Browse Articles
Discover research articles across all indexed journals
I grow medicinal mushrooms in my renewable-energy laboratory
Underwater target depth estimation: A shallow-water broadband acoustic source depth estimation method based on corrected warping transformation
A broadband sound source depth estimation method based on the BDRM model is proposed for the shallow sea to address the problem that the traditional Warping transform is limited by the ideal waveguide and cannot handle the real marine environment localization of structures with variable sound speed profiles. The modified Warping transform operator compensates for the propagation and reflection phases of the received signals in double phase, compresses the modes of the received signals under different sound speed profiles to approximate a single frequency, and projects the modified signals using time-frequency analysis (TFR). As a result, the modes of the received signals are clearly separated in the time-frequency domain. Then, the corresponding bandpass filters are designed considering the different marine environments. After that, the modal energies are extracted, and the depth estimation function is constructed based on the matched modal energies to achieve the depth estimation of the pulsed signal target under the non-ideal waveguide conditions with a variable speed of sound profile. Compared to the short-time Fourier transform (STFT) and the dispersion-dissipation transform (DDT), the modified Warping transform (MWT) achieves a clearer separation of the modes of the target received signal under the variable sound speed profile of the non-ideal waveguide. The depth estimation method proposed in this study effectively resolves the issues of passive source depth estimation under variable sound speed profiles, which typically result in a significant decrease in estimation accuracy. Simulation results indicate that under the conditions of the Pekeris waveguide, negative sound speed gradient waveguide, and Qingdao shallow sea waveguide, the success rate of the method for target depth estimation reaches more than 95% under a signal-to-noise ratio (SNR) of 10 dB. This demonstrates both high accuracy and stability. In Qingdao, the target depth can be estimated accurately when the signal-to-noise ratio is above 5 dB. In real sea area experiments, the method effectively separates the first four orders of the normal modes and achieves the estimation of the depth of the airgun pulse source. Compared to the traditional Warping transform, the method proposed in this study provides a wider application range and greater practical value in engineering.
Author Correction: Dissection of X chromosome dosage compensation for quantitative traits in sheep using different statistical models
Global prevalence of elevated estimated pulmonary artery systolic pressure in clinically stable children and adults with sickle cell disease: A systematic review and meta-analysis
Background The current study sought to determine the prevalence of elevated estimated pulmonary artery systolic pressure (ePASP) in clinically stable children and adults with sickle cell disease)SCD(worldwide. Methods The studies included were identified through a search of databases such as PubMed, Scopus, Science Direct, Web of Science, and Embase, as well as Google Scholar engine, adhering to specific inclusion and exclusion criteria. Heterogeneity among the primary study results was assessed using the I-squared index, while publication bias was evaluated through funnel plots, Egger’s test, and trim and fill analysis. All statistical analyses were conducted using R software, version 4.3.0. Results 79 primary studies were included, comprising 6,256 children (<18 years old) and 6,582 adults (≥18 years old) with SCD from 22 countries. The prevalence of elevated ePASP was found to be 21.8% (95% confidence interval [CI]: 18.46 to 25.07) in children and 30.6% (95% CI: 27.1 to 34.1) in adults. The prevalence of elevated ePASP among studies with severe SCD genotypes including HbSS and HbS/β0 was found to be 19.45% (95% CI: 14.95 to 23.95) in children and 29.55% (95% CI: 24.21 to 34.89) in adults. Furthermore, sex-specific prevalence among SCD patients with elevated ePASP indicated the highest prevalence in male children at 60.35% (95% CI: 54.82 to 65.88) and adult female patients at 54.41% (95% CI: 47.3 to 61.5). A comparative analysis of the mean values of clinical and laboratory results revealed significant differences in several characteristics, including age, oxygen saturation, hemoglobin levels, fetal hemoglobin, white blood cell counts, platelet counts, and reticulocyte counts between patients with elevated ePASP and those without, in both children and adult SCD populations. Conclusion Our findings regarding clinically stable SCD patients highlight a high prevalence of elevated ePASP.
