Search Results (61,461)

Cystic echinococcosis is a zoonosis caused by the cestode Echinococcus granulosus sensu stricto. Population genetic studies and phylogeographic patterns are essential to understanding the transmission dynamics of this parasite under varying environmental conditions. In this study, the genetic diversity of E. granulosus s.s. was evaluated using 46 hydatid cyst samples obtained from sheep, goats, cattle, and humans across three regions of Chile: Coquimbo, La Araucanía, and Magallanes. Mitochondrial cox1 gene sequences were analyzed and compared with reference sequences reported from South America, Europe, Africa, Asia, and Oceania. In Chile, the EG01 haplotype was the predominant haplotype. A total of four haplotypes were identified, with low haplotype diversity (Hd = 0.461 ± 0.00637) and low nucleotide diversity (π = 0.00181 ± 0.00036). The haplotype network displayed a star-like configuration, with the EG01 genotype at the center, suggesting a potentially ancestral or widely distributed lineage. In Coquimbo (Tajima’s D = −0.93302, p = 0.061; Fu’s Fs = −0.003, p = 0.502) and Magallanes (Tajima’s D = −0.17406, p = 0.386; Fu’s Fs = −0.121, p = 0.414), both neutrality tests were non-significant, indicating no strong evidence for recent population expansion or selection. Star-like haplotype network patterns were also observed in populations from Europe, the Middle East, Asia, Africa, and Oceania, with the EG01 genotype occupying the central position. The population genetic structure of Echinococcus granulosus s.s. in Chile demonstrates considerable complexity, with EG01 as the predominant haplotype. Further comprehensive studies are required to assess the intraspecific genetic variability of E. granulosus s.s. throughout Chile and to determine whether this variability influences the key biological traits of the parasite. This structure may prove even more complex when longer fragments are analyzed, which could allow for the detection of finer-scale microdiversity among isolates from different hosts. We recommended that future cystic echinococcosis control programs take into account the genetic variability of E. granulosus s.s. strains circulating in each endemic region, to better understand their epidemiological, immunological, and possibly pathological differences. Full article

In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article

This research considers a preliminary comparative technical evaluation of two micro-gas turbine (MGT) systems in combined heat and power (CHP) mode (100 kWe), aimed at supplying heat to a residential community of 15 average-sized buildings located in Central Europe over a year. Two systems were modelled in Ebsilon 15 software: a natural gas case (benchmark) and an ammonia-fueled case, both based on the same on-design parameters. Off-design simulations evaluated performance over variable ambient temperatures and loads. Idealized, unrecuperated cycles were adopted to isolate the thermodynamic impact of the fuel switch under complete combustion assumption. Under these assumptions, the study shows that the ammonia system produces more electrical energy and less excess heat, yielding marginally higher electrical efficiency and EUF (26.05% and 77.63%) than the natural gas system (24.59% and 77.55%), highlighting ammonia’s utilization potential in such a context. Future research should target validating ammonia combustion and emission profiles across the turbine load range, and updating the thermodynamic model with a recuperator and SCR accounting for realistic pressure losses. Full article

Urban parks play an essential role in mitigating the urban heat island (UHI) effect driven by urbanization. A rigorous understanding of the cooling effects of urban parks can support urban planning efforts aimed at mitigating the UHI effect and enhancing urban sustainability. However, previous research has primarily focused on the maximum cooling impact, often overlooking the accumulative effects arising from spatial continuity. The present study fills this gap by investigating 74 urban parks located in the central area of Jinan and constructing a comprehensive cooling evaluation framework through two dimensions: maximum impact (Park Cooling Area, PCA; Park Cooling Efficiency, PCE) and cumulative impact (Park Cooling Intensity, PCI; Park Cooling Gradient, PCG). We further systematically examined the influence of park attributes and the surrounding urban structures on these metrics. The findings indicate that urban parks, as a whole, significantly contribute to lowering the ambient temperatures in their vicinity: 62.3% are located in surface temperature cold spots, reducing ambient temperatures by up to 7.77 °C. However, cooling intensity, range, and efficiency vary significantly across parks, with an average PCI of 0.0280, PCG of 0.99 °C, PCA of 46.00 ha, and PCE of 5.34. For maximum impact, PCA is jointly determined by park area, boundary length, and shape complexity, while smaller parks generally exhibit higher PCE—reflecting diminished cooling efficiency at excessive scales. For cumulative impact, building density and spatial enclosure degree surrounding parks critically regulate PCI and PCG by influencing cool-air aggregation and diffusion. Based on these findings, this study classified urban parks according to their cooling characteristics, clarified the functional differences among different park types, and proposed targeted recommendations. Full article

(1) Insulin-like growth factor-1 (IGF-1) is a neurotrophin with anti-inflammatory properties. Neuroinflammation and stress activate peripheral immune mechanisms, which may contribute to the development of depression and anxiety in sporadic Alzheimer’s disease (sAD). This study aims to evaluate whether intracerebroventricular (ICV) premedication with IGF-1 in a rat model of streptozotocin (STZ)-induced neuroinflammation can prevent the emergence of anhedonia and anxiety-like behavior by impacting the peripheral inflammatory responses. (2) Male Wistar rats were subjected to double ICVSTZ (total dose: 3 mg/kg) and ICVIGF-1 injections (total dose: 2 µg). We analyzed the level of anhedonia (sucrose preference), anxiety (elevated plus maze), peripheral inflammation (hematological and cytometric measurement of leukocyte populations, interleukin (IL)-6), and corticosterone concentration at 7 (very early stage, VES), 45 (early stage, ES), and 90 days after STZ injections (late stage, LS). (3) We found that ICVIGF-1 administration reduces behavioral symptoms: anhedonia (ES and LS) and anxiety (VES, ES), and peripheral inflammation: number of leukocytes, lymphocytes, T lymphocytes, monocytes, granulocytes, IL-6, and corticosterone concentration (LS) in the rat model of sAD. (4) The obtained results demonstrate beneficial effects of central IGF-1 administration on neuropsychiatric symptoms and peripheral immune system activation during disease progression in the rat model of sAD. Full article

This study investigates mechanisms that influence how users perceive a technology’s usefulness after adoption as they continue to use the technology. The Expectation Confirmation Model (ECM) has been widely used to examine the key drivers of IT continuance, emphasizing perceived usefulness as a central factor. Although researchers have explored factors, such as ease of use, trust, and site quality, affecting post-adoption perceived usefulness, the mechanisms shaping post-adoption perceived usefulness remain underexplored. This study proposes that post-adoption perceived usefulness is shaped through the actualization of the technology’s affordances. Using a survey focused on fitness app usage (e.g., Fitbit), we examined various affordances users actualize and whether actualization of an affordance shapes their perception of usefulness. Results show that some affordances are actualized widely by most users (e.g., exercise status updating) while others are actualized by fewer users (e.g., reminders to exercise or guiding users how to exercise). Moreover, when an affordance is widely actualized, it significantly influences users’ perceptions of usefulness within the ECM framework. Given that perceived usefulness is a key factor in predicting IT continuance, our findings contribute to the literature by highlighting the influence of actualized affordances on perceptions of usefulness and hence IT continuance. Full article

Internal fins are commonly utilized as a passive technique to enhance natural convection, but their efficiency depends on complex interplay between fin design, material properties, and convective strength. This study presents an extensive numerical analysis of buoyancy-driven flow in square cavities containing a single horizontal fin on the hot wall. Over 9000 simulations were conducted, methodically varying the Rayleigh number (Ra = 10 to 10⁵), Prandtl number (Pr = 0.1 to 10), and fin characteristics, such as length, vertical position, thickness, and the thermal conductivity ratio (up to 1000), to assess their overall impact on thermal efficiency. Thermal enhancements compared to scenarios without fins are quantified using local and average Nusselt numbers, as well as a Nusselt number ratio (NNR). The results reveal that, contrary to conventional beliefs, long fins positioned centrally can actually decrease heat transfer by up to 11.8% at high Ra and Pr due to the disruption of thermal plumes and diminished circulation. Conversely, shorter fins located near the cavity’s top and bottom wall edges can enhance the Nusselt numbers for the hot wall by up to 8.4%, thereby positively affecting the development of thermal boundary layers. A U-shaped Nusselt number distribution related to fin placement appears at Ra ≥ 10³, where edge-aligned fins consistently outperform those positioned mid-height. The benefits of high-conductivity fins become increasingly nonlinear at larger Ra, with advantages limited to designs that minimally disrupt core convective patterns. These findings challenge established notions regarding passive thermal enhancement and provide a predictive thermogeometric framework for designing enclosures. The results can be directly applied to passive cooling systems in electronics, battery packs, solar thermal collectors, and energy-efficient buildings, where optimizing heat transfer is vital without employing active control methods. Full article

