Search Results (99,176)

Background: Bayesian model-informed precision dosing (MIPD) is increasingly used to individualize drug therapy; therefore, this review aimed to identify and characterize its implementation in routine clinical practice. Methods: A focused systematic review was conducted. Web of Science Core Collection and PubMed were searched from inception to February 2026. Eligible studies were original research articles evaluating Bayesian MIPD in routine clinical practice using software platforms that supported dosing decisions. Data were synthesized descriptively. No formal risk-of-bias assessment was performed due to heterogeneity in study design. Results: Fifteen studies met the inclusion criteria. Anti-infective therapy predominated, particularly vancomycin (n = 11), with additional studies involving busulfan, mycophenolate mofetil, amikacin, and tobramycin. Commonly reported software platforms included InsightRx (n = 6) and DoseMeRx (n = 4), along with Abbottbase, NextDose, and ISBA. MIPD was mainly applied with therapeutic drug monitoring, reflecting predominant a posteriori use in routine care. Across studies, implementation was associated with improved pharmacokinetic target attainment, while a subset reported clinical benefits, including reduced nephrotoxicity and favorable effectiveness-related outcomes. Pharmacist involvement was commonly described. Conclusions: Published evidence indicates that Bayesian MIPD is being implemented in routine clinical settings, but current published experience is dominated by vancomycin-focused studies. Although the evidence base remains limited, it has grown since 2020 and suggests that software-supported Bayesian dosing can improve pharmacokinetic target attainment and may support better clinical outcomes. Full article

Heart failure with reduced ejection fraction (HFrEF) is increasingly recognized as a systemic metabolic disorder. The aim of this study was to characterize amino acid-related metabolic differences between heart failure with moderately reduced ejection fraction (HFmrEF) (LVEF 40–49%) and HFrEF (LVEF < 40%) and to derive a biologically interpretable composite metabolomic index capable of discriminating between these two stages of systolic dysfunction. We conducted a cross-sectional metabolomic analysis of 42 patients stratified by left ventricular ejection fraction (LVEF < 40% vs. 40–49%). The reference group comprised patients with mildly reduced ejection fraction (LVEF 40–49%), without inclusion of individuals with preserved or normal cardiac function. Targeted amino acid profiling was performed using liquid chromatography-tandem mass spectrometry (LC–MS/MS). Metabolites were standardized and analyzed individually and in combination. A composite index (Heart Failure Amino Acid-Derived Systolic Index: HASI-40), integrating markers of proteolysis and metabolic resilience, was derived to distinguish patients with HFrEF from those with HFmrEF. Discrimination was assessed using receiver operator curve (ROC) analysis with internal validation and multivariable adjustment. Patients with LVEF < 40% exhibited a coordinated metabolic phenotype characterized by reduced methionine, sarcosine, serine, and taurine. While individual metabolites did not retain significance after multiple-testing correction, the composite HASI-40 index remained strongly associated with HFrEF (OR 5.56, 95% CI: 1.70–18.14; p = 0.004), although the wide confidence interval indicates limited precision due to sample size. The index demonstrated good discrimination with an area under the curve (AUC) of 0.862, which improved when combined with age (AUC 0.932). The index represents a standardized composite measure and does not define a diagnostic cutoff for individual patients. These findings suggest that HFmrEF and HFrEF exhibit partially distinct metabolic phenotypes despite overlapping clinical characteristics. These findings suggest that HASI-40 captures metabolic differences between patients with HFmrEF (LVEF 40–49%) and those with HFrEF (LVEF < 40%), reflecting progression toward more advanced systolic dysfunction. However, due to the absence of a control group with preserved ejection fraction, small sample size, and lack of external validation, the index should be considered exploratory and hypothesis-generating rather than clinically applicable. Full article

