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Introduction: At the end of 2019, the COVID-19 broke out, and spread to Guizhou province in January of 2020. Methodology: To acquire the epidemiologic characteristics of COVID-19 in Guizhou province, we collected data from 169 laboratory-confirmed COVID-19 related cases. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and the effective reproduction number. We also presented two representative case studies in Guizhou province: Case Study 1 was an example of the asymptomatic carrier; while Case Study 2 was an example of a large and complex infection chain that involved four different regions, spanning three provinces and eight families. Results: Two peaks in the incidence distribution associated with COVID-19 in Guizhou province were related to the 6.04 days (95% CI: 5.00 – 7.10) of incubation period and 6.14±2.21 days of serial interval. We also discussed the effectiveness of the control measures based on the instantaneous effective reproduction number that was a constantly declining curve. Conclusions: As of February 2, 2020, the estimated effective reproduction number was below 1, and no new cases were reported since February 26. These showed that Guizhou Province had achieved significant progress in preventing the spread of the epidemic. The medical isolation of close contacts was consequential. Meanwhile, the asymptomatic carriers and the super-spreaders must be isolated in time, who would cause a widespread infection.

Hysteresis-based energy management strategy for microgrid containing photovoltaic, ESS and heating loads is proposed in this study. In this real-time optimisation method, economic cost, operation cost, comfort level, renewable energy penetration and other performance indices are optimised in real time. To solve the complex energy management problem, when power forecasting is unavailable, different operational cases are clarified and calculated accordingly. In this way, the management process is turned into a finite-solution problem. Furthermore, a hysteresis loop is also established based on the cost function to optimise the system continuously, of which the loop width is offline designed. As a result, the computation burden is reduced. Finally, simulation results are presented to verify the effectiveness of the proposed energy management method. The application of the proposed method can reduce the economic cost and improve the operational performances of a microgrid significantly, including comfort, power quality and health of ESS.

The integration of an Energy Storage System (ESS) is difficult to commercialize within the current Chinese market mainly because of the lacking of supporting policy and breakthrough technologies. However, it is considered an appropriate approach in a certain circumstance, for instance, in a remote rural area. This work presents a power-balanced ESS integration model and its control method. By using a rural distributed network in southern China, the results show that the proposed model is able to improve the three-phase power imbalance issue.

Land use activities mainly for economic and agricultural purposes have converted one third to one half of our planet's land surface into urban expansion and agricultural practice, which has had significant impacts on natural ecosystems, food production, and environmental quality, attracting the attention of researchers and policymakers. Consequently, land use is emerging as a fundamental issue in global environmental change and sustainable development. This study represents an addition to the prevailing literature by investigating the asymmetric impacts of land-use and land-cover changes on environmental quality in Pakistan using time series data from 1961 to 2016. Carbon dioxide (CO2) emissions were deemed a dependent variable (a proxy for environmental quality), whereas built-up land, cropland, water bodies, and grazing land were considered independent. A nonlinear ARDL bound testing technique (NARDL) was used to investigate dynamic cointegration among the study variables. Moreover, this study used the BDS test and structural break unit root test to confirm nonlinearity and stationarity of the data set. The results confirm that the variables exhibit asymmetrical co-integration. There is a symmetric unidirectional causation, running from built-up land and grazing land towards CO2 emissions with coefficients of 10.570 and 17.045, respectively. Furthermore, asymmetric causality shows that any positive shocks to built-up land (6.134) and water bodies (20.335) significantly cause CO2 emissions. Similarly, a negative shock to grazing land (16.470) also causes CO2 emissions. By contrast, a neutral effect was found between cropland and CO2 emissions.

What is already known on this topic? Brucellosis is a zoonotic infectious disease caused by Brucella spp. The main source of infection in human brucellosis is sick animals, mainly including sheep, goat, and cattle, but sika deer (Cervus nippon) can also cause human brucellosis. The first human brucellosis case in Guizhou Province was reported in 2009, and no brucellosis outbreak was reported caused by sika deer ever before. What is added by this report? This is the first reported outbreak of human brucellosis caused by sika deer in Guizhou Province. Inappropriate regulation of animal movement may be the main driver of introducing and spreading brucellosis in southern areas. The ability to diagnose brucellosis in both humans and animals was weak in the county where the outbreak took place. What are the implications for public health practice? It was suggested to prioritize occupational protection and health education for sika deer breeders. The inspection of the movement of animals and the reimbursement policy need to be improved.

