Abstract: Automated face mask detection remains an important component of hygiene compli-ance, occupational safety, and public health monitoring, even in post-pandemic envi-ronments where real-time, non-intrusive surveillance is required. Traditional deep learning models offer strong recognition performance but are often impractical for de-ployment on embedded and edge devices due to their computational complexity. Re-cent research has therefore emphasized lightweight and hybrid architectures that maintain high detection accuracy while reducing model size, inference latency, and energy consumption. This review provides an architecture-centered examination of face mask detection systems, analyzing conventional convolutional models, light-weight convolutional networks such as the MobileNet family, and hybrid frameworks that integrate efficient backbones with optimized detection heads. A comparative per-formance analysis highlights key trade-offs between accuracy and computational effi-ciency, emphasizing the constraints of real-world and edge-oriented deployments. Open challenges, including improper mask detection, domain adaptation, model com-pression, and extending detection systems toward broader compliance-monitoring ap-plications, are discussed to outline a forward-looking research agenda. This work con-solidates current understanding of architectural strategies for mask detection and of-fers guidance for developing scalable, robust, and real-time deep learning solutions suitable for embedded and mobile platforms.

Abstract: The rapid expansion of artificial intelligence (AI) and digitalization in contemporary ed-ucation has reshaped global debates on sustainable education, often emphasizing effi-ciency, personalization, and technological innovation. However, this transformation has coincided with increasing technologization and commodification of education, raising critical questions about whether AI-driven education can genuinely support sustainability as a value-based and human-centered project. This study examines sustainable education in the age of artificial intelligence and digitalization through a value-critical analytical ap-proach grounded in a conceptual distinction between sustainable education, sustainabil-ity in education, and education for sustainable development. Methodologically, the article adopts a qualitative critical analysis of contemporary literature and policy-oriented de-bates to assess the ethical, social, and educational implications of AI integration. The analysis reveals a dual and context-dependent impact of AI on sustainable education: while AI can enhance educational quality, access, and personalization in well-resourced and well-governed contexts, it may also intensify educational inequalities, reinforce the commodification of knowledge, undermine academic integrity, and marginalize the hu-man dimension of education under market-driven and weakly regulated conditions. These challenges are particularly evident in culturally and religiously grounded educa-tional contexts, where AI reshapes epistemic authority and educational meaning. The study concludes that achieving sustainable education in the digital age depends not on AI adoption per se, but on reframing AI and digitalization within a coherent ethical and val-ue-based framework that subordinates technology to educational aims, social justice, and human dignity.

Abstract: Modern information systems suffer from a fundamental architectural flaw: data coherence depends on external validation layers, creating systemic entropy and computational waste. We present the G Model, a mathematical framework that redefines informationas points in a geometric space where incoherence is mathematically impossible. Through a triaxial formalization (Meaning, Location, Connection) and an intrinsic coherence operator (Φ), the system guarantees that only valid data can exist within the managed universe (Ω). We formalize this with four fundamental axioms ensuring coherence, uniqueness, acyclicity, and deterministic propagation. Our implementation, the SRGD system (Sistema Relacional Gestión de Datos), demonstrates practical viability through a stateless three-layer architecture and unified flow patterns. Preliminary results show significant advantages in critical infrastructure scenarios where error is inadmissible, providing a foundation for trustworthy AI training data and eliminating the validation overhead present in traditional RDBMS and NoSQL systems. This work represents a paradigm shift from “data storage systems” to “coherent information spaces".

Abstract: Drug discovery is a complex, multi-parameter optimization process. I argue that a greater emphasis on optimizing binding affinity will accelerate the development of new medicines. Note that “optimizing” is not always synonymous with “maximizing.” While affinity is certainly not the only thing that matters, the value of optimizing drug – receptor interactions is profound and often underappreciated. Optimizing affinity provides seven distinct benefits: achieving potent tool compounds more quickly; making compounds with increased potency; making more selective compounds; optimizing drug candidates more quickly; encouraging the pursuit of more synthetically challenging compounds; expanding chemical diversity during lead optimization; and minimizing interactions with "avoid-ome” targets that lead to poor ADME and tox properties. Affinity should be viewed as a key strategic component throughout the entire discovery process – balancing the level of on-target engagement appropriate to the specific mechanism being pursued alongside the need for chemical diversity and the proactive de-risking of off-targets including the avoid-ome. A “checklist” of practical suggestions is offered to enable project teams to more fully embrace the challenges of affinity optimization.

