Search Results (57,646)

Pavement damage classification is crucial for road maintenance and driving safety. However, restricted to the varying scales, irregular shapes, small area ratios, and frequent overlap with background noise, traditional methods struggle to achieve accurate recognition. To address these challenges, a novel pavement damage classification model is designed based on the MambaOut named Multi-scale Damage Enhancement MambaOut (MDEM). The model incorporates two key modules to improve damage classification performance. The Multi-scale Dynamic Feature Fusion Block (MDFF) adaptively integrates multi-scale information to enhance feature extraction, effectively distinguishing visually similar cracks at different scales. The Damage Detail Enhancement Block (DDE) emphasizes fine structural details while suppressing background interference, thereby improving the representation of small-scale damage regions. Experiments were conducted on multiple datasets, including CQU-BPMDD, CQU-BPDD, and Crack500-PDD. On the CQU-BPMDD dataset, MDEM outperformed the baseline model with improvements of 2.01% in accuracy, 2.64% in precision, 2.7% in F1-score, and 4.2% in AUC. The extensive experimental results demonstrate that MDEM significantly surpasses MambaOut and other comparable methods in pavement damage classification tasks. It effectively addresses challenges such as varying scales, irregular shapes, small damage areas, and background noise, enhancing inspection accuracy in real-world road maintenance. Full article

The Strait of Sicily represents a biogeographically rich and complex region. The diverse geological origin and past continental connection of its islands have shaped a highly heterogeneous fauna, mainly composed of both African and European taxa. The Italian sea-slater, Ligia italica (Fabricius, 1798), is a small isopod inhabiting rocky shores of the Mediterranean Sea, Black Sea, and Atlantic Ocean. Despite its wide distribution, the phylogeography of this species is poorly understood, with limited available data suggesting a remarkable level of cryptic diversity. In this study, we investigated the mitochondrial genetic diversity (COX1) of L. italica across nine Italian and Maltese islands across the Strait of Sicily, aiming to clarify the biogeographic patterns underlying the distribution of these insular populations. Our results reveal an unexpectedly high genetic diversity within our study area, with eight different haplogroups, each characterized by low internal genetic variation and mutual distances ranging from 5.5% to 17.9%. These values are comparable to those associated with species-level rank within the genus Ligia. Overall, the phylogenetic relationships between the lineages appear well supported; however, the same relationships are not clearly correlated with geographic proximity or connectivity among the sampled localities. The distribution patterns of some of the detected haplogroups suggest possible passive dispersal mechanisms (e.g., rafting), while others indicate more intricate biogeographic scenarios. The overall diversity of L. italica within the Strait of Sicily, as well as the unclear origin of some insular populations, cannot be fully explained with the current data. In particular, the high genetic structure observed within the Maltese Archipelago, may partially reflect human-mediated dispersal (e.g., maritime transport), possibly involving source populations that remain unsampled or genetically uncharacterized. Our results highlight that the Strait of Sicily can be considered a diversity hot spot for L. italica and support the designation of this taxon as a putative species complex, with a cryptic diversity worthy of an exhaustive taxonomic revision. Full article

The Indian Ocean is globally significant in terms of capture fisheries, and understanding the species composition of fish in the Indian Ocean is of great importance for the protection and development of its fishery resources. While coastal fish communities in the Indian Ocean are relatively well-documented, studies on pelagic zones remain sparse, especially for non-target species constituting fishery bycatch. Traditional biodiversity surveys rely on labor-intensive, inefficient trawling methods. To address these limitations, this study aims to apply environmental DNA (eDNA) metabarcoding for a species diversity survey in the Western Indian Ocean, offering a more reliable, efficient, and non-invasive alternative to traditional methods. The results will provide important insights into the region’s fish biodiversity, supporting sustainable management and conservation of fisheries resources in the area. Samples were collected from 130 stations in different water layers in the Western Indian Ocean, and species diversity was analyzed through 12S rRNA gene amplicon sequencing. The results showed that 98 fish species were detected from 176 seawater eDNA samples, belonging to two classes (Actinopteri and Chondrichthyes), 20 orders, 35 families, and 60 genera. Within a depth range of 300 m, there were no significant differences in species diversity parameters among samples from different depths. The orders with the highest relative abundance detected include Scombriformes, Aulopiformes, and Myctophiformes. The species with the highest relative abundance include Thunnus albacares, Alepisaurus ferox, Xiphias gladius, Diaphus fragilis, Decapterus macarellus, Thunnus maccoyii, and Platycephalus cultellatus. The species composition and relative abundance of economic species observed in this study showed, as expected, differences from fishery catch statistics. These results suggest that eDNA technology can not only monitor marine fish diversity more efficiently but also complement the lack of fisheries data. Integrating eDNA technology into routine monitoring in the Western Indian Ocean in the future could promote sustainable management of fisheries resources in the region. Full article

