Search Results (5,183)

Ultra-dense heterogeneous cellular networks in 6G and beyond face an escalating vulnerability to cell outages stemming from complex issues like parameter misconfigurations, hidden conflicts among Autonomous Network Functions (ANFs), multivendor incompatibility, and software/hardware failures. While ANF-based automated fault detection is a core capability for next-generation networks, existing solutions are predominantly reactive, identifying faults only after reliability is compromised. To overcome this critical limitation and maintain high service quality, a proactive fault prediction capability is essential. We introduce a novel Discrete-Time Markov Chain (DTMC)-based stochastic framework designed to model network reliability dynamics. This framework forecasts the transition of a cell from normal operation to suboptimal or degraded states, offering a crucial shift from reactive to proactive fault management. Our model rigorously quantifies the effects of fault arrivals, estimates the fraction of time the network remains degraded, and, uniquely, identifies sensitive parameters whose misconfigurations pose the most significant threat to performance. Numerical evaluations demonstrate the model’s high applicability in accurately predicting both the timing and probable causes of faults. By enabling true anticipation and mitigation, this framework is a key enabler for significantly reducing the cell outage time and enhancing the reliability and resilience of next-generation wireless networks. Full article

Background/Objectives: The further application of high-coverage whole genome sequencing in fields such as paleogenomics, forensic investigations, and conservation genomics is impeded by two major barriers: extremely high costs and stringent sample requirements. Utilizing low-coverage sequencing offers a practical solution to these constraints; however, this approach introduces a primary challenge—the necessity to reconstruct distorted genomic information for downstream analysis. Methods: Analytical experiments conducted on low- to medium-coverage sequencing data confirmed the accuracy of several existing methods for inferring relationships up to the third degree and distinguishing unrelated individuals. Subsequently, efforts were made to evaluate allele-frequency-independent methods within animal genomics, where analyses are likely to encounter challenges such as uncertain allele frequencies, diverse sample types, and suboptimal sample quality. Kinship inference was performed on a total of 33 pairs of animal samples across three species, comprising nine parent–offspring pairs and four full-sibling pairs. Results: The analysis revealed that two efficient algorithm implementations (READ and KIN) successfully identified all unrelated pairs. Notably, among the various algorithms utilized, only KIN exhibited confusion between first- and second-degree relationships when subjected to. Conclusions: This study has filled a critical gap in the existing literature by conducting a comprehensive evaluation of various algorithms on low-coverage sequencing data derived from authentic human and animal samples, accompanied by detailed ground truth—a vital task that has been overlooked. Full article

The ongoing global monkeypox outbreak since 2022 has highlighted the urgent need for vaccine development. Current vaccination strategies rely on cross-protective immunity provided by orthopoxvirus-based live-attenuated vaccines. However, these vaccines not only exhibit suboptimal efficacy against monkeypox virus (MPXV) but also raise safety concerns, particularly given the significant global overlap between MPXV infections and HIV. Owing to their superior safety profile and accessibility, recombinant subunit vaccines represent a highly promising platform for monkeypox vaccine development. In this study, we developed a subunit vaccine comprising A29L, B6R, and M1R antigens formulated with a proprietary nanoemulsion adjuvant and evaluated its immunogenicity and protective efficacy. In mice immunized with a prime-boost regimen of the three individual antigens combined with the nanoemulsion adjuvant, comparable serum IgG levels against each antigen were elicited. Both A29 and M1 formulations induced serum antibodies with potent neutralizing activity against MPXV and Vaccinia virus Western Reserve strain (VACV-WR). Notably, M1 antiserum exhibited stronger neutralization than A29 antiserum, whereas B6R immune serum showed no significant neutralizing activity. Splenocytes from B6R-immunized mice mounted a robust IFN-γ response, which was markedly lower in those immunized with A29 or M1. All three monovalent vaccines conferred complete survival following an intranasal lethal MPXV challenge, with M1 providing the strongest protection. In a lethal VACV-WR challenge model, only M1 immunization conferred significant protection. Histopathological analysis of lung tissues on day 5 post-infection revealed more pronounced inflammatory features in B6R-immunized mice compared to the nanoemulsion adjuvant control group. Furthermore, the nanoemulsion-adjuvanted bivalent A29L + B6R formulation induced significantly higher IgG and neutralizing antibody titers and demonstrated superior protective efficacy compared to the aluminum hydroxide-adjuvanted formulation. This comparative preclinical evaluation provides important evidence to support the development of a safe and effective subunit vaccine against monkeypox. Full article

