Search Results (2,008)

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

This study offers the first systematic exploration of the Greek e-Government team, a public sector innovation unit that operated within the Office of the Prime Minister of Greece from 2009 to 2012—the sole example of such a unit in the country. It illustrates how strategically positioned innovation units can function as change agents within government bureaucracies. The purpose of this work was to analyze how this distinctive unit functioned by bridging policy formulation, legislative drafting, and technological implementation at the highest government levels. The research involved thematic analysis of original interviews conducted with most core members of the team. The findings highlight successes, notably the Diavgeia transparency platform, which markedly improved administrative transparency, accountability, and citizen access to government decisions. Important challenges were also identified, particularly regarding the sustainability of the unit, issues of institutionalization, and meaningful citizen engagement. The experience of the Greek e-Government team suggests that public sector innovation (PSI) units are most effective when they combine high-level political access with multidisciplinary expertise and operational flexibility. The analysis also reveals inherent tensions between the need for centralized coordination and the benefits of decentralized implementation, as well as challenges in maintaining citizen participation throughout the policy development process. Full article

This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studies that applied ML or GenAI within LA contexts. Records came from 12 databases (last search 15 March 2025), and the results were synthesized via thematic clustering. ML approaches dominate LA tasks, such as engagement prediction, dropout-risk modelling, and academic-performance forecasting, whereas GenAI—mainly transformer models like GPT-4 and BERT—is emerging in real-time feedback, adaptive learning, and sentiment analysis. Studies spanned world regions. Most ML papers (n = 75) examined engagement or dropout, while GenAI papers (n = 26) focused on adaptive feedback and sentiment analysis. No formal risk-of-bias assessment was conducted due to heterogeneity. While ML methods are well-established, GenAI applications remain experimental and face challenges related to transparency, pedagogical grounding, and implementation feasibility. This review offers a comparative synthesis of paradigms and outlines future directions for responsible, inclusive, theory-informed AI use in education. Full article

Purpose: Following George Floyd’s death, the push for law enforcement accountability policies has intensified. Despite robust legislative action, challenges in enacting and implementing meaningful reforms persist. This study analyzes police accountability policies (PAP) in the U.S. from 2020 to 2022, identifying barriers and facilitators through expert perspectives in enforcement oversight, policy advocacy, and community engagement. Methods: The study used a dual approach: analyzing 226 police accountability bills from all 50 U.S. states, D.C., and Puerto Rico via the National Conference of State Legislatures database, and categorizing them into six key areas such as training, technology use, and certification. Additionally, a survey was conducted among experts to identify the challenges and drivers in passing police accountability legislation. Findings: A legislative analysis showed that although 48 states passed police accountability laws, California, New Jersey, Oklahoma, and Colorado have made significant strides by passing multiple pieces of legislation aimed at enhancing law enforcement accountability and ensuring better policing practices. The most common policies focused on training and technology, enacted by 16 and 12 states, respectively. However, crucial certification and decertification policies were adopted in just 13 states, highlighting the inconsistent implementation of measures critical for police accountability and transparency. The survey identified several barriers to passing PAP, including inadequate support from local governments (72.7%). Structural exclusion of poor and minority communities from policing resources was also a significant barrier (54.5%). Facilitators included community support (81.8%) and a cultural shift in policing towards viewing officers as “guardians” rather than “warriors” (63.6%). Conclusions: While some progress has been made in passing PAP, considerable gaps remain, particularly in enforcement and comprehensive reform. Resistance from law enforcement institutions, lack of community support, and structural inequalities continue to impede the adoption of effective PAP. Full article

The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). The research was conducted on inland shallow water areas, while images of bottom objects were obtained using a UAV platform. The work consisted in preparing SSOs, thanks to which composite images were created. On such training data, 120 models based on the YOLO (You Only Look Once) network were obtained. The study confirmed the effectiveness of models created using YOLOv3, YOLOv5, YOLOv8, YOLOv9, and YOLOv10. A comparison was made between versions of YOLO. The influence of the amount of training data, SSO type, and augmentation parameters used in the training process was analyzed. The main parameter of model performance was the F1-score. The calculated statistics of individual models indicate that the most effective networks use partial augmentation, trained on sets consisting of 2000 SSOs. On the other hand, the increased transparency of SSOs resulted in increasing the diversity of training data and improving the performance of models. This research is developmental, and further research should improve the processes of obtaining detection models using deep networks. Full article

