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SUMMARY A novel model of electricity market dynamics is proposed in this paper. The model consists of the difference equations embedded within the problem of optimization of market clearing. Decision-making processes of different market participants under bounded rationality and adaptive expectations are considered in the proposed model. Moreover, the model can accurately reflect the process of market clearing and take into account the complex network constraints. Different market status, such as stable Nash equilibrium, periodic oscillation and chaos, are quantitatively analyzed. For unfavorable chaotic fluctuations in economic performance, use of the parameter perturbation control method in electricity market is proposed. The measures required for driving the market from the chaotic state to stable Nash equilibrium are suggested to provide an important theoretical basis for effectively regulating the electricity market. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A wireless battery charger with inductive power transfer (IPT) was proposed in this paper. The commonly used constant-current constant-voltage (CC-CV) charging method is accomplished by a closed-loop controlled IPT with a hybrid resonant circuit on the secondary side. A smooth transition between the CC stage and the CV stage can be made simply by swapping exactly the associated switches on resonant capacitors. The required charging voltage and current are regulated by controlling the phase-shifted angle of the high-frequency inverter on the primary side. To stabilize the charging current and voltage, a closed-loop digital controller was introduced with infrared (IR) transmission feedback. Precise regulation of the resonant inverter on a relative small ranged phase-shifted angle can be realized by two 16-bit microcontroller units (MCUs) with compact encoding and decoding techniques. A hybrid resonant inverter was designed for a 600 W prototype of the proposed IPT battery charger. Experimental results from exemplar cases have demonstrated that the battery charger can provide a stable charging current at the CC stage and then transit smoothly into the CV stage. [ABSTRACT FROM AUTHOR]

Fig. Surface Modulation induced Oxygen Vacancies/Stacking faults and Spinel-Carbon Composite Coatings Toward High-performance Li-rich Mn-Based Cathode. [Display omitted] • The surface structure of Li-rich cathodes is regulated by VC modification. • The modification mainly improves the rate performance and the ICE. • The mechanism of enhanced performance by VC modification is revealed. Lithium-rich manganese-based cathode (LRM) is considered to be the most promising cathode materials for next-generation lithium-ion batteries due to its high energy density. However, the low initial coulombic efficiency and poor rate performance are severe problems in the commercialization of LRM. Herein, we use an ascorbic acid (VC) modulation strategy to create spinel-carbon composite coatings and dual defects (oxygen vacancies, stack faults) on the surface of LRM. The composite surface coating and defects play a synergistic role in inhibiting interfacial side reactions, enhancing structural stability, and improving electrical conductivity as well as lithium-ion diffusion kinetics. As a result, the modified LRMs exhibit a specific capacity of 251.7mAh/g with an improved initial coulombic efficiency (ICE) of 82.3 % (pristine 72.9 %), enhanced rate capability (135 mAh/g at 5C), and long-term cyclability of 90 % retention after 200 cycles compared with the pristine (78 % retention after 200 cycles). The performance improvement of the modified LRMs is attributable to the composite coating and the dual defects, which ensure the LRM with a more stable structure (smaller volume change of 2.4 % compared with the original sample of 3.65 %). This strategy provides an efficient and environmentally friendly idea of surface modification for boosting the electrochemical performance. [ABSTRACT FROM AUTHOR]

This letter introduces a parallel and distributed computation method for dynamical economic dispatch over a cyber-physical system. To achieve a faster economic dispatch operation, accelerated consensus approach is proposed. The simulation illustrates the better performance of accelerated consensus algorithm. [ABSTRACT FROM PUBLISHER]

This letter introduces a deterministic global optimization methods for unit commitment (UC) problem based on outer approximation method (OAM). The proposed Multi-cuts OAM (MCs-OAM) decomposes the UC problem into a mixed integer linear programming (MILP) master problem and several nonlinear programming (NLP) subproblems, whereas only one NLP in classic OAM. After elaborately designing the terminating criterion for solving the bigger but tighter MILP master problems, MCs-OAM can obtained higher quality solutions with fewer main iterations and less total CPU times, although solving more NLPs consumes more CPU times than OAM. The numerical results on 42 test systems of up to 200 units show that the MCs-OAM is very promising for large scale UC problems because that it can obtain high-quality solutions in reasonable time. [ABSTRACT FROM AUTHOR]

