Search Results (2,319)

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. This study proposes a predictive framework that combines validated CFD simulations with deep learning techniques to estimate key combustion and emission parameters in a methanol–diesel dual-fuel engine. A three-dimensional CFD model was developed to simulate turbulent combustion, methanol injection, and pollutant formation, using the RNG k-ε turbulence model. A temporal dataset consisting of 1370 samples was generated, covering the compression, combustion, and early expansion phases—critical regions influencing both emissions and in-cylinder pressure dynamics. The optimal configuration identified involved a 63° spray injection angle and a 25% methanol proportion. A Gated Recurrent Unit (GRU) neural network, consisting of 256 neurons, a Tanh activation function, and a dropout rate of 0.2, was trained on this dataset. The model accurately predicted in-cylinder pressure, temperature, NOx emissions, and impact-related parameters, achieving a Pearson correlation coefficient of ρ = 0.997. This approach highlights the potential of combining CFD and deep learning for rapid and reliable prediction of engine behavior. It contributes to the development of more efficient, cleaner, and robust design strategies for future dual-fuel combustion systems. Full article

Synthetic data has become an increasingly important tool for semantic segmentation, where collecting large-scale annotated datasets is often costly and impractical. Prior work has leveraged computer graphics and game engines to generate training data, but many pipelines remain limited to single modalities and constrained environments or require substantial manual setup. To address these limitations, we present a fully automated pipeline built within Unreal Engine 5 (UE5) that procedurally generates diverse, labeled environments and collects multimodal visual data for semantic segmentation tasks. Our system integrates UE5’s biome-based procedural generation framework with a spline-following drone actor capable of capturing both RGB and depth imagery, alongside pixel-perfect semantic segmentation labels. As a proof of concept, we generated a dataset consisting of 1169 samples across two visual modalities and seven semantic classes. The pipeline supports scalable expansion and rapid environment variation, enabling high-throughput synthetic data generation with minimal human intervention. To validate our approach, we trained benchmark computer vision segmentation models on the synthetic dataset and demonstrated their ability to learn meaningful semantic representations. This work highlights the potential of game-engine-based data generation to accelerate research in multimodal perception and provide reproducible, scalable benchmarks for future segmentation models. Full article

University students are subject to various demands in their role as academics. Such pressures tend to amplify emotional distress, making them more susceptible to mental health hazards. This study investigates the influence of enclosed courtyards on students’ mental health within educational facilities, focusing on their distinct spatial configurations, such as semi-open layouts and vegetation cover, as well as their effects on intellectual functioning and well-being. The research used electroencephalography (EEG) to examine brainwave activity and quantify the influence of the spatial design of enclosed courtyards on the mental and emotional well-being of students. An experiment with 16 students and EEG measurements was conducted in the Faculty of Engineering courtyard at Egypt’s Ain Shams University in Cairo, providing 60–70% statistical power to detect medium effect sizes (Cohen’s d = 0.5, α = 0.05), which is sufficient for exploratory research. The study explores the psychophysiological implications of the brain’s electrical signals as neurological measurements, such as alpha and theta brainwaves, in order to assess individuals’ relaxation, restoration, and attention levels. The findings show that natural characteristics of the courtyard, expansive space, and visual stimuli have a significant effect on restoration and attention. While the sample size is limited and the design is context-specific, the results provide preliminary evidence that meticulously designed enclosed courtyards can improve students’ mental well-being. These findings invite further multi-site validation to assess generalizability. This study contributes to the expanding domain of neurolandscape” by demonstrating the interplay between built environments and mental health in educational contexts. Full article

Microbial Induced Carbonate Precipitation (MICP) is regarded as a promising eco-friendly alternative to traditional Portland cement for soil stabilization. However, the feasibility of applying bio-cemented soil as a road base material remains inadequately studied, particularly in terms of the relationships between MICP treatment parameters—such as solution content, curing age, and the ratio of bacterial solution (BS) to cementation solution (CS) —and key mechanical and durability properties under realistic road conditions. In this study, an optimal curing condition for bio-cemented sand was first determined through unconfined compression strength (UCS) tests and calcium carbonate content (CCC) determination. Subsequently, dynamic triaxial tests were conducted to evaluate its resistance to cyclic loading. Further road performance tests, including splitting tensile strength, freeze-thaw resistance, temperature shrinkage, and arch expansion assessments, were carried out to comprehensively evaluate the material’s applicability. Scanning electron microscopy (SEM) was employed to elucidate the microstructural mechanisms underlying strength development. The results show that the strength (4.28 MPa) of bio-cemented sand cured under optimal conditions (12% bio-cured solution content, a BS-to-CS ratio of 1:4 and 7-d curing age) satisfies the criteria for road base applications. MICP treatment significantly improved the dynamic properties of aeolian sand (AS), reducing the cumulative plastic axial strain (ε_p) by nearly 11–46% and increasing the dynamic elastic modulus (E_d) by approximately 7–31% compared to untreated sand. The material also demonstrates satisfactory performance across all four road performance metrics. Microstructural analysis reveals enhanced interparticle bonding due to calcium carbonate precipitation, with samples prepared near the optimum moisture content exhibiting superior integrity and strength. Overall, bio-cemented sand demonstrates excellent potential as a sustainable road base material. These findings provide a theoretical foundation for practical applications of similar bio-cemented soils in road engineering. Full article

