Search Results (2,017)

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

As voice cloning technology rapidly advances, the risk of personal voices being misused by malicious actors for fraud or other illegal activities has significantly increased, making the collection of speech data increasingly challenging. To address this issue, this study proposes a data augmentation method based on XText-to-Speech (XTTS) synthesis to tackle the challenges of small-sample, multi-class speech recognition, using profanity as a case study to achieve high-accuracy keyword recognition. Two models were therefore evaluated: a CNN model (Proposed-I) and a CNN-Transformer hybrid model (Proposed-II). Proposed-I leverages local feature extraction, improving accuracy on a real human speech (RHS) test set from 55.35% without augmentation to 80.36% with XTTS-enhanced data. Proposed-II integrates CNN’s local feature extraction with Transformer’s long-range dependency modeling, further boosting test set accuracy to 88.90% while reducing the parameter count by approximately 41%, significantly enhancing computational efficiency. Compared to a previously proposed incremental architecture, the Proposed-II model achieves an 8.49% higher accuracy while reducing parameters by about 98.81% and MACs by about 98.97%, demonstrating exceptional resource efficiency. By utilizing XTTS and public corpora to generate a novel keyword speech dataset, this study enhances sample diversity and reduces reliance on large-scale original speech data. Experimental analysis reveals that an optimal synthetic-to-real speech ratio of 1:5 significantly improves the overall system accuracy, effectively addressing data scarcity. Additionally, the Proposed-I and Proposed-II models achieve accuracies of 97.54% and 98.66%, respectively, in distinguishing real from synthetic speech, demonstrating their strong potential for speech security and anti-spoofing applications. Full article

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Soil acidity is one of the main limiting factors for coffee production in Peruvian rainforests. The objective of this study is to predict the spatial acidity variability for recommending site-specific liming and phosphorus fertilization treatments. We analyzed thirty-six edaphoclimatic variables, eight methods for estimating liming doses, and three geospatial variables from 552 soil samples in the Pichanaqui district of Peru. Multivariate statistics, nonparametric comparison, and geostatistical analysis with Ordinary Kriging interpolation were used for data analysis. The results showed low coffee yields (0.70 ± 0.16 t ha⁻¹) due to soil acidification. The interquartile ranges (IQR) were found to be 3.80–5.10 for pH, 0.21–0.87 cmol Kg⁻¹ for Al⁺³, and 2.55–6.53 mg Kg⁻¹ for available P, which are limiting soil conditions for coffee plantations. Moreover, pH, Al⁺³, Ca⁺², and organic matter (OM) were the variables with the highest accuracy and quality in the spatial prediction of soil acidity (R² between 0.77 and 0.85). The estimation method of liming requirements, MPM (integration of pH and organic material method), obtained the highest correlation with soil acidity-modulating variables and had a high spatial predictability (R² = 0.79), estimating doses between 1.50 and 3.01 t ha⁻¹ in soils with organic matter (OM) > 4.00%. The MAC (potential acidity method) method (R² = 0.59) estimated liming doses between 0.51 and 0.88 t ha⁻¹ in soils with OM < 4.00% and potential acidity greater than 0.71 cmol Kg⁻¹. Regarding phosphorus fertilization (DAP), the results showed high requirements (median = 137.21 kg ha⁻¹, IQR = 8.28 kg ha⁻¹), with high spatial predictability (R² = 0.74). However, coffee plantations on Ferralsols, with Paleogene parental material, mainly in dry forests, had the lowest predicted fertilization requirements (between 6.92 and 77.55 kg ha⁻¹ of DAP). This research shows a moderate spatial variation of acidity, the need to optimize phosphorus fertilization, and an optimal prediction of liming requirements using the MPM and MAC methods, which indicate high requirements in the southwest of the Pichanaqui district. Full article

Maternal and child health during pregnancy is an important issue in global public health, and the classification accuracy of fetal cardiotocography (CTG), as a key tool for monitoring fetal health during pregnancy, is directly related to the effectiveness of early diagnosis and intervention. Due to the serious category imbalance problem of CTG data, traditional models find it challenging to take into account a small number of categories of samples, increasing the risk of leakage and misdiagnosis. To solve this problem, this paper proposes a two-step innovation: firstly, we design a method of adaptive adjustment of misclassification loss function weights (MAAL), which dynamically identifies and increases the focus on misclassified samples based on misclassification rates. Secondly, a primary and secondary correction network model (MAC-NET) is constructed to carry out secondary correction for the misclassified samples of the primary model. Experimental results show that the method proposed in this paper achieves 99.39% accuracy on the UCI publicly available fetal health dataset, and also obtains excellent performance on other domain imbalance datasets. This demonstrates that the model is not only effective in alleviating the problem of category imbalance, but also has very high clinical utility. Full article