Hinge-FM2I: an approach using image inpainting for interpolating missing data in univariate time series
Abstract Accurate time series forecasts are crucial for various applications, such as traffic management, electricity consumption, and healthcare. However, limitations in models and data quality can significantly impact forecasts’ accuracy. One common issue with data quality is the absence of data points, referred to as missing data values. It is often caused by sensor malfunctions, equipment failures, or human errors. This paper proposes Hinge-FM2I, a novel method for handling missing data values in univariate time series data. Hinge-FM2I builds upon the strengths of the Forecasting Method by Image Inpainting (FM2I). FM2I has proven effective, but selecting the most accurate forecasts remains a challenge. To overcome this issue, we proposed a selection algorithm. Inspired by door hinges, Hinge-FM2I drops a data point either before or after the gap (left/right-hinge), then uses FM2I for imputation. In fact, it selects the imputed gap based on the lowest error of the dropped data point. Hinge-FM2I was evaluated on a comprehensive sample composed of 1356 time series. These latter are extracted from the M3 competition benchmark dataset, with missing value rates ranging from 3.57 to 28.57%. Experimental results demonstrate that Hinge-FM2I significantly outperforms established methods such as linear/spline interpolation, K-Nearest Neighbors, and ARIMA. Notably, Hinge-FM2I achieves an average Symmetric Mean Absolute Percentage Error score of 5.6% for small gaps and up to 10% for larger ones. These findings highlight the effectiveness of Hinge-FM2I as a promising new method for addressing missing values in univariate time series data.
Passive acoustic monitoring of baleen whale seasonal presence across the New York Bight
The New York Bight is an ecologically and economically important marine region along the U.S. Atlantic Coast. Extensive assessments have characterized the habitats and biota in this ecosystem; however, most have focused on fishes, benthic habitats, and human impacts. To investigate the spatial and temporal occurrence of whales in this region, we conducted a three-year passive acoustic monitoring survey that documented the acoustic presence of five baleen whale species that occur within the New York Bight and are of conservation concern: North Atlantic right whales (Eubalaena glacialis), humpback whales (Megaptera novaeangliae), fin whales (Balaenoptera physalus), sei whales (Balaenoptera borealis), and blue whales (Balaenoptera musculus). Data were recorded with 14 bottom-mounted acoustic sensors across the continental shelf between 2017 and 2020. Right whales were detected across all seasons, with most detections in autumn closer to New York Harbor and spring detections at sites closer to the continental shelf edge. Humpbacks were detected during all months of the year with varying distribution of detections across the shelf. The year-round presence of right and humpback whales challenges previous hypotheses that this region is primarily a stopover location along their migration paths. Fin whales were detected at all sites on most days. Sei whales were detected primarily during the spring at offshore sites. Blue whales were detected in the winter at sites closer to the continental shelf edge, but were rare. These data improve our understanding of baleen whale seasonal occurrences in the New York Bight and can inform monitoring and mitigation efforts associated with the management and conservation of these species.
Embryo vitrification impacts learning and spatial memory by altering the imprinting genes expression level in the mouse offspring’ hippocampus
How to end outrage and detoxify politics: share stories, not statistics
Financial cost of assisted reproductive technology for patients in high-income countries: A systematic review protocol
Background Infertility affects one in six people globally, with similar prevalence rates across high-income and low- and middle-income countries. Technological advancements, particularly in Assisted Reproductive Technology (ART), have improved fertility treatment options. Although access to ART is presumed to be better in high-income countries (HICs), economic factors and eligibility restrictions could still impact effective utilization in these settings. Informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-P), this protocol outlines the methodological and analytical approaches to examine the ART costs paid by patients in HICs and the correlation with economic indicators and ART regulatory frameworks. Methods Following the PRISMA approach, we will search for articles indexed in PubMed, EMBASE, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Web of Science, PsycINFO, and Latin American & Caribbean Health Sciences Literature (LILACS). Grey literature from relevant organizations’ virtual databases will also be searched. The review will encompass studies published between 2001 and 2024, with the primary outcome being ART direct medical and direct non-medical costs, while secondary outcomes shall include ART financing arrangements. The review will synthesize ART costs, adjusting them to USD Purchasing Power Parity for cross-country comparison, and correlate findings with GNI per capita and ART financing policies. The Integrated Quality Criteria for Review of Multiple Study Designs (ICROMS) tool will be utilized to evaluate the quality of the included studies. We will conduct a meta-analysis if the studies provide sufficient cost-effect size estimates. Discussion The review findings will contribute to our understanding of the potential financial burden faced by (disadvantaged) individuals in HICs due to ART costs. Additionally, the review shall highlight the implications that ART financing policies have in enhancing access and affordability, offering valuable insights for healthcare planning and policy formulation. The results will be disseminated through a peer-reviewed journal article and relevant international conferences. Trial registration Systematic review registration: PROSPERO number: CRD42023487655.
