200R; PBMC transcript levels were assessed. Group comparisons, sex stratification, and ROC analyses were performed. Results: Lymphocyte distributions were preserved, but IgAN patients showed anemia and impaired renal function, while MPGN patients had greater proteinuria and dyslipidemia. GN patients displayed increased PD-1/PD-L1 and CD200R/CD200, with reduced CTLA-4/CD86, compared to HV. Serum analysis revealed elevated sPD-1, sPD-L1, sCD200, sCD200R and decreased sCTLA-4, sCD86. PBMC transcripts paralleled these trends, with PD-1/PD-L1 mainly increased in MPGN. Sex had minimal impact. ROC analyses showed strong GN vs. HV discrimination by CD19⁺CTLA-4⁺, PD-1/PD-L1, and CD200/CD200R, but limited ability to separate IgAN from MPGN. Conclusions: IgAN and MPGN share a sex-independent checkpoint signature: PD-1/PD-L1 and CD200R/CD200 upregulation with CTLA-4/CD86 downregulation. CD19⁺, CTLA-4⁺, and soluble PD-1/PD-L1/CD200(R) emerge as promising biomarkers requiring further validation. Full article

Gender-based violence (GBV) and traffic offenses pose significant public health challenges and contribute to widespread social issues globally. This study examines the sociodemographic and psychological profiles of individuals who commit traffic offenses and GBV, focusing on three alternative penal programs: TASEVAL (for traffic offenses), PRIA-MA, and reGENER@r (both for GBV). The study involved 54 participants distributed across these programs, using various psychometric tests to assess their profiles. Participants across the three programs (TASEVAL, PRIA-MA, and reGENER@R) were comparable in age (mean range 39.13–40.69 years) and nationality, with roughly half having prior contact with the justice system. Educational levels varied, with TASEVAL participants mainly completing secondary education (43.8%), PRIA-MA participants primary education (43.8%), and reGENER@R participants post-secondary education (59.1%). Employment status differed slightly, with TASEVAL and reGENER@R participants mainly employed (62.5% and 63.6%, respectively), while most PRIA-MA participants were unemployed (56.3%). Family characteristics varied across groups. In TASEVAL, having a partner and no children predominated (62.5% and 31.3%); in PRIA-MA, not having a partner and having two children predominated (62.5% and 37.5%); and, in reGENER@R, not having a partner and having one child predominated (59.1% and 31.8%). No significant differences were observed in sociodemographic variables. Regarding psychological characteristics, results across all groups indicate a marked presence of psychopathological symptoms and difficulties in emotional intelligence domains, with a significant correlation between psychological traits and coping strategies. These findings highlight the importance of tailoring alternative penal measures to the specific characteristics of each group to enhance effectiveness and reduce recidivism. Full article

Objectives: To systematically review and analyze electronic systems in competitive motorcycles (2020-2025), examining their technical specifications, performance impacts, and technological evolution across MotoGP, World Superbike (WSBK), MotoE, British Superbike (BSB), and Spanish Championship (ESBK) categories. Eligibility criteria: Included studies reporting technical specifications or performance data of electronic systems in professional motorcycle racing, published between January 2020 and December 2025 in English, Spanish, Italian, or Japanese. Excluded: opinion pieces, amateur racing, and studies without quantitative data. Information sources: IEEE Xplore, SAE Technical Papers, Web of Science, Scopus, and specialized motorsport databases were searched through December 15, 2025. Risk of bias: Modified Cochrane Risk of Bias tool for experimental studies and Newcastle-Ottawa Scale for observational studies. Synthesis of results: Synthesis of results: Random-effects meta-analysis using DerSimonian-Laird method for homogeneous outcomes; narrative synthesis for heterogeneous data. The complete PRISMA 2020 checklist is provided in Appendix . Included studies: 87 studies met inclusion criteria (52 experimental, 38 simulation, 23 technical descriptions, 14 comparative analyses). Electronic systems were categorized into six domains: Engine Control Units (ECU, 28 studies, 22%), Vehicle Dynamics (23 studies, 18%), Traction Control (19 studies, 15%), Data Acquisition (21 studies, 17%), Braking Systems (18 studies, 14%), and Emerging Technologies (18 studies, 14%). Note that studies could address multiple domains. Limitations of evidence: Proprietary restrictions limited access to 31% of technical details; 43% lacked cross-category comparisons. Interpretation: Electronic systems are primary performance differentiators, with computational power following Moore’s Law. Future developments point toward distributed architectures and 5G telemetry. Funding: This project has been funded by the R&D programme with reference TEC-2024/TEC-62 and acronym iRoboCity2030-CM, granted by the Comunidad de Madrid through the Dirección General de Investigación e Innovación Tecnológica, Orden 5696/2024. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Primary Care Pharmacy (PCP) plays a vital role in healthcare systems. This study evaluated the competencies of pharmacy graduates from a community-focused curriculum, emphasizing their skills and personal traits. A structured questionnaire assessed four domains: general characteristics (11 items), PCP skills (16 items: 13 home visit and 3 community engagement skills), PCP personal traits (7 items), and readiness for PCP practice. Two sets of questionnaires were distributed in 2018 to recent pharmacy graduates: one for self-assessment and the other for evaluation by supervisors or co-workers. A 5-point scale (1 = least competent, 5 = most competent) was used. Co-workers gave higher scores than the graduates themselves. In home visit skills, “providing medicine advice” scored highest (4.4 ± 0.6 by graduates; 4.5 ± 0.2 by co-workers), while “performing essential physical exams” scored the lowest (3.5 ± 0.7). For co-workers, the lowest score was “working with a multidisciplinary team” (3.9 ± 0.9). Among community engagement skills, “solving health-related problems” rated highest (3.4 ± 0.7), and “identifying community health needs” rated lowest (3.2 ± 0.7). “Being friendly” and “responsibility” were top-rated personal traits by graduates and co-workers, respectively. The lowest was “coordinating with local organizations.” Graduates showed strong PCP traits and home visit skills but moderate community engagement. Community-based exposure is recommended to enhance these competencies. Full article

