Search Results (460)

Background/Objectives: Bloodstain pattern analysis (BPA) is widely used in crime scene investigation (CSI), yet its practical application, evidential limits, and interpretive role are often discussed in fragmented or technique-focused terms. This systematic literature review examines how BPA is used in CSI, with emphasis on its operational functions, interpretive scope, and scientific robustness. Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in Scopus using predefined Boolean strings. After screening, eligibility assessment, and manual review, 18 peer-reviewed research articles published between 1996 and 2026 were included. Data were extracted systematically and analysed using thematic synthesis. Results: The findings show that BPA is applied in CSI as an integrated evidential pathway rather than as a single analytical procedure. Its uses include bloodstain detection and documentation, geometric reconstruction through trajectory and area-of-origin analysis, differentiation of mechanisms and sources to prevent misclassification, activity-level inference based on transfer and contact phenomena, and temporal reasoning related to trace formation. The review also highlights the role of validation infrastructures, including blood substitutes, animal analogues, and computational methods, which support training, experimentation, and reproducibility under ethical and practical constraints. Across the literature, reconstruction accuracy is shown to be sensitive to documentation quality, measurement assumptions, environmental conditions, and contextual limitations. Conclusions: Overall, BPA contributes to CSI by enabling structured, context-aware interpretation of blood evidence while remaining subject to measurement assumptions, contextual influences, and cognitive factors that may affect reconstruction outcomes. Its evidential value lies not only in reconstructing events, but also in supporting transparent, testable, and defensible forensic reasoning. Full article

Background: Radish seeds are our model on glucosinolates (GSLs), which is a class of secondary metabolites in medicinal plants of the Brassicaceae family. Multilayer perceptron (MLP) network is highly effective in the study of complex plants. This study came up with a smart plan through the Python language. Methods: First, we used the MLP network to pick out GSL precursor ions, running them through a deep learning filter. Next, we set up an automated screening system and looked at how standard chemicals break down. To speed things up, we created a scoring system that flagged promising compounds. After that, we built a tracer molecular network, basically connecting compounds according to how the plant makes them, which helped us label everything accurately. Finally, we brought in a math-based tool that pieces together different chemical parts to predict new GSL structures. Results: With this workflow, we annotated 195 glucosinolate-related compounds in radish seeds. That includes 86 regular GSLs, 34 malonyl products, 40 sinapoyl compounds, and 35 diglycosides. Among them, eight compounds were confirmed by comparison with authentic standards (retention time and MS/MS data), whereas the remaining compounds were tentatively annotated based on accurate mass measurements, diagnostic fragment ions, Tracer Molecular Nnetworking, and literature/database matching. In addition, 36 compounds were considered putatively novel derivatives pending further structural confirmation. Conclusions: This new approach reduces the time spent on determining chemicals in complicated samples. This can be done with other vegetables and medicinal herbs by researchers. It assists us in knowing the chemistry of plants in a deeper manner. Full article

Background: Unhoused individuals face disproportionately high rates of preventable chronic disease due to fragmented access to care and prolonged exposure to environmental stressors. Street medicine programs offer a mobile, low-barrier model to assess and address these unmet needs. Despite well-documented disparities, no publications in the current literature provide numerically specific screening recommendation guidelines tailored to unhoused populations. This study fills that gap using clinical data from Street Medicine Phoenix (SMP), a mobile healthcare initiative serving urban Arizona. Methods: We retrospectively reviewed 1322 clinical encounters recorded by SMP between August 2023 and October 2024. Diagnoses and treatments were manually categorized. Blood pressure (BP) and glucose values were analyzed using descriptive statistics and compared against national norms (CDC 50th percentile and ADA guidelines). Kruskal–Wallis and Dunn’s tests assessed age-based differences, while chi-square and Mann–Whitney U tests examined glucose patterns. Results: The mean patient age was 51.4 years; 34.5% identified as female. Cardiovascular issues (39.4%) and routine screenings (39.6%) were most frequently documented. Systolic and diastolic BP values were significantly elevated across all age groups except those 60+, with even the 18–39 group showing median systolic BP above CDC norms (124.0 mmHg). Among 60 patients with fasting glucose data, 41.4% met ADA criteria for diabetes, and 10.7% of those without a known diagnosis had diabetic-range values. Conclusions: Our findings suggest that cardiometabolic disease may emerge earlier and more aggressively among unhoused individuals than in the general U.S. population, reflecting patterns of accelerated biological aging. The elevation of cohort-based BP percentiles suggests that current national benchmarks may underrepresent clinical risk in this group. We propose initiating blood pressure screening at age 18 and fasting glucose screening by age 35 in unhoused individuals—adaptations of existing USPSTF recommendations based on cohort-specific trends. These screening thresholds can be feasibly implemented in street medicine settings to promote earlier detection and improve long-term health outcomes. Full article

