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Abstract Background Intracerebral hemorrhage (ICH) results from the rupture of blood vessels causing bleeding within the brain and is one of the major causes of death and long-term disability globally, particularly in low- and middle-income countries. Despite having a lower incidence than ischemic stroke, ICH imposes a greater social and economic burden. To our knowledge, since the release of the 2021 Global Burden of Disease (GBD) report, there has been no comprehensive update on the epidemiology and trends of ICH. This study aims to analyze the impact of gender, age, and the Sociodemographic Index (SDI) on the burden of ICH at global, regional, and national levels. Methods Data on the incidence, deaths, and disability-adjusted life years (DALYs) of ICH and its related risk factors from 1990 to 2021 were extracted from the GBD 2021 project, encompassing 203 countries and regions. Furthermore, temporal trends of the global intracerebral hemorrhage burden were assessed through Joinpoint analysis. Results In 2021, there were 3.444 million new cases of ICH worldwide, with an age-standardized prevalence rate of 40.8 per 100,000 people, representing a 31.4% decrease compared to 1990. In 2021, ICH caused 3.308 million deaths, with an age-standardized mortality rate of 39.1 per 100,000 people, a reduction of 36.6% since 1990. Globally, ICH accounted for 79.457 million DALYs, with an age-standardized DALY rate of 92.4 per 100,000 people, representing a 39.1% decrease since 1990. Regionally, Central Asia, Oceania, and Southeast Asia had the highest age-standardized prevalence rates of ICH, whereas Australasia, high-income North America, and Western Europe had the lowest rates. Nationally, the Solomon Islands, Mongolia, and Kiribati had the highest age-standardized prevalence rates, whereas Switzerland, New Zealand, and Australia had the lowest. Hypertension, smoking, and environmental pollution were identified as the primary risk factors for ICH. This study also validated the significant association between SDI and the burden of ICH, with the age-standardized DALY rate of ICH decreasing significantly as SDI increased. Conclusion Despite the decreasing burden of intracerebral hemorrhage, it remains a significant public health issue in countries with a lower SDI. Prevention strategies should prioritize hypertension management, air quality improvement, and smoking control to further mitigate the impact of intracerebral hemorrhage.

CO2 sequestration via mineralization of steel slag has received widespread attention due to its ability to achieve in-situ CO2 sequestration in steel industries and high-value utilization of steel slag. The final CO2 sequestration capacity of steel slag is closely related to the reaction parameters (i.e., reaction duration, reaction temperature, CO2vol concentration, and liquid-solid ratios) of mineralization. The reaction parameters may have synergistic effects on the ability of steel slag to sequestrate CO2. Therefore, the single-factor (one-factor-at-a-time) experimental strategy can't obtain optimal process parameters. Herein, response surface methodology and the Box-Behnken Design were employed in determining optimal conditions. It is found that the combined effects of CO2 concentration in combination with reaction temperature and liquid-solid ratio significantly influence the sequestration process (P = 0.0082 and P < 0.0001, respectively). Conversely, the combined effects of reaction duration with liquid-solid ratio and CO2 concentration were found to be less significant (P = 0.6905 and P = 0.6114, respectively). The reasons behind this observation can be ascribed to the focus of this research on the later stages of the reaction, during which it proceeds smoothly. Additionally, alterations in temperature, liquid-solid ratio, and CO2 concentration not only affect the initial pH, CO2 dissolution rate and quantity, and reaction kinetics but also alter the patterns of their collective impact on CO2 sequestration. The CO2 capture could reach 179.1 g-CO2/kg-steel slag at the optimal condition (i.e., 56.14 °C reaction temperature, 6.66 ml/g liquid-solid ratio, 44.53 wt.% CO2 concentration, and 286.73 mins reaction time), compared to single-factor stepwise optimization, which improves by about 9.84 %. Implementing this optimized mineralization process could enable the Chinese steel industry to capture an estimated 27.4 million tons of CO2 annually, based on an annual production of 153 million tons of steel slag.

