In the new manufacturing area, the Chinese textile industry is facing the opportunities and challenges under the sustainable development model and advanced manufacturing technology. It is urgent to ...use digital technology to realize digital transformation. In this process, digital dynamic capability and digital innovation are significant for textile firms to achieve digital transformation performance. Drawing on the data from 367 questionnaires of Chinese textile firms, we apply multiple regression to examine how a firm's digital transformation is affected by adopting digital technology. A conceptual model based on framework of resource–capability–performance is developed to examine how adoption of digital technology, digital dynamic capability, and digital innovation orientation affects a firm's digital transformation performance. The results indicate that the positive relationship between digital technology adoption and digital transformation performance is less significant; this path is fully mediated through digital dynamic capability. Digital innovation orientation has a positive moderating effect and that the efficiency digital innovation has the most significant moderating effect among all digital innovation types. In addition, compared with textile firms in the low level of digital technology adoption, the firms in the high‐level group show a significant positive relationship between digital technology adoption and digital transformation performance. These findings confirm the validity of the model and contribute to existing literature on technology efforts in firms and provide guidelines to help managers implement informed decisions about digital transformation.
With the development of the economy and society, people pay more and more attention to physical health. In order to make the big health enterprises in the long term at the level of smooth ...development, it is necessary to carry out an in-depth study on the investment efficiency and potential risk of the big health industry. This study constructs an investment efficiency evaluation method based on the DEA model. Firstly, the comprehensive efficiency is decomposed through the CCR model to further obtain the output results. Then, the effectiveness of enterprise investment is evaluated. The changes in the investment efficiency of the big health industry and other sample decision-making units are analyzed through the DEA-Malmquist model to output the trend of the overall investment efficiency. Logistic regression, support vector machine, and random forest models are used to assess the risk of the large health industry, respectively, and several classifiers are trained. When predicting the final sample, the voting or mean value method is used to count the effect of classification. The overall mean value of big health enterprises hovered between 0.96 and 0.98 in five years, indicating that the comprehensive investment efficiency of the big health industry is relatively stable. The average AUC value of the random forest model is 0.635, which is 0.028 higher than the average AUC value of the support vector machine; thus, it is concluded that there is no great fluctuation in the investment efficiency of the big health industry under the background of the digital economy, and the random forest model is more suitable for the risk assessment of the big health industry.
Chronic inflammation in adipose tissue, possibly related to adipose cell hypertrophy, hypoxia, and/or intestinal leakage of bacteria and their metabolic products, likely plays a critical role in the ...development of obesity-associated insulin resistance (IR). Cells of both the innate and adaptive immune system residing in adipose tissues, as well as in the intestine, participate in this process. Thus, M1 macrophages, IFN-γ-secreting Th1 cells, CD8+ T cells, and B cells promote IR, in part through secretion of proinflammatory cytokines. Conversely, eosinophils, Th2 T cells, type 2 innate lymphoid cells, and possibly Foxp3+ Tregs protect against IR through local control of inflammation.
•The present study undertakes a comprehensive qualitative meta-analysis based on published case studies.•We reveal 5 orchestration practices: strategic design, relational, resource integrating, ...technological and innovation.•Ecosystem strategic design, relational, resource integrating practices form the basic ecosystem orchestration framework.•Ecosystem technological leveraging and innovation practices are complementary ecosystem orchestration practices.•These five practices constitute the Stirring Model of ecosystem orchestration.
This study ventures into the dynamic realm of ecosystem orchestration for industrial firms, emphasizing its significance in maintaining competitive advantage in the digital era. The fragmented research on this important subject poses challenges for firms aiming to navigate and capitalize on ecosystem orchestration. To bridge this knowledge gap, we conducted a comprehensive qualitative meta-analysis of 31 case studies and identified multifaceted orchestration practices employed by industrial firms. The core contribution of this research is the illumination of five interdependent but interrelated orchestration practices: strategic design, relational, resource integration, technological, and innovation. Together, these practices are synthesized into an integrative framework termed the “Stirring Model,” which serves as a practical guide to the orchestration practices. Furthermore, the conceptual framework clarifies the synergy between the identified practices and highlights their collective impact. This study proposes theoretical and practical implications for ecosystem orchestration literature and suggests avenues for further research.
Existing Transformer-based models have achieved impressive success in facial expression recognition (FER) by modeling the long-range relationships among facial muscle movements. However, the size of ...pure Transformer-based models tends to be in the million-parameter level, which poses a challenge for deploying these models. Moreover, the lack of inductive bias in Transformer usually leads to the difficulty of training from scratch on limited FER datasets. To address these problems, we propose an effective and lightweight variant Transformer for FER called VaTFER. In VaTFER, we firstly construct action unit (AU) tokens by utilizing action unit-based regions and their histogram of oriented gradient (HOG) features. Then, we present a novel spatial-channel feature relevance Transformer (SCFRT) module, which incorporates multilayer channel reduction self-attention (MLCRSA) and a dynamic learnable information extraction (DLIE) mechanism. MLCRSA is utilized to model long-range dependencies among all tokens and decrease the number of parameters. DLIE's goal is to alleviate the lack of inductive bias and improve the learning ability of the model. Furthermore, we use an excitation module to replace the vanilla multilayer perception (MLP) for accurate prediction. To further reduce computing and memory resources, we introduce a binary quantization mechanism, formulating a novel lightweight Transformer model called variant binary Transformer for FER (VaBTFER). We conduct extensive experiments on several commonly used facial expression datasets, and the results attest to the effectiveness of our methods.
Cancer cachexia is a progressive and multi-factorial metabolic syndrome characterized by loss of adipose tissue and skeletal muscle. White adipose tissue (WAT) lipolysis and white-to-brown ...transdifferentiation of WAT (WAT browning) are proposed to contribute to WAT atrophy in cancer cachexia. Chronic inflammation, mediated by cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6), has been reported to promote cancer cachexia. However, whether chronic inflammation promotes cancer cachexia by regulating WAT metabolism and the underlying mechanism remains unclear.
In this study, we first analyzed the association between chronic inflammation and WAT metabolism in gastric and colorectal cancer cachectic patients. In cachectic mice treated with anti-IL-6 receptor antibody, we clarified whether WAT lipolysis and browning were regulated by IL-6.
Clinical analyses showed positive significant association between serum IL-6 and free fatty acid (FFA) both in early- and late-stage cancer cachexia. However, serum TNF-α was positively associated with serum FFA in the early- but not late-stage cachexia. WAT lipolysis was increased in early- and late-stage cachexia, while WAT browning was detected only in late-stage cachexia. Anti-IL-6 receptor antibody inhibited WAT lipolysis and browning in cachectic mice.
Based on these findings, we conclude that chronic inflammation (especially that mediated by IL-6) might promote cancer cachexia by regulating WAT lipolysis in early-stage cachexia and browning in late-stage cachexia.
Since December 2019, a novel coronavirus SARS-CoV-2 has emerged and rapidly spread throughout the world, resulting in a global public health emergency. The lack of vaccine and antivirals has brought ...an urgent need for an animal model. Human angiotensin-converting enzyme II (ACE2) has been identified as a functional receptor for SARS-CoV-2. In this study, we generated a mouse model expressing human ACE2 (hACE2) by using CRISPR/Cas9 knockin technology. In comparison with wild-type C57BL/6 mice, both young and aged hACE2 mice sustained high viral loads in lung, trachea, and brain upon intranasal infection. Although fatalities were not observed, interstitial pneumonia and elevated cytokines were seen in SARS-CoV-2 infected-aged hACE2 mice. Interestingly, intragastric inoculation of SARS-CoV-2 was seen to cause productive infection and lead to pulmonary pathological changes in hACE2 mice. Overall, this animal model described here provides a useful tool for studying SARS-CoV-2 transmission and pathogenesis and evaluating COVID-19 vaccines and therapeutics.