As a core component of high-speed trains, the traction drive system is also one of the main sources of both pass-by noise and interior noise. Current research primarily focuses on the modeling and design of its dynamic characteristics, while studies on its sound radiation remain relatively scarce. Existing investigations mainly rely on experimental and finite element methods. This paper proposes a rapid modeling method for the sound radiation of traction drive systems and analyzes the acoustic characteristics under different train speeds and gear helix angles. Taking an electric freight locomotive operating on China’s railways as the subject, the primary noise sources were identified through real-vehicle testing, thereby simplifying the non-dominant noise sources. By integrating a gear system dynamic model with theoretical models of gear meshing noise and motor noise, the proposed approach avoids the complexity and high computational cost associated with traditional finite element methods. The results show that at lower train speeds, the main noise source is the motor, while at higher speeds, it is the gearbox. As the train speed and helix angle increase, the radiated sound pressure of the traction drive system first increases and then decreases, though the sound field distribution and directivity remain largely unchanged. Full article

Against the backdrop of an intensifying global climate change and energy crisis, energy system decarbonization constitutes a primary sector for carbon mitigation. Integrated Energy Systems (IES) of district heating systems (DHS), a critical component of district energy networks (DEN), enable energy cascade utilization and enhance renewable energy integration efficiency when coupled with incremental distribution networks (IDN). However, retrofitting coupled systems necessitates significant capital investment and sustained operational expenditures. To evaluate the economic and environmental benefits of system retrofitting and assess cross-sector coordinated optimization potential, this study develops a multi-objective optimization framework for IES transition planning of DHS. Using an operational DHS energy station as a case study, we establish multi-scenario retrofitting strategies and operational protocols with comprehensive feasibility assessments, incorporating sensitivity analysis of cross-sector optimization potential while evaluating how varying electricity-to-heat load ratios affect optimization performance. Results demonstrate that intelligent operation optimization is essential for coordinating multi-equipment operations and maximizing energy conservation. Significant long-term economic and carbon mitigation potential remains untapped in ground source heat pumps and combined cooling, heating, and power (CCHP) systems. Coordinated optimization with campus incremental distribution networks further enhances energy cascade utilization in urban energy systems. Full article

This study aims to investigate the syntactic complexity in individuals with Alzheimer’s disease (AD) by conducting a comprehensive analysis that incorporates mean dependency distance (MDD), fine-grained grammatical metrics, and dependency network structures. A total of 150 adults with AD and 150 healthy controls (HC) responded in English to interview prompts based on the Cookie Theft picture description task, and the results were compared. The key findings are as follows: (1) The primary syntactic change is a strategic shift from hierarchical, clause-based constructions to linear, phrase-based ones, a direct consequence of working memory deficits designed to minimize cognitive load. (2) This shift is executed via a resource reallocation, where costly, long-distance clausal dependencies are systematically avoided in favor of a compensatory reliance on local dependencies, such as intra-phrasal modification and simple predicate structures. (3) This strategic reallocation leads to a systemic reorganization of the syntactic network, transforming it from a flexible, distributed system into a rigid, centralized one that becomes critically dependent on the over-leveraged structural role of function words to maintain basic connectivity. (4) The overall syntactic profile is the result of a functional balance governed by the principle of cognitive economy, where expressive richness and grammatical depth are sacrificed to preserve core communicative functions. These findings suggest that the syntactic signature of AD is not a random degradation of linguistic competence but a profound and systematic grammatical adaptation, where the entire linguistic system restructures itself to function under the severe constraints of diminished cognitive resources. Full article

