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This meta-analysis examined literature from the last two decades to identify factors that correlate with teachers' classroom management self-efficacy (CMSE) and to estimate the effect size of these relationships. Online and reference list searches from international and Chinese databases yielded 1085 unique results. However, with a focus on empirical research the final sample consisted of 87 studies and 22 correlates. The findings cluster the correlates of CMSE into three categories: teacher-level factors (working experience, constructivist beliefs, teacher stress, job satisfaction, teacher commitment, teacher personality, and teacher burnout), classroom-level factors (classroom climate, classroom management, students' misbehaviour, students' achievement, classroom interaction, and student-teacher relationship), and school-level factors (principal leadership and school culture). The results of this meta-analysis show small to large correlations between these 15 factors with CMSE. How these factors are associated with teachers' CMSE and recommendations for future CMSE research are discussed.

[Introduction] The rapid development of new energy vehicles (NEVs) brings higher requirements for the power demand of highways. Based on the analysis of the power loads of highways, the photovoltaic endowment, and the energy storage technologies suitable for highway service areas in China, this paper explores the self-consistency of the highway transportation and energy integration mode of the PV-Storage-Charging integrated development path, and combines practical engineering project to demonstrate and analyze the feasibility of the highway transportation and energy integration mode. [Method] By analyzing the total mileage of highways in China, the market penetration rate of NEVs, and the PV & energy storage resources of highway transportation, based on the potential increase in highway power load due to the growth of NEVs and the utilization space of photovoltaic resources in highway transportation, a comprehensive energy utilization mode of PV-Storage-Charging for highway service areas was proposed. Combined with existing projects of self-consistent modes of transportation and energy integration, suggestions were proposed for the integrated development mode of highway PV-Storage-Charging. [Result] Data indicates that by 2025, the national stock of NEVs will exceed 25 million, consuming 37.5 TWh of electricity, showing tremendous potential for electricity demand. At the same time, the abundant photovoltaic resources along and around roads in China have a photovoltaic generation potential of approximately 1022.8 TW, while the calculated annual energy consumption of highway infrastructure is approximately 17.99 TW. [Conclusion] The main path of integration between transportation and energy should start with developing wind and photovoltaic natural resource endowments based on the infrastructure of road transportation itself, and forming a self-consistent supply of transportation energy systems, thereby constructing a distributed, green, renewable, and net zero-emission energy supply system for transportation systems. The integrated development path of PV-Storage-Charging transportation and energy integration can consume renewable energy locally, alleviate grid pressure while promoting the clean energy utilization of highways, showing immense potential. This paper can provide theoretical support and reference for the development of highway transportation and energy integration in China.

Abstract Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.

Objective Virtual Reality (VR) and Augmented Reality (AR) are innovative technologies that can serve as effective tools for creating health interventions by altering psychological distance. Based on construal level theory and the reality-virtuality continuum, we designed, tested, and compared VR and AR campaigns to encourage proactive measures against COVID-19. Methods 143 participants were randomly assigned to one of three messages: VR, AR, and a CDC video, and completed surveys before, immediately, and one week following message exposure. Results VR and AR increased preventive intentions and behaviors against COVID-19 both in the short and long run. VR was particularly effective as it also increased risk perceptions, more preventive intentions in the short term, and more preventive behaviors, including social distancing and mask wearing, in the long term. VR was more efficient than AR in enhancing risk perceptions and preventive intentions right after being exposed to the messages as well as promoting behaviors such as avoiding crowds, maintaining social distance from others, and wearing a mask in indoor public areas one week later. Moreover, among the three conditions, VR was the only intervention that generated actual behavior change after one week, which indicated potential long-term advantages of VR compared to other mediums. VR decreased social, spatial, and hypothetical distances to a greater degree than AR. VR was more effective than video. However, AR was not more persuasive than video. Conclusions Insights gained from the findings extend beyond the pandemic phase, offering practical applications for employing VR and AR technologies in health campaigns.

Abstract Integrated Sensing and Communications (ISAC) need to process data streams in high‐speed sensor data acquisition or high‐speed wireless communications. To process the data can require more computing and communication resources, resulting in higher power consumption. Halved‐Phase Only Multiple Input Multiple Output (HPO‐MIMO) communication technology can solve this problem by using low‐power nonlinear detection devices. In ISAC, Channel Estimation (CE) technology can provide key channel characteristics and state information for sensing and collaborative work of perception and communication tasks. However, HPO‐MIMO system cannot realize CE using traditional receiver schemes because of the missing amplitude. In order to solve this problem, two HPO‐MIMO CE schemes based on model‐driven deep learning are proposed in this paper. The proposed schemes include a Densely Residual Network (DRN) and a Inception‐Resnet (IR), which is suitable for the case of sufficient data and insufficient data, respectively. The simulation results show that the performance of DRN based scheme is better than that of IR based scheme when the data amount is sufficient, and the performance of IR based scheme is better when the dataset is small. In addition, the proposed CE schemes work well with a range of antenna sizes and distances.

Abstract To realize the resourceful use of soilbags filled with graphite tailings, their load-bearing and deformation characteristics must be fully understood. In this study, the following results were obtained by performing geometric testing of water-filled sealing bags and uniaxial compression tests of soilbags filled with graphite tailings. The volume of the soilbag expressed in rectangular form was approximately 0.773 times the actual volume. The types of compression damage to soilbags can be defined as surface damage and overall damage. The surface damage load increases with decreasing filling density and decreases with decreasing soilbag size. Moreover, the higher the tensile capacity of the soilbag material and the lower the friction between the soilbags, the greater the surface damage load. The overall damage load increased with an increase in the tensile strength of the soilbag material and decreased with an increase in the degree of filling; the overall damage load was greater for large-sized soilbags at high degrees of filling. Thus, the existing theoretical calculation method cannot accurately calculate the damage load of soilbags filled with graphite tailings, and the test results deviate from the theoretical calculation results, with the latter showing an increasing damage load with a decreasing filling degree.

Summary: Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study. The bigger picture: Spatial transcriptomics (ST) enables the localization of cell types and their associated gene expression within tissue samples. In multi-cellular resolution ST, a tissue is divided into spots consisting of several cells, and this sometimes creates difficulties for cell characterization and identification in complex tissue samples. There are several methods for spot deconvolution, but most are limited to single-sample analysis and require a reference cellular profile. Here, we present MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), a data analysis framework that permits multi-sample spot cellular deconvolution without a reference expression profile.

Farmers' decisions on crop choice, management practices, and livelihood strategies are essential to agricultural sustainability. This data article describes three datasets on crop production in Quzhou, a county in the central part of North China Plain. The three datasets cover different scales. The village dataset assembles basic data on all 342 villages of Quzhou county, including information on population, land area, crop grown, labour, irrigation and markets. Data was sourced from the yearbook data of 2017 and a village cadres survey in 2018. The village dataset was used to create a village typology from which 35 villages belonging to seven village types (five for each type) were selected for stratified random sampling to collect information on farm characteristics and cropping practices. We surveyed these 35 villages, interviewing fifteen farmer households per village (525 in total) in 2020. The interviewees represented two farm management models: smallholder farms and business farms. The resulting household dataset provides farm-level data, including demographic data of farming decision-makers and the number of household members, land use and machinery resources, crop production management practices, and government subsidies. The crop-level dataset was derived from the household survey and included input-output inventories for each crop grown during one year on each field greater than 1/30th ha (1/2 mu) on the 525 surveyed farms within a year. This dataset comprises information on cropping practices in 1352 fields. The three datasets provide a basis for analyses on cropping practices and sustainability attributes of farms and crops in a typical agricultural county on the North China Plain.

Rapid and effective healing of irregular wounds caused by burns, lacerations, and blast injuries remains a persistent challenge in wound care. Hydrogel microsphere dressings that can adaptively fill and adhere to the wounds without external force are desired to treat irregular wounds, providing an external barrier and accelerating healing. Herein, we created multifunctional cellulose‐based surface‐wrinkled microspheres with antioxidant, antibacterial, hygroscopicity, wet‐adhesion and shape‐adaptive capabilities to relieve inflammation, bacteria and excess exudate situations in healing irregular wounds. This dressing rapidly adsorbs exudate and reversibly adheres wetly to the wounds upon being filled, effectively inhibiting bacterial infection and reducing the flooded exudate to accelerate wound healing. Polydopamine (PDA) provides catechol‐based tissue bioadhesion to microspheres through π–π stacking or hydrogen bond interaction, and also establishes a bond bridge with an antimicrobial component (thymol), which not only enables the microspheres to stably adhere to the wound to maintain hygroscopicity, but also improves the release of the introduced antimicrobial component (thymol). In vivo assays, as well as histopathological and immunofluorescence studies have shown that multifunctional cellulose‐based microspheres have excellent pro‐healing abilities and are promising candidates for dehumidification and healing of irregular wound in clinical applications.

A series of lead-free Rb2ZrCl6:xTe4+ (x = 0%, 0.1%, 0.5%, 1.0%, 2.0%, 3.0%, 5.0%, 10.0%) perovskite materials were synthesized through a hydrothermal method in this work. The substitution of Te4+ for Zr in Rb2ZrCl6 was investigated to examine the effect of Te4+ doping on the spectral properties of Rb2ZrCl6 and its potential applications. The incorporation of Te4+ induced yellow emission of triplet self-trapped emission (STE). Different luminescence wavelengths were regulated by Te4+ concentration and excitation wavelength, and under a low concentration of Te4+ doping (x ≤ 0.1%), different types of host STE emission and Te4+ triplet state emission could be achieved through various excitation energies. These luminescent properties made it suitable for applications in information encryption. When Te4+ was doped at high concentrations (x ≥ 1%), yellow triplet state emission of Te4+ predominated, resulting in intense yellow emission, which stemmed from strong exciton binding energy and intense electron-phonon coupling. In addition, a Rb2ZrCl6:2%Te4+@RTV scintillating film was fabricated and a spatial resolution of 3.7 lp/mm was achieved, demonstrating the potential applications of Rb2ZrCl6:xTe4+ in nondestructive detection and bioimaging.

A UAV-assisted space–air–ground integrated network (SAGIN) can provide communication services for remote areas and disaster-stricken regions. However, the increasing types and numbers of ground terminals (GTs) have led to the explosive growth of communication data volume, which is far from meeting the communication needs of ground users. We propose a mobile edge network model that consists of three tiers: satellites, UAVs, and GTs. In this model, UAVs and satellites deploy edge servers to deliver services to GTs. GTs with limited computing capabilities can upload computation tasks to UAVs or satellites for processing. Specifically, we optimize association control, bandwidth allocation, computation task allocation, caching decisions, and the UAV’s position to minimize task latency. However, the proposed joint optimization problem is complex, and it is difficult to solve. Hence, we utilize Block Coordinate Descent (BCD) and introduce auxiliary variables to decompose the original problem into different subproblems. These subproblems are then solved using the McCormick envelope theory, the Successive Convex Approximation (SCA) method, and convex optimization techniques. The simulation results extensively illustrate that the proposed solution dramatically decreases the overall latency when compared with alternative benchmark schemes.

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.

Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients’ pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.