Investigation on Aluminum Alloy Reflector Mirror Processing Technology Combining Ultrasonic Rolling and Single-Point Diamond Turning.

In the process of aluminum alloy reflector mirror processing, the structural defects of aluminum alloys present bottlenecks restricting the development of aluminum alloy reflector mirror processing technologies. Therefore, this study proposes an aluminum alloy reflector mirror processing method involving ultrasonic rolling and single-point diamond turning. The core idea of this method is to use ultrasonic rolling to pretreat the surface of the workpiece to refine the grains and increase the hardness, then perform single-point diamond turning to improve the optical reflection performance.

Study on High-Temperature Constitutive Model and Plasticity of the Novel Cr-Mo-V Hot-Work Die Steel Forging.

In response to the increasingly strict performance requirements of large molds, a novel Cr-Mo-V hot-work die steel has been developed. In order to study the high-temperature hot deformation behavior and plasticity of the novel steel, hot compression tests were conducted on the Gleeble-1500D thermal simulation testing machine at a deformation temperature of 950~1200 °C and a strain rate of 0.001~5 s.

Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model.

Acupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling generalized linear model is adopted to encode the neuronal spiking activity evoked by different acupuncture manipulations. Then, maximum likelihood estimation is used to fit the model parameters and estimate the coupling parameters of stimulus, the self-coupling parameters of the neuron's own spike history and the cross-coupling parameters of other neurons' spike history.

Optimized preparation of alginate/nanocellulose/polypyrrole composite hydrogel via in-situ polymerization for high-efficiency solar desalination and wastewater purification.

In the field of solar steam generation, hydrogels with interfacial evaporation configurations stand as a promising candidate for solar evaporators. Hydrogel-based photothermal materials provide excellent hydration channels for supplying water to an evaporative layer due to their porous structure and hydrophilic nature. This work proposed a facile and in-situ fabrication of sodium alginate hydrogel incorporated with cellulose nanocrystals and polypyrrole as an effective photothermal material.

Fabricating ZnO@C composites based on shell-derived cellulose for high performance lithium-ion battery anodes.

In this study, shell-derived cellulose was successfully produced, and the hydrothermal method was employed to generate ZnO@C (ZOC) composites, which were then subjected to calcination in N gas at a temperature of 600 °C for varying durations. X-ray diffraction and thermogravimetric analyses demonstrated that the annealing duration had a substantial impact on the quantities of C and ZnO in the ZOC composites. The scanning electron microscope images indicated the presence of ZnO nanoparticles on the surface of the C phase and revealed a similar morphology among the ZOC composites.

Performance analysis of dual-hop mixed RF-FSO systems combined with NOMA.

This paper investigates the performance of hybrid radio frequency/free space optical (RF/FSO) systems combined with non-orthogonal multiple access communications technology. We examine a scenario where the source and destination are separated by a large distance, with no direct link between them. The relay, denoted R, operates using the decode-and-forward (DF) protocol.

Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals.

Objective: Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.

Approach: We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain.

Enhancing the Ground Truth Disparity by MAP Estimation for Developing a Neural-Net Based Stereoscopic Camera.

This paper presents a novel method to enhance ground truth disparity maps generated by Semi-Global Matching (SGM) using Maximum a Posteriori (MAP) estimation. SGM, while not producing visually appealing outputs like neural networks, offers high disparity accuracy in valid regions and avoids the generalization issues often encountered with neural network-based disparity estimation. However, SGM struggles with occlusions and textureless areas, leading to invalid disparity values.

Strong exciton-photon coupling in self-hybridized organic-inorganic lead halide perovskite microcavities.

Controlling coherent light-matter interactions in semiconductor microcavities is at the heart of the next-generation solid-state polaritonic devices. Organic-inorganic hybrid perovskites are potential materials for room-temperature polaritonics owing to their high exciton oscillator strengths and large exciton binding energies. Herein, we report on strong exciton-photon coupling in the micro-platelet and micro-ribbon shaped methylammonium lead bromide single crystals.

Melting-free integrated photonic memory with layered polymorphs.

Chalcogenide-based nonvolatile phase change materials (PCMs) have a long history of usage, from bulk disk memory to all-optic neuromorphic computing circuits. Being able to perform uniform phase transitions over a subwavelength scale makes PCMs particularly suitable for photonic applications. For switching between nonvolatile states, the conventional chalcogenide phase change materials are brought to a melting temperature to break the covalent bonds.

Optimization of arc quenching parameters for enhancing surface hardness and line width in S45C steel using Taguchi method.

This study investigates the impact of arc length, current intensity, travel speed, and gas flow rate on surface hardness and line width during arc quenching process of S45C steel. The current intensity has the greatest influence on the surface hardness of S45C steel, followed by the travel speed, gas flow rate, and arc length. Using the Taguchi method, the optimal values of the parameters such as the arc length of 1.

An Indicator Based on Spatial Coordinate Information for Assessing the Capability for Dynamic Machining Performance of Five-Axis Flank Milling.

As a spatial coordinate sensor, the touch-trigger on-machine probe is a key equipment in manufacturing that ensures machining quality, and it has played an important role in five-axis flank milling. However, in flank milling, the utilization of the deviation as a conventional indicator for quality assessment of the machining performance is incomprehensive without considering the characteristics of the machining method. In this paper, the error mutual moment is introduced as an indicator to assess the capability for dynamic machining performance of the machine tool in flank milling based on the spatial coordinate information of the touch-trigger on-machine probe considering the characteristic of the error distribution of the flank milling.

Effect of task interruption on the situation awareness of air traffic controllers.

Multiple intersecting situational tasks in the field of aviation often cause air traffic controllers to face issues pertaining to interruption and task switching. To investigate the impact of task interruption on the situation awareness of air traffic controllers, two experiments were conducted. Experiment 1, which focused on 44 new graduates preparing to work in the field of air traffic control, revealed that task interruption reduced the participants' levels of situation awareness.

The radical scavenging activity of 1-methyl-1,4-dihydronicotinamide: theoretical insights into the mechanism, kinetics and solvent effects.

1,4-Dihydronicotinamide derivatives, including 1-methyl-1,4-dihydronicotinamide (MNAH), are derivatives of the active center of nicotinamide coenzyme (NADH) and are therefore potent radical scavengers. MNAH serves as a useful model of NADH that allows for modeling studies to address the activity of this important biomolecule. In this work, MNAH activity was evaluated against typical free radicals using quantum chemical calculations in physiological environments, with a secondary aim of comparing activity against two physiologically relevant radicals of markedly different stability, HO˙, and HOO˙, to establish which of these is a better model for assessing antioxidant capacity in physiological environments.

Optimizing flexural strength of RC beams with recycled aggregates and CFRP using machine learning models.

This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions of construction materials, particularly the inclusion of recycled aggregates and carbon fiber-reinforced polymer (CFRP), affect the structural performance of concrete beams. Eight beams, including those with natural aggregates, recycled aggregates, fly ash, and CFRP, were tested.

Generalized observer for road profile estimation in the automotive suspension system.

This study develops a generalized observer to estimate the road profile in the semi-active automotive suspension system. The dynamics of the quarter-car model are represented in the descriptor nonlinear parameter-varying system framework, with the road profile as a system's state that is of arbitrary dynamics. Then, a road profile estimation method is designed by using a generalized observer, which utilizes the onboard accelerometers as the observer's input.

Automatic detection of Alzheimer's disease from EEG signals using an improved AFS-GA hybrid algorithm.

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by energy diffusion and partial disconnection in the brain, with its main feature being an insidious onset and subtle clinical symptoms. Electroencephalogram (EEG) as a primary tool for assessing and aiding in the diagnosis of brain diseases has been widely used in AD detection. Accurate diagnosis is crucial for preventing the transition from early cognitive impairment to AD and providing early treatment for AD patients.

Research on recognition of slippery road surface and collision warning system based on deep learning.

Aiming at the problems of slow detection speed, large prediction error and weak environmental adaptability of current vehicle collision warning system, this paper proposes a recognition method of slippery road surface and collision warning system based on deep learning. Firstly, this paper uses the on-board camera to monitor the environment and road conditions in front of the vehicle in real time, and a residual network model FS-ResNet50 is proposed, which integrated SE attention mechanism and multi-level feature information based on the traditional ResNet50 model. The FS-ResNet50 model is used to identify the slippery states of the current road, such as wet and snowy.

A Novel-Potential Wave-Bump Yarn of Plain Weave Fabric for Fog Harvesting.

With the variety of fibers and fabrics, the studies of the surface structure of the textile yarns, the weave fabric, and their surface wettability are still potential factors to improve and optimize the fog harvesting efficiency. In this work, inspired by the fog harvesting behavior of the desert beetle dorsal surface, a wavy-bumpy structure of post-weave yarn (obtained from woven fabric) was reported to improve large droplet growth (converge) efficiency. In which, this study used tetrabutyl titanate (Ti(OCH)) to waterproof, increase hydrophobicity, and stabilize the surface of yarns and fabric (inspired by the feather structure and lotus leaf surface).

AIRHF-Net: an adaptive interaction representation hierarchical fusion network for occluded person re-identification.

To tackle the high resource consumption in occluded person re-identification, sparse attention mechanisms based on Vision Transformers (ViTs) have become popular. However, they often suffer from performance degradation with long sequences, omission of crucial information, and token representation convergence. To address these issues, we introduce AIRHF-Net: an Adaptive Interaction Representation Hierarchical Fusion Network, named AIRHF-Net, designed to enhance pedestrian identity recognition in occluded scenarios.