Browse Articles

Operation mechanism analysis and parameter optimization of airflow-rotating disc separation device for agricultural film fragments

A study of the effect of motor structural components on harmonic noise

Development of a PANoptosis-related LncRNAs for prognosis predicting and immune infiltration characterization of gastric Cancer

Nickel silicide nanowire anodes for microbial fuel cells to advance power production and charge transfer efficiency in 3D configurations

High-efficiency stepdown/step-up converter for series-connected energy storage system

Blood predictive biomarkers for cognitive impairment among community-dwelling older adults: a cross-sectional study in China

Self-supervised learning reduces label noise in sharp wave ripple classification

Abstract In the field of electrophysiological signal analysis, the classification of time-series datasets is essential. However, these datasets are often compromised by the prevalent issue of incorrect attribution of labels, known as label noise, which may arise due to insufficient information, inappropriate assumptions, specialists’ mistakes, and subjectivity, among others. This critically impairs the accuracy and reliability of data classification, presenting significant barriers to extracting meaningful insights. Addressing this challenge, our study innovatively applies self-supervised learning (SSL) for the classification of sharp wave ripples (SWRs), high-frequency oscillations involved in memory processing that were generated before or after the encoding of spatial information. This novel SSL methodology diverges from traditional label correction techniques. By utilizing SSL, we effectively relabel SWR data, leveraging the inherent structural patterns within time-series data to improve label quality without relying on external labeling. The application of SSL to SWR datasets has yielded a 10% increase in classification accuracy. While this improved classification accuracy does not directly enhance our understanding of SWRs, it opens up new pathways for research. The study’s findings suggest the transformative capability of SSL in improving data quality across various domains reliant on precise time-series data classification.

Performance assessment of disposable carbon-based immunosensors for the detection of SARS-CoV-2 infections

Abstract We designed, developed, and clinically tested two rapid antigen-based immunosensors for SARS-CoV-2 detection, enabling diagnosis and viral load quantification for under USD $2. In a first clinical study, a screen-printed disposable carbon-based (SPC) sensor was assessed on prospectively recruited adult participants classified into three study groups: healthy donors (n = 46); SARS-CoV-2-infected symptomatic patients (n = 58); and co-habitants of patients without prior testing (n = 38). Nasopharyngeal aspirates (NA), oropharyngeal swabs (OS), and saliva (SA) samples were obtained from all participants. Performance was measured in terms of clinical sensitivity and specificity against a reference diagnostic RT-qPCR kit and analytical sensitivity (limit of detection, LoD) and specificity using recombinant material in lab tests. A second study was performed using the same sensor design, albeit with laser-induced graphene (LIG) electrodes, using nasopharyngeal swabs (NS) on 224 patient samples obtained at different stages of the pandemic, of which 110 tested negative and 114 positive via RT-qPCR. We find OS was the most informative sample, when compared to NA and SA. The SPC-based sensors had a 93.8% sensitivity and 61.5% specificity with OS samples, while the LIG-based sensors with NS had a lower sensitivity of 68.93%, albeit a significantly higher specificity of 86.17%. We believe specificity values for the SPC sensors were driven by positive results from co-habitants and healthy donors and were affected by the low sensitivity (75.5%) and high LoD (> 20,000 viral copies/mL) of the reference RT-qPCR kit used, and the lower sensitivity of the LIG-based was due to a reduced set of effective antigen-binding sites caused by the non-covalent LIG-mAb ligands used. The immunosensor’s LoD to spike protein in phosphate-buffered saline (PBS) for both types of sensors was near 1 fg/mL and showed no cross-reactivity to recombinant structural proteins of Epstein-Barr and Influenza. Performance metrics and time-to-result (5 < 12 min) provide proof-of-principle of the immunosensor’s applicability as a low-cost, rapid technology for determining SARS-CoV-2 infections. Changing the working electrode material to LIG, instead of SPC, improved specificity even in the presence of pathogen variants. Discordant results between our two immunosensor versions and RT-qPCR tests are attributed not only to limited antibody effectiveness in the former but also to the quality of RT-qPCR probes used at the height of the pandemic.

