Search Results (13,189)

Cerebral amyloid angiopathy (CAA) is an increasingly recognized cause of cognitive decline and lobar intracerebral hemorrhage in older adults. Recent research highlights that neuropsychiatric symptoms (NPSs)—including depression, anxiety, apathy, and irritability—are highly prevalent in CAA, often emerging prior to overt cognitive impairment or major vascular events. Compared to other cerebrovascular diseases, CAA presents a distinctive and multifaceted NPS profile, with symptoms closely linked to disease severity and neuroimaging biomarkers such as white matter hyperintensities and microbleeds. Critically, NPSs in CAA can complicate cognitive assessment and predict worse functional outcomes, yet remain underappreciated in clinical and research contexts. Management is complicated by pharmacologic risks—including heightened bleeding risk associated with SSRIs and novel anti-amyloid therapies—underscoring the need for individualized and multidisciplinary approaches. We highlight the urgent need for standardized NPS assessment, targeted research into mechanisms and treatment, and greater integration of neuropsychiatric evaluation into CAA care. We suggest that recognizing NPSs as core clinical features—not secondary complications—of CAA is essential to improving both patient outcomes and scientific understanding. Future studies should focus on longitudinal analyses, the development of tailored interventions, and robust comparative research to clarify the pathophysiology, clinical trajectory, and optimal management of NPSs in CAA. Full article

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies. Full article

This paper contributes conceptually and empirically to a more rigorous understanding of the role of intellectual capital in the sustainable development of the BRICS+ bloc. We investigate the growing command of technical competencies over social competencies across the entire knowledge process. A range of factors, including the ever-increasing tension between AI and humans, the multidimensional nature of intellectual capital, and a focus on competency-based approaches, shape the theory of a knowledge economy. This study presents a spatial modeling approach to analyze the sustainable development of economic systems, reevaluates the importance of intellectual capital in the era of Industry 4.0, introduces the concept of scientific management of intellectual capital by categorizing it into the AI, individual, and collective human mind, and enhances the methodology of managing the knowledge economy to foster intellectual capital development. The primary finding of the research is that the advancement of the knowledge economy is driving digital communication and network-based collaboration on a larger scale within the BRICS+ bloc. Policy implications are intricately linked to the necessity for the holistic development of intellectual capital, encompassing both human and artificial intelligence. This development requires enhancements in quality of life and living standards, advancements in education and healthcare, optimization of the labor market, and reinforcing its connection with the educational sector. Concurrently, it is vital to stimulate research and development (R&D), support the commercialization of high-tech innovations, and accelerate the process of robotization. These combined efforts are essential to fostering economic growth effectively. Full article

Modern artificial intelligence methods are increasingly applied in hydrology, particularly for forecasting water inflow into reservoirs. However, their limited interpretability constrains practical deployment in critical water resource management systems. Explainable AI offers solutions aimed at increasing the transparency of models, which makes the topic relevant in the context of developing sustainable and trusted AI systems in hydrology. Articles published in leading scientific journals in recent years were selected for the review. The selection criteria were the application of XAI methods in hydrological forecasting problems and the presence of a quantitative assessment of interpretability. The main attention is paid to approaches combining LSTM, GRU, CNN, and ensembles with XAI methods such as SHAP, LIME, Grad-CAM, and ICE. The results of the review show that XAI mechanisms increase confidence in AI forecasts, identify important meteorological features, and allow analyzing parameter interactions. However, there is a lack of standardization of interpretation, especially in problems with high-dimensional input data. The review emphasizes the need to develop robust, unified XAI approaches that can be integrated into next-generation hydrological models. Full article

Non-Hodgkin B-cell lymphomas are a heterogeneous group of lymphoid malignancies with variable biological behavior, clinical presentation and treatment response. While chemoimmunotherapy remains the cornerstone of their management, growing evidence implicates the gut microbiota as a critical modulator of both lymphomagenesis and therapeutic efficacy. Gut microbiota dysbiosis, characterized by reduced microbial diversity and pathogenic taxonomic shifts, has been observed also in newly diagnosed patients and not just after therapy. This microbial imbalance contributes to mucosal barrier disruption, systemic inflammation, and altered immune responses, affecting treatment outcomes and toxicity profiles. Antibiotic exposure, especially broad-spectrum agents, exacerbates dysbiosis and has been associated with inferior responses to immunochemotherapy and CAR T-cell therapy. Conversely, certain commensal taxa, like Faecalibacterium prausnitzii and Lactobacillus johnsonii, may exert protective effects by preserving mucosal homeostasis and promoting antitumor immunity. Targeted interventions, including prudent antibiotic stewardship, prebiotics, probiotics, dietary modulation, and fecal microbiota transplantation, are under investigation to restore eubiosis and improve clinical outcomes. Preliminary clinical trials suggest a strong correlation between baseline microbiome composition and therapeutic response. Further mechanistic studies and randomized trials are warranted to define the causal role of the microbiome in non-Hodgkin B-cell lymphomas pathophysiology and to develop personalized microbiome-modulating strategies as adjuncts to standard treatment. Full article