Introduction: Traumatic brain injury (TBI) is a significant global public health issue, with long-term impacts on patients. This study examines the relationship between TBI severity, as measured by the Abbreviated Injury Scale (AIS) and the Injury Severity Score (ISS) at admission, and various sociodemographic, clinical, and injury-related factors. Methods: We conducted a retrospective analysis using data from 164 adult TBI patients. All were admitted between March 2020 and June 2023 to two Eastern European tertiary hospitals. Variables included sex, age, education, employment, marital status, injury type and cause, place of injury, and clinical measures such as the Marshall score, AIS, and ISS. Statistical methods included Pearson’s Chi-squared, Fisher’s exact, Spearman correlation, Wilcoxon, and Kruskal–Wallis tests. Results: Most patients were male (65.9%), retired (59.8%), and urban residents (73.8%), with a mean age of 64.98 years. The most frequent mechanism of injury was falls (76.2%), typically occurring at home (61%). The predominant injury type was closed head trauma (93.3%). Most patients had mild AIS scores (75%), and the mean ISS was 6.52 (SD: 4.55). Statistically significant group differences were found for AIS among categories of Modified Marshall Score, injury type, and education categories and for ISS among categories of the Modified Marshall Score, injury type, cause and place of injury, employment status, and sex. No significant correlations were found between AIS or ISS and age or hospital length of stay. Conclusions: AIS is more anatomically focused. ISS reflects broader systemic injury patterns and is more influenced by contextual factors. These findings are particularly relevant for the Eastern European population and can help develop better healthcare policies for the region. Full article

Trochoidea caroni (Gastropoda, Geomitridae) is a land snail previously found only in Sicily and Capri (Naples), but it has recently been found in other regions of the Italian peninsula. In this study, we performed karyological and molecular analysis on T. caroni from different sites across this regional range. Karyological analysis was performed on specimens from Palermo (Sicily), Capri (Campania), and Terracina (Lazio) using standard staining and NOR-FISH methods; the latter method was also performed on samples of T. elegans from Rome (Lazio). All T. caroni specimens had 2n = 48 chromosomes, but the 8th and 17th pairs differed morphologically between specimens from Capri and Terracina and those from Sicily. The mitochondrial 16S rRNA analysis grouped Sicilian and non-Sicilian populations of T. caroni in distinct subclades. Superimposing karyological data on their phylogenetic tree suggests that possible chromosomal rearrangements occurred during the diversification of Trochoidea. Our findings provide karyological and molecular evidence for a diversification between Sicilian and non-Sicilian populations of T. caroni. Furthermore, NOR-FISH revealed hybridisation signals on the 16th chromosome pair in both T. caroni and T. elegans (tribe Trochoideini). Similar NOR localisation has also been identified in Cernuella virgata (tribe Cernuellini), suggesting that it was inherited from the common ancestor of Trochoideini and Cernuellini. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

Large-scene space-borne SAR images with a high resolution are particularly effective for monitoring vast oceanic areas globally. However, ships are easily overlooked in such large scenes due to their small size and cluttered backgrounds, making SAR ship detection challenging for the existing methods. To address this challenge, we propose a progressive saliency-guided (PSG) method, which uses saliency-derived positional priors to guide the model in focusing on small targets and extracting their features. Specifically, a dual-guided perception enhancement (DGPE) module is developed, which introduces additional target saliency maps as prior information to cross-guide and highlight key regions in SAR images at the feature level, enhancing small object feature representation. Additionally, a saliency confidence aware assessment (SCAA) mechanism is designed to strengthen small object proposal learning at the proposal level, guided by classification and localization scores at key locations. The DGPE and SCAA modules jointly enhance small object learning across different network levels. Extensive experiments demonstrate that the PSG method significantly improves the detection performance (+4.38% AP on LS-SSDD and +4.35% on HRSID) for small ships in large-scene SAR images compared to that of the baseline, providing an effective solution for small ship detection in large scenes. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

