Search Results (7,429)

Segmenting white matter tracts in diffusion-weighted magnetic resonance imaging (dMRI) is of vital importance for brain health analysis. It remains a challenging task due to the intersection and overlap of tracts (i.e., multiple tracts coexist in one voxel) and the data complexity of dMRI images (e.g., 4D high spatial resolution). Existing methods that demonstrate good performance implement direct volumetric tract segmentation by performing on individual 2D slices. However, this ignores 3D contextual information, requires additional post-processing, and struggles with the boundary handling of 3D volumes. Therefore, in this paper, we propose an efficient 3D direct volumetric segmentation method for segmenting white matter tracts. It has three key innovations. First, we propose to deeply interleave convolutions and transformer blocks into a U-shaped network, which effectively integrates their respective strengths to extract spatial contextual features and global long-distance dependencies for enhanced feature extraction. Second, we propose a novel channel-wise transformer, which integrates depth-wise separable convolution and compressed contextual feature-based channel-wise attention, effectively addressing the memory and computational challenges of 4D computing. Moreover, it helps to model global dependencies of contextual features and ensures each hierarchical layer focuses on complementary features. Third, we propose to train a fully symmetric network with gradually sized volumetric patches, which can solve the challenge of few 3D training samples and further reduce memory and computational costs. Experimental results on the largest publicly available tract-specific tractograms dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods. Full article

Laser Metal Deposition (LMD) is an advanced Additive Manufacturing (AM) technology widely used for metal component fabrication, cladding, and repair. Despite its potential, issues such as geometrical inaccuracies and deposition flaws can significantly affect part quality and process efficiency. Existing optical monitoring approaches mainly focus on geometric features and generally do not provide real-time estimates of deposition efficiency, which is critical for both product performance and resource utilization. Furthermore, evaluating deposition efficiency in industrial settings is often time-consuming and difficult to implement. This preliminary study introduces an innovative in-process methodology for assessing deposition efficiency during multi-track deposition. The approach exploits end-track scan data acquired by a laser line scanning system to estimate the deposited volume and the corresponding deposition efficiency for each track. A validation test on a two-layer sample demonstrates the capability of the method to detect defects induced by partially clogged and non-clogged nozzle conditions. Comparison with metallographic measurements shows an average deviation of 4.3%. By enabling timely identification of powder feeding anomalies and supporting improved powder utilization, the proposed methodology contributes to waste reduction, enhanced process stability, and more sustainable industrial implementation of LMD. Full article

Microfluidic chip technologies, also known as lab-on-a-chip systems, have profoundly transformed laboratory medicine by enabling the miniaturization, automation, and rapid processing of complex diagnostic assays using minimal sample volumes. Recent advances in chip design, fabrication methods—including 3D printing, modular and flexible substrates—and biosensor integration have significantly enhanced the performance, sensitivity, and clinical applicability of these devices. Integration of advanced biosensors allows for real-time detection of circulating tumor cells, nucleic acids, and exosomes, supporting innovative applications in cancer diagnostics, infectious disease detection, point-of-care testing (POCT), personalized medicine, and therapeutic monitoring. Notably, the convergence of microfluidics with artificial intelligence (AI) and machine learning has amplified device automation, reliability, and analytical power, resulting in “smart” diagnostic platforms capable of self-optimization, automated analysis, and clinical decision support. Emerging applications in fields such as neuroscience diagnostics and microbiome profiling further highlight the broad potential of microfluidic technology. Here, we present findings from a comprehensive review of recent innovations in microfluidic chip design and fabrication, advances in biosensor and AI integration, and their clinical applications in laboratory medicine. We also discuss current challenges in manufacturing, clinical validation, and system integration, as well as future directions for translating next-generation microfluidic technologies into routine clinical and public health practice. Full article

