High-Energy-Density Waterborne Dielectrics from Polyelectrolyte-Colloid Complexes

To meet the demands of miniaturization and integration of next-generation power systems, a major challenge is to improve the energy density of used dielectric capacitors. Polymer nanocomposites are of great potential for high-energy-density capacitors. However, most of them are prepared via melt blending at high temperatures or solution processing in hazardous organic solvents, which are energy consuming and environmentally problematic. It has long remained economically and ecologically challenging to develop new dielectric materials. Here, a class of high-energy-density dielectrics made by electrostatically complexing polyvinylidene fluoride (PVDF) latex with oppositely charged chitosan in an aqueous phase is reported. At the charge neutralization point, the film of PVDF@Chitosan complexes demonstrates the highest breakdown strength (630 MV m⁻¹) and recoverable energy density (10.1 J cm⁻³), which are respectively 279% and 421% higher than the bare PVDF latex film, and far beyond most of the conventional solvent- or melt-processed polymer films. The largely improved capacitive performances are ascribed to the significant minimization of losses at the critical charge neutralization point. The concept can be extended to a wide range of colloids, including polystyrene latex and aqueous bentonite suspension, highlighting the versatility of the proposed approach to develop environmentally friendly high-performance capacitors.     

Factorial design–machine learning approach for predicting incident durations

This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed, including linear regression, decision trees, support vector machines, ensemble trees, Gaussian process regression, and artificial neural networks. The study results are validated using data from the Houston TranStar incidents archive with over 90,000 records. The accuracy of the developed predictive models is compared based on multiple techniques (i.e., no feature selection–ML, FSFS–ML, and DOE–ML). The results revealed that the significant factors affecting incident duration identified by both DOE and FSFS include the type of vehicles involved, type of lanes affected, number of vehicles involved, number of emergency responses dispatched, incident severity level, and day of the week. The comparative results of the different feature selection and modeling approaches revealed that the hybrid DOE–ML approach outperformed the other tested analysis approaches. The best-performing model under the DOE–ML approach was the SVM with cubic kernel model. It reduced the modeling time by 83.8% while increasing the prediction error by merely 0.02%, which is not significant. Therefore, the prediction accuracy could be slightly downgraded in return for a substantial reduction in the number of variables utilized, resulting in substantial savings in the modeling time and required dataset.     

Gamma-Rays Induced Synthesis of Ag-Decorated ZnCo2O4–MoS2 Heterostructure as Novel Photocatalyst and Effective Antimicrobial Agent for Wastewater Treatment Application

Journal of Inorganic and Organometallic Polymers and Materials - The development of novel semiconductors-based-photocatalysts is a promising strategy for addressing environmental pollution. In the...     

Age and Gender Classification Using Backpropagation andBaggingAlgorithms

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data. Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.     

An approach for performance requirements verification and test environments generation

Requirements Engineering - Model-based testing (MBT) is a method that supports the design and execution of test cases by models that specify the intended behaviors of a system under test. While...