The European Union (EU) relies heavily on voluntary tax compliance, yet evidence on how tax literacy (TL) and tax education (TE) relate to tax morale (TM) and voluntary tax compliance or compliance intentions (VTC) remains fragmented across partly disconnected strands of the literature. This systematic review examined EU-relevant evidence on the stakeholder contexts in which TL/TE are discussed in relation to TM and VTC, with particular attention to schools, communities, and public institutions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, searches in Scopus and Web of Science (2000–2025) applied two complementary query streams focused on TL/TE and TM/VTC-related mechanisms. The searches identified 1327 records; after deduplication and screening, 402 studies were included. Based on structured coding of titles, abstracts, and author keywords, the review maps patterns of emphasis and framing rather than causal effects. Public-institutional and education-related contexts were the most frequently signposted stakeholder environments, while digital and outreach-oriented delivery cues were more visible than classroom-based cues. Trust and fairness/justice dominated the explanatory vocabulary. Overall, the review supports an ecosystem-oriented interpretation of stakeholder coordination in EU tax literacy research. Full article

Maritime traffic complexity (MTC) is a term that has gained increased importance in the last decade in the maritime safety domain. It is a concept for understanding navigational safety and operational challenges in congested maritime environments. Although research interest in MTC has grown, it is a concept that remains fragmented, with various interpretations of definitions, indicators, and modeling approaches present in the literature. This study presents a comprehensive literature review and bibliometric analysis to synthesize the current state of research on MTC as a scientific construct and clarify its conceptual foundations from an analytical perspective. In accordance with PRISMA guidelines and systematic literature review (SLR) methodology, relevant studies were identified and screened across major scientific databases. A detailed analysis was conducted on 40 scientific publications. The findings indicate that most existing MTC models rely mainly on Automatic Identification System (AIS) data and corresponding derived metrics. MTC is primarily assessed through geometric vessel–vessel interactions, relative motion parameters, and collision-risk indicators. Bibliometric analysis demonstrates a rapid increase in scientific interest in this topic since 2015, with research concentrated in several leading journals. The study identifies a significant methodological limitation in current frameworks, which often overlook the heterogeneity of marine traffic, environmental conditions, vessel reliability, and human factors. Therefore, this study highlights the need for a more comprehensive MTC evaluation framework that incorporates operational, geographical constraint-based, environmental, and behavioral variables alongside traditional AIS-based metrics. Full article

The proliferation of marine data presents both an opportunity for ocean governance and a challenge, contributing to fragmentation across disciplines, institutions, and sectors. Marine Spatial Data Infrastructure (MSDI) stands out as a major framework for integrating marine information. However, an integrated synthesis that combines quantitative mapping of publication patterns with qualitative analysis of thematic evolution remains absent. This study employs a two-step approach combining systematic review and bibliometric analysis of Scopus-indexed literature (2000–2024). Based on a focused corpus of 20 publications rigorously screened for explicit MSDI relevance, we examine publication trends, collaboration patterns, thematic structures, and evolutionary trajectories. Results indicate accelerating scholarly interest in MSDI, with European institutions contributing 75% of the analysed publications. Policy frameworks such as the INSPIRE Directive (Infrastructure for Spatial Information in the European Community) and the Marine Strategy Framework Directive (MSFD) emerge as key drivers of research activity. Temporal analysis of this corpus suggests a tentative five-phase evolution in MSDI research: (1) foundational technical standardisation, (2) governance model implementation, (3) semantic interoperability enhancement, (4) policy integration, and (5) advanced applications incorporating FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles and Artificial Intelligence (AI). These phases, derived from systematic coding of thematic focus across publications, represent observed patterns within the analysed literature rather than definitive stages. This paper concludes that MSDI is moving toward a more socio-technical approach that requires the consideration of a technical-focused tool in present-day ocean governance. Future work should combine semantic AI, decentralised architectures, polycentric governance models, and impact assessment frameworks to align MSDI development with the objectives of equity, inclusion, and sustainability. Full article