The aim of this study was to investigate the effect of phyto-additive mixture supplementation on semen quality and on some reproductive parameters after artificial insemination in rabbits. The trial run 120 days on 20 adult New Zealand white rabbit bucks that were allocated into two different groups, first was control (CON; n = 10) fed with commercial pelleted-feed and second was considered experimental group (EXP; n = 10) which received in feed a natural feed additive mixture (0.1% of dried Chlorella vulgaris powder and 0.1% of dried Laurus nobilis leaves powder). Consequently, the quality assessment of semen by the Computer Assisted Semen Analyzer (CASA) system, samples were instrumentally inseminated on rabbit does for two consecutive reproductive cycles, and productive and reproductive indexes were evaluated. Results demonstrate that while spermatozoa concentration and ejaculate volume did not differ significantly among experimental groups or between reproduction cycles, spermatozoa motility parameters were significantly enhanced in rabbits receiving the phyto-additive mixture, as evidenced by increased total motility (87.83% vs. 70.63%) and progressive motility (75.68% vs. 50.10%) compared with the control group (p < 0.01). No differences were observed in prolificacy traits during the first reproductive cycle, whereas in the second cycle the phyto-additive treatment increased the number of kits born alive per litter (12.29 vs. 10.19; p < 0.05) and improved kit growth performance at birth (79.17 vs. 66.75 g), at weaning (1085.28 vs. 963.15 g), and in average daily gain (28.75 vs. 25.61 g/day). The study provides evidence of alternative practises based on feeding programme to enhance reproductive traits in rabbit production. The goal is to provide farmers with examples of good farming practise (such as precision farming), focused on sustainability and efficiency, and a certain transfer of knowledge. Full article

Hiking poles significantly benefit hikers by improving balance, reducing strain on lower limbs and spine, and redistributing workload to the upper extremities. However, exercise at moderate altitude often causes respiratory muscle fatigue (RMF), which limits performance. This study investigated the effect of mountaineering poles use on RMF during submaximal uphill walking, examining cardiovascular and pulmonary responses, perceived exertion (RPE) and perceived dyspnea (DYS). Seventeen hikers (36.2 ± 11.9 years) walked a 6.4 km trail (1010 m elevation) at 65–85% of their heart rate maximum (HR_max), with and without poles (wp/np). Maximum voluntary ventilation (MVV₁₂), inspiratory capacity (IC), expiratory reserve volume (ERV), vital capacity (VC), tidal volume (VT), ventilation (VE), forced expiratory volume (FEV₁), forced vital capacity (FVC), respiration rate (RR), heart rate (HR), oxygen saturation (SaO₂), blood lactate accumulation (BLC), energy expenditure (EE), RPE, DYS, and performance (Time) were measured at the trail’s end (2070 m). Paired samples t-tests and Wilcoxon signed-rank tests were used for comparison. IC was higher when using poles compared to hiking without poles (Δ = 0.21 L, p = 0.011, adjusted p = 0.187). Non-significant differences were observed for MVV₁₂, ERV, VT, VE, RR, and BLC. In conclusion, under the investigated submaximal conditions, pole use did not significantly alter the overall physiological load or respiratory muscle endurance. These findings suggest that recreational hikers can utilize poles for mechanical support, without additional ventilatory or cardiovascular strain. Full article