Material Extrusion (ME) is a layer-by-layer additive manufacturing technique that has gained prominence due to its simplicity, cost-effectiveness, design freedom, and adaptability to a wide range of thermoplastic materials. However, the mechanical performance of ME-printed parts often remains suboptimal, primarily due to complex thermal phenomena that govern microstructural development during the printing process, which are key determinants of mechanical strength. As a result, optimizing thermodynamic printing parameters has become essential for improving the overall quality of the printed parts. Extensive research articles and reviews have been published to explore the effect of many ME printing parameter settings on the resultant product characteristics. Despite this focus, the effect of cooling rate, a critical thermodynamic parameter of the process, has been largely overlooked in current research when they are critically reviewed. Cooling rate plays a central role in determining the thermal history of printed material, which in turn influences polymer chain mobility and microstructural features of the extruded material, all of which are crucial to the mechanical integrity of the printed part. Thus, it has been concluded by this review that analytical and empirical investigations into the influence of cooling rate on the microstructural properties of ME parts represent a valuable and novel contribution to the academic field. Full article

Rapid advancements in surveying technology have necessitated the development of more accurate and efficient tools. Leica Geosystems AG (Heerbrugg, Switzerland), a leading provider of measurement and surveying solutions, has initiated a study to enhance the capabilities of its GNSS INS-based surveying systems. This research focuses on integrating the Leica GS18 I GNSS receiver and the AP20 AutoPole with a Time of Flight (ToF) camera through sensor fusion. The primary objective is to leverage the unique strengths of each device to improve accuracy, efficiency, and usability in challenging surveying environments. Results indicate that the fused AP20 configuration achieves decimetre-level accuracy (2.7–4.4 cm on signalized points; 5.2–20.0 cm on natural features). In contrast, the GS18 I fused configuration shows significantly higher errors (17.5–26.6 cm on signalized points; 16.1–69.4 cm on natural features), suggesting suboptimal spatio-temporal fusion. These findings confirm that the fused AP20 configuration demonstrates superior accuracy in challenging GNSS conditions compared to the GS18 I setup with deviations within acceptable limits for most practical applications, while highlighting the need for further refinement of the GS18 I configuration. Full article

Percutaneous coronary intervention (PCI) has evolved significantly over the past two decades, yet challenges in achieving optimal stent deployment and long-term outcomes persist, particularly in complex coronary anatomy. Intravascular imaging (IVI) modalities such as intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near-infrared spectroscopy (NIRS) have transformed the precision of PCI by providing detailed cross-sectional visualization of vessel architecture, plaque morphology, and stent apposition. Compared to angiography-guided PCI, imaging-guided PCI enables more accurate lesion assessment, appropriate stent sizing, and detection of suboptimal results including under-expansion, malapposition, and edge dissections, factors strongly linked to restenosis and stent thrombosis. Large-scale randomized trials (e.g., ULTIMATE, ILUMIEN) and meta-analyses have demonstrated that imaging-guided PCI reduces major adverse cardiovascular events (MACE) and improves long-term stent patency, particularly in left main, bifurcation, and calcified lesions. Despite these benefits, adoption remains variable due to cost, procedural complexity, and training gaps. Emerging advances, including artificial intelligence-enhanced imaging, hybrid devices, and fusion of imaging with physiologic assessments, promise to integrate imaging more seamlessly into routine practice. This review summarizes current evidence, practical applications, and future directions of IVI-guided PCI, underscoring its growing role in contemporary interventional cardiology and its potential to personalize and optimize coronary revascularization strategies. Full article

