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Targeting the issues of seed leakage and cutting segment adhesion due to poor seed feeding and cutting in real-time seed-cutting cassava planters, this study developed a seeding quality monitoring system. Based on the structure and working principle of the seed cutting and discharging device, the installation methods of the matrix fiber optic sensor and rotary encoder were determined. By combining the operational characteristics of the planter’s ground wheel drive with seed cutting and seed dropping, a monitoring model correlating the sowing parameters with seed dropping time was established; a monitoring window was created by extracting and processing the rotary encoder pulse signal, and the number of seeds sown after each opposing cutter’s operation was calculated based on the pulse width information within the monitoring window. The monitoring system’s statistics were compared and analyzed with the manual statistics, and the bench test showed that the monitoring system designed in this study offers high accuracy. When the simulated rotational speed of the opposing cutter ranges from 10 to 30 rpm, the average monitoring error between the monitored and actual seeding quantities for the left and right rows is less than 1.4%. The monitoring system can promptly and accurately activate sound and light alarms for faults, achieving a 100% success rate in alarms and an average fault response time of less than 0.4 seconds. Field tests demonstrate that the average error in seeding volume is 0.91%, and the monitoring system can timely alert to faults occurring in the planter. The system fulfills the requirements for real-time monitoring of cassava seeding volume at various operating speeds in field conditions, and can serve as a reference for monitoring operational parameters in subsequent cassava combine harvesters.

IntroductionThe crown clade is one of two major groups in the Russula subg. Russula.Methods/materialAn analysis of Chinese samples was performed based on the morphology, internal transcribed spacer (ITS) sequences, and multi-gene phylogenies of 28S nrLSU, 16S mtSSU, rpb1, rpb2, and tef1-α.ResultsThe results supported the independence of six new species: Russula alboflava (sect. Amethystinae), R. chrysantha (subsect. Chamaeleontinae), R. liyui (subsect. Laricinae), R. lutescens (subsect. Olivaceinae), R. paraxerampelina, and R. prunicolor (subsect. Xerampelinae) from Fagaceae forest habitats. Subsect. Alboflavinae was newly proposed in sect. Amethystinae. Members of the new subsection include R. alboflava, R. burlinghamiae, and possibly R. ballouii.DiscussionOur analyses also supported the claim that two species of R. fulvograminea (subsect. Laricinae) and R. subrubens (subsect. Xerampelinae) have a Eurasian distribution. The habitat and primary hosts of the main phylogenetic clades within related subsections were summarized and discussed.

Abstract Charging behaviours of electric vehicles (EVs) exhibit substantial randomness, making accurate prediction or modelling challenging. Furthermore, as the number of EVs continues to increase, charging stations are diversifying their offerings to accommodate distinct charging characteristics, addressing a wide spectrum of EV charging needs. Previous research mostly focused on the randomness of EVs while neglecting the heterogeneity in charging infrastructure. Therefore, this paper introduces a decentralized collaborative optimal method for EV charging stations, taking into account the varying facility types and the power limitations. First, a decentralized collaborative framework is proposed. The energy boundary model and the average laxity of EVs contribute to transforming the optimization problem into a Markov Decision Process (MDP) with uncertain transitions. Then, multi‐agent deep deterministic policy gradient multi‐individuals (MADDPG‐MI) algorithm is developed to train several heterogeneous agents presenting different types of charging facilities. Each agent makes decisions for multiple homogenous charging piles. Numerous simulation studies validate that the proposed method can effectively reduce charging costs and manages in scenarios involving either homogeneous or multiple heterogeneous charging facilities. Moreover, the MADDPG‐MI algorithm demonstrates performance consistency among multiple decision‐making units while consuming lower training resources offering enhanced scalability.

At present, artificial intelligence continues to heat up.Large language models have attracted much attention and set off a wave of enthusiasm around the world.The success of artificial intelligence is not essentially a ＆quot;miracle＆quot; of large computing power, but a change in computing models.Firstly, this paper affirms the fundamental role of data in AI, and points out that synthetic data will be the main source of data in the future.Then, this paper reviews the development of computing models, highlights the historic competition between neural network models and Turing models.We points out that the important hallmark of large language models is the emergence of intelligence in machines, emphasizes that the essence of large language models is ＆quot;compression＆quot;, and analyzes the reasons for the ＆quot;illusion＆quot; of large language models.Finally, we call on the scientific community to attach importance to large scientific models in ＆quot;AI for research(AI4R)＆quot;.