Evolution law of surrounding rock stress field for ultra-deep shaft development blasting
Effect of Chinese herbal medicine (CHM) as an adjunctive therapy in distinct stages of patients with COVID-19: A systematic review and meta-analysis
Background The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory disease coronavirus 2 (SARS-CoV-2), has led to millions of infected cases and deaths worldwide. Clinical practice and clinical trials in China suggested that integrated Chinese herbal medicine (CHM) and conventional Western monotherapy (ICW) have achieved significant clinical effectiveness in treating COVID-19 patients. Objectives This article aims to systematically evaluate the effects of ICW in treating patients at distinct stages of COVID-19. The most frequently used components of the CHM formulas have been summarized to define the most promising drug candidates. Methods In this meta-analysis, seven databases up to May 20, 2024, were systematically searched to collect relevant randomized controlled trials (RCTs) and cohort studies (CSs). Difference in mean (MD) or ratio risk (RR) with 95% confidence interval (CI) was utilized for data processing analysis. Results A total of 46 studies, consisting of 24 RCTs and 22 CSs, and 10492 patients were included. ICW group showed significant improvement over the conventional Western monotherapy (CWM) group at all stages of COVID-19 patients. ICW therapy was effective in improving recovery rate of chest CT (RR = 1.21, 95%CI [1.13,1.29]), shortening negativity time of nucleic acid (MD = -2.14,95% CI [-3.70, -0.58]), suppressing the transition of mild/moderate patients into severe conditions (RR = 0.45, 95% CI [0.33,0.62]), and reducing mortality (RR = 0.45, 95% CI [0.37,0.55]) for severe/critical COVID-19. Furthermore, compared with severe/critical patients, mild/moderate COVID-19 patients proved more effective after being treated with ICW therapy. They had a higher recovery rate of chest CT manifestations (75.4% vs. 69.1%), shorter negativity time of nucleic acid (9.21 d vs. 14.89 d), reduced time to clinical symptom reduction (3.85d vs. 11d) and shortened days of hospital stays (15.9d vs 19.1d). As for inflammatory markers analysis, ICW regimens decreased the level of lymphocytes in mild/moderate and severe/critical patients (MD = -0.15, 95% CI [-0.18, -0.13]), but no statistical difference was observed in white blood cell count and neutrophils count (MD = 0.02, 95% CI [-0.14, -0.18]; MD = 0.22,95% CI [-0.7, 1.15], respectively). A different tendency was found in the C-reactive protein level, which significantly decreased at the early stage of COVID-19 in the ICW group (MD = 2.56, 95%CI [1.28,3.83]). Conclusion This meta-analysis demonstrates the significant superiority of ICW over single western monotherapy in improving clinical efficacy at distinct stages of Chinese COVID-19 patients. Subgroup analysis further showed that the earlier intervention of CHM may contribute to a better therapeutic effect. Trial registration PROSPERO ID: CRD42023401200.
A novel fixed-time prescribed performance sliding mode control for uncertain wheeled mobile robots
Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder
ELM (Extreme learning machine) has drawn great attention due its high training speed and outstanding generalization performance. To solve the problem that the long training time of kernel ELM auto-encoder and the difficult setting of the weight of kernel function in the existing multi-kernel models, a multi-kernel cost-sensitive ELM method based on expectation kernel auto-encoder is proposed. Firstly, from the view of similarity, the reduced kernel auto-encoder is defined by randomly selecting the reference points from the input data; then, the reduced expectation kernel auto-encoder is designed according to the expectation kernel ELM, and the combination of random mapping and similarity mapping is realized. On this basis, two multi-kernel ELM models are designed, and the output of the classifier is converted into posterior probability. Finally, the cost-sensitive decision is realized based on the minimum risk criterion. The experimental results on the public and realistic datasets verify the effectiveness of the method.
