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With the rapid development of portable and wearable electronic devices, research on flexible energy storage devices has gradually shifted to the directions of miniaturization, softness and intelligence. At the same time, people have higher requirements for the energy density, power density and mechanical properties of the device. As the core part of flexible energy storage devices, electrode material is the key to determining device performance. With the development of flexible energy storage electronic devices, there is an urgent need for new battery technology and fast, low cost and precise control of their microstructure preparation methods. Therefore, the research and development of new energy storage devices such as flexible lithium/sodium-ion batteries, flexible lithium-sulfur batteries, and flexible zinc-air batteries have become the current research hotspots in academia. The current research status of flexible energy storage battery electrodes in recent years was discussed in this paper, the design of flexible electrode materials (independent flexible electrodes and flexible substrate electrodes), and the preparation process of flexible electrode materials of different dimensions (one-dimensional materials, two-dimensional materials and three-dimensional materials) and applications of flexible energy storage electrodes (flexible lithium/sodium ion batteries, flexible lithium-sulfur batteries, flexible zinc-air batteries) were compared and analyzed, and the structural characteristics and electrochemical properties of electrode materials were discussed. Finally, the current problems faced by flexible energy storage devices were pointed out, and the future focus of flexible energy storage devices was the research and development of new solid electrolytes, the rational design of device structures and the continuous optimization of packaging technology.

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others.

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.

Vibration damping fastener is an important means for vibration and noise reduction in urban rail transit. At present, a common type of double-layer nonlinear vibration damping fastener has such problems as large consumables and high installation height. In this paper, a design scheme for a double-layer nonlinear vibration damping fastener is provided by means of structural design, simulation analysis and indoor test, which can effectively overcome the defects existing in a certain type of fastener. After indoor drop hammer test, the vibration reduction effect reaches 8. 9 dB. The Fatigue test indicates that the vertical stiffness of the system is 17 kN/mm, which meets the requirements of medium vibration damping stiffness. The traffic safety analysis shows that the derailment coefficient is low and meets the safety requirements. It is thus ensured that the fastener satisfies the requirements of medium vibration reduction fasteners and can be used for medium vibration reduction tracks of urban rail transit. [ABSTRACT FROM AUTHOR]

Epidemiological data on cerebral venous thrombosis in China are still lacking at present on the aspects of incidence, recurrence, risk factors, and so on. Herein, we aimed to fill the gap, based on the result of this multicenter prospective cohort study. A total of 26 top tertiary hospitals distributed in China Mainland will take part in this study. For the first time, a dataset of cerebral venous thrombosis cohort (including multiethnic patients of all ages in almost all regions of Mainland China, regardless of gender) will be built. Inclusion criteria were as follows: (1) aged ≥14 years, (2) neuroimaging-confirmed cerebral venous thrombosis, (3) symptom onset was within 30 days prior to enrollment, (4) signed the informed consent form. Demographic data, risk factors, clinical and neuroimaging features, ophthalmologic and aural results, blood tests, cerebrospinal fluid examination, therapeutic strategies, and adverse events were analyzed. Two milliliters of fasting venous blood and 2 mL of cerebrospinal fluid will be collected and stored. Furthermore, patients will be followed up at months 1, 3, 6, and 12 after baseline assessment. Primary outcome will be all-cause mortality. Secondary outcomes: (1) cerebrospinal fluid pressure and Frisen grade; (2) recanalization rate on imaging; (3) rating scales such as GCS, NIHSS, mRS, Mini-Mental State Examination, Montreal Cognitive Assessment, Patient Health Questionnaire 9-item, HIT-6, and Tinnitus Handicap Index. This study will for the first time provide strong evidence on the incidence rate, recurrence rate, and demographic data, as well as special risk factors, clinical outcomes, symptomatic and imaging features of cerebral venous thrombosis in Chinese population. The results of this study will also provide an important reference on prevention, early diagnosis, and customized treatment of cerebral venous thrombosis in Chinese patients. [ABSTRACT FROM AUTHOR]

In order to study the stiffness characteristics of Vossloh W300 fastener system, which is widely used in high-speed railway in China, the MTS-25 servo hydraulic fatigue testing machine is used to study the dynamic and static stiffness characteristics of the fastener system under four working conditions of different preload, material under plate, clip bolt torque and frequency. The results show that: (1) With the increase of preload, the static stiffness of fastener system will also increase. The static stiffness under 30 kN preload is 44. 3% higher than that without preload; (2) The static stiffness is also affected by the material under the plate, the static stiffness of nylon fastener system is 1. 61 and 1. 78 times higher than that of thermoplastic and polyurethane respectively; (3) Compared with the standard state, the dynamic stiffness of the clip bolt in the torque relaxation state is reduced by 27. 3%, while the dynamic stiffness of the clip bolt in the torque in the over-tightening state is increased by 5. 0%; (4) In the range of 5 ~ 100 Hz, the dynamic stiffness of the fastener system at 80 Hz is 38% higher than that at 5 Hz, which exceeds the reasonable design limit of the dynamic stiffness of the fastener system of high-speed railway and directly impacts the elastic vibration isolation of the fastener system and the strength of the railseat. [ABSTRACT FROM AUTHOR]