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The frequent operation of the switching devices inside the converter valve (mainly composed of thyristors) and other equipment in the converter station of ultra-high voltage direct current (UHVDC) transmission system generates a large number of high harmonics and high-frequency interference, which is the main source of error of the DC electronic current transformers (ECT). In order to better understand the transfer characteristics of DC ECT, this paper considers the effects of temperature and frequency on the shunt and low-pass filter at the same time, and combines the field test at different temperatures with different current frequencies, the results are compared and fitted to obtain the broadband model of the transformer taking into account the temperature. Based on this model, the mechanism and degree of influence of different equipment parameters on the broadband transfer characteristics of the transformer are further analyzed by setting different analog circuit parameters and digital sampling frequencies. The results show that different temperatures, shunt radius, shunt residual inductance, low-pass filter cut-off frequency, sampling frequency of the combined unit affect the broadband transfer characteristics of the transformer, and when a certain threshold is reached, the accuracy of the transformer does not meet the current standard requirements, which should be taken into account during the analysis process, and the results have a reference value for the parameter selection of the transformer.

Aiming at the shortcomings of a traditional centralized control in an active distribution network (AND), this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN. The distributed information exchange protocols of the distributed generation (DG) group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable. On these bases, this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system. Furthermore, a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system. The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy. In addition, the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.

The Shuitoushang landslide in Ershe in Qijiagou Village of Xinchang Town in Tongjiang County is a typical red-bed terrace landslide in eastern Sichuan. There are few studies on the seepage analyses of such landslides in this area, but similar slopes are widely distributed in the area. In this paper, on the basis of a large number of field investigations, surveys and data collection of disaster sites, the satellite remote sensing, UAV aerial photography, airborne LiDAR, indoor tests and other technical means are comprehensively used to analysis the landslide. Based on the mathematical model of Fredlund & Xing soil-water characteristic curve, the SEEP/W module in Geo-Studio is used to conduct the seepage analysis, and the analysis results at different time stages are coupled with the SLOPE/W module. Then the dynamic relationship between the stability coefficient, rainfall and time is obtained, revealing the deformation process and formation mechanism of the landslide. The results show that: (1) the continuous rainfall causes the groundwater level to rise and the slope stability to decrease, eventually leading to the overall instability and failure of the Shuitoushang landslide. (2) The deformation process of the landslide and the characteristics of groundwater exposure are basically consistent with the results calculated by Geo-Studio, indicating that the results based on Fredlund & Xing model can provide more accurate simulation results in similar areas. (3) Reclamation of terraced fields (paddy fields) will reduce slope stability. The results can provide theoretical support for hidden danger investigation and active prevention of similar landslide disasters in mountainous areas of Sichuan, and provide a reference for disaster prevention and mitigation.

Abstract Objective This paper aims to explore the diagnostic value of enhanced magnetic resonance imaging (MRI) combined with a carcinoembryonic antigen (CEA) and carbohydrate antigen in terms of the liver metastasis of colorectal cancer. Methods A total of 167 colorectal cancer patients with liver metastasis and 167 colorectal cancer patients without liver metastasis were selected as the subjects. An automatic electrochemiluminescence analyser was then used to detect the tumour markers CEA, CA19-9, CA125 and CA72-4. The consistency between the MRI examination and clinical pathological examination was also analysed, and the sensitivity, specificity and positive and negative predictive values of various combined detection methods were compared. Results The abnormal rates of CEA, CA19-9, CA125 and CA72-4 in the two groups were statistically significant (P < 0.05), while the results of the enhanced MRI and clinicopathological examination for liver metastasis in patients with colon cancer were largely consistent (Kappa coefficient = 0.788, P < 0.000). However, the two methods were inconsistent. The false positive rate of the enhanced MRI examination was 15.3%, while the false negative rate was 6.0%. The specificity (94.61%), positive predictive value (92.68%) and positive likelihood ratio (12.67%) were the highest for the MRI combined with serial CEA, while the sensitivity (98.80%) and negative predictive value (97.22%) were the highest with the MRI combined with parallel CEA, and this combination returned the lowest negative likelihood ratio (0.03). Conclusion The combination of MRI and CEA excludes non-metastatic patients and identifies colorectal liver metastasis cancer patients. Overall, it has a higher diagnostic value.