Electronic imaging of photoisomerisation process in photochromic crystals with scanning tunnelling spectroscopy
Men deny more than they believe about climate change on Twitter (X)
Climate change and twitter have been in scholarly and academic attention for study of human behaviour expressed on the popular social media platform. The sentiment of the tweets has been the subject of previous studies, and the most recent study used Twitter texts to examine seven aspects of climate change: denier/believer stance, sentiment, aggressiveness, temperature, gender, subjects and disasters, and their relationships. Amid the big pictures across these vital variables, we know very little about the extent to which the comparative gendered differences in views exist in the climate denier and believer groups shaping the climate change discussion. Using the large scale global twitter data from the past 13 years, this paper has examined the differences in the views of deniers and believers on climate change in comparison to the people neutral to climate change. Based on the expression on twitter, results of a sound multinomial regression model of this study indicates a globally strong climate denier stance of men.
Sociodemographic and work-related determinants of self-rated health trajectories: a collaborative meta-analysis of cohort studies from Europe and the US
Investigating geohazard risk in mountainous areas for underground gas storage using InSAR and development of a protocol for hazard prevention
XiangGuoSi reservoir is a depleted gas reservoir that has recently (in 2014) been converted to an underground gas storage facility. It stores gas in the reservoir during the summer season and produces gas in the winter season. In this work, we present a case report on using InSAR to monitor the mountainous area beneath where the XiangGuoSi gas reservoir is located, along with its supporting pipeline infrastructures. Data, containing 159 scenes, from C-band Synthetic Aperture Radar (SAR) aboard Sentinel-1 satellite is used here, the processing period covered a timespan of 5.6 years. Importantly, we find that the surface deformation is not correlated with the reservoir’s gas injection/extraction history. This indicates that the gas storage’s underground operation does not increase geohazard risk in the area. Further, this indicates the reservoir rock’s pore structure is rather stable even during the cycles of injection/extraction, suggesting a stable reservoir performance even into the far future. Nevertheless, the natural movement of the mountain still poses a landslide risk for the pipeline structure. Given our observed deformation is mostly monotonically downward (subsidence) and in many points, linear, we develop a protocol using 1. the local maximum deformation rate point’s proximity to the pipeline and 2. the rate and total deformation magnitude reported during the monitoring period. After all, this report shows the capability of InSAR as a tool for mapping geohazards for mountainous areas where critical infrastructures are in the vicinity.
Evaluation of reference genes for qPCR in human liver and kidney tissue from individuals with obesity
Predicting software reuse using machine learning techniques—A case study on open-source Java software systems
Software reuse is an essential practice to increase efficiency and reduce costs in software production. Software reuse practices range from reusing artifacts, libraries, components, packages, and APIs. Identifying suitable software for reuse requires pinpointing potential candidates. However, there are no objective methods in place to measure software reuse. This makes it challenging to identify highly reusable software. Software reuse research mainly addresses two hurdles: 1) identifying reusable candidates effectively and efficiently, and 2) selecting high-quality software components that improve maintainability and extensibility. This paper proposes automating software reuse prediction by leveraging machine learning (ML) algorithms, enabling future research and practitioners to better identify highly reusable software. Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. Software metrics were extracted from Maven artifacts and used to train classification and regression models to predict and estimate software reuse. The average F1-score of the ML classification models is 77.19%. The best-performing model, Ridge Regression, achieved an F1-score of 79.17%. Additionally, this research aims to assist developers by identifying key metrics that significantly impact software reuse. Our findings suggest that the file-level PUA (Public Undocumented API) metric is the most important factor influencing software reuse. We also present suitable value ranges for the top five important metrics that developers can follow to create highly reusable software. Furthermore, we developed a tool that utilizes the trained models to predict the reuse potential of existing GitHub projects and rank Maven artifacts by their domain.