Background: Plantar pressure distribution is a valuable tool for studying how the ground reaction forces are transmitted from the feet to the body and for detecting abnormalities in foot biomechanics. Objectives: The objective of this study was to determine the effect of the foot type (normal foot, flatfoot, and cavus foot) on plantar pressure distribution in healthy Mexican men and women aged from 3 to 74 years. Methods: A database of the plantar pressure distribution under dynamic and static conditions for both feet was studied using descriptive statistics, regression analysis, and statistical factorial design. The database contained images of the soles of the feet and pressure distribution of 996 persons between 3 and 74 years old (53.9% females and 46.1% males). Two different conditions were evaluated; the first was in a static condition, and the second was during walking. The Chippaux–Smirak Index (CSI) was used to classify the type of feet. Results: In the left foot, a linear regression analysis of the soles of the feet shows that the prevalence of flatfoot (p-value = 3.45 × E⁻⁵) decreased with age, while the normal foot (p-value = 7.39 × E⁻⁵) increased. When people are standing (static), the hindfoot (55.64 ± 18.80%) presents more pressure than the forefoot (45.18 ± 19.50%), while in dynamic, the forefoot (55.95 ± 13.36%) supports more pressure than the hindfoot (44.05 ± 13.36%). Similar behavior occurs in the right foot. A statistical factorial design ANOVA shows that the plantar pressure in the forefoot and hindfoot regions is significantly different (p < 0.05). Conclusions: The prevalence of flatfoot decreased with age, while the proportion of normal foot type increased. Under static conditions, the hindfoot bore more load than the forefoot, whereas under dynamic conditions, the forefoot bore more load than the hindfoot. This research contributes to generating a comprehensive database of reference values of the plantar pressure of different foot types in a Mexican population; this will be useful to podiatrists, clinicians, and physiotherapists for the analysis or treatment of abnormal foot postures. Full article

Alcohol Use Disorder (AUD) profoundly impacts individuals and society, driven by neurobiological adaptations that sustain chronicity and relapse. Emerging research highlights neuroinflammation, particularly microglial activation, as a central mechanism in AUD pathology. Ethanol engages microglia—the brain’s immune cells—through key signaling pathways such as Toll-like receptor 4 (TLR4) and the NLRP3 inflammasome, triggering the release of proinflammatory cytokines (IL-1β, TNF-α, IL-6). These mediators alter synaptic plasticity in addiction-related brain regions, including the ventral tegmental area, nucleus accumbens, amygdala, and lateral habenula, thereby exacerbating cravings, withdrawal symptoms, and relapse risk. Rodent models reveal that microglial priming disrupts dopamine signaling, heightening impulsivity and anxiety-like behaviors. Human studies corroborate these findings, demonstrating increased microglial activation markers in postmortem AUD brains and neuroimaging analyses. Notably, sex differences influence microglial reactivity, complicating AUD’s neuroimmune landscape and necessitating sex-specific research approaches. Microglia-targeted therapies—including minocycline, ibudilast, GLP-1 receptor agonists, and P2X7 receptor antagonists—promise to mitigate neuroinflammation and reduce alcohol intake, yet clinical validation remains limited. Addressing gaps such as biomarker identification, longitudinal human studies, and developmental mechanisms is critical. Leveraging multi-omics tools and advanced neuroimaging can refine microglia-based therapeutic strategies, offering innovative avenues to break the self-sustaining cycle of AUD. Full article

The application of artificial intelligence in education, particularly in learning programming, is gaining increasing significance. However, research on secondary school students specializing in IT at an early stage has received relatively little attention in this field. The aim of this study is to assess how vocational secondary school IT students utilize Generative artificial intelligence in learning programming. The study employed a survey-based methodology, where students with varying levels of knowledge were surveyed to understand their AI usage patterns. The sample consisted of students from vocational IT schools, and data were analyzed using descriptive statistics and independent samples t-tests. The results indicate that students with different levels of knowledge use AI tools differently, with ChatGPT being the most popular tool. The study further highlights that AI usage brings significant benefits, such as providing a personalized learning experience and enabling quick error correction. However, excessive reliance on AI tools may hinder students from acquiring fundamental programming skills. The findings support the idea that while AI can effectively complement teachers’ explanations, overdependence on it can be risky, potentially reducing students’ creativity and problem-solving abilities. The study emphasizes the crucial role of educators in teaching the responsible and ethical use of artificial intelligence. The results of this research offer new perspectives on the effective integration of Generative artificial intelligence into vocational secondary school programming education and suggest further studies to compare its applications at the university level. However, the study acknowledges certain limitations, such as the potential bias of self-reported data, which may affect the generalizability of the results. Unlike other studies, the age groups we surveyed, and the cohorts formed from them are nearly evenly distributed, making our sample representative of the region in question. Full article