Soybean (Glycine max) is a globally important crop, but its productivity is often limited by suboptimal nodulation and nitrogen fixation, particularly under stress conditions. Bradyrhizobium diazoefficiens strain USDA110 is widely applied to enhance nodulation, yet its efficiency can be further improved by phytohormone modulation. This study examined the effects of seed coatings containing plant growth regulators (PGRs)—acetylsalicylic acid (ASA), aminoethoxyvinylglycine (AVG), Indole-3-butyric acid (IBA), and 6-benzylaminopurine (BAP)—at varying concentrations (5, 50, and 500 nM), in combination with USDA110, on nodulation, nitrogenase activity, ethylene emission, physiological traits, and yield of soybean cultivar CM60. Laboratory assays identified 50 nM AVG, 5 nM IBA, and 5 nM ASA as optimal treatments, significantly enhancing nodule number and nitrogenase activity more than 32% and 28%, as, respectively, compared to untreated seeds. Greenhouse trials in pots, both under well-watered and water stress conditions, showed that USDA110 + AVG/IBA significantly improved photosynthetic rate (+21 and +18% compared to USDA110 alone) and increased plant height. Notably, USDA110 + AVG/IBA treatments sustained higher seed weight under drought, increasing it by over 25%, indicating strong synergistic effects in mitigating stress impacts. These findings highlighted that integrating USDA110 with specific PGRs represented a promising strategy to optimize nitrogen fixation and enhanced soybean productivity under both favorable and challenging conditions. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

In light of ongoing advancements in smart manufacturing, there is a growing need for intelligent fault diagnosis methods that maintain reliability under noisy, high-variability operating conditions. Conventional feature selection strategies often struggle when data contain outliers or suboptimal feature subsets, limiting their diagnostic utility. This study introduces a density-based feature space optimization (DBFSO) framework that integrates feature selection with localized density estimation to enhance feature space separability and classifier efficiency. Using k-nearest neighbor density estimation, the method identifies and removes low-density feature vectors associated with noise or outlier behavior, thereby sharpening the feature space and improving class discriminability. Experiments using roll-to-roll (R2R) manufacturing data under mechanical disturbances demonstrate that DBFSO improves classification accuracy by up to 36–40% when suboptimal feature subsets are used and reduces training time by 60–71% due to reduced feature space volume. Even with already-optimized feature sets, DBFSO provides consistent performance gains and increased robustness against operational variability. Additional validation using a bearing fault dataset confirms that the framework generalizes across domains, yielding improved accuracy and significantly more compact, noise-resistant feature representations. These findings highlight DBFSO as an effective preprocessing strategy for intelligent fault diagnosis in intelligent manufacturing systems. Full article

Lithofacies, as an integrated representation of key reservoir attributes including mineral composition and organic matter enrichment, provides crucial geological and engineering guidance for identifying “dual sweet spots” and designing fracturing strategies in deep shale gas reservoirs. However, reliable lithofacies characterization remains particularly challenging owing to significant reservoir heterogeneity, scarce core data, and imbalanced facies distribution. Conventional manual log interpretation tends to be cost prohibitive and inaccurate, while existing intelligent algorithms suffer from inadequate robustness and suboptimal efficiency, failing to meet demands for both precision and practicality in such complex reservoirs. To address these limitations, this study developed a super-integrated lithofacies identification model termed SRLCL, leveraging well-logging data and lithofacies classifications. The proposed framework synergistically combines multiple modeling advantages while maintaining a balance between data characteristics and optimization effectiveness. Specifically, SRLCL incorporates three key components: Newton-Weighted Oversampling (NWO) to mitigate data scarcity and class imbalance, the Polar Light Optimizer (PLO) to accelerate convergence and enhance optimization performance, and a Stacking ensemble architecture that integrates five heterogeneous algorithms—Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—to overcome the representational limitations of single-model or homogeneous ensemble approaches. Experimental results indicated that the NWO-PLO-SRLCL model achieved an overall accuracy of 93% in lithofacies identification, exceeding conventional methods by more than 6% while demonstrating remarkable generalization capability and stability. Furthermore, production simulations of fractured horizontal wells based on the lithofacies-controlled geological model showed only a 6.18% deviation from actual cumulative gas production, underscoring how accurate lithofacies identification facilitates development strategy optimization and provides a reliable foundation for efficient deep shale gas development. Full article

