Stable Results Research Articles

Music Style Transfer with Time-Varying Inversion of Diffusion Models

With the development of diffusion models, text-guided image style transfer has demonstrated great controllable and high-quality results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the limited availability of matched audio-text datasets. Music, being an abstract and complex art form, exhibits variations and intricacies even within the same genre, thereby making accurate textual descriptions challenging. This paper presents a music style transfer approach that effectively captures musical attributes using minimal data. We introduce a novel time-varying textual inversion module to precisely capture mel-spectrogram features at different levels. During inference, we utilize a bias-reduced stylization technique to get stable results. Experimental results demonstrate that our method can transfer the style of specific instruments, as well as incorporate natural sounds to compose melodies. Samples and code are available at https://lsfhuihuiff.github.io/MusicTI/.

Parallel Empirical Evaluations: Resilience despite Concurrency

Computational evaluations are crucial in modern problem-solving when we surpass theoretical algorithms or bounds. These experiments frequently take much work, and the sheer amount of needed resources makes it impossible to execute them on a single personal computer or laptop. Cluster schedulers allow for automatizing these tasks and scale to many computers. But, when we evaluate implementations of combinatorial algorithms, we depend on stable runtime results. Common approaches either limit parallelism or suffer from unstable runtime measurements due to interference among jobs on modern hardware. The former is inefficient and not sustainable. The latter results in unreplicable experiments. In this work, we address this issue and offer an acceptable balance between efficiency, software, hardware complexity, reliability, and replicability. We investigate effects towards replicability stability and illustrate how to efficiently use widely employed cluster resources for parallel evaluations. Furthermore, we present solutions which mitigate issues that emerge from the concurrent execution of benchmark jobs. Our experimental evaluation shows that – despite parallel execution – our approach reduces the runtime instability on the majority of instances to one second.

G–LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint)

To explain the prediction result of a Deep Neural Network (DNN) model based on a given sample, LIME [1] and its derivatives have been proposed to approximate the local behavior of the DNN model around the data point via linear surrogates. Though these algorithms interpret the DNN by finding the key features used for classification, the random interpolations used by LIME would perturb the explanation result and cause the instability and inconsistency between repetitions of LIME computations. To tackle this issue, we propose G-LIME that extends the vanilla LIME through high-dimensional Bayesian linear regression using the sparsity and informative global priors. Specifically, with a dataset representing the population of samples (e.g., the training set), G-LIME first pursues the global explanation of the DNN model using the whole dataset. Then, with a new data point, -LIME incorporates an modified estimator of ElasticNet-alike to refine the local explanation result through balancing the distance to the global explanation and the sparsity/feature selection in the explanation. Finally, G-LIME uses Least Angle Regression (LARS) and retrieves the solution path of a modified ElasticNet under varying -regularization, to screen and rank the importance of features [2] as the explanation result. Through extensive experiments on real world tasks, we show that the proposed method yields more stable, consistent, and accurate results compared to LIME.

A reconstruction method for structural stress distribution of wind turbine tower using optimised mathematical model

Wind turbine towers experience complex dynamic loads during actual operation, and these loads are difficult to accurately predict in advance, which may lead to inaccurate structural strength assessment during the structural design phase, thereby posing safety risks to the wind turbine tower. Therefore, a reconstruction method for structural stress distribution of wind turbine tower was developed based on modal expansion and linear superposition theory to estimate the full-field stress distribution of a tower, which can be used for online monitoring of structural strength. The dynamic stress distribution of the tower is assumed to be the superposition of several structural modal stress distributions, in the reconstruction method for structural stress distribution developed in this study. The modes with the greatest contribution to the wind turbine structural response were selected based on the modal effective mass, and used for the base functions of an initial mathematical model for the reconstruction of structural stress distribution of the tower, in which many candidate strain gauge locations and directions were considered. Then, the initial mathematical model was expressed as a linear system of equations. Two numerical examples were used to verify the accuracy and stability of the initial mathematical model for the reconstruction of structural stress distribution of the tower, and the reconstruction results indicate that the initial mathematical model combined with the Moore–Penrose inverse algorithm can provide stable and accurate reconstruction results. However, the initial mathematical model uses too many sensors, which is not conducive to engineering applications. Therefore, D-optimal and C-optimal design methods were used to reduce the dimensions of the initial mathematical model, optimise the system of equations, and determine the location, direction and number of strain gauges. Finally, the numerical examples of stress distribution reconstruction show that dimensionality reduction of the mathematical model leads to higher accuracy, in which the C-optimal design algorithm providing a more robust reconstruction outcome.