Introduction/Objective: Patient satisfaction with nursing care quality is an important patient-reported indicator of hospitalization experience. Previous studies have mainly examined sociodemographic, clinical, and organizational factors, while personality traits have rarely been included in explanatory models. This study examined the association of sociodemographic characteristics, hospitalization-related variables, and personality traits with patient satisfaction. Methods: A single-center cross-sectional study was conducted among hospitalized patients in a general hospital in Croatia. Data were collected at discharge using a demographic and hospitalization questionnaire, the NEO Five-Factor Inventory, and the Croatian version of the Patient Satisfaction with Nursing Care Quality Questionnaire. Group differences were analyzed using non-parametric tests, and hierarchical regression analysis was performed. Results: Younger age, employment, male gender, and better self-rated health were associated with higher satisfaction. Patients admitted on a scheduled basis and those staying alone or with one other person in the room were more satisfied. Sociodemographic variables explained 21.5% of the variance in satisfaction (R² = 0.215; adjusted R² = 0.168). After hospitalization-related variables were added, the explained variance increased to 30.1% (R² = 0.301; adjusted R² = 0.232). The addition of personality traits further increased the explained variance to 45.6% (R² = 0.456; adjusted R² = 0.385). In the final model, staying with two or more persons was negatively associated with satisfaction, whereas agreeableness and conscientiousness were positively associated with satisfaction. Conclusions: Patient satisfaction with nursing care quality was associated with patient characteristics, hospitalization conditions, and personality traits. Accommodation conditions and individual psychological differences should be considered when interpreting satisfaction as an indicator of nursing care quality. Full article

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process. Full article

Effective laboratory training is essential in engineering education, yet conventional on-site instruction is often constrained by time, accessibility, and safety considerations. To address these challenges, this study presents the design, implementation, and evaluation of a web-based virtual reality (WebVR) representation of a large-scale engineering laboratory constructed from massive colorized point cloud data. This study proposes a novel WebVR approach that integrates Unity and Potree for high-fidelity point-cloud visualization combined with advanced interactive capabilities in a browser-based virtual laboratory. It supports immersive first-person exploration, guided navigation, interactive hotspots conveying equipment and safety information, and emergency evacuation simulations. The usability, usefulness, and acceptance of the virtual laboratory were evaluated through an anonymous questionnaire administered to students and laboratory staff. User evaluation results indicated consistently positive feedback, with 100% of respondents rating the interface/navigation and visual/interactive content as good or excellent, 88.6% identifying scene realism as the biggest system strength (the most frequently selected), 74.3% reporting significantly higher engagement compared with traditional online laboratory training, and 82.9% indicating they would definitely recommend the system as a learning resource. In addition, a thematic analysis of qualitative feedback was performed to inform future enhancements of the WebVR environment. Overall, the findings demonstrate that the WebVR-based virtual laboratory can effectively complement conventional on-site laboratory instruction, offering a scalable, accessible, and low-risk platform that enhances learning experiences in engineering education. Full article

Enterprise bankruptcy prediction is a critical research issue in financial risk early warning, credit evaluation, and investment decision-making. To address the limitations of traditional methods in handling high-dimensional, nonlinear, and complex financial data, including parameter sensitivity, susceptibility to local optima, and insufficient prediction stability, this study proposes a multi-strategy enhanced Beaver Behavior Optimizer and applies it to optimize kernel extreme learning machines, constructing the MEBBO KELM prediction model. Three improvement mechanisms are introduced, including an elite pool enhanced exploration strategy, a stochastic centroid reverse learning strategy, and a leader guided boundary control strategy, which improve population diversity, global search capability, boundary handling capacity, and convergence accuracy. The proposed algorithm is evaluated on CEC2017 and CEC2022 benchmark datasets and compared with EWOA, HPHHO, MELGWO, TACPSO, CFOA, ALA, AOO, RIME, and BBO. Statistical analyses are conducted using the Wilcoxon rank sum test and the Friedman test. The results demonstrate that MEBBO achieves superior solution accuracy and stability, indicating strong global optimization capability and robustness. Further experiments on the Wieslaw Corporate Bankruptcy Dataset show that MEBBO-KELM achieves strong and robust performance across multiple evaluation metrics, including ACC, MCC, Sensitivity, Specificity, Precision, Recall, and F1 score. Specifically, ACC reaches 79.7578, MCC reaches 0.6050, and F1 score reaches 78.8504, confirming its effectiveness. Full article

Global climate warming has led to the frequent occurrence of natural disasters, threatening the stability of agricultural production and the survival of agricultural enterprises. The existing literature presents mixed evidence regarding the impact of natural disasters on corporate ESG performance. Some studies argue that natural disasters promote ESG performance; however, such conclusions only hold for non-agricultural enterprises. Agricultural enterprises are highly dependent on natural conditions, and their core production factors are vulnerable to direct damage from natural disasters. Meanwhile, they are characterized by long production cycles and high asset specificity. After disaster shocks, agricultural enterprises have to prioritize production recovery, so natural disasters exert a dominant negative effect on their ESG performance. Based on the above context, here we take the performance of Chinese A-share listed agricultural companies between 2010 and 2023 as the research sample to explore the impact of natural disasters on the ESG performance of agricultural enterprises. The empirical results show that natural disasters significantly inhibit the ESG performance of agricultural enterprises. Mechanism tests indicate that natural disasters weaken ESG performance by damaging supply chain resilience, hindering green innovation, and disrupting internal control. A cross-sectional heterogeneity analysis reveals that the inhibitory effect is more pronounced for large-scale enterprises, enterprises with lower executive green cognition, and enterprises located in areas that are not major grain-selling areas. This study enriches the research on the economic consequences of natural disasters and the factors influencing corporate ESG performance. It also provides important practical implications for strengthening the ESG fulfillment of agricultural enterprises and accelerating the cultivation of new productive forces in agriculture. Full article

