Search Results (16,885)

Alzheimer’s disease is a growing global health concern, demanding innovative solutions for early detection, continuous monitoring, and patient support. This article reviews recent advances in Smart Wearable Medical Devices (SWMDs), Internet of Things (IoT) systems, and mobile applications used to monitor physiological, behavioral, and cognitive changes in Alzheimer’s patients. We highlight the role of wearable sensors in detecting vital signs, falls, and geolocation data, alongside IoT architectures that enable real-time alerts and remote caregiver access. Building on these technologies, we present LumiCare, a conceptual, context-aware mobile system that integrates multimodal sensor data, chatbot-based interaction, and emerging 6G network capabilities. LumiCare uses machine learning for behavioral analysis, delivers personalized cognitive prompts, and enables emergency response through adaptive alerts and caregiver notifications. The system includes the LumiCare Companion, an interactive mobile app designed to support daily routines, cognitive engagement, and safety monitoring. By combining local AI processing with scalable edge-cloud architectures, LumiCare balances latency, privacy, and computational load. While promising, this work remains at the design stage and has not yet undergone clinical validation. Our analysis underscores the potential of wearable, IoT, and mobile technologies to improve the quality of life for Alzheimer’s patients, support caregivers, and reduce healthcare burdens. Full article

Cities are significant contributors to climate change, environmental degradation, and resource depletion. To address these challenges, sustainable strategies in building design, construction, and management are essential, and digitalisation through the integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) can enable it. However, the environmental benefits of BIM–LCA integration remain underexplored, limiting broader practical adoption. This study systematically reviews 80 case studies (2015–2025) to assess how recent applications address known barriers and to identify enablers of successful BIM–LCA workflows. The analysis highlights a growing alignment between technological, regulatory, and methodological advancements and practical implementation needs, especially as technical barriers are increasingly overcome. Nevertheless, systemic challenges related to institutional, behavioural, and socio-economic factors persist. From a stakeholder perspective, four thematic drivers were identified: material circularity and resource efficiency; integration with complementary assessment tools; energy-performance strategies for comfort and efficiency; and alignment with international certification systems. The study offers a stakeholder-oriented framework that demonstrates the multi-level value of BIM–LCA integration not only for environmental impact assessment but to support informed decision-making and reduce resource consumption. These insights aim to bridge the gap between academic research and practical implementation, contributing to the advancement of sustainable practices in the built environment. 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

Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article

Chronic kidney disease (CKD) is a progressive global health burden often diagnosed in late stages due to reliance on invasive and centralized blood and urine tests. Saliva, as a non-invasive diagnostic fluid, has emerged as a promising alternative for assessing renal function. This scoping review aims to evaluate the diagnostic accuracy of salivary biomarkers compared to traditional methods, and to explore the potential of emerging biosensing technologies for CKD detection and monitoring. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Cochrane Library up to 1 July 2025, following the PRISMA-ScR guidelines. Studies involving adult CKD patients and healthy controls that assessed the diagnostic performance of salivary biomarkers against validated reference standards (e.g., serum creatinine, eGFR) were included. A total of 29 eligible studies were selected after applying predefined inclusion and exclusion criteria. Results: Salivary creatinine and urea were the most frequently assessed biomarkers and demonstrated strong correlations with serum levels (AUCs up to 1.00; sensitivity and specificity frequently >85%). Several studies reported high diagnostic potential for novel salivary markers such as Trimethylamine N-oxide (TMAO), cystatin C, and amino acids. Technological innovations, including electrochemical biosensors and ATR-FTIR spectroscopy, showed promise for enhancing sensitivity and enabling point-of-care testing. However, heterogeneity in sampling protocols and limited data for early-stage CKD were notable limitations. Conclusions: Salivary diagnostics, supported by biosensor technologies, offer a feasible and non-invasive alternative for CKD screening and monitoring. Standardization, broader clinical validation, and integration into dental workflows are key to clinical implementation. Full article