Abstract: Climate change is a major environmental determinant of health, capable of altering exposure pathways to toxic contaminants such as (Pb) [1,2]. Lead is a persistent global pollutant with no safe exposure threshold and disproportionately affects children and socioeconomically vulnerable populations [3–5,17,24]. This review examines how climate-related processes amplify lead mobilization and associated public health risks within a One Health framework. We conducted an integrated bibliometric and narrative review of peer-reviewed literature published between 1990 and 2025 using Web of Science, Scopus, and PubMed. Bibliometric mapping was combined with thematic synthesis. A total of 89 studies were analyzed. Results reveal a fragmented research landscape across disciplines and identify five convergent climate-sensitive lead exposure pathways: flood-driven remobilization [8,58], drought-related dust resuspension [7,22], temperature-mediated increases in bioavailability [6,28], urban amplification [9,20,21], and climate-influenced transport through water and food systems [13,40]. Climate change acts as a risk multiplier for lead exposure, reinforcing environmental health inequities. Integrating climate-sensitive exposure pathways into environmental surveillance and One Health–oriented public health policies is essential to reduce future lead-related disease burdens [35–38]. This review provides an integrated bibliometric and conceptual framework to support climate-sensitive lead surveillance and policy development.

Abstract: After approximately 60 years of service the 2856 MHz LINAC injector, of the Canadian Light Source (CLS), has been retired to make space for a new 3000.24 MHz LINAC injector, the frequency of which is a multiple of the 500.04 MHz CESR-B type superconductive radio frequency cavity used in the CLS storage ring. The new CLS LINAC injector has been designed and built by RI Research Instruments GmbH. The design is based on their robust S-band RF traveling wave accelerating structures technology, already serving other laboratories in the USA, Australia, Taiwan, Switzerland, and Sweden. In order to reduce cost and optimize space, the CLS has replaced its six accelerating RF structures, each 3.05 meters long, delivering 250 MeV electron beam with three 5.26 m long accelerating structures that will deliver the same beam energy. In order to do so, one RF structure is powered by one modulator-klystron and the last two RF structures receive their RF power from a second modulator-klystron that passes through a SLED system. The SLED system multiplies the peak power by a factor 5 to 6 and is then equally split to power each structure. We are reporting on the issues encountered during the commissioning of this new injector, on how we have tackled them and where the injector, compared to its technical specification, is standing today.

Abstract: This present study adopted the Box-Behnken Design (BBD) with Response Surface Methodology (RSM) to identify the optimum input processing factors (heating temperature: 40, 50 and 60 °C, heating time: 30, 45 and 60 min and pressing height: 60, 80 and 100 mm) for estimating the oil output parameters (mass of oil, oil yield and oil expression efficiency) and deformation energy. The mechanical properties examined were the hardness and secant modulus of elasticity. Based on the full quadratic model, which includes both significant and non-significant terms, the optimal input processing factors were determined to be a heating temperature of 60 °C, a heating time of 52.5 min, and a sample pressing height of 100 mm, with coefficient of determination (R²) values ranging from 0.68 to 0.95. The linear models with the significant terms predicted the mass of oil of 33.36 g, oil yield of 21.5 %, oil expression efficiency of 65.47 % and the experimental deformation energy of 1080.82 J. The percentage error values between the experimental and theoretical deformation energies were from 1.35 to 28.31%, suggesting that the varying input processing factors affected the coefficients of the tangent curve model for fitting the experimental force-deformation curves. The hardness and secant modulus of elasticity values ranged between 3.65 and 7.09 kN/mm and 123.98 to 150.39 MPa, indicating that the varying input processing factors had a significant effect on the stiffness of the bulk hemp seeds. These findings are useful for modelling and optimising the mechanical behaviour of oilseeds using a mechanical screw press to enhance oil recovery efficiency.

