Search Results (10,521)

A new era in wireless communication has been witnessed by the emergence of fifth generation (5G) technology, characterized by high data rates, ultra-low latency, and massive device connectivity. To address the growing demand for efficient spectrum utilization, Non-Orthogonal Multiple Access (NOMA) has been introduced as a promising multiple access scheme. This study investigates the energy efficiency (EE) and spectral efficiency (SE) performance of NOMA in comparison with Orthogonal Multiple Access (OMA) under varying bandwidth conditions. In addition to conventional analytical and simulation-based evaluations, artificial intelligence (AI) techniques, including Deep Learning (DL), Decision Tree (DT), K-Nearest Neighbours (KNN), and Logistic Regression (LR), are employed to model and predict system performance. The AI models are trained using simulation-generated datasets to capture complex relationships between bandwidth, transmit power, and user distribution. Simulation results demonstrate improvement in SE and EE of NOMA across different bandwidth scenarios. Furthermore, DL and DT models achieve higher prediction accuracy. The consistency between AI predictions and simulation outcomes confirms the robustness of the proposed framework. These findings highlight the superiority of NOMA over OMA and demonstrate the effectiveness of integrating AI techniques for performance optimization in 5G and beyond wireless networks. Full article

Contemporary post-war cities increasingly require adaptive urban systems capable of addressing climate vulnerability, infrastructural instability, environmental degradation, and human well-being simultaneously. This study develops an interdisciplinary framework for adaptive biophilic infrastructure and resource governance within the context of sustainable post-war reconstruction in Ukraine. The research combines literature analysis, comparative urban assessment, and experimental evaluation of eco-modified construction materials. Particular attention is given to vertical greening systems, adaptive underground infrastructure, daylight-integrated public environments, multifunctional urban systems, and environmentally responsive concrete composites incorporating porous minerals and plant-based biomass. Comparative examples from Montreal, New York, Seoul, and Singapore are examined alongside differentiated Ukrainian urban contexts, including Kyiv, Kharkiv, Dnipro, Odesa, Kherson, Lviv, and Uzhhorod. The findings demonstrate that adaptive biophilic infrastructure may improve urban microclimates, strengthen thermal and acoustic regulation, enhance infrastructural adaptability, and support psycho-emotional comfort within dense and post-conflict urban environments. The study further indicates that underground and layered urban systems increasingly function as multifunctional socio-ecological infrastructures integrating mobility continuity, environmental regulation, public accessibility, emergency protection, and human-centered spatial resilience. The experimental assessment demonstrates that eco-modified materials contribute to moisture stabilization, thermal buffering, acoustic moderation, and passive environmental regulation within adaptive urban systems. The incorporation of porous mineral additives and plant biomass improved the environmental responsiveness of the investigated composites while supporting more resource-efficient construction approaches. The study concludes that sustainable post-war reconstruction requires a transition from fragmented technological interventions toward integrated socio-ecological urban frameworks capable of combining environmental regulation, infrastructural resilience, resource efficiency, adaptive governance, and human-centered spatial design within long-term urban sustainability strategies. Full article

Background: The administration of intravenous contrast media using automated power injectors is fundamental for high-quality computed tomography (CT), particularly in CT angiography (CTA). The selection of an appropriate vascular access device (VAD) and adherence to technical safety standards are critical for ensuring diagnostic efficacy and patient safety. This systematic review aims to synthesize current scientific literature regarding the efficacy and safety of contrast media infusion across various vascular access routes, including peripheral intravenous cannulas (PIVC), central venous catheters (CVC), and totally implantable venous access devices (TIVAD). Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in PubMed, CINAHL, Web of Science, and Scopus for studies published between 2000 and 2026. A total of 19 studies—including randomized controlled trials (RCTs), cohort studies, and systematic reviews—were analyzed. Methodological quality was assessed using Joanna Briggs Institute (JBI) appraisal tools. Results: Modern PIVCs utilizing diffuser technology (side holes) significantly reduce distal jet pressure, minimizing vessel wall damage during high-flow injections. For patients with difficult vascular access, “power-injectable” certified devices (e.g., PICCs or TIVADs) serve as a safe alternative. Standard, non-power-injectable central lines must be avoided due to the risk of catheter rupture. The selection of an appropriate vascular access device is particularly challenging in patients of older age or those with chronic conditions, such as peripheral vascular disease, obesity, or chemotherapy-related venous damage, which often lead to difficult intravenous access (DIVA). Conclusions: Utilizing certified power-injectable devices and advanced cannula designs improves the safety of high-pressure contrast administration. Adherence to technical protocols and the identification of high-risk patients are essential for mitigating complications such as contrast extravasation. Full article