The growing demand for sustainable biorefinery approaches calls for efficient, environmentally benign strategies to valorize agricultural residues and ensure their complete utilization. This study explores the combination of deep eutectic solvents (DESs) and microwave heating technology as a greener process for the selective fractionation of agri-food waste residues in a zero-waste perspective. Within this framework, five representative biomasses were thoroughly investigated, namely brewer’s spent grain, raw and parboiled rice husks, rapeseed cakes, and hemp hurds. DES formulation was selected for its ability to solubilize and separate lignocellulosic components, enabling the recovery of a polysaccharide-rich fraction, lignin, and bioactive compounds. DES extraction was performed using both microwave heating and conventional batch heating, enabling a direct comparison of the two methods, the optimization of a more sustainable fractionation process, and the maximization of yields while preserving the functional integrity of the recovered fractions. A comprehensive characterization of the separated fractions was carried out, revealing that the two fractionation methods do not yield significant differences in the composition of the primary components. Moreover, a ¹³C CP-MAS NMR analysis of the recovered lignins demonstrates how this analytical technique is a real fingerprint for the biomass source. The results demonstrate the great potential of microwave DES-mediated fractionation as a mild, tunable, and sustainable alternative to conventional methods, aligning with green chemistry principles and opening new approaches for the full valorization of waste byproducts Full article

As artificial intelligence (AI) becomes increasingly embedded within service-oriented High-Performance Work Systems (HPWSs), understanding its implications for employee well-being and organizational sustainability is critical. This study examines the relationship between AI service quality and job satisfaction, considering the mediating effect of perceived organizational justice and the moderating influence of supervisor support. Drawing on the ISS model, equity, organizational justice, and Leader–Member Exchange (LMX) theory, data were collected from a diverse sample of service sector employees through a cross-sectional design. The findings indicate that higher AI service quality significantly enhances job satisfaction, particularly in environments with strong supervisor support. Contrary to expectations, perceived organizational justice did not mediate the AI-satisfaction link, suggesting that perceived organizational justice constructs may be less influential in AI-mediated contexts. Instead, supervisor support emerged as a key contextual enabler, strengthening employees’ positive perceptions and emotional responses to AI systems. These results emphasize that technological optimization alone is insufficient for building sustainable service workplaces. Effective leadership and human-centered practices remain essential to fostering trust, satisfaction, and long-term engagement in digitally transforming organizations. This study offers practical and theoretical insights into integrating AI and human resource strategies in support of socially sustainable service systems. Full article

As many German SMEs approach the end of their photovoltaic (PV) feed-in tariff period, the challenge of maintaining economic viability for these installations intensifies. This study addresses the integration of intermittent renewable energy sources (iRES) into production processes by proposing a method to identify and exploit industrial flexibility. A detailed case study of a German carpentry factory designed as a Net-Zero Energy Factory (NZEF) illustrates the approach, combining energy monitoring with blockchain technology to enhance transparency and traceability. Flexibility is exploited through a three-layer control system involving passive operator guidance, battery storage, and electric vehicle charging. The installation of a 40 kWh battery raises self-consumption from 50 to 70%, saving approximately EUR 4270 annually. However, this alone does not offset the investment. Blockchain-based transparency adds economic value by enabling premium pricing, potentially increasing revenue by up to 10%. This dual benefit supports the financial case for NZEFs. The framework is replicable and particularly relevant for low-automation industries, offering small and medium enterprises (SMEs) a viable pathway to decarbonization. The results align with the European Clean Industrial Deal, demonstrating how digitalization and industrial flexibility can reinforce competitiveness, sustainability, and digital trust in Europe’s transition to a resilient, low-carbon economy Full article

Silver nanoparticles (AgNPs) have garnered significant attention due to their potent antimicrobial properties and broad-spectrum efficacy against pathogens. Recent advances in polymer science have enabled the development of AgNPs-integrated hydrogels and membranes, offering multifunctional platforms for biomedical and food-related applications. This review provides a comprehensive overview of recent strategies for synthesizing and incorporating AgNPs into polymeric matrices, highlighting both natural and synthetic polymers as carriers. The structural and functional properties of these nanocomposite systems, such as biocompatibility, mechanical stability, controlled silver ion release, and antimicrobial activity, are critically examined. The focus is placed on their application in wound healing, drug delivery, food packaging, and preservation technologies. Challenges such as cytotoxicity, long-term stability, and regulatory concerns are discussed alongside emerging trends and safety paradigms. This work underscores the potential of AgNPs–polymer hybrids as next-generation materials and outlines future directions for their sustainable and targeted application in biomedical and food systems. Full article

