Search Results (56)

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

The complementary dual of entropy has received significant attention in the literature. Due to the emergence of many generalizations and extensions of entropy, the need to generalize the complementary dual of uncertainty arose. This article develops the residual cumulative generalized fractional extropy as a generalization of the residual cumulative complementary dual of entropy. Many properties, including convergence, transformation, bounds, recurrence relations, and connections with other measures, are discussed. Moreover, the proposed measure’s order statistics and stochastic order are examined. Furthermore, the dynamic design of the measure, its properties, and its characterization are considered. Finally, nonparametric estimation via empirical residual cumulative generalized fractional extropy with an application to blood transfusion is performed. Full article

Lithium-ion batteries experience nonlinear degradation characteristics during long-term operation. Accurate estimation of their remaining useful life (RUL) is of significant importance for early fault diagnosis and residual value evaluation. However, existing RUL prediction approaches often suffer from limited accuracy and insufficient specificity. To address these limitations, this study proposes an RUL prediction methodology based on Gaussian process regression, which incorporates degradation pattern recognition and auxiliary features derived from knee points. First, 9 health-related features are extracted from the first 100 charge/discharge cycles of the battery. Based on these extracted features, clustering and classification techniques are employed to categorize the batteries into three distinct degradation patterns. Moreover, feature importance is assessed to identify and eliminate redundant indicators, thereby enhancing the relevance of the feature set for prediction. Subsequently, for each degradation pattern, GPR-based models with composite kernel functions are constructed by integrating knee point positions and their corresponding slopes. The model hyperparameters are further optimized through the particle swarm optimization (PSO) algorithm to improve the adaptability and generalization capability of the predictive models. Experimental results demonstrate that the proposed method achieves a high level of predictive accuracy, with an overall mean absolute percentage error (MAPE) of approximately 8.70%. Furthermore, compared with conventional prediction methods, the proposed approach exhibits superior performance and can serve as an effective evaluation tool for diverse application scenarios, including lithium-ion battery health monitoring, early prognostics, and echelon utilization. Full article

All beta- and gamma-herpesviruses utilize a set of six viral proteins to facilitate transcription from specific promoters that become active late in the viral life cycle. These proteins form a complex that interacts with a TA-rich sequence upstream of the late transcription start sites and recruits RNA polymerase II (Pol II). The structure of any of the late transcription factors (LTFs) alone or in complexes has not been solved by standard means yet, but a fair amount is known about how the proteins interact and where the complex is positioned over the late promoters. Here, AlphaFold3 was used to predict and analyze the LTF complex using proteins from the beta-herpesviruses HCMV, MCMV, HHV6, and HHV7, and from the gamma-herpesviruses EBV and KSHV. The predicted structures had high levels of confidence and were remarkably similar even though there is little sequence conservation in the LTFs across the viruses. The results are consistent with most of the previously determined information concerning the interaction of the factors with each other and with DNA. A conserved threonine phosphorylation in one of the subunits that is critical to the function of the LTFs is predicted to be at the junction of five subunits. AlphaFold 3 predicts seven metal ion binding sites in each of the four beta-herpesviruses and either five or six in the gamma-herpesviruses created by conserved residues in three of the subunits. The structures also provide insights into the function of the subunits and which host general transcription factors (GTFs) may or may not be utilized during initiation. Full article