The solid acid catalysts SO42−/Fe2O3 were prepared by impregnation technique, and the preparation conditions were different in calcination temperature, concentration of impregnation solution of H2SO4 and impregnation time. The characterization was performed by using Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), Temperature programed desorption of NH3 (NH3‒TPD), N2‒BET and microwave absorbing test. As shown by FTIR spectra, the S=O functional group existed in the sample, which was essential for the strong acidity of the SO42‒/MxOy type solid acids. The XRD results indicated that when the calcination temperature exceeded 400℃, iron in SO42‒/Fe2O3 transformed from amorphous to crystalline phase. The results from NH3-TPD showed that the prepared sample possessed strong acid and superacid sites. As shown by N2-BET results, the BET surface area of the samples was up to 200m2/g, and their pore size distributions essentially belonged to mesoporous characteristic distribution. The SO42−/Fe2O3 solid acid catalysts were used for the transesterification of castor oil under microwave radiation to produce biodiesel. The amounts of FAME in the product were analyzed by high-performance liquid chromatography. The highest yield of product was 65.3 wt.% when the reaction temperature was 65 ℃, alcohol/oil molar ratio was 30/1, catalyst loading was 20 wt.%, the reaction time was 3 h and the power of microwave was 300 w. Furthermore, the reaction results showed that SO42‒/Fe2O3 had better catalytic activity under microwave radiation than under conventional heating condition. [ABSTRACT FROM AUTHOR]

Existing classical ciphertext retrieval schemes are mainly developed according to the homomorphic encryption that is a cryptographic technique based on computational complexity theory of mathematical puzzles. In quantum computing, on the one hand, most of traditional asymmetric encryption can be quickly cracked as the computational capacity of quantum computer is much higher than that of traditional digital computer. Hence, the quantum homomorphic encryption scheme is widely used in data encryption for the issue of privacy protection in quantum computing. But on the other the retrieval efficiency of homomorphic encrypted data is insufficient especially in quantum cloud computing. Therefore, this paper proposes a novel quantum homomorphic encryption ciphertext retrieval (QHECR) scheme basing on the Grover algorithm to solve the problem of homomorphic encrypted ciphertext data retrieval in quantum cloud computing. Our scheme is to improve such retrieval efficiency mentioned above where the interaction process is not required even if the T-gate exists in the circuit. In the experiment, two qubits without the T-gate are conducted on both simulations and real quantum devices by using IBM quantum information science kit (Qiskit). The results show that the proposed QHECR scheme is capable of achieving the retrieval encrypted data when the T-gate does not exist in the evaluation circuit in ciphertext environment. Moreover, a verification experiment about the T-gate key update algorithm is implemented to verify the feasibility and reliability of the proposed scheme in the Qiskit as well, which indicates that the QHECR is still available when the T-gate exists in the circuit. Since the decryption is inefficient when there are exponential T-gates in the circuit, our proposed scheme is suitable for low T-gate complexity. [ABSTRACT FROM AUTHOR]