This study systematically investigates the influence of short carbon-fiber (SCF) content on the mechanical, thermal, and tribological properties of self-lubricating polyphenylene sulfide (PPS) composites filled with PTFE and MoS₂, addressing the critical need for high-wear resistance in Carbon-Fiber-Reinforced Thermoplastic (CFRTP) structural applications. The results identified 10 wt% SCF as the optimal content that achieved the best balance between load-bearing capacity and friction performance. The coefficient of friction μ and wear amount were reduced by 29.28% and 29.29%, respectively, compared to the PPS/PTFE/MoS₂ composite material without SCF, and by 14.67% and 20.75%, respectively, compared to the material with excessive SCF filling (20 wt%). Finite-Element Analysis-Representative Volume Element (FEA-RVE) reveals the mechanism by which excessive content of SCF at the microscopic level leads to a slight decrease in mechanical properties. Critically, the tribological performance exhibited a discrepancy with bulk mechanical properties: above 15 wt% SCF, the wear rate worsened despite high mechanical strength, revealing that increased fiber agglomeration and micro-abrasion effects were the primary causes of performance deterioration. Further in-depth XPS analysis revealed a synergistic lubrication mechanism: In the optimal sample, an ultra-dense PTFE transfer film was formed to mask the underlying MoS₂. This masking, coupled with the high surface activity of MoO₃ particles leads to stronger physicochemical interactions with the polymer matrix, ensures the exceptional durability and stability of the tribo-film. This research establishes a complete structure–performance relationship by integrating mechanical, thermal, and tribo–chemical mechanisms, offering critical theoretical guidance for the design of next-generation high-performance self-lubricating CFRTPs. Full article

Air pollution is a major environmental health hazard. This study evaluates the health risks of air pollution exposure in the megacity Karachi, Pakistan, using the cigarette-equivalent technique developed previously for translating air pollution exposure into passive cigarette equivalents. Sampling of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and black carbon (BC) was performed at various fixed locations throughout the four seasons of the year. We evaluated the health risks of pollutants exposure using four different health endpoints including low birth weight (<2500 g at term after 37 weeks of gestation), decreased lung function (Forced Expiratory Volume in 1 s), cardiovascular mortality, and lung cancer in residents of Karachi. The average risks of low birth weight from PM_2.5, NO₂, and BC were 37.2, 14.8, and 1.01, respectively, (expressed as the equivalent number of passively smoked cigarettes, PSCs) while the average risks of decreased lung function were 93.9, 38.8, and 2.87. Risks of cardiovascular mortality were 51.9, 14.3, and 2.79, and those of lung cancer were 31.3, 6.47, and 1.32, respectively. The remarkably high risks are attributed to high concentrations of air pollutants. These results suggests that residents of Karachi may experience other adverse health effects beyond those typically attributed to air pollution. These PSC equivalent risks indicate a substantial potential health burden in Karachi and support the need for emission reduction efforts targeting traffic, industrial activity, and open burning. PM_2.5 and BC were measured in 2008–2011 and NO2 in 2008–2009, so the results should be interpreted as baseline risk estimates for that period rather than current (2025) concentrations. Full article

Italy is estimated to host thousands of abandoned mines, many of which contain large volumes of mine residues that negatively affect land and aquatic ecosystems, also posing a risk to human health. This study evaluates the effectiveness of spaceborne imaging spectroscopy combined with laboratory spectroscopy for characterizing the mineralogy and geochemistry of residues from the abandoned Montevecchio sulfide mine in southwestern Sardinia, a site recognized as a significant source of environmental pollution. Mine tailings and their downstream dispersion along the Rio Irvi River were systematically studied and sampled in the field. Collected samples were analyzed in the lab using an Analytical Spectral Device (ASD) spectroradiometer, complemented by powder X-ray Diffraction (XRD) for mineralogical characterization. Affected zones were subsequently mapped using the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite data at a 30 m spatial resolution, by applying a polynomial fitting technique to the image spectra. The results reveal the presence of Fe- and Zn-bearing sulfates and oxy/hydroxides, indicative of acidic-to-circum-neutral drainage conditions in the mine tailings and along affected streams. Specifically, EnMAP was able to detect jarosite and subtle chemical and physical variations in Fe-hydroxides. This integrated approach enabled the delineation of environmental conditions and zones with varying acidity based on the spectral characteristics of secondary minerals. Overall, the study demonstrates the potential of EnMAP data for mapping acid mine drainage and assessing environmental impacts in legacy mining areas. Full article

