Search Results (1,474)

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

Moringa oleifera (MO), a nutritionally and pharmacologically potent species, is emerging as a sustainable candidate for applications across bioenergy, agriculture, textiles, pharmaceuticals, and biomedicine. This review explores recent advances in MO-based biotechnologies, highlighting novel extraction methods, green nanotechnology, and clinical trial findings. Although MO’s resilience offers promise for climate-smart agriculture and public health, challenges remain in standardizing cultivation and verifying therapeutic claims. This work underscores MO’s translational potential and the need for integrative, interdisciplinary research. MO is used in advanced materials, like electrospun fibers and biopolymers, showing filtration, antibacterial, anti-inflammatory, and antioxidant properties—important for the biomedical industry and environmental remediation. In textiles, it serves as an eco-friendly alternative for wastewater treatment and yarn sizing. Biotechnological advancements, such as genome sequencing and in vitro culture, enhance traits and metabolite production. MO supports green biotechnology through sustainable agriculture, nanomaterials, and biocomposites. MO shows potential for disease management, immune support, metabolic health, and dental care, but requires further clinical trials for validation. Its resilience is suitable for land restoration and food security in arid areas. AI and deep learning enhance Moringa breeding, allowing for faster, cost-effective development of improved varieties. MO’s diverse applications establish it as a key element for sustainable development in arid regions. Full article

Based on experimental data regarding the local distribution of metallic impurities in raw selenium and the composition of its vapor phase, the potential composition of the vapor–droplet suspension that leads to reduced condensate quality due to impurities with low partial vapor pressures relative to selenium, as well as metals with vapor pressures comparable to selenium, has been hypothesized. Due to selenium’s high aggressiveness towards structural materials and based on economic feasibility, the use of low-alloy steel of ordinary quality for the technical design of the distillation process, instead of alloyed steel, has been thermodynamically justified. A method has been developed, and a device to refine selenium has been manufactured, which differs from existing ones by the inertial purification of the vapor phase from droplet suspension. The development is protected by a security document (patent KZ No. 37275). Based on the completed developments, an industrial prototype of such equipment has been designed and implemented in production. Full article

Global resource consumption continues to grow each year, exerting increasing pressure on their availability. This trend could lead to a shortage of raw materials in the coming years. Aware of the risks associated with this situation, the European Union has implemented policies and strategies aimed at diversifying its supply sources, including waste recycling. In this context, the present study was conducted with the objective of developing innovative processes to concentrate valuable metals present in the non-recovered fractions of waste electrical and electronic equipment (WEEE). Three types of samples were studied: washing table residues (WTRs), printed circuit boards (PCBs), and powders from cathode-ray tube screens (CRT powders). Several separation techniques, based on the physical properties of the elements, were implemented, including electrostatic separation, magnetic separation, and density and gravity-based separations. The results obtained are promising. For WTRs and PCBs, the recovery rates of targeted metals (Cu, Al, Pb, Zn, Sn) reached approximately 91% and 80%, respectively. In addition to these metals, other valuable metals, present in significant quantities, deserve further exploration. Regarding CRT powders, the performances are also encouraging, with recovery rates of 54.7% for zinc, 57.1% for yttrium, and approximately 71% for europium. Although these results are satisfactory, optimizations are possible to maximize the recovery of these critical elements. The techniques implemented have demonstrated their effectiveness in concentrating target metals in the treated fractions. These results confirm that recycling constitutes a viable alternative to address resource shortages and secure part of the supplies needed for the European Union’s industry. Full article

Tin is an indispensable metal for contemporary society owing to its extensive application. China is a major tin manufacturer and consumer worldwide. Nonetheless, the crucial characteristics of its tin metabolism remain limited. Therefore, a dynamic material flow analysis (MFA) from 2001 to 2022 was performed in this study to trace China’s tin flows and stocks. Findings show that China became a net tin exporter from a life cycle perspective, and annual tin consumption embodied in various final products varied between 49.3 kilo tons (Kt) in 2001 and 161.5 Kt in 2022, with home appliances and electronics being the dominant consumption sectors. A total of 913.3 Kt of tin became in-use stocks. In addition, the imported tin embodied in various final products varied between 13.9 Kt in 2001 and 21.6 Kt in 2022, with machinery being the dominant consumption sector. The exported tin embodied in various final products varied between 12.0 Kt in 2001 and 76.3 Kt in 2022, with machinery being the dominant consumption sector. Finally, this study proposes some suggestions, in view of the Chinese reality, like enhancing tin recycling, promoting tin geological prospecting, optimizing the structure of the tin trade, and promoting regional cooperation, to improve the supply security of tin resources. Full article