The use of distributed photovoltaics (PVs) on a large scale often causes voltage over-limit problems in distribution networks. This paper proposes a distributed photovoltaic cluster collaborative optimization voltage control strategy based on an improved community algorithm to address the issue of centralized control being unable to respond quickly to the randomness of distributed photovoltaics and the difficulty of achieving overall coordination with local control. First, by improving the community algorithm, the division of reactive and active clusters, considering the power balance and node coupling degree, is realized. Then, the cluster-coordinated voltage control strategy is proposed by making full use of the power control ability of a photovoltaic inverter. Finally, a voltage regulation ability evaluation index is proposed to assess the node regulation ability within the cluster and select key nodes. This effectively reduces the number of control nodes. The simulation analysis of the improved IEEE 69 distribution network shows that the proposed voltage control strategy can mitigate the issue of voltage over-limit in high-permeability distributed photovoltaic access distribution and enhance the photovoltaic consumption capacity.

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

PurposePhiladelphia-chromosome negative myeloproliferative neoplasms (MPN) exhibit phenotypic similarities with JAK/STAT-unmutated idiopathic erythrocytosis and thrombocytosis (IE/IT). We aimed to develop a clinical diagnostic model to discern MPN and IE/IT.MethodsA retrospective study was performed on 77 MPN patients and 32 IE/IT patients in our center from January 2018 to December 2023. We investigated the role of hemogram, cytokine and spleen size in differentiating MPN and IE/IT among newly onset erythrocytosis and thrombocytosis patients. Independent influencing factors were integrated into a nomogram for individualized risk prediction. The calibration and discrimination ability of the model were evaluated by concordance index (C-index), calibration curve.ResultsMPN had significantly higher TNF-α level than IE/IT, and the TNF-α level is correlated with MF-grade. Multivariable analyses revealed that TNF-α, PLT count, age, size of spleen were independent diagnostic factors in differentiating MPN and IE/IT. Nomograms integrated the above 4 factors for differentiating MPN and IE/IT was internally validated and had good performance, the C-index of the model is 0.979.ConclusionThe elevation of serum TNF-α in MPN patients is of diagnostic significance and is correlated with the severity of myelofibrosis. The nomogram incorporating TNF-α with age, PLT count and spleen size presents a noteworthy tool in the preliminary discrimination of MPN patients and those with idiopathic erythrocytosis or thrombocytosis. This highlights the potential of cytokines as biomarkers in hematologic disorders.

Background: Central nervous system lymphoma (CNSL) is considered an aggressive lymphoma with a poor prognosis. Studies investigating CNSL have shown that chimeric antigen receptor (CAR) T-cell therapy has demonstrated an effective response in limited sample sizes. Therefore, we conducted this systematic review and meta-analysis to clarify the sustained efficacy and factors associated with the sustained efficacy of CAR T-cell therapy in the treatment of CNSL.Methods: We searched studies from PubMed, Embase, Medline, and the Cochrane Center Register of Controlled Trials up to July 2023. Studies that included individual data on the duration of response (DoR) after receiving CAR T-cell therapy were enrolled. Pooled response rates were calculated using fixed-effects or random-effects models. Subgroup analysis was performed to analyze the heterogeneity, and a Cox regression model was performed to identify the factors associated with sustained efficacy.Results: In total, 12 studies including 69 patients were identified and included in this meta-analysis. The pooled relapse rate was 45% [95% CI 35, 56]. Subgroup analyses of relapse rates revealed that CAR T-cells using the CD28/4-1BB domain (CD28/4-1BB vs. CD28 vs. 4-1BB, p = 0.0151), parenchymal or leptomeningeal involvement (parenchymal or leptomeningeal vs. both parenchymal and leptomeningeal, p < 0.0001), and combined treatment with CAR T-cell therapy [Autologous stem cell transplantation (ASCT) plus CAR T-cell therapy vs. CAR T cells with maintenance therapy vs. CAR T-cell therapy alone, p = 0.003] were associated with lower relapse rates in patients. Time-to-event endpoints were assessed using reconstructed individual patient survival data to explore key modulators of DoR. Partial response status at CAR-T infusion and the use of ASCT plus CAR T-cell therapy were associated with longer DoR at the multivariate level, with hazard ratios of 0.25 and 0.26, respectively.Conclusion: CAR T-cell therapy shows promising and sustained efficacy in CNSL patients. However, further prospective large-scale studies are needed to assess these effect modifiers to optimize patient selection and improve the sustained efficacy of CAR T-cell therapy in the treatment of CNSL.Systematic review registration:https://clinicaltrials.gov/, identifier PROSPERO CRD42023451856.

