Search Results (257)

The response time of Artificial Neural Network (ANN) inference is critical in embedded systems processing sensor data close to the source. This is particularly important in applications such as predictive maintenance, which rely on timely state change predictions. This study enables estimation of model response times based on the underlying platform, highlighting the importance of benchmarking generic ANN applications on edge devices. We analyze the impact of network parameters, activation functions, and single- versus multi-threading on response times. Additionally, potential hardware-related influences, such as clock rate variances, are discussed. The results underline the complexity of task partitioning and scheduling strategies, stressing the need for precise parameter coordination to optimise performance across platforms. This study shows that cutting-edge frameworks do not necessarily perform the required operations automatically for all configurations, which may negatively impact performance. This paper further investigates the influence of network structure on model calibration, quantified using the Expected Calibration Error (ECE), and the limits of potential optimisation opportunities. It also examines the effects of model conversion to Tensorflow Lite (TFLite), highlighting the necessity of considering both performance and calibration when deploying models on embedded systems. Full article

Transition-metal and rare-earth element co-doped ZnO nanoparticles have attracted significant attention due to their potential applications in spintronics and optoelectronics. In this study, Zn_0.95Co_0.01EuxO (x = 0.01–0.05) nanoparticles were synthesized using the sol–gel technique. The estimated stress, strain, and crystallite sizes of the synthesized Co/Eu co-doped ZnO nanoparticles were calculated using the Williamson–Hall method, and their electron spin resonance (ESR) properties were investigated to examine the effect on their magnetic and structural properties. X-ray diffraction (XRD) analysis confirmed the presence of a single-phase structure. Surface morphology, elemental composition, crystal quality, defect types, density, and magnetic behavior were characterized using scanning electron microscope (SEM), electron-dispersive spectroscopy (EDS), and ESR techniques, respectively. The effect of Eu concentration on the linewidth (ΔBpp) and g-factor in the ESR spectra was studied. By correlating ESR results with the obtained structural properties, room-temperature ferromagnetic behavior was identified. Full article

Background and Objectives: The worldwide prevalence of chronic kidney disease (CKD) continues to increase, representing a major concern for public health systems. CKD is associated with gut microbiota dysbiosis, which may exacerbate disease progression by increasing the levels of uremic toxins, systemic inflammation, and oxidative stress. Modulation of the gut microbiota through biotic supplementation has been proposed as a potential therapeutic strategy to slow CKD progression and mitigate its complications. This study aimed to evaluate the effect of 10-month synbiotic supplementation on estimated glomerular filtration rate (eGFR), circulating concentrations of indoxyl sulfate (IS), p-cresyl sulfate (p-CS), interleukin-6 (IL-6), and malondialdehyde (MDA) in patients with stage IV–V CKD not receiving dialysis, in comparison to placebo. Materials and Methods: Fifty non-dialysis CKD IV–V patients were assigned (n = 25 each) via matched, non-randomized allocation (age, sex, and primary disease) to synbiotic or placebo. This single-blind, placebo-controlled trial blinded participants and laboratory personnel. The synbiotic group received daily capsules containing Lactobacillus acidophilus La-14 (2 × 10¹¹ CFU/g) + fructooligosaccharides; controls received identical placebo. Adherence was monitored monthly (pill counts, diaries), with < 80% over two visits resulting in withdrawal. The eGFR, IS, p-CS, IL-6, and MDA were measured at baseline and month 10. Results: Forty-two patients (21/arm) completed the study; eight withdrew (4 per arm). At 10 months, the change in eGFR was −1.2 ± 2.5 mL/min/1.73 m² (synbiotic) vs. −3.5 ± 3.0 mL/min/1.73 m² (placebo); between-group difference in change was 2.3 mL/min/1.73 m² (95% CI: 0.5–4.1; p = 0.014; adjusted p = 0.07). IS decreased by −15.4 ± 8.2 ng/L vs. −3.1 ± 6.5 ng/L; between-group difference in change was −12.3 ng/L (95% CI: −17.8 to −6.8; p < 0.001; adjusted p = 0.005). No significant differences were observed for p-CS, IL-6, or MDA after correction. Conclusions: Synbiotic supplementation over a 10-month period resulted in a trend toward decreased serum IS levels in patients with advanced CKD, suggesting potential benefits of microbiota-targeted therapies. However, no significant effects were observed on renal function, inflammatory, or oxidative stress markers. Further large-scale studies are warranted to confirm these findings and explore the long-term impact of synbiotics in CKD management. Full article

Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. This study introduces HUE-Net—a Human-centric, Uncertainty-aware, Event-fused Network—designed specifically to thrive under severe environmental stress. HUE-Net marries the visible RGB band with near-infrared (NIR) imagery and high-temporal-event data through an early-fusion pipeline, proven more responsive than serial approaches. A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. Central to the architecture is the perturbed multi-branch variational module, which distills probabilistic identity embeddings while delivering calibrated confidence scores. Complementing this, an Adaptive Spectral Attention mechanism dynamically reweights each stream to amplify the most reliable facial features in real time. Unlike previous efforts that compartmentalize uncertainty handling, spectral blending, or computational thrift, HUE-Net unites all three in a lightweight package. Benchmarks on the IJB-C and N-SpectralFace datasets illustrate that the system not only secures state-of-the-art accuracy but also exhibits unmatched spectral robustness and reliable probability calibration. The results indicate that HUE-Net is well-positioned for forensic missions and humanitarian scenarios where trustworthy identification cannot be deferred. Full article

Lipopolysaccharide (LPS) endotoxin is a well-characterized microbe-associated molecular pattern (MAMP) that forms the outer membrane of both pathogenic and commensal Gram-negative bacteria. It plays a crucial role in triggering inflammatory disorders such as mastitis, acidosis, and septicemia. In heifers, an LPS challenge induces a dynamic stress response, marked by elevated cortisol levels, increased body temperature, and altered immune function. Research indicates that LPS administration leads to a significant rise in cortisol post-challenge. Building on this understanding, the present study aimed to estimate genetic parameters for serum cortisol response to LPS challenge in Holstein heifers and its linear associations with production, health, reproduction, and conformation traits. Additionally, a genome-wide association study (GWAS) was conducted to identify genetic regions associated with cortisol response. A total of 252 animals were evaluated for cortisol response, with correlations estimated between cortisol levels and 55 genomic breeding values for key traits. Genetic parameters and heritability for cortisol response were estimated using Residual Maximum Likelihood (REML) in the Blupf90+ v 2.57 software. Single-Step GWAS (ssGWAS) employing a 10-SNP window approach and 42,123 SNP markers was performed to identify genomic regions that explained at least 0.5% of additive genetic variance. Finally, candidate genes and QTLs located 50 kb up and downstream of those windows were identified. The cortisol response showed significant but weak linear associations with cystic ovaries, body maintenance requirements, lactation persistency, milk yield, and protein yield (p-value ≤ 0.05) and showed suggestive weak linear associations with udder texture, clinical ketosis, heel horn erosion, and milking speed (p-value ≤ 0.15). Cortisol response showed significant additive genetic variance, along with moderate heritability of 0.26 (±0.19). A total of 34 windows explained at least 0.5% of additive genetic variance, and 75 QTLs and 11 candidate genes, comprising the genes CCL20, DAW1, CSMD2, HMGB4, B3GAT2, PARD3, bta-mir-2285aw, CFH, CDH2, ENSBTAG00000052242, and ENSBTAG00000050498, were identified. The functional enrichment analysis allowed us to infer two instances where these gene products could interfere with cortisol production: the first instance is related to the complement system, and the second one is related to the EMT (Epithelium–Mesenchymal Transition) and pituitary gland formation. Among the QTLs, 13 were enriched in the dataset, corresponding to traits related to milk (potassium content), the exterior (udder traits, teat placement, foot angle, rear leg placement, and feet and leg conformation), production (length of productive life, net merit, and type), and reproduction (stillbirth and calving ease). In summary, the cortisol response to LPS challenge in Holstein heifers seems to be moderately heritable and has weak but significant linear associations with important production and health traits. Several candidate genes identified could perform important roles, in at least two ways, for cortisol production, and QTLs were identified close to regions of the genome that explained a significant amount of additive genetic variance for cortisol response. Therefore, further investigations are warranted to validate these findings with a larger dataset. Full article