This study aimed to investigate the dynamic characteristics of shafts joined by friction welding and to update their finite element models. The first five bending mode resonance frequencies, damping ratios, and mode shapes of SAE 304 steel beams, friction-welded at three different rotational speeds (1200, 1500, and 1800 rpm), were determined using the Experimental Modal Analysis method. This approach allowed for an examination of how the dynamic properties of friction-welded beams change at varying rotational speeds. A slight decrease in resonance frequency values was observed with the transition from lower to higher rotational speeds. The largest difference of 3.28% was observed in the first mode, and the smallest difference of 0.19% was observed in the second mode. Different trends in damping ratios were observed for different modes. In the first, second, and fourth modes, damping ratios tended to increase with increasing rotational speeds, while they tended to decrease in the third and fifth modes. The largest difference was calculated as 52.83% in the third vibration mode. However, no significant change in mode shapes was observed for different rotational speeds. Based on the examined Modal Assurance Criterion (MAC) results, cross-comparisons of the mode shapes obtained for all three different speeds yielded a minimum similarity of 93.8%, reaching up to 99.9%. For model updating, a Frequency Response Assurance Criterion (FRAC)-based method utilizing frequency response functions (FRFs) was employed. Initially, a numerical model of the welded shaft was created using MATLAB-R2015a, based on the Euler–Bernoulli beam theory. Since rotational coordinates were not used in the EMA analyses, static model reduction was performed on the numerical model to reduce the effect of rotational coordinates to translational coordinates. For model updating, experimentally obtained FRFs from EMA and FRFs from the numerical model were used. The equivalent modulus of elasticity and equivalent density of the friction weld region were used as updating parameters. Successful results were achieved by developing an algorithm that ensured the convergence of the numerical model’s FRFs and natural frequencies. Full article

Hypertension (HTN) is a major contributor to global morbidity and manifests in several variants, including salt-sensitive hypertension (SSHTN). SSHTN is defined by an increase in blood pressure (BP) in response to high dietary salt, and is associated with heightened cardiovascular risk, renal damage, and immune system activation. However, the role of granulocyte-macrophage colony-stimulating factor (GM-CSF) has not yet been explored in the context of SSHTN. Previously, we reported that GM-CSF is critical in priming bone marrow-derived (BMD)-macrophages (BMD-Macs) and BMD-dendritic cells (BMD-DCs) to become activated (CD38+) in response to salt. Further exploration revealed these cells differentiated into BMD-M1 Macs, CD38+ BMD-M1 Macs, BMD-type-2 conventional DCs (cDC2s), and CD38+ BMD-cDC2s. Additionally, BMD-monocytes (BMDMs) grown with GM-CSF and injected into SSHTN mice traffic to the kidneys and differentiate into Macs, CD38+ Macs, DCs, and CD38+ DCs. In the current study, we treated SSHTN mice with an anti-GM-CSF antibody (aGM) and found that preventive aGM treatment mitigated BP, prevented renal inflammation, and altered renal immune cells. In mice with established SSHTN, aGM treatment attenuated BP, reduced renal inflammation, and differentially affected renal immune cells. Adoptive transfer of aGM-treated BMDMs into SSHTN mice resulted in decreased renal trafficking. Additionally, aGM treatment of BMD-Macs, CD38+ BMD-M1 Macs, BMD-DCs, and CD38+ BMD-cDC2s led to decreased pro-inflammatory gene expression. These findings suggest that GM-CSF plays a role in SSHTN and may serve as a potential therapeutic target. Full article

Considering the importance of the automotive industry, reducing the environmental impact of automotive component manufacturing is crucial. Additionally, lightening of the latter promotes a reduction in fuel consumption throughout the vehicle’s life cycle, limiting emissions. This study presents a comprehensive approach to optimizing and manufacturing a MacPherson steering knuckle using topology optimization (TO), additive manufacturing, and rapid investment casting (RIC). Static structural simulations confirmed the mechanical integrity of the optimized design, with stress and strain values remaining within the elastic limits of the SG A536 iron alloy. The TO process achieved a 30% reduction in mass, resulting in lower material use and production costs. Additive manufacturing of optimized geometry reduced resin consumption by 27% and printing time by 9%. RIC simulations validated efficient mold filling and solidification, with porosity confined to removable riser regions. Life cycle assessment (LCA) demonstrated a 27% reduction in manufacturing environmental impact and a 31% decrease throughout the component life cycle, largely due to vehicle lightweighting. The findings highlight the potential of integrated TO and advanced manufacturing techniques to produce structurally efficient and environmentally sustainable automotive components. This workflow offers promising implications for broader industrial applications that aim to balance mechanical performance with ecological responsibility. Full article