Современные технологии рефракционной экстракции лентикулы

Purpose. To present modern literature data on the main aspects of refractive lenticular extraction technology. Material and methods. To perform the review, the search for literature sources on PubMed abstract databases was analyzed using the keywords “keratorefractive surgery”, “corneal lenticular”, “femtosecond laser” and data from Russian publications on the topic of the article for the period up to and including 2023. Results. According to the literature data, the strengths and weaknesses of the implementation of each of the existing technologies of keratorefractive surgery have been studied, and the postulates of choosing the optimal method for correcting ametropia have been formulated individually for each patient. Conclusion. Today, keratorefractive surgery is a high-tech industry in ophthalmology and provides a predictable, high-precision and stable refractive result in patients with a wide range of refractive disorders. The analysis of accumulated scientific and clinical experience has shown that one of the most pathogenetically oriented technologies in keratorefractive surgery, minimally affecting the reduction of the biomechanical properties of the cornea, not inducing dry eye syndrome, providing a highly predictable postoperative result in the correction of myopia and myopic astigmatism is refractive lenticular extraction, opening a new era in laser vision correction. Keywords: ametropy, keratorefractive surgery, refractive lenticular extraction

The Long-Term Esthetic and Radiographic Outcome of Implants Placed in the Anterior Maxilla after Ridge Preservation, Combining Bovine Xenograft with Collagen Matrix.

The objective of the study was to evaluate the long-term esthetic and radiographic results of implants placed in the anterior maxilla after ridge preservation, combining bovine xenograft with collagen matrix. Fifteen patients who required a single tooth extraction because of fracture, root resorption, or extended caries were included in the study. After extraction, all sites were grafted using Deproteinized Bovine Bone Mineral (DBBM) with collagen and covered by a resorbable collagen matrix (CM). Five months after socket grafting, implants were successfully installed. The implant diameter range was between 3.8 and 4.2 mm. All patients were monitored for over 7 years, both clinically and radiographically. Three independent observers evaluated the long-term esthetic outcome, employing the Pink Esthetic Score (PES) technique. Over a period exceeding seven years, a 100% survival rate was observed for all 15 implants, with minimal marginal bone loss. The mean PES was 11.40 (±1.44) at the first assessment and 11.38 (±1.63) at the second assessment. The difference was not statistically significant (p = 0.978), and the scores of PES measurements indicated excellent esthetic results even after seven years. Based on these preliminary results, it seems that placing collagen bovine bone in a fresh extraction socket, covered with a collagen matrix, can preserve the alveolar ridge and provide long-term stable esthetic results.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents

US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit. AHIs. Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling. Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs. In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

An Investigation of Representative Customer Load Collectives in the Development of Electric Vehicle Drivetrain Durability

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.

Association between gut microbiota and male infertility: a two-sample Mendelian randomization study.

Previous research has confirmed the significant association between gut microbiota (GM) and male infertility (MI), but the causality between them remains unclear. This study aims to investigate the causal relationship between GM and MI using Mendelian randomization (MR) and provide supplementary information for the optimization of future randomized controlled trials (RCTs). Instrumental variables for 211 GM taxa were obtained from genome-wide association studies (GWAS), and inverse variance weighted (IVW) method was used as the main analysis method for two-sample MR analysis to assess the impact of GM on the risk of MI. Four methods were used to test for horizontal pleiotropy and heterogeneity of MR results to ensure the reliability of the MR findings. A total of 50 single-nucleotide polymorphisms (SNPs) closely related to GM were included, and ultimately identified 1 family and 4 general are causally associated with MI. Among them, Anaerotruncus (OR = 1.96, 95% CI 1.31-3.40, P = 0.016) is significantly associated with increased MI risk. Furthermore, we used four MR methods to evaluate the causality, and the results supported these findings. The leave-one-out analysis showed stable results with no instrumental variables exerting strong influence on the results. The causal direction indicated a positive effect, and the effects of heterogeneity and horizontal pleiotropy on the estimation of causal effect were minimized. We confirmed a causal relationship between GM taxa and MI, providing new insights into the mechanisms underlying GM-mediated MI.

Longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Platinum and palladium price forecasting through neural networks

To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models’ potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hundred and twenty model settings are examined, including different training algorithms, numbers of hidden neurons and delays, and ratios used to segment the data. With the analysis, two models leading to stable and accurate forecast results are constructed for the prices of the two commodities. In particular, the models’ performance in terms of the relative root mean square error is 1.86% and 3.61% for platinum and palladium, respectively, for the overall sample. Results in this work could help technical forecasts and policy analysis. The forecast framework might be extended to other different commodities.