This study investigates left-turn safety at urban intersections using surrogate safety measures derived from field video observations. Time-to-Collision (TTC) among motorized traffic and Post-Encroachment Time (PET) among pedestrian and motorized traffic were extracted for left-turn conflicts across five intersection types in Thessaloniki, Greece, and linked to geometric attributes, signal operations, and traffic conditions. Count-based models (Poisson, Negative Binomial) were estimated alongside machine-learning approaches (Random Forest, Gradient Boosting with Poisson loss). For PET events, the Poisson model had the best balance of parsimony and predictive accuracy, whereas the Negative Binomial model provided a superior fit for TTC events. Results indicate that PET-defined conflicts increased with pedestrian volume and the presence of shared and protected left-turn lanes, and decreased with higher opposing flow, greater average acceleration, and wider end-approach lanes. By contrast, TTC events were associated with lower average speeds, the presence of protected signal phasing for left turns, and the number of passenger cars. Machine-learning models underperformed relative to classical count models, reflecting limited sample size and the discrete event structure. The analysis indicates that the determinants of TTC and PET differ, with certain variables such as pedestrian activity and lane configuration having contrasting effects on the two surrogate safety measures. The analysis reveals that pedestrian demand and shared lane configurations significantly increase PET occurrences, whereas TTC events are more strongly associated with vehicle volumes, speeds, and signal phasing. This distinction underscores the importance of tailoring safety assessment and intervention strategies to the type of interaction being evaluated. Full article

The aim of the presented study was to compare changes in acid-base balance during high-intensity exercise preceded and not preceded by a warm-up that ended with breathing using additional respiratory dead space volume (ARDSv). The study involved 24 cyclists. Each participant completed two 3 min exercise tests performed at 110% of maximal power from a graded exercise test. A standardized warm-up preceded each 3 min test. Between the warm-up and the test was an 8 min passive rest, during which, in one test, participants breathed using ARDSv increased by 1000 mL, in the second test, ARDSv was not used. After the warm-up and after the 3 min test, a sample of arterialized blood was taken to measure changes in hydrogen ion concentration (ΔH⁺), lactate (ΔLa⁻), blood gas parameters, and morphological parameters, and changes in plasma volume (ΔPV) were calculated. Additionally, after the 3 min tests, the rating of perceived exertion (RPE) was assessed. It was demonstrated that ΔH⁺ (20.4 ± 6.3 and 17.5 ± 6.3, respectively; p = 0.016) and ΔPV (−8.25 ± 4.81 and −4.86 ± 5.58, respectively; p = 0.029) were greater in the test not preceded by breathing restrictions compared with the test preceded by breathing using ARDSv. Simultaneously, no significant differences in ΔH+ were found between tests when post-exercise values were corrected for changes in plasma volume. Furthermore, lower RPE was observed after ARDSv use (Z = 1.97, p = 0.048). Breathing using ARDSv between a warm-up and a high-intensity 3 min exercise induces mild respiratory acidosis, limits the exercise-induced increase in H⁺ concentration and changes in plasma volume, while affecting lower RPE. Full article

Heart Rate Variability (HRV) biofeedback could be considered as a tool to help athletes to optimize their performance. This study aimed to examine the effects of a one-week HRV biofeedback (HRV-BFB) program on physiological indices, sensorimotor functions, and kata performance in Eastern martial arts athletes. Forty high-level martial arts athletes (karate, wushu, taekwondo, kyokushinkai) aged 17–27 years were divided into two groups: a control group (n = 20) and a biofeedback group (BFB, n = 20). Athletes from both groups underwent assessment of sensorimotor functions and the technical quality of their kata routines. The primary outcome was the expert-rated kata performance score. All routines were video-recorded and independently rated by three certified judges. The BFB group completed a hybrid HRV-BFB program consisting of supervised resonance-frequency breathing sessions in the laboratory and one week of home-based practice. During supervised sessions, athletes performed slow abdominal-paced breathing (6 breaths/min). At home, they practiced the same breathing pattern twice daily for one week (5 min per session, smartphone-guided). Nonparametric tests were used because several variables deviated from normality, and the sample size per group was limited (n = 20). After completing the HRV-BFB training, movement oscillation frequency improved significantly, reflected by lower movement oscillation frequency (p = 0.0009, r = 0.79), faster choice reaction time at a tendency level (p = 0.0793, r = 0.39), and an increase in blood volume pulse (BVP) (p = 0.037, r = 0.48) in BFB group compared to control group. Following BFB training, the judges’ scores did not change in the control group, while a significant increase was observed in the BFB group (p = 0.038, r = 0.44), indicating a positive effect of BFB training on kata performance. Regular HRV-BFB training emphasizing slow-paced abdominal breathing may enhance autonomic regulation, fine motor control, and improve the technical execution of kata routines in athletes. Full article