The aim of this study was to determine, through meta-analysis, the effects of malic acid/malate addition on ruminal and blood parameters and diet digestibility in cattle. The literature search was conducted in Web of Science, Science Direct, and Google Scholar using the terms “organic acids”, “malic acid”, “malate”, and “bovine”. The database was composed of papers published between 1980 and 2023. The average effect of malate/malic acid inclusion was calculated using the “DerSimonian and Laird” random effects model. Meta-regression and subgroup analyses were conducted to explore sources of heterogeneity. Overall, malic acid (MAC) addition did not significantly affect rumen pH (ES = 0.310, p = 0.17), but subgroup analysis showed that malate increased pH (ES = 1.420, p < 0.01). MAC increased rumen propionate (ES = 0.560, p < 0.01) and total volatile fatty acids (VFAs; ES = 0.508, p = 0.03), while reducing the acetate-to-propionate ratio (p < 0.01). Starch and NDF intake were significant covariates affecting pH and VFA-related variables. MAC improved total-tract digestibility of dry matter (DM; ES = 0.547, p ≤ 0.05), crude protein (CP; ES = 0.422, p ≤ 0.05), and acid detergent fiber (ADF; ES = 0.635, p ≤ 0.05). It increased glucose levels (Overall ES = 0.170, p = 0.05) and reduced NEFA (Overall ES = −0.404, p = 0.03). In conclusion, the effectiveness of MAC depends on its chemical form. Improvements in rumen pH, fiber degradation, and blood parameters suggest more efficient energy use and potential metabolic benefits. The influence of diet-related covariates suggests that the response to MAC may vary depending on the nutritional composition of the diet. Full article

Unconventional food plants (UFPs) are increasingly valued for their nutritional composition and bioactive potential. This study proposes a comprehensive characterization of the chemical and bioactive properties of Pereskia aculeata Miller (Cactaceae) (PA); Xanthosoma sagittifolium (L.) Schott (Araceae) (XS); Stachys byzantina K. Koch (Lamiaceae) (SB); and inflorescences from three cultivars of Musa acuminata (Musaceae) var. Dwarf Cavendish, var. BRS Platina, and var. BRS Conquista (MAD, MAP, and MAC), including the assessment of physical, nutritional, phytochemical, and biological parameters. Notably, detailed phenolic profiles were established for these species, many of which are poorly documented in the literature. XS was characterized by a unique abundance of C-glycosylated flavones, especially apigenin and luteolin derivatives, rarely described for this species. SB exhibited high levels of phenylethanoid glycosides, particularly verbascoside and its isomers (up to 21.32 mg/g extract), while PA was rich in O-glycosylated flavonols such as quercetin, kaempferol, and isorhamnetin derivatives. Nutritionally, XS had the highest protein content (16.3 g/100 g dw), while SB showed remarkable dietary fiber content (59.8 g/100 g). Banana inflorescences presented high fiber (up to 66.5 g/100 g) and lipid levels (up to 7.35 g/100 g). Regarding bioactivity, PA showed the highest DPPH radical scavenging activity (95.21%) and SB the highest reducing power in the FRAP assay (4085.90 µM TE/g). Cellular antioxidant activity exceeded 2000% in most samples, except for SB. Cytotoxic and anti-inflammatory activities were generally low, with only SB showing moderate effects against Caco-2 and AGS cell lines. SB and PA demonstrated the strongest antimicrobial activity, particularly against Yersinia enterocolitica, methicillin-resistant Staphylococcus aureus (MRSA), and Enterococcus faecalis, with minimum inhibitory concentrations ranging from 0.156 to 0.625 mg/mL. Linear discriminant analysis revealed distinctive chemical patterns among the species, with organic acids (e.g., oxalic up to 7.53 g/100 g) and fatty acids (e.g., linolenic acid up to 52.38%) as key discriminant variables. Overall, the study underscores the nutritional and functional relevance of these underutilized plants and contributes rare quantitative data to the scientific literature regarding their phenolic signatures. Full article

Background/Objective: One third of “non-functioning adrenal tumours” (NFAs) have mild autonomous cortisol secretion (MACS). An updated analysis of the hormonal biomarkers profile, including risk factors and the rate of post-surgery adrenal insufficiency (PSAI), the duration of restoring the normal adrenocortical function in MACS/NFA and potential impacts on clinical comorbidities. Methods: Comprehensive review based on PubMed search (January 2020–January 2025). Results: The studies (n = 14) included 2623 patients (N = 1158 underwent unilateral adrenalectomy), aged 18–93 (mean = 57.49 years), with a female-to-male ratio = 1.54. Post-adrenalectomy (n = 9, N = 753) analysis: the PSAI risk correlated with the severity of baseline hypercortisolism. PSAI incidence: 50% of MAC. The rate after 4–6 weeks follow-up was 71.9% (adrenal Cushing’s syndrome) vs. 50% (MACS) vs. 14.4% (NFA). PSAI duration was up to 35 months. Early PSAI diagnosis was reflected by post-operative cortisol assay on day 1 (cut-off ≤ 5 µg/dL) and an ACTH (Cosyntropin) stimulation test (CST) (cortisol cut-off ≤ 14 µg/dL). Pre-operatory PSAI predictors: higher serum cortisol-DST (1 mg dexamethasone testing) and lower baseline plasma ACTH (not all studies agreed). Conclusions: A stratified strategy is encouraged following a unilateral adrenalectomy in MACS; PSAI is expected in almost half of patients, with a potential improvement of hypertension. Serum cortisol assays serve as most useful biomarker as pre-operatory PSAI predictor (after DST) and, potentially, in addition with baseline ACTH. Post-surgery basal cortisol measurement (± CST) helps the decision of glucocorticoids replacement since first post-operative day and during follow-up, serial testing at 3 months is a useful tool. Full article