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

Modern distributed computing systems and applications with strict privacy requirements demand robust data confidentiality. A primary challenge involves enabling parties to exchange data or perform joint computations. These interactions must avoid revealing private information about the data. Protocols with the obliviousness property, known as oblivious protocols, address this issue. They ensure that no party learns more than necessary. This survey analyzes the security and performance of post-quantum oblivious protocols, with a focus on oblivious transfer and oblivious pseudorandom functions. The evaluation assesses resilience against malicious adversaries in the Universal Composability framework. Efficiency is quantified through communication and computational overhead. It identifies optimal scenarios for these protocols. This paper also surveys related primitives, such as oblivious signatures and data structures, along with their applications. Key findings highlight the inherent trade-offs between computational cost and communication complexity in post-quantum oblivious constructions. Open challenges and future research directions are outlined. Emphasis is placed on quantum-resistant designs and formal security proofs in stronger adversarial models. Full article

Rivers, vital for life and civilizations, face severe threats from human activities such as hydropower development, with heavy metal pollution emerging as a critical concern due to altered biogeochemical cycles. Understanding how river damming affects heavy metal transport processes and developing targeted remediation strategies are essential for safeguarding the health of river-reservoir ecosystems and enabling the sustainable utilization of hydropower resources. Therefore, this review first summarizes the global hydropower development, details how damming disrupts hydrology, environments, and ecosystems, and analyzes heavy metal distribution and transport in reservoir water, suspended sediments, and riverbed sediments. It reveals that river damming promotes heavy metal adsorption onto suspended particles, deposition in riverbed sediments, and re-release during reservoir regulation, and anthropogenic activities are a primary driver of significant pollution in key reservoirs worldwide. Additionally, we further evaluate in situ (e.g., stabilizing agents, sediment capping, and phytoremediation) and ex situ (e.g., dredging, chemical washing, electrochemical separation, and ultrasonic extraction) remediation techniques, highlighting the challenges of phytoremediation in deep, stratified reservoir environments. Moreover, solidification/stabilization emerges as a promising in situ strategy, emphasizing the need for specific approaches to balance pollution control with hydropower functionality in dammed river systems. Full article

The formation of strategies and tactics of destructive impact (DI) at the stages of complex computer attacks (CCAs) largely depends on the content of intelligence data obtained by the intruder about the attacked elements of distributed information systems (DISs). This study analyzes scientific papers, methodologies and standards in the field of assessing the indicators of awareness of the intruder about the objects of DI and symmetrical indicators of intelligence security of the elements of the DIS. It was revealed that the aspects of changing the quantitative and qualitative characteristics of intelligence data (ID) at the stages of CCA, as well as their impact on the possibilities of using certain types of simple computer attacks (SKAs), are poorly studied and insufficiently systematized. This paper uses technologies for modeling the process of an intruder obtaining ID based on the application of the methodology of black, grey and white boxes and the theory of fuzzy sets. This allowed us to identify the relationship between certain arrays of ID and the possibilities of applying certain types of SCA end-structure arrays of ID according to the levels of identifying objects of DI, and to create a scale of intruder awareness symmetrical to the scale of intelligence protection of the elements of the DIS. Experiments were conducted to verify the practical applicability of the developed models and techniques, showing positive results that make it possible to identify vulnerable objects, tactics and techniques of the intruder in advance. The result of this study is the development of an intruder awareness scale, which includes five levels of his knowledge about the attacked system, estimated by numerical intervals and characterized by linguistic terms. Each awareness level corresponds to one CCA stage: primary ID collection, penetration and legalization, privilege escalation, distribution and DI. Awareness levels have corresponding typical ID lists that can be potentially available after conducting the corresponding type of SCA. Typical ID lists are classified according to the following DI levels: network, hardware, system, application and user level. For each awareness level, the method of obtaining the ID by the intruder is specified. These research results represent a scientific contribution. The practical contribution is the application of the developed scale for information security (IS) incident management. It allows for a proactive assessment of DIS security against CCAs—modeling the real DIS structure and various CCA scenarios. During an incident, upon detection of a certain CCA stage, it allows for identifying data on DIS elements potentially known by the intruder and eliminating further development of the incident. The results of this study can also be used for training IS specialists in network security, risk assessment and IS incident management. Full article

Microstructural analysis of hot-compressed magnesium alloys is crucial for understanding the plastic formability of magnesium alloys during thermo-mechanical processing. Thermal compression tests and finite element simulations were conducted on a low rare-earth (RE) Mg-1.8Nd-0.4Zr-0.3Ca alloy. Multiple microstructural characterization techniques were employed to analyze slip systems, twinning mechanisms, dynamic recrystallization (DRX), and precipitate phases in the hot-compressed alloy. The results demonstrated that the equivalent strain distribution within compressed specimens exhibits heterogeneity, with a larger equivalent strain in the core. After thermal compression, the original microscopic structure formed a necklace-like structure. The primary DRX mechanisms comprise continuous dynamic recrystallization (CDRX), twin-induced dynamic recrystallization (TDRX), and particle-stimulated nucleation (PSN). Pyramidal slip and recrystallization constitute primary contributors to peak texture weakening and tilting. Mg₄₁Nd₅ and α-Zr phases enhanced dislocation density by impeding dislocation motion and promoting cross-slip activation. Hot compression provided the necessary thermal activation energy and stress conditions for solute atom diffusion and clustering, triggering dynamic precipitation of Mg₄₁Nd₅ phases. Full article