External archive guided radial-grid multi objective differential evolution.

Differential evolution (DE) is a robust evolutionary algorithm for solving single-objective and multi-objective optimization problems (MOPs). While numerous multi-objective DE (MODE) variants exist, prior research has primarily focused on parameter control and mutation operators, often neglecting the issue of inadequate population distribution across the objective space. This paper proposes an external archive-guided radial-grid-driven differential evolution for multi-objective optimization (Ar-RGDEMO) to address these challenges.

Temporal-Quality Ensemble Technique for Handling Image Blur in Packaging Defect Inspection.

Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights.

Clean mucosal area detection of gastroenterologists versus artificial intelligence in small bowel capsule endoscopy.

Studies comparing the detection of clean mucosal areas in capsule endoscopy (CE) using human judgment versus artificial intelligence (AI) are rare. This study statistically analyzed gastroenterologist judgments and AI results. Three hundred CE video clips (100 patients) were prepared.

-Step Pre-Training and Décalcomanie Data Augmentation for Micro-Expression Recognition.

Facial expressions are divided into micro- and macro-expressions. Micro-expressions are low-intensity emotions presented for a short moment of about 0.25 s, whereas macro-expressions last up to 4 s.

Semantic Segmentation Dataset for AI-Based Quantification of Clean Mucosa in Capsule Endoscopy.

: Capsule endoscopy (CE) for bowel cleanliness evaluation primarily depends on subjective methods. To objectively evaluate bowel cleanliness, we focused on artificial intelligence (AI)-based assessments. We aimed to generate a large segmentation dataset from CE images and verify its quality using a convolutional neural network (CNN)-based algorithm.

Concentration Gradient Induced Delithiation Failure of MoO for Li-Ion Batteries.

Electric vehicle manufacturers worldwide are demanding superior lithium-ion batteries, with high energy and power densities, compared to gasoline engines. Although conversion-type metal oxides are promising candidates for high-capacity anodes, low initial Coulombic efficiency (ICE) and poor capacity retention have hindered research on their applications. In this study, the ICE of conversion-type MoO is investigated, with a particular focus on the delithiation failure.

SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map.

Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image.

Interplay between electrochemical reactions and mechanical responses in silicon-graphite anodes and its impact on degradation.

Durability of high-energy throughput batteries is a prerequisite for electric vehicles to penetrate the market. Despite remarkable progresses in silicon anodes with high energy densities, rapid capacity fading of full cells with silicon-graphite anodes limits their use. In this work, we unveil degradation mechanisms such as Li crosstalk between silicon and graphite, consequent Li accumulation in silicon, and capacity depression of graphite due to silicon expansion.

Metabolic and Lipidomic Profiling of Vegetable Juices Fermented with Various Probiotics.

Fermented vegetable juices have gained attention due to their various beneficial effects on human health. In this study, we employed gas chromatography-mass spectrometry, direct infusion-mass spectrometry, and liquid chromatography-mass spectrometry to identify useful metabolites, lipids, and carotenoids in vegetable juice (VJ) fermented with HY7712, HY7715, HY7801, and ssp. HY8002.

Nanoscale Electrical Degradation of Silicon-Carbon Composite Anode Materials for Lithium-Ion Batteries.

High-performance lithium-ion batteries (LIBs) are in increasing demand for a variety of applications in rapidly growing energy-related fields including electric vehicles. To develop high-performance LIBs, it is necessary to comprehensively understand the degradation mechanism of the LIB electrodes. From this viewpoint, it is crucial to investigate how the electrical properties of LIB electrodes change under charging and discharging.

Sn-Based Nanocomposite for Li-Ion Battery Anode with High Energy Density, Rate Capability, and Reversibility.

To design an easily manufactured, large energy density, highly reversible, and fast rate-capable Li-ion battery (LIB) anode, Co-Sn intermetallics (CoSn, CoSn, and CoSn) were synthesized, and their potential as anode materials for LIBs was investigated. Based on their electrochemical performances, CoSn was selected, and its C-modified nanocomposite (CoSn/C) as well as Ti- and C-modified nanocomposite (CoSn/ a-TiC/C) was straightforwardly prepared. Interestingly, the CoSn, CoSn/C, and CoSn/ a-TiC/C showed conversion/nonrecombination, conversion/partial recombination, and conversion/full recombination during Li insertion/extraction, respectively, which were thoroughly investigated using ex situ X-ray diffraction and extended X-ray absorption fine structure analyses.

Protective Oxide Coating for Ionic Conductive Solid Electrolyte Interphase.

To employ Li-based batteries to their full potential in a wide range of energy-storage applications, their capacity and performance stability must be improved. Si is a viable anode material for Li-based batteries in electric vehicles due to its high theoretical capacity and good economic feasibility. However, it suffers from physical and chemical degradation, leading to unstable electrochemical performance and preventing its incorporation in new Li-based battery systems.

Silicon/Carbon Nanotube/BaTiO₃ Nanocomposite Anode: Evidence for Enhanced Lithium-Ion Mobility Induced by the Local Piezoelectric Potential.

We report on the synergetic effects of silicon (Si) and BaTiO3 (BTO) for applications as the anode of Li-ion batteries. The large expansion of Si during lithiation was exploited as an energy source via piezoelectric BTO nanoparticles. Si and BTO nanoparticles were dispersed in a matrix consisting of multiwalled carbon nanotubes (CNTs) using a high-energy ball-milling process.

Novel multi-layered 1-D nanostructure exhibiting the theoretical capacity of silicon for a super-enhanced lithium-ion battery.

Silicon/carbon (Si/C) nanocomposites have recently received much attention as Li-ion battery negative electrodes due to their mutual synergetic effects in capacity and mechanical integrity. The contribution of Si to the total capacity of the Si/C nanocomposites determines their structural efficiency. Herein, we report on a multi-layered, one-dimensional nanostructure that exhibits the theoretical specific capacity of Si in the nanocomposite.