The increasing demand for high-speed, energy-efficient computing has propelled the development of integrated photonic logic gates, which utilize the speed of light to surpass the limitations of traditional electronic circuits. These gates enable ultrafast, parallel data processing with minimal power consumption, making them ideal for next-generation computing, telecommunications, and quantum applications. Recent advancements in nanofabrication, nonlinear optics, and phase-change materials have facilitated the seamless integration of all-optical logic gates onto compact photonic chips, significantly enhancing performance and scalability. This paper explores the latest breakthroughs in photonic logic gate design, key material innovations, and their transformative applications. While challenges such as fabrication precision and electronic–photonic integration remain, integrated photonic logic gates hold immense promise for revolutionizing optical computing, artificial intelligence, and secure communication. Full article

The Maowusu Desert is still suffering from serious ecological and environmental security issues such as wind erosion and desertification, influenced by both natural and human factors. The amendment of aeolian sandy soil with soft rock material presents an effective erosion control strategy, leveraging the complementary structural and compositional properties of both materials to enhance soil stability and rehabilitate degraded environments. However, there are few studies that investigate the effect of soil surface electrochemical properties and particle interaction forces on the structural stability of compound soils with soft rock and sandy soil. This decade-long field study quantified the electrochemical properties and interparticle forces and their synergistic effects on structural stability across five soft rock-to-aeolian sandy soil blend volume ratios (0:1, 1:5, 1:2, 1:1, 1:0) within the 0–30 cm soil profile. The results showed that the soil organic matter (SOM), specific surface area (SSA), and cation exchange capacity (CEC) significantly increased with the incorporation of soft rock material. For five different proportions, with the addition of soft rock and the extension of planting years, the content of SOM increased from 5.65 g·kg⁻¹ to 11.36 g·kg⁻¹, the CEC varied from 4.68 cmol kg⁻¹ to 17.91 cmol kg⁻¹, while the σ₀ importantly decreased from 1.8 to 0.47 c m⁻² (p < 0.05). For the interaction force at 2.4 nm between soil particles, the absolute value of van der Waals attractive force increased from 0.10 atm to 0.38 atm, and the net force decreased from 0.09 atm to −0.30 atm after the incorporation ratios of soft rock from 0:1 to 1:1. There was a significant negative correlation between the resultant net force between the particles of compound soil and the SSA and CEC. These results indicate that the addition of soft rock material positively improves the surface electrochemical properties and internal forces between aeolian sandy soil particles, further enhancing its structural stability. This study establishes a foundational theoretical framework for advancing our mechanistic understanding of aeolian sand stabilization and ecosystem rehabilitation in the Mu Us Desert. Full article

The increasing demand for critical raw materials, such as antimony—a semimetal with strategic relevance in fire-retardant applications, electronic components, and national security—has made the identification of European sources essential for the European Union’s strategic autonomy. Remote sensing offers a valuable tool for detecting alteration minerals associated with subsurface gold and antimony deposits that reach the surface. However, the coarse spatial resolution of the most freely available satellite data remains a limiting factor. The PlanetScope satellite constellation presents a promising low-cost alternative for the academic community, providing 3 m spatial resolution and eight spectral bands. In this study, we evaluated PlanetScope’s capacity to detect Fe³⁺-bearing iron oxides—key indicators of hydrothermal alteration—by applying targeted band ratios (BRs) in northern Portugal. A comparative analysis was conducted to validate its performance using established BRs from Sentinel-2, ASTER, and Landsat 9. The results were assessed through relative comparison methods, enabling both quantitative and qualitative evaluation of the spectral similarity among sensors. Spatial patterns were analyzed, and points of interest were identified and subsequently validated through fieldwork. Our findings demonstrate that PlanetScope is a viable option for mineral exploration applications, capable of detecting iron oxide anomalies associated with alteration zones while offering finer spatial detail than most freely accessible satellites. Full article

Deep-sea mining presents a critical sustainability paradox; it offers access to essential minerals for the technologies of the green transition (e.g., batteries, wind turbines, electric vehicles) yet threatens fragile marine ecosystems. As the terrestrial sources of these materials face mounting geopolitical, environmental, and ethical constraints, undersea deposits are increasingly being viewed as alternatives. However, the extraction technologies remain unproven at large scales, posing risks related to biodiversity loss, sediment disruption, and altered oceanic carbon cycles. This paper explores how deep-sea mining might be reconciled with sustainable development, arguing that its viability hinges on addressing five interdependent challenges—technological readiness, environmental protection, economic feasibility, robust governance, and social acceptability. Progress requires parallel advancements across all domains. This paper reviews the current knowledge of deep-sea resources and extraction methods, analyzes the ecological and sociopolitical risks, and proposes systemic solutions, including the implementation of stringent regulatory frameworks, technological innovation, responsible terrestrial sourcing, and circular economy strategies. A precautionary and integrated approach is emphasized to ensure that the securing of critical minerals does not compromise marine ecosystem health or long-term sustainability objectives. Full article