Efficacy and safety of arthroscopy in femoroacetabular impingement syndrome: a systematic review and meta-analysis of randomized clinical trials

Intensity-difference squeezing from four-wave mixing in hot 85Rb and 87Rb atoms in single diode laser pumping system

Predicting workability and mechanical properties of bentonite plastic concrete using hybrid ensemble learning

Prefabricated building construction in materialization phase as catalysts for hotel low-carbon transitions via hybrid computational visualization algorithms

Abstract This study examines the carbon emissions of star-rated hotels in Hangzhou, comparing the environmental impact of prefabricated construction (PC) and conventional construction (CC) methodologies. The research reveals that PC generally results in lower carbon emissions during the materialization phase, with notable variations across different hotel star levels and administrative regions. Higher-star hotels exhibit higher total emissions, primarily due to larger scale and reliance on conventional construction methods. In contrast, lower-tier hotels benefit more consistently from the adoption of prefabricated construction, leading to significant reductions in carbon emissions. Regional analysis shows that the impact of the COVID-19 pandemic on hotel turnover and carbon decoupling trends varies, with core urban areas experiencing a more pronounced decoupling effect, while suburban regions exhibited slower recovery. The findings underscore the potential for prefabricated construction to reduce carbon footprints, particularly in mid-tier and lower-tier hotels. This study contributes to the understanding of sustainable construction practices in the hotel industry and provides a foundation for future research focused on refining carbon emission assessments, incorporating real-world data, and exploring the integration of renewable energy and lifecycle emissions.

Spawning in a threatened freshwater mussel shifts to earlier dates as a result of increasing summer mortality

Investigating the kinetics of single-chain expansion upon release in theta conditions

Experimental performance examination of a coherence technique-based numerical differential current relay for AC machine stator windings protection

Abstract A computational technique based on a coherence method for fault detection and classification for electrical machine stator windings is presented in this article. The coherence algorithm can identify clearly and concisely inter-turn and shunt faults situated on the 3-phase stator windings of the AC machine. Besides, it can categorize the different types of internal shunt faults. The cross-coherence algorithm performs the functional role of digital differential current to find and classify the internal faults; while, the auto-coherence algorithm acts as an overcurrent detector to define the occurrence of external, internal, or inter-turn faults. A new setup of three-phase induction machine stator windings, where each winding is re-winded to produce 20 taps per phase, is used to examine the approach. The new design is intended to build current transformers at the neutral and supply sides of the three windings, and to simplify conducting comprehensive examinations to verify the efficacy and efficiency of the advanced algorithm. The protection characteristics of the developed algorithm will be analyzed and estimated using the new setup. The test results indicate that the reliability and accuracy of the protection are above 98.7%. The coherence criterion is also useful for monitoring electrical faults, sensing inter-turn faults, distinguishing between external and internal shunt faults, classifying diverse internal shunt faults within the equipment protection zone, and estimating the tripping time when inter-turn faults occur. Furthermore, a new design of protection tripping-characteristic curves is established, and the time response of the computational technique is fast.

Enhanced perovskite solar cells performance with TiOx and SnOx thin films as electron transport layers

Molecular delineation and haplotype analysis of domain membrane protein (DMP) gene influencing in-vivo haploid induction in maize

A prospective single center non randomized clinical trial of autologous skin cells with platelet rich plasma for diabetic ulcer and trauma injuries patients

A safety risk assessment method for TBM tunnel construction based on attribute interval identification theory

Choice Behaviors and Prefrontal–Hippocampal Coupling Are Disrupted in a Rat Model of Fetal Alcohol Spectrum Disorders

Fetal alcohol spectrum disorders (FASDs) are characterized by a range of physical, cognitive, and behavioral impairments. Determining how temporally specific alcohol exposure (AE) affects neural circuits is crucial to understanding the FASD phenotype. Third trimester AE can be modeled in rats by administering alcohol during the first two postnatal weeks, which damages the medial prefrontal cortex (mPFC) and hippocampus (HPC), structures whose functional interactions are required for working memory and executive function. Therefore, we hypothesized that AE during this period would impair working memory, disrupt choice behaviors, and alter mPFC–HPC oscillatory synchrony. To test this hypothesis, we recorded local field potentials from the mPFC and dorsal HPC as male and female AE and sham-intubated (SI) rats performed a spatial working memory task in adulthood and implemented algorithms to detect vicarious trial and errors (VTEs), behaviors associated with deliberative decision-making. We found that, compared with the SI group, the AE group performed fewer VTEs and demonstrated a disturbed relationship between VTEs and choice outcomes, while spatial working memory was unimpaired. This behavioral disruption was accompanied by alterations to mPFC and HPC oscillatory activity in the theta and beta bands, respectively, and a reduced prevalence of mPFC–HPC synchronous events. When trained on multiple behavioral variables, a machine learning algorithm could accurately predict whether rats were in the AE or SI group, thus characterizing a potential phenotype following third trimester AE. Together, these findings indicate that third trimester AE disrupts mPFC–HPC oscillatory interactions and choice behaviors.