Search Results (122)

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

Background/Objectives: Cyclin-dependent kinase 4/6 inhibitors combined with endocrine therapy have become the standard of care for HR+/HER2− metastatic breast cancer. Given the metabolic functions of CDK4/6 and the endocrine activity of adipose tissue, body mass index has been proposed as a potential prognostic or predictive factor in this setting. This systematic review aimed to summarize current evidence on the association between BMI and treatment outcomes in HR+/HER2− MBC patients receiving CDK4/6i. Methods: A systematic literature search was conducted in PubMed and Scopus databases, covering publications from 2015 to 2025. We included real-world studies and clinical cohorts reporting survival outcomes of HR+/HER2− MBC patients treated with CDK4/6i in relation to BMI and other body composition parameters. Results: From 69 records identified, 14 studies met the inclusion criteria. Findings were heterogenous; four studies reported improved survival outcomes in higher BMI patients, whereas most identified no significant association. Studies incorporating computed tomography-based metrics demonstrated that body composition parameters such as visceral adiposity and skeletal muscle area were more reliable predictors of prognosis than BMI alone. Conclusions: Our findings indicate that BMI as a standalone metric is an insufficient predictor of clinical outcomes or treatment response for those receiving CDK4/6i, highlighting the need for precise body composition evaluation. More detailed anthropometric and metabolic profiling could clarify the clinical significance of adiposity in HR+/HER2− MBC. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, typically diagnosed at an advanced stage. The identification of prodromal symptoms could support earlier detection. Among these, depression is frequently reported, raising the question of whether it may represent not only a reactive response but also a paraneoplastic manifestation. Methods: We conducted a narrative review of clinical, epidemiological and biological literature published between 1988 and 2025. Searches were performed in PubMed/MEDLINE, Scopus, and Web of Science using predefined keywords related to pancreatic cancer, depression, prodromal symptoms, cytokines, and the kynurenine pathway. Eligible studies included clinical cohorts, population-based analyses, biological investigations, and case reports exploring the temporal or mechanistic link between depression and PDAC. Results: A substantial proportion of patients (10–20%) exhibit depressive symptoms in the months preceding the clinical diagnosis of pancreatic cancer. In several cases, depression occurs independent of weight loss and new-onset diabetes. Biological evidence highlights the involvement of pro-inflammatory cytokines (IL-6, IL-1β, TNF-α), NF-κB signaling, and activation of the tryptophan–kynurenine pathway (IDO), suggesting a link between tumor-related processes and mood alterations. These mechanistic findings are actually hypothesis-generating, deriving mainly from small clinical cohorts and preclinical models. Clinically, depression is associated with reduced adherence to treatment, poorer quality of life, and shorter survival. However, no specific depressive phenotype has been identified. Conclusions: Depression may represent a potential prodromal symptom of pancreatic cancer, reflecting systemic biological processes as well as psychological reactions. Its utility as a standalone marker remains limited; future studies should integrate psychiatric, clinical and biological biomarker assessments to enhance early clinical diagnosis. Full article

Background: Idiopathic intracranial hypertension (IIH), also known as primary pseudotumor cerebri, is characterized by increased intracranial pressure (ICP) without an identifiable cause. It can lead to significant morbidity, including permanent vision loss, especially in younger children. The exact cause of IIH is still unclear, but excess adiposity seems to be a key risk factor. Current treatment options are unsatisfactory, but research is exploring novel therapies targeting obesity-related mechanisms. Methods: Narrative review of the literature aimed at summarizing current knowledge regarding the epidemiology, pathophysiology, clinical features, treatment options and long-term outcomes for pediatric IIH, with a particular focus on the link with obesity. Results: The incidence of IIH is rising, mirroring the obesity epidemic. Excess adiposity, predominantly visceral, might cause IIH through several factors such as decreased venous return, hormone dysregulation, inflammation, obstructive sleep apnea, and dysfunction of the glymphatic system. The extent of weight loss required and the most appropriate strategy to achieve it are still uncertain. Given the difficulty in achieving and maintaining weight loss with dietary strategies, bariatric surgery and weight loss medications are emerging as effective options for long-term remission of both obesity and IIH. Conclusions: IIH is a rare and poorly understood disease. At present, weight loss represents the only treatment that addresses the pathophysiology of IIH. The role and potential as standalone or synergistic therapies of weight loss drugs and bariatric surgery for IIH in adolescents require future research. Full article

