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Abstract This study introduces a constrained many‐objective optimization approach for the optimal design of 20 MW direct drive (DD) permanent magnet synchronous generators (PMSGs). Designing a high‐performance, competitive DD‐PMSG requires considering the generator's performance as well as its weight and material cost. Therefore, we focus on four main characteristics as our design objectives: (1) specific power (power per weight), (2) power‐per‐cost, (3) efficiency, and (4) power factor. To achieve this, we apply an advanced constrained nondominated sorting genetic algorithm III (NSGA‐III), a many‐objective optimization method utilizing evolutionary computation, capable of optimizing four or more objectives with constraints. Additionally, the electromagnetic finite element method is employed to evaluate the generator's characteristics. Through our proposed design process, we optimize three distinct 20 MW DD‐PMSG configurations: a 320‐pole/300‐slot, a 350‐pole/300‐slot, and a 350‐pole/336‐slot topology. Following this optimization, we perform additional multiphysics simulations (covering electromagnetic, structural, overload, and thermal aspects) and control response simulations on four selected models from the Pareto‐optimal solutions to validate their effectiveness as preliminary DD‐PMSG designs. Finally, we conduct a comprehensive analysis of all simulation results.

Abstract Two-dimensional electrides can acquire topologically non-trivial phases due to intriguing interplay between the cationic atomic layers and anionic electron layers. However, experimental evidence of topological surface states has yet to be verified. Here, via angle-resolved photoemission spectroscopy (ARPES) and scanning tunnelling microscopy (STM), we probe the magnetic Weyl states of the ferromagnetic electride [Gd2C]2+·2e−. In particular, the presence of Weyl cones and Fermi-arc states is demonstrated through photon energy-dependent ARPES measurements, agreeing with theoretical band structure calculations. Notably, the STM measurements reveal that the Fermi-arc states exist underneath a floating quantum electron liquid on the top Gd layer, forming double-stacked surface states in a heterostructure. Our work thus not only unveils the non-trivial topology of the [Gd2C]2+·2e− electride but also realizes a surface heterostructure that can host phenomena distinct from the bulk.

Abstract The pursuit of advanced brain‐inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin‐film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3‐based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

Abstract Background Tumor cells of diffuse-type gastric cancer (DGC) are discohesive and infiltrate into the stroma as single cells or small subgroups, so the stroma significantly impacts DGC progression. Cancer-associated fibroblasts (CAFs) are major components of the tumor stroma. Here, we identified CAF-specific secreted molecules and investigated the mechanism underlying CAF-induced DGC progression. Methods We conducted transcriptome analysis for paired normal fibroblast (NF)-CAF isolated from DGC patient tissues and proteomics for conditioned media (CM) of fibroblasts. The effects of fibroblasts on cancer cells were examined by transwell migration and soft agar assays, western blotting, and in vivo. We confirmed the effect of blocking tubulointerstitial nephritis antigen-like 1 (TINAGL1) in CAFs using siRNA or shRNA. We evaluated the expression of TINAGL1 protein in frozen tissues of DGC and paired normal stomach and mRNA in formalin-fixed, paraffin-embedded (FFPE) tissue using RNA in-situ hybridization (RNA-ISH). Results CAFs more highly expressed TINAGL1 than NFs. The co-culture of CAFs increased migration and tumorigenesis of DGC. Moreover, CAFs enhanced the phosphorylation of focal adhesion kinase (FAK) and mesenchymal marker expression in DGC cells. In an animal study, DGC tumors co-injected with CAFs showed aggressive phenotypes, including lymph node metastasis. However, increased phosphorylation of FAK and migration were reduced by blocking TINAGL1 in CAFs. In the tissues of DGC patients, TINAGL1 was higher in cancer than paired normal tissues and detected with collagen type I alpha 1 chain (COL1A1) in the same spot. Furthermore, high TINAGL1 expression was significantly correlated with poor prognosis in several public databases and our patient cohort diagnosed with DGC. Conclusions These results indicate that TINAGL1 secreted by CAFs induces phosphorylation of FAK in DGC cells and promotes tumor progression. Thus, targeting TINAGL1 in CAFs can be a novel therapeutic strategy for DGC.

AbstractPhilodendron hederaceum (Jacq.) Schott 1829, a species of the Araceae family, is a foliage plant of ornamental value. The complete chloroplast genome sequence of Philodendron hederaceum was obtained by the de novo assembly of NovaSeq 6000 (Illumina Co., San Diego, CA) paired-end short reads and Oxford Nanopore long reads. The complete chloroplast genome of P. hederaceum was 168,609 bp in length, with a large single-copy (LSC) region of 94,393 bp, a small single-copy (SSC) region of 25,774 bp, and a pair of identical inverted repeat regions (IRs) of 24,221 bp. The genome contained a total of 129 genes, including 85 protein-coding genes, 36 transfer RNA (tRNA) genes, and eight ribosomal RNA (rRNA) genes. The phylogenetic analysis of P. hederaceum with 19 related species and two outgroup species revealed the closest taxonomical relationship with Philodendron lanceolatum in the Araceae family.