Fatigue failure, generated by local multi-axial random state stress, frequently occurs in many engineering fields. Therefore, it is customary to perform experimental vibration tests for a structural durability assessment. Over the years, a number of testing methodologies, which differ in terms of the testing machines, specimen geometry, and type of excitation, have been proposed. The aim of this paper is to describe a new testing procedure for random multi-axial fatigue testing. In particular, the paper presents the experimental set-up, the testing procedure, and the data analysis procedure to obtain the multi-axial random fatigue life estimation. The originality of the proposed methodology consists in the experimental set-up, which allows performing multi-axial fatigue tests with different normal-to-shear stress ratios, by choosing the proper frequency range, using a single-axis exciter. The system is composed of a special designed specimen, clamped on a uni-axial shaker. On the specimen tip, a T-shaped mass is placed, which generates a tunable multi-axial stress state. Furthermore, by means of a finite element model, the system dynamic response and the stress on the notched specimen section are estimated. The model is validated through a harmonic acceleration base test. The experimental tests validate the numerical simulations and confirm the presence of bending–torsion coupled loading. Full article

This work investigates the applicability of Bayesian inverse analysis for identifying parameters from non-standard aluminum specimens with notches that induce stress concentrations. Unlike conventional standardized specimens, the notched samples used in this work are typically unsuitable for direct parameter extraction due to geometric irregularities and size effects. The experimental procedure involved tensile tests conducted using a universal testing machine, with deformation data collected via LVDT sensors and optical measurements with digital image correlation. The numerical simulations were performed using a quadrilateral finite element model with embedded strong discontinuities to capture the complete material response, including elastic, plastic, and fracture behavior. The proposed identification procedure successfully provided reliable posterior parameter estimates on aluminum rectangular and single-notch specimens. Furthermore, the identified parameters were validated on a double-notch specimen made of the same material. The results highlight the importance of parameter interpretation and show that the Bayesian framework can reliably identify key material and model-dependent parameters from non-standard specimens while accounting for uncertainty in both measurements and model formulation. Full article

Accurate prediction of air demand in free-surface flows through high-head spillway tunnels with multiple vents represents a critical design challenge. Existing empirical formulas for estimating air demand, derived from studies of single vents using experimental and prototype data, are not directly applicable to multi-vent configurations. This study investigates the combined effects of key parameters on ventilation requirements: (1) flow characteristics (velocity range of 6–12 m/s and depth varying between 0.06 and 0.1 m); (2) vent geometry (total vent area from 28 to 140 cm² and spatial distribution). Through an experimental analysis, an empirical formula is derived to correlate wall roughness with interfacial shear stress, enabling an improved method for estimating air demand in spillway tunnels with multiple air vents. The resulting predictive model achieves ±25% agreement with two prototype case studies and model tests. These experimentally validated relationships provide quantitative guidelines for optimizing ventilation system designs. Full article

Bilirubin, albumin, and uric acid are established endogenous antioxidant biomarkers, whereas the antioxidant role of creatinine has not yet been fully clarified. As a byproduct of creatine metabolism, creatinine may reflect underlying metabolic activity and redox balance, particularly under conditions of oxidative stress such as cigarette smoking. This study aimed to evaluate the associations between serum creatinine and other antioxidant biomarkers and lung cancer risk, stratified by smoking status. We analyzed 83,371 cancer-free men from the Korean Cancer Prevention Study II (KCPS II) cohort. During a mean follow-up of 13.5 years, 533 incident lung cancer cases were identified. Serum creatinine, total bilirubin, albumin, and uric acid were measured. Smoking status classified participants as never-, former, and ever-smokers, with ever-smokers including both current and former smokers. Cox proportional hazards regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs), stratified by smoking status. Biomarkers were also analyzed by quartiles and linear trends. A single standard deviation increase in serum creatinine was significantly and inversely associated with lung cancer risk among former smokers (HR: 0.774, 95% CI: 0.620 to 0.967) and ever-smokers (HR: 0.823, 95% CI: 0.716 to 0.945). Total bilirubin also showed significant inverse associations in former smokers (HR: 0.826, 95% CI: 0.705 to 0.967) and ever-smokers (HR: 0.785, 95% CI: 0.708 to 0.870). Albumin was inversely associated only with ever-smokers (HR: 0.878, 95% CI: 0.807 to 0.955), while uric acid showed inverse associations with both former smokers (HR: 0.832, 95% CI: 0.699 to 0.989) and ever-smokers (HR: 0.847, 95% CI: 0.760 to 0.944). None of the biomarkers showed significant associations among never-smokers. Serum creatinine and other endogenous antioxidant biomarkers were inversely associated with lung cancer risk, particularly in individuals with a history of smoking exposure. Full article