Background: Botulinum toxin injection is one of the most common esthetic procedures, yet complications may occur due to anatomical variability or suboptimal injection technique. This study aimed to evaluate the upper facial muscles using ultrasound, focusing on inter- and intraindividual variability. Methods: The study involved volunteers aged 21–40 years, excluding those with prior facial treatments, trauma, or muscle disorders. The muscles examined included the occipitofrontalis (frontal belly), procerus, corrugator supercilii, and orbicularis oculi. Muscle thickness and distance from the epidermis were measured using high-frequency ultrasound. Statistical analyses included descriptive statistics, correlation with age and BMI, sex comparisons, and symmetry assessment. Results: A total of 127 participants (103 women and 24 men) were enrolled, with a mean age of 28.8 ± 4.4 years. Age showed no significant correlation with muscle thickness or depth, supporting the internal consistency of the studied age group. BMI showed moderate correlations with the depth of the selected forehead muscles. Males showed greater thickness in the frontal and procerus muscles. Relative side-to-side asymmetry coefficients reached 40% for both thickness and depth, indicating notable individual laterality. Conclusions: The study provides normative ultrasound parameters for the upper facial muscle in healthy adults. The results demonstrate significant anatomical variability depending on sex, BMI, and facial laterality, supporting individualized ultrasound-guided approaches for botulinum toxin injection. Full article

Purpose: This study investigates the association between obstructive sleep apnea (OSA), dry eye disease (DED), and glaucoma, focusing on the impact of positive airway pressure (PAP) usage and air leakage. Methods: This retrospective cross-sectional study included 57 adults with polysomnography-confirmed OSA between 2010 and 2023. Participants were grouped into PAP users (PAP+, n = 40) and non-users (PAP−, n = 17). Ocular assessments included tear film break-up time, Schirmer’s test, Oxford staining, meibomian gland evaluation, intraocular pressure, cup-to-disc (C/D) ratio, and retinal nerve fiber layer thickness. PAP device data (usage duration and air leak rate) and OSA severity metrics were recorded. Group comparisons used chi-square and Student’s t-test, and regression analyses identified associations between PAP leakage and ocular parameters. Results: Among the 57 OSA patients, PAP users showed a trend toward a higher risk of glaucoma (OR = 0.83) and DED (OR = 0.69) compared to non-users, but neither trend was statistically significant. PAP users had significantly more severe OSA, including longer N1 sleep stage (p = 0.0005), higher apnea-hypopnea index (AHI, p = 0.0001), and poorer oxygenation. PAP leakage: 95% (mean = 25.84 L/min) exceeded the 24 L/min threshold specified in ResMed’s clinical guidelines, suggesting suboptimal therapy. Higher PAP leak was significantly associated with a lower Schirmer’s test value (p = 0.031) and a higher C/D ratio (p = 0.040) on regression analysis. However, no significant differences were found in ophthalmic parameters between PAP+ and PAP− groups. Conclusions: Suboptimal PAP therapy as mask leakage or nocturnal hemodynamic changes may worsen evaporative dry eye and affect intraocular pressure. Our findings highlight the association between PAP mask leakage and reduced tear production, and suggest that OSA-related optic nerve stress may persist unless both hypoxia and nocturnal IOP fluctuations are properly managed. However, due to the relatively small sample size and retrospective cross-sectional design, future prospective studies with larger cohorts are needed to confirm these associations. Full article