Introduction: Magnolia kobus DC has been used as herbal medicine to treat coughs and is known to exert biological effects such as anti-inflammatory, antioxidant, and antibacterial properties. We aimed to define the pharmacological effects of M. kobus leaf ethanol extract (MLEE) on acute lung inflammation and explore the underlying mechanisms of action. Methods: For in vitro investigations, RAW 264.7 cells were pretreated with MLEE (1, 10, and 100 μg/mL) and stimulated with lipopolysaccharide (LPS). For in vivo investigations, BALB/c mice were intratracheally administered with LPS for 24 hours after injection of MLEE (0.3, 3, and 30 mg/kg). Hematoxylin and eosin staining was used for histopathology analysis of lung tissue. The phytochemical constituents of MLEE were analyzed using high-performance liquid chromatography. Results: In RAW 264.7 cells, MLEE reduced the activation of the inflammatory mediators (inducible nitric oxide synthase and cyclooxygenase-2) and the nuclear translocation of nuclear factor (NF)-κB and nuclear factor erythroid-2-related factor 2 (Nrf2). The intraperitoneal injection of MLEE (30 mg/kg) attenuated interstitial edema and immune cell infiltration in LPS-induced acute lung inflammation. MLEE also inhibited the activation of cyclooxygenase-2, NF-κB, and Nrf2 in the lung tissue. Conclusion: Taken together, MLEE exerted an anti-inflammatory effect by inhibiting inflammatory and oxidative mediators on acute lung inflammation suggesting that it might be used as a natural drug for treating acute lung inflammatory diseases.

Sorting single‐walled carbon nanotubes (SWNTs) that are heterogeneous into homogeneous groups based on their electronic type, chirality, and handedness is crucial for optoelectronic and biological applications. To achieve this, researchers have utilized dispersants with different binding affinities to sort SWNTs according to their chiralities. This review article provides an overview of the methods developed for sorting SWNTs using dispersants, with a particular focus on the role played by dispersant binding affinities. The article is organized into six sections, including an introduction, a background on SWNTs and dispersant‐based individualization of SWNTs, information on dispersants and their binding affinities, separation methods for SWNTs based on controlling the dispersant binding affinity, parameters used to control dispersant binding affinity, and a conclusion. It is hoped that this review will enhance understanding of the intricate interactions between dispersants and SWNTs, leading to the development of new SWNT sorting methods for future applications.

Abstract Innate immune training involves myelopoiesis, dynamic gene modulation, and functional reprogramming of myeloid cells in response to secondary heterologous challenges. The present study evaluates whether systemic innate immune training can protect tissues from local injury. Systemic pretreatment of mice with β‐glucan, a trained immunity agonist, reduces the mortality rate of mice with bleomycin‐induced lung injury and fibrosis, as well as decreasing collagen deposition in the lungs. β‐Glucan pretreatment induces neutrophil accumulation in the lungs and enhances efferocytosis. Training of mice with β‐glucan results in histone modification in both alveolar macrophages (AMs) and neighboring lung epithelial cells. Training also increases the production of RvD1 and soluble mediators by AMs and efferocytes. Efferocytosis increases trained immunity in AMs by stimulating RvD1 release, thus inducing SIRT1 expression in neighboring lung epithelial cells. Elevated epithelial SIRT1 expression is associated with decreased epithelial cell apoptosis after lung injury, attenuating tissue damage. Further, neutrophil depletion dampens the effects of β‐glucan on macrophage accumulation, epigenetic modification in lung macrophages, epithelial SIRT1 expression, and injury‐mediated fibrosis in the lung. These findings provide mechanistic insights into innate immune training and clues to the potential ability of centrally trained immunity to protect peripheral organs against injury‐mediated disorders.