Diarrhea-predominant irritable bowel syndrome (IBS-D) is a functional gastrointestinal disorder characterized by altered motility, abdominal pain, and dysbiosis—particularly reduced biodiversity and a lower abundance of butyrate-producing bacteria. Strategies that modulate the gut microbiota may offer therapeutic benefit. Clostridium butyricum (C. butyricum) CBM588 is a butyrate-producing probiotic with immunomodulatory properties and potential efficacy in treating gastrointestinal disorders. This pragmatic, prospective, open-label, single-arm interventional study assessed the clinical, microbial, and safety-related effects of an 8-week CBM588 supplementation, along with a low-fiber and low-residue diet, in 205 patients with IBS-D who attended Quisisana Nursing Home Hospital, Rome, Italy, between November 2024 and February 2025. The primary outcomes included the global symptom response, the Bristol Stool Scale (BSS), stool frequency, diarrhea episodes, abdominal pain (severity and frequency), bloating, bowel dissatisfaction, quality of life (QoL), safety, and treatment tolerability—measured using the IBS Symptom Severity Scale (IBS-SSS) and a standardized tolerability scale. CBM588, in patients treated with a low-fiber and low-residue diet, significantly improved all clinical endpoints, with a >80% reduction in diarrhea episodes; ~60% reductions in stool frequency and abdominal pain; and >50% improvements in bloating, bowel dissatisfaction, and QoL. Treatment was well tolerated (mean tolerability score 8.95 ± 0.88), with >95% adherence, and no serious adverse events were reported. The secondary outcomes included changes in gut microbiota. In a subset of patients, 16S rRNA gene sequencing showed increased α-diversity and enrichment of butyrate-producing genera (Agathobacter, Butyricicoccus, Coprococcus), which correlated with symptom improvement. Bloating increased in some patients, possibly related to fermentation activity. These findings support the C. butyricum CBM588 probiotic strain as a safe, well-tolerated, and microbiota-targeted intervention for IBS-D. Randomized controlled trials are warranted to confirm efficacy. Full article

The increasing complexity of modern lifetime data necessitates the development of more flexible probability models. To address this need, we propose the power length-biased new XLindley (PLNXL) distribution, a novel two-parameter model tailored to model a wide range of lifetime datasets. Characterized by both shape and scale parameters, the PLNXL distribution effectively captures diverse hazard rate functions, including increasing, decreasing, and inverted bathtub-shaped forms. Additionally, its mean residual life function is capable of exhibiting decreasing, increasing, and bathtub-shaped behaviors, thereby enhancing its practical relevance. We derive key mathematical properties of the distribution, including moments, reliability measures, and entropy. The parameters are estimated using the maximum likelihood method, and simulation studies confirm the consistency and efficiency of the estimators. The applicability of the proposed model is illustrated using real-world datasets, where it consistently outperforms the existing models. These results highlight the robustness and adaptability of the PLNXL distribution for lifetime data analysis across a wide array of applications. Full article

We present a new one-parameter discrete Muth (DsMuth) distribution, a flexible probability mass function designed for modeling count data, particularly over-dispersed data. The proposed distribution is derived through the survival discretization approach. Several of the proposed distribution’s characteristics and reliability measures are investigated, including the mean, variance, skewness, kurtosis, probability-generating function, moments, moment-generating function, mean residual life, quantile function, and entropy. Different estimation approaches, including maximum likelihood, moments, and proportion, are explored to identify unknown distribution parameters. The performance of these estimators is assessed through simulations under different parameter settings and sample sizes. Additionally, a real dataset is used to emphasize the significance of the proposed distribution compared to other available discrete probability distributions. Full article

The digitization of uninterrupted operation in the distribution network is of great significance for improving people’s quality of life and promoting economic development. As an important means of achieving digitization, point cloud technology is crucial to the intelligent transformation of distribution network. To this end, the authors embedded the improved RSA (residual spatial attention) module and modified the loss function of network, proposing a deep learning network called RSA-PT for the semantic segmentation of a distribution network scene point cloud. According to the requirements of uninterrupted operation in the distribution network, the authors segmented the point cloud into the following ten classes: high-voltage line, low-voltage line, groundline, tower, ground, road, house, tree, obstacle, and car. Model and attention mechanism comparison experiments, as well as ablation studies, were conducted on the distribution network scene point cloud dataset. The experimental results showed that RSA-PT achieved mIoU (mean intersection over union), mA (mean accuracy), and OA (overall accuracy) indicators of 90.55%, 94.20%, and 97.20%, respectively. Furthermore, the mIoU of RSA-PT exceeded the baseline model by 6.63%. Our work could provide a technical foundation for the digital analysis of conditions for uninterrupted operation in distribution networks. Full article