The gut microbiota plays an essential role in human health, influencing gut–brain communication. Imbalances in this microbial ecosystem, termed dysbiosis, have been associated with increased gut permeability and gastrointestinal symptoms commonly reported in autism spectrum disorder (ASD), without implying a direct causal role in ASD itself. This study aimed to determine whether alterations in gut microbiota exist in individuals with Fragile X Syndrome (FXS), with or without ASD, compared to ASD patients and neurotypical controls, and to identify microbiota biomarkers associated with these disorders. Stool samples from Caucasian individuals aged 3–18 years belonging to four groups (ASD, FXS, FXS + ASD, and controls) were analysed by amplifying the V3–V4 region of the bacterial 16S rRNA gene to characterize microbiota composition. Significant differences were found among patient groups compared to neurotypical controls, with notable similarities between the ASD and FXS + ASD groups. Additionally, specific microbiota biomarkers were identified for each patient group. These findings suggest that distinct microbiota alterations are associated with FXS and ASD, which may contribute to a more accurate characterization of symptoms in these disorders and could serve as potential biomarkers for assessing neurodevelopmental risk. Full article

In this paper, we use molecular phylogenetics, micro-CT scanning, and morphological analyses to describe a new species of goby, Pascua marecoralliensis, and demonstrate that the genus Pascua is distinct from Hetereleotris, as supported by five diagnostic characters, including modified basicaudal scales and reduced sensory papillae patterns. Phylogenetic analysis places Pascua as sister to the Gobiodon group, while Hetereleotris forms a separate clade. The new species, P. marecoralliensis, differs from congeners in fin ray counts, cephalic pore patterns, and head morphology and exhibits unique live colouration. Additionally, we reclassify Hetereleotris readerae and H. sticta as Pascua readerae and P. sticta based on shared genus-specific traits. The distribution of Pascua spans the southern Pacific, suggesting a relict lineage or undiscovered diversity in the genus. This work underscores the importance of integrative taxonomic approaches for resolving cryptic diversity in gobioid fishes and highlights the need for further sampling in understudied regions. Full article

Building geometric parameters are key factors influencing energy-efficient building design. However, the systematic influence of building geometric parameters on energy use intensity (EUI) across varying climate regions and building envelope thermal performance levels remains incompletely elucidated, hindering the quantitative assessment of their energy-saving benefits in diverse regions and operational scenarios. This study employs a zonal sensor-optimized coupled daylighting–thermal simulation to analyze the impact of building geometric parameters and their values on annual total EUI across different climate regions and building envelope thermal performance levels. The interquartile range (IQR), sensitivity analysis (SA), and energy saving rate (ESR) analysis are utilized. The results showed the following: (1) The energy-saving benefits of geometric parameters were the greatest in severe cold (SevC) and temperate regions (TRs), with IQRs ranging from 28.50 to 39.87 kWh/m², followed by hot summer–warm winter (HS-WW), cold (Cld), and hot summer–cold winter (HS-CW) regions. While high-performance building envelopes significantly reduce EUI, the energy-saving benefits associated with geometric parameters remain undiminished. (2) The WWR is the parameter most sensitive to EUI, with SA reaching a maximum of 41.19%, notably exceeding 20% in HS-CW regions, HS-WW regions, and TRs; floor height has the lowest sensitivity, with SA reaching a maximum of 5.65%. (3) In different climate regions, the influence of floor height and building footprint area on the ESR shifts between positive and negative correlations, while the WWR and window sill height consistently exhibit positive correlations with the ESR in all climate regions. This study provides a quantitative decision-making basis for optimizing building geometric parameters in different climate regions to achieve high-performance building shapes during the early stages of architectural design. Full article

A series of shear interface experiments on a type of loess and rough concrete interface under conditions of different initial water contents (16%, 21%, and 26%), dry densities (1.30 g/cm³, 1.52 g/cm³, 1.70 g/cm³) and normal stresses (50 kPa, 100 kPa, 200 kPa) were conducted to further understand shear deformation and strength characteristics of a loess and rough concrete interface combined with loess deformation monitoring method of gypsum powder line method. A discrete element method (DEM) model was then established, calibrated against the experimentally obtained shear stress–displacement curves, and run to investigate the shear deformation, contact force chain and fabric evolution processes at the microscopic level. The results show the following: (1) The shear deformation and strength behaviors of the loess and rough concrete interface were significantly impacted by the initial moisture content, dry density and normal stress. (2) The shear deformation of the loess increased with the increase in initial moisture content, and decreased with dry density and normal stress. (3) The shear strength of the loess and rough concrete interface increased with the increase in dry density and normal stress, and decreased with the increase in initial moisture content. (4) The evolution of the shear deformation, contact force chain and fabric of the loess-concrete rough interface were explored and analyzed from a microstructural perspective. This study contributes insights critical to construction of the pile-loess systems in Chinese Loess Plateau regions. Full article