Search Results (2,489)

Background: Artificial intelligence is transforming forensic fingerprint analysis by introducing probabilistic demographic inference alongside traditional pattern matching. This study explores how AI integration reshapes the role of forensic experts from interpreters of physical traces to epistemic corridors who validate algorithmic outputs and translate them into legally admissible evidence. Methods: A conceptual proof-of-concept exercise compares traditional AFIS-based workflows with AI-enhanced predictive models in a simulated burglary scenario involving partial latent fingermarks. The hypothetical design, which does not rely on empirical validation, illustrates the methodological contrasts between physical and algorithmic inference. Results: The comparison demonstrates how AI-based demographic classification can generate investigative leads when conventional matching fails. It also highlights the evolving responsibilities of forensic experts, who must acquire competencies in statistical validation, bias detection, and explainability while preserving traditional pattern-recognition expertise. Conclusions: AI should augment rather than replace expert judgment. Forensic practitioners must act as critical mediators between computational inference and courtroom testimony, ensuring that algorithmic evidence meets legal standards of transparency, contestability, and scientific rigor. The paper concludes with recommendations for validation protocols, cross-laboratory benchmarking, and structured training curricula to prepare experts for this transformed epistemic landscape. Full article

Rural areas across Europe, particularly in the Balkans, are confronting a challenging and uneven negative transformation, marked by depopulation, economic stagnation and the degradation of their vernacular heritage. Assessing the unique dynamics and historical reality of these settlements proves difficult because conventional sustainability assessment systems are typically urban-focused and static. To address the methodological shortfall, this research introduces the Living Palimpsest Profile (LPP), a novel framework that conceptualizes rural settlements as layered landscapes in which time is treated as an endogenous variable in the sustainability equation. Employing the palimpsest metaphor, the LPP integrates a rigorous qualitative assessment, validated through convergent verification, with a hierarchical Framework of Visions. The framework was applied successfully to two Balkan case studies, demonstrating capacity to capture local specificity and inform contextual policy segmentation in line with the Sustainable Development Goals (SDGs). Given its significant transferability to other heritage-rich regions, the LPP is positioned as an essential methodological solution for the sustainable development of vernacular settlements worldwide. Full article

The shift towards renewable resources has positioned agar, a natural seaweed polysaccharide, as a pivotal and sustainable material for developing next-generation energy storage technologies. This review highlights the transformative role of agar-based composites as a game-changing and eco-friendly platform for supercapacitors, batteries, and fuel cells. Moving beyond the traditional synthetic polymers, agar introduces a novel paradigm by leveraging its natural gelation, superior film-forming ability, and inherent ionic conductivity to create advanced electrolytes, binders, and matrices. The novelty of this field lies in the strategic fabrication of synergistic composites with polymers, metal oxides, and carbon materials, engineered through innovative techniques like electrospinning, solvent casting, crosslinking, 3D printing, and freeze-drying. We critically examine how these innovative composites are breaking new ground in enhancing device efficacy, flexibility, and thermal stability. Ultimately, this analysis not only consolidates the current landscape but also charts future pathways, positioning agar-based materials as a pivotal and sustainable solution for powering the future. Full article

Breast cancer remains a major global health challenge. In 2022, there were an estimated 2.3 million new cases and 670,000 deaths among women worldwide. Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) breast cancer accounts for approximately 70% of breast cancer diagnoses. The treatment landscape for advanced HR+)/HER2− breast cancer has been transformed by the introduction of CDK4/6 inhibitors in the first-line setting. However, therapeutic strategies following progression on CDK4/6 inhibitors remain heterogeneous and uncertainty exists in their optimal integration in clinical practice. This review aims to systematically examine available second-line and subsequent treatment options for HR+/HER2− metastatic breast cancer after progression on CDK4/6 inhibitors, with a focus on biomarker-driven strategies and emerging therapies. The therapeutic landscape beyond CDK4/6 inhibitors includes targeted agents guided by actionable biomarkers as well as novel selective estrogen receptor degraders (SERDs). In biomarker-unselected populations, options include CDK4/6 continuation strategies, endocrine monotherapy in selected cases, and cytotoxic therapy. The integration of molecular testing via next-generation sequencing has become standard of care in guiding these decisions. However, overlapping molecular alterations and a lack of consensus on treatment sequencing pose significant challenges. Prognostic factors such as circulating tumor DNA dynamics may further refine treatment personalization. Post-CDK4/6 therapy in HR+/HER2− metastatic breast cancer is an evolving and increasingly complex area of practice. Optimal treatment selection should be tailored to both tumor biology and patient-specific factors, supported by molecular testing and high-quality evidence. Full article