The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection

Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. To address these challenges, an innovative crack detection framework, CrackDiff, based on the generative diffusion model, is proposed. It leverages the learning capabilities of the generative diffusion model for the data distribution and latent spatial relationships of cracks across different sample timesteps and generates more accurate and continuous crack segmentation results. CrackDiff uses crack images as guidance for the diffusion model and employs a multi-task UNet architecture to predict mask and noise simultaneously at each sampling step, enhancing the robustness of generations. Compared to other models, CrackDiff generates more accurate and stable results. Through experiments on the Crack500 and DeepCrack pavement datasets, CrackDiff achieves the best performance (F1 = 0.818 and mIoU = 0.841 on Crack500, and F1 = 0.841 and mIoU = 0.862 on DeepCrack).

Stacked ensemble learning approach for PCM-based double-pipe latent heat thermal energy storage prediction towards flexible building energy

Dynamic performance prediction of the PCM-based double-pipe latent heat thermal energy storage (LHTES) system plays an important role for building peak load shifting. This study presents a stacked ensemble learning framework for the phase change performance prediction of LHTES systems. The proposed model has a two-layer structure involving base and meta models: Regression Tree, Support Vector Regression and Linear Regression. Sensitivity analysis has been utilized for the feature extraction considering possible geometrical and physical parameters of LHTES systems with a fixed PCM volume that can store 0.94 kWh of energy within 255 min. Case studies show that the proposed model outperforms the base models in terms of both prediction accuracy and robustness. Compared with base models, the proposed model has a maximum 7.82% (charging) enhancement in Mean Absolute Percentage Error (MAPE) over all-range data sets and a maximum 16.43% enhancement in MAPE for the initial discharging stage, and has more stable predictive results within the high accuracy zone (accuracy > 95%) with a maximum 7.3% (charging) increase in the distribution density over all-range data sets and a maximum interquartile range reduction of 25.6% (middle discharging stage) in Mean Absolute Error. Application of the proposed model achieved more building peak load reduction.

Long-term experiences with high-energy shock wave therapy in the management chronic phase Peyronie’s disease using two different electromagnetic lithotripters

BackgroundExtracorporeal shock wave lithotripsy represents one option for the non-surgical management of Peyronie’s disease. Despite promising results, several questions are still pending. We want to present the long-term results of a retrospective study using high-energy extracorporeal shock wave lithotripsy.Material and methodsWe evaluated retrospectively 110 patients treated between 1996 and 2020 at the Department of Urology, SLK Kliniken Heilbronn for chronic phase Peyronie’s disease using two electromagnetic lithotripters (Siemens Lithostar Plus Overhead Module, Siemens Lithoskop) applying high-energy shock waves under local anesthesia and sonographic or fluoroscopic control. A standardized questionnaire focused on the change in pain, curvature, sexual function and the need of penile surgery.ResultsIn 85 of the 110 patients (mean age 54 years) we had sufficient data for evaluation. The median follow-up was 228 (6–288) months. There were no significant complications. Pain reduction was achieved in all patients, 65 (76%) patients were free of pain. Improvement of penile curvature was achieved in 43 patients (51%) ranging from 25% improvement (deflected angle < 30°) to 95% (angle 30–60°). 59 patients (69%) reported problems with sexual intercourse, 40 of those (68%) reported improvement. Only 9 (10.5%) patients underwent surgical correction. We did not observe any significant differences between both electromagnetic devices with stable long-term results.ConclusionsHigh-energy shock wave therapy delivered by two standard electromagnetic lithotripters is safe and efficient providing stable long-term results. In cases with significant plaque formation, the concept of high-energy ESWT should be considered in future studies.

Prevalence of depressive symptoms in patients with advanced schistosomiasis in China: A systematic review and meta-analysis.

Advanced schistosomiasis is the most serious outcome of infection and has a negative impact on both physical fitness and mental health of patients, the latter of which has long been overlooked. Therefore, we performed this systematic review and meta-analysis to estimate the overall prevalence of depressive symptoms, one of the most common mental problems, in patients with advanced schistosomiasis in China. Six electronic databases were searched for studies reporting the prevalence of depressive symptoms in the targeted patients. Assessments were pooled using a fixed- or random-effects model based on heterogeneity test. Subgroup analyses were further performed and differences between/among groups were examined using the chi-squared test. The protocol had previously been registered in PROSPERO (CRD42023406708). A total of 11 studies with 1,673 participants were included. The pooled prevalence of depressive symptoms in advanced schistosomiasis in China was 62.01% (95% CI: 51.30% - 72.72%), with a significant heterogeneity among studies. Depressive symptoms were more prevalent in patients with complications and more than half of the patients suffered a mild- or moderate-level of depression. No publication bias was found, and sensitivity analysis showed a stable result. The overall prevalence of depressive symptoms in advanced schistosomiasis in China was high enough to warrant psychotherapeutic interventions, especially for patients with complications. This would greatly prevent or/and reduce depression and improve their quality of life.