This study explores the development of a topical film-forming spray infused with phytobiotic herbs to extend egg shelf life and maintain its quality. Unlike traditional surface treatments, film-forming sprays provide uniform drug distribution, better bioavailability, effective CO₂ retention by sealing pores, and antibacterial effects. The spray includes a polymer to encapsulate phytoconstituents and form the film. The resulting film is highly water-resistant, glossy, transparent, and dries within two minutes. SEM analysis showed a fine, uniform morphology, while zeta analysis revealed a negative potential of −0.342 mV and conductivity of 0.390 mS/cm, indicating stable dispersion. The spray’s effectiveness was tested on 640 chicken eggs stored at varying temperatures. Eggs treated and kept at 2–8 °C showed the best results, with smaller air cells, higher specific gravity, and superior quality indicators such as pH, albumen weight, albumen height and index, Haugh unit, yolk weight, and yolk index. Additionally, the spray significantly reduced microbial load, including total plate count and E. coli. Eggs stored at 28 °C remained safe for 24–30 days, while those at 2–8 °C lasted over 42 days. This innovative film-forming spray offers a promising approach for preserving internal and external egg quality during storage. Full article

Background and Objectives: Diabetes is a global public health challenge, with increasing prevalence worldwide. The implementation of artificial intelligence (AI) in the management of this condition offers potential benefits in improving healthcare outcomes. This study primarily investigates the barriers and facilitators perceived by healthcare professionals in the adoption of AI. Secondarily, by analyzing both quantitative and qualitative data collected, it aims to support the potential development of AI-based programs for diabetes management, with particular focus on a possible bottom-up approach. Materials and Methods: A scoping review was conducted following PRISMA-ScR guidelines for reporting and registered in the Open Science Framework (OSF) database. The study selection process was conducted in two phases—title/abstract screening and full-text review—independently by three researchers, with a fourth resolving conflicts. Data were extracted and assessed using Joanna Briggs Institute (JBI) tools. The included studies were synthesized narratively, combining both quantitative and qualitative analyses to ensure methodological rigor and contextual depth. Results: The adoption of AI tools in diabetes management is influenced by several barriers, including perceived unsatisfactory clinical performance, high costs, issues related to data security and decision-making transparency, as well as limited training among healthcare workers. Key facilitators include improved clinical efficiency, ease of use, time-saving, and organizational support, which contribute to broader acceptance of the technology. Conclusions: The active and continuous involvement of healthcare workers represents a valuable opportunity to develop more effective, reliable, and well-integrated AI solutions in clinical practice. Our findings emphasize the importance of a bottom-up approach and highlight how adequate training and organizational support can help overcome existing barriers, promoting sustainable and equitable innovation aligned with public health priorities. Full article

Objectives: Digitalization has revolutionized dentistry, introducing advanced technological tools that improve diagnostic accuracy and access to healthcare. This study aims to examine the effects of integrating digital technologies into the dental field, analyzing the associated benefits and risks, with particular paid attention to the therapeutic relationship and decision-making autonomy. Materials and Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and Cochrane Library, complemented by Google Scholar for non-indexed studies. The selection criteria included peer-reviewed studies published in English between 2014 and 2024, focusing on digital dentistry, artificial intelligence, and medical ethics. This is a narrative review. Elements of PRISMA guidelines were applied to enhance transparency in reporting. Results: The analysis highlighted that although digital technologies and AI offer significant benefits, such as more accurate diagnoses and personalized treatments, there are associated risks, including the loss of empathy in the dentist–patient relationship, the risk of overdiagnosis, and the possibility of bias in the data. Conclusions: The balance between technological innovation and the centrality of the dentist is crucial. A human and ethical approach to digital medicine is essential to ensure that technologies improve patient care without compromising the therapeutic relationship. To preserve the quality of dental care, it is necessary to integrate digital technologies in a way that supports, rather than replaces, the therapeutic relationship. Full article

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article

Graphene is considered one of the most promising flexible transparent electrode materials as it has high charge carrier mobility, high electrical conductivity, low optical absorption, excellent mechanical strength, and good bendability. However, graphene-based flexible transparent electrodes face a critical challenge in balancing electrical conductivity and optical transmittance. Here, we present a green and scalable direct ink writing (DIW) strategy to fabricate graphene grid patterns by optimizing ink formulation with sodium dodecyl sulfate (SDS) and ethanol. SDS eliminates the coffee ring effect via Marangoni flow, while ethanol enhances graphene flake alignment during hot-pressing, achieving a high conductivity of 5.22 × 10⁵ S m⁻¹. The grid-patterned graphene-based flexible transparent electrodes exhibit a low sheet resistance of 21.3 Ω/sq with 68.5% transmittance as well as a high stability in high-temperature and corrosive environments, surpassing most metal/graphene composites. This method avoids toxic solvents and high-temperature treatments, demonstrating excellent stability in harsh environments. Full article

Systematic reviews are a cornerstone of evidence-based research, providing comprehensive summaries of existing studies to answer specific research questions. This article offers a detailed guide to conducting high-quality systematic reviews in biology, health and social sciences. It outlines key steps, including developing and registering a protocol, designing comprehensive search strategies, and selecting studies through a screening process. The article emphasizes the importance of accurate data extraction and the use of validated tools to assess the risk of bias across different study designs. Both meta-analysis (quantitative approach) and narrative synthesis (qualitative approach) are discussed in detail. The guide also highlights the use of frameworks, such as GRADE, to assess the certainty of evidence and provides recommendations for clear and transparent reporting in line with the PRISMA 2020 guidelines. This paper aims to adapt and translate evidence-based review principles, commonly applied in clinical research, into the context of biological sciences. By highlighting domain-specific methodologies, challenges, and resources, we provide tailored guidance for researchers in ecology, molecular biology, evolutionary biology, and related fields in order to conduct transparent and reproducible evidence syntheses. Full article