As a key AI technology, Machine learning (ML) has witnessed growing adoption in landscape architecture through advanced algorithms and computational techniques. Despite this progress, a critical gap persists in systematically analyzing ML’s transformative impacts and emerging opportunities through an application-driven lens. This study integrates bibliometric analysis with a systematic literature review to synthesize methodological advancements and domain-specific applications. After systematically reviewing the applications of machine learning in the field of landscape architecture, five categories were identified: simulation and prediction, layout generation, image post-processing, management and evaluation, and text analysis. Furthermore, this paper proposes strategic implementation frameworks for ML integration while establishing methodological benchmarks for intelligent design systems. Full article

Background/Objectives: Digital twins are emerging as a transformative technology in diabetes management, promising a shift from standardized protocols to highly personalized care. This scoping review aims to systematically map the current landscape of digital twin applications in diabetes, synthesizing evidence on their implementation architectures, analytical models, performance metrics, and clinical integration strategies to identify key trends and critical gaps. Methods: A systematic search was conducted across five electronic databases in accordance with PRISMA-ScR guidelines to identify empirical studies on digital twins for diabetes. Data from the selected articles were extracted to analyze bibliographic characteristics, population data, technological components, performance outcomes, and integration levels. A narrative synthesis was performed to map the evidence. Results: Seventeen studies were included, revealing a rapid increase in publications since 2022, with a notable concentration of research in India. The technological architecture shows a convergence toward machine learning models (e.g., LSTM) powered by data from IoT devices and wearables. Certain interventional studies have reported significant clinical impacts, including HbA1c reductions of up to 1.9% and T2DM remission rates as high as 76.5% in one trial. However, major implementation barriers were identified, including fragmented interoperability standards and low rates of full integration into clinical workflows (35.3%). Conclusions: Digital twins are emerging as powerful tools that show the potential to drive significant clinical outcomes in diabetes care. However, to translate this promise into widespread practice, future efforts must focus on overcoming the critical challenges of standardized interoperability and deep clinical integration. Rigorous, independently validated, long-term trials in diverse populations are essential to confirm these promising findings. Full article

We introduce an IoT architecture that addresses critical mobility challenges in Morocco’s urban transportation ecosystem. Using Design Science Research methodology, we developed a complete system integrating smart infrastructure, edge computing, and accessible interfaces to enhance service quality while prioritizing inclusivity for vulnerable populations. Our five-layer architecture targets institutional capacity limitations, inadequate service levels, and accessibility barriers present in Morocco’s transportation landscape. An evaluation of our proposed solution shows how technology integration can advance eco-friendly transport goals while accommodating limited resources in developing contexts. The research contributes novel insights into IoT architectural models for inclusive design alongside practical recommendations for transportation authorities seeking to leverage digital transformation for more equitable urban mobility. Full article

A primary objective of the European Green Deal is the shift towards a sustainable and circular economy, necessitating profound alterations in production and consumption practices. The Digital Product Passport (DPP) is central to this transformation, fulfilling the information requirements and transparency expectations of the Ecodesign Regulation for Sustainable Products (ESPR) and facilitating the effective application of Extended Producer Responsibility (EPR) principles. This research presents a concept-focused bibliometric analysis of the evolving academic landscape of digital product passports (DPPs) from 2021 to 2025, based on publications indexed in Scopus and Web of Science (WoS), analyzed with the R-based Biblioshiny package. Key findings reveal that research predominantly focuses on digital product passports and the circular economy, with rapid growth in DPP publications driven by regulatory mandates under the European Green Deal. Geographically, Germany and Austria emerge as leading contributors, while thematically, circular economy and sustainability dominate the discourse. However, research gaps persist in the implementation of technology and its cross-disciplinary applications. The thematic and structural classification of existing knowledge is crucial for conceptual clarity and addressing research gaps in DPP literature. This research is expected to provide a foundational roadmap for future research on sustainable production and digital traceability ecosystems. Full article

The conformational landscape of 3-indoleacetamide, a key intermediate in plant hormone biosynthesis, has been comprehensively investigated using state-of-the-art laser-ablation chirped-pulse Fourier transform microwave (LA-CP-FTMW) and laser-ablation molecular beam Fourier transform microwave (LA-MB-FTMW) spectroscopy. Remarkably, 3-indoleacetamide exhibits unprecedented conformational rigidity within the tryptophan-derived molecule family, displaying only a single stable conformer characterized by distinctive a-, b-, and c-type rotational transitions. This singular conformational behavior contrasts dramatically with the structural flexibility observed in closely related tryptophan derivatives such as tryptophan, serotonin, tryptamine, and 3-indoleacetic acid. The unique structural constraint imposed by the acetamide functional group provides unprecedented insights into the molecular determinants governing the distinct biological roles of tryptophan-derived compounds. This work establishes a potential correlation between conformational flexibility and biological function, from neurotransmission to plant hormone regulation, offering new perspectives on structure-activity relationships in bioactive natural products. Full article