Cassava is one of the world’s staple crops and an important economic and food crop for one billion people. It holds significant value in food, feed, and industrial raw materials. However, cassava production is faced with the following problems: agricultural machinery and agronomy are not integrated, work efficiency is not high, it is not suitable for large-scale planting plots, and there is a lack of large-scale equipment, automation, and intelligent cassava production equipment. This study reviews research on cassava planting agronomy and mechanized production technologies and equipment, including planting, stem–tuber separation, and harvesting, both globally and in China. The shortcomings are analyzed, and recommendations are provided for current mechanized cassava production to accelerate the development of intelligent equipment and strengthen the shortcomings. Meanwhile, we used typical and extensive Chinese planting and harvesting machines as case studies. The future trends in cassava production are forecasted, underscoring the importance of advancing global mechanized technology and equipment for cassava production to enhance its efficiency, competitiveness, and modernization.

Sugarcane is an important economic crop in tropical and subtropical regions. Presawing planting is an important method for achieving automated and precise planting with sugarcane planting machines. The sawing process is a key stage in planting management, affecting not only the germination and survival rates of sugarcane, but also reflecting the mechanical performance of sawing. To reduce the peak sawing force and enhance the sawing surface quality of sugarcane seedlings, this study utilized a central composite experimental design method. Single-factor and multi-factor experiments were conducted with a specially designed sugarcane stalk sawing experimental rig to investigate the impact of factors such as the stalk diameter feeding speed, and sawing speed on the peak sawing force and sawing surface quality. Upon being developed and validated, multivariate mathematical regression models elucidated the relationships among these factors. The experimental results showed that the order of influence of each factor on the peak sawing force was the stalk diameter, feed speed, and sawing speed, while for the sawing surface quality, the sequence was the sawing speed, stalk diameter, and feed speed. Correspondingly, the determination coefficients for the peak sawing force and sawing surface quality prediction models were 0.9708 and 0.9675. With a maximum error of 7.6% for the peak sawing force and an average relative error of 7.1%, and a maximum error of 3.5% for the sawing surface quality and an average relative error of 2.83%, the calculated results from the regression models were in good agreement with the experimental findings. This indicates that the models are capable of quickly and accurately predicting the peak sawing force and sawing surface quality of sugarcane stalks under different conditions. The research findings provide valuable insights for the development and optimization of sugarcane stalk presawing equipment and related experimental studies.

Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches.

Cordyceps militaris is one of the commercially cultivated mushrooms, valued for its medicinal and nutritional benefits. However, the fruiting body development mechanism has remained elusive. Chitin synthases (CHSs) are ubiquitous enzymes involved in the regulation of fungal growth, development and virulence. In this study, a total of eight CmChs genes were identified. Chromosomal localization analysis revealed an uneven distribution of CmCHSs across the C. militaris genome. Based on the phylogenetic analysis, 100 CHSs from Cordyceps sensu lato, encompassing C. militaris, were categorized into three divisions and seven classes, shedding light on their evolutionary relationships. There was no significant difference in the number of CHSs between ascomycetes and basidiomycetes in general (p = 0.067), as well as between pathogenic and saprotrophic fungi in general (p = 0.151 and 0.971 in Ascomycota and Basidiomycota fungi, respectively). This underscored the essential and conserved nature of these CHSs across various fungal lifestyles and ecological niches. The different transcript patterns of the eight CmChss during key life cycle stages, such as conidia germination, infection, and fruiting body development, indicated that each CHS gene may have a distinct role during specific stages of the life cycle. In conclusion, these findings indeed lay the groundwork for a further exploration of the functional roles of CHSs in the regulatory mechanism of fruiting body development in C. militaris.

There are huge differences in the layouts and numbers of sensors in different smart home environments. Daily activities performed by residents trigger a variety of sensor event streams. Solving the problem of sensor mapping is an important prerequisite for the transfer of activity features in smart homes. However, it is common practice among most of the existing approaches that only sensor profile information or the ontological relationship between sensor location and furniture attachment are used for sensor mapping. The rough mapping seriously restricts the performance of daily activity recognition. This paper presents a mapping approach based on the optimal search for sensors. To begin with, a source smart home that is similar to the target one is selected. Thereafter, sensors in both source and target smart homes are grouped by sensor profile information. In addition, sensor mapping space is built. Furthermore, a small amount of data collected from the target smart home is used to evaluate each instance in sensor mapping space. In conclusion, Deep Adversarial Transfer Network is employed to perform daily activity recognition among heterogeneous smart homes. Testing is conducted using the public CASAC data set. The results have revealed that the proposed approach achieves a 7–10% improvement in accuracy, 5–11% improvement in precision, and 6–11% improvement in F1 score, compared with the existing methods.

Abstract The associated discrete circuit (ADC) model for switches is widely used in electromagnetic transient simulation of power electronic system. When using ADC model, the problem of virtual power loss caused by L/C switching cannot be ignored and small time‐step (≤2 μs) is usually required to guarantee accuracy, both of which will limit the application of ADC model. To cope with this, a fractional time‐step simulation method (FTSSM) suitable for the ADC model of power electronic system is proposed. The method is derived from the compact form of electro‐magnetic transient program (EMTP) algorithm, and the simulation iteration is implemented by small‐step synthesis calculation with different fractional time‐steps, which are designed to locate the accurate switching actions. In this way, the simulation can be conducted under larger time‐step (to tens of μs) while the accuracy is still acceptable. Further analysis on time domain error and numerical stability of the FTSSM are discussed. Simulations of grid‐connected inverter system and permanent magnet synchronous motor (PMSM) driving system are done, using FTSSM and on PSCAD respectively for comparison. The results verify the accuracy of proposed FTSSM, and indicate that the FTSSM is helpful to accelerate the offline simulation and improve the real‐time simulation performance.

Abstract Large numbers of marine glaciers in the Qinghai-Tibet Plateau are especially sensitive to changes of climate and surface conditions. They have suffered fast accumulation and melting and retreated quickly in recent years. In 2017, we surveyed the cold-adapted fungi in these unique habitats and obtained 1208 fungal strains. Based on preliminary analysis of ITS sequences, 41 isolates belonging to the genus Cadophora were detected. As one of the most frequently encountered genera, the Cadophora isolates were studied in detail. Two phylogenetic trees were constructed: one was based on the partial large subunit nrDNA (LSU) to infer taxonomic placement of our isolates and the other was based on multi-locus sequences of LSU, ITS, TUB and TEF-1α to investigate more exact phylogenetic relationships between Cadophora and allied genera. Combined with morphological characteristics, nine Cadophora species were determined, including seven new to science. Among the new species, only C. inflata produces holoblastic conidia and all the others express phialidic conidiogenesis. All isolates have optimum growth temperature at 20 °C or 25 °C. With more species involved, the currently circumscribed genus became obviously paraphyletic. All members are clustered into two main clades: one clade mainly includes most of the Cadophora species which have phialidic conidiogenesis and we refer to as ‘Cadophora s. str.’; the remaining Cadophora species have multiform conidiogenesis and are clustered in the second clade, with members of other genera in Ploettnerulaceae interspersed among the subclades. The results show a high diversity of Cadophora from marine glaciers in the Qinghai-Tibet Plateau and most of them are novel species.

BackgroundLow temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear.MethodsThis study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed.ResultsExperimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze−thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability.ConclusionOur studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment.

Abstract To evaluate the changes of left atrial (LA) geometry and function in patients with rheumatoid arthritis (RA) by conventional echocardiography and two-dimensional speckle tracking imaging (2D-STI). We enrolled 46 RA patients with a duration of

ABSTRACT In this study, to meet the requirements of mechanized planting of the wide and narrow double-row ridging cassava cultivation mode, solve the problem of the real-time cutting cassava planter in which cassava stalk cutting length has poor uniformity, and improve the operation performance of the existing precut cassava planters, a rotary dibble-type cassava planter was designed. With its rotary dibble-type stalk metering device, transmission system and other components, the planter can complete the sowing, ditching, soil covering and suppression operations simultaneously. The results of a performance evaluation and an analysis of field trials showed that the variation coefficient of the planting distance was less than 30%, and the planting qualification rate was greater than 95%. The order of factors affecting the variation coefficient of the planting distance was determined to be transmission ratio followed by the forward speed. The order of factors affecting the planting qualification rate was determined to be transmission ratio followed by the forward speed. The results of the variance analysis showed that the influences of the transmission ratio and forward speed on the accuracy of planting distance and the planting qualified rate were significant. The forward speed of the planter operation was recommended to be 0.56 m/s.

Novel coronavirus disease 2019 (COVID-19), a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has brought an unprecedented public health crisis and continues to threaten humanity due to the persistent emergence of new variants. Therefore, developing more effective and broad-spectrum therapeutic and prophylactic drugs against infection by SARS-CoV-2 and its variants, as well as future emerging CoVs, is urgently needed. In this study, we screened several US FDA-approved drugs and identified phenothiazine derivatives with the ability to potently inhibit the infection of pseudotyped SARS-CoV-2 and distinct variants of concern (VOCs), including B.1.617.2 (Delta) and currently circulating Omicron sublineages XBB and BQ.1.1, as well as pseudotyped SARS-CoV and MERS-CoV. Mechanistic studies suggested that phenothiazines predominantly inhibited SARS-CoV-2 pseudovirus (PsV) infection at the early stage and potentially bound to the spike (S) protein of SARS-CoV-2, which may prevent the proteolytic cleavage of the S protein, thereby exhibiting inhibitory activity against SARS-CoV-2 infection. In summary, our findings suggest that phenothiazines can serve as a potential broad-spectrum therapeutic drug for the treatment of SARS-CoV-2 infection as well as the infection of future emerging human coronaviruses (HCoVs).

Abstract Globally abundant wave energy for power generation attracts ever increasing attention. Because of non-linear dynamics and potential uncertainties in ocean energy conversion systems, generation productivity needs to be increased by applying robust control algorithms. This paper focuses on control strategies for a small ocean energy conversion system based on a direct driven permanent magnet synchronous generator (PMSG). It evaluates the performance of two kinds of control strategies, i.e., traditional field-oriented control (FOC) and robust adaptive control. The proposed adaptive control successfully achieves maximum velocity and stable power production, with reduced speed tracking error and system response time. The adaptive control also guarantees global system stability and its superiority over FOC by using a non-linear back-stepping control technique offering a better optimization solution. The robustness of the ocean energy conversion system is further enhanced by investigating the Lyapunov method and the use of a DC-DC boost converter. To overcome system complexity, turbine-generator based power take-off (PTO) is considered. A Matlab/Simulink study verifies the advantages of a non-linear control strategy for an Oscillating Water Column (OWC) based power generation system.

COVID-19 infection causes collapse of glomerular capillaries and loss of podocytes, culminating in a severe kidney disease called COVID-19–associated nephropathy (COVAN). The underlying mechanism of COVAN is unknown. We hypothesized that cytokines induced by COVID-19 trigger expression of pathogenic APOL1 via JAK/STAT signaling, resulting in podocyte loss and COVAN phenotype. Here, based on 9 biopsy-proven COVAN cases, we demonstrated for the first time, to the best of our knowledge, that APOL1 protein was abundantly expressed in podocytes and glomerular endothelial cells (GECs) of COVAN kidneys but not in controls. Moreover, a majority of patients with COVAN carried 2 APOL1 risk alleles. We show that recombinant cytokines induced by SARS-CoV-2 acted synergistically to drive APOL1 expression through the JAK/STAT pathway in primary human podocytes, GECs, and kidney micro-organoids derived from a carrier of 2 APOL1 risk alleles, but expression was blocked by a JAK1/2 inhibitor, baricitinib. We demonstrate that cytokine-induced JAK/STAT/APOL1 signaling reduced the viability of kidney organoid podocytes but was rescued by baricitinib. Together, our results support the conclusion that COVID-19–induced cytokines are sufficient to drive COVAN-associated podocytopathy via JAK/STAT/APOL1 signaling and that JAK inhibitors could block this pathogenic process. These findings suggest JAK inhibitors may have therapeutic benefits for managing cytokine-induced, APOL1-mediated podocytopathy.

Abstract The electric vehicle (EV) charging station is a critical part of the infrastructure for the wide adoption of EVs. Real-time simulation of an EV station plays an essential role in testing its operation under different operating modes. However, the large numbers of high-frequency power electronic switches contained in EV chargers pose great challenges for real-time simulation. This paper proposes a compact electromagnetic transient program (C-EMTP) algorithm for FPGA-based real-time simulation of an EV station with multiple high-frequency chargers. The C-EMTP algorithm transforms the traditional EMTP algorithm into two parallel sub-tasks only consisting of simple matrix operations, to fully utilize the high parallelism of FPGA. The simulation time step can be greatly reduced compared with that of the traditional EMTP algorithm, and so the simulation accuracy for high-frequency power electronics is improved. The EV chargers can be decoupled with each other and simulated in parallel. A CPU-FPGA-based real-time simulation platform is developed and the proposed simulation of the EV station is implemented. The control strategy is simulated in a CPU with 100 μs time-step, while the EV station circuit topology is simulated in a single FPGA with a 250 ns time-step. In the case studies, the EV station consists of a two-level rectifier and five dual-active bridge (DAB) EV chargers. It is tested under different scenarios, and the real-time simulation results are validated using PSCAD/EMTDC.

Retroviruses, especially the pathogenic human immunodeficiency virus type 1 (HIV-1), have severely threatened human health for decades. Retroviruses can form stable latent reservoirs via retroviral DNA integration into the host genome, and then be temporarily transcriptional silencing in infected cells, which makes retroviral infection incurable. Although many cellular restriction factors interfere with various steps of the life cycle of retroviruses and the formation of viral latency, viruses can utilize viral proteins or hijack cellular factors to evade intracellular immunity. Many post-translational modifications play key roles in the cross-talking between the cellular and viral proteins, which has greatly determined the fate of retroviral infection. Here, we reviewed recent advances in the regulation of ubiquitination and SUMOylation in the infection and latency of retroviruses, focusing on both host defense- and virus counterattack-related ubiquitination and SUMOylation system. We also summarized the development of ubiquitination- and SUMOylation-targeted anti-retroviral drugs and discussed their therapeutic potential. Manipulating ubiquitination or SUMOylation pathways by targeted drugs could be a promising strategy to achieve a “sterilizing cure” or “functional cure” of retroviral infection.

Two transition metal complexes were synthesized with Ni(II) and Cu(II) using a tetradentate Schiff-base ligand, (R,R) and (S,S)-N,N′-Bis(3,5-di-tert-butylsalicylidene)-1,2-cyclohexanediamine. The stereochemical properties of the ligand and the metal complexes were investigated using a combined experimental and theoretical approach. Multiple spectroscopic techniques, which include IR, vibrational circular dichroism (VCD), UV-Vis and electronic circular dichroism (ECD), as well as Raman and the newly discovered ECD-circularly polarized Raman (i.e., eCP-Raman) spectroscopies were utilized. The good agreement achieved between the experimental and simulated IR, VCD, UV-Vis and ECD spectra of the ligand allowed one to identify the presence of three main ligand conformers in solution, thanks, especially to the high VCD sensitivity to the conformations associated with the tertbutyl groups. The helicity of the metal complexes was identified to be M and P for those with the (R,R) and (S,S) ligands, respectively. Furthermore, eCP-Raman measurements were carried out for the two metal complexes under (near) resonance. Their induced solvent chiral Raman features were explained, and the potential application of eCP-Raman was discussed.

This paper proposes a novel decentralized control strategy for multiparallel grid-forming distributed generations (DGs) in an islanded microgrid. Different from most existing droop-based hierarchical control methods, the proposed scheme imposes a fixed system frequency that is independent of the load conditions. Additionally, a proper point of common coupling (PCC) voltage amplitude maintaining and proportional power sharing can be simultaneously achieved when dealing with variations in the load and DGs plug in/off. The transient performance of the system, as well as the power supply reliability, can be improved, as no extra restoration control layer or communications between the inverters are needed. Furthermore, the power-sharing ratio among the DG units can be changed online without affecting the voltage regulation performance, which enhances the power management flexibility. The PCC voltage regulation and the power-sharing performance of the proposed control strategy are ensured via the Lyapunov method. Finally, the effectiveness and practicability of the proposed approach are verified through real-time simulations and hardware experimental tests.

Abstract Parallel operation of inverter modules is the solution to increase the reliability, efficiency, and redundancy of inverters in microgrids. Load sharing among inverters in distributed generators (DGs) is a key issue. This study investigates the feasibility of power-sharing among parallel DGs using a dual control strategy in islanded mode of a microgrid. PQ control and droop control techniques are established to control the microgrid operation. P-f and Q-E droop control is used to attain real and reactive power sharing. The frequency variation caused by load change is an issue in droop control strategy whereas the tracking error of inverter power in PQ control is also a challenge. To address these issues, two DGs are interfaced with two parallel inverters in an islanded AC microgrid. PQ control is investigated for controlling the output real and reactive power of the DGs by assigning their references. The inverter under enhanced droop control implements power reallocation to restore the frequency among the distributed generators with predefined droop characteristics. A dual control strategy is proposed for the AC microgrid under islanded operation without communication link. Simulation studies are carried out using MATLAB/SIMULINK and the results show the validity and effective power-sharing performance of the system while maintaining a stable operation when the microgrid is in islanding mode.

An overview and summary was provided on the convergence of communication and computing and the future research directions were clarified.As two key technologies of information science,communication and computing were born to be converged.In current mobile communication systems,from a single device or technique point of view,communication and computing had been well converged.However,to break the bottleneck of traditional mobile communication systems,it was necessary to converge communication and computing in a system level.Existing research demonstrates that a systematic convergence of communication and computing will reduce the channel capacity requirement on the mobile system and thus enhancing the system capability to support more services.Facing future mobile communication system,communication and computing convergence should be further studied in-depth from defining system service capabilities (basic theory),building a scalable,remodelable,transparent,modular system architecture and investigating service-aware cross-layer information exchange mechanisms,thus the sustainable development of mobile communication system could be promoted.