Women in vulnerable living situations with schizophrenia face intersecting challenges, including migration-related trauma, caregiving burdens, and systemic barriers such as cultural dislocation, limited healthcare access, and stigma. These factors heighten vulnerability compared with men and contribute to delayed diagnoses, poor treatment adherence, and adverse outcomes. Advances in artificial intelligence (AI) and digital tools offer potential support, though they should be regarded as complementary rather than stand-alone solutions. This review synthesizes literature on gender-specific care for women with schizophrenia, examining clinical, social, and reproductive needs alongside the impact of migration and psychosocial adversity. Emerging models, including women-focused psychiatric units, perinatal services, and community therapeutic spaces, illustrate holistic approaches that integrate the medical, psychological, and social dimensions of care. Digital interventions, such as smartphone applications, mobile health tools, and digital participation strategies, are considered supportive extensions that offer opportunities to improve access, reduce costs, and enhance continuity of care. Despite this promise, digital tools remain under-validated for women in precarious contexts. Ethical challenges, including algorithmic bias, data privacy risks, and the exclusion of undocumented or marginalized groups, further constrain equitable implementation. This review aims to articulate conceptual linkages among gender, migration, and digital innovation in schizophrenia care, identifying thematic patterns, ethical tensions, and structural limitations in the existing literature. The synthesis provides a foundation for future hypothesis development and interdisciplinary research to advance inclusive and equity-driven mental health interventions. Full article

This paper presents a comprehensive review of motion capture (MoCap) data recovery techniques, with a particular focus on the suitability of artificial intelligence (AI) for addressing missing or corrupted motion data. Existing approaches are classified into three categories: non-data-driven, data-driven (AI-based), and hybrid methods. Within the AI domain, frameworks such as generative adversarial networks (GANs), transformers, and graph neural networks (GNNs) demonstrate strong capabilities in modeling complex spatial–temporal dependencies and achieving accurate motion reconstruction. Compared with traditional methods, AI techniques offer greater adaptability and precision, though they remain limited by high computational costs and dependence on large, high-quality datasets. Hybrid approaches that combine AI models with physics-based or statistical algorithms provide a balance between efficiency, interpretability, and robustness. The review also examines benchmark datasets, including CMU MoCap and Human3.6M, while highlighting the growing role of synthetic and augmented data in improving AI model generalization. Despite notable progress, the absence of standardized evaluation protocols and diverse real-world datasets continues to hinder generalization. Emerging trends point toward real-time AI-driven recovery, multimodal data fusion, and unified performance benchmarks. By integrating traditional, AI-based, and hybrid approaches into a coherent taxonomy, this review provides a unique contribution to the literature. Unlike prior surveys focused on prediction, denoising, pose estimation, or generative modeling, it treats MoCap recovery as a standalone problem. It further synthesizes comparative insights across datasets, evaluation metrics, movement representations, and common failure cases, offering a comprehensive foundation for advancing MoCap recovery research. Full article

Hybrid Gas–Magnetic Bearings (HGMBs) are an emerging technology ready to completely change high-speed oil-free rotor support in aerospace electric motors. Because HGMBs combine the stiffness and load capacity of gas bearings with the active control of magnetic bearings, enabling oil-free, contactless rotor support from zero to ultra-high speeds. They offer more load capacity of standalone magnetic bearings while maintaining full levitation across the entire speed range. Dual-mode operation, magnetic at low speeds and gas film at high speeds, minimizes control power and thermal losses, making HGMBs ideal for high-speed aerospace systems such as cryogenic turbopumps, electric propulsion units, and hydrogen compressors. While not universally optimal, HGMBs excel where extreme speed, high load, and stringent efficiency requirements converge. Advances in modeling, control, and manufacturing are expected to accelerate their adoption, marking a shift toward hybrid electromagnetic–aerodynamic rotor support for next-generation aerospace propulsion. This review provides a thorough overview of emerging HGMBs, emphasizing their design principles, performance metrics, application case studies, and comparative advantages over conventional gas or magnetic bearings. We include both a historical perspective and the latest developments, supported by technical data, experimental results, and insights from recent literature. We also present a comparative discussion including future research directions for HGMBs in aerospace electrical machine applications. Full article