Introduction: Coronavirus disease 2019 (COVID-19) has caused an outbreak around the world. Early detection of severe illness is crucial for patients’ survival. We analysed initial clinical characteristics of 146 patients with COVID-19 reported in Guizhou province, China to explore risk factors for transforming mild illness to severe. Methodology: Data of 146 laboratory-confirmed cases were collected and evaluated by the survival analysis of univariate and multivariate Cox proportional hazards model. Results: On initial presentation, patients had fever (51.05%), dry cough (45.45%), headache (16.08%), shortness of breath (7.75%) and gastrointestinal symptoms (13.99%). Among 146 laboratory-confirmed cases, 30 patients (20.55%) had severe illness and needed Intensive Care Unit care for supportive treatment. The remaining patients (116, 79.45%) were non-severe cases. Nineteen (19/146, 13.01%) of 30 patients in the Intensive Care Unit had comorbidities, including hypertension (12, 40.00%), diabetes (5, 16.67%), cardiovascular disease (5, 16.67%) and pulmonary disease (4, 13.33%). For survival analysis, patients who had fever (HR = 3.30, 95% CI = 1.31, 8.29) and comorbidities (HR = 9.76, 95% CI = 4.28, 22.23) at baseline were more likely to be admitted into the Intensive Care Unit. Few variables were not related to the survival time of discharge from baseline to discharge and from Intensive Care Unit care to discharge. Conclusions: Severe patients with COVID-19 should be paid more attention. On initial symptoms, many patients did not have fever, but those with fever were more likely to be admitted to the Intensive Care Unit. Comorbidities were likewise a risk factor of severe COVID-19.

Loose wide-grading soils are commonly found in the source areas of debris flows, and in landslides after an earthquake. During rainfall events, fine particles (fines) in the soils gradually migrate downward, and eventually the loss of fines results in an increase in the pore volume of the soil and a reduction in the stability of the soil skeleton, which can lead to subsequent slope failure. To gain more understanding of the fine migration process at the microscopic scale, a 3D discrete element-fluid flow sequentially coupled model is developed, based on Darcy’s Law, to simulate fluid flow through a porous medium and calculate the transportation of soil solids. The erosion model is verified using experimental data. Parametric studies are carried out to investigate the effects of coarse particle size. The results reveal that changes in pore structure caused by fine particle migration can change the local permeability of the material. For the case of the average pore throat diameter to fine particle ratio ( $$J$$ ) of 2.41, changes in local porosity with time from internal erosion in the sample can be divided into four stages: (1) a rapid increase with some variations in porosity, (2) a slow increase in porosity, (3) a rapid increase in porosity, and (4) a steady state with no change in porosity. Not all stages are present for all value of $$J$$ . Stages (1) (2) (4) are present for 2.48 ≤ $$J\le$$ 2.58 and stages (1) (4) are present for $$J$$ ≤ 2.24 and $$J\hspace{0.17em}$$ ≥ 2.74. A sharp increase in the fine’s erosion possibility occurs for a $$J$$ value lies between 2.58 and 2.74. The erosion possibility sensibility shows an exponential relationship with $$J$$ . The model provides an effective and efficient way to investigate the process of pore blockage and internal soil erosion.

Intelligent plant cultivation robots play a vital role in plant intelligent cultivation. Aiming at the problem of low accuracy and low efficiency of intelligent plant cultivation robots in searching for target plants in unknown environments, this paper proposes an intelligent plant cultivation robot based on key marker algorithm and improved A* algorithm. In terms of target plant positioning, a key marker algorithm based on YOLO V3 is proposed. Accurately find the target plant in the location environment, mark the key coordinate location of the target plant, and plan the routes. In terms of route planning, the self-node search strategy for the traditional A* algorithm has disadvantages such as many path turning points and large turning angles. By analyzing annealing algorithm, ant colony algorithm and A* algorithm, this paper proposes an improved A* algorithm. Finally, experiments with many different scenes prove that the intelligent plant cultivation robot proposed in this paper can effectively improve the accuracy of target plant detection and the efficiency of route planning.

What is already known on this topic? Brucellosis is a zoonotic infectious disease caused by Brucella spp. The main source of infection in human brucellosis is sick animals, mainly including sheep, goat, and cattle, but sika deer (Cervus nippon) can also cause human brucellosis. The first human brucellosis case in Guizhou Province was reported in 2009, and no brucellosis outbreak was reported caused by sika deer ever before. What is added by this report? This is the first reported outbreak of human brucellosis caused by sika deer in Guizhou Province. Inappropriate regulation of animal movement may be the main driver of introducing and spreading brucellosis in southern areas. The ability to diagnose brucellosis in both humans and animals was weak in the county where the outbreak took place. What are the implications for public health practice? It was suggested to prioritize occupational protection and health education for sika deer breeders. The inspection of the movement of animals and the reimbursement policy need to be improved.