Submarine geological disasters occur frequently in the Chengdao area of the Yellow River subaqueous delta, which seriously threaten the safe operation of marine engineering in the region. Therefore, it is of great significance to carry out risk zonation for this region. The current study presents a detailed assessment of risk zones related to submarine geological hazards in this region based on analytical hierarchy process (AHP). The system of assessment index for risk zonation was established with four aspects of hydrodynamic condition, engineering geological environment, disaster geological conditions and human engineering activities. Eight geological hazard evaluation factors were selected and the distribution characteristics of each evaluation factor were discussed in detail by combining qualitative analysis and quantitative calculation. The risk level of submarine geological hazards in the Chengdao area are divided into four types: low risk, relatively low risk, relatively high risk, and high risk. The results show that the areas with a high geological hazard risk in this region are mainly distributed in the areas with water depths of 9–12 m, where the hydrodynamic effect is strong, with many human engineering activities, and seriously suffered from geologic hazards. The study results can provide scientific basis for engineering construction and hazard prevention in the Chengdao area.

In order to effectively quantify the uncertainty of PV power and improve the forecasting accuracy, a day-ahead interval forecasting method of PV power based on multi-correlation parameter scenarios generation is proposed. The historical PV power data is divided into a limited set of scenarios representing different output and fluctuation characteristics through the K-means clustering algorithm; Combined with the strong correlation feature parameters determined by the correlation coefficient, the characteristic curves of PV power and multi-correlation parameter in different scenarios are generated through the deep convolutional generative adversarial networks (DCGAN) model; The average value of each characteristic curve and other explanatory variables are input together into a quantile regression long short-term memory (QRLSTM) model to achieve day-ahead PV power forecasting, and obtain the forecasting interval under different confidence levels. The results of the experiment of a PV power station in Yangzhou, Jiangsu Province, China show that the forecasting performance of the proposed method under different evaluation indicators is 17.9755% and 18.1571% higher than that of the Gaussian process regression (GPR) model and the single QRLSTM model, respectively, which has obvious advantages.

In order to achieve high-precision stability evaluation and time-domain simulation of AC/DC distribution systems, and improve the analysis efficiency of complex systems, an average dynamic phasor modeling method of AC/DC distribution systems was proposed with considering the current reconstruction of dual-active bridge (DAB) high frequency transformer and the error correction of phase shifting duty cycle. Based on the average dynamic phasor model of voltage source converters (VSCs) and DABs established, the equivalent phase shift duty cycle of DAB was used as a modified algebraic variable to reduce the deviation caused by ignoring high-order dynamic phasors in modeling process and Fourier series are used to reconstruct the winding current of the high frequency transformer, respectively, to realize the accurate characterization of the DC and AC variables in average dynamic phasor models. Then an expandable global model of AC/DC distribution systems has been established by introducing the distribution line equations. Finally, according to the time-domain simulation based on MATLAB/Simulink software, the influence of model stability when the operating frequency and the leakage inductance of high frequency transformer changes was analyzed, and the correctness and accuracy of the proposed average dynamic phasor model was verified under the two scenarios of load mutation and disconnection fault.

Accurately measuring the critical shear stress is crucial for numerous applications, such as sediment transport modeling, erosion prediction, and the design of sustainable coastal engineering structures. However, developing reliable and precise in-situ measurement devices faces significant challenges due to the harsh and dynamic nature of aquatic environments. Factors like turbulence and waves introduce complexities that must be considered when designing and calibrating these devices. The newly developed Openable Underwater Carousel In-situ Flume (OUC-IF) was used to determine the critical shear stress (τc) and quantify erosion rates. Acoustic Doppler Velocimeter (ADV) was employed to measure 3D near-bottom velocities, which were then used to estimate and pre-calibrate bed shear stress (τ) applied on the seabed in the annular flume. Three computation methods of shear stress were evaluated: turbulent kinetic energy (TKE), direct covariance (COV), and log profile (LP). In-situ erosion experiments were conducted for the first time at two sites in the tidal flat of the Yellow River Delta (site 1 with a water depth of 1.32 m and site 2 with a water depth of 0.75 m). The critical shear stress was found to be 0.10 Pa at site 1 and 0.19 Pa at site 2, and the erosion rates of the sediments were successfully measured. The effect of wave-seabed interactions on erosion resistance was explored by theoretically estimating the wave-induced pore pressure of the seabed based on the observed data. The max liquefaction degree of the seabed at site 1 and site 2 was 0.035 and 0.057, respectively, and the average erosion coefficient Me was 2.63E-05 kg m-2s-1 at site 1 and 3.48E-05 kg m-2s-1 at site 2.

At present, the traditional machine learning diagnosis method has some problems, such as the imbalance of various fault types and the great difference of recognition effect between different fault types. In order to solve the problem of low accuracy of traditional machine learning diagnosis methods, a multilevel fault diagnosis model of transformer based on hierarchical classification, generalized learning and ensemble learning is established. According to the imbalanced degree of each category sample, the method established the corresponding classifier, and carried on the diagnosis step by step. At the first level, the broad learning system was selected to extract three generalized feature labels—normal, discharge and overheat, which were fused with the original parameter input to guide the nine more detailed state types. In the second-level classifier, EasyEnsemble learning method is used to generate multiple data-balanced training subsets through under-sampling, fully balancing the fault information of most classes and a few classes, and then synthesized the final classifier through parallel training sub-classifiers, avoiding the problem of missing data information due to under-sampling. Experimental results show that, compared with the traditional diagnosis methods, the proposed method improved the generalization characteristics of a few types of faults, improved the overall accuracy rate, and had a more accurate and balanced fault diagnosis effect of power transformer.

Miniature hydraulic actuators are especially suitable for narrow-space and harsh environment arrangement. However, when using thin and long hoses to connect components, the volume expansion caused by pressurized oil inside can have significant adverse effects on the performance of the miniature system. Moreover, the volumetric variation relates to many uncertain factors that are difficult to describe quantitatively. This paper conducted an experiment to test the hose deformation characteristics and presents the Generalized Regression Neural Network (GRNN) to describe the hose behavior. On this basis, a system model of a miniature double-cylinder hydraulic actuation system was established. To decrease the impact of nonlinearity and uncertainty on the system, this paper proposes a Model Predictive Control (MPC) based on Augmented Minimal State-Space (AMSS) model and Extended State Observer (ESO). The extended state space acts as the prediction module model for the MPC, and the disturbance of the ESO estimates is fed to the controller to improve the anti-disturbance capability. The full system model is validated by comparison between the experiment and the simulation. For a miniature double-cylinder hydraulic actuation system, the proposed MPC-ESO control strategy contributes to a better dynamic than conventional MPC and fuzzy-PID. In addition, the position response time can be reduced by 0.5 s and achieves a 4.2% reduction in steady-state error, especially for high-frequency motion. Moreover, the actuation system with MPC-ESO exhibits better performance in suppressing the influence of the load disturbance.