We present a transdisciplinary Teaching–Learning Sequence (TLS) for lower secondary school students that integrates climate change education with the promotion of gender equality in science. The TLS connects theoretical understanding with practical engagement through laboratory demonstrations, simulations, and accessible experiments. The sequence addresses key topics in sustainability education, including incoming and outgoing radiation, the greenhouse effect, energy transformations, and energy sources, through activities involving the electromagnetic spectrum, infrared imaging, selective transparency, absorption, and albedo. It also includes inquiry-based explorations of electromagnetic induction, miniature hydroelectric and wind power systems, Stirling engines, photovoltaic and concentrated solar technologies, and combustion-related CO₂ acidification. A distinctive feature of the TLS is the explicit integration of the social dimension of sustainability through discussion of the Matilda Effect and the historical case of Eunice Newton Foote, with the aim of challenging persistent gender stereotypes in STEM. The intervention was implemented with 12–13-year-old students and evaluated through pre- and post-tests, written explanations, closed-ended questions, drawings, and the Draw-A-Scientist Test. The results indicate a significant improvement in students’ understanding of climate-related scientific concepts and in their critical awareness of misinformation and climate denial strategies. While the sequence did not significantly increase students’ engagement in climate action, the gender-focused activities promoted strong critical reflection on stereotypes and on the role of women in science. Full article

This study examines motivational and capability barriers to internet non-adoption in Thailand’s near-saturated digital economy. Using the 2025 Q4 ICT Household Survey conducted by Thailand’s National Statistical Office, the analysis focuses on 20,633 adult non-adopters who report either motivational or capability-related barriers. The dependent variable distinguishes capability non-adoption, defined as lack of skill or awareness, from motivational non-adoption, defined as lack of perceived need or privacy/security concerns. Weighted logistic regression with normalised population weights, PSU-clustered robust standard errors, and average marginal effects is used to estimate associations between household ICT access, age, education, employment, smartphone access, and barrier type. Motivational barriers account for 56.2% of the two-category non-adopter population and capability barriers for 43.8%. Although motivational reasons are the more common, household ICT access is positively—if modestly—associated with capability rather than motivational barriers (average marginal effect +1.7 percentage points): capability-constrained non-adopters are concentrated in connected households, the compositional signature predicted by the second-level digital divide. Age does not significantly moderate this association. Among older non-adopters, education, employment, and smartphone access are negatively associated with capability barriers, while household ICT access is not. The findings suggest that in post-access digital economies, household connectivity is insufficient for digital market inclusion; individual-level skills and device access become central to expanding effective digital market reach. Full article

The development of digital learning technologies has introduced innovative tools to enhance science and chemistry education, including PhET simulations. This study aims to evaluate the effectiveness of PhET simulations on students’ learning outcomes through a systematic literature review following the PRISMA 2020 guidelines. A systematic search of Scopus and Crossref databases was conducted (last search: January 2026) using predefined keywords. Eligible studies were empirical research published between 2020 and 2026 that investigated PhET simulations in science-related education and reported learning outcomes, while non-empirical studies and non-Scopus-indexed articles were excluded. Risk of bias was assessed using an adapted Joanna Briggs Institute critical appraisal tool. Due to heterogeneity in study designs and outcome measures, the results were synthesized using a narrative approach. A total of 14 studies across elementary to higher education levels were included. The findings indicate that PhET simulations consistently improve learning outcomes, particularly academic achievement and conceptual understanding, with effects generally favoring simulation-based instruction over traditional methods. However, higher-order skills and affective outcomes such as motivation and attitude remain less frequently investigated. The evidence is limited by variability in study designs, incomplete reporting of non-cognitive outcomes, and the absence of quantitative synthesis. Overall, PhET simulations demonstrate strong potential as an effective interactive learning medium, although their impact depends on instructional design, teacher facilitation, and technological accessibility. Full article

Equine sports medicine is increasingly oriented toward objective, field-based monitoring systems that support both performance optimization and welfare assessment. In this context, smartphone-based technologies have emerged as accessible tools capable of integrating data acquisition, processing, and interpretation within a single platform. This narrative review aims to examine the role of smartphones in equine sports medicine, focusing on their function as standalone sensing devices and as gateways for wearable and external sensor systems. The analysis is based on a structured synthesis of current literature addressing technological foundations, including embedded sensors, connectivity architectures, and artificial intelligence-driven data processing, as well as their clinical applications across locomotor, cardiovascular, respiratory, behavioural, and thermoregulatory domains. Evidence indicates that smartphone-based systems improve the feasibility of longitudinal monitoring and facilitate real-time decision-making in field conditions, while enhancing communication between veterinarians, trainers, and owners. However, their performance remains influenced by acquisition conditions, system variability, and algorithmic constraints, requiring careful validation and contextual interpretation. In addition, challenges related to data governance, privacy, and ethical use remain insufficiently addressed. Overall, smartphone-based technologies represent enabling tools that support a transition toward more integrated, data-driven, and welfare-oriented management of the equine athlete, while highlighting the need for standardisation and regulatory development. Full article

