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Compared with conventional lithium‐ion battery systems, anion‐intercalation in graphite cathodes opens avenues for the development of batteries with groundbreaking power density. This study explores the enhancement of dual‐ion batteries (DIBs) by gel polymer electrolyte (GPE) enhanced with surface‐charged nanoclays, focusing on overcoming traditional challenges such as electrolyte decomposition, anion‐solvent co‐intercalation, and interfacial instability at the graphite cathode. Three nanoclays including montmorillonite, kaolinite, and halloysite are compared by incorporating them into GPE and evaluating their effect on the electrochemical properties, ion conduction, and mechanical integrity of DIBs. Particular attention is paid to the halloysite because of its differential internal and external surface charges, which generate charge separation, facilitate optimal lithium‐ion transport, and mitigate undesirable anion‐solvent interactions. These results indicate that halloysite‐incorporated GPE (HS‐G) significantly improves DIB performance, as demonstrated by extended cycle life, robust capacity retention at fast charge/discharge rates of 20 C, and operational stability over a wide temperature range (0–60 °C). These improvements are attributed to the unique structural advantages of HS‐G, including effective charge separation, enhanced anion diffusion, and reduced solvent co‐intercalation, which provide an environmentally friendly and cost‐effective approach to advanced DIB applications. [ABSTRACT FROM AUTHOR]

The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks. [ABSTRACT FROM AUTHOR]

High‐capacity silicon (Si) materials hold a position at the forefront of advanced lithium‐ion batteries. The inherent potential offers considerable advantages for substantially increasing the energy density in batteries, capable of maximizing the benefit by changing the paradigm from nano‐ to micron‐sized Si particles. Nevertheless, intrinsic structural instability remains a significant barrier to its practical application, especially for larger Si particles. Here, a covalently interconnected system is reported employing Si microparticles (5 µm) and a highly elastic gel polymer electrolyte (GPE) through electron beam irradiation. The integrated system mitigates the substantial volumetric expansion of pure Si, enhancing overall stability, while accelerating charge carrier kinetics due to the high ionic conductivity. Through the cost‐effective but practical approach of electron beam technology, the resulting 500 mAh‐pouch cell showed exceptional stability and high gravimetric/volumetric energy densities of 413 Wh kg−1, 1022 Wh L−1, highlighting the feasibility even in current battery production lines. [ABSTRACT FROM AUTHOR]

Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance improvement. To improve the performance of small object detection, we propose DCEF   2 -YOLO. Our proposed method enables efficient real-time small object detection by using a deformable convolution (DFConv) module and an efficient feature fusion structure to maximize the use of the internal feature information of objects. DFConv preserves small object information by preventing the mixing of object information with the background. The optimized feature fusion structure produces high-quality feature maps for efficient real-time small object detection while maximizing the use of limited information. Additionally, modifying the input data processing stage and reducing the detection layer to suit small object detection also contributes to performance improvement. When compared to the performance of the latest YOLO-based models (such as DCN-YOLO and YOLOv7), DCEF   2 -YOLO outperforms them, with a mAP of +6.1% on the DOTA-v1.0 test set, +0.3% on the NWPU VHR-10 test set, and +1.5% on the VEDAI512 test set. Furthermore, it has a fast processing speed of 120.48 FPS with an RTX3090 for 512 × 512 images, making it suitable for real-time small object detection tasks. [ABSTRACT FROM AUTHOR]

Objective: The study aims to develop a mHealth application for seizure management based on the human system integration (HSI) approach. Background: Unmet healthcare needs among people with epilepsy continue to exist despite the advancement in healthcare technology. Current seizure management methods are found to be ineffective. Therefore, a more efficient strategy such as mHealth technology is necessary to aid seizure management. Method: The needs identification phase involved identifying the user requirements by interviewing 10 stakeholders and conducting thematic analysis and needs interpretation technique. In the solution identification phase, the system requirements were derived using various human-centered design and systems engineering approaches and were evaluated through quality function deployment to determine design targets. For the design and evaluation phase, the design targets were reflected in the app through the iterative prototyping process, and the interface and functional design were evaluated by seven human factors and ergonomics experts and four stakeholders, respectively. Results: Three primary needs and ten user requirements were derived from the needs identification phase. Ten out of fifteen system requirements were selected as design targets to be included in the final prototype. Results of the evaluation showed that the interface design of the proposed app showed superior usability compared to a competitor app and that the app functions were beneficial for the stakeholders. Conclusion: The mHealth app designed through the HSI framework showed good potential in addressing the main issues in seizure management. Application: The mHealth app design methodology based on the HSI approach can be applied to the design of small-scale systems in various domains. [ABSTRACT FROM AUTHOR]

Histone‐lysine N‐methyltransferase SETD2 (SETD2), the sole histone methyltransferase that catalyzes trimethylation of lysine 36 on histone H3 (H3K36me3), is often mutated in clear cell renal cell carcinoma (ccRCC). SETD2 mutation and/or loss of H3K36me3 is linked to metastasis and poor outcome in ccRCC patients. Epithelial‐to‐mesenchymal transition (EMT) is a major pathway that drives invasion and metastasis in various cancer types. Here, using novel kidney epithelial cell lines isogenic for SETD2, we discovered that SETD2 inactivation drives EMT and promotes migration, invasion, and stemness in a transforming growth factor‐beta‐independent manner. This newly identified EMT program is triggered in part through secreted factors, including cytokines and growth factors, and through transcriptional reprogramming. RNA‐seq and assay for transposase‐accessible chromatin sequencing uncovered key transcription factors upregulated upon SETD2 loss, including SOX2, POU2F2 (OCT2), and PRRX1, that could individually drive EMT and stemness phenotypes in SETD2 wild‐type (WT) cells. Public expression data from SETD2 WT/mutant ccRCC support the EMT transcriptional signatures derived from cell line models. In summary, our studies reveal that SETD2 is a key regulator of EMT phenotypes through cell‐intrinsic and cell‐extrinsic mechanisms that help explain the association between SETD2 loss and ccRCC metastasis. [ABSTRACT FROM AUTHOR]

The van der Waals (vdW) heterostructures based on two-dimensional (2D) semiconducting materials have been thoroughly investigated with regard to practical applications. Recent studies on 2D materials have reignited attraction in the p-n junction, with promising potential for applications in both electronics and optoelectronics. 2D materials provide exceptional band structural diversity in p-n junction devices, which is rare in regular bulk semiconductors. In this article, we demonstrate a p-n diode based on multiheterostructure configuration, WTe 2 -GaTe-ReSe 2 -WTe 2 , where WTe 2 acts as heterocontact with GaTe/ReSe 2 junction. Our devices with heterocontacts of WTe 2 showed excellent performance in electronic and optoelectronic characteristics as compared to contacts with basic metal electrodes. However, the highest rectification ratio was achieved up to ∼2.09 × 10 6 with the lowest ideality factor of ∼1.23. Moreover, the maximum change in photocurrent ( I ph ) is measured around 312 nA at V ds = 0.5 V. The device showed a high responsivity ( R ) of 4.7 × 10 4 m·AW -1 , maximum external quantum efficiency (EQE) of 2.49 × 10 4 (%), and detectivity ( D *) of 2.1 × 10 11 Jones at wavelength λ = 220 nm. Further, we revealed the bipolar photoresponse mechanisms in WTe 2 -GaTe-ReSe 2 -WTe 2 devices due to band alignment at the interface, which can be modified by applying different gate voltages. Hence, our promising results render heterocontact engineering of the GaTe-ReSe 2 heterostructured diode as an excellent candidate for next-generation optoelectronic logic and neuromorphic computing.