Carbon emissions prediction considering environment protection investment of 30 provinces in China.

In the backdrop of carbon peaking and carbon neutrality, carbon emissions have always been a major concern. The approach of the heterogeneity grey model is proposed, aiming to predict carbon emissions of 30 provinces in China. This model combines the manta ray foraging optimization algorithm to search for the optimal heterogeneity coefficient. By using the heterogeneity grey model, the carbon emissions are analyzed in 30 provinces of China from 2022 to 2030 considering different environmental protection investment scenarios. The results indicate that in 19 provinces from 2022 to 2030, there is a significant decrease in carbon emissions as government investment increases. In 11 provinces during the same period, there is a rising trend in carbon emissions with the increase of government investment. Hence, achieving a reduction in carbon emissions necessitates not only relying on government investment in environmental protection but also exploring alternative approaches to mitigate carbon emissions. The methodologies and conclusions proposed in this study can provide technical references and making decision references for provincial carbon emission efforts.

Microplastics increase the microbial functional potential of greenhouse gas emissions and water pollution in a freshwater lake: A metagenomic study.

Aquatic ecosystems are being increasingly polluted by microplastics (MPs), which calls for an understanding of how MPs affect microbially driven biogenic element cycling in water environments. A 28-day incubation experiment was conducted using freshwater lake water added with three polymer types of MPs (i.e., polyethylene, polypropylene, polystyrene) separately or in combination at a concentration of 1 items/L. The effects of various MPs on microbial communities and functional genes related to carbon, nitrogen, phosphorus, and sulfur cycling were analyzed using metagenomics. Results showed that Sphingomonas and Novosphingobium, which were indicator taxa (genus level) in the polyethylene treatment group, made the largest functional contribution to biogenic element cycling. Following the addition of MPs, the relative abundances of genes related to methane oxidation (e.g., hdrD, frhB, accAB) and denitrification (napABC, nirK, norB) increased. These changes were accompanied by increased relative abundances of genes involved in organic phosphorus mineralization (e.g., phoAD) and sulfate reduction (cysHIJ), as well as decreased relative abundances of genes involved in phosphate transport (phnCDE) and the SOX system. Findings of this study underscore that MPs, especially polyethylene, increase the potential of greenhouse gas emissions (CO2, N2O) and water pollution (PO43-, H2S) in freshwater lakes at the functional gene level.

Flubendazole Enhances the Inhibitory Effect of Paclitaxel via HIF1α/PI3K/AKT Signaling Pathways in Breast Cancer.

Paclitaxel, a natural anticancer drug, is widely recognized and extensively utilized in the treatment of breast cancer (BC). However, it may lead to certain side effects or drug resistance. Fortunately, combination therapy with another anti-tumor agent has been explored as an option to improve the efficacy of paclitaxel in the treatment of BC. Herein, we first evaluated the synergistic effects of paclitaxel and flubendazole through combination index (CI) calculations. Secondly, flubendazole was demonstrated to synergize paclitaxel-mediated BC cell killing in vitro and in vivo. Moreover, we discovered that flubendazole could reverse the drug resistance of paclitaxel-resistant BC cells. Mechanistically, flubendazole was demonstrated to enhance the inhibitory effect of paclitaxel via HIF1α/PI3K/AKT signaling pathways. Collectively, our findings demonstrate the effectiveness of flubendazole in combination with paclitaxel for treating BC, providing an insight into exploiting more novel combination therapies for BC in the future.

Two novel nonlinear multivariate grey models with kernel learning for small-sample time series prediction.

For many applications, small-sample time series prediction based on grey forecasting models has become indispensable. Many algorithms have been developed recently to make them effective. Each of these methods has a specialized application depending on the properties of the time series that need to be inferred. In order to develop a generalized nonlinear multivariable grey model with higher compatibility and generalization performance, we realize the nonlinearization of traditional GM(1,N), and we call it NGM(1,N). The unidentified nonlinear function that maps the data into a better representational space is present in both the NGM(1,N) and its response function. The original optimization problem with linear equality constraints is established in terms of parameter estimation for the NGM(1,N), and two different approaches are taken to solve it. The former is the Lagrange multiplier method, which converts the optimization problem into a linear system to be solved; and the latter is the standard dualization method utilizing Lagrange multipliers, that uses a flexible estimation equation for the development coefficient. As the size of the training data increases, the estimation results of the potential development coefficient get richer and the final estimation results using the average value are more reliable. The kernel function expresses the dot product of two unidentified nonlinear functions during the solving process, greatly lowering the computational complexity of nonlinear functions. Three numerical examples show that the LDNGM(1,N) outperforms the other multivariate grey models compared in terms of generalization performance. The duality theory and framework with kernel learning are instructive for further research around multivariate grey models to follow. Supplementary Information: The online version contains supplementary material available at 10.1007/s11071-023-08296-y.

