High energy density of BaTiO3@TiO2 nanosheet/polymer composites via ping-pong-like electron area scattering and interface engineering

Dielectric substances exhibit great potential for high-power capacitors due to their high stability and fast charge–discharge; however, a long-term challenge is to enhance energy density. Here, we propose a poly(vinylidene fluoride) (PVDF) composite utilizing BaTiO3 nanoparticle@TiO2 nanosheet (BT@TO ns) 2D nanohybrids as fillers, aiming at combining the interfacial strategy of using a core–shell filler and the electron scattering of a 2D filler to improve the energy density. With 4 wt% filler, the composite possesses the largest breakdown strength (Eb) of 561.2 MV m−1, which is significantly enhanced from the 407.6 MV m−1 of PVDF, and permittivity of 12.6 at 1 kHz, which is a 23% increase from that of PVDF. A superhigh energy density of 21.3 J cm−3 with an efficiency of 61% is obtained at 550 MV m−1. The 2D BT@TO ns-filled composite exhibits a higher energy density than composites filled with core–shell 1D BT@TO nws or non-core–shell 0D BT, 1D TO, or 2D TO particles. The Eb and energy density improvements are attributed to the buffer layer-based interface engineering and enhanced area scattering of electrons caused by the 2D hybrids, an effect similar to that of a ping-pong paddle to scatter electric field-induced charge migrations in composites. Thus, an effective hybrid strategy is presented for achieving high-performance polymer composites that can be used in energy storage devices.

From Contact to Enact: Reducing Prejudice Toward Physical Disability Using Engagement Strategies

The contact hypothesis has dominated work on prejudice reduction and is often described as one of the most successful theories within social psychology. The hypothesis has nevertheless been criticized for not being applicable in real life situations due to unobtainable conditions for direct contact. Several indirect contact suggestions have been developed to solve this “application challenge.” Here, we suggest a hybrid strategy of both direct and indirect contact. Based on the second-person method developed in social psychology and cognition, we suggest working with an engagement strategy as a hybrid hypothesis. We expand on this suggestion through an engagement-based intervention, where we implement the strategy in a theater performance and investigate the effects on prejudicial attitudes toward people with physical disabilities. Based on the results we reformulate our initial engagement strategy into the Enact (Engagement, Nuancing, and Attitude formation) hypothesis. To deal with the application challenge, this hybrid hypothesis posits two necessary conditions for prejudice reduction. Interventions should: (1) work with engagement to reduce prejudice, and (2) focus on the second-order level of attitudes formation. Here the aim of the prejudice reduction is not attitude correction, but instead the nuancing of attitudes.

PCR-free, label-free detection of sequence-specific DNA with single-molecule sensitivity using in vitro N-hybrid system in microfluidic drops

We propose a novel method that can detect DNA with high specificity at the single-molecule level by employing the in vitro N-hybrid strategy realized in sub-picoliter microfluidic drops. It detects target DNA based on the specific interactions of the target-encoded proteins with their partner molecules, and achieves single-molecule sensitivity via signal-transduction and signal-amplification during gene-expression processes in a sub-picoliter droplet, therefore effectively avoiding complicated procedures in labeling-based methods or biases and artifacts in PCR-based methods.

A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

Monitoring and assessment of forest cover disturbance in the Middle Volga region of Russia using Landsat images

The knowledge of the disturbance effect on the forest ecosystems is crucial for sustainable development on the global level. It is important to quantify, map and monitor forest cover resulting from natural and anthropogenic disturbances. This research presents spatio-temporal trend analyses of forest cover disturbance in the Middle Volga region of Russia, using a time series of Landsat images. We generated a series of image composites at different year intervals between 1985 and 2018 and utilized a hybrid strategy consisting of Tasseled Cap transformation, sampling ground truth data and post-classification analyses. For validation of the disturbance maps, we used a point-based accuracy assessment, using local forest inventory reports and ground truth sample plots data for 2016-2018. The produced Landsat 1985, 2001 и 2018 thematic maps for 7 classes of forest cover show that coniferous area decreased by 4%. At the same time, there is a decrease in small-leaved (19%), mixed (8%) and an increase in young stands (23%). A significant disturbed forest area 85,120 ha was observed between 2014-2018, where much of the loss occurs due to severe wildfires. More research is needed with the inclusion of the additional number of anthropogenic and natural factors to increase the accuracy of monitoring and detection of forest disturbance of the region.

An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

Accurate prediction of floods is the first step in formulating flood control strategies and reducing flood disasters. This research proposes a deep learning model based on Gate Recurrent Unit (GRU), Random Forest Algorithm (RF), Whale Optimization Algorithm (WOA) and Optimal Variational Mode Decomposition (OVMD) for flood prediction. First, the random historical time series is decomposed using OVMD. Secondly, combined with the RF feature importance measurement, select features with high importance to obtain the optimal input set. Third, use the GRU model to predict all sub-models, and use the WOA algorithm to optimize the hyperparameters in the GRU model. This study also proposes a hybrid strategy to improve the traditional WOA algorithm and enhance the optimization ability of the WOA algorithm. Finally, the prediction results of all sub-modes were aggregated to generate the final prediction result. The model was validated using data from three hydrological stations in the upper, middle and lower reaches of the Minjiang river basin in China. Through the results of the experiment, it can be seen that the proposed prediction model can effectively predict the flood time series, and has better accuracy than other models.