The COVID-19 pandemic catalyzed unprecedented innovation in indoor disinfection technologies, fundamentally transforming the patent landscape and commercial development in this sector. This comprehensive analysis examined patent filings from global databases and commercial market data spanning January 2020 to December 2025. Patent data were collected up to September 2022, while market data include both historical figures (2020–2023) and future projections (2024–2025) derived from industry research reports. A systematic review identified significant technological developments across five major categories: ultraviolet-C (UV-C) systems, ozone generators, photocatalytic oxidation systems, plasma disinfection technologies, and electromagnetic field applications. The analysis revealed that while patent activity surged dramatically during the pandemic period, commercial success rates varied significantly across technology categories. UV-C systems demonstrated the highest market penetration with established commercial viability, while emerging technologies such as electromagnetic disinfection faced substantial barriers to commercialization. Geographic analysis showed concentrated innovation in developed economies, with China leading in patent volume and South Korea achieving notable commercial success despite smaller patent portfolios. The study provides critical insights into the relationship between patent activity and commercial viability in emergency-driven innovation contexts. Full article

This paper represents the results of ten years of monitoring using satellite imagery and aerial reconnaissance, followed by in-depth analysis utilizing LiDAR data and geomagnetic prospection techniques of the first two Neolithic rondels detected in Croatia—Markušica Brošov salaš and Gorjani Topole. Through the exclusive use of satellite and aerial image analysis, we were able to accurately determine the general size, shape, and number of ditches present at the sites under investigation. The wealth of information obtained from these images was sufficient for us to confidently interpret these formations as Neolithic rondels—meeting all the criteria commonly used. The addition of LiDAR data and geomagnetic prospection further enhanced our understanding by revealing a range of additional features and peculiarities across both sites, including within all identified ditch systems. These advanced methods allowed us to uncover details that would otherwise remain invisible through surface observation alone. Our research demonstrates the remarkable power of publicly available satellite imagery as a primary tool for archeological site detection and preliminary interpretation. The results from Markušica and Gorjani emphasize the scientific necessity of combining complementary remote sensing and geophysical techniques to overcome individual methodological limitations, providing robust documentation and interpretation of prehistoric enclosures in highly transformed landscapes. This research contributes novel insights into Neolithic social landscapes, monumentality, and land use strategies in Croatia while offering a methodological model for archeological prospection applicable across Central and Southeastern Europe. Full article

The rapid advancement of large language models (LLMs) has significantly transformed the development and deployment of generative AI chatbots across various domains. This systematic literature review (SLR) analyzes 39 primary studies published between 2020 and 2025 to explore how these models are utilized, the sectors in which they are deployed, and the broader trends shaping their use. The findings reveal that models such as GPT-3.5, GPT-4, and LLaMA variants have been widely adopted, with applications spanning education, healthcare, business services, and beyond. As adoption increases, research continues to emphasize the need for more adaptable, context-aware, and responsible chatbot systems. The insights from this review aim to guide the effective integration of LLM-based chatbots, highlighting best practices such as domain-specific fine-tuning, retrieval-augmented generation (RAG), and multi-modal interaction design. This review maps the current landscape of LLM-based chatbot development, explores the sectors and primary use cases in each domain, analyzes the types of generative AI models used in chatbot applications, and synthesizes the reported limitations and future directions to guide effective strategies for their design and deployment across domains. Full article

The rapid evolution of healthcare technology is being driven by advancements in Micro-Electro-Mechanical Systems (MEMS), BioMEMS (Biological MEMS), and the expanding concept of the Internet of Bodies (IoB). This review explores the convergence of these three domains and their transformative impact on personalized medicine (PM), with a focus on smart, connected biomedical devices. Starting from the historical development of MEMS for medical sensing and diagnostics, the review traces the emergence of BioMEMS as biocompatible, minimally invasive solutions for continuous monitoring and real-time intervention. The integration of such devices within the IoB ecosystem enables data-driven, remote, and predictive healthcare, offering tailored diagnostics and treatment for chronic and acute conditions alike. The paper classifies IoB-associated technologies into non-invasive, invasive, and incorporated devices, reviewing wearable systems such as smart bracelets, e-tattoos, and smart footwear, as well as internal devices including implantable and ingestible. Alongside these opportunities, significant challenges persist, particularly in device biocompatibility, data interoperability, cybersecurity, and ethical regulation. By synthesizing recent advances and critical perspectives, this review aims to provide a comprehensive understanding of the current landscape, clinical potential, and future directions of MEMS, BioMEMS, and IoB-enabled personalized healthcare. Full article