Optimization of Work Environment and Community Labor Health Based on Digital Model—Empirical Evidence from Developing Countries

As far as we know, for large manufacturing enterprises, there is often a community of labor gathered around such enterprises, which is especially used as a place for the enterprise to place the labor force. This paper aimed to update the industry model of Chinese Manufacturing Enterprises (CMEs) to improve workers’ health management. This work first discusses the value, mode, and process of Enterprise Digital Transformation (EDT) and Worker Health and Safety Management (WHSM). Then, it proposes the CMEs-oriented EDT model and WHSM system based on Big Data Technology (BDT) and the Internet of Things (IoT). The proposed model and system are verified through a case study on the Shanghai BYD manufacturing enterprise (short for BYD) using the Fuzzy Comprehensive Evaluation Method (CFEM). The EDT model verification considers the adaptation and performance of enterprises after EDT. The WHSM system considers workers’ oxygen inhalation status to evaluate their heart and cardiovascular health. The results show that EDT improves the enterprise’s revenue and reshuffles the revenue structure. The EDT model has absolute adaptability to BYD. It has greatly improved BYD’s indexes, especially financial performance, market capability, and technical capability

Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China

Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem

Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China

Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem

Solution-Processed Efficient Nanocrystal Solar Cells Based on CdTe and CdS Nanocrystals

Solution-processed CdTe nanocrystals solar cells have attracted much attention due to their low cost, low material consumption, and potential for roll-to-roll production. Among all kinds of semiconductor materials, CdS exhibits the lowest lattice mismatch with CdTe, which permits high junction quality and high device performance. In this study, high quality CdS nanocrystals were prepared by a non-injection technique with tetraethylthiuram disufide and 2,2′-dithiobisbenzothiazole as the stabilizers. Based on the CdTe and CdS nanocrystals, devices with the architecture of ITO/ZnO/CdS/CdTe/MoOx/Au were fabricated successfully by a solution process under ambient condition. The effects of annealing conditions, film thickness, and detailed device structure on the CdTe/CdS nanocrystal solar cells were investigated and discussed in detail. We demonstrate that high junction quality can be obtained by using CdS nanocrystal thin film compared to traditional CdS film via chemical bath deposition (CBD). The best device had short circuit current density (Jsc), open circuit voltage (Voc) and fill factor (FF) of 17.26 mA/cm2, 0.56 V, and 52.84%, respectively, resulting in a power conversion efficiency (PCE) of 5.14%, which is significantly higher than that reported using CBD CdS as the window layer. This work provides important suggestions for the further improvement of efficiency in CdTe nanocrystal solar cells

CdTe Nanocrystal Hetero-Junction Solar Cells with High Open Circuit Voltage Based on Sb-doped TiO2 Electron Acceptor Materials

We propose Sb-doped TiO2 as electron acceptor material for depleted CdTe nanocrystal (NC) hetero-junction solar cells. Novel devices with the architecture of FTO/ZnO/Sb:TiO2/CdTe/Au based on CdTe NC and TiO2 precursor are fabricated by rational ambient solution process. By introducing TiO2 with dopant concentration, we are able to tailor the optoelectronic properties of NC solar cells. Our novel devices demonstrate a very high open circuit voltage of 0.74 V, which is the highest Voc reported for any CdTe NC based solar cells. The power conversion efficiency (PCE) of solar cells increases with the increase of Sb-doped content from 1% to 3%, then decreases almost linearly with further increase of Sb content due to the recombination effect. The champion device shows Jsc, Voc, FF, and PCE of 14.65 mA/cm2, 0.70 V, 34.44, and 3.53% respectively, which is prospective for solution processed NC solar cells with high Voc

Rationally Controlled Synthesis of CdSexTe1−x Alloy Nanocrystals and Their Application in Efficient Graded Bandgap Solar Cells

CdSexTe1−x semiconductor nanocrystals (NCs), being rod-shaped/irregular dot-shaped in morphology, have been fabricated via a simple hot-injection method. The NCs composition is well controlled through varying molar ratios of Se to Te precursors. Through changing the composition of the CdSexTe1−x NCs, the spectral absorption of the NC thin film between 570–800 nm is proved to be tunable. It is shown that the bandgap of homogeneously alloyed CdSexTe1−x active thin film is nonlinearly correlated with the different compositions, which is perceived as optical bowing. The solar cell devices based on CdSexTe1−x NCs with the structure of ITO/ZnO/CdSe/CdSexTe1−x/MoOx/Au and the graded bandgap ITO/ZnO/CdSe(w/o)/CdSexTe1−x/CdTe/MoOx/Au are systematically evaluated. It was found that the performance of solar cells degrades almost linearly with the increase of alloy NC film thickness with respect to ITO/ZnO/CdSe/CdSe0.2Te0.8/MoOx/Au. From another perspective, in terms of the graded bandgap structure of ITO/ZnO/CdSe/CdSexTe1−x/CdTe/MoOx/Au, the performance is improved in contrast with its single-junction analogues. The graded bandgap structure is proved to be efficient when absorbing spectrum and the solar cells fabricated under the structure of ITO/ZnO/CdSe0.8Te0.2/CdSe0.2Te0.8/CdTe/MoOx/Au indicate power conversion efficiency (PCE) of 6.37%, a value among the highest for solution-processed inversely-structured CdSexTe1−x NC solar cells. As the NC solar cells are solution-processed under environmental conditions, they are promising for fabricating solar cells at low cost, roll by roll and in large area