The genus Bacillus is widely recognized for its metabolic versatility, enabling it to colonize extreme environments, including sites contaminated with metals. In this study, we report the genome of B. velezensis strain HM1, isolated from sulfur-rich mine tailings from silver mining activities in southwestern Mexico. Isolation was performed by heat treatment followed by selective cultivation in a medium enriched with mine tailings extract (metals and sulfates), resulting in a single dominant morphotype corresponding to strain HM1. Whole-genome sequencing was carried out using the Illumina NovaSeq platform (2 × 250 bp). The assembled genome of strain HM1 has a size of 4,044,128 bp, distributed across 20 contigs, with an N50 of 700,388 bp and an L50 of 3, and an average coverage of 66.8×. The GC content was 46.31%, with an estimated completeness of 99.81% and contamination of 0.01%. Genome analyses indicate that the assembly corresponds to a single chromosome, with no evidence of plasmid replicons. Genome annotation identified 3950 coding sequences (CDSs), 83 tRNAs, 11 rRNAs, 26 ncRNAs, and 4 sORFs. Phylogenomic analysis, together with genomic similarity metrics (ANI > 98.6%, AAI > 98.8%, dDDH > 87%), confirms its classification as Bacillus velezensis. Functionally, the genome encodes multiple genes involved in resistance to metals and metalloids (including ABC transporters, efflux pumps, and biotransformation enzymes), as well as a complete pathway for sulfate assimilation. Collectively, these genomic features reveal a broad repertoire of adaptive strategies employed by strain HM1 to thrive in metal-contaminated environments. Full article

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

Contactless vital signs monitoring is becoming increasingly relevant in scenarios where conventional sensors are impractical or not recommended. In this manuscript, a radar-based contactless system for the joint reconstruction of phonocardiogram (PCG) waveforms and cardiac state segmentation is illustrated. The proposed method exploits a self-attention one-dimensional (1D) U-Net fed by a pre-processed radar-derived input to estimate a PCG-like waveform, its envelope, and the four main cardiac phases: S1, systole, S2, and diastole. The accuracy of our method has been assessed on a public synchronized radar–PCG dataset acquired by means of a 24 GHz Doppler radar and a digital stethoscope. On the test subset, the proposed model achieved a 13.4885 dB reduction in log-spectral distance relative to the radar input signal, indicating a marked improvement in waveform fidelity. Segmentation performance also improved, with Micro-${\mathrm{F}}_1$ increasing from 74.41% to 84.17% and Macro-${\mathrm{F}}_1$ from 68.40% to 80.43% on average. Experimental results demonstrated the viability of real-time low-power embedded hardware deployment for contactless auscultation and continuous cardiac monitoring applications. The findings confirm that respiratory interference and low-amplitude signals complicate S2 detection, especially when exacerbated by subject motion. Full article

The pantograph–catenary system is a crucial component of rail transit vehicles, performing the vital function of electric energy transmission. During train operation, the current-carrying components continuously emit particulate matter into the surrounding environment due to friction, and these particulate emissions have a significant impact on human health. However, research on the correlation between the current-carrying friction of carbon contact strips and particulate matter emission characteristics is rarely reported. Based on a semi-enclosed pin-on-disc current-carrying friction and wear test rig, this paper investigates the effects of varying current intensity under different contact load conditions on the friction and wear performance of carbon/copper pairs, as well as the associated particulate matter emission behavior. It reveals the damage characteristics of carbon contact strips, the particulate matter emission characteristics, and the relationship between them under different service conditions. The results indicate that the wear mechanism and particulate matter emission behavior of carbon contact strips are jointly influenced by current magnitude and contact load. In the absence of current, increasing the load exacerbates the mechanical wear on the carbon friction pair surface, while elevating the emission concentration of particles of various sizes and stabilizing the particle size distribution. Under current-carrying conditions, a higher contact load effectively reduces the frequency of arc discharges between the friction pair. Meanwhile, the degree of arc erosion on the contact surface worsens with increasing current intensity. Arc discharges instantaneously lead to a sharp increase in particulate emissions, and the higher the discharge intensity or the greater the number of discharges, the higher the particulate concentration around the contact pair. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

