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To enrich the research methods for studying the development law of bed separation, this paper takes the upper 08 working face of 221 in Shilawusu Mine as the research object, and comprehensively determines the location of bed separation through distributed optical fiber (BOTDR) and fixed-point optical fiber. The particle flow code (PFC2D) is used to study the pattern of the separation layer development under the conditions of large mining height and large mining depth. The results show that, as the working face advances away from the cutting hole, the separated layer develops intermittently from the bottom to the bottom of the key layer, and the depth of separation layer development is around 440 m, 396 m, 376 m, 331 m, 321 m and 296 m, and the layer stops at the bottom of the main key layer of coarse sandstone. The overburden transport mainly goes through the stages of subsidence of the lower key layer, rapid subsidence of the upper sub-key layer, development of overburden movement towards the bottom of the main key layer of coarse sandstone, and transport of the main key layer. The separated layer position is proportional to the advancement distance of the working face, and the development of the separated layer is mainly divided into three stages, which are gestation (Ⅰ)-accelerated expansion (Ⅱ)- compression stage (Ⅲ).

Abstract Background Joint contracture causes a decrease in range of motion (ROM), which severely affects activities of daily living of patients. We have investigated the effectiveness of a multidisciplinary rehabilitation on joint contracture by rat model. Methods We used 60 Wistar rats in this study. The rats were divided into five groups as follows: group 1 was the normal control group; except the group 1, we created left hind limb knee joint contracture using Nagai method for other four groups. The joint contracture modeling group 2 was the model control group for monitoring the spontaneous recovery, and other three groups were given different rehabilitation treatments; for example, group 3 was treadmill running group; group 4 was medication group; group 5 was treadmill running plus medication group. The left hind limbs knee joint ROM and the femoral blood flow indicators (FBFI) including PS, ED, RI, and PI were measured right before and after the 4 weeks of rehabilitation. Result After 4 weeks of rehabilitation treatments, the measured values of ROM and FBFI are compared with the corresponding values of group 2. Firstly, we did not see clear difference in the values of ROM and FBFI for group 2 before and after 4 weeks spontaneous recovery. The improvement of left lower limb ROM for group 4 and group 5 as compared to the group 2 was statistically significant (p

Soil analysis using near-infrared spectroscopy has shown great potential to be an alternative to traditional laboratory analysis, and there is continuously increasing interest in building large-scale soil spectral libraries (SSLs). However, due to issues such as high non-linearity in soil spectral data and complexity in soil spatial variation, the establishment of robust prediction models for soil spectral libraries remains a challenge. This study aimed to investigate the performance of deep learning algorithms, including long short-term memory (LSTM) and LSTM–convolutional neural networks (LSTM–CNN) integrated models, to predict the soil organic matter (SOM) of a provincial-scale SSL, and compare it to the normally used local weighted regression (LWR) model. The Hebei soil spectral library (HSSL) contains 425 topsoil samples (0–20 cm), of which every 3 soil samples were collected from dry land, irrigated land, and paddy fields, respectively, in different counties of Hebei Province, China. The results show that the accuracy of the validation dataset rank as follows: LSTM–CNN (R2p = 0.96, RMSEp = 1.66 g/kg) > LSTM (R2p = 0.83, RMSEp = 3.42 g/kg) > LWR (R2p = 0.82, RMSEp = 3.79 g/kg). The LSTM–CNN model performed the best, mainly due to its comprehensive ability to effectively extract spatial and temporal features. Meanwhile, the LSTM model achieved higher accuracy than the LWR model, owing to its built-in memory unit and its advantage of faster feature band extraction. Thus, it was suggested to use deep learning algorithms for SOM predictions in SSLs. However, their performance on larger-scale SSLs such as continental/global SSLs still needs to be further investigated.

In this study, the thermoset–thermoplastic structure was produced through a co-curing technique together with an injection overmolding technique. Continuous fiber reinforced thermoset composite (TSC) was selected as thermoset material, while polyamide 6 (PA 6) was chosen as thermoplastic material. The influence of injection temperature, preheating temperature and injection speed on the interfacial bonding strength of hybrid thermoset–thermoplastic composites was investigated. The results show that increasing injection temperature and preheating temperature have significant effects on the increase in bonding strength, while injection speed has little effect on it. In addition, the bonding strength of the co-cured interface is enhanced after the injection overmolding process, which is further studied through molecular dynamic (MD) simulation. The molecular dynamic simulation result shows that the high temperature and pressure during the injection process only have a weak effect on enhancing the bonding strength of the co-cured interface, while the chemical reaction at the co-cured interface is the main reason for the enhancement. Furthermore, the more chemical reactions occur at the interface, the stronger the interface will be.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSEp = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSEp = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSEp = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSEp = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.

Umbilical cord mesenchymal stem cells (UC-MSCs) have been suggested as a candidate for various clinical applications, however, major limitations include the lack of organ-specific accumulation and low survival rates of transplanted cells. In the present study, it was hypothesized that the paracrine effects of UC-MSCs may enhance stem cell-based tissue repair and regeneration by promoting the specific homing of stem/progenitor cells and the overall ability to drive them to the damaged area. UC-MSCs-derived conditioned medium (UC-CM) was analyzed using liquid chip and ELISA techniques. In vitro tube formation assays of human umbilical vein endothelial cells (HUVECs) and UC-MSCs were then performed to assess the angiogenic properties of UC-CM. Subsequently, UC-MSCs, HUVECs and fibroblasts were labeled with PKH26 for an in vivo cell migration assay. The expression levels of C-X-C chemokine receptor 4 (CXCR4), C-C chemokine receptor 2 (CCR2) and c-met were determined in the UC-MSCs, HUVECs and fibroblasts using reverse transcription-quantitative polymerase chain reaction and flow cytometry. UC-CM was incubated with or without antibodies, and the contribution of stromal cell-derived factor 1 (SDF-1), monocyte chemotactic protein 1 (MCP-1) and hepatocyte growth factor (HGF) on the migration of cells was investigated in vitro. The results demonstrated that UC-MSCs secreted different cytokines and chemokines, including increased quantities of SDF-1, MCP-1 and HGF, in addition to the angiogenic factors, vascular cell adhesion protein-1, interleukin- 8, insulin-like growth factor-1 and vascular endothelial growth factor. The total lengths of the tubes were significantly increased in the UC-MSCs and HUVECs incubated in UC-CM compared with those incubated in Dulbecco's modified Eagle's medium. In vivo cell migration assays demonstrated that UC-CM was a chemotactic stimulus for the UC-MSCs and HUVECs. In vitro Matrigel migration and scratch healing assays demonstrated that UC-CM increased the migration of CXCR4-postive or/and CCR2-positive cells in a dose-dependent manner. In addition, different molecules were screened under antibody-based blocking migration conditions. The data revealed that the SDF-1/CXCR4 and MCP-1/CCR2 axes were involved in the chemoattractive activity of UC-CM and suggested that the effective paracrine factor of UC-CM is a large complex rather than a single factor. The results of the present study supported the hypothesis that UC-MSCs release soluble factors, which may extend the therapeutic applicability of stem cells. [ABSTRACT FROM AUTHOR]