Haematopota pluvialis is a widely distributed hematophagic insect occurring across Eurasia. This horse fly may be a highly efficient mechanical vector of pathogens, including viruses, bacteria, and protozoa. Furthermore, its painful bites can cause local skin lesions and systemic symptoms. The aim of this study was to determine human exposure to H. pluvialis attacks in various types of open space habitats in Eastern Poland and to perform molecular screening of these tabanids for the presence of hematopathogens: Bartonella spp. and Anaplasma phagocytophilum. Specimens of H. pluvialis were collected at three distinct sites in Eastern Poland. The presence of Bartonella spp. and A. phagocytophilum was investigated using PCR-based methods. In total, 141 H. pluvialis females were analyzed. The molecular analysis of the rpoB gene fragment yielded one new haplotype of Bartonella sp. in 0.7% (1) of all studied samples, which may hypothetically exhibit zoonotic potential. Anaplasma phagocytophilum was not detected in the studied material. Moreover, a high level of human and animals exposure to horse fly bites was noted in the studied areas of Eastern Poland. The present results highlight the need for further targeted research on H. pluvialis to quantify pathogen prevalence, transmission efficiencies, and conditions facilitating pathogen transmission in natural settings. Full article

Background/Objectives: Chronic diseases pose a major challenge for healthcare systems, requiring integrated, patient-centered approaches that combine clinical management, prevention, and self-care. Lifestyle Medicine (LM) and lifestyle in general offers complementary frameworks to address these needs. However, the potential integration of LM within community nursing—particularly through the role of Family and Community Nurse (FCN)—has not been comprehensively synthesized. This narrative review aimed to synthesize international evidence on the role of community nursing—particularly FCN—in integrating chronic care management and LM view. Methods: For quality assessment, a narrative review was conducted in accordance with the SANRA criteria to enable the integration of heterogeneous evidence and a comprehensive synthesis of this complex topic. Literature searches were performed in the PubMed–Medline database, and the final screening of references from included studies was used to identify relevant manuscripts. Primary studies published in English over the past ten years were screened and analyzed using the PICOS framework. Sixteen eligible studies were included in the final synthesis. Results: The included studies indicated that nurse-led community interventions in LM view were associated with improvements in self-management, treatment adherence, and selected clinical outcomes, such as blood pressure, glycated hemoglobin, and physical activity levels. Empowerment-based approaches and the use of digital or telehealth tools supported patient engagement and health literacy. At the organizational level, multidisciplinary collaboration, shared protocols, and professional leadership emerged as key factors in sustaining continuity and quality of care, while organizational fragmentation and limited training in behavioral counseling were commonly reported barriers. Conclusions: Community nursing, particularly through FCNs, plays a relevant role in integrating chronic care management and LM approaches, contributing to improved self-management, treatment adherence, and selected clinical outcomes. The evidence highlights the importance of empowerment-based interventions, digital support tools, and multidisciplinary collaboration in enhancing care continuity and patient engagement. Addressing organizational barriers and strengthening behavioral counseling training remain essential to support effective implementation in community settings. Full article