Abstract: Investigating the impact of factor mobility (FM) on the economic efficiency of marine fisheries (EEMF) holds scientific reference value for promoting high-quality development of the marine fisheries economy in China's coastal regions. This study is based on panel data from 11 coastal provinces and municipalities in China covering the period from 2008 to 2023. Utilizing Tobit models and mediation effect models, it empirically analyzes the direct and indirect impacts of FM on the EEMF, as well as regional heterogeneity in these effects. Research findings indicate that: (1) The level of FM and the EEMF in coastal regions both exhibit fluctuating upward trends, though regional variations exist across different provinces. (2) The FM in coastal regions enhances the EEMF. For every additional unit of FM, the EEMF increases by 0.0825 units. (3) Technological innovation levels and industrial structure upgrading serve as key pathways through which FM influences the EEMF, acting as mediating variables. (4) This impact exhibits regional heterogeneity, with the Eastern Marine Economic Circle being most significantly affected. The research findings expand the scope of studies on FM and the EEMF, providing practical advice for promoting the optimal allocation of factors in coastal regions and enhancing the EEMF development.

Abstract: Autophagy is a highly conserved cellular degradation process essential for maintaining cellular homeostasis, yet its role in cancer is fundamentally context dependent. Increasing evidence indicates that autophagy suppresses tumor initiation by preserving genomic and metabolic integrity, while paradoxically supporting tumor progression, therapy resistance, and immune evasion at advanced stages. This functional duality presents a major challenge for therapeutic targeting and largely reflects the spatiotemporal heterogeneity of autophagy regulation across tumor stages, cancer cell subpopulations, and the tumor microenvironment (TME). In this review, we argue that autophagy-related proteins should be conceptualized as context-dependent therapeutic nodes rather than universally actionable targets. We systematically examine key autophagy regulators—including Beclin-1, p62/SQSTM1, mTOR, and p53， and analyze how their functions are shaped by tumor stage, genetic background, and microenvironmental cues such as hypoxia, immune pressure, and stromal interactions. We further highlight the pivotal role of the TME in determining autophagy dependency and therapeutic vulnerability, providing mechanistic insight into why autophagy modulation without microenvironmental consideration often yields inconsistent outcomes. From a precision medicine perspective, we discuss how nanotechnology-based delivery systems enable spatially and temporally controlled modulation of autophagy, thereby addressing intratumoral heterogeneity and reducing systemic toxicity. By integrating molecular profiling, TME characteristics, and nanomedicine-enabled targeting strategies, this review outlines a rational framework for exploiting autophagy in cancer therapy. Together, these insights provide a foundation for the development of context-aware, autophagy-targeted interventions and advance the pursuit of more effective and personalized cancer treatments.

Abstract:

Mycophenolic acid (MPA), a secondary metabolite derived from fungal strains, is a therapeutic agent drawing significant attention due to its potential applications in organ transplant rejection, autoimmune disorders, and cancer cell inhibition. It also exhibits potent antiviral, antifungal, and antibacterial properties, positioning it as a candidate for next-generation antibiotics. Research is presently focused on bioprospecting for MPA-producing fungal strains with a broad activity spectrum to enhance clinical efficacy. In this study, 304 fungal strains were isolated from diverse marine sediments in central and southern Vietnam. Thin-layer chromatography (TLC) identified 25 strains capable of synthesizing MPA. Based on morphological characteristics, these were classified into three genera—Penicillium, Aspergillus, and Cladosporium—alongside two unidentified strains. Notably, high-performance liquid chromatography (HPLC) confirmed that strain MBLC9-138 possesses high MPA-producing potential, reaching 463.25 to 632.03 mg/L after 5–7 days of cultivation. Internal transcribed spacer (ITS) sequencing identified this strain as Cladosporium sp. MBLC9-138, marking the first report of MPA biosynthesis within this genus. Furthermore, MPA extracted from this strain exhibited significant antimicrobial activity against Escherichia coli (Gram-negative), Staphylococcus aureus, and Bacillus cereus (Gram-positive), with MIC values of 32, 64, and 16 µg/mL, respectively. These results highlight a promising bioactive candidate that could offer dual therapeutic benefits while potentially minimizing gastrointestinal side effects and antibiotic resistance. Simultaneously, Vietnamese marine sediments continue to be a rich source of material for isolating bioactive microorganisms, particularly MPA-producing strains.