White-light-emitting diodes (wLEDs) are central to next-generation lighting technologies, yet their reliance on critical raw materials (CRMs), such as rare-earth elements, raises concerns regarding sustainability and supply security. In this work, we present a simple, low-cost method to produce photoluminescent carbon-based nanostructures—known as oxidative debris (OD)—via alkaline treatment of graphene oxide (GO) using KOH solutions ranging from 0.04 M to 1.78 M. The resulting OD, isolated from the supernatant after acid precipitation, exhibits strong and tunable photoluminescence (PL) across the visible spectrum. Emission peaks shift from blue (~440 nm) to green (~500 nm) and yellow (~565 nm) as a function of treatment conditions, with excitation wavelengths between 300 and 390 nm. Optical, morphological. and compositional analyses were performed using UV-Vis, AFM, FTIR, and Raman spectroscopy, confirming the presence of highly oxidized aromatic domains. The blue-emitting (S2) and green/yellow-emitting (R2) fractions were successfully separated and characterized, demonstrating potential color tuning by adjusting KOH concentration and treatment time. This study highlights the feasibility of reusing GO-derived byproducts as sustainable phosphor alternatives in wLEDs, reducing reliance on CRMs and aligning with green chemistry principles. Full article

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements–compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system’s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

Testbeds are widely used in experiential learning, providing practical assessments and bridging classroom material with real-world applications. However, manually managing and provisioning student lab environments consumes significant preparation time for instructors. The growing demand for advanced technical skills, such as network administration and cybersecurity, is leading to larger class sizes. This stresses testbed resources and necessitates continuous design updates. To address these challenges, we designed an efficient Environment Management Platform (EMP). The EMP is composed of a set of 4 Command Line Interface scripts and a Web Interface for secure administration and bulk user operations. Based on our testing, the EMP significantly reduces setup time for student virtualized lab environments. Through a cybersecurity learning environment case study, we found that setup is completed in 15 s for each student, a 12.8-fold reduction compared to manual provisioning. When considering a class of 20 students, the EMP realizes a substantial saving of 62 min in system configuration time. Additionally, the software-based management and provisioning process ensures the accurate realization of lab environments, eliminating the errors commonly associated with manual configuration. This platform is applicable to many educational domains that rely on virtual machines for experiential learning. Full article

The safe transport of cylindrical loads such as metal, paper, or polymer rollers requires specialized securing structures that address the complex dynamic forces encountered during rail movement. This paper presents a structured methodology for the design and verification of such securing systems, combining theoretical analysis, standardized load models, and numerical simulations. The method includes load calculations based on EN 12195-1:2010, EN 15551, and Eurocode 1, and validation through finite element modeling in Ansys Workbench. The proposed structure ensures stability under static and dynamic loads, including acceleration, braking, turning, and wind forces, while optimizing wagon space utilization. Simulation results confirm that the design meets strength and safety criteria without exceeding material stress limits, offering a reliable solution for the secure transport of cylindrical rollers. Full article

Some additives currently used to enhance drilling mud’s rheological qualities have a substantial economic impact on society. Carboxymethyl cellulose (CMC) and calcium carbonate (CaCO₃) are currently imported. Food crops have influences on food security; hence, this research explored the potential of utilizing cow bone powder (CBP), a bio-waste product and a renewable resource, as an environmentally friendly fluid-loss additive for drilling applications, in comparison with CaCO₃. Both samples (CBP and CaCO₃) were evaluated to determine the most efficient powder sizes (coarse, medium, and fine powder), concentrations (5–15 g), and aging conditions (before or after aging) that would offer improved rheological and fluid-loss control. The results obtained showed that CBP had a significant impact on mud rheology when compared to CaCO₃. Decreasing the particle size (coarse to fine particles) and increasing the concentration from 5 to 15 g positively impacted mud rheology. Among all the conditions analyzed, fine-particle CBP with a 15 g concentration produced the best characteristics, including in the apparent viscosity (37 cP), plastic viscosity (29 cP), and yield point (25.5 lb/100 ft²), and a gel strength of 16 lb/100 ft² (10 s) and 28 lb/100 ft² (10 min). The filtration control ability of CaCO₃ was observed to be better than that of the coarse and medium CBP particle sizes; however, fine-particle-size CBP demonstrated a 6.1% and 34.6% fluid-loss reduction at 10 g and 15 g concentrations when compared to respective amounts of CaCO₃. The thermal behavior of the Mud Samples demonstrated that it positively impacted rheology before aging. In contrast, after aging, it exhibited a negative effect where samples grew more viscous and exceeded the API standard range for mud properties. Therefore, CBP’s excellent rheological and fluid-loss control ability makes it a potential, sustainable, and economically viable alternative to conventional materials. This superior performance enhances the thinning properties of drilling muds in stationary and circulating conditions. Full article