Objective: This scoping review identifies and analyzes factors influencing the effectiveness of orthodontic mini-implants and temporary anchorage devices in orthodontic treatments, including clinical applications, success rates, and associated complications. Methods: A systematic search was conducted across EBSCOhost, Ovid Medline, PubMed, Scopus, and Web of Science for peer-reviewed, English-language human studies published between 2013 and 2023 that examined determinants of mini-implants/temporary anchorage devices success or failure. Inclusion/exclusion criteria were predefined, and screening was performed in duplicate. Thirty-six studies met criteria. Results: Placement site and peri-implant oral hygiene/soft-tissue health were the most consistent contributors to success. Optimal sites varied by indication, supporting individualized planning. Greater implant length generally improved stability but must be balanced against anatomic limits and patient comfort. Temporary anchorage devices supported diverse movements (e.g., molar distalization; posterior/anterior intrusion). Findings for loading protocol, patient age, bone quality, and operator experience were mixed, reflecting heterogeneity in primary stability, force magnitude/vector, and outcome definitions. Conclusion: Mini-implants/temporary anchorage devices success is multifactorial. Emphasis on site-specific selection, hygiene management, appropriate implant dimensions, and patient-specific modifiers can optimize outcomes and minimize complications. Future studies should report standardized outcomes and explicit loading parameters to enable granular analyses of movement-specific biomechanics and evidence-based decision-making. Full article

The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient requirements management and validation. While digital twins promise transformative real-time decision-making, reliance on static unstructured data formats inhibits progress. This paper presents a novel framework that integrates Building Information Modelling (BIM) and Model-Based Systems Engineering (MBSE), using Linked Data principles to preserve semantic meaning during information exchange between physical abstractions and requirements. The proposed approach automates a step of compliance validation against regulatory standards explored through a case study, utilising requirements from a high-speed railway station fire safety system and a modified duplex apartment digital model. The workflow (i) digitises static documents into machine-readable MBSE formats, (ii) integrates structured data into dynamic digital models, and (iii) creates foundations for data exchange to enable compliance validation. These findings highlight the framework’s ability to enhance traceability, bridge static and dynamic data gaps, and provide decision-making support in digital twin environments. This study advances the application of Linked Data in infrastructure, enabling broader integration of ontologies required for dynamic decision-making trade-offs. Full article

Acute ischemic stroke (AIS) is one of the leading global causes of mortality and morbidity. Clearer understanding of stroke etiology is a major clinical objective to determine appropriate strategies for secondary stroke prevention. Histological and molecular analysis of clots retrieved during mechanical thrombectomy (MT) in AIS offers a unique opportunity to study clot composition and its relation to stroke etiology. The field of clot composition analysis has undergone substantial growth in recent years, driven in part by the establishment of MT as the standard of care, as well as its expanding indications. Although many features differ between large-artery atherosclerosis (LAA) and cardioembolic (CE) clots, application of these findings to predicting stroke etiology at a clinical level remains challenging. Moreover, a significant number of patients have multiple comorbidities or suffer a cryptogenic subtype. Next-generation techniques such as multiomic sequencing offer a powerful potential to elevate our understanding of clot pathology and provide the level of granularity required for clinical diagnosis and management. Herein, we provide an updated review of the current state of the field by exploring stroke etiologies and their relationship to clot pathology, including classic histologic features as well as more recent, emerging results from proteomic and transcriptomic analyses. Full article