Abstract: Background and Aims: Since the early 2000s, scholarship and policy analysis on Nigeria’s extractive sectors have expanded beyond oil bunkering to encompass the illegal mining of solid minerals, artisanal economies and environmental degradation. These developments have produced new framings and critiques of the “resource curse,” linking extraction to governance, security and justice. This paper aims to elucidate how the idea of “resource governance” has been discussed and perceived across Nigerian scholarly and policy texts from 1999 to 2025. Methods: Terms like “resource governance in Nigeria,” “extractive industries,” “mining” and “illegal mining" were searched across academic databases and institutional repositories. 36 english-language publications explicitly or implicitly addressing Nigeria’s extractive governance, published from 1999 to 2025, were included in the final analysis. Texts were analyzed for discursive themes using a combined scoping review and critical discourse analysis framework. Metadata related to author identity, geography, institutional affiliation, and publication type were also recorded. Results: The criminal-economy discourse (linking extraction to illegality and insecurity) dominated the archive. Other discourses include ecological justice (framing harm as both environmental and moral) and displacement (highlighting exclusion and inequality). Conclusion: Findings indicate that resource governance in Nigeria is framed less as a technical challenge than as a field of political struggle and moral negotiation. These discourses collectively reveal how coercive governance, legitimized through security and reform narratives, helps sustain extractive inequality. The results underscore the need to integrate local agency and justice frameworks into national and transnational debates over resource policy.

Abstract: Banana bract mosaic virus (BBrMV) is the most economically damaging and deleterious Potyvirus pathogen (family, Potyviridae) of banana (2n = 3x = 33) that causes significant losses to banana production Asia. The BBrMV has a single-stranded, positive sense ssRNA genome of 9708 nucleotides. RNA interference (RNAi) is an evolutionarily conserved biological potent intracellular response mechanism in eukaryotic organisms and is an antagonist of virus replication. The current study focuses on the role of banana genome-encoded microRNAs (mac-miRNAs) targeting +ssRNA genome of the BBrMV using in-silico predictive algorithms, RNA22, RNAhybrid, TAPIR and psRNATarget. Mature banana locus-derived mac-miRNA sequences (n = 32) were tested for alignment with the BBrMV genomic +ssRNA (NCBI accession No. MG758140). In total we extrapolated 32 mature banana miRNAs, only two of which are potentially efficient miRNAs (mac-miR157b and mbg-miR397a) to have high-affinity target sites in the BBrMV genome. To identify emerging therapeutic targets, we utilized Circos software to develop a network illustrating potential mechanistic RNA-RNA interactions, based on robust prediction algorithms. Our findings provide the first in silico evidence of multiple dynamic tenacious interaction between banana high-confidence miRNAs and Potyvirus genome. This work represents a critical step towards proactive biosecurity preparedness, offering a predictive framework for engineering BBrMV-resistant banana plants and safeguarding domestic banana production.

Abstract: Artificial intelligence (AI) is increasingly integrated into scientific discovery processes, such as protein design, gene analysis, and materials research, significantly enhancing the efficiency of discoveries in these fields. While much recent literature emphasizes fully automated pipelines, it is crucial to acknowledge that scientific discovery is inherently a creative and high-stakes endeavor. Therefore, it relies heavily on human expertise for judgment and guidance, especially in the face of uncertainty. Despite rapid growth in human-in-the-loop and collaborative systems, the field lacks a unifying survey that explains how humans and AI actually collaborate across the scientific discovery life-cycle. In this paper, we present a systematic review of human–AI (HAI) collaboration for scientific discovery. Specifically, we have identified four representative roles of humans and AI. Using this lens, we then distill common HAI collaboration patterns across three distinct stages in the scientific discovery process (i.e., observation, hypothesis, and experiment). Finally, we identify key gaps in existing approaches and outline future research directions for developing trustworthy, role-aware human–AI systems in scientific discovery.