Background: Contemplative practices encompass a variety of static and dynamic practices. These practices, by fostering insights, heightened awareness, and a deeper connection to a broader framework of meaning, may play a crucial role in significantly reducing trauma-related symptoms in both young and adult populations. Methods: The current narrative literature review used Scopus and PubMed to search for studies published between January 2018 and August 2023 that examined the effects of contemplative practices, an umbrella term that includes mindfulness-based interventions, yoga, tai chi, qigong, and meditation, on trauma recovery and PTSD symptoms among adults and youths. Results: The literature search identified 281 articles. Forty-two studies met the inclusion criteria and were critically evaluated. Among the various approaches, encompassing stand-alone contemplative practices and combined interventions, mindfulness emerged as the most employed and investigated practice for supporting trauma recovery. Contemplative practices have been shown to effectively reduce various dimensions of traumatic experience, such as reactivity, intrusion, hyperarousal, and negative cognitions and mood. Controversial results were found on avoidance symptoms and physiological parameters. Conclusions: The results give support to the idea of combining contemplative practices with trauma-focused psychotherapeutic interventions to foster a sense of safety and enhance emotional expression and awareness of feelings of fear, shame, guilt, or inferiority while improving metacognitive processes. This, in turn, supports healing the sense of self, restoring a sense of basic trust in self and others, which is often deeply affected in individuals who have experienced trauma. Full article

The growing world population has continued to drive up the demand for food and energy resources, putting substantial strain on the finite land, water, and fossil resources of the earth. Given the current climate crisis, the necessity of implementing renewable energy-generation strategies has become clear. Although solar energy is one of the most abundant and consistent forms of renewable energy available, conventional ground-mounted solar arrays require large amounts of land area, and solar energy generation may come into competition with agriculture with increasing installation capacity. Agrivoltaics has been presented as a solution to integrate agricultural activities with solar energy generation to enhance the land efficiency of both activities. Through this method, agriculture and solar energy become synergistic, generating multiple profit streams from the same land with additional potential environmental benefits. The review presented herein studies the literature pertaining to the triple bottom line for agrivoltaics systems: people, planet, and profit. Despite the early-stage nature of many available studies, researchers have reported that certain agrivoltaics systems could be up to 270% more profitable than standalone cropping systems and reduce the greenhouse gas potential of traditional agriculture and energy generation by up to 99%. By synthesizing the information from multiple techno-economic analyses, life-cycle assessments, and policy recommendations, we hope to provide some insight into the key parameters driving the long-term sustainability of agrivoltaics systems. Full article

The study aims to identify optimum retrofitting strategies that mitigate climate change and support Australia’s net-zero emissions target by 2050. Current heating and cooling demands, as well as the energy performance of three stand-alone houses built before 2003, were evaluated to determine optimal retrofitting measures. Based on a comprehensive literature review and physical building surveys and energy simulations using FirstRate5 of three selected case studies of stand-alone houses in Australia’s climate zone 5, the study identifies and proposes effective retrofitting opportunities in Western Australia. Additionally, the outcomes from FirstRate5 illustrate that improving ceiling and exterior wall insulation in living and dining areas, sealing air leaks, reducing overshading, and replacing single-glazed windows with double-glazed units while enlarging north-facing windows, following the recommended wall–window ratio significantly improve the energy rating of the selected houses. The average energy rating performance of the three selected stand-alone houses increases from an average below 3.5 stars (211.5 MJ/m²) to above 7.5 stars (46.7 MJ/m²), representing around 76.6% improvement in energy efficiency. Just to contextualise the scale up, such retrofitting of all old stand-alone houses built before 2003 would potentially reduce emissions by 12.73 Mt CO₂-e/year, representing a 3.16% contribution toward Australia’s national emission reduction target by 2035. Additionally, installing solar energy systems could reduce an extra 4.5 Mt CO₂-e/year. The study’s findings demand robust retrofitting strategies for Australia to achieve its 2050 net-zero emissions targets. Full article

Introduction: While low back pain (LBP) is most often associated with musculoskeletal issues, in a minority of cases, it can be caused by serious underlying conditions such as cancer. Recognizing malignancy early remains a major clinical challenge, as the warning signs, known as red flags (RFs), are often vague and inconsistent. Methods: A comprehensive search of six databases (PubMed, Scopus, Google Scholar, Web of Science, Cochrane Library, and SciELO) and grey literature was conducted for studies published from January 1999 to March 2025. Eligible sources included studies describing adult patients with cancer presenting with LBP. Study selection and data extraction were independently performed by two reviewers. Results: We included 70 studies, most of which were case-based, along with reviews and observational research. In these studies, cancer prevalence among patients with LBP ranged from 0.1% to 1.6%, with metastatic disease being the most common finding. A prior history of cancer emerged as the most reliable red flag (specificity up to 0.99), while other signs and symptoms were less consistent. Notably, combining multiple RFs, such as a history of cancer and unexplained weight loss, significantly improved the diagnostic accuracy (LR+ = 10.25 in one study). Conclusions: While current evidence is limited and largely based on case-based studies, some RFs, particularly a history of cancer, show greater diagnostic value. In patients with LBP associated with underlying malignancy, RFs seem to be more useful for ruling in rather than ruling out (i.e., screening) serious pathologies. Most RFs have poor standalone accuracy; however, considering combinations of RFs within the broader clinical context may improve early detection of spinal malignancy in patients with LBP. Full article