This study presents a simulation-based damage modeling and fatigue risk assessment of a reusable ceramic matrix composite thruster designed for short-duration, green bipropellant propulsion systems. The thruster is constructed from a fiber-reinforced ultra-high temperature ceramic matrix composite composed of zirconium diboride, silicon carbide, and carbon fibers. Time-resolved thermal and structural simulations are conducted on a validated thruster geometry to characterize the severity of early-stage thermal shock, stress buildup, and potential degradation pathways. Unlike traditional fatigue studies that rely on empirical fatigue constants or Paris-law-based crack-growth models, this work introduces a simulation-derived stress-margin envelope methodology that incorporates ±20% variability in temperature-dependent material strength, offering a physically grounded yet conservative risk estimate. From this, a normalized risk index is derived to evaluate the likelihood of damage initiation in critical regions over the 0–10 s firing window. The results indicate that the convergent throat region experiences a peak thermal gradient rate of approximately 380 K/s, with the normalized thermal shock index exceeding 43. Stress margins in this region collapse by 2.3 s, while margin loss in the flange curvature appears near 8 s. These findings are mapped into green, yellow, and red risk bands to classify operational safety zones. All the results assume no active cooling, representing conservative operating limits. If regenerative or ablative cooling is implemented, these margins would improve significantly. The framework established here enables a transparent, reproducible methodology for evaluating lifetime safety in ceramic propulsion nozzles and serves as a foundational tool for fatigue-resilient component design in green space engines. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Smart contracts and cryptocurrency wallets are foundational components of decentralized applications (dApps) on blockchain platforms such as Ethereum. While these technologies enable secure, transparent, and automated transactions, their integration also introduces complex security challenges. This study presents a security-oriented analysis of smart contract and wallet integration, focusing on BlockScribe—a decentralized Ethereum-based application for digital record certification. We systematically identify and categorize security risks arising from the interaction between wallet interfaces and smart contract logic. In particular, we analyze how user authorization flows, transaction design, and contract modularity affect the security posture of the entire dApp. To support our findings, we conduct an empirical evaluation using static analysis tools and formal verification methods, examining both contract-level vulnerabilities and integration-level flaws. Our results highlight several overlooked attack surfaces in wallet–contract communication patterns, including reentrancy amplification, permission mismanagement, and transaction ordering issues. We further discuss implications for secure dApp development and propose mitigation strategies that improve the robustness of wallet–contract ecosystems. This case study contributes to a deeper understanding of integration-layer vulnerabilities in blockchain-based systems and offers practical guidance for developers and auditors aiming to strengthen smart contract security. Full article

Background and Objectives: Posterior chamber phakic implantable contact lenses (Phakic-ICL) are widely used for refractive correction due to their efficacy and safety, including minimal corneal endothelial cell loss. The Collamer-based EVO+ Visian implantable contact lens (ICL), manufactured from Collamer, which is a blend of collagen and hydroxyethyl methacrylate (HEMA), has demonstrated excellent long-term biocompatibility and optical clarity. Recently, hydrophilic acrylic Phakic-ICLs, such as the Implantable Phakic Contact Lens (IPCL), have been introduced. This study investigated the material differences among Phakic-ICLs and their interaction with fibronectin (FN), which has been reported to adhere to intraocular lens (IOL) surfaces following implantation. The aim was to compare Collamer, IPCL, and LENTIS lenses (used as control) in terms of FN distribution and cell adhesion using a small number of explanted Phakic-ICLs. Materials and Methods: Three lens types were analyzed: a Collamer Phakic-ICL (EVO+ Visian ICL), a hydrophilic acrylic IPCL, and a hydrophilic acrylic phakic-IOL (LENTIS). FN distribution and cell adhesion were evaluated across different regions of each lens. An in vitro FN-coating experiment was conducted to assess its effect on cell adhesion. Results: All lenses demonstrated minimal FN deposition and cellular adhesion in the central optical zone. A thin FN film was observed on the haptics of Collamer lenses, while FN adhesion was weaker or absent on IPCL and LENTIS surfaces. Following FN coating, Collamer lenses supported more uniform FN film formation; however, this did not significantly enhance cell adhesion. Conclusions: Collamer, which contains collagen, promotes FN film formation. Although FN film formation was enhanced, the low cell-adhesive properties of HEMA resulted in minimal cell adhesion even with FN presence. This characteristic may contribute to the long-term transparency and biocompatibility observed clinically. In contrast, hydrophilic acrylic materials used in IPCL and LENTIS demonstrated limited FN interaction. These material differences may influence extracellular matrix protein deposition and biocompatibility in clinical settings, warranting further investigation. Full article