Abstract: Two major global trends shaping 21st-century society are population aging and urbanization. Consequently, the living conditions of older adults have become an increasing focus of societal attention. Social interaction plays a crucial role in the mental health, emotional well-being, and social identity of older adults. Urban streets, as key sites for walking and social activity among older adults, can be seen as extensions of their homes—places where they regularly interact with neighbors and build new connections. Compared to built environments often termed "gray spaces," exposure to green spaces has been shown to offer greater benefits to residents' well-being. Among streetscape features, the Spatial Openness Level is closely associated with the psychological well-being of elderly individuals. The Gray-Green space Exposure Ratio (GER) and Spatial Openness Level(SOL) serve as key indicators for evaluating streetscape quality. In this study, conducted in Wuhan City, objective physiological monitoring of brainwave activity was employed to examine the responses of older adults to variations in GER and SOL. The results indicate that both GER and SOL significantly influence the emotional states of older adults.(correlation coefficient R² = 0.6062, p < 0.01) .These findings can inform human-centered urban design criteria, thereby promoting social interaction among older adults. Future research should incorporate additional environmental factors to establish a more comprehensive assessment framework for age-friendly urban spaces.

Abstract: With an emphasis on the distinct labor market structures of Africa and emerging markets, this paper offers a data-guided analysis of the effects of automation and artificial intelligence (AI) on the evolution of jobs. The study employs a mixed-methods approach and bases its conclusions on both detailed task-level data from the "Anthropic Economic Index" and a quantitative regression model of more than 1,000 occupations. The empirical findings show a strong negative correlation between automation probability and wages, meaning that median annual salaries fall by about $176 for every percentage point increase in an occupation's automation risk. The study also reveals that the use of AI is currently divided between automation (43%) and augmentation (57%), with advantages disproportionately favoring high-skill, cognitive jobs like writing and software development. On the other hand, low-skilled and low-wage jobs—which are common in emerging markets—benefit the least from current AI augmentation and are most at risk of being replaced. These trends point to the possibility of worsening labor disparities and upending established routes for economic growth. The study suggests evidence-based policy solutions to reduce these risks, such as sector-specific industrial strategies, focused reskilling programs to support "Job Zone transitions," and the encouragement of AI-human cooperation.

Abstract: The smart grid concept is based on the full integration of renewable energy sources. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity. So hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand and in order to achieve a sustainable general energy system (GES) it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolysers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (also German case), an additional 20–30 GW of generation capacity in H2 operated gas turbines and strong H₂ transportation infrastructure will be required over the next ten years. This paper describes the systematic transformation from today's power system to one that includes a hydrogen economy. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors).

Abstract: The Webometrics University Ranking website ceased to function in 2025 due to an inability to obtain citation data from Google Scholar. Since then, Webometrics University Ranking data has been published on the Figshare server, but the values of the three individual indicators have not been ranked. From July 2025 onwards, the Openness indicator values for citations have been calculated using OpenAlex via the ROR identifier. Data on the ranking of all three indicators will be provided twice a year in the form of an Excel file on a paid basis. Examples of universities included in the TOP-2,000 of the January 2025 and July 2025 Webometrics Ranking, which had missing legitimate Institutional Google Scholar Citation profiles (IGSCPs), demonstrate a sharp increase in their rankings when switching to the new methodology for calculating Openness indicator values. Experiments on the webometric ranking of universities with missing IGSCPs included in the TOP-2,000, when restored, showed identical average changes in world rankings, both in experiments using the old methodology and when transitioning from the old methodology to the new one together with the strong rank correlation of two corresponding layers in the Top 1,000 Webometrics University Ranking in time led to the conclusion that the transition to the new methodology will have virtually no effect on university rankings. This conclusion allows researchers and university managers to conduct comparative analyses of university positioning and benchmarking exercises starting in 2016, when IGSCPs were introduced into the Webometrics University Ranking calculation. The importance of creating and maintaining Institutional Google Scholar Citation profiles, despite changes in the Webometrics University Ranking calculation methodology, has been demonstrated.