Honeybees (Apis mellifera) serve as biological sentinels because their foraging behavior links colony health to environmental conditions. Traditional hive inspections are invasive, observer-dependent, and often detect problems only after symptoms appear. This review synthesizes advances in precision beekeeping, environmental DNA (eDNA) metabarcoding, exposomics, and artificial intelligence to propose the Honeybee-Based Early Warning System (H-BEWS), a unified framework that integrates digital sensors, molecular and chemical monitoring, and ecological data into a predictive early warning system for both colony and ecosystem health. By linking anomalies detected by hive sensors to targeted molecular and chemical analyses, H-BEWS enables proactive interventions and environmental surveillance, supporting a One Health perspective. Unlike previous reviews that focus on individual technologies, H-BEWS emphasizes multi-layered integration, predictive risk assessment, and ecosystem-level insights, providing a novel conceptual framework for early detection of colony stress and environmental hazards. The approach offers practical applications for beekeepers, researchers, and policymakers by converting real-time data into actionable insights and informing management decisions. Challenges include sensor standardization, data integration, AI validation, and equitable access for small-scale beekeepers. Future directions will focus on real-time sequencing, multimodal AI models, digital twin creation, and the development of global surveillance networks. H-BEWS demonstrates how an integrative, multi-layered approach can transform honeybee colonies into living biosensors, providing actionable insights for both apiculture management and ecosystem monitoring. Full article

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article

Artificial intelligence (AI) is increasingly being integrated into higher education. Among these innovations, AI teaching assistants have emerged as tools that can provide immediate academic support, personalized feedback, and improved access to learning resources. Despite the growing adoption, limited research has explored students’ knowledge, attitudes, and acceptance of AI teaching assistants in African higher education contexts. The Technology Acceptance Model was adopted as a theoretical lens to explore South African university students’ Knowledge, Attitudes and Acceptance of AI teaching assistants in a clinical learning environment. A qualitative study design within an interpretivist paradigm was employed. Data were collected through semi-structured interviews with six undergraduate students who had experienced both traditional teaching approaches and AI-assisted learning. The data were analysed using thematic analysis. The findings revealed three key themes: students’ understanding of AI teaching assistants, attitudes toward AI-assisted learning, and acceptance and concerns regarding AI in clinical education. The results indicate that students generally demonstrate positive attitudes toward AI teaching assistants and recognize their usefulness for supporting independent learning. However, participants also expressed concerns regarding the accuracy of AI-generated information and emphasized the continued importance of human educators in clinical training. The study contributes context-specific insights into technology acceptance in African higher education, highlighting how perceived usefulness may remain strong even in resource-constrained environments. Full article

The energy transition constitutes not only a technological and regulatory challenge but also a communicative and cultural one, in which corporate narratives play a decisive role in shaping social understanding, legitimacy, and trust. This study examines how major energy companies operating in Spain construct the narrative of the energy transition through their corporate discourse and evaluates the extent to which these narratives integrate pedagogical and relational dimensions oriented toward society. Using a qualitative content analysis approach supported by lexical frequency analysis as a heuristic tool, the study analyzes the CEO or Chair letters published in sustainability reports by four energy companies—Iberdrola, Endesa, Naturgy, and Holaluz—over a five-year period (2020–2024), comprising a total of 20 reports, from which 18 CEO/Chair letters were extracted and treated as a single analytical unit. Two reports (Iberdrola and Naturgy, 2024) adopted the ESRS/CSRD format directly, eliminating the traditional chairperson’s letter. To triangulate and contextualize the documentary analysis, a two-round Delphi study was conducted with 11 independent experts. The findings reveal a predominantly technical and self-referential discourse focused on corporate strategy, performance, and regulatory compliance, with a limited presence of explanatory or citizen-oriented narratives. Despite increasing terminological convergence driven by regulatory standardization, the analysis reveals persistent divergence in narrative framing, with the challenger company articulating purpose-driven and citizen-empowerment frames largely absent from incumbent discourse. The Delphi results reinforce these findings, emphasizing the need to strengthen pedagogical clarity, accessibility, and relational orientation in energy communication. On this basis, the study proposes a relational model of energy communication that highlights narrative mediation, social intelligibility, and stakeholder-oriented discourse as key factors for enhancing legitimacy and trust in the context of the energy transition. The analysis further identifies a structural tension between regulatory standardization and narrative capacity, exemplified by the elimination of the CEO letter in one company’s 2024 report following ESRS adoption. Full article

CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications. Full article

Private Networks or Standalone Non-Public Networks (SNPNs) are essential for Industry 4.0 and enterprise connectivity. However, most existing studies rely on simulations, evaluate only a single radio access technology, or report raw key performance indicators (KPIs) without an interpretable quality assessment framework. In practical deployment, operators require measurement-driven evidence to assess the performance and feasibility of 4G LTE and 5G SNPN solutions. This study presents a controlled experimental comparison of software-defined radio (SDR)-based 4G LTE and 5G SNPNs using the same Universal Software Radio Peripheral (USRP) platform, Open5GS, srsRAN, and commercial off-the-shelf user equipment (COTS-UE). The evaluation was conducted in an indoor environment under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Experimental iPerf3 results show that the SDR-based 5G SNPN achieves higher downlink and uplink throughput than the SDR-based 4G LTE SNPN across all tested scenarios. The 5G deployment reaches up to 55 Mbps downlink and 40.5 Mbps uplink under LOS conditions, while maintaining 42 Mbps downlink and 28 Mbps uplink under NLOS conditions. Furthermore, 5G achieves lower latency than 4G LTE, with average values ranging from 21 ms to 31 ms. To provide interpretable network quality assessment, a Mamdani fuzzy logic-based Network Quality Index (NQI) with 81 inference rules is proposed to map signal-to-interference-plus-noise ratio (SINR), throughput, latency, and jitter into linguistic quality levels. The proposed approach enables nonlinear integration of heterogeneous KPIs and provides a technology-agnostic framework for practical SNPN deployment. Full article

Cellulose is the most promising abundant renewable polymer material with the highest potential for the future low-carbon biorefineries. However, its utilization in industry is limited by the structural recalcitrance as a result of organization of crystalline domains, fibrillar architecture hierarchy and intramolecular and intermolecular hydrogen bonding which is responsible for access restriction for the catalysts and consequent cleavage of the glycosidic bonds. Therefore, efficient depolymerization of cellulose is of paramount importance as a step in biomass conversion into the low molecular products. In this review, the recent advances in cellulose depolymerization are discussed. The chemical, enzymatic, thermal, thermochemical, mechanochemical, oxidative and hybrid catalytic method is thoroughly discussed. Attention is paid to the mechanism of the depolymerization reaction steps as glycosidic bond activation as hydrolytic, radical mediated, and energy assisted pathways. Selectivity and conversion efficiency based on substrate morphology, solvent system and catalyst design are also discussed. Further, there is a comparison of key performance metrics which are relevant for the industrial process as product yield, carbon efficiency, energy demand, stability of the catalyst, solvent recyclability and impact to the environmental lifecycle. The pros and cons of the various methods are also represented. Processes based on mineral acids enable rapid conversion. However, they suffer from corrosion, waste handling issues and degradation by-products. On the other hand, enzymatic depolymerization processes offer relatively high selectivity but they are limited in terms of feedstock sensitivity and slow reaction kinetics. The downstream valorization mechanisms are also described with the result being that no single available technology is capable of satisfying all industrial requirements. Thus, future progress expects integrated circular processes where advanced catalysis, process intensification and digital optimization strategies take place. Full article

Theranostics is a novel approach that integrates diagnostic and therapeutic efficacy on a single platform, holding great promise for precision medicine by enabling real-time monitoring of disease progression and therapeutic response. Despite significant advances, the successful development and delivery of theranostic systems are critically limited by multiple biological barriers present at systemic, tissue, cellular, anatomical, and immunological levels. These barriers restrict bioavailability, target accessibility, and therapeutic efficacy, while often increasing off-target accumulation and adverse effects. This review provides a comprehensive overview of the major biological barriers encountered in theranostic development, including physiological barriers such as plasma protein binding, renal clearance, and hepatic metabolism; anatomical barriers like endothelial linings, the blood–brain barrier (BBB), and the tumor microenvironment; cellular barriers involving membrane permeability, intracellular trafficking, and endo-lysosomal entrapment; and immunological barriers such as immune recognition, inflammatory responses, and complement activation. Special emphasis is placed on the BBB, highlighting its structural complexity, transport mechanisms, and strategies such as molecular Trojan-horse technology, receptor-mediated and adsorptive-mediated transcytosis, and nanocarrier-based approaches to enhance central nervous system delivery. The review further discusses targeted delivery challenges, including receptor heterogeneity and multidrug resistance, and critically evaluates current strategies to overcome these barriers through surface functionalization, stimuli-responsive systems, biomimetic carriers, and controlled-release mechanisms. Finally, recent advances, clinical challenges, and future perspectives—including personalized theranostics, artificial intelligence—assisted design, and next-generation barrier-penetrating systems—are explored. Overall, this review aims to provide a structured understanding of biological barriers in theranostics and highlight innovative approaches to improve their translational potential. Full article