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Found 6273 publications
Generational differences and patterns in climate anxiety symptoms among adolescents and young adults in Budapest, Hungary
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
A growing number of young people now perceive climate change as an existential threat rather than a distant environmental issue. This study investigates the relationship between environmental awareness and symptoms of climate anxiety among two generational groups – Generation Z (aged 28–29) and Generation Alpha (aged 14–15) – residing in Budapest, and fills an empirical gap in the Hungarian context regarding how urban youth segments process ecological distress. Data were collected via an anonymous online questionnaire, distributed between January 12 and March 31, 2025, through targeted social media groups. Screening questions limited participation to the target age groups. The survey covered environmental awareness, sustainable behaviors, and psychological and physical symptoms, resulting in 701 valid responses. Four hypotheses were tested using non-parametric statistical analyses, primarily Spearman’s rank correlation and Mann–Whitney U tests. All statistical analyses were conducted with SPSS software version 27, and selected calculations were cross-validated using the WolframAlpha analytics tool. The findings indicate that climate-related distress is a multidimensional phenomenon shaped by environmental attitudes, sex, and generational context. Among young adult males in Generation Z, higher levels of environmental engagement and knowledge were associated with a reduced sense of helplessness, suggesting that taking action can serve as a psychological protective factor. Sex-based comparisons revealed that female participants reported significantly higher levels of clinical symptoms, particularly anxiety, irritability, and nervousness. In contrast, male participants were more likely to report physical manifestations such as muscle pain. Generation Z females exhibited higher levels of apathy, whereas Generation Alpha females reported more acute nervousness. The study also highlights the critical link between mental health and environmental action, contributing to the broader framework of the United Nations Sustainable Development Goals (SDGs). These results underscore the importance of age- and sex-sensitive approaches when addressing the psychological impact of the climate crisis. The research emphasizes that promoting mental health (SDG 3) is viewed as an essential catalyst for effective climate action (SDG 13).
Open Access: Yes
Discovery of potential antiviral compounds and accelerating the therapeutic discovery against monkeypox virus
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Monkeypox virus is a zoonotic virus of the genus Orthopox viruses. It can be transmitted through direct or indirect contact with animals or infected ones. Owing similarity of pathogenesis with smallpox, the same drugs can be used for both viruses, but they are not specific and only help to relieve the symptoms only. Therefore, the absence of antiviral treatment or licensed vaccine highlights an urgent need, especially due to its rapid prevalence. The study screened the library of compounds to retrieve drug-like molecules that can act against monkeypox virus. The highly virulent target gene B8R having uniport ID Q3I8J0 was chosen. Targeting B8R is substantial for global health and can align with SDG 3 and awareness of disease management. The B8R was modelled via Artificial intelligence (AI) AlphaFold method and then exposed to a library of compounds. Complementary interactions in the active site were shown by molecular docking. The Complex-1 had the greatest binding affinity (–8.4 kcal/mol), followed by Complex-2 (–8.1 kcal/mol) and Complex-3 (–7.7 kcal/mol). After 125 ns, Complex-1 reached equilibrium at 7.5 Å RMSD, according to MD simulations, exhibiting stable ligand retention and reliable interactions with crucial residues Gly135 and Lys136. Complex-3 shown intermediate protein stability (6 Å RMSD) but notable ligand fluctuation (48 Å RMSF), while Complex-2 displayed increased protein RMSD (8 Å RMSD) and delayed ligand stabilisation (16 Å RMSF). These results were corroborated by PCA analysis, which showed that Complex-1 exhibits coherent structural development whereas Complex-2 and Complex-3 show scattered and compact trajectories, respectively. Complex-1 promise for Mpox viral inhibition was highlighted by the fact that it was the most stable and dynamically favourable contender overall. The N-terminal follows the folding trend. The insilico analysis not only proposed a potent compound but also provides deep insight into the behavior of protein. The proposed potent compound against this zoonotic virus can be helpful to combat the monkeypox virus by subjecting it further towards experimental investigation.
Open Access: Yes
Trends and insights from bibliometric analysis for mapping artificial intelligence and machine learning in sustainable development
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Rapid population growth, environmental degradation and persistent urgency of climate change have intensified the global search for sustainable development solutions. Governments, researchers and institutions alike face the challenge of balancing economic progress with social equity and environmental protection. In response, recent scholarships have increasingly turned to digital technologies as potential enablers of sustainable transformation. This study addresses the need to understand how artificial intelligence (AI) and machine learning (ML) are being incorporated into sustainable development strategies, with a particular focus on mapping knowledge trends and research patterns. Using bibliometric analysis of SCOPUS data spanning 2015 to 2024, the study uncovers the evolution of research topics, highlights influential authors and institutions, and traces the diffusion of ideas across disciplines. The findings reveal that AI and ML are emerging as key drivers of sustainability, with strong applications in energy and emission management, environmental monitoring, climate change mitigation, precision agriculture and water resource management. Research in this area has grown rapidly over the past decade, shifting from theory to real applications. It also highlights that China's and the United States dual dominance in both publication volume and citation impact, while also recognizing the contributions of other countries like India, the United Kingdom and Australia in shaping global research landscapes. Three main implications arise from these results. For policymakers, the evidence underscores the urgency of designing inclusive policies, investing in digital infrastructure, and fostering global cooperation to ensure the equitable distribution of technological benefits. For the research community, the study points to opportunities for cross-disciplinary collaborations that link technological innovation with real-world sustainability challenges. From a broader societal perspective, the findings emphasize the importance of knowledge sharing and technology transfer, enabling both developed and developing countries to advance collectively toward achieving the Sustainable Development Goals.