Abstract: Multivariable optimization is an essential mathematical exercise in daily engineering design and troubleshooting. To this end, simplex multivariable optimization method is a powerful optimization approach that has served many engineering disciplines well over the years. One such simplex algorithm is the Nelder Mead algorithm. But, the reflection step of the Nelder Mead algorithm may increase the number of iterations needed to arrive at the optimal point, as it reflects the starting point to the opposite side of the function. This work proposes an automated two-stage adjustable ratio simplex optimization method that first search within and around the optimization surface for a good starting point, followed by a narrow and more refined search for the optimal point. For both stages of the new simplex algorithm, only contraction and extension steps are used, and this helps to remove possible oscillatory effects common to other simplex algorithms as the iterations progress. Demonstrative use of the new simplex algorithm on optimizing the coefficients of a quadratic function reveals good accuracy and speed as compared to the Nelder Mead algorithm which uses significantly more iterations. Future testing should be conducted with other optimization functions as well as objective functions. Interested readers are invited to explore and expand on the work reported herein.

Abstract: Exercise is an effective intervention at mitigating many of the sequalae of cancer and its treatments. However, a scarcity of exercise services for cancer survivors remains, highlighting a research-to-practice gap. Accordingly, there is considerable rationale to explore strategies to enhance the implementation of exercise oncology trial findings into clinical practice. Dissemination is the active process of spreading research findings to key stakeholders and is crucial to the implementation of evidence base practice. However, little is known regarding the optimal methods of disseminating results of exercise oncology trials. To this end, this project aimed to explore the viewpoints of stakeholders (patients/ health care professionals (HCPs)/ policy makers/ researchers) on the dissemination of exercise oncology trials. Stakeholders were invited to take part in a one-to-one semi-structured interview exploring their experiences of and preferences for exercise oncology trial dissemination. Interviews were audio recorded, transcribed verbatim, and analyzed using a thematic approach. Thirty stakeholders were recruited: patients with a history of cancer (n=14), health care professionals (HCPs) (n=3), researchers (n=10), and policy makers/ health care management (n=3). Median interview length was 14 minutes and 10 seconds (range 8 minutes 16 seconds to 37 minutes and 23 seconds). Three main themes were identified: i) The need for enhanced dissemination strategies, ii) engaging stakeholders throughout the study lifespan is key to facilitating effective dissemination and iii) tools to facilitate closing the research to practice gap. Stakeholders acknowledged that there is limited awareness amongst the public regarding the benefits of exercise across the cancer trajectory, and that accessible and trustworthy information delivered through a variety of mediums to target different stakeholders is required. Stakeholders felt strongly that research outputs need to be targeted to the interests of key stakeholders to aid the integration of evidence into practice, and that buy-in from clinicians is paramount to integrating exercise into usual care. Results of this qualitative study highlight there is a need for more widespread and targeted dissemination of exercise oncology trial results. Stakeholders recommended a comprehensive approach to dissemination to help mitigate the research to practice gap.

Abstract: Brief post-learning wakeful resting periods and local sleep mechanisms have been proposed to support offline memory consolidation processes. Mind-wandering (MW), thought to reflect the occurrence or need for local sleep, has been linked to momentary attentional disengagement and may index transitions toward offline processing states. We hypothesized that resting opportunities administered immediately after probe-caught MW episodes reflecting local sleep need may selectively enhance memory consolidation. In a first experiment, participants learned 5 blocks of 8 paired-associate words; a MW thought probe was administered after each block. In the MW condition, participants were allowed a 3-minute quiet, offline pause after the block if they reported MW. In the control condition, no pause was administered. Consolidation was better in the MW than the control condition, supporting the hypothesis. However, Experiment 2 tested the MW-related pause effect by comparing the MW condition to a condition in which pauses were allowed irrespective of MW. Results showed that performance equally improved in both conditions, suggesting that post-learning pause effects would not be MW-specific. However, additional analyses evidenced a positive relationship between MW intensity and memory consolidation in both experiments. Our findings suggest that transient interruption of input during a declarative learning session may favor memory consoli-dation at wake, partially independently of the attentional state.