The 1.9 billion metric tons of steel globally manufactured in 2023 justify the steel industry’s pivotal role in modern society’s growth. Considering the rapid development of countries that have not fully taken part in the global market, such as Africa, steel production is expected to increase in the next decade. However, the environmental burden associated with steel manufacturing must be mitigated to achieve sustainable production, which would align with the European Green Deal pathway. Such a burden is associated both with the GHG emissions and with the solid residues arising from steel manufacturing, considering both the integrated and electrical routes. The valorisation of the main steel residues from the electrical steelmaking is the central theme of this work, referring to the steel electric manufacturing in the Dalmine case study. The investigation was carried out from two different points of view, comprising the action of a plasma electric reactor and a RecoDust unit to optimize the recovery of iron and zinc, respectively, being the two main technologies envisioned in the EU-funded research project ReMFra. This work focuses on those preliminary steps required to detect the optimal recipes to consider for such industrial units, such as thermodynamic modelling, testing the mechanical properties of the briquettes produced, and the smelting trials carried out at pilot scale. However, tests for the usability of the dusty feedstock for RecoDust are carried out, and, with the results, some recommendations for pretreatment can be made. The outcomes show the high potential of these streams for metal and mineral recovery. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

This study investigates the concept of landscape services (LS), which integrate environmental and sociocultural dimensions of sustainable development. Recognizing landscapes as essential to daily life and well-being, the research aims to support sustainable spatial planning by analyzing both their potential and their actual use. The study has three main objectives: (1) to assess the potential of 16 selected landscape types to provide six key LS through expert evaluation; (2) to determine actual LS usage patterns among the local community (residents); and (3) to identify agreements and discrepancies between expert assessments and resident use. The services analyzed include providing space for daily activities; regulating spatial structure through diversity and compositional richness; enhancing physical and mental health; enabling passive and active recreation; supporting personal fulfillment; and fostering social interaction. Expert-based surveys and participatory mapping with residents were used to assess the provision and use of LS. The results indicate consistent evaluations for forest and historical urban landscapes (high potential and use) and mining and transportation landscapes (low potential and use). However, significant differences emerged for mountain LS, rated highly by experts but used minimally by residents. These insights highlight the importance of aligning expert planning with community needs to promote sustainable land use policies and reduce spatial conflicts. Full article

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

Physical activity, particularly when practiced in natural settings, has well-established benefits for overall health, sleep, and body composition. These effects are especially important for postmenopausal women, although research specifically targeting this population remains limited. The study evaluated a 16-week multicomponent outdoor exercise program (cardiorespiratory, strength, balance, coordination, and flexibility training) in postmenopausal women, consisting of three 60 min sessions per week. Participants were non-randomly assigned to an experimental group (EG, n = 55) and a control group (CG, n = 20). Measurements were taken at baseline and after 16 weeks, including body composition, sleep (duration and quality), and connection with nature. No significant differences were observed between groups at baseline. After the intervention, the EG and CG presented significant differences (p ≤ 0.01) in the rates of change in body mass, fat mass (FM; −9.26% and −1.21%, respectively), and visceral fat level (VFL; −13.46 points and −3.80 points). These differences were also observed for the sleep fragmentation index (p ≤ 0.01), but not for connection with nature. A significant interaction effect (p < 0.01) of time × group was observed for %FM, VFL, and appendicular skeletal muscle mass. Exercise duration had an effect (p = 0.043) on participants’ personal and affective identification with nature, and the time × group × medication interaction significantly influenced sleep efficiency (p = 0.034). The exercise program proved effective in reducing total and central adiposity levels; however, it did not lead to improvements in sleep duration, sleep quality, or connection with nature. Full article

Neural networks (NNs) achieve breakthroughs in computer vision and natural language processing,yet their “black box” nature persists. Traditional methods prioritise parameter optimisation and loss design, overlooking NNs’ fundamental structure as topologically organised nonlinear computational systems. This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. To overcome limitations of heuristic compression techniques, we develop a topology-sensitive adaptive pruning algorithm that evaluates critical paths via node strength centrality, preserving structural–functional integrity. On CIFAR-10, our method achieves 55.5% parameter reduction with only 7.8% accuracy degradation—significantly outperforming traditional approaches. Crucially, retrained pruned networks exceed original model accuracy by up to 2.63%, demonstrating that topology optimisation unlocks latent model potential. This research establishes a paradigm shift from empirical to topologically rationalised neural architecture design, providing theoretical foundations for deep learning optimisation dynamics. Full article

Genomic islands (GIs) including integrative and conjugative elements (ICEs), prophages, and integrative plasmids are central drivers of horizontal gene transfer in bacterial plant pathogens. These elements often carry cargo genes encoding virulence factors, antibiotic and metal resistance determinants, and metabolic functions that enhance environmental adaptability. In plant-pathogenic species such as Pseudomonas syringae, GIs contribute to host specificity, immune evasion, and the emergence of novel pathogenic variants. ICEclc and its homologs represent integrative and mobilizable elements whose tightly regulated excision and transfer are driven by a specialized transcriptional cascade, while ICEs in P. syringae highlight the ecological impact of cargo genes on pathogen virulence and fitness. Pathogenicity islands further modulate virulence gene expression in response to in planta stimuli. Beyond P. syringae, GIs in genera such as Erwinia, Pectobacterium, and Ralstonia underpin critical traits like toxin biosynthesis, secretion system acquisition, and topoisomerase-mediated stability. Leveraging high-throughput genomics and structural biology will be essential to dissect GI regulation and develop targeted interventions to curb disease spread. This review synthesizes the current understanding of GIs in plant-pathogenic gammaproteobacteria and outlines future research priorities for translating mechanistic insights into sustainable disease control strategies. Full article

The oral–gut microbiota axis is a relatively new field of research. Although most studies have focused separately on the oral and gut microbiota, emerging evidence has highlighted that the two microbiota are interconnected and may influence each other through various mechanisms shaping systemic health. The aim of this review is therefore to provide an overview of the interactions between oral and gut microbiota, and the influence of diet and related metabolites on this axis. Pathogenic oral bacteria, such as Porphyromonas gingivalis and Fusobacterium nucleatum, can migrate to the gut through the enteral route, particularly in individuals with weakened gastrointestinal defenses or conditions like gastroesophageal reflux disease, contributing to disorders like inflammatory bowel disease and colorectal cancer. Bile acids, altered by gut microbes, also play a significant role in modulating these microbiota interactions and inflammatory responses. Oral bacteria can also spread via the bloodstream, promoting systemic inflammation and worsening some conditions like cardiovascular disease. Translocation of microorganisms can also take place from the gut to the oral cavity through fecal–oral transmission, especially within poor sanitary conditions. Some metabolites including short-chain fatty acids, trimethylamine N-oxide, indole and its derivatives, bile acids, and lipopolysaccharides produced by both oral and gut microbes seem to play central roles in mediating oral–gut interactions. The complex interplay between oral and gut microbiota underscores their crucial role in maintaining systemic health and highlights the potential consequences of dysbiosis at both the oral and gastrointestinal level. Some dietary patterns and nutritional compounds including probiotics and prebiotics seem to exert beneficial effects both on oral and gut microbiota eubiosis. A better understanding of these microbial interactions could therefore pave the way for the prevention and management of systemic conditions, improving overall health outcomes. Full article