Background: Indigenous populations worldwide face a disproportionate burden of HIV due to structural inequities, cultural marginalization, and limited access to health services. Despite growing recognition of the need for culturally adapted responses, the effectiveness of combination HIV prevention strategies in these communities remains underexplored. Objectives: This study aimed to evaluate and compare the effectiveness of multiple HIV prevention strategies among Indigenous populations using a systematic review and network meta-analysis (NMA), to inform equity-oriented public health interventions. Methods: Following PRISMA-NMA 2020 guidelines, a comprehensive literature search was conducted across four databases (PubMed, SciELO, LILACS, Science Direct) for quantitative studies published between January 2000 and June 2025. Eligible studies evaluated HIV prevention interventions among Indigenous populations and reported risk or odds ratios. A frequentist NMA model was used to calculate effect estimates (OR, 95% CI) and SUCRA rankings for seven types of interventions, combining biomedical, behavioral, and structural approaches. Results: Four high-to-moderate quality studies enclosing 4523 participants were included. The most effective intervention was home-based counseling and testing for HIV, followed by medical consultation combined with HIV testing. Standalone testing, while effective, was significantly less impactful than when combined with culturally sensitive educational strategies. Information-only strategies showed the least efficacy. The SUCRA analysis ranked home-based testing highest (45.17%), highlighting the importance of decentralization, community participation, and intercultural mediation. Conclusions: Culturally adapted combination prevention strategies—especially those integrating home-based testing and counseling—are more effective than isolated biomedical interventions in Indigenous populations. These findings reinforce the urgent need for participatory, context-driven public health responses that center Indigenous knowledge, reduce stigma, and expand equitable access to HIV care and prevention. Full article

This review provides a critical and technically grounded assessment of continuous electrocoagulation processes (CEPs) for the treatment of industrial inorganic pollutants, emphasizing recent innovations, methodological developments, and practical outcomes. A comprehensive literature survey indicates that 53 studies published over the past 25 years have investigated CEPs for inorganic contaminant removal, with 36 focusing on standalone electrocoagulation systems and 17 exploring integrated CEPs approaches. Recent advancements in reactor design, such as enhanced internal mixing, optimized electrode geometry, and modular configurations, have significantly improved treatment efficiency, scalability, and operational stability. Evidence indicates that CEPs can achieve high removal efficiencies for a wide range of inorganic contaminants, including fluoride, arsenic, heavy metals (e.g., chromium, lead, nickel, iron), nitrates, and phosphates, particularly under optimized operating conditions. Compared to conventional treatment methods, CEPs offer several advantages, such as simplified operation, reduced chemical consumption, lower sludge generation, and compatibility with renewable energy sources and complementary processes like membrane filtration, flotation, and advanced oxidation. Despite these promising outcomes, industrial-scale implementation remains constrained by non-standardized reactor designs, variable operational parameters, electrode passivation, high energy requirements, and limited long-term field data. Furthermore, few studies have addressed the modeling and optimization of integrated CEPs systems, highlighting critical research gaps for process enhancement and reliable scale-up. In conclusion, CEPs emerge as a novel, adaptable, and potentially sustainable approach to industrial inorganic wastewater treatment. Its future deployment will rely on continued technological refinement, standardization, validation under real-world conditions, and alignment with regulatory and economic frameworks. Full article

Green Firebreaks (GFBs), strips of strategically placed low-flammability vegetation, represent a proactive complement to other approaches to wildfire management. This review, which summarises the literature to elucidate GFBs’ potential to reduce fire spread and intensity, revealed that empirical studies validating their effectiveness remain scarce. It also revealed that comparisons of GFB techniques are challenging due to spatial and temporal complexity combined with inconsistent methods and terminology. Several researchers note that GFB effectiveness requires that their design is appropriate for the site conditions. Furthermore, GFBs are not a stand-alone solution to the wildfire problem, and a lack of consideration for trade-offs may undermine their effectiveness, particularly under extreme weather conditions. As climate change intensifies drought and heat, vegetation moisture content must be a key design factor given that even low-flammability vegetation becomes fuel under extreme drought conditions. In addition, poorly designed GFBs may unintentionally alter wind dynamics and increase ember transport and fire spread. There is a broad consensus in the literature that appropriately designed GFBs can complement wildfire management while providing additional biodiversity and other benefits. To achieve their potential, research is required for GFB designs to be site-specific, responsive to trade-offs, and effective in providing multiple benefits under different climate change scenarios. Full article