Neuronal iron overload contributes to synaptic damage and neuropsychiatric disorders. However, the molecular mechanisms underlying iron deposition in depression remain largely unexplored. Our study aims to investigate how nuclear factor-erythroid 2 (NF-E2)-related factor 2 (Nrf2) ameliorates hippocampal synaptic dysfunction and reduces brain functional connectivity (FC) associated with excessive iron in depression. We treated mice with chronic unpredictable mild stress (CUMS) with the iron chelator deferoxamine mesylate (DFOM) and a high-iron diet (2.5% carbonyl iron) to examine the role of iron overload in synaptic plasticity. The involvement of Nrf2 in iron metabolism and brain function was assessed using molecular biological techniques and in vivo resting-state functional magnetic resonance imaging (rs-fMRI) through genetic deletion or pharmacologic activation of Nrf2. The results demonstrated a significant correlation between elevated serum iron levels and impaired hippocampal functional connectivity (FC), which contributed to the development of depression-induced CUMS. Iron overload plays a crucial role in CUMS-induced depression and synaptic dysfunction, as evidenced by the therapeutic effects of a high-iron diet and DFOM. The observed iron overload in this study was associated with decreased Nrf2 levels and increased expression of transferrin receptors (TfR). Notably, inhibition of iron accumulation effectively attenuated CUMS-induced synaptic damage mediated by downregulation of brain-derived neurotrophic factor (BDNF). Nrf2−/− mice exhibited compromised FC within the limbic system and the basal ganglia, particularly in the hippocampus, and inhibition of iron accumulation effectively attenuated CUMS-induced synaptic damage mediated by downregulation of brain-derived neurotrophic factor (BDNF). Activation of Nrf2 restored iron homeostasis and reversed vulnerability to depression. Mechanistically, we further identified that Nrf2 deletion promoted iron overload via upregulation of TfR and downregulation of ferritin light chain (FtL), leading to BDNF-mediated synapse damage in the hippocampus. Therefore, our findings unveil a novel role for Nrf2 in regulating iron homeostasis while providing mechanistic insights into poststress susceptibility to depression. Targeting Nrf2-mediated iron metabolism may offer promising strategies for developing more effective antidepressant therapies. [ABSTRACT FROM AUTHOR]

We study the problem of intentional attacks targeting to interdependent networks generated with known degree distribution (in-degree oriented model) or distribution of interlinks (out-degree oriented model). In both models, each node’s degree is correlated with the number of its links that connect to the other network. For both models, varying the correlation coefficient has a significant effect on the robustness of a system undergoing random attacks or attacks targeting nodes with low degree. For a system with an assortative relationship between in-degree and out-degree, reducing the broadness of networks’ degree distributions can increase the resistance of systems against intentional attacks. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cell vehicles have been promoted as a complement to electric vehicles (EVs) to facilitate the decarbonization of transportation networks (TNs). The coordinated operation of a networked hydrogen-power-transportation system with distributed hydrogen supplies is proposed in this research. To maximize the synergistic effect, TN's couplings in hydrogen transport delays and refueling/charging demand are further integrated into the conventional hydrogen-power system. The objective is to maximize the total profits, subject to both the coupling and network constraints. To present the storage effect of hydrogen tube trailers with delays, an extended discrete user equilibrium (UE) is developed and the traditional UE is adopted to formulate refueling-charging demand couplings. Considering the hydrogen supply process, it especially involves the constraints of hydrogen production, storage, utilization, and dispensation. A data-driven robust chance-constrained programming is provided to account for multiple uncertainties from energy/traffic demand and renewable power. Simulation results of the 48-node system show that the proposed model improves the total profits. • The coordinated operation of a networked hydrogen-power-transportation system is proposed. • The traffic flow couplings with hydrogen transport delays and refueling/charging demand are integrated to maximize the synergistic effect. • An extended discrete user equilibrium is developed to present the storage and delay effect of hydrogen trasport. • A data-driven robust chance-constrained programming is used to deal wth the uncertainties in demand and generation. [ABSTRACT FROM AUTHOR]

The output of renewable generation depends on the real-time weather conditions and changes rapidly; so the economic operating point of the power system varies over time. This paper aims to find the explicit mapping from variable renewable power to optimal power flow solutions. To this end, we propose a parametric distribution optimal power flow (P-DOPF) method, which gives the optimal dispatch strategy and power flow status as analytical functions of the renewable output. With the established distribution optimal power flow problem based on the relaxed Distflow model, the first step is to perform a global polyhedral approximation on the second-order cone constraints to develop a linearized formulation. The second step is to obtain the P-DOPF model by treating renewable power output as parameters; then, the P-DOPF problem gives rise to a multi-parametric linear program (mp-LP). Third, we prove that the optimal solution and optimal value of the P-DOPF are piecewise linear functions of the parameters and we design an adaptive-sampling algorithm to construct the optimal value and optimal solution functions, as well as the partition of the parameter set, subject to a given error tolerance; this algorithm is not influenced by model degeneracy, a common difficulty of existing mp-LP algorithms. The P-DOPF framework provides an explicit real-time control policy of generators in response to the renewable output. Case studies on the IEEE 33 and 69-bus systems verify the effectiveness and performance of the proposed method; by comparison, the proposed method outperforms the established affine policy method in computational efficiency and optimality by 24.5% and 4.62%, respectively. [ABSTRACT FROM AUTHOR]

