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When optimizing the interface between low-dimensional materials and flexible substrates, the surface properties are essential for preserving their exceptional electronic properties. This study presents an approach to fabricating flexible low-dimensional electronic devices using a laser liftoff process that enables optimal interface quality and batch fabrication. Graphene Hall sensors on 6-μm polyimide substrates were batch-fabricated to demonstrate the utility of this approach. The sensors showed a linear response to magnetic fields up to 25 mT and an average current-normalized sensitivity of 140 V AT -1 , outperforming other flexible graphene Hall devices without gate modulation or encapsulation. Owing to the ultrathin substrate, sensor-bending tests showed stable electrical characteristics under strain. The developments in this study represent a step toward extending the applications of low-dimensional flexible electronics.

Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates., Competing Interests: Declarations. Conflict of interest: The authors declare no conflicts of interest for this study., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Purpose: The prevention of preterm birth is a challenging task for obstetricians. Cervical cerclage, used as both a primary and secondary prevention method for spontaneous preterm birth, is a crucial surgical intervention. It is essential that obstetricians can learn this procedure in a simulated environment before performing the stitches on high-risk patients. This study aimed to develop a simulator based on 3D printing and evaluate its validity for clinical training., Methods: The objectives of this study were (1) to design and construct a cost-effective simulator for McDonald cerclage with two different cervix models-a closed cervix and a cervix with bulging membranes-using common material from a DIY store and 3D printing technology and (2) to validate its effectiveness through feedback from learners and experts in cervical cerclage. The self-made simulator was evaluated by obstetricians using a questionnaire with Likert scale., Results: Obstetricians and gynecologists assessed the simulator and found it useful for learning and practicing cervical cerclage. The simulator was deemed valuable for skill training., Conclusion: Cervical cerclage is a complex procedure that should be mastered through simulation rather than initial practice on real patients. Our simulator is a cost-effective model suitable for various clinical settings. It has been validated by obstetricians for both preventive and therapeutic cerclage, demonstrating its efficacy for training in cerclage techniques. Future research should focus on less skilled obstetricians and gynecologists and investigate how repeated use of the simulator can enhance their performance in cerclage stitching., Competing Interests: Declarations Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Ethics approval A certificate of non-objection was obtained from the Ethics Committee of the University Hospital Wuerzburg (application number 2024030502). Consent to participate Informed consent was obtained from all individual participants included in the study., (© 2024. The Author(s).)

Ultrasound (US) has emerged as a noninvasive neurostimulation method for motor control in Parkinson's disease (PD). Previous in vivo US neuromodulation studies for PD were single-target stimulation. However, the motor symptoms of PD are linked with neural circuit dysfunction, and multi-target stimulation is conducted in clinical treatment for PD. Thus, in the present study, we achieved multi-target US stimulation using holographic lens transducer based on the Rayleigh-Sommerfeld diffraction integral and time-reversal methods. We demonstrated that holographic US stimulation of the bilateral dorsal striatum (DