Digital Evaluation of Sitting Posture Comfort in Human-vehicle System under Industry 4.0 Framework

Most of the previous studies on the vibration ride comfort of the human-vehicle system were focused only on one or two aspects of the investigation. A hybrid approach which integrates all kinds of investigation methods in real environment and virtual environment is described. The real experimental environment includes the WBV（whole body vibration） test, questionnaires for human subjective sensation and motion capture. The virtual experimental environment includes the theoretical calculation on simplified 5-DOF human body vibration model, the vibration simulation and analysis within ADAMS/VibrationTM module, and the digital human biomechanics and occupational health analysis in Jack software. While the real experimental environment provides realistic and accurate test results, it also serves as core and validation for the virtual experimental environment. The virtual experimental environment takes full advantages of current available vibration simulation and digital human modelling software, and makes it possible to evaluate the sitting posture comfort in a human-vehicle system with various human anthropometric parameters. How this digital evaluation system for car seat comfort design is fitted in the Industry 4.0 framework is also proposed.

Accurate prediction of oxygen vacancy concentration with disordered A-site cations in high-entropy perovskite oxides

Entropic stabilized ABO_(3) perovskite oxides promise many applications,including the two-step solar thermochemical hydrogen(STCH)production.Using binary and quaternary A-site mixed{A}FeO_(3) as a model system,we reveal that as more cation types,especially above four,are mixed on the A-site,the cell lattice becomes more cubic-like but the local Fe–O octahedrons are more distorted.By comparing four different Density Functional Theory-informed statistical models with experiments,we show that the oxygen vacancy formation energies(E^(f)_(V))distribution and the vacancy interactions must be considered to predict the oxygen non-stoichiometry(δ)accurately.For STCH applications,the E^(f)_(V) distribution,including both the average and the spread,can be optimized jointly to improveΔδ(difference ofδbetween the two-step conditions)in some hydrogen production levels.This model can be used to predict the range of water splitting that can be thermodynamically improved by mixing cations in{A}FeO_(3) perovskites.

Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation.Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET.However,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of misclassification.Therefore,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models.Here,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of classifier.Then,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier.Moreover,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations.During evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

A general bottom-up synthesis of CuO-based trimetallic oxide mesocrystal superstructures for efficient catalytic production of trichlorosilane

Mesocrystals, the non-classical crystals with highly ordered nanoparticle superstructures, have shown great potential in many applications because of their newly collective properties. However, there is still a lack of a facile and general synthesis strategy to organize and integrate distinct components into complex mesocrystals, and of reported application for them in industrial catalytic reactions. Herein we report a general bottom-up synthesis of CuO-based trimetallic oxide mesocrystals (denoted as CuO-M1Ox-M2Oy, where M1 and M2 = Zn, In, Fe, Ni, Mn, and Co) using a simple precipitation method followed by a hydrothermal treatment and a topotactic transformation via calcination. When these mesocrystals were used as the catalyst to produce trichlorosilane (TCS) via Si hydrochlorination reaction, they exhibited excellent catalytic performance with much increased Si conversion and TCS selectivity. In particular, the TCS yield was increased 19-fold than that of the catalyst-free process. The latter is the current industrial process. The efficiently catalytic property of these mesocrystals is attributed to the formation of well-defined nanoscale heterointerfaces that can effectively facilitate the charge transfer, and the generation of the compressive and tensile strain on CuO near the interfaces among different metal oxides. The synthetic approach developed here could be applicable to fabricate versatile complicated metal oxide mesocrystals as novel catalysts for various industrial chemical reactions.