Water quality sampling and monitoring are fundamental to water environmental protection. The purpose of this study was to develop a water quality sampling and multi-parameter monitoring system mounted on a multi-rotor unmanned aerial vehicle (UAV). The system consisted of the UAV, water sampling and multi-parameter detection device, and path planning algorithm. The water sampling device was composed of a rotating drum, a direct current (DC) reduction motor, water suction hose, high-pressure isolation pump, sampling bottles, and microcontroller. The multi-parameter detection device consisted of sensors for potential of hydrogen (pH), turbidity, total dissolved solids (TDS), and a microcontroller. The flight path of the UAV was optimized using the proposed layered hybrid improved particle swarm optimization (LHIPSO) and rapidly-exploring random trees (RRT) obstacle avoidance path planning algorithm, in order to improve the sampling efficiency. Simulation experiments were conducted that compared the LHIPSO algorithm with the particle swarm optimization (PSO) algorithm and the dynamic adjustment (DAPSO) algorithm. The simulation results showed that the LHIPSO algorithm had improved global optimization capability and stability compared to the other algorithms, validating the effectiveness of the proposed algorithm. Field experiments were conducted at an aquaculture fish farm, and the device achieved real-time monitoring of three water quality parameters (pH, TDS, turbidity) at depths of 1 m and 2 m. A rapid analysis of three parameters (ammonia nitrogen, nitrite, dissolved oxygen) was performed in the laboratory on the collected water samples, and validated the feasibility of this study.

This paper established the system of assessment in dex for regionalization of geological hazards in Bohai Sea with four aspects of hydrodynamic condition, engineering geological environment, disaster geological conditions and human engineering activities based on analytic hierarchy process, eight evaluation factors selected and the distribution characteristics of each geological hazard evaluation factor which were discussed in detail. The integrated geological disaster evaluation and regionalization were carried out combined with qualitative analysis and quantitative calculation. The risk level of submarine geological hazards in Bohai Sea are divided into four types: high, medium-high, medium-low and low.The study results can provide scientific basis for engineering construction and hazard prevention in Bohai Sea area.

Laser direct metal deposition (LDMD) enables not only the preparation of high-performance coatings on the surfaces of low-property materials but also the three-dimensional direct manufacturing and re-manufacturing of parts. In the LDMD process, the spatial coupling characteristics of the powder flow and the laser beam are the key factors affecting the forming quality of the cladding layer. Based on the gas–solid two-phase flow theory, a numerical model of coaxial powder feeding was established by CFD. The powder flow characteristics of the lower part of the nozzle, the powder particle motion trajectory, and the optical-powder spatial coupling morphology and law were studied, and the relationship between the powder flow morphology, laser beam, and powder utilization was explored. On this basis, the law between the optical-powder coupling characteristics and the geometric characteristics of the cladding layer is discussed in conjunction with LDMD experiments. The results show that the powder concentration scalar located in the focal plane of the laser beam can be used to visualize the optical-powder coupling morphology. When the powder feeding speed exceeds the loading capacity of the carrier gas flow, the powder concentration in the center of the spot and the powder utilization rate decrease. When the carrier gas flow rate is 4.0 L/min and the powder feeding rate is 4.0 g/min, the best utilization rate achieved is 81.4%. In addition, the H (height) of the cladding layer is more sensitive to changes in the powder concentration than the W (width). These findings provide new ideas for nozzle structure design and the optimization of LDMD parameters.

In recent years, nitrogen pollutants have become one of the main causes of water pollution and eutrophication; thus, it is very important to increase the research on nitrogen removal in wastewater. In this study, a bacterium with outstanding ammonia nitrogen degradation capability was isolated from piggery wastewater and identified as Bacillus tequilensis (designated as A2). The ammonia nitrogen degradation rate of A2 reached the highest level (95%) when the incubation temperature was 42 °C, the initial pH was 7, the seed volume was 5%, the rotation speed was 160 r·min−1, the C/N was 10:1, and the carbon source was sodium citrate. A new nitrite reductase gene was successfully expressed in E. coli BL21 (DE3), and the result showed that the enzyme gene contained 2418 bp and 805 encoding amino acids, the recombinant enzyme was purified through an Ni2+ affinity chromatography column, it had a molecular weight of about 94 kDa, it displayed the maximum enzyme activity at 40 °C and pH 6.0, it exhibited good stability in the range of 25 °C to 35 °C, and it showed a pH of 6.0 to 7.0. A 1 mM concentration of Fe3+ promoted the enzyme activity, followed by a 1 mM concentration of Fe2+ and Mg2+. The kinetic parameters of Km, Kcat, and the Vmax of NiR-A2 were calculated to be 1.37 μmol·mL−1, 4.9 × 102 s−1, and 23.75 μmol·mg−1·min−1, respectively. This strain shows good prospects for wastewater treatment, especially in the treatment of high concentration ammonia nitrogen and nitrite degradation, because of its tolerance to and high degradation rate of high concentrations of ammonia nitrogen and high nitrite.

In view of the operational risk issues such as safety and economy caused by the connection of new energy and multiple types of electric vehicles (EVs) to the AC–DC distribution network, an AC–DC distribution network operational risk assessment method that takes into account multiple risk factors is proposed. First, a probability distribution model of scenery output and EV timing is constructed, and the starting charge state of multiple types of EVs is replaced by the number of daily driving miles subjectively set; then, based on the complex network theory, timing safety indicators, such as voltage overrun risk and branch power overload operation risk, are proposed, and the economic risk is established according to the economic operation of the distribution grid; Furthermore, a risk assessment matrix for grid-connected EVs with different capacities is constructed, and the principal component analysis (PCA) method is used to reduce the dimension of the risk assessment matrix and calculate the objective weight coefficient; finally, taking the improved IEEE 33 node AC/DC power distribution system as an example, the comprehensive risk evaluation based on PCA is compared with the traditional one, and the results show that when the safety and economic risk factors are considered at the same time, the operation risk in a certain range has a downward trend when the proposed method is adopted, which has a positive guiding significance for the planning of EV capacity in a certain area.

In order to realize the unbalanced power optimally allocated and the DC voltage stably controlled after disturbance, an adaptive droop control method considering power and voltage deviation is proposed based on the traditional voltage–power droop control of a voltage source converter-based multi-terminal direct current (VSC-MTDC) distribution network. The inherent constraint that the unbalanced power is proportionally distributed according to its capacity under the traditional droop control is broken in the proposed method to realize the reasonable transfer of unbalanced power and to reduce the overload risk of smaller capacity VSCs; the “dead zone” is appropriately set to relax the operating range of the VSC to a certain extent by a power deviation factor being introduced in the droop characteristic curve. The corresponding MATLAB/Simulink simulation model of the five-terminal DC power distribution network is established and compared with the electromagnetic transient model under the traditional droop control. Finally, the simulation results verify the effectiveness and control effects of the proposed control method.

Flexible direct current (DC) grid can realize large-scale renewable energy, wide-area coordinated complementation, and reliable power transmission. It is an important development that can be used to support high-voltage and large-capacity flexible DC transmission in the future. The short-circuit current of the DC line is one of the important bases for the selection of key main equipment parameters such as converter valves, DC circuit breakers, and reactors in the flexible DC grid. In this paper, a flexible DC grid equivalent circuit network model with alternating current (AC) feed-in is established. Aiming at the monopolar ground fault of the flexible DC grid grounded through the metal loop, an optimized traditional matrix calculation method is proposed to obtain the accurate line fault current value. On this basis, with an actual engineering background, the equivalent circuit model of the four-terminal bipolar flexible DC power grid is established, and the influence of grounding position, grounding parameters, and current-limiting reactor on the fault current of the DC line is analyzed. Finally, simulation using the PSCAD software verifies the effectiveness and accuracy of the proposed method. The method proposed in this paper can provide the necessary bases and references for the selection of flexible DC grid equipment.

Naked eye augmented reality technology has the capability of satisfying the immersion feeling. Therefore, light field three-dimensional (3-D) display technology has received considerable attention. In this paper, a novel pose estimation method in light field 3D display based on H∞ optimal control is proposed. Compared with the current pure hardware platform method, the proposed method releases hardware resources such as the graphic processing unit and avoids numerous parameter adjustments. The proposed method comprises three key parts. First, we calculate the normal of the rigid objects and apply it to obtain the normal estimation. Second, we calculate H∞ of the normal estimation result on the Hardy space, which is the maximum singular value of the rational function matrix parsed in the right half plane of the complex plane. Finally, we use the optimal result of H∞ for pose estimation and transformation. The experiments are carried out on the virtual and real classical datasets acquired by Kinect, Mian, and Clutter. The experiments show that the proposed method can be used to obtain high-quality display effects with low cost and high efficiency.

Tailings dams are constructed as storage dams for ore waste, serving as industrial waste piles and for drainage. The dam is negatively affected by rainfall, infiltration lines and its own gravity, which can cause its instability to gradually increase, leading to dam deformation. To predict the irregular changes of tailings dam deformation, empirical mode decomposition (EMD) is applied to the deformation data to obtain the trend and periodic components. The attention mechanism is used to assign different weights to the input variables to overcome the limitation that the long short-term memory (LSTM) model can only generate fixed-length vectors. The lagged autocorrelation coefficient is applied to each decomposed subregion to solve the lagging effect of external factors on dam deformation. Finally, the model is used to predict deformation in multiple directions to test the generalization ability. The proposed method can effectively mitigate the problems of gradient disappearance and gradient explosion. The applied results show that, compared with the control model EMD-LSTM, the evaluation indexes RMSE and MAE improve 23.66% and 27.90%, respectively. The method also has a high prediction accuracy in the remaining directions of the tailings dam, which has a wide practical application effect and provides a new idea for tailings dam deformation mechanism research.

In order to analyze the impact of large-scale photovoltaic system on the power system, a photovoltaic output prediction method considering the correlation is proposed and the optimal power flow is calculated. Firstly, establish a photovoltaic output model to obtain the attenuation coefficient and fluctuation amount, and analyze the correlation among the multiple photovoltaic power plants through the k-means method. Secondly, the long short-term memory (LSTM) neural network is used as the photovoltaic output prediction model, and the clustered photovoltaic output data is brought into the LSTM model to generate large-scale photovoltaic prediction results with the consideration of the spatial correlation. And an optimal power flow model that takes grid loss and voltage offset as targets is established. Finally, MATLAB is used to verify that the proposed large-scale photovoltaic forecasting method has higher accuracy. The multi-objective optimal power flow calculation is performed based on the NSGA-II algorithm and the modified IEEE systems, and the optimal power flow with photovoltaic output at different times is compared and analyzed.

To satisfy the grid-connected voltage level, both photovoltaic modules and energy storage modules are connected in series. However, the multiple photovoltaic modules often fall into local maximum power point under partial shading conditions during practical operation, and the multiple energy storage modules may suffer from a reduction in the effective capacity caused by characteristic differences among modules. To solve this problem, a novel cascaded modular photovoltaic-energy storage system is proposed in this paper. In the proposed topology, the energy storage modules achieve maximum power point tracking of the corresponding distributed photovoltaic module, and the proposed energy optimization strategy based on particle swarm optimization can ensure the efficient constant active power transmission from a photovoltaic energy storage (PV/ES) system to the grid in a certain time period under capacity constraints. Compared with conventional photovoltaic systems, the proposed scheme can avoid hot spots or the hot strings phenomena for PV modules and the large current and voltage stresses for DC/DC converters. Furthermore, the proposed energy optimization strategy for the coordination of all ES modules can realize the independent MPPT of each PV module and the constant active power between the PV/ES system and the grid under inconsistency of the light intensity under partial shading conditions. A hardware-in-loop photovoltaic-energy platform is established to verify the feasibility and effectiveness of the proposed topology and control strategy, and the proposed system achieves efficiency of about 97% under partial shading conditions, thus providing an effective and practical solution for power generation system.

