Professional Characteristics Influencing Work Engagement of Rural Early Childhood Teachers

Rural early childhood teachers' work engagement directly impacts their professional development and significantly influences the quality of the early childhood education system. However, rural early childhood teachers in China face numerous challenges in maintaining high levels of work engagement. This study used the Utrecht Work Engagement Scale (UWES) to examine the relationship between professional characteristics and work engagement among 530 rural early childhood teachers. The results showed that teaching tenure, educational level, professional background, and other factors significantly affected their work engagement

A Signature Based Approach Towards Global Channel Charting with Ultra Low Complexity

Channel charting, an unsupervised learning method that learns a low-dimensional representation from channel information to preserve geometrical property of physical space of user equipments (UEs), has drawn many attentions from both academic and industrial communities, because it can facilitate many downstream tasks, such as indoor localization, UE handover, beam management, and so on. However, many previous works mainly focus on charting that only preserves local geometry and use raw channel information to learn the chart, which do not consider the global geometry and are often computationally intensive and very time-consuming. Therefore, in this paper, a novel signature based approach for global channel charting with ultra low complexity is proposed. By using an iterated-integral based method called signature transform, a compact feature map and a novel distance metric are proposed, which enable channel charting with ultra low complexity and preserving both local and global geometry. We demonstrate the efficacy of our method using synthetic and open-source real-field datasets.Comment: accepted by IEEE ICC 2024 Workshop

A Crop Pests Image Classification Algorithm Based on Deep Convolutional Neural Network

Conventional pests image classification methods may not be accurate due to the complex farmland background, sunlight and pest gestures. To raise the accuracy, the deep convolutional neural network (DCNN), a concept from Deep Learning, was used in this study to classify crop pests image. On the ground of our experiments, in which LeNet-5 and AlexNet were used to classify pests image, we have analyzed the effects of both convolution kernel and the number of layers on the network, and redesigned the structure of convolutional neural network for crop pests. Further more, 82 common pest types have been classified, with the accuracy reaching 91%. The comparison to conventional classification methods proves that our method is not only feasible but preeminent

Vinyl chloride accident unleashes a toxic legacy

A railroad accident on February 3, 2023, led to the release and combustion of 115,580 gallons, equivalent to over 437,000 L, of vinyl chloride monomer (VCM) in East Palestine, Ohio [1]. This monomer is used in polyvinyl chloride (PVC) production, and its burning produces additional toxins such as hydrochloric acid and lethal phosgene, known as a notorious chemical weapon during World War

Differential responses of soil phosphorus fractions to nitrogen and phosphorus fertilization: a global meta-analysis

Anthropogenic inputs of nitrogen (N) and phosphorus (P) to terrestrial ecosystems alter soil nutrient cycling. However, the global-scale responses of soil P fractions to N and P inputs and their underlying mechanisms remain elusive. We conducted a global meta-analysis based on 818 observations of soil P fractions from 99 field N and P addition experiments in forest, grassland, and cropland ecosystems ranging from temperate to tropical zones. Our global meta-analysis revealed distinct responses of soil P fractions to N and P enrichment. For studies using the Chang and Jackson inorganic (Pi) method, we found that high N addition promoted the transformation of immobile Pi fractions into Ferrum/Aluminum-bound Pi and available Pi in surface soils through soil acidification. However, this acid-induced transformation of Pi fractions by N addition was observed only in Calcium-rich soils, while in acidic soils, further acidification led to increase P binding. In contrast, additions of P alone or combined with N significantly increased all soil Pi fractions. Regarding the Hedley P fractions, N addition generally decreased labile organic P by enhancing soil acid phosphatase activity. The responses of other P fractions were influenced by soil pH, fertilization rates, ecosystem type, and other factors. P addition increased most soil P fractions. Overall, both P fractionation methods consistently demonstrate that N inputs deplete soil P and accelerate P cycling, while P inputs increase most soil P fractions, alleviating P limitation. These findings are crucial for predicting the effects of future atmospheric N and P deposition on P cycling processes

The performance of large-pitch AC-LGAD with different N+ dose

AC-Coupled LGAD (AC-LGAD) is a new 4D detector developed based on the Low Gain Avalanche Diode (LGAD) technology, which can accurately measure the time and spatial information of particles. Institute of High Energy Physics (IHEP) designed a large-size AC-LGAD with a pitch of 2000 {\mu}m and AC pad of 1000 {\mu}m, and explored the effect of N+ layer dose on the spatial resolution and time resolution. The spatial resolution varied from 32.7 {\mu}m to 15.1 {\mu}m depending on N+ dose. The time resolution does not change significantly at different N+ doses, which is about 15-17 ps. AC-LGAD with a low N+ dose has a large attenuation factor and better spatial resolution. Large signal attenuation factor and low noise level are beneficial to improve the spatial resolution of the AC-LGAD sensor

Simultaneous measurement of multiple organic tracers in fine aerosols from biomass burning and fungal spores by HPLC-MS/MS

Three monosaccharide anhydrides (MAs: levoglucosan, mannosan, and galactosan) and sugar alcohols (arabitol and mannitol) are widely used as organic tracers for source identification of aerosols emitted from biomass burning and fungal spores, respectively. In the past, these two types of organic tracer have been measured separately or conjointly using different analytical techniques, with which a number of disadvantages have been experienced during the application to environmental aerosol samples, including organic solvent involved extraction, time-consuming derivatization, or poor separation efficiency due to overlapping peaks, etc. Hence, in this study a more environment-friendly, effective and integrated extraction and analytical method has been developed for simultaneous determination of the above mentioned organic tracers in the same aerosol sample using ultrasonication and high performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). The ultrasonication assisted extraction process using ultrapure water can achieve satisfactory recoveries in the range of 100.3 ± 1.3% to 108.4 ± 1.6% for these tracers. All the parameters related to LC and MS/MS have been optimized to ensure good identification and pronounced intensity for each compound. A series of rigorous validation steps have been conducted. This newly developed analytical method using ultrasonication and HPLC-MS/MS has been successfully applied to environmental aerosol samples of different pollution levels for the simultaneous measurement of the above mentioned five organic tracers from biomass burning and fungal spores

Identification and characterization of senescent macrophages in renal allograft rejection: a cross-species MultiOmics study

BackgroundAllograft rejection remains a main hindrance for long-term graft survival. Cellular senescence (CS) contributes to graft injury, but the role of immune cell senescence in rejection remains unclear.MethodsMicroarray data from renal transplant biopsy cohorts and age-matched rat allograft models were integrated to characterize senescence phenotypes. Immune cell infiltration algorithms and histopathology were employed to recognize major senescent alloimmune subpopulation. Then, novel senescent infiltrating macrophages (SnIMs) were identified using cross-species single-cell transcriptomics and validated in rat models. Finally, the clinical values of SnIMs were evaluated in renal transplant datasets.ResultsCS gene sets were enriched in rejecting allografts, correlating with graft loss and pathological injury. Alloimmune responses amplified stress-induced senescence in rat allografts, with p21+ macrophages emerging as the important senescent immune subtype. SnIMs exhibited cell cycle arrest, upregulation of senescence-associated secretory phenotype, and conserved transcriptional signatures driven by NF-κB/Cebpb across species through single-cell analysis. These cells accumulated along pseudotime during rejection and interacted with effector T cells via CXCL chemokines. Clinically, SnIM infiltration predicted T cell–mediated rejection and correlated with Banff lesion grades and poor graft survival.ConclusionsOur findings identify novel stress-induced SnIMs in renal allograft rejection and highlight their pathogenic role in rejection injury, providing a therapeutic target to improve renal transplant outcome