This cross-sectional study aimed to assess the interrelationships between PM2.5 exposure, heavy metal concentrations, and oxidative stress indicators, while evaluating the impact of antioxidant intake, such as vitamins, on these associations. PM2.5 exposure assessments were conducted using portable sensor-based monitors; biomarker analyses for heavy metals and oxidative stress were performed in 114 non-smoking adults. We observed that personal or ambient PM2.5 exposure levels were not associated with increased levels of heavy metals in blood and urine, nor with oxidative stress levels in urine. However, the concentrations of cadmium and lead in blood, and those of chromium and nickel in urine, were significantly associated with the urinary malondialdehyde (MDA) concentration. Additionally, increases in blood cadmium, urinary chromium, and nickel levels were significantly associated with higher urinary MDA concentrations in the non-vitamin-supplement group, but this trend was not observed in the regular vitamin supplement group. Our findings suggest that a regular intake of vitamin supplements might modulate the relationship between heavy metal exposure and oxidative stress, indicating potential protective effects against oxidative damage induced by PM2.5 and heavy metals. This study highlights the complexity of environmental pollutant exposure and its impact on human health, emphasizing the need for further research to elucidate the underlying mechanisms and explore potential protective strategies.

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection.

In dye-sensitized solar cells (DSSCs), the counter electrode (CE) plays a crucial role as an electron transfer agent and regenerator of the redox couple. Unlike conventional CEs that are generally made of glass-based substrates (e.g., FTO/glass), polymer substrates appear to be emerging candidates, owing to their intrinsic properties of lightweight, high durability, and low cost. Despite great promise, current manufacturing methods of CEs on polymeric substrates suffer from serious limitations, including low conductivity, scalability, process complexity, and the need for dedicated vacuum equipment. In the present study, we employ and evaluate a fully additive manufacturing route that can enable the fabrication of CEs for DSSCs in a high-throughput and eco-friendly manner with improved performance. The proposed approach sequentially comprises: (1) material extrusion 3-D printing of polymer substrate; (2) conductive surface metallization through cold spray particle deposition; and (3) over-coating of a thin-layer catalyzer with a graphite pencil. The fabricated electrodes are characterized in terms of microstructure, electrical conductivity, and photo-conversion efficiency. Owing to its promising electrical conductivity (8.5 × 104 S·m−1) and micro-rough surface structure (Ra ≈ 6.32 µm), the DSSCs with the additively manufactured CEs led to ≈2.5-times-higher photo-conversion efficiency than that of traditional CEs made of FTO/glass. The results of the study suggest that the proposed additive manufacturing approach can advance the field of DSSCs by addressing the limitations of conventional CE manufacturing platforms.

Humans share a similar body structure, but each individual possesses unique characteristics, which we define as one’s body type. Various classification methods have been devised to understand and assess these body types. Recent research has applied artificial intelligence technology utilizing noninvasive measurement tools, such as 3D body scanner, which minimize physical contact. The purpose of this study was to develop an artificial intelligence somatotype system capable of predicting the three body types proposed by Heath-Carter’s somatotype theory using 3D body images collected using a 3D body scanner. To classify body types, measurements were taken to determine the three somatotype components (endomorphy, mesomorphy, and ectomorphy). MobileNetV2 was utilized as the transfer learning model. The results of this study are as follows: first, the AI somatotype model showed good performance, with a training accuracy around 91% and a validation accuracy around 72%. The respective loss values were 0.26 for the training set and 0.69 for the validation set. Second, validation of the model’s performance using test data resulted in accurate predictions for 18 out of 21 new data points, with prediction errors occurring in three cases, indicating approximately 85% classification accuracy. This study provides foundational data for subsequent research aiming to predict 13 detailed body types across the three body types. Furthermore, it is hoped that the outcomes of this research can be applied in practical settings, enabling anyone with a smartphone camera to identify various body types based on captured images and predict obesity and diseases.

The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using R2, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an R2 of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance.

(1) Background: This study aimed to investigate the effects of proprioceptive neuromuscular facilitation pattern kinesio taping on arm swing, balance, and gait parameters among chronic stroke patients. (2) Methods: Twenty-eight participants were randomized into proprioceptive neuromuscular facilitation pattern kinesio taping during gait training (n = 14) and gait training (n = 14) groups. The proprioceptive neuromuscular facilitation pattern kinesio taping during gait training group employed proprioceptive neuromuscular facilitation pattern kinesio taping during 15 min treadmill-based gait training five times a week for four weeks, while the gait training group underwent the same gait training without proprioceptive neuromuscular facilitation pattern kinesio taping. Arm swing angle was measured using the Image J program, static balance was assessed with an AMTI force plate, dynamic balance was evaluated through the Timed Up and Go test, and gait parameters were recorded using the GAITRite system and the Dynamic Gait Index. (3) Results: After 4 weeks of training, the proprioceptive neuromuscular facilitation pattern kinesio taping during gait training group exhibited significant improvements in all variables compared to the baseline (p < 0.05), whereas the gait training group did not show statistically significant differences in any variables (p > 0.05). (4) Conclusions: This study demonstrates the effectiveness of proprioceptive neuromuscular facilitation pattern kinesio taping during gait training in enhancing arm swing angle, balance, and gait parameters.