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts on road safety outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review aimed to identify hazardous driving behaviors, the internal and external factors contributing to these behaviors, and their consequences for traffic safety. Inclusion criteria targeted original research published in English between 2009 and 2024 specifically focused on truck driver behavior and road safety outcomes. Systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore yielded 104 studies meeting these criteria. The synthesis revealed prevalent risky behaviors—such as speeding, fatigue-related impairments, distracted driving, and substance use—driven by internal factors (e.g., health conditions, psychological stress) and external pressures (e.g., occupational demands, regulatory constraints). These behaviors were consistently associated with increased crash risk. Nonetheless, limitations including the exclusion of non-English studies, reliance on self-reported data, and lack of standardized metrics constrained cross-study comparability and generalizability. Effective interventions identified include fatigue management programs, driver monitoring technologies, and positive safety climates. Findings underscore the urgent need for evidence-based, multifaceted strategies to enhance truck driver safety and inform policy, industry practices, and future research. Full article

Parking management and operation represent a major challenge for both users and administrators, who seek to ensure efficient utilization, accommodate as many demands as possible, and reduce maintenance costs. This paper presents a theoretical model for an integrated IT system designed for parking management and administration. The modeling process involved designing a parking facility using the AutoCAD Vehicle Tracking v25.00.2775 software package, in accordance with current design standards. To simulate system operation, a dedicated Python v2025.12.0 program was developed to assign parking spaces to arriving vehicles based on specific allocation criteria. Three allocation strategies were applied: random allocation, allocation aimed at minimizing the driving distance within the parking lot, and allocation aimed at reducing the walking distance from the assigned space to the destination. The simulation results show that, in the absence of allocation criteria, parking spaces are utilized in a quasi-uniform manner. The calculated values of variance and standard deviation are significantly lower in this case, increasing as allocation restrictions are introduced, but then returning to reduced values as the occupancy rate grows, since under intensive use the potential for controlled allocation decreases. The relationship between the number of allocations of each parking space and the applied allocation strategies was examined using Pearson and Spearman correlation coefficients. The results reveal a direct linear dependence under moderate demand and an inverse dependence under high demand—patterns consistent with situations observed in practice. The proposed software application provides a practical tool for effective parking management, contributing to the rational use of parking spaces, reduced travel distances within the facility, lower fuel consumption, and consequently, reduced pollution. Full article

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects multiple organs and systems with a broad and heterogeneous spectrum of clinical manifestations. National disease-specific datasets and registries are crucial for clinical research since they can provide real-world and long-term data about clinical aspects, biomarkers, and treatments. Registries collect data from actual patients over time, outside the controlled environment of randomized controlled trials. This can help enhance the understanding of the natural history of a disease, provide information about how treatments work in everyday settings and elucidate potential variations in care and outcomes across different geographic areas. Here, we present a protocol for the creation of a standardized national disease-specific dataset for patients with SLE—the Systemic Lupus Erythematous Network (LUNET) Registry—which will facilitate data sharing, cross-comparison, and interoperability among centers. The LUNET registry is intended to serve as a comprehensive primary data source, capturing real-world longitudinal clinical information and the heterogeneity of patient presentations that are often underrepresented in traditional clinical trials. Ultimately, the LUNET registry will help to optimize SLE management in routine clinical practice by enabling the compilation of real-world evidence to inform clinical decision-making and health policy. Full article

Urban forests provide many ecosystem services in urbanized landscapes, including biodiversity conservation. The sustainable management of urban forests requires a thorough understanding of biodiversity changes in the context of rapid urbanization. As biodiversity in rapidly changing urban areas is very dynamic, we need a better understanding of long-term biodiversity changes in urban forests. Birds are very good bioindicators of urban forest biodiversity because they are strongly habitat-sensitive. However, a major knowledge gap exists in long-term trends in bird diversity in temperate urban forests. This study analyzed a comparison of bird-species richness in a temperate Central European urban forest over a time span of 37 years. Bird-counts using the standard line-transect method conducted in 2023–2024 were compared with older field data from 1987 gained using the same method in a lowland hardwood floodplain forest in the Czech Republic. The results revealed significant faunistic similarities in the bird-species diversity of an urban forest during the 1987–2024 period. The high local alpha diversity of the bird community (42 nesting bird species) as well as the relatively high long-term stability in bird richness indicated the importance of the studied urban forest as a stable biodiversity refugium in a dynamic urbanized landscape. Therefore, urban forests can be considered very stable biodiversity refugia in dynamically changing urban areas. Full article