Renewable energy resources (RESs) have been considered to be a promising solution for providing clear and economical power in isolated islands. Due to the low predictive nature, conventional unit commitment with a deterministic optimization algorithm is no long suitable for grids with high RES scale. This paper proposes a coordinated multi-time scale robust scheduling framework for isolated system with energy storage units. The framework decomposes the adjustable dispatch process into three coordinated stages and specifies the operational policy of thermal and hydraulic units. We formulate mathematical models for each dispatch stage and add additional constraints for inter-stage coordination. Then, we develop a multi-stage robust solution based on Bender's decomposition and a cutting plane algorithm. Finally, we test the proposed framework with real data from Nanji Island, China. Compared with traditional scheduling, robust schedule approach can significantly improve the “robustness”of grids and allow higher RES penetration level.

This work aims to establish the mathematical model with the high effectiveness in predicting the damping force of an MR damper with nonmagnetized passages in piston. The pressure drops due to viscous loss, MR effect, and the minor losses at the inlet and outlet of passages are considered in the mathematical model. The widely reported Bingham model is adopted to describe the mechanical property of MR fluid. The mechanical behaviours of the MR damper are experimentally evaluated under different excitations and current. The yield stress of MR fluid with respect to the current applied to piston coil is obtained by finite element analysis in Ansoft Maxwell 14.0. The proposed model is validated by comparing the simulated damping characteristics with the measured data under various currents applied to the piston coil. The simulated results are also compared with those obtained from the mathematical model without the pressure drop due to the minor losses at the inlet and outlet of passages. The comparisons show that the proposed mathematical model can yield more accurate predictions of damping force. This indicates that the pressure drop due to the minor losses is significant and nonnegligible. The nonlinearity of force-velocity characteristics is discussed. In order to quantitatively explain the necessity of taking the minor losses into account for modelling the MR damper, the proportion of pressure drop due to the minor losses to the total pressure drop is investigated and discussed. Pressure drops due to the minor losses and viscous loss are also investigated and discussed. At last, the proposed mathematical model is used to analyse the working principle of nonmagnetized passages.

Dual active bridge (DAB) DC/DC converter is an ideal high-power bidirectional DC/DC converter with multiple control methods. In this paper, the working characteristics of the DAB converters are analysed by the example of medium power mode, in which the voltage transfer ratio is less than 1. For the situation that the DAB converter is difficult to achieve soft switching at light load, a triple phase shift (TPS) control method is used. Combining with RMS current optimisation, a control method to achieve soft switching for full-power range is proposed. The control strategy enables the converter to realise zero voltage switching (ZVS) of all the switching device, which not only improves the operating conditions of the switching device, but also improves the converter efficiency. Finally, a prototype of DAB was built. Compared with the traditional control method, the experimental results verify the effectiveness of the proposed control strategy.

The increasing wind capacity integration brings new challenges to power system planning and operation, due to the intermittency and uncertainty of its power generations. This paper determines the optimal allocation of wind capacity with considerations of the impact of uncertainty in wind generation using the zonotope-based method, for the sack of reducing the possible violations of operation limits caused by wind power fluctuations. The uncertain variations of wind power are bounded by a zonotope and then propagated through a linearised power flow to get another zonotope, which captures all possible values of the system state variables. The state-bounding zonotope is then added to the optimal model to quantify and assess the impact of generation uncertainty on system static performance. The proposed method is applied to IEEE 33-bus system, the results prove the validity of the method and show that the zonotope based set-theoretic method is more efficient than probabilistic methods when focusing on the operation limits with considerations of the uncertainty of multiple renewable energy sources.

The idea of ‘energy router’ (ER) is put forward as a critical application of the energy Internet (EI), especially in the new generation of electric power system to fulfil the power management and to deal with the integration of massive distributed generations. Furthermore, AC/DC hybrid network becomes a kind of development trend since it can improve the power quality and efficiency compared with adding converters for DC loads. This article proposes a decentralised control method for multi-port converter (MPC)-based ERs to achieve flexible power regulation and ‘plug and play’ operation. With the proposed schemes, DC bus voltage can be maintained together by multi-ER through appropriately adjusting their transmitted power according to the pre-set power-sharing ratio. Furthermore, the sharing ratio can be online changed for flexible power regulation. The stability of the system is ensured by Lyapunov method, detailed simulation results are presented to validate the feasibility of the proposed scheme under different operating conditions.