Using information technology to optimize the identification process for outpatients having blood drawn and improve patient satisfaction.

BACKGROUND: This study explored the application effect of information technology in optimizing the patient identification process. METHODS: The method for optimizing the identification process involved in drawing blood among outpatients using information technology was executed from July 2020. In this paper, 959 patients who had blood drawn from January to June 2020 were included as the pre-optimization group, and 1011 patients who had blood drawn from July to December 2019 were included as the post-optimization group. The correct rate of patient identification, waiting time, and patient satisfaction before and after the optimization were statistically analyzed. The changes in these three indexes before and after the optimization implementation, as well as the application effects, were compared. RESULTS: The correct rate of patient identification after optimization (99.80%) was higher than before optimization (98.02%) (X2 = 13.120; P < 0.001), and the waiting time for having blood drawn was also significantly shortened (t = 8.046; P < 0.001). The satisfaction of patients was also significantly improved (X2 = 20.973; P < 0.001). CONCLUSIONS: By combining information technology with the characteristics of blood collection in our hospital, using the call system to obtain patient information, then scan the QR code of the guide sheet for automatic verification, and finally manually reconfirm patient information, which can significantly reduce the occurrence of identification errors, improve work efficiency and improve patients' satisfaction.

Application of grey model in influencing factors analysis and trend prediction of carbon emission in Shanxi Province.

In recent years, global warming has attracted extensive attention. The main cause of global warming is the emission of greenhouse gases, known as carbon emissions. Therefore, it is of great significance to explore the influencing factors of carbon emissions and accurately predict carbon emissions for reducing carbon emissions and slowing down climate warming. This paper takes the carbon emissions of Shanxi Province in China as the research object. Firstly, the emission factor method is used to calculate the carbon emissions, and then the grey correlation model is used to screen out the factors that have a greater impact on carbon emissions (per capita GDP, urbanization rate, resident population, energy consumption, expenditure on R&D projects). Then, an improved grey multi-variable convolution integral model (AGMC(1, N)) is used to accurately predict carbon emissions. The results show that the application of the AGMC(1,N) model to carbon emission prediction has a good prediction effect. In addition, the carbon emissions of Shanxi Province will increase with the growth rate of per capita GDP, energy consumption, resident population, and expenditure on R&D projects, while the carbon emissions will gradually decrease with the increase of urbanization level. The prediction results provide the direction for carbon emission reduction in Shanxi Province. At the same time, theAGMC(1,N) model can also be applied to the prediction of carbon emissions in other provinces or other fields.

Using a fractional order grey seasonal model to predict the dissolved oxygen and pH in the Huaihe River.

To accurately forecast the seasonal fluctuations of dissolved oxygen (DO) and pH in Huaihe River, a grey seasonal model with fractional order accumulation is proposed, optimized by particle swarm optimization (PSO-FGSM(1,1)). We use this new model to carry out an empirical analysis based on the DO and pH data from 2014 to 2018 from Huaibin, Bengbu, Chuzhou monitoring points. The comparison results show that the PSO-FGSM(1,1) model accuracy is significantly higher than the Holt-Winters model with grey wolf optimization (GWO-Holt-Winters). The prediction results indicated that the pollution of the Huaihe River has regional characteristics. The Huaibin and Chuzhou sections of the Huaihe River are slightly polluted, and the Bengbu section is seriously polluted.

Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China.

In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using chemical oxygen demand (COD) as the water quality indicator).

Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM).

Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important parameters for predicting the concentration of indoor culturable fungi (ICF). For model training and statistical analysis, parameters that contained indoor or outdoor PM10 and PM2.5, RH, Temperature, CO2 and ICF were measured in 85 residential buildings of Baoding, China, from November 2016 to March 2017. The variable selection process contains four different cases to identify the optimal input combination. The results indicate that root mean square error of the optimal input combinations can be improved 5.6% from 1 to 2 input variables, while that could be only improved 1.9% from 2 to 3 input variables. However, considering both precision and simplicity, the combination of indoor PM10 and RH provides a more suitable selection for predicting the ICF.

Food safety knowledge, attitudes, and behavior of street food vendors and consumers in Handan, a third tier city in China.