Artificial reefs serve as a crucial measure for preventing habitat degradation, enhancing primary productivity in marine areas, and restoring and increasing fishery resources, making them an essential component of marine ranching development. Accurate identification and detection of artificial reefs are vital for ecological conservation and fishery resource management. To achieve precise segmentation of artificial reefs in multibeam sonar images, this study proposes an improved YOLOv11-based model, YOLO-AR. Specifically, the DCCA (Dynamic Convolution Coordinate Attention) module is introduced into the backbone network to reduce the model’s sensitivity to complex seafloor environments. Additionally, a small-object detection layer is added to the neck network, along with the ultra-lightweight dynamic upsampling operator DySample (Dynamic Sampling), which enhances the model’s ability to segment small artificial reefs. Furthermore, some standard convolution layers in the backbone are replaced with ADown (Advanced Downsampling) to reduce the model’s complexity. Experimental results demonstrate that YOLO-AR achieves an mAP@0.5 of 0.912, an intersection-over-union (IOU) of 0.832, and an F1 score of 0.908. Meanwhile, the parameters and model size of YOLO-AR are 2.67 million and 5.58 MB. Compared to other advanced segmentation models, YOLO-AR maintains a more lightweight structure while delivering a superior segmentation performance. In real-world multibeam sonar images, YOLO-AR can accurately segment artificial reefs, making it highly effective for practical applications. Full article

Tavapadon, a novel oral dopamine-D1R/D5R partial agonist, has been studied in recent years for the treatment of late-stage development Parkinson’s disease (PD). Levodopa, a dopamine precursor that currently remains the gold-standard first-line therapy for PD motor symptoms, serves as a benchmark against emerging dopaminergic agents. By selectively activating D1-family receptors on direct-pathway medium neurons, Tavapadon differs in that it delivers levodopa-level motor benefit while avoiding its many D2R/D3R-mediated adverse effects. In placebo-controlled trials, Tavapadon produced clear, clinically meaningful gains in motor function and day-to-day activities, as captured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Recent late-stage results have revealed that Tavapadon maintains superior UPDRS outcomes in de novo patients and, when added to levodopa, extended “ON” time periods of reliable motor control free of troublesome dyskinesia, without introducing new safety concerns. In studies, nausea, headache, and somnolence were the most frequent adverse events. Hallucinations, orthostatic hypotension, and impulse-control disorders remained comparable to placebo, reflecting minimal D2R/D3R-mediated effects. Preclinical primate studies have demonstrated levodopa-like motor rescue with markedly less dyskinesia, a pattern mirrored in clinical add-on trials. Collectively, evidence indicates that Tavapadon can match levodopa-mediated symptomatic efficacy, lower dyskinesia liability compared with levodopa or earlier full D1 receptor (D1R) agonists, and offer the convenience of once-daily dosing characteristics, which may bridge the therapeutic gap between levodopa and the current D2R/D3R agonists in PD management. In the present investigation, the emerging clinical role for Tavapadon is described, along with the mechanism of action, clinical efficacy, safety, and future directions. Full article

With the rapid evolution of 5G wireless communications, millimeter-wave (mmWave) technology has become a crucial enabler for high-speed, low-latency, and large-scale connectivity. As the critical interface for signal transmission, mmWave antennas directly affect system performance, reliability, and application scope. This paper reviews the current state of mmWave antenna technologies in 5G systems, focusing on antenna types, design considerations, and integration strategies. We discuss how the multiple-input multiple-output (MIMO) architectures and advanced beamforming techniques enhance system capacity and link robustness. State-of-the-art integration methods, such as antenna-in-package (AiP) and chip-level integration, are examined for their importance in achieving compact and high-performance mmWave systems. Material selection and fabrication technologies—including low-loss substrates like polytetrafluoroethylene (PTFE), hydrocarbon-based materials, liquid crystal polymer (LCP), and microwave dielectric ceramics, as well as emerging processes such as low-temperature co-fired ceramics (LTCC), 3D printing, and micro-electro-mechanical systems (MEMS)—are also analyzed. Key challenges include propagation path limitations, power consumption and thermal management in highly integrated systems, cost–performance trade-offs for mass production, and interoperability standardization across vendors. Finally, we outline future research directions, including intelligent beam management, reconfigurable antennas, AI-driven designs, and hybrid mmWave–sub-6 GHz systems, highlighting the vital role of mmWave antennas in shaping next-generation wireless networks. Full article