Due to the increasing penetration, intermittent renewable energy sources (RESs), such as wind power, have added additional uncertainties to AC–DC power system operation. The strong correlation between wind power will bring larger computational errors. It makes generation dispatching incompatible with the current single-period optimal power flow practice. This study presents an AC–DC affinely adjustable robust dynamic optimal power flow (AARDOPF) model. Ellipsoidal uncertainty set is adopted to well fit the spatial-temporal correlated wind power. Affine policies are utilised in the re-dispatch process. To apply AC–DC AARDOPF, the base-point generation is calculated and determined to match the power with forecasted RES output. And once the uncertainty is revealed, generators reschedule its output through participation factors responding to the uncertain fluctuation of RES output to ensure a feasible solution for all realisations of RES output. Then original non-linear models are transformed into SOCP models. Finally, GUROBI is used to solve this model. Numerical results are obtained on modified AC–DC IEEE 30-bus and 118-bus system to minimise the expected operational cost. Comparing with stochastic dynamic optimal power flow (DOPF), this model is proved to be more practical and effective.

The electric spring (ES) is a kind of electric device used for energy buffering, based on voltage and power management. ES transfers the voltage (or energy) fluctuation in the main bus to the non-critical load (NCL), stabilises the main voltage for critical load, and actively adjusts the power consumption of the NCL to achieve regulation goals of distribution network with wide voltage operational range of NCL. With lots of intermittent and fluctuating renewable energy resources into the energy Internet, the smart load integrated with the ES will play a greater role in the optimal operation of the distribution network. Here, the power flow optimal model of distribution network with the ES is proposed. Under different load levels, the flexible regulation of ES can reduce the overall voltage drop and optimise the operation of distribution network. The benefits of ES for the optimisation of distribution network are verified based on an improved IEEE 33-node case. Numerical results show that the ES relieves the voltage drop of the distribution network and improves the reliability of distribution network based on the interaction between the ES and NCL. The overall energy efficiency and reliability of the distribution network system are improved.

Hybrid AC/DC distribution network has developed rapidly owing to its convenience for renewable energy integration, thus the authors are interested in quantifying the impact of uncertainty from renewable power on system static performance. This paper proposes a zonotope-based set-theoretic method for quantifying the impact of renewable power fluctuations on system-state variables in hybrid AC/DC distribution network, both for the assessment of the capabilities for integrating renewable resources by hybrid AC/DC system, and for the appropriate selection of operating method in hybrid power systems. The authors bound the uncertain variations of generation by a zonotope and propagate it through a linearised power flow to get another zonotope, which captures all possible values of the system state variables. The zonotope-based method is computationally tractable and takes less computation than probabilistic methods especially when considering the generation uncertainty of multiple renewable energy resources. The proposed method is applied to a hybrid AC/DC test system with different control strategies of converters. The results show that hybrid AC/DC system with proper control mode may help reduce the impact of generation uncertainty.

Inn for electric vehicles (EVs) and photovoltaic (PV) is a typical integration of EVs locally consuming renewable energy sources (RES), with features of EV charging, energy storage, and RES consumption. In the existing research works, scholars focus on the energy storage system (ESS) as a medium to charge EVs and send back the power to the utility grid and alleviating the information asymmetry between EV owners and grid dispatching agencies with lower costs of distribution network. In this study, a mixed operational mode based on inn for EV and PV with a replaceable EV battery is analysed. The fragmented energy transferring can shift the load more effectively, balance fluctuation of RES, of great significance to optimise the operation of distribution network system. In summary, a comprehensive model of power flow in distribution network with inn for EV and PV is proposed. Based on an improved IEEE 33-node case, the effects of fragmented energy management mode are analysed. Numerical results show that replacing the full-charged battery for EV is more effective for peak load time-shifting and improving consuming of RES power based on the interaction among RES, ESS, and the replaceable EV battery.

A novel empirical data analysis methodology based on the random matrix theory (RMT) and time series analysis is proposed for the power systems. Among the ongoing research studies of big data in the power system applications, there is a strong necessity for new mathematical tools that describe and analyze big data. This paper used RMT to model the empirical data which also treated as a time series. The proposed method extends traditional RMT for applications in a non-Gaussian distribution environment. Three case studies, i.e., power equipment condition monitoring, voltage stability analysis and low-frequency oscillation detection, illustrate the potential application value of our proposed method for multi-source heterogeneous data analysis, sensitive spot awareness and fast signal detection under an unknown noise pattern. The results showed that the empirical data from a power system modeled following RMT and in a time series have high sensitivity to dynamically characterized system states as well as observability and efficiency in system analysis compared with conventional equation-based methods.