A quartz crystal microbalance-based biosensor for the specific detection of the first transgenic common bean (L.) cultivar (BRS FC401 RMD) with resistance to Bean golden mosaic virus (BGMV) was developed. The immobilization chemistry relies on the strong bond between the thiolated probe and the gold electrode surface. The probe sequence is internal to a region of the BGMV rep gene that was introduced into the common bean genome. The sensor’s analytical performance was determined using synthetic oligonucleotides. Real samples of transgenic and wild-type bean seeds were also tested. Sample pretreatment consisted only of enzymatic fragmentation, followed by a thermal denaturation step combined with blocking oligonucleotides. Different biosensor regeneration approaches were studied. Immobilization showed good reproducibility (CV% of 5.8%). The biosensor proved specific for both synthetic oligonucleotides and non-amplified genomic DNA. A linear detection range of 0–1.4 ng/µL was observed, with a detection limit of 0.18 ng/µL. Three sequential detections were performed without loss of surface activity. The results demonstrate the biosensor’s potential for direct, real-time, label-free detection of DNA samples for field screening of transgenic common bean cultivars. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Urban areas are pivotal to achieving the Sustainable Development Goals (SDGs), yet sustainability monitoring at the municipal level remains fragmented, difficult to operationalize, and weakly comparable across cities. Although the SDGs provide a comprehensive global agenda and ISO 37120 offers a standardized set of city indicators, municipalities still face practical barriers in translating global targets into actionable, jurisdiction-sensitive, and measurable metrics aligned with local responsibilities and available data. This study addresses this gap by presenting the design of an integrated, target-level urban sustainability assessment framework grounded in SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) principles and explicitly tailored to municipalities in developed-country contexts. The framework contributes (i) a structured procedure for disaggregating and reallocating SDG targets according to municipal responsibilities, (ii) a six-dimension architecture that consolidates SDG targets and ISO 37120 themes into a coherent, governance-oriented structure (Government and Economic Development; Civic & Social Infrastructure; Environment and Climate; Infrastructure and Urban Planning; Health; Urban Living Conditions), and (iii) a SMART-based indicator screening logic that prioritizes feasibility, data availability, and benchmarking potential, thus supporting the green transition in Urban Areas. The framework is empirically examined through validation against sustainability reporting practices of the Porto City Council, quantifying indicator coverage, assessing alignment with municipal mandates, and identifying systematic gaps—particularly in cross-cutting areas such as governance transparency, equity monitoring, and long-term climate adaptation. Overall, the results indicate that the proposed approach strengthens coherence, measurability, and comparability in urban sustainability assessment, supporting evidence-based municipal decision-making, performance benchmarking, and more strategically aligned SDG localization. Full article

This study systematically investigated the dynamic diversity, potential sources, and antifungal activities of small molecular peptides during the pile-fermentation process of post-fermented tea. By analyzing the damaging effects of small molecular peptide extracts from tea samples at different pile-fermentation stages on the spore cell membranes of Aspergillus carbonarius (A. carbonarius) and the inhibitory activity against β-1,3-glucan synthase (β-1,3-GS), it was confirmed that some small molecular peptides exhibit significant antifungal effects. The main findings are as follows: (1) The number of identified small molecular peptides showed a trend of first increasing and then decreasing with the progress of pile-fermentation, peaking at 4453 species on the 35th day of pile-fermentation, and were dominated by hexapeptides and heptapeptides with molecular weights ranging from 600 to 800 Da. (2) Based on orthogonal partial least squares discriminant analysis (OPLS-DA), the samples were divided into three characteristic stages according to the differences in small molecular peptide composition at different stages, and 156 characteristic peptides with a relative abundance higher than 0.1% were screened out. Their precursor proteins were derived from 148 proteins belonging to 16 genera, including Camellia, Aspergillus, Saccharomyces, Penicillium, and Bacillus. (3) BLAST alignment results showed that five out of the 156 characteristic peptides were degradation fragments of known antifungal peptides originating from Aspergillus and Bacillus. (4) Combining molecular docking screening and in vitro verification of synthetic peptides, a total of 27 small molecular peptides with antifungal activity were obtained, and their mechanism of action was the inhibition of β-1,3-GS activity. (5) The small molecular peptides related to antifungal activity could be classified into two categories: enzymatic hydrolysates of known antifungal peptides, and the enzymatic hydrolysates of tea-derived proteins or macromolecular peptides. Both categories were mainly distributed in the three stages of pile-fermentation, and there was a significant positive correlation among the population size of dominant microorganisms, microbial peptidase activity, and the abundance of small molecular peptides. This study reveals the dynamic generation pattern and antifungal potential of small molecular peptides during the pile-fermentation of post-fermented tea, providing a new scientific basis for evaluating the dynamic changes in microbial communities in tea and effectively controlling the contamination of harmful fungi during the pile-fermentation process. Full article