Leaf area index (LAI) serves as a crucial indicator for assessing vegetation growth status, and unmanned aerial vehicle (UAV) optical remote sensing technology provides an effective approach for forest pest-related research. This study investigated the feasibility of LAI estimation in Moso bamboo (Phyllostachys pubescens) forests with different damage levels using UAV data while simultaneously exploring the scale effects of various spatial resolutions. Through image resampling using 10 distinct spatial resolutions and field data classification based on Pantana phyllostachysae Chao pest severity (healthy and mild damaged as Scheme 1, moderate damaged and severe damaged as Scheme 2, and all as Scheme 3), three machine learning algorithms (SVM, RF, and XGBoost) were employed to establish LAI estimation models for both single and mixed damage levels. Comparative analysis was conducted across different schemes, algorithms, and spatial resolutions to identify optimal estimation models. The results showed that (1) XGBoost-based regression models achieved superior performance across all schemes, with optimal model accuracy consistently observed at 3 m spatial resolutions; (2) minimal scale effects occurred at a 3 m resolution for Schemes 1 and 2, while Scheme 3 showed lowest scale effects at 1.5 m followed by 3 m resolutions; (3) Scheme 3 exhibited significant advantages in mixed damaged bamboo forest inversion with robust performance across all damage levels, whereas Schemes 1 and 2 demonstrated higher accuracy for single damaged scenarios compared to mixed damaged. This research validates the feasibility of incorporating pest stress factors into LAI estimation through different pest damage models, offering novel perspectives and technical support for parameter inversion in Moso bamboo forests. Full article

Numerical modeling of the direct magnetoelectric (ME) effect in a PVDF–cobalt ferrite (CFO) composite film has been performed. The problem is solved within the framework of the mesoscopic RVE approach, where each elementary cell contains three particles with varying mutual positions. Both modes of mechanical stress generation are taken into account: magnetostrictive and magnetorotational, i.e., changes in the shape and rotation of the particle as a whole. Depending on the sign of the magnetostriction constants, these sources of piezopolarization can either enhance or reduce the overall ME effect. A significant dependence of the ME effect on the mutual arrangement of CFO particles in the cell has been discovered; for instance, the effect is minimal when the particles are closest to each other. In other words, clustering is a negative factor. In a system where the magnetic moments of the magnetically hard CFO particles are ordered, the maximum ME effect is attained when the poling direction is at an angle of about ${40}^{\circ }$ to the film plane. As it turns out, a fairly good estimate of this angle can be obtained from the solution of a single-particle problem; the main contribution here comes from the ‘diagonal’ components of the piezotensor: $d_{31}$ and $d_{33}$. The ‘tangential’ component $d_{15}$ plays a special role: changing its sign can reverse the polarity of the charge generated on the film. Full article

The stay-green (SG) trait enhances photosynthetic activity during the late grain-filling period, benefiting grain yield under drought and heat stresses. CH7034 is a wheat breeding line with SG. To clarify the SG loci carried by CH7034 and obtain linked molecular markers, in this study, a recombinant inbred line (RIL) population derived from the cross between CH7034 and non-SG SY95-71 was genotyped using the Wheat17K single-nucleotide polymorphism (SNP) array, and a high-density genetic map covering 21 chromosomes and consisting of 2159 SNP markers was constructed. Then, the chlorophyll content of flag leaf from each RIL was estimated for mapping, and one QTL for SG on chromosome 7D was identified, temporarily named QSg.sxau-7D, with the maximum phenotypic variance explained of 8.81~11.46%. A PCR-based diagnostic marker 7D-16 for QSg.sxau-7D was developed, and the CH7034 allele of 7D-16 corresponded to the higher flag leaf chlorophyll content, while the 7D-16 SY95-71 allele corresponded to the lower value, which confirmed the genetic effect on SG of QSg.sxau-7D. QSg.sxau-7D located in the 526.4~556.2 Mbp interval is different from all the known SG loci on chromosome 7D, and 69 high-confidence annotated genes within the interval expressed throughout the entire period of flag leaf senescence. Moreover, results of an association analysis based on the diagnostic marker showed that there is a positive correlation between QSg.sxau-7D and thousand-grain weight. Our results revealed a novel QTL QSg.sxau-7D whose CH7034 allele had a strong effect on SG, which can be applied in further wheat molecular breeding. Full article