BACKGROUND: Food safety has long been the subject of scholarly research, and street food is a weak link in food safety supervision. Street food not only provides convenience for many people, but is also the livelihood for millions of low income people, making a great contribution to the economy of many developing countries. METHODS: Street food safety is essential, and yet it has been rarely studied in China. Therefore, a typical city in China was selected as the research object to assess food safety knowledge, attitudes, and street food suppliers and consumer behaviors using questionnaires based on previous studies, and considering China's particular characteristics and reasonable impacts identified in previous studies, such as increased income, work experience, licenses, and locations. The food safety knowledge and attitude questionnaire conformed with the national conditions in China. It was used to assess the food safety knowledge and attitudes toward food suppliers and consumers, where three main areas were addressed in the surveys and statistical analysis, as follows. (1) Statistical information including gender, age, education, income, food safety training, and specific elements related to the work experience of suppliers. (2) Knowledge of food safety including the awareness of consumers and suppliers regarding food poisoning pathogens, food and personal hygiene, high-risk groups, and correct cleaning. (3) A list of food handling behaviors was used to determine the behaviors and characteristics of subjects. RESULTS: The results show that street food suppliers have generally poor food handling practices, and most are operating under unsanitary conditions. Food safety knowledge of street vendors in the High-tech Industries Development Zone was the lowest, most likely because these regions are located in rural-urban fringe zones, where education levels are generally relatively low. Food safety attitudes of the youngest consumers were significantly better than those of older age groups. Their educational level was also different, with correspondingly relatively high income for younger individuals. Most vendors chose locations near schools or supermarkets. Consumers and street food vendors had good understanding of food safety, but street vendors were relatively poor in carrying out safe food handling, with only 26.7% using or being fully equipped withhand-washing facilities, although more than 60% of vendors wore clean and tidy clothes and masks. CONCLUSIONS: Street food vendor training should be prioritized to improve the safety of street food. Other policies and measures should also be propagated to improve the food safety knowledge, attitudes, and behavior of vendors in Handan. Steps should be taken to improve street food stall operating conditions and facilities, including providing clean protected structures, access to potable water, and efficient waste collection and disposal systems. These findings should encourage government agencies to further promote strategies to improve street food safety.

[Research progress in adiponectin and cognitive impairment].

Adipocytokines are polypeptides or proteins that are secreted by fat cells with a wide range of biological activities. Adiponectin is a fatty cytokine with insulin sensitization. It possesses the function of anti- diabetes, atherosclerosis and anti-inflammation. Adiponectin may participate in regulating the development of cognitive impairment, which is considered as a new regulatory factor for cognitive impairment.

[Protective effects and mechanisms of Xingnaojing Injection on early global brain ischemic-induced deep coma in rats].

OBJECTIVE: To study the protective effect of Xingnaojing Injection on early global brain ischemia-induced deep coma in rats.
 Methods: The deep coma model was induced by global brain ischemia by using four-vessel occlusion method in male SD rats. According to the body weight, the rats were randomly divided into 8 groups: a model control group, three different dose of Xingnaojing Injection (1.8, 3.6 and 5.4 mL.kg-1) groups, a Xingnaojing Injection (3.6 mL.kg-1) plus PI3K inhibitor group, a naloxone injection (0.04 mL.kg-1) group and a naloxone injection (0.04 mL.kg-1) plus Xingnaojing Injection (3.6 mL.kg-1) group (n=8 per group). In addition, eight animals served as the sham group were performed same operation with the model group excepting no blockage of the blood vessels. After the operation, three different doses of Xingnaojing Injection and/or naloxone injection were given intravenously once a day for three days. Ten µL PI3K inhibitor (LY294002, 10 mmol/L) was injected via anterior cerebral ventricle at once after global brain ischemia. The awakening time after the first drug treatment, the grasping power and the autonomous activity within 10 min after the last drug treatment were recorded. The levels of both dopamine (DA) and glutamate (Glu) in cerebrospinal fluid were detected by ELISA. The pathological changes were observed in brain tissue slices with HE staining and the protein levels of Akt/p-Akt and cAMP-response element binding protein (CREB)/p-CREB in hippocampus were detected by Western blotting.
 Results: Comparing with the model group, single administration of Xingnaojing Injection could significantly shorten the waking time (P<0.05) and continuous administration of Xingnaojing Injection for 3 d could increase grasping power, distance, frequency and duration of autonomous activities (P<0.05 or P<0.01) in the deep coma rat. Also, Xingnaojing Injection could inhibit these increases in neurotransmitters DA and Glu contents (P<0.05 or P<0.01), and improve pathological changes of hippocampal tissue. Xingnaojing Injection significantly induced protein phosphorylation of both Akt and CREB (P<0.05 or P<0.01); this effect was inhibited by PI3K inhibitor (P<0.05 or P<0.01). Moreover, the protective effects of naloxone on awakening time, grasping power, the autonomous activity and hippocampus damage in global brain ischemia-induced deep coma could be enhanced by joint use of Xingnaojing Injection (P<0.05 or P<0.01).
 Conclusion: Xingnaojing Injection could significantly improve deep coma induced by global brain ischemia in rat, which is related to inducing PI3K/Akt-dependent protein phosphorylation of CREB, and reducing hippocampal damage. The protective effect of Xingnaojing Injection is synergistically enhanced by naloxone.