This study proposes a generalized family of distributions to enhance flexibility in modeling complex engineering and biomedical data. The framework unifies existing models and improves reliability analysis in both engineering and biomedical applications by capturing diverse system behaviors. We introduce a novel hybrid family of distributions that incorporates a flexible set of hybrid functions, enabling the extension of various existing distributions. Specifically, we present a three-parameter special member called the hybrid-Weibull–exponential (HWE) distribution. We derive several fundamental mathematical properties of this new family, including moments, random data generation processes, mean residual life (MRL) and its relationship with the failure rate function, and its related asymptotic behavior. Furthermore, we compute advanced information measures, such as extropy and cumulative residual entropy, and derive order statistics along with their asymptotic behaviors. Model identifiability is demonstrated numerically using the Kullback–Leibler divergence. Additionally, we perform a stress–strength (SS) reliability analysis of the HWE under two common scale parameters, supported by illustrative numerical evaluations. For parameter estimation, we adopt the maximum likelihood estimation (MLE) method in both density estimation and SS-parameter studies. The simulation results indicated that the MLE demonstrates consistency in both density and SS-parameter estimations, with the mean squared error of the MLEs decreasing as the sample size increases. Moreover, the average length of the confidence interval for the percentile and Student’s t-bootstrap for the SS-parameter becomes smaller with larger sample sizes, and the coverage probability progressively aligns with the nominal confidence level of 95%. To demonstrate the practical effectiveness of the hybrid family, we provide three real-world data applications in which the HWE distribution outperforms many existing Weibull-based models, as measured by AIC, BIC, CAIC, KS, Anderson–Darling, and Cramer–von Mises criteria. Furthermore, the HLW exhibits strong performance in SS-parameter analysis. Consequently, this hybrid family holds immense potential for modeling lifetime data and advancing reliability and survival analysis. Full article

Background/Objectives: The objective of this study was to assess the unidimensionality and internal structure of the Arabic version of the Oswestry Disability Index (ODI) in patients with lower back pain (LBP) using the Rasch measurement model. Methods: Patients with LBP (N = 113) completed the Arabic ODI during their first visit to physical therapy departments. The Arabic ODI was examined by assessing its fit to the requirements of the Rasch measurement model. Chi-square statistics for item–trait interaction alongside mean item and person fit residuals were used for overall model fit assessment. Additionally, the analysis included assessments for the fit of individual items, the sequence of thresholds, local dependency, unidimensionality using the t-test method, and differential item functioning (DIF) by sex, age, chronicity, and the presence of radiating pain. Results: The overall fit of the Arabic ODI to the Rasch measurement model was supported by non-significant Chi-square statistics (χ² = 25.32, p = 0.19) and acceptable mean item and person fit residuals. All items showed acceptable fit (standardized fit residual −1.89 to 1.62) with no violation of local item independence. The t-test method supported the scale’s unidimensionality. The ODI showed good internal consistency with a person separation index of 0.85, with good overall targeting of item thresholds to the participants’ lower back function. Items 2, 7, and 10 showed disordered thresholds, and potential bias by sex was detected in item 9 (social life). Conclusions: The Arabic ODI is a unidimensional measure valid for assessing disability due to low back pain; however, indications of the inappropriate functioning of some response options along with potential bias by sex need to be revisited. Full article

Monitoring marine life through underwater object detection technology serves as a primary means of understanding biodiversity and ecosystem health. However, the complex marine environment, poor resolution, color distortion in underwater optical imaging, and limited computational resources all affect the accuracy and efficiency of underwater object detection. To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Module (SimAM) and Squeeze-and-Excitation Attention (SE) to form the Simple Enhancement attention Module (SME) and optimize AConv, improving the sensitivity of the model to object details. Furthermore, we introduce the lightweight DySample operator to optimize feature recovery, enabling better adaptation to the complex characteristics of underwater targets. Finally, we employ Wise-IoU version 3 (WIoU v3) as the loss function to balance the loss weights for targets of different sizes. In comparison with the YOLOv9s model, according to the experiments conducted on the UPRC and Brackish underwater datasets, YOLOv9s-SD achieves an improvement of 1.3% and 1.2% in the mean Average Precision (mAP), reaching 83.0% and 94.3% on the respective datasets and demonstrating better adaptability to intricate underwater environments. Full article

In this paper the exponentiated generalized xgamma distribution is introduced. Some of its properties are presented through some models of stress–strength, moments, mean residual life, mean past lifetime, and order statistics. The maximum likelihood estimators, confidence intervals for the parameters, and the reliability and the hazard rate functions of the exponentiated generalized xgamma distribution based on dual generalized order statistics are obtained. Bayesian estimators for the unknown parameters, reliability, and hazard rate functions of the exponentiated generalized xgamma distribution based on dual generalized order statistics are considered. The results based on lower record values are verified using simulations as well as three real sets of data are adopted to demonstrate the flexibility and potential applications of the distribution. Full article