This paper leverages Google Trends search volume data from 2004 to 2008 as a proxy for investor information demand. The analysis documents that greater search activity prior to earnings announcements is positively associated with future market reaction to earnings announcements, pre-earnings announcement drift, and buying pressure. The results are consistent with investors gathering value-relevant information through online research, which is subsequently incorporated into prices through trading around earnings announcements. Notably, search volume is positively associated with market reaction to earnings announcements and pre-announcement drifts for more obscure firms where data is scarce. Overall, this paper provides large-sample evidence validating theoretical models where dispersed private information is incorporated into stock prices. The findings suggest that broader data access may facilitate pricing efficiency by promoting more informed market participation. Full article

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring. Full article

Microplastics spread through the environment in various ways. Inland waters are an ideal medium for their dispersal, as they collect pollutants from various sources and transport them over long distances. From there, microplastics can enter the marine environment, break down into smaller particles or end up in drinking water treatment plants. However, the fate, transport and potential health effects of microplastics after ingestion of drinking water and water in food are not yet fully understood. It is therefore necessary to evaluate the quantification and identification of microplastics in drinking water by analysing real samples in order to assess the potential impact on human health. To this end, microplastic contamination in 32 treated drinking water samples from a surface water treatment plant in north-eastern Italy were analysed using micro-Fourier transform infrared spectroscopy (µ-FT-IR). The results indicated low levels of contamination, with all the samples containing less than 170 microplastics per litre, which is in line with European drinking water levels. Polyolefins with size 50–500 µm, such as polypropylene and polyethylene, were the predominant polymers detected (50.2%), while surprisingly polyethylene terephthalate was scarcely present (0.1%, size range 10–50 µm). Statistical analysis revealed a significant negative correlation between microplastic concentration and sampling volume, with larger volumes yielding fewer particles. This inconsistency likely results from the lack of bottle rinsing when only a fraction of the sampling volume is filtered. It was also found that rinsing the sampling bottles with ethanol alone prior to analysis was sufficient to ensure accurate quantification. These results highlight the challenges in standardising the detection of microplastics in drinking water and underline the need for optimised sampling protocols. Full article

Anaerobic digestion (AD) is a proven and promising technology for recovering energy from biowastes, such as pig slurry (PS) from the fattening/finishing phase. The mechanisms of AD are widely studied, and nowadays, it is of the utmost importance to investigate strategies that give end-users the confidence to choose this technology and to adapt it to their reality, promoting the energy transition and circular economy. This study investigated how collection and storage period affect PS samples, and how hydraulic retention time (HRT) (15 versus 20 days) influences AD performance and stability. Seasonality was the primary factor influencing feedstock characteristics. Samples presented no significant differences during the storage period. A 20-day HRT led to higher digestate pH, total ammonia nitrogen (TAN), and free ammonia nitrogen (FAN) concentrations, which can cause process instability and methanogenesis inhibition. However, 20-day HRT led to a specific methane production that was 7% higher and to a methane quality (expressed in % v/v CH₄) that was 6% higher than 15-day HRT. Overall, methane quality, digestate pH, TAN, and FAN values may be considered key points that need to be monitored to prevent the AD system from being compromised. Nevertheless, these results provide the operational freedom to choose either HRT, allowing reduced reactor volume and investment. Full article