Electrified transportation systems and renewable energy resources have been recognized as effective environmental-friendly technologies against global warming contributed by greenhouse gas (GHG). Remarkably, hydrogen fuel cell-powered electric vehicles can outperform battery-powered electric vehicles largely in the sense of the driving range and the refueling time. However, both of them require a better coordination of infrastructure system and renewable energy resources to achieve a significant reduction of GHG emissions. This article aims to maximize the long-term profitability for the planning model of hydrogen refueling stations, where the capacitated flow refueling location model is leveraged for maximal traffic flow coverage. Furthermore, we discuss various real-world constraints, such as traffic network constraints, distribution network constraints, hydrogen balance constraints, and energy constraints for electric vehicles, to make the planning model more practical. By considering the uncertainty of the short-term refueling demand across the city, an approach for geographically redistributing hydrogen among the stations is also presented where the minimal cost of redistribution is modeled by one-commodity pickup-and-delivery traveling salesman problem. A real-life case study of Western Sydney is adopted to testify the efficiency of the planning model under current and future cost levels. Finally, a numerical simulation is utilized to demonstrate the validity of the hydrogen redistribution method. [ABSTRACT FROM AUTHOR]

Many countries have set power system emissions reduction goals. However, in developing the electricity‑carbon system, regulators may prioritize final targets while overlooking the planning of pathways. The paper aims to develop a simulation framework for the long-term interactive evolution of electricity‑carbon systems and optimize a developing trajectory. To begin, the electricity system is modeled as a daily spot market over 365 days, and simulated by fast unit commitment (FUC) method. Then, considering the internal multi-player gaming, the multi-class mean field game (MMFG) theory is introduced to simulate the carbon market. Subsequently, a state transition equation for the electricity‑carbon evolution is formulated based on the concept of trajectory optimization from optimal control theory. Here, the regulator can steer the evolution by adjusting the carbon emission intensity benchmark (CEIB) in the carbon market. Finally, employing the Twin Delayed Deep Deterministic Policy Gradients (TD3) technique, the problem characterized by high-dimensional state space and continuous action space is efficiently solved. The effectiveness of the proposed method is examined by case studies on a provincial-scale grid with over 200 units, where the optimal CEIB can be achieved within a second and the control precision of trajectory evolution can be limited to 2%. • Meanfield based simulationfor continuous carbon tradingamongmulti-classgenerators. • Multi-year evolution trajectoryframeworkfor interactive electricity-carbon systems. • Efficient resolution of complexelectricity-carbon trajectory optimization. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) are widely utilized in various applications, e.g. electric vehicles, microgrids, and data centres. However, the structure of multiple cell/module/pack BESSs causes a battery imbalance problem that severely affects BESS reliability, capacity utilization, and battery lifespan. The available balance schemes introduce extra equalizers and suffer from slow balance speed due to the equalizer limits. To tackle this issue, a modular reconfigurable BESS (MR-BESS) topology is introduced in this paper, for which a fast battery balance method is proposed. This combination provides reconfiguration flexibility and fault tolerance capability without the need for any extra components, such as equalizers. Experimental results were performed on a scale-down prototype to verify the operation of the proposed reconfigurable BESS topology and the effectiveness of the fast battery balance method. • A novel modular reconfigurable BESS (MR-BESS) topology is proposed. • An equalizer-free active battery balance method for proposed topology is proposed. • A control algorithm for balance procedure that realizes fast balance speed. [ABSTRACT FROM AUTHOR]

Strategic bidding which aims to optimally harvest the price difference in the wholesale electricity market can efficiently allocate VPPs' aggregated resources to provide large flexibility for energy and frequency regulation (FR), aiding in the real-time rebalancing of supply and demand. However, the uncertainty of renewable energy results in substantial imbalances between supply and demand, which causes high randomness of system frequency deviation and significant price fluctuations in the electricity market, making bidding challenging. To tackle these issues, a risk-averse optimal bidding strategy is proposed for VPPs to participate in both energy and FR markets. Moreover, a payment recovery mechanism is designed to recover the cost of FR undersupply. Specifically, each VPP submits bids to maximize profit, while the market operator clears the market and penalizes undersupply through the payment recovery mechanism. The existence of Nash equilibrium is proved, and a distributed best response algorithm is implemented to calculate the risk-averse optimal bidding strategy, taking into account Denial-of-Service attacks. The mean-convergence and variance-convergence of our proposed algorithm are derived. A case study of Australian National Electricity Market (NEM) validates the effectiveness of the proposed method in reducing overbidding risk, strengthening FR service reliability and improving profits for VPPs. • A risk-averse two-stage stochastic game model is proposed for VPPs to bid in energy and FCAS markets. • A pricing mechanism is proposed to describe the relationship between price fluctuations and VPP's supply in FCAS markets. • A payment recovery is designed to recover the cost of FCAS undersupply. • A distributed best response algorithm considering DoS attacks is proposed to solve the stochastic bidding game. [ABSTRACT FROM AUTHOR]

• An improved method considering correlated stochastic variables is proposed for PLF. • A twice-permutation technique is proposed to ensure the desired correlations. • Singular value decomposition extends the scope of the method. As the increasing integration of large-scale renewable energy sources in power systems, the stochastic characteristics of loads and renewable energy systems become much more complex and impacts power systems much more than ever. Probabilistic load flow analysis is a powerful tool to discover the stochastic characteristics of power systems. There are two important issues for probabilistic load flow analysis based on Monte Carlo simulation: (i) How to generate random samples with the specific distribution and correlation; and (ii) how to make the simulation method to work well even when the correlation matrices are not positive definite. In order to handle the two issues, Nataf transformation combined with Latin hypercube sampling and singular value decomposition method is proposed for solving probabilistic load flow problems with correlated variables in this paper. By using the singular value decomposition (SVD), the proposed method works well even when the correlation matrices are not positive definite. And the twice-permutation technique based on SVD ensures that the samples have the desired correlations. The investigation on modified IEEE 14-bus system and modified IEEE 118-bus system shows that the proposed method is accurate and efficient. [ABSTRACT FROM AUTHOR]

Highlights • Conventional tillage with no tillage rotation annually provided more suitable soil temperature for crop growth. • Conventional tillage with no tillage rotation annually can improve soil properties. • The field would store more soil water when conventional tillage with no tillage rotation annually applied. • Tillage rotations would have more chance to obtain high crop yields and economic profits. Abstract Intensive tillage in conventional tillage systems reinforces water stress effects on crop growth, limiting yields from dryland agriculture. Conservation tillage can reduce soil evaporation and conserve more soil water in fields, but long-term, mono-conservation tillage may lead to low crop yields. The rotation of conventional tillage with conservation tillage may offset some of the defects generated by the mono-tillage practices of either conventional or conservation tillage, improve crop yields and provide better soil conditions. A long-term tillage rotation experiment (2007–2016) was established to assess the effects of rotating conservation tillage with conventional tillage on soil water and crop productivity in a winter wheat-spring maize rotation field in Heyang County, Shaanxi Province, a typical semi-arid region of the Loess Plateau, China. Four tillage treatments were applied over ten years, as follows: ST/CT (subsoiling was performed during the first year then rotated with conventional tillage in the second year), CT/NT (conventional tillage during the first year and then rotated with no-tillage in the second year), NT (no tillage applied in any year) and CT (conventional tillage applied annually). Compared with CT and NT, the CT/NT rotation significantly decreased the soil bulk density and increased the soil porosity in the 0–60 cm soil layer after ten years (P < 0.05). The CT/NT and ST/CT rotations increased the minimum soil temperature during the wheat growth season and decreased the maximum soil temperature during the maize growth season. In fallow periods, the mean soil water storage and soil water content values for the CT/NT rotation were 8.7% and 4.8–10.1% higher than those of the CT treatment, respectively. The CT/NT rotation consumed more soil water during the winter wheat growth season and less soil water during the spring maize growth season compared to the NT and CT treatments. Over ten years, the ST/CT rotation produced higher crop yields (winter wheat: 5231 kg ha−1, spring maize: 8388 kg ha−1), while the CT/NT rotation had a higher WUE (winter wheat: 15.5 kg ha−1 mm−1, ST/CT: 20.6 kg ha−1 mm−1) than either NT or CT alone. The CT/NT rotation also had an increased straw yield (mean value: 9952 kg ha−1) and economic profit (6776 yuan ha−1). Moreover, the CT/NT and ST/CT rotations provided an optimal SOC distribution in the 0–60 cm soil layer. With respect to comprehensive productivity, the CT/NT rotation provided relatively better soil conditions and crop yields during the winter wheat-spring maize rotations. We recommend using the CT/NT rotation as the optimal tillage system for the sustainable production of crops under conditions of semi-arid agricultural production in the Loess Plateau of China. [ABSTRACT FROM AUTHOR]