Background: Androgen deprivation therapy (ADT) is a fundamental component of treatment for metastatic hormone-sensitive prostate cancer (mHSPC), but it accelerates bone mineral density loss and increases fracture risk. International guidelines recommend calcium and vitamin D supplementation, baseline dual-energy X-ray absorptiometry (DXA), and antiresorptive therapy in patients with osteoporosis. Methods: We conducted a retrospective review of 156 mHSPC patients treated with ADT at a tertiary hospital between January 2022 and December 2024. We assessed adherence to guideline-recommended bone health measures. Collected variables included age, ADT duration, calcium/vitamin D supplementation, DXA testing, antiresorptive treatment, and fracture events. Exploratory stratified analyses were performed, and proportions were reported with 95% confidence intervals (CIs). Results: Calcium/vitamin D supplementation was prescribed in 50.6% of patients (95% CI: 42.9–58.4), baseline DXA was performed in 12.8% (95% CI: 8.5–18.9), and denosumab was administered in 5.1% of the cohort (95% CI: 2.6–9.8). The median follow-up was 23 months, with a fracture incidence of 0.67 events per 100 person-years. Stratified analyses showed lower adherence in older patients, those with prolonged ADT exposure, and those with high metastatic burden. Conclusions: Adherence to guideline-recommended bone health measures in patients with mHSPC receiving ADT was markedly suboptimal. These findings underscore the need to implement standardized institutional protocols to ensure systematic supplementation, routine DXA monitoring, and appropriate antiresorptive therapy. Full article

Background: Highly pathogenic avian influenza (HPAI) remains a significant threat to poultry production in China. Layer specialized households (LSHs)—characterized by medium-scale operations (1000–15,000 birds)—are particularly vulnerable due to frequently suboptimal biosecurity measures. Cooperative prevention and control (CPC) strategies, including unified disinfection and joint monitoring, present a viable method for reducing HPAI risks. However, participation among LSHs remains low. Objectives: This study seeks to identify the key determinants influencing LSHs’ willingness to participate in CPC measures against HPAI, and further compare these driving factors across villages with versus without a documented history of HPAI outbreaks. Methods: A survey of 130 LSHs was conducted in two Chinese villages: Village A (with HPAI history) and Village B (without HPAI history). Data on socio-economic characteristics, production practices, and attitudes were collected via structured questionnaires. An Ordered Probit model was employed to analyze determinants of willingness to participate, measured on a 5-point ordinal scale. Results: Full-sample regression analysis demonstrated that older age, higher educational attainment, risk-tolerant attitudes, larger household scale, and higher annual household income exerted a significantly positive impact on the willingness to participate in the program. Surprisingly, greater knowledge of avian influenza exerted a significant negative effect. Regional subgroup analyses identified distinct drivers: in Village A (with HPAI history), prior experience of bird deaths positively influenced willingness, whereas longer breeding experience had a negative effect. In Village B (no HPAI history), frequent social interaction and greater breeding experience emerged as positive determinants. Conclusions: The findings underscore the critical role of regional context and specific household characteristics in shaping LSHs’ willingness to cooperate. Policy interventions to promote CPC should be regionally tailored: in previously affected areas, leveraging past outbreak experiences and ensuring equitable compensation is essential; in unaffected areas, fostering social networks and demonstrating CPC benefits through peer influence are more effective. Full article

Sleep disturbances, affecting nearly half of middle-aged adults, have emerged as modifiable determinants of brain health and dementia risk. Recent advances in machine learning applied to MRI enable the estimation of “brain age,” a biomarker that quantifies deviation from normative neural aging. This review synthesizes and critically evaluates converging evidence that poor sleep accelerates biological brain aging, identifies mechanistic pathways, and delineates translational barriers to clinical application. Across large-scale cohorts comprising more than 25,000 participants, suboptimal sleep independently predicts 1–3 years of MRI-derived brain age acceleration, even after adjusting for vascular and metabolic confounders. Objective sleep fragmentation and altered sleep-stage architecture exhibit sleep-specific neuroanatomical signatures, independent of amyloid and tau pathology, while inflammatory, vascular, and glymphatic mechanisms mediate a small fraction of the effect. Experimental sleep deprivation studies demonstrate reversibility of accelerated brain aging, highlighting opportunities for early intervention. Translation to clinical practice is constrained by methodological heterogeneity, reliance on self-reported sleep metrics, limited population diversity, and the absence of randomized intervention trials demonstrating causal reversibility. Addressing these gaps through standardized MRI-based biomarkers, longitudinal mechanistic studies, and interventional trials could establish sleep optimization as a viable neuroprotective strategy for dementia prevention. Full article