Enhancing egg production in geese without antibiotics remains a challenge in poultry science. This study compared the effects of Lactobacillus (LAB) and Bacillus (BAC) probiotics on laying performance, gut microbiota, and serum metabolism in Zhedong White geese. Birds were fed a control diet or diets supplemented with LAB or BAC. Egg production and quality were monitored throughout the trial. Serum metabolomics and fecal 16S rRNA sequencing were integrated with KEGG enrichment and correlation analyses to uncover functional mechanisms. Both probiotics improved laying performance and egg quality. Total egg production of the LAB group was 8.5% higher than that of the BAC group (p < 0.05). The LAB group’s advantage in egg production was consistent with its stronger activation of the steroid hormone biosynthesis pathway (elevated serum corticosterone and tetrahydrocorticosterone indicated an overall enhancement of steroidogenic flux). Simultaneously, the LAB group exhibited a more efficient conversion of L-phenylalanine to catecholamine precursors, which drove activation of the neuroendocrine reproductive axis. The BAC group showed more significant changes in nitrogen and energy metabolism pathways and a more pronounced expansion of energy-harvesting Firmicutes. These findings reveal two strain-specific regulatory pathways: LAB functions through the “aromatic amino acid–neuroendocrine–steroid hormone axis,” while BAC relies on the “gut microbiota–energy metabolism” pathway, with direct implications for the precise application of probiotics under antibiotic-free farming conditions. Full article

To address the low mechanization level, high labor intensity, and severe substrate disturbance in intertidal shellfish harvesting, a vibratory harvesting method based on local vibration-induced substrate fluidization was proposed, and a vibratory harvesting device for Mactra veneriformis was developed. Bench and intertidal field tests were conducted to systematically investigate the effects of vibration frequency, vibration pressure, and vibration amplitude on substrate fluidization, clam uplift, and harvesting performance. The single-factor results showed that all three parameters significantly affected the pore water pressure ratio, substrate viscosity, uplift distance, and harvesting rate, with better fluidization obtained at 8 Hz, 30 kPa, and 25 mm. A Box–Behnken response surface design was further used to establish quadratic regression models for these responses, and all models were highly significant with a non-significant lack of fit. The optimized parameter combination was 10 Hz, 35 kPa, and 25 mm, under which the predicted pore water pressure ratio and uplift distance were 101.3% and 97.2 mm, respectively, and the substrate viscosity was 1364 Pa·s. Field tests showed that the pore water pressure ratio remained above 85.3%, viscosity decreased to 1331–2639 Pa·s, shear strength decreased by 57.2–64.9%, and the average uplift distance at 100 mm burial depth reached 80–92 mm. The results indicate that vibratory harvesting can effectively promote substrate fluidization and reduce clam uplift resistance, providing a reference for the development of low-disturbance mechanized harvesting equipment for intertidal shellfish. Full article

Background: Antigenic drift limits the protective efficacy of influenza vaccine. Glycosylation of hemagglutinin (HA) represents a promising immunofocusing strategy that enhances neutralizing antibody responses by masking immunodominant non-neutralizing epitopes. Methods: B-cell epitopes of influenza viruses were retrieved from the Immune Epitope Database and were mapped onto the HA structure of A/Puerto Rico/8/1934 (H1N1). Structure-guided analysis identified residues 136 and 137 as candidate sites for N-linked glycosylation (NLG). Single-site mutants (136NLG and 137NLG) were generated using reverse genetics and evaluated for stability, receptor binding, viral replication, and immunogenicity in a murine model with inactivated whole-virus vaccines. Results: Both mutants exhibited increased thermostability at 42 °C. Glycosylation reduced the HA–sialic acid affinity, resulting in decreased viral adsorption and internalization efficiency in MDCK cells, and delayed viral replication at low multiplicity of infection (MOI). In vivo, all vaccine groups provided complete protection against lethal challenge; notably, the 136NLG group exhibited reduced weight loss, indicating improved protective efficacy compared with wild-type (WT). Conclusions: Targeted glycosylation at residue 136 in the HA head domain effectively enhances the viral stability and elicits a 1.78-fold increase in hemagglutination inhibition titer (GMT) relative to the WT, thereby improving vaccine performance. These findings establish a rational and structure-based design strategy for developing more stable and effective influenza vaccines. Full article