Phenomenological studies on magnetic and mechanical remanence effects in magnetorheological fluids

Although magnetorheological fluids (MRF) have been used in science and engineering for many decades and several products based on this technology are known, there is still no consistent routine that properly takes into account major effects in the material. The aim of this work is to establish a series of steps, from the preparation of the fluid through the demagnetization routine to the choice of the angular evaluation point, which will allow to achieve stable and reproducible results regarding the material’s response to magnetic fields and mechanical actuation. As a major result of this work, a general demagnetization routine and a method for subsequent reproducible evaluation accounting for the long-term time-dependent behavior of the MRF were identified. Our efforts are aimed at exploring the following targets and questions: how can a stable initial state be ensured? Why is the initial measurement of MRF commonly hardly reproducible? How can reproducible measurements for characterizing the material be obtained? For each of these items, we developed procedures or suggest measures and discuss to what extent they solve the underlying issues and what had to be left for future investigations.

Online analysis of iron ore slurry using PGNAA technology with artificial neural network

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

A 10-year in-depth follow-up of post-lingual hearing loss patients with Chinese domestic cochlear implants

Background Follow-up of cochlear implant effectiveness is mainly focused on 3 years postoperatively, and studies with more than 5 years of observation are rare, especially for local Chinese brands. Objectives Nurotron (Chinese domestic cochlear implant brand) CI recipients who participated in the clinical trial in 2009 were followed-up for 10 years prospectively, providing data to guide doctors and patients. Material and Methods From December 2009 to April 2010, 57 subjects underwent Nurotron Venus CI surgery at multiple-centers, and were continued to be followed up and assessed at 1, 2, 3, 4, 5, and 10 years after switch on. Results All recipients were successfully implanted with CIs with no difficulty in subsequent use with one reported case of re-implantation at 9 years after implantation. The aided hearing thresholds were significantly improved at one month after switch on (p < 0.0001) and remained stable afterwards for 10 years. Speech recognition scores were significantly higher than pre-operative results (p < 0.05) and continued to improve till 3 years after switch on. At 10 years post-operation, most subjects had improved QOL scores in most sub-items. Conclusions and Significance Nurotron Venus CI System provides long-term, stable results in hearing speech assistance capabilities and can improve the quality of life of CI recipients.

МИНИИНВАЗИВНАЯ ТОРАКОПЛАСТИКА У ПАЦИЕНТОВ С ВОРОНКООБРАЗНОЙ ДЕФОРМАЦИЕЙ ГРУДНОЙ КЛЕТКИ

The article is devoted to a description of our own observations and treatment of patients with pectus excavatum. It is noted that approaches to surgical treatment of patients with pectus excavatum require increased efficiency and safety, which makes this issue relevant. Attention is focused on videothoracoscopic technologies in the form of inserting a plate under the sternum under the control of a videothoracoscopy. It has been established that the operation using the Nuss technique is the optimal method of surgical correction of pectus excavatum. The main advantage of surgery using the Nuss method is a good cosmetic effect and a stable long-term correction result. The use of thoracoscopic navigation reduces the risk of intraoperative complications, minimizes blood loss, reduces the invasiveness of the intervention, and reduces the duration of the operation and the patient’s time in the hospital.

Innovative Application of GalaFLEX Poly-4-Hydroxybutyrate Scaffold in Breast Reduction/Mastopexy with Inferiorly Based Dermo-adipose Flap.

Breast reduction is one of the most required plastic surgery procedures worldwide, improving significantly the quality of life for patients with macromastia and gigantomachia. Despite various proposed approaches aiming to yield more stable results postreduction, no single technique has demonstrated unequivocal superiority. Recurrence ptosis at 6-12 months postoperative remains a challenging concern. To improve this issue, the authors propose a novel application of the poly-4-hydroxybutyrate (P4HB) scaffold on the anterior surface of the Ribeiro dermo-adipose flap. The primary aim was to establish a durable and resilient biological connection between the mammary gland and the flap, thereby promoting long-lasting outcomes in breast reduction procedures. The P4HB scaffold is a monofilament, single-layer, biologically derived, fully resorbable, rapidly integrating within breast tissue; this biological process stimulates the formation of freshly vascularized connective tissue up to a thickness of 2-3 mm. Within 18-24 months, the scaffold undergoes gradual resorption through hydrolysis-based mechanism, providing enhanced strength and resistance to the native tissue, as shown in animal model. A key innovation proposed by the authors involves the division of a 15 × 20 cm rectangular sheet of the P4HB scaffold along its diagonal, resulting in two right triangles. This modification ensures increased height of the device if compared with the traditional splitting technique of the scaffold. The strategic establishment of a biological bridge between the mammary gland and flap through the implementation of the P4HB scaffold could potentially enhance the longevity and aesthetics of breast reduction outcomes.