Background: The rate of morbidity after liver surgery is estimated at 30% and can be even higher when considering higher-risk subgroups of patients. Frailty is believed to better predict surgical outcomes by showcasing the patient’s ability to withstand major surgical stress and selecting frail ones. Methods: This is a single-centre, observational case–control study on patients diagnosed with liver malignancies who underwent liver resections between 2013 and 2024. The five-item modified Frailty Index (mFI-5) was used to split patients into frail and non-frail. The two groups were compared in terms of preoperative, operative and postoperative outcomes using a chi-squared and logistic regression model. Results: A total of 230 patients were included and split into two groups: non-frail, NF, n = 90, and frail patients, F, n = 140. Overall, F patients had a higher rate of morbidity (p = 0.04) but with similar mortality and length of stay. When considering only major liver resections, F patients had a higher probability of posthepatectomy liver failure (LR 6.793, p = 0.009), postoperative bleeding (LR 9.541, p = 0.002) and longer ICU stay (LR 8.666, p = 0.003), with similar rates of bile leak, surgical site infections, length of stay and mortality. Conclusions: Frailty seems to be a solid predictor of posthepatectomy liver failure in patients undergoing major liver resections and is associated with a longer ICU stay. However, mortality and surgical morbidity seem to be comparable between frail and non-frail patients. Full article

Anxiety and stress are major challenges for public health and represent significant symptoms in long COVID. Despite the repercussions on quality of life and mental health, their impacts have not been systematically consolidated in the Brazilian population. Our objective was to analyze the indicators and predictors of anxiety and perceived stress in people who have experienced long COVID in different regional contexts in Brazil. This cross-sectional survey was carried out in the five regions of Brazil and included 4239 adult individuals who had at least one diagnosis of COVID-19. Participants responded to questions on the Depression, Anxiety, and Stress Scale (DASS-21). The GAMLSS class of regression models estimated the predictors associated with the outcomes investigated. The results showed a predominance of participants with a single diagnosis of COVID-19 (65.4%), mild clinical conditions (89.5%), and high adherence to immunization strategies (98.4%). Overall, 48.5% of participants had residual symptoms that started between 4 and 12 weeks after the acute phase of COVID-19 infection. Positive screening for anxiety and perceived stress was associated with female gender, diagnosis of chronic diseases, presence of physical symptoms, moderate or severe clinical condition in the acute phase of the infection, and the need for hospitalization. Through this study, we confirmed that anxiety and stress, developed or exacerbated during the post-COVID-19 phase, represent significant challenges in the Brazilian population. Sociodemographic, clinical, and care conditions were predictors of the outcomes assessed. Knowing these repercussions can allow for personalizing mental health care and help structure evidence-based public policies. Full article

Pile foundation failures during earthquakes can cause severe structural damage, emphasizing the importance of accurate strength evaluation. This study focuses on concrete-filled steel pipe pile heads with inner ribs, which play a crucial role in resisting compressive loads. Compression tests were conducted on specimens simulating pile heads to investigate stress transfer between the steel pipe and infill concrete. A numerical analysis model was developed using ABAQUS 6.14 and validated against experimental results, successfully reproducing load-deformation relationships and stress transfer mechanisms. Simulations extended the study by analyzing the bearing strength of the infill concrete under rib-induced pressure, with varying diameter-to-thickness ratios D/t. The results show that the compressive strength is primarily governed by the combined effects of steel pipe buckling resistance and concrete bearing resistance of a single layer of inner ribs. The proposed evaluation formula provides a lower-bound estimate of compressive strength and effectively captures key parameters influencing performance. Full article