In order to model fluctuation process characteristics of photovoltaic (PV) outputs, this paper proposes a novel mixed Gaussian model with the expectation maximization (EM) algorithm. Firstly, random components of PV outputs are obtained through computing the difference between the measured data of PV output and its theoretical outputs. Secondly, the EM algorithm is used to determine the weight of different Gaussian distribution functions. Finally, the mixed Gaussian model is obtained by linearly superimposing these Gaussian functions with the weight. Based on the simulation results on the measured data in Xichang City, China, the effectiveness of the proposed model is verified. Furthermore, this model has proven to be significantly better than other traditional models including t location-scale (TLS) distribution model.

Historically, the Yellow River in China discharges > 1 × 109 ton/yr sediment to the sea, and has formed a large delta in the western Bohai Sea. Its river mouth is characterized by an extremely high suspended sediment concentration (SSC), up to 50 g/L. However, the hydrodynamic factors controlling the high suspended sediments in the Yellow River estuary are not well understood. Here, we conducted two hydrodynamic observations and SSC measurements in the winter and spring low-flow seasons of 2014−2015 and 2016−2017 under five sea conditions, including calm-rippled, smooth-wavelet, slight, moderate, and rough, in the Yellow River Delta-front during the observation period. Under calm-rippled conditions, the contribution of currents to the total resuspended sediment concentration (RSC) was 77.7%−100.0%. During the smooth-wavelet and slight periods, the currents’ contribution decreased as low as 30% and 3.0% of the total RSC, respectively. Under moderate and rough-sea conditions, waves accounted for at least 70% and 85% of the total RSC, respectively. The results indicate that 20 cm-thick lutoclines were created after a significant increase in the wave height to a peak value followed by a decrease. When the SSC is over 3 g/L and hydrodynamic conditions could not break the lutoclines, the flocculent settling of suspended sediment changes to hindered settling in the Yellow River Delta. Under hindered settling, the settling velocity decreases, and the resuspended sediments remains in the lutoclines and their lower water layers. This study reveals different controlling factors for the high SSC near a river-influenced delta, and helps us get a better understanding of a delta’s resuspension and settling mechanisms.

Partial discharge (PD) detection can effectively achieve the status maintenance of XLPE (Cross Linked Polyethylene) cable, so it is the direction of the development of equipment maintenance in power systems. At present, a main method of PD detection is the broadband electromagnetic coupling with a high-frequency current transformer (HFCT). Due to the strong electromagnetic interference (EMI) generated among the mass amount of cables in a tunnel and the impedance mismatching of HFCT and the data acquisition equipment, the features of the pulse current generated by PD are often submerged in the background noise. The conventional method for the stationary signal analysis cannot analyze the PD signal, which is transient and non-stationary. Although the algorithm of Shannon wavelet singular entropy (SWSE) can be used to analyze the PD signal at some level, its precision and anti-interference capability of PD feature extraction are still insufficient. For the above problem, a novel method named Renyi wavelet packet singular entropy (RWPSE) is proposed and applied to the PD feature extraction on power cables. Taking a three-level system as an example, we analyze the statistical properties of Renyi entropy and the intrinsic correlation with Shannon entropy under different values of α . At the same time, discrete wavelet packet transform (DWPT) is taken instead of discrete wavelet transform (DWT), and Renyi entropy is combined to construct the RWPSE algorithm. Taking the grounding current signal from the shielding layer of XLPE cable as the research object, which includes the current pulse feature of PD, the effectiveness of the novel method is tested. The theoretical analysis and experimental results show that compared to SWSE, RWPSE can not only improve the feature extraction accuracy for PD, but also can suppress EMI effectively.

The ultrafast dynamic evolution of an atomic system under medium-strength laser fields is studied by performing transient absorption measurement. An analytical model developed from perturbation theory with a modified transition dipole moment is presented to explain the spectral features of the multilevel system. By fitting the measured absorption spectra to the model, the system’s dynamic evolution is quantified by different amplitude and phase modulation factors in the pump-probe and probe-pump scenarios. This study provides a way to understand laser-matter interaction in the transition area between the strong-field and weak-field regimes.

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.