Industrial organisations increasingly rely on machine learning (ML) to improve quality, maintenance, and planning in Industry 4.0/5.0 ecosystems. However, turning experimental models into reliable services on the production floor remains complex due to the heterogeneity of operational technologies (OTs) and information technologies (ITs), including implementation constraints, latency in edge-fog-cloud scenarios, governance requirements, and continuous performance degradation caused by data drift. Although Machine Learning Operations (MLOps) provides lifecycle practices for deployment, monitoring, and retraining, the evidence is fragmented across tool-centric descriptions, case-specific pipelines, and conceptual architectures, offering limited guidance on which industrial constraints should inform architectural decisions and how to evaluate solutions. This work addresses that gap through a PRISMA-guided systematic review of 49 studies on industrial MLOps (with the search and screening primarily targeting Industry 4.0/IIoT operationalisation contexts, as reflected in the search strategy and corpus) and an evidence-based synthesis of principles, challenges, lifecycle practices, and enabling technologies. From this synthesis, industrial requirements are derived that encompass OT/IT integration, edge-fog-cloud orchestration, security and traceability, and observability-based lifecycle control. On this basis, a reference architecture is proposed that maps these requirements to functional layers, data and control flows, and verifiable responsibilities. To support reproducibility and practical inspectability, the article also presents an open-source architectural instantiation aligned with the proposed decomposition. Finally, the evaluation is illustrated through a predictive maintenance use case (tool breakage) in a single CNC machining cell, where the objective is to demonstrate end-to-end feasibility under realistic operational constraints rather than cross-scenario superiority or broad industrial generalisability. Full article

Translating heterogeneous user requirements (URs) into robust engineering specifications for public-access products is a critical challenge, often impeded by information uncertainty and fragmented design processes. To address this, we propose an integrated decision-making framework underpinned by Rough Set Theory (RST) as a unified mathematical language for uncertainty management. The framework systematically guides customer-driven product development by integrating a series of RST-based methods: a Kano model analysis to screen URs, a novel rough-Shapley value model to determine their interdependent weights, a rough-QFD approach to translate them into weighted design requirements (DRs), and the rough-VIKOR method to select the optimal design alternative. A case study on public-access faucets validates the framework’s efficacy. The results demonstrate its capability to identify critical URs, derive robust DRs by systematically resolving technical attribute conflicts, and select a superior design solution that optimally balances hygiene, durability, and user experience. The application of the framework successfully identified Alternative ${\mathrm{A}}_1$ (Push-Activated Spout) as the optimal solution, demonstrating superior performance in proactive hygiene and core functionality. The results prove that maintaining data integrity through a unified RST pipeline effectively resolves early-stage design conflicts. This research contributes a rigorous, data-driven decision support system that enhances objectivity and information fidelity, providing a transparent and auditable methodology for designing human-centered public infrastructure. Full article

As private landownership becomes increasingly fragmented and socially diverse, understanding how new types of landowners interact with wildlife and engage in management practices is essential for achieving landscape-scale conservation outcomes. This issue has taken on renewed urgency in the post-pandemic era, as amenity migration expanded beyond the traditionally studied ultra-wealthy purchasers of large ranches to include a broader socioeconomic spectrum acquiring small-acreage properties. This shift has introduced a more heterogeneous population of land stewards, many operating with limited financial, technical, and institutional resources in highly fragmented landscapes. This paper examines the intersection of sociodemographic change and private land conservation, focusing on the implications of amenity migration for wildlife management in rural private ecosystems. Through an integrative review of the amenity migration literature informed by a PRISMA-based search and screening protocol, we show that although wildlife is frequently referenced in this literature, wildlife management is rarely examined as an intentional and coordinated social–ecological practice. We argue that the implications of contemporary amenity migration for wildlife management extend beyond individual landowners to include institutional systems, shifting community dynamics, and cross-boundary governance challenges that shape wildlife outcomes across private landscapes. Recognizing the post-pandemic transformation of rural landownership is therefore essential for aligning conservation resources and institutional support systems with emerging patterns of private land stewardship. Full article