Random fluctuation in photovoltaic (PV) power plants is becoming a serious problem affecting the power quality and stability of the grid along with the increasing penetration of PVs. In order to solve this problem, by the adding of energy storage systems (ESS), a grid-connected microgrid system can be performed. To make this system feasible, this paper proposes a model predictive control (MPC) based on power/voltage smoothing strategy. With the receding horizon optimization performed by MPC, the system parameters can be estimated with high accuracy, and at the same time the optimal ESS power reference is obtained. The critical parameters, such as state of charge, are also taken into account in order to ensure the health and stability of the ESSs. In this proposed control strategy, communication between PVs and ESS is not needed, since control command can be calculated with local measured data. At the same time, MPC can make a great contribution to the accuracy and timeliness of the control. Finally, experimental results from a grid-connected lab-scale microgrid system are presented to prove effectiveness and robustness of the proposed approach.

At the end of 2019, a coronavirus disease 2019 (COVID-19) outbroke in Wuhan, China, and spread to Guizhou province on January of 2020. To acquire the epidemiologic characteristics of COVID-19 in Guizhou, China, we collected data on 162 laboratory-confirmed cases related to COVID-19. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and basic reproduction number. We also presented two representative case studies in Guizhou province -- Case Study 1 was an example of asymptomatic carrier; and Case Study 2 was an example of a large and complex infection chain that involved four different districts spanning three provinces and eight families. With an estimation of 8 days incubation period and 6 days serial interval, our results indicate that there may exist infectiousness during the incubation period for 2019-nCoV. This increases the difficulty of screening or identifying cases related to COVID-19.

Understanding the causality between energy consumption, carbon emissions and economic growth is helpful for policymakers to formulate energy, environmental and economic policies. For the first time, based on nonlinear dynamics, this paper employs multispatial convergent cross mapping (CCM) to revisit the energy-carbon-economy causation for China, India and the G7 countries using both aggregate data and per capita data. The findings indicate that there are significant differences between developing countries and developed countries. A bidirectional nexus between energy consumption, carbon emissions and economic growth is found in China and India, but various causal relationships are identified in the G7 countries, including bidirectional, unidirectional and neutral nexus. The results confirm that the decoupling phenomenon is common in most G7 countries. By leveraging a variety of samples and a new approach, this study provides new evidence for policy authorities to formulate country-specific policies to obtain better environmental quality while achieving sustainable economic growth.

Many viruses can cause respiratory diseases in humans. Although great advances have been achieved in methods of diagnosis, it remains challenging to identify pathogens in unexplained pneumonia (UP) cases. In this study, we applied next-generation sequencing (NGS) technology and a metagenomic approach to detect and characterize respiratory viruses in UP cases from Guizhou Province, China. A total of 33 oropharyngeal swabs were obtained from hospitalized UP patients and subjected to NGS. An unbiased metagenomic analysis pipeline identified 13 virus species in 16 samples. Human rhinovirus C was the virus most frequently detected and was identified in seven samples. Human measles virus, adenovirus B 55 and coxsackievirus A10 were also identified. Metagenomic sequencing also provided virus genomic sequences, which enabled genotype characterization and phylogenetic analysis. For cases of multiple infection, metagenomic sequencing afforded information regarding the quantity of each virus in the sample, which could be used to evaluate each viruses’ role in the disease. Our study highlights the potential of metagenomic sequencing for pathogen identification in UP cases.

This paper proposes a novel current control method based on Model Predictive Control (MPC) for three-phase inverters. The proposed method is based on an Adaptive MPC (A-MPC) with a PWM modulation. An innovative model parameter estimation and modification method is also proposed, leading to enhanced control accuracy. Comparing with traditional current control methods, such as PI and PR control, the proposed method has better dynamic performance. The transient dynamics, i.e., recovery time and overshoot, have been considerably improved. Simulation and experimental results are presented to validate the effectiveness of the proposal.

Random fluctuation of PV power is becoming a more and more serious problem affecting the power quality and stability of grid as the PV penetration keeps increasing recentyears. Aiming at this problem, this paper proposed a control strategy of energy storage system based on Model Predictive Control (MPC). By the continuous optimizing of MPC, we can obtain the system parameters accurately, and then calculate the energy storage power. At the same time, this control took state of charge (SOC) and other parameters into account to ensure the health and stability of the energy storage units. Finally, simulation results proved the proposed control strategy had good performance and robustness.