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data.

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.

Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions.

Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

Total thyroidectomy as primary surgical management for thyroid disease: surgical therapy experience from 5559 thyroidectomies in a less-developed region.

BACKGROUND: The objective of this study was to evaluate the safety of total thyroidectomy for thyroid disorders and summarise the treatment experience in a less-developed region. METHODS: This was a retrospective observational cohort study using the computerised database of the First Affiliated Hospital of Harbin Medical University. All consecutive thyroidectomy patients from 2003 to 2014 were included in this study. Demographics, surgical procedure, diagnoses, morbidity and mortality were retrospectively reviewed. RESULTS: There were a total of 714 men and 4845 women in this study, with a mean age of 55 (range 9-87) years. A total of 4632 patients underwent total thyroidectomy for primary surgical treatment, and 189 patients previously underwent partial thyroidectomy. A total of 56.2% of the patients had multinodular goitre, including 12.23% who were thyrotoxic. Graves' disease and Hashimoto's disease were diagnosed in 2.82 and 7.23% of the patients, respectively. Papillary thyroid cancer was identified in 1336 patients, 44.99% of whom had papillary microcarcinoma. The total prevalence of permanent complications of first-time and secondary surgeries was 0.35 and 7.41%, respectively. During thyroid surgery, 945 patients underwent parathyroid autotransplantation. CONCLUSIONS: Initial total thyroidectomy can be safely performed for both benign and malignant thyroid diseases in a less-developed region. The morbidity of a secondary surgical procedure after subtotal thyroidectomy is significantly high compared to first-time surgery.

Online sensor fault detection based on an improved strong tracking filter.

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model.

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

A non-intrusive method for monitoring the degradation of MOSFETs.

Highly reliable embedded systems have been widely applied in the fields of aerospace, nuclear power, high-speed rail, etc., which are related to security and economic development. The reliability of the power supply directly influences the security of the embedded system, and has been the research focus of numerous electronic information and energy studies. The degradation of power modules occupies a dominant position among the key factors affecting the power supply reliability. How to dynamically determine the degradation state and forecast the remaining useful life of working power modules is critical. Therefore, an online non-intrusive method of obtaining the degradation state of MOSFETs based on the Volterra series is proposed. It uses the self-driving signal of MOSFETs as a non-intrusive incentive, and extracts the degradation characteristics of MOSFETs by the frequency-domain kernel of the Volterra series. Experimental results show that the identification achieved by the method agrees well with the theoretical analysis.

[Association between androgenetic alopecia and metabolic syndrome: a meta-analysis].

OBJECTIVE: To investigate the association between androgenetic alopecia (AGA) and metabolic syndrome (MS). METHODS: Literature on association between AGA and MS up to December 26, 2013 was searched from PubMed, Web of Knowledge, Scopus, Cochrance library, SinoMed, CNKI, Wanfang and VIP databases, and the studies met the eligibility criteria were selected. Meta-analysis was performed by using StataSE 12.0 software to determine the association between AGA and MS. RESULTS: Four case-control studies and 2 cross-sectional studies met the eligibility criteria, including 950 AGA subjects and 3056 control subjects were entered the analysis. Meta-analysis showed that AGA was significantly correlated with MS (OR=2.70, 95%CI: 1.67-4.37, P<0.01). Stratification analysis showed that AGA was significantly correlated with MS in male (OR=2.30, 95%CI: 1.33-3.98, P<0.01) and female subjects (OR=4.61, 95%CI: 1.26-16.94, P<0.05); and AGA was significantly correlated with MS in European (OR=5.29, 95%CI: 2.86-9.80, P<0.01) and Asian subjects (OR=1.92, 95%CI: 1.18-3.10, P<0.01). CONCLUSION: Based on the available data, AGA may be a risk factor for MS, indicating that AGA patients would be a targeting population for screening of metabolic syndrome.