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Two-dimensional superlattice nanocatalysts unlock multimodal energy transformation-driven catalytic therapy
The gender gap in scholarly self-promotion on social media
Influence of inner meshing profile on noise of hybrid electric vehicles coupler chain based on vibration analysis
Abstract Chain noise is an important factor affecting the ride comfort of hybrid electric vehicles (HEVs) with chain coupler. The inner meshing profile can effectively reduce the polygonal action, which has already been proved, but its influence on the noise is controversial. In this paper, the basic vibration locus, velocities, and acceleration for HEVs coupler chain are analyzed. Based on a specific example, under the action of the inner meshing profile, the indicators that represent the low, medium and high frequency vibration are proved to be reduced. Based on meshing relations, it is proved that the error of the inner meshing profile will cause the deviation of the chain pitch line and the meshing disorder, which may weaken the influence of inner meshing profile on reducing noise. Through conducting the dynamics simulation and the noise and wear experiment, the results show that the influence of the inner meshing profile on the noise has dual effect, and it can only reduce the noise after running-in. Moreover, under the action of the inner meshing profile, the noise level will be decreased with the increase of running time. This paper not only resolves the controversy but also provides an effective method for chain noise.
Identification of key genes associated with cellular aging and mitochondria in acute myocardial infarction
Retrospective analysis of clinical and epidemiological characteristics of Chinese cobra bites in a South China emergency department
Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures
Broadband circularly polarized dielectric rod antenna excited by a cavity backed spiral
Development and evaluation of an automated classification and counting system for rice planthoppers captured on survey boards
Abstract Rice planthoppers are the most economically important insect pests of rice in Asia. Traditional surveys to examine their abundance and composition in paddy fields involve human visual inspection, which requires considerable time and effort by expert entomologists. We previously developed a deep learning-based object detection system which can detect rice planthopper individuals from scanned images of survey boards, categorize, and count planthopper individuals by 18 categories, based on species, developmental stages, adult sexes, and adult wing-forms, with a mean average precision (mAP) of 79%. In this study, we modified the system by reconsidering the categories of planthopper individuals to be counted and by additional supervised training. The modified system can count rice planthopper individuals captured on survey boards across 17 categories with a mAP of 91%. We also showed that by using the system developed here, classification and counting of rice planthopper individuals can be completed in approximately six minutes per survey board, which can take more than an hour for human experts. Thus, this high-performance system can greatly save time and reduce labor costs for monitoring the occurrence, reproduction, and population growth of the rice planthoppers in paddy fields, which could increase the efficiency of their management.
Cross-sectional associations between meal timing patterns and diet quality indices in Iranian women
Macro and micro damage characteristics of Red-bed sandstone in Urumqi under freeze–thaw cycles
All-printed chip-less wearable neuromorphic system for multimodal physicochemical health monitoring
Abstract Recent advancements in wearable sensor technologies have enabled real-time monitoring of physiological and biochemical signals, opening new opportunities for personalized healthcare applications. However, conventional wearable devices often depend on rigid electronics components for signal transduction, processing, and wireless communications, leading to compromised signal quality due to the mechanical mismatches with the soft, flexible nature of human skin. Additionally, current computing technologies face substantial challenges in efficiently processing these vast datasets, with limitations in scalability, high power consumption, and a heavy reliance on external internet resources, which also poses security risks. To address these challenges, we have developed a miniaturized, standalone, chip-less wearable neuromorphic system capable of simultaneously monitoring, processing, and analyzing multimodal physicochemical biomarker data (i.e., metabolites, cardiac activities, and core body temperature). By leveraging scalable printing technology, we fabricated artificial synapses that function as both sensors and analog processing units, integrating them alongside printed synaptic nodes into a compact wearable system embedded with a medical diagnostic algorithm for multimodal data processing and decision making. The feasibility of this flexible wearable neuromorphic system was demonstrated in sepsis diagnosis and patient data classification, highlighting the potential of this wearable technology for real-time medical diagnostics.
Evidence of water promotes cyclohexanol adsorption and dehydration over H-ZSM-5 zeolite
LC‐MS and High‐Throughput Data Processing Solutions for Lipid Metabolic Tracing Using Bioorthogonal Click Chemistry
Abstract Tracing lipid metabolism in mammalian cells presents a significant technological challenge due to the vast structural diversity of lipids involved in multiple metabolic routes. Bioorthogonal approaches based on click chemistry have revolutionized analytical performance in lipid tracing. When adapted for mass spectrometry (MS), they enable highly specific and sensitive analyses of lipid transformations at the lipidome scale. Here, we advance this approach by integrating liquid chromatography (LC) prior to MS detection and developing a software‐assisted workflow for high‐throughput data processing. LC separation resolved labeled and unmodified lipids, enabling qualitative and quantitative analysis of both lipidome fractions, as well as isomeric lipid species. Using synthetic standards and endogenously produced alkyne lipids, we characterized LC‐MS behavior, including preferential adduct formation and the extent of in‐source fragmentation. Specific fragmentation rules, derived from tandem MS experiments for 23 lipid subclasses, were implemented in Lipostar2 software for high‐throughput annotation and quantification of labeled lipids. Applying this platform, we traced metabolic pathways of palmitic and oleic acid alkynes, revealing distinct lipid incorporation patterns and metabolic bottlenecks. Altogether, here we provide an integrated analytical and bioinformatics platform for high‐throughput tracing of lipid metabolism using LC‐MS workflow.
Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution
Association between educational attainment and mental health conditions among Africans working and studying in selected African countries
Transcriptomic and network toxicology approaches reveal potential mechanisms of DEHP induced intrahepatic cholestasis during pregnancy
Selective nanocomposite hydrogels developed from cashew gum/laponite for selective removal
Land use impacts on soil aggregate associated organic carbon in reclaimed coal mining subsidence areas: mechanisms and implications
Abstract Under the background of ecological restoration and soil structure improvement in coal mining subsidence areas, understanding the impact of land use on soil organic carbon pools and aggregate characteristics is of great significance to evaluate the quality change and ecological effect of reclaimed soil and guide the improvement of reclaimed soil. Although the dynamics of soil organic carbon under land use changes have been studied, the mechanism of aggregate carbon protection in reclaimed mining soil is still unclear. Taking the reclaimed soils of farmland, woodland, and wasteland in the Dongtan mining area of Zoucheng City in Shandong Province as the research object, by comparing the particle size composition of soil aggregates, the distribution characteristics of organic carbon in 0–20 cm and 20–40 cm soil layers, and analyzing the differences of different organic carbon components, it was found that the organic carbon content of aggregates in the 0–20 cm soil layer was higher than that in the 20–40 cm soil layer; In topsoil, the contribution of fine aggregates to total organic carbon was the largest; the contents of free light fraction (free LF) organic carbon in fine aggregates, micro-aggregates, and total free light fraction organic carbon in forest soil were significantly higher than those in farmland and wasteland. These results suggest that fine aggregates may serve as primary carriers of reclaimed soil organic carbon. The study on the surface soil aggregate organic carbon helps reveal the transformation and accumulation process of reclaimed soil organic carbon. The study found that woodland reclamation may enhance microbial-driven soil organic carbon storage, suggesting that targeted land-use strategies have the potential to maximize the carbon sequestration capacity in degraded ecosystems. Findings are specific to the 7-year reclamation period and Dongtan mining area, requiring long-term regional validation.