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•Human ACE2 knockin mice were generated by using CRISPR/Cas9 technology•SARS-CoV-2 leads to robust replication in lung, trachea, and brain•SARS-CoV-2 causes interstitial pneumonia and elevated cytokine in aged hACE2 mice•High dose of SARS-CoV-2 can establish infection via intragastric route in hACE2 mice
The COVID-19 pandemic has brought an urgent need for small animal models. Here, Sun et al. established an ACE2 humanized mouse by CRISPR/Cas9 knockin technology. These hACE2 mice are susceptible to SARS-CoV-2 infection upon intranasal inoculation, and the resulting pulmonary infection and pathological changes resemble those observed in COVID-19 patients.
This paper presents the concept of magnetic functional integrated DC motor drive. It uses the field winding of a wound-field DC motor as the inductor of its drive circuit. The field winding in a ...wound-field DC motor not only builds the required magnetic field for the electric-mechanic energy conversion, but also acts as the inductor to buffer the energy for the drive circuit. One magnetic component is shared to realize different functions for motor and its drive circuit, simultaneously. The weight, loss and cost of the drive system can be reduced due to the saving of extra bulky inductors for the drive circuit. This concept can be applied to all DC-DC drive circuits using inductor as its energy buffer. An example of magnetic functional integrated DC motor drive using the buck-boost converter is used to explain the concept in detail. The feasibility and the new features are verified experimentally by comparing with the conventional motor drive.
Previous studies have estimated emissions from China's cement industry for some specific periods, but a trend analysis of historical emissions has not yet been carried out. Based on changes in ...clinker quality and developments in energy-saving technologies of different clinker kilns, we calculated carbon dioxide (CO2) emissions due to cement production in China for the continuous period from 1980 to 2014. Our analysis showed that total CO2 emissions from cement production in China were 1270.55 Mt in 2014, which is 18 times higher than that in 1980. There was a slight reduction in the share of emissions from fuel consumption, while the percentage of process emissions rose at a stable rate. The cement emission factor fell sharply from 852.12kg/t in 1980 to 513.15kg/t in 2014. Two scenarios were considered to predict future emissions, namely a baseline scenario (BS) and a best practice scenario (BPS). We found that CO2 emissions could be reduced to 856.62–957.91 Mt in 2020, which corresponds to a cement output of 1.86 billion tons. This implies that, by 2020, clinker and cement emission factors will fall to 789.11–840.618kg/t and 460.55–513.15kg/t, respectively. By 2020, energy efficiency improvements will be the main driver of emission reductions. Our projections also indicated that emission reductions resulting from process improvements would only account for 1.48% of total direct emissions, even under the BPS scenario. Thus, we suggest that the development of alternative industrial byproducts and fuels, substituting for natural resources, should be a main focus of future innovation efforts toward a sustainable cement industry in China.
•Historical and future emission trends for China's cement industry are addressed.•Estimated emissions derive from the input method rather than default values.•The future energy intensity of the cement industry is analyzed.•Energy efficiency improvements will be the main driver of emission reductions.
Carbon price, to a certain extent, reflects the intensity of a national emission reduction target, whereas carbon price forecasting is the basis for improving crisis management competence and ...strengthening market enthusiasm. This paper advances a novel hybrid carbon price forecasting methodology consisting of the empirical wavelet transform (EWT) and the gated recurrent unit (GRU) neural network. First, the carbon price data is decomposed through the EWT approach into the more stable and regular sub-components. These sub-components are divided into trend, low-frequency and high-frequency component using the fuzzy C-means clustering algorithm. Next, the lag order of different classes of components is determined as the input variables of the GRU model by the partial auto-correlation function method. Then, all values of each component predicted by the GRU method are aggregated to produce a final combined prediction result for the original carbon price. Finally, the EWT-GRU model is compared with the individual Autoregressive Integrated Moving Average (ARIMA), Back Propagation Neural Network (BPNN), GRU and EWT-BPNN models. The simulation results demonstrate that the proposed EWT-GRU combined forecasting model is superior to other models in terms of prediction effect, prediction accuracy, etc. They also confirm the validity and accuracy of the EWT-GRU model in carbon price prediction and show it deserves popularization.
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