The reshaping of End-of-Life (EoL) components by means of the sheet metal forming process has been considered largely attractive, even from the social and economic point of view. At the same time, EoL parts can often be characterized by non-uniform thicknesses or alternation of work-hardened/undeformed zones as the result of the manufacturing process. Such heterogeneity can hinder a proper reshaping of the EoL part, and residual marks on the reformed blanks can still be present at the end of the reshaping step. In a previous analysis, the authors evaluated the effectiveness of reshaping a blank with a deep-drawn feature by means of the Sheet Hydroforming (SHF) process: it was demonstrated that residual marks were still present if the deep-drawn feature was located in a region not enough strained during the reshaping step. Starting from this condition and adopting a numerical approach, additional investigations were carried out, changing the profile of the load applied by the blank holder and the maximum oil pressure. Numerical results were collected in terms of overall strain severity and residual height of the residual marks from the deep-drawn feature at the end of the reshaping step. Data were then fitted by accurate Response Surfaces trained by means of interpolant Radial Basis Functions and anisotropic Kriging algorithms, subsequently used to carry out a virtual optimization managed by multi-objective evolutionary algorithms (MOGA-II and NSGA-II). Optimization results, subsequently validated via experimental trials, provided the optimal working conditions to achieve a remarkable reduction of the marks from the deep-drawn feature without the occurrence of rupture. Full article

Background: Lower limb length discrepancy (LLD) in children and adolescents, often due to congenital or acquired conditions, is treated to achieve limb equality and alignment, optimizing function and minimizing cosmetic concerns for an active adulthood. This study evaluated the Health-Related Quality of Life (HRQoL) and physical functioning of adults who underwent unilateral limb lengthening with circular external fixators (EFs) in childhood. Methods: Fifty patients treated at a median age of 14.9 years completed the Short Form 36 (SF-36) and Stanmore Limb Reconstruction Score (SLRS) questionnaires in adulthood, with a median follow-up of 8.9 years. Results: Among the 50 patients, 38 underwent a single limb lengthening (21 tibia, 12 femur, 5 both), while 12 required multiple cycles. The median residual LLD was 0.4 cm, with 12 patients (24%) having over 2 cm. Complications occurred in 67% of procedures, mainly due to prolonged healing. Physical and mental health scores were significantly lower than normative data. The mean Physical Component Summary was 52.2 ± 7.2 (p = 0.20). The mean Mental Component Summary was 43.9 ± 8.6 (p = 0.001), notably lower in congenital LLD cases. Many SLRS items (Pain, Social, Physical Function, Work, and Emotions) strongly correlated with SF-36 items. Conclusions: Adults treated with distraction osteogenesis for congenital LLD show normal physical but lower mental health scores compared to peers. Lengthening procedure characteristics did not significantly impact mental health. Routine psychological and social assessments are recommended to prevent long-term distress by providing appropriate support. Full article

Type 1 diabetes (T1D) is a chronic disease characterized by the immune-mediated destruction of the pancreatic beta cells responsible for insulin production. The secreted insulin and C-peptide are equimolar. Due to its longer half-life, C-peptide has become a safer means of assessing the pancreatic reserve. C-peptide levels were evaluated in a population of patients with T1D, focusing on the relationship between this variable and other factors. In addition, the influence of C-peptide on metabolic control and microvascular complications was investigated. This cross-sectional study included 95 patients who had been diagnosed with T1D at least five years earlier. These patients were evaluated using a clinical demographic survey, anthropometric data, laboratory tests, and fundoscopy. This study showed that 29.5% of patients had residual insulin secretion, which correlated directly with their age at diagnosis. No statistically significant differences in metabolic control or microvascular complications were observed between the C-peptide level groups. In addition, our results indicate that ancestry does not influence the persistence of residual C-peptide function in our highly mixed population. It is recommended that future research consider incorporating new variables, such as HLA and pancreatic autoimmunity, as factors that may influence residual β-cell function. Full article