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers "prosumers" arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs. • A non-intrusive carbon emission accounting method for industrial corporations. • A novel perspective of modern power systems for carbon emission accounting. • Calculating carbon emissions considering distributed photovoltaics' impacts. • The method becomes superior as the photovoltaic output proportion increases. [ABSTRACT FROM AUTHOR]

To decrease carbon dioxide emission, a high penetration level of renewable energy will be witnessed over the world in the future. By then, energy storage will play an important role in power balancing and peak shaving. This paper considers the capacity sizing problem during the transition to a low-carbon power system: the retirement plan of conventional fossil-fuel generators and the growth of demands are given. The renewable generation capacities at given sites are to be determined in coordination with the upgrade of transmission lines and installation of energy storage units. In order to capture the inaccuracy of empirical probability distributions for uncertain renewable output and load profiles, a novel distributionally robust bi-objective sizing method using Wasserstein-metric-based ambiguity sets is proposed. The total investment cost and expected carbon dioxide emission subject to operating conditions and a load shedding risk constraint are minimized. The distributionally robust shortfall risk of load shedding and the worst-case expectation of carbon dioxide emission are reformulated into computable forms based on calculating the Lipschitz constants. The final problem comes down to solving mixed-integer linear programming problems. The numerical results demonstrate the effectiveness of the proposed method and the necessity of using distributionally robust optimization. • A generation-transmission-storage sizing model for power systems is developed. • Wasserstein-metric-based ambiguity set is used to model uncertain distributions. • Cost, emission, and load-shedding risk under inexact distribution are considered. • Lipschitz constants are calculated, and the sizing problem is solved via MILP. • The Pareto frontier compromising cost and carbon emission is obtained. [ABSTRACT FROM AUTHOR]

Soil water deficit in apple orchards is a common phenomenon related to land degradation on the Loess Plateau. However, little is known regarding the degree of soil water depletion and its effects on orchard productivity in rainfed apple orchards. We used a combination of field sampling and modeling to understand the impact of apple orchards on soil water availability and changes in orchard productivity over a long-term time series. A total of 51 soil profiles from eight experimental sites in the Loess Plateau, were collected in 2010 and 2016 and modeling was used to evaluate the long-term effects of apple plantations on soil water depletion and orchard productivity. A process-based model, the Environmental Policy Integrated Climate (EPIC), was calibrated and validated using survey data from field experiments. The calibrated EPIC model could well simulate soil moisture and orchard productivity with relative root mean square errors (RRMSE) of 11.12 % and 2.71 %, respectively. Field sampling and modeling showed that conversion from farmlands to orchards decreased soil moisture, leading to severe soil water depletion (SWD) in the 0–15.0 m soil profiles. Stand age was the main factor influencing soil moisture, and SWD gradually increased with increasing stand age. Depleted soil water led to land degradation and decreased orchard productivity, especially in water-limited regions. Simulation results suggested that the optimal planting density and fertilizer application rates were often related to variability in climatic conditions; therefore, appropriate management practices need to be adapted to local natural conditions. Although SWD in apple orchards is inevitable, the detrimental effects could be minimized during orchard development, provided that appropriate management measures are selected based on precipitation and soil water conditions. These findings may provide a basis for evaluating the extent of SWD and its effect on orchard productivity in dryland apple orchards. • The effects of soil water depletion (SWD) on orchard productivity were studied. • Field sampling and modeling methods were used to evaluate SWD. • Soil water depletion results in orchard productivity degradation. • High planting density and fertilizer rates did not improve the orchard productivity in rainfed agroecosystems. • Optimal management practice of rainfed orchards varied among sites. [ABSTRACT FROM AUTHOR]

• A distributed method for DAS of IEGS is proposed. • The two-stage mixed-integer nonlinear DAS model can be effectively solved. • Limited information is shared between electricity and gas system operators. • A high-quality DAS solution can be obtained. • The security of IEGS against uncertainties can be strictly guaranteed. The urgency to address the uncertainty in the day-ahead scheduling (DAS) of integrated electricity and natural gas systems (IEGSs) has been highlighted, and the robust optimization has been proven to be an effective method. However, the gas system and electricity system are generally operated by different utilities in practice, i.e., gas system operator (GSO) and electricity system operator (ESO), which poses a considerable challenge to solve this two-stage mixed-integer nonlinear DAS model due to the limit on information sharing. In this paper, a novel distributed calculation method is proposed to solve the robust DAS model of IEGSs in a decentralized way. First, a mixed-integer master problem and a non-convex max–min subproblem can be obtained from the original DAS model by employing the column and constraint generation (CCG) method. Afterwards, based on the limited information exchange between ESO and GSO, an innovative decomposition method is proposed for the master problem to obtain the optimal DAS solution, and then a novel inner CCG algorithm is presented for the subproblem to guarantee the security of IEGSs. By doing so, the robust DAS of IEGS is decomposed into several mixed-integer linear programming (MILP) problems, and GSO and ESO can coordinately solve these MILPs with limited information sharing. Last, numerical tests on two IEGSs verify the superiority of the proposed method in hedging against the wind uncertainty and achieving the solution optimality. [ABSTRACT FROM AUTHOR]

• A novel energy bank model is presented based on sharing economy. • Coordination of shared BESS and individual VESS is investigated. • A two-stage planning model for the optimal allocation and dispatch is presented. Under the pressure of climate change, renewable energy development has been assisted by subsidies or preferential policies such as feed-in-tariff. However, renewable energy cannot rely on policies as before. To stimulate the further development of renewable energy, this paper proposed an energy bank system (EBS) using a sharing economy model, i.e. a trading platform for multiple microgrids with renewable distributed generation (RDG) or hybrid energy storage devices. EBS is a similar idea with a virtual bank in the financial sector. Money is electrical energy, whereas storing energy is an analogy to depositing money, RDG becomes a kind of investment. To maximize the utilization efficiency of the RDG, participants of EBS can trade electricity with each other and earn revenues. In this paper, the figure of distributed energy charging (FDEC) is proposed for the energy management and dispatch, the call-auction method with the rule of maximum transaction volume is proposed for the energy trading and calculation. In the case studies, based on the practical data in Australia, the simulation results prove advanced economic advantages and high effectiveness of EBS. [ABSTRACT FROM AUTHOR]