Bio-based surfactants have demonstrated significant potential as economically viable and environmentally sustainable alternatives to petroleum-derived surfactants, with the global biosurfactant market expanding from USD 4.41 billion in 2023 to a projected USD 6.71 billion by 2032, representing a compound annual growth rate of 5.4%. While conventional surfactants such as alkyl aryl sulfates and alkyl benzene sulfonates exhibit extremely high aquatic toxicity and impose substantial ecological costs, biosurfactants including lipopeptides (surfactin, iturin, fengycin, lichenysin) produced by Bacillus species and glycolipids (rhamnolipids, sophorolipids, trehalose lipids, mannosylerythritol lipids) from Pseudomonas demonstrate superior biodegradability. However, current biosurfactant production costs, ranging from 5 to20 USD/kg, cannot compete effectively with synthetic surfactants, averaging approximately 2 USD/kg, necessitating comprehensive process improvements to achieve commercial viability. The utilization of renewable agricultural feedstocks containing 65–70% carbohydrates, including corn stover, sugarcane bagasse, rice bran, and palm oil mill effluent, has achieved production costs as low as 3.8 USD/kg through advanced optimized pretreatment technologies, enzyme catalysis, simultaneous saccharification and fermentation (SSF), and downstream processes, resulting in cost reductions compared to conventional methods. The implementation of artificial intelligence and machine learning algorithms for bioprocess optimization enables simultaneous optimization of genetic engineering, metabolic pathways, and fermentation parameters, achieving yield improvements and cost reductions, with projections indicating production costs below 2.50 USD/kg being needed in the next decade to achieve cost parity with synthetic surfactants, maintaining economic viability. Full article

Background: Dental implants have become integral in restoring partially or completely edentulous patients due to their reported long-term success. While titanium remains the primary material for implants and abutments due to its mechanical properties and biocompatibility, zirconia has emerged as a promising alternative, especially for aesthetic regions. This systematic review aimed to assess whether zirconia abutments present a rational alternative to titanium in modern implantology, focusing on their mechanical and clinical performances. Method: The workflow used for this review included the PRISMA checklist. The eligibility criteria included various study types, with a preference given to clinical trials. The search strategy employed the PICO model, including a large number of relevant studies, and online research was carried on the online databases PubMed and Scopus, with “implant” AND “abutment” AND “zirconia” and “zirconia abutment” AND “mechanical properties” used as search strings. Results: Six clinical studies were included with an adequate follow-up and patient cohort; they suggest that while zirconia abutments offer improved aesthetics and biological integration, concerns persist regarding their mechanical properties, particularly regarding their fatigue resistance and connection stability. In vitro studies have revealed differences between titanium and zirconia abutments, with the latter showing greater susceptibility to fatigue-induced deformation and fretting wear. The clinical outcomes, however, demonstrate favourable long-term performance, with zirconia abutments promoting healthy soft tissue conditions. CAD/CAM technologies enable the precise customization of zirconia abutments, enhancing their compatibility and aesthetic outcomes. Conclusions: Although this review faces limitations due to the scarcity of comparative studies and varied methodologies, it underscores the potential of zirconia abutments in implantology. In conclusion, while zirconia abutments offer promising advantages, the careful consideration of patient-specific factors and the long-term outcomes is warranted for their optimal utilisation in implant-supported prostheses. Full article