Radio frequency identification (RFID) technology is an automatic identification method, which relies on the use of radio repeaters called RFID tags to quickly store and retrieve data. Because RFID tags do not need linear contact when communicating with readers, it is possible to collect a large amount of data in a short time. However, the collected data also produce problems such as false negative readings, false positive readings, duplicated readings, out-of-order readings and so on. In this case, how to efficiently clean the large-scale RFID data in short time has become an important research topic in the field of database. This paper mainly summarizes the existing RFID data cleaning technology. Firstly, the relevant definitions and descriptions of RFID system and RFID data cleaning problem are given, and typical datasets and evaluation criteria are listed. Then the existing RFID data cleaning work is compared and summarized in detail from the classification, subcategory, basic idea, advantages, limitations, application scenarios and other aspects of related technologies. At the same time, the relevant application systems are compared and analyzed. Then, for the key problems of the false negative reading, false positive reading, duplicated reading and out-of-order reading, the existing studies are compared and summarized in detail. Finally, this paper proposes next five research directions worthy of paying attention to in the field of RFID data cleaning, such as the construction of RFID original data and benchmark dataset, the cleaning strategy of encryption and privacy protection data, the accuracy of data collection, the timeliness of cleaning results, and scene self-learning.

Measurement(s) debris flow • water-based rainfall • check dam Technology Type(s) satellite imaging • weather station Sample Characteristic - Organism earthquake induced debris flows and mitigation measures Sample Characteristic - Environment earthquake Sample Characteristic - Location Wenchuan County

Nowadays, the high penetration of renewable energy, with variable and unpredictable nature, poses major challenges to operation and planning studies of power systems. Employing energy storage plants has been introduced as an effective solution to alleviate these challenges. Several studies have been presented in the literature to provide a framework for planning studies of energy storage plants. However, they usually have two main drawbacks: (i) ignoring the lifetime varying characteristic of energy storage, (ii) inability to model the charge/discharge schedule of energy storage accurately. This paper amends the abovementioned shortcomings by proposing a distributionally robust planning method based on Kullback–Leibler divergence. According to the power function of lifespan of electrochemical energy storage, the lifespan model of energy storage plants with equivalent full cycles times is established. Considering the lifespan model constraints of the energy storage plant and system operating constraints, the planning model of energy storage plants is constructed with the lifespan cycle cost and units’ operation cost as the objective. Furthermore, the ambiguity set of wind farm output based on Kullback–Leibler divergence is embedded into the planning model of energy storage plants, and the distributionally robust planning model of energy storage plant is transformed into mixed integer linear programming model by sample average approximation method. A modified IEEE-30 bus system with two wind farms is studied to demonstrate the effectiveness. The results show distributionally robust planning model reduces about 10% planning cost compared with the traditional robust planning model, and increases the energy storage capacity by 12MW compared with the stochastic planning method. Other, the planning cost of energy storage plants will decrease by 5% ∼23% when the wind abandoned rate increases by 2% and decreases slowly with the increase of abandoned wind rate.

Lifestyle factors are responsible for a considerable portion of cancer incidence worldwide, but credible estimates from the World Health Organization and the International Agency for Research on Cancer (IARC) suggest that the fraction of cancers attributable to toxic environmental exposures is between 7% and 19%. To explore the hypothesis that low-dose exposures to mixtures of chemicals in the environment may be combining to contribute to environmental carcinogenesis, we reviewed 11 hallmark phenotypes of cancer, multiple priority target sites for disruption in each area and prototypical chemical disruptors for all targets, this included dose-response characterizations, evidence of low-dose effects and cross-hallmark effects for all targets and chemicals. In total, 85 examples of chemicals were reviewed for actions on key pathways/mechanisms related to carcinogenesis. Only 15% (13/85) were found to have evidence of a dose-response threshold, whereas 59% (50/85) exerted low-dose effects. No dose-response information was found for the remaining 26% (22/85). Our analysis suggests that the cumulative effects of individual (non-carcinogenic) chemicals acting on different pathways, and a variety of related systems, organs, tissues and cells could plausibly conspire to produce carcinogenic synergies. Additional basic research on carcinogenesis and research focused on low-dose effects of chemical mixtures needs to be rigorously pursued before the merits of this hypothesis can be further advanced. However, the structure of the World Health Organization International Programme on Chemical Safety 'Mode of Action' framework should be revisited as it has inherent weaknesses that are not fully aligned with our current understanding of cancer biology.

According to the “World Population Prospects 2022” released by the United Nations in August 2022, the world will officially enter an “aging society”. In order to provide the elderly with an improved quality of daily life, “health promotion” and “prevention of disease” will be important. With respect to care of the elderly, the concepts of “therapeutic environment” and “green care” have been explored and developed. Therefore, in this study, we combine the currently popular Internet of Things (IoT) into an aquaponics system and proposes a smart green care system (SGCS). The proposed system uses face recognition technology to record the labor and rehabilitation history of the elderly, in combination with environmental data analysis, to enable automatic control decisions for equipment in conjunction with a voice control system to reduce the obstacles faced by the elderly in operating the information system. It also uses image recognition technology to monitor and notify about plant diseases and insect pests to achieve automatic management and enhance the interaction between the elderly and the SGCS through human–computer interaction. The SGCS allows the elderly to guide it to participate in appropriate activities through direct contact with the natural environment, thereby enhancing the quality of green healing life. In this study, taking long-term care institutions as an example, we verified proof of concept (PoC), proof of service (PoS), and proof of business (PoB), confirming the feasibility of the SGCS. The SGCS proposed in this study can be successfully used in long-term care institutions and various other environments, such as medical units and home care contexts. It can take full advantage of the functions associated with the concept of “healing environment” and “green care” widely recognized by users. Therefore, it can be widely used in the field of long-term care in the future.

Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.

Distributed state estimation system is beneficial in power grids with complex structures and large quantities of measurements because of its advantages, such as high computational efficiency and enhanced reliability. This study investigates a novel multi-area distributed state estimation algorithm for large-scale interconnected power systems. The state estimation model of the entire power grid is effectively decomposed into a group of estimation models that can be locally solved using an area estimator. The global information required by an estimator in solving its estimation model is effectively synthesized as two average values, and an average consensus-based algorithm is developed for an area estimator to access the required global information in a distributed fashion. Bad data are identified and eliminated after the convergence of the iterative estimation process. Simulation results of the IEEE 14-bus test system are provided to show the correctness and effectiveness of the proposed algorithm. Solution accuracy, computational efficiency and state estimation speed are compared with those of existing centralized, hierarchical and distributed methods by the numerical example in the IEEE 300-bus test system.

Incorporating carbon-carbon triple bonds into conjugated chains typically requires acetylenic precursors. Here, the authors synthesize poly(p-phenylene ethynylene) molecular wires on Cu(111) by directly coupling trichloromethyl-containing precursors, forming C-C triple bonds in situ

Abstract The presence of distributed generators (DGs) with high penetration poses new challenges in the management and operation of electrical grids. Due to the local character of DGs, they could in principle be used in emergency situations to prevent a voltage instability event of the grid. In this paper, a certain method is proposed to coordinate the operation of virtual power plant (VPP) and conventional voltage regulation device to improve the static voltage stability of distribution network with the multi-agent framework. The concept and the general framework of this coordinated control system is introduced, and the voltage instable nodes are determined based on the voltage instability indicator. The voltage coordinated control model of the distribution system is established according to the multi-agent consistency control theory and the coordinated controllers for agents are designed by solving a problem with bilinear matrix inequality constraints. The suggested method is implemented on an IEEE 33 nodes test system and the simulation results show its efficiency and validity.

Abstract Power line carrier (PLC) technology plays an increasingly important role in the realization of cost-effective communication in a smart distribution grid. No current channel modeling method is universally applicable to more complex topologies that may emerge in smart grids, such as ring and mesh topologies. This paper presents a novel PLC channel modeling method based on the information node concept, and the universality and feasibility of the proposed method are demonstrated with applications in modeling networks with ring and mesh topologies. The factors that affect the channel characteristics of the networks and the laws that govern their behaviors for different types of topologies are analyzed. The validity and effectiveness of the proposed method are proven using simulation and laboratory tests. This paper provides the necessary theoretical basis and technical means to design the PLC modulation method for smart distribution grids.

Background: Tenofovir disoproxil (TDF) is a promising salvage therapy for patients with chronic hepatitis B (CHB) who failed regimens of other nucleoside analogues (NAs). In this study, we aimed to investigate the clinical efficacy and safety of TDF monotherapy in Chinese CHB patients with genotypic resistance. Methods: A total of 33 CHB patients who had failed treatment with other NAs and had genotypic resistance were switched to TDF monotherapy for 48 weeks. Patients' demographic data (age, sex, history of hepatitis B virus [HBV] therapy), laboratory testing results (hepatitis B e antigen [HBeAg] status, HBV DNA levels, alanine aminotransferase [ALT] levels, serum creatinine, urinary protein, genotypic assay), clinical symptoms, and liver color ultrasound examinations were collected for evaluation at day 0 (baseline) and the 12th, 24th, 36th, and 48th weeks after initiating treatment. Statistical analyses were carried out using rank sum test or rank correlation. Results: With regard to efficacy, the study found that all patients who switched to TDF monotherapy had undetectable HBV DNA levels after 48 weeks. In addition, patients with lower baseline HBV DNA levels realized earlier virological undetectability (rs = 0.39, P = 0.030). ALT levels were normal in 30 of 33 patients (91%). HBeAg negative conversion occurred in 7 of 25 patients (28%), among whom HBeAg seroconversion (12%) and HBeAg seroclearance (16%) occurred. The time of complete virological response was significantly affected by the number of resistance loci (rs = 0.36, P = 0.040). Concerning safety, the study found that no adverse events were observed during the 48 weeks. Conclusion: TDF monotherapy is an effective and safe salvage treatment for CHB patients who are resistant to other NAs.