Open Access: Yes
Intelligent predictive neural network analysis of stefan blowing impacts on chemical reactive flow of Boger nanofluid with thermophoresis and brownian motion
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
This study scrutinizes the effect of thermal radiation and Stefan blowing on the chemical reactive flow of Boger nanofluid across a stretched sheet with Darcy Forchheimer medium and heat generation using an intelligent computational framework based on Artifice neural network—Bayesian regularization. Furthermore, Brownian motion and thermophoresis properties have been examined. The suggested model of how Stefan blowing affects the chemical reactive flow of a Boger nanofluid with thermophoresis effects and Brownian motion has useful applications in a number of industrial and engineering operations. In chemical reactors, nano-coating technologies, and polymer processing, this model is essential for improving heat and mass transport processes. While the Boger nanofluid model accurately depicts non-Newtonian behaviour pertinent to biofluids and complex lubricants, Stefan blowing consideration offers insights on evaporation or suction effects. For the purpose of maximizing nanoparticle dispersion in cooling systems, fuel cells, and medicinal devices like targeted drug delivery systems where exact control over particle motion and chemical reactivity is crucial, Brownian motion and thermophoresis are also critical. The velocity profile improves as the Stefan blowing parameter values rise, but the thermal and concentration profiles decrease.
Open Access: Yes
Effect of Spirulina platensis on the content values of wheat bread
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Due to their nutritional composition, algae are promising ingredients in the development of new foods. The aim of our work was to prepare bread containing Spirulina platensis (new name Arthrospira platensis) in different percentages (0.5, 1.0, 2.5%) within the framework of the MSZ 6369/8-1988 standard, and to determine its content (dry matter, ash, fat, protein/nitrogen, fiber content, carbohydrate content, and polyoxide, color), as well as its texture and color. Furthermore, we assessed consumer opinions through a sensory evaluation. We found that increasing the width and shape fraction while decreasing the height. The results showed that the antioxidant and polyphenols properties of Spirulina-enriched breads increased. The protein, nitrogen, fibre content increased and carbohydrate, energy value properties of Spirulina-enriched breads decreased with increasing concentration of algae. Spirulina powder increased the greenness of the bread and decreased the lightness of the crumb. The hardness, cohesiveness and springiness increased with the addition of Spirulina to bread, while the gumminess and chewiness values became lower compared to the control. Consumer acceptability results showed that the addition of Spirulina at a concentration of 2.5% significantly reduced overall acceptance. Our results indicated that Spirulina cyanobacteria, can be a suitable raw material for making bread, also from the point of view of healthier and sustainable nutrition.
Open Access: Yes
Ensemble deep learning approach for traffic video analytics in edge computing
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Video analytics is the new era of computer vision in identifying and classifying objects. Traffic surveillance videos can be analysed to using computer vision to comprehend the road traffic. Monitoring the real-time road traffic is essential to control them. Computer vision helps in identifying the vehicles on the road, but the present techniques either perform the video analysis on the cloud platform or the edge platform. The former introduces more delay in processing while controlling is needed in real-time, the latter is not accurate in estimating the current road traffic. YOLO algorithms are the most notable ones for efficient real-time object detection. To make such object detections feasible in lightweight environments, its tinier version called Tiny YOLO is used. Edge computing is the efficient framework to have its computation done on the edge of the physical layer without the need to move data into the cloud to reduce latency. A novel hybrid model of vehicle detection and classification using Tiny YOLO and YOLOR is constructed at the edge layer. This hybrid model processes the video frames at a higher rate and produces the traffic estimate. The numerical traffic volume is sent to Ensemble Learning in Traffic Video Analytics (ELITVA) which uses F-RNN to make decisions in reducing the traffic flow seamlessly. The experimental results performed on drone dataset captured at road signals show an increase in precision by 13.8%, accuracy by 4.8%, recall by 17.4%, F1 score by 19.9%, and frame rate processing by 12.8% compared to other existing traffic surveillance systems and efficient controlling of road traffic.
Open Access: Yes
Molecular characterisation of the invasive terrestrial nemertean Geonemertes pelaensis: long and complex mitogenome and presence of NUMTs
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
The complete mitochondrial genome of the invasive terrestrial nemertean Geonemertes pelaensis Semper, 1863 (Nemertea: Prosorhochmidae) was sequenced from two specimens collected in geographically distant French overseas territories—Martinique in the Caribbean and New Caledonia in the South-West Pacific. In both specimens, the mitogenome contained 13 protein-coding genes, two rRNA genes, and 21 tRNA genes, and was unusually large, approaching 32 kb. The two genomes differed by only four single nucleotide polymorphisms and one indel. A comparison with 22 cox1 sequences available in GenBank confirmed this high level of genetic conservation, suggesting a recent introduction from related source populations. The extraordinary length of the mitogenome was largely attributable to two extended regions comprising only tRNA genes and long intergenic sequences. These results were contrasted with data from an unpublished SRA sequencing project (SRS20559370) of an unlocalized specimen identified as G. pelaensis; its reconstructed mitogenome was only 18 kb in length (14 kb shorter) and showed extensive sequence divergence. Phylogenetic analyses placed this specimen as the sister lineage to G. pelaensis, highlighting the need for further investigation of this taxon. In the Martinique specimen, several NUMTs (nuclear mitochondrial pseudogenes) were also detected, which could complicate future studies relying solely on Sanger sequencing. Sequencing additionally revealed prey DNA from the gut contents of both worms: the New Caledonian specimen had consumed an unidentified noctuid moth, while the Martinique specimen had likely fed on the invasive cockroach Periplaneta australasiae (Fabricius, 1775), itself an introduced species.
Open Access: Yes
Sequential model predictive direct speed control of PMSM
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Finite control set model predictive control (FCS-MPC) has emerged as a powerful strategy for permanent magnet synchronous motor (PMSM) drives. However, its performance strongly depends on appropriately chosen weighting factors, which directly affect control quality and, in some cases, may even lead to instability. Despite the crucial role of weighting factors, there is no systematic or generally accepted procedure for selecting their values, which limits the robustness and practical applicability of conventional FCS-MPC methods. To overcome this limitation, this paper presents the experimental validation of a sequential direct speed predictive control strategy for PMSM. The individual cost functions are evaluated sequentially, thereby tuning is simplified and weighting factors are reduced. Experimental results show that the original version of sequential direct speed control, as proposed in the literature, exhibits promising dynamic performance but suffers from instability and current ripples under certain conditions. To address these issues, an enhanced version of the sequential direct speed predictive control is proposed in the paper. It effectively suppresses instabilities and enhances the speed dynamic response of the drive. The proposed approach was experimentally validated using the OP 5600 rapid control prototyping platform running RT-LAB software and a 1.1 kW PMSM machine.
Open Access: Yes
Acylglycerol Kinase 2-mediated Inhibition of Sirtuin 2 Restores AMPK/AKT/mTOR Signaling Balance in Podocytes: A Pharmacological Strategy for Diabetic Nephropathy
Publication Name: Iranian Journal of Pharmaceutical Research
Publication Date: 2026-12-01
Volume: 25
Issue: 1
Page Range: Unknown
Description:
Background: Diabetic nephropathy is a major cause of end-stage renal disease, driven in part by molecular dysfunctions in podocytes. Sirtuin 2 (Sirt2), a cytoplasmic NAD+-dependent deacetylase, has emerged as a potential regulator of key metabolic pathways, but its specific role in podocyte biology remains poorly defined. Objectives: This study aimed to investigate the function of Sirt2 in human podocytes (hPodo), delineate its interaction with histone deacetylase 6 (HDAC6), and evaluate the therapeutic potential of Sirt2 inhibition in restoring metabolic balance and protecting against diabetic nephropathy-associated podocyte stress. Methods: Comparative expression analysis was performed between hPodo and HEK293T kidney cells. Pharmacological inhibition of Sirt2 was carried out using acylglycerol kinase 2 (AGK2), alongside siRNA-mediated Sirt2 knockdown. AMPK/AKT/mTOR signaling activity was assessed by Western blotting and functional assays to determine metabolic and growth responses. Results: Human podocytes exhibited significantly elevated Sirt2 expression and high levels of HDAC6, forming a unique Sirt2–HDAC6 regulatory complex. Inhibition or silencing of Sirt2 induced robust AMPK activation while suppressing AKT/mTOR signaling. This signaling reprogramming restored energy sensing and attenuated hyperactive growth pathways, alleviating podocyte stress. Acylglycerol kinase 2 treatment reestablished metabolic homeostasis by disrupting Sirt2-mediated repression of AMPK. Conclusions: Sirtuin 2 inhibition, particularly through AGK2, emerges as a novel pharmacological strategy to protect podocytes, restore metabolic regulation, and potentially slow the progression of diabetic nephropathy. Significance Statement By inhibiting one of the important intracellular signaling pathways in human kidney cells, we could reduce the cellular stress that is commonly observed in diabetic kidney injury. This could serve as a drug target to slow the progression of kidney disease associated with diabetes mellitus.
Open Access: Yes
DOI: 10.5812/ijpr-165603