Background: Hepatocellular carcinoma (HCC) remains a major contributor to global cancer mortality. Increasing evidence suggests that the gut microbiota is a key modulator of liver disease progression and a promising source of non-invasive biomarkers. However, regional disparities in microbial profiles, influenced by ethnicity, diet, and environment, limit the generalizability of the existing data. This study aimed to characterize gut microbiota alterations in Thai patients with advanced untreated HCC compared to healthy individuals. Method: Fecal samples from 27 untreated Thai advanced HCC patients were collected, and data from 31 healthy individuals retrieved from a previous study were employed. Gut microbiota profiles were analyzed using 16S rRNA gene sequencing. Results: Alpha diversity was significantly reduced in HCC patients (p < 0.001). At the phylum level, Proteobacteria and Firmicutes were enriched in HCC, whereas Actinobacteria were more abundant in controls. Proteobacteria and Bacteroidota levels were positively correlated with serum alpha-fetoprotein (AFP) levels, whereas Firmicutes were negatively correlated with ALBI scores. Conclusions: Thai patients with advanced HCC exhibited distinct gut microbial signatures, characterized by dysbiosis and expansion of Proteobacteria. These findings support the role of the gut microbiota as a potential non-invasive biomarker for disease severity and prognosis in HCC, underscoring the importance of population-specific microbial studies. Full article

The introduction of alien insect species is increasingly facilitated by global plant trade, particularly through the movement of ornamental plants. Takecallis nigroantennatus, a host-specific aphid associated with cold-hardy Fargesia bamboo, has recently expanded its range in Europe. To examine its invasion dynamics, we conducted a population-level survey across 13 locations in six countries, sampling individuals from botanic and private gardens, specialized bamboo nurseries, garden centers, and urban horticultural environments in the UK, Belgium, The Netherlands, Germany, Poland, and Norway. A total of 117 specimens were analyzed using mitochondrial COI sequences, revealing a single dominant haplotype without geographic structure based on Bayesian and Maximum Likelihood phylogenetic analyses. This striking genetic uniformity indicates a narrow introduction bottleneck, suggesting a single or highly restricted introduction event followed by clonal spread. Despite the species’ ability for sexual reproduction, the data support a founder effect and rapid recent expansion closely linked to the introduction history of Fargesia in Europe. The results are also consistent with a possible time lag between the arrival of ornamental bamboo and the subsequent establishment of its associated herbivore, a scenario that warrants further investigation. Importantly, our study provides a practical framework for applied monitoring and early detection in bamboo nurseries, botanical gardens, and other high-risk introduction sites, illustrating how molecular tools can inform biosecurity and the management of emerging invasive species. Full article

Agricultural productivity is closely linked to the spatial variability of soil physical properties. However, high variability makes it difficult to implement effective management strategies, and the constant expansion of eucalyptus plantations in certain areas alters the soil’s physical properties. This study conducted a geostatistical analysis of the physical properties of a soil in Sogamoso, Boyacá (Colombia), which contains areas with different management practices and vegetation cover, among which the presence of Eucalyptus globulus stands out. Ninety-seven points were sampled in an area of 29.1 ha, with multiple land uses. The data were analyzed using descriptive statistics and geostatistical analysis, which determined the semivariogram parameters, the degree of spatial dependence, and the best-fitting interpolation model for mapping. A correlation analysis between variables was also performed. Analysis of variance showed no significant differences among vegetation covers (dense forest, grass-crop mosaic, weedy grassland, and crop mosaic), indicating structural homogeneity. The hydraulic conductivity (K_sat) had the highest coefficient of variation (CV), at 141.9%, while particle density had the lowest CV, at 9.25%. K_sat (exponential model, range = 207 m) and porosity (spherical model, range = 98 m) showed a strong spatial dependence. K_sat was lower in areas with eucalyptus (0.01 to 0.2 m day⁻¹), attributed to hydrophobicity induced by organic compounds emitted by these plantations. Soil moisture contents showed lower values in areas with eucalyptus, corroborating their high water consumption. Soil aggregates were lower when eucalyptus plantations were on slopes greater than 15%. Porosity showed an inverse correlation with apparent density (r² = −0.86). Full article