The combined heat and power (CHP) systems can provide heat and electricity simultaneously. They are promising in the future energy landscape because of high efficiency and low emissions. This paper proposes a new operation optimization model of CHPs in deregulated energy markets. Both CHPs' overall efficiency and heat to electricity ratio are closely linked with the loading level, which are dynamically determined in this paper. A discrete optimization model is then proposed to determine the optimal real-time operation strategies for the CHPs. The optimization problem is solved by the interior point method with discrete time intervals, in which the discrete optimal operation points can be identified effectively. This step projects the potential operation strategies that could produce maximum benefits. Finally, a dynamic programming algorithm is developed to maximize the profits of CHPs through dynamically modifying the operation strategies projected in the previous step considering transient constraints. The proposed new methodology is demonstrated on a 1-MW CHP system with real-time data.

The paper presents an overview of the traditional methods to obtain wind parameters such as wind speed, wind direction and air density. After analyzing wind turbines’ arrangements and communication characteristics and the correlation of operation data between wind turbines, the paper proposes a novel recombination-sensing method route of “measuring–correlating–sharing–predicting–verifying” (MCSPV) and explores its feasibility. The analysis undertaken in the paper shows that the wind speed and wind direction instrument fixed on the wind turbine nacelle is simple and economical. However, it performs in-process measurement, which restricts the control optimization of wind turbines. The light detection and ranging (LIDAR) technology which is accurate and fast, ensures an early and super short-time sensing of wind speed and wind direction but it is costly. The wind parameter predictive perception method can predict wind speed and wind power at multiple time scales statistically, but it has limited significance for the control of the action of wind turbines. None of the traditional wind parameter-sensing methods have ever succeeded in air density sensing. The MCSPV recombination sensing method is feasible, both theoretically and in engineering, for realizing the efficient and accurate sensing and obtaining of such parameters as wind speed, wind direction and air density aimed at the control of wind turbines.

Blood is in fact a suspension of different cells with yield stress, shear thinning, and viscoelastic properties, which can be represented by different non-Newtonian models. Taking Casson fluid as an example, an unsteady solver on unstructured grid for non-Newtonian fluid is developed to simulate transient blood flow in complex flow region. In this paper, a steady solver for Newtonian fluid is firstly developed with the discretization of convective flux, diffusion flux, and source term on unstructured grid. For the non-Newtonian characteristics of blood, the Casson fluid is approximated by the Papanastasiou's model and treated as Newtonian fluid with variable viscosity. Then considering the transient property of blood flow, an unsteady non-Newtonian solver based on unstructured grid is developed by introducing the temporal term by first-order upwind difference scheme. Using the proposed solver, the blood flows in carotid bifurcation of hypertensive patients and healthy people are simulated. The result shows that the possibility of the genesis and development of atherosclerosis is increased, because of the increase in incoming flow shock and backflow areas of the hypertensive patients, whose WSS was 20~87.1% lower in outer vascular wall near the bifurcation than that of the normal persons and 3.7~5.5% lower in inner vascular wall downstream the bifurcation.

Big data is the source of the city’s wisdom,making good use of big data could solve or relieve the problems caused by the city’s development.The big data managed by the government are of high value because they are closely related to people’s life and public service.How to govern the government big data and explore its value is one of the key challenges during the construction of smart city.Taking Ningbo as a case study,the challenges of governing government big data were described,which include the needs and obstacle of sharing big data by internal government,and the problems of opening government big data and helping to bring it about market utilization,solutions and policies were also proposed.

The government big data is a large data source with high density value.Making good use of the government big data is of great benefit to promote the economic development and improve the public service.The enterprise which could propose solutions is a bridge between the government big data and the public demands.Open government big data and develop big data business market is an effective way for the government to release the value of big data.Taking the government big data of Ningbo under its smart city construction as an example,the obstacles and challenges of the government big data market utilization were analyzed,and the mode and countermeasures of the government big data market utilization were also discussed.

The vigorous cytokine response of immune cells to Gram-negative bacteria is primarily mediated by a recognition molecule, Toll-like receptor 4 (TLR4), which recognizes lipopolysaccharide (LPS) and initiates a series of intracellular NF-kappaB-associated signaling events. Recently, bladder epithelial cells (BECs) were reported to express TLR4 and to evoke a vigorous cytokine response upon exposure to LPS. We examined intracellular signaling events in human BECs leading to the production of IL-6, a major urinary cytokine, following activation by Escherichia coli and isolated LPS. We observed that in addition to the classical NF-kappaB-associated pathway, TLR4 triggers a distinct and more rapid signaling response involving, sequentially, Ca(2+), adenylyl cyclase 3-generated cAMP, and a transcriptional factor, cAMP response element-binding protein. This capacity of BECs to mobilize secondary messengers and evoke a more rapid IL-6 response might be critical in their role as first responders to microbial challenge in the urinary tract.