Perilla seed has long been recognized in traditional diets for its health-promoting properties, but its potential role in hair loss prevention remains underexplored. This study compared three extraction methods—maceration (MAC), screw pressing (SC), and supercritical fluid extraction (SFE)—to determine their efficiency in recovering bioactive compounds and their effects on androgenetic alopecia (AGA)-related pathways. The SFE extract contained the highest levels of polyunsaturated fatty acids and tocopherols, while MAC uniquely recovered a broader range of polyphenols. Among all extracts, SFE-derived perilla seed extract showed the most consistent biological effects, promoting proliferation of human hair follicle dermal papilla cells (HFDPCs) by 139.4 ± 1.1% at 72 h (p < 0.05). It also reduced TBARS and nitrite levels in HFDPCs to 66.75 ± 0.62% of control and 0.87 ± 0.01 μM, respectively, indicating strong antioxidant and anti-inflammatory effects. Importantly, the SFE extract significantly downregulated SRD5A1-3 and TGF-β1 expression—key genes involved in androgen-mediated hair follicle regression—outperforming finasteride, dutasteride, and minoxidil in vitro by approximately 1.10-fold, 1.25-fold, and 1.50-fold, respectively (p < 0.05). These findings suggest that perilla seed extract obtained via supercritical fluid extraction may offer potential as a natural candidate to prevent hair loss through multiple biological mechanisms. These in vitro results support its further investigation for potential application in functional food or nutraceutical development targeting scalp and hair health. Full article

Background: Several blood biomarkers have shown a major role in predicting major adverse complications (MACs) in patients who have undergone cardiac surgery. Here, we aimed to investigate the possible role of the blood urea nitrogen (BUN) to serum albumin ratio (BAR) and the inflammatory prognostic index (IPI) in predicting major adverse complication after surgical aorta valve replacement (SAVR). Methods: The clinical, echocardiographic, and clinical-chemistry laboratory data of 195 patients who underwent SAVR were evaluated. The post-surgical MACs (death, surgical re-exploration, myocardial infarction and cerebral ischemia) during the hospitalization were recorded. Univariate and multivariate logistic regression analyses were studied by comparing the basic clinical features, echocardiographic parameters, and patients’ hematological indices between patients with or without MACs. Results: The mean age was 66.1 years, and 62.5% were males. Logistic regression analysis showed that the left atrium volume (LAV), BAR, and IPI as either continuous or categorical variables were independently associated with MACs. Moreover, we found a combined effect of higher LAV with a higher value of BAR or IPI. Combined higher levels of LAV and BAR increased the risk of developing MACs by 9.8 (CI 95% = 2.8–34.3, p = 0.0003), while higher values of LAV and IPI increased the risk of developing MACs by 4.5. Conclusions: Higher levels of BAR and IPI, alone or in combination with higher LAVs, showed an independent predictive value of MACs after SAVR. These findings strongly support the importance of evaluating easily available biomarkers of the pre-operative status of patients in order to predict adverse outcomes. Full article

This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios. Full article

African swine fever virus (ASFV) causes global swine outbreaks, but its cellular pathogenesis is poorly understood. Using single-cell RNA data from ASFV-infected pig spleens across four timepoints, we identified macrophages as the primary viral reservoir, with infection driving lymphoid depletion and myeloid expansion. We characterized four functionally distinct macrophage subsets, including a metabolically reprogrammed SusceptibleMac population serving as the major viral niche and an AntiviralMac subset rapidly depleted during infection. Viral gene expression analysis revealed E165R as a central hub in viral replication networks, while host transcriptomics uncovered disruption of Netrin signaling pathways that may facilitate immune evasion. Pseudotime analysis revealed dynamic macrophage state transitions during infection. These findings provide a high-resolution cellular atlas of ASFV pathogenesis, revealing macrophage subset-specific responses that shape disease outcomes and identifying potential targets for therapeutic intervention. Full article