Cartilage tissue engineering aims to restore damaged cartilage using biomaterials, cells, and/or biological cues to support cell growth and tissue repair. Although in the past decades scientific advances have moved the field forward, their translation to a clinical setting is still hampered. One major hurdle to take is to reduce process variability to ensure a predictable biological outcome. Using enabling technologies such as bioprinting has shown the potential to improve process robustness. However, developing bioinks that balance printability with biological functionality remains a major challenge. This study presents the development and structure–property relationships of a novel gelatin-based hydrogel blend, GelMA/GelMA-AEMA, optimized for extrusion-based bioprinting (EBB) while maintaining the crucial biological properties of GelMA for tissue engineering applications. The novel GelMA/GelMA-AEMA blend demonstrated superior flowability and printability compared to GelMA, effectively addressing common 3D-printing defects such as filament shape inhomogeneity. A systematic rheological characterization revealed that the blend exhibits a softer, elastically dominated structure with improved compliance. The blend behaves as a yield-stress fluid with a strong shear-thinning degree, making it highly suitable for EBB. The superior flow properties of the blend are deemed to enhance bond slippage and stress-induced orientation of its more imperfect gel structure, resulting in greater macroscopic deformation and enhanced print fidelity. In addition, histological assessment of a 21-day in vitro study with iPSC-derived chondrocytes suggested that the blend is at least equally performant as GelMA in supporting matrix formation. Histological analysis shows similar matrix deposition profiles, whereas gene expression analysis and compression tests even have suggested superior characteristics for cartilage TE. This study emphasizes the central role of rheology in bioink development and provides foundations for future material development for EBB, with potential implications for cartilage tissue engineering. Full article

Somatic mutations in KRAS and TP53 are among the most common genetic alterations in pancreatic ductal adenocarcinoma (PDAC). Advances in PCR-based technologies now enable the detection of these mutations in tumor tissue and cell-free DNA (cfDNA), providing a minimally invasive approach to assess tumor burden. However, in resectable PDAC, circulating tumor DNA (ctDNA) may represent less than 0.1% of total cfDNA, requiring highly sensitive detection methods. The aim of our study was to assess two PCR-based assays—competitive allele-specific PCR (castPCR) and digital PCR (dPCR)—for detecting selected somatic mutations in tumor tissue, cfDNA, and extracellular vesicle-associated DNA (EV-DNA) from plasma. Matched primary tumor and preoperative plasma samples were collected from 50 patients undergoing upfront resection for PDAC. CastPCR was used for detecting selected KRAS, TP53, SMAD4, and CDKN2A mutations in tumor DNA. Additionally, dPCR was used to analyze KRAS and TP53 mutations in tumor DNA as well as cfDNA and EV-DNA. The concordance between both platforms was 71.4% for KRAS p.G12D and 58.3% for the analysis of TP53 p.R273H mutations in tumor tissue. However, dPCR detected these mutations in an additional 28.6% and 39.6% of samples, respectively. In cfDNA, dPCR identified KRAS p.G12D in 10.2% and TP53 p.R273H in 2.0% of samples. Mutation detection in EV-DNA was limited by low DNA yield. Both platforms proved effective for tumor DNA analysis, with dPCR offering greater sensitivity. Somatic mutation detection from liquid biopsy using dPCR further supports its potential utility in the preoperative setting. 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

Urban growth and rising vehicle usage have intensified congestion, accidents, and environmental impact, exposing the limitations of traditional traffic management systems. This study introduces a dual-incident simulation framework to investigate the potential of Vehicle-to-Everything (V2X) technologies in enhancing urban mobility. Using the Eclipse MOSAIC platform integrated with SUMO, a realistic network in Iași, Romania, was modeled under single- and dual-incident scenarios with three V2X penetration levels: 0%, 50%, and 100%. Unlike prior works that focus on single-incident cases or assume full penetration, our approach evaluates cascading disruptions under partial adoption, providing a more realistic transition path for mid-sized European cities. Key performance indicators, i.e., average speed, vehicle density, time loss, and waiting time, were calculated using mathematically defined formulas and validated across multiple simulation runs. Results demonstrate that full V2X deployment reduces average time loss by 18% and peak density by more than 70% compared to baseline conditions, while partial adoption delivers measurable yet limited benefits. The dual-incident scenario shows that V2X-enabled rerouting significantly mitigates cascading congestion effects. These contributions advance the state of the art by bridging microscopic vehicle dynamics with network-level communication modeling, offering quantitative insights for phased V2X implementation and the design of resilient, sustainable intelligent transportation systems. Full article