• SNS improves depression-like behavior in MS-combined CUMS rats. • SNS increases CaSR expression and synaptic plasticity in the hippocampus and PFC. • SNS regulates the CaSR-PKC-ERK signaling pathway in rats. Adverse stress in early life negatively influences psychiatric health by increasing the risk of developing depression and suicide in adulthood. Clinical antidepressants, such as fluoxetine, exhibit unsatisfactory results due to their low efficacy or intolerable side effects. SiNiSan (SNS), a traditional Chinese herbal formula, has been proven to have affirmatory antidepressive effects. However, the underlying mechanism remains poorly understood. Therefore, this study aimed to explore the impact and molecular mechanism of SNS treatment in rats exposed to neonatal maternal separation (MS)-combined young–adult chronic unpredictable mild stress (CUMS). Seventy-two neonatal male Sprague–Dawley rats were randomly divided into six groups of 12 rats each: control + ddH 2 O, model + ddH 2 O, positive (fluoxetine: 5 mg/kg), SNS-low dose (2.5 g/kg), SNS-medium dose (5 g/kg), and SNS-high dose (10 g/kg). Behavioral tests included sucrose preference test, open-field test, and forced swimming test. Calcium sensitive receptor (CaSR), protein kinase C (PKC), ERK1/2, and synapse-associated proteins (PSD-95, GAP-43, and synaptophysin [Syn]) in the hippocampus (HIP) and prefrontal cortex (PFC) were assayed using Western blot. CaSR and Syn protein expression was measured by immunohistochemistry. MS-combined CUMS rats exhibited depression-like behavior. SNS exerted antidepressant effects on stress-induced depression-like behavior. The levels of CaSR, PKC, and p-ERK 1/2 in the HIP and PFC decreased in stressed rats. SNS treatment significantly upregulated the expression of CaSR, PKC, and p-ERK1/2 in the HIP and PFC of adult stressed rats. MS-combined CUMS could develop depression-like behavior in adult. SNS exhibited antidepressive effects accompanied by improving synaptic plasticity by activation of the CaSR-PKC-ERK signaling pathway. [ABSTRACT FROM AUTHOR]

Introduction: This case report aims to describe in detail the acute isolated cilioretinal artery occlusion (CLRAO) secondary to complicated therapeutic percutaneous coronary intervention (PCI)., Case Description: A 68-year-old Chinese man with coronary artery disease (CAD) complained of sudden, sharp chest pain. Coronary angiography revealed severe stenoses of the coronary arteries. The patient was then treated with PCI. One hour after the procedure, the patient presented with a sudden reduction in vision in the right eye. The patient was diagnosed with acute isolated CLRAO and treated with Salvia miltiorrhiza injections., Conclusions: This is the report to provide a detailed description of acute isolated CLRAO secondary to therapeutic PCI treated with Salvia miltiorrhiza. The visual prognosis of the untreated patients is poor. Suitable management and prevention are essential for interventional cardiologists to prevent these complications., (© 2023. BioMed Central Ltd., part of Springer Nature.)

A prevailing problem in many machine learning tasks is that the training (i.e., source domain) and test data (i.e., target domain) have different distribution [i.e., non-independent identical distribution (i.i.d.)]. Unsupervised domain adaptation (UDA) was proposed to learn the unlabeled target data by leveraging the labeled source data. In this article, we propose a guide subspace learning (GSL) method for UDA, in which an invariant, discriminative, and domain-agnostic subspace is learned by three guidance terms through a two-stage progressive training strategy. First, the subspace-guided term reduces the discrepancy between the domains by moving the source closer to the target subspace. Second, the data-guided term uses the coupled projections to map both domains to a unified subspace, where each target sample can be represented by the source samples with a low-rank coefficient matrix that can preserve the global structure of data. In this way, the data from both domains can be well interlaced and the domain-invariant features can be obtained. Third, for improving the discrimination of the subspaces, the label-guided term is constructed for prediction based on source labels and pseudo-target labels. To further improve the model tolerance to label noise, a label relaxation matrix is introduced. For the solver, a two-stage learning strategy with teacher teaches and student feedbacks mode is proposed to obtain the discriminative domain-agnostic subspace. In addition, for handling nonlinear domain shift, a nonlinear GSL (NGSL) framework is formulated with kernel embedding, such that the unified subspace is imposed with nonlinearity. Experiments on various cross-domain visual benchmark databases show that our methods outperform many state-of-the-art UDA methods. The source code is available at https://github.com/Fjr9516/GSL.