Losses resulting from food processing have encouraged the search for sustainable alternatives for the use of agro-industrial by-products. Melon is a fruit widely consumed in Brazil, but during its processing, by-products are generated, such as peels. This study utilized yellow melon peels to produce flour and incorporated it into the formulation of vegan and gluten-free muffins. Three muffin formulations were processed, with the partial replacement of rice flour by melon peel flour (MPF): Control (0%), F10 (10%), and F20 (20%). The flour and muffins were characterized according to their physicochemical properties. The muffins were evaluated based on color, texture, expansion factor, height, specific volume, and sensory acceptance. Considering the fiber and ash content (17.38 g/100 g and 10.14 g/100 g, respectively), MPF can be classified as a food with “high fiber and mineral content”. The specific volume of the muffins ranged from 1.403 to 1.756 cm³/g, with the lowest value found for the muffins made with the highest amount of MPF (F20). The muffins with 20% MPF had the lowest expansion factor (1.213 mm) due to the higher amount of fiber (4.45%). The most accepted sample was the F10 formulation (7.21), with an acceptance index of 88.88%. Full article

Objective: In this paper, we propose a novel approach to heart rate (HR) detection based on the evaluation of oscillatory patterns of continuous wavelet transform as a method of time-frequency analysis. HR detection based on electrocardiogram (ECG) or photoplethysmogram (PPG) signals can be performed using the same technique. Methods: The developed approach was tested on ECG (lead V1) and PPG (standard recording on the ring finger of the left hand and differential signal) for 10 min in 40 generally healthy volunteers (aged 26.8 ± 3.22 years). A comparison was made with the traditional HR detection method based on R-peak shape analysis. Results: Based on a number of statistical evaluations, the comparison yielded an acceptable degree of agreement between the results of the proposed method and the traditional method (the discrepancy between the results did not exceed 3.41%). The distortion of the signal shape and its noise do not affect the quality of HR detection by the proposed method; so, additional filtering or changes in the implemented algorithm are not required, as demonstrated by processing both the differential PPG signal and the PPG signals recorded during the patient’s walking. Conclusions: The proposed method allows obtaining HR information with a higher equidistant sampling frequency and expansion of the information on the frequency composition of HRV. Full article

Soil samples of different tree ages from the core area and expansion area of Korla City were selected to determine their nutrients and yield, and the analysis was combined with a Principal Component Analysis (PCA) biplot. The soil fertility and yield in the core area were superior to those in the expansion area. PCA biplot analysis showed that the cumulative variance contribution rate of the principal components of the orchard with a tree age of 10–20 years was 80.60%. PC1 had strong positive loadings for calcium, available phosphorus, organic matter, total nitrogen, and yield, and a strong negative loading for pH. PC2 had strong loadings for manganese, zinc, copper, selenium, and iron, as well as for magnesium, boron, available nitrogen, and electrical conductivity. For the core area, soil conditions need to be maintained. For the expansion area, salinization should be addressed; the input of Mg and B should be controlled; and the application of calcium, phosphorus fertilizers, and organic fertilizers should be increased to improve production and quality. Full article

Palestinian female students from East Jerusalem face multiple forms of discrimination as Palestinians in Israeli universities, as women in traditional society, and as residents of a politically contested area. These intersecting challenges create unique difficulties that traditional educational systems cannot adequately address. Despite worldwide higher education expansion, severe inequalities persist, particularly affecting students with multiple disadvantaged identities who face institutional barriers designed to maintain disparities. This study examines how Palestinian women transform structural challenges into opportunities for empowerment within Israeli higher education. This qualitative research used in-depth semi-structured interviews with 40 Palestinian female students from East Jerusalem enrolled in Israeli institutions. Participants were recruited through snowball sampling until data saturation was achieved. An interview analysis examined educational choices, institutional navigation strategies, experiences of discrimination, cultural identity preservation, and career planning approaches using a thematic methodology. Seven interconnected themes emerged, highlighting empowerment strategies: strategic educational choice-making, institutional navigation skills, academic resilience development, cultural identity preservation, intercultural bridge-building, community-oriented career planning, and the development of critical political consciousness. The findings demonstrate how these women utilize community cultural strengths to succeed in environments not designed for their advancement, positioning themselves as institutional change agents rather than passive recipients of support. This research contributes to diversity science by revealing how intersectional marginalization creates distinctive forms of empowerment, providing insights for developing antiracist teaching approaches that recognize the cultural assets that multiply-marginalized students bring to higher education. Full article

To ensure the safety and quality of grain during storage requires distributed monitoring of temperature and relative humidity within the bulk material, where hundreds of sensors may be needed. Conventional multi-channel systems are often constrained by the limited number of sensors connectable to a single acquisition unit, high hardware cost, and poor scalability. To address these challenges, this study proposes a novel design method for a multi-channel temperature and relative humidity acquisition system (MTRHAS). The system integrates sequential sampling control and a time-division multiplexing mechanism, enabling efficient data acquisition from multiple sensors while reducing hardware requirements and cost. This system employs sequential sampling control using a single complex programmable logic device (CPLD), and uses multiple CPLDs for multi-channel sensor expansion with a shared address and data bus for communication with a microcontroller unit (MCU). A prototype was developed using two CPLDs and one MCU, achieving data collection from 80 sensors. To validate the approach, a simulated grain silo experiment was conducted, with nine sensors deployed to monitor temperature and relative humidity during aeration. Calibration ensured sensor accuracy, and real-time monitoring results revealed that the system effectively captured spatial and temporal variation patterns of intergranular air conditions. Compared with conventional designs, the proposed system shortens the sampling cycle, decreases the number of acquisition units required, and enhances scalability through the shared bus architecture. These findings demonstrate that the MTRHAS provides an efficient and practical solution for large-scale monitoring of grain storage environments. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

Lance nematodes, Hoplolaimus spp., feed on the roots of many kinds of plants, including agronomic crops. In this study, morphological and molecular analyses of several Hoplolaimus species and populations are provided. We were able to collect and characterize the topotype materials of H. galeatus from Arlington, Virginia; H. stephanus syn. n. from Nichols, South Carolina; and H. concaudajuvencus from Pensacola, Florida, and several additional populations and species from the United States, Israel, and India. Phylogenetic analyses of several hundred sequences of the D2–D3 expansion regions of 28S rRNA, ITS rRNA, and COI genes of Hoplolaimus species obtained from published and original datasets were given. Fifty-three new D2–D3 of 28S rRNA, 43 new ITS rRNA, and 47 new COI sequences from 23 isolates of Hoplolaimus spp. and one isolate of Peltamigratus christiei were obtained in this study. New molecular identities for H. concaudajuvencus and H. galeatus were proposed. Hoplolaimus stephanus syn. n. was considered a synonym of H. galeatus based on the morphological and molecular similarity of these two species. Analysis of morphology and molecular data did not reveal significant differences among H. columbus syn. n., H. indicus syn. n., and H. seinhorsti, and the first two species were synonymized with H. seinhorsti. A new species, H. floridensis sp. n., was described from many locations in Florida, USA. It was separated from other representatives of the genus Hoplolaimus by its morphological and molecular characteristics. Maps with geographical distribution of several lance nematode species in North America were reconstructed based on published and original molecular identification of samples. Full article

The circular economy (CE) aims to reduce environmental degradation by ensuring the continuous use of materials and energy resources, aligning with the decarbonization agenda. However, despite the rising acceptance of CE concepts, the economic and managerial aspects remain underexplored in policy and practice. Therefore, this study seeks to bridge the knowledge–practice gap by studying how technology-driven innovation, renewable energy, and energy efficiency interact with CE principles to advance sustainable environmental connections in a detailed manner. The economic analysis of this study was conducted using two base and moderation models, utilizing global data from 78 developing and developed countries, and applying Fixed Effect, Random Effect, and Generalized Method of Moments estimates. The samples were selected based on data availability from internationally recognized databases from 2010 to 2021. The key findings suggest that technology-driven innovation and renewable energy reduce carbon emissions, whereas gross domestic product (GDP) growth and energy efficiency show no standalone positive effects. Notably, moderation effects reveal that the integration of technology with GDP promotes sustainability outcomes, but energy efficiency and renewable energy interact negatively with emissions, a contradictory result warranting further policy investigation. CE-driven innovation promotes decarbonization by striking a balance between environmental preservation, economic expansion, and technology uptake. This study emphasizes region-specific techniques and offers policy insights for combining the CE with natural capital and green GDP. It increases the knowledge of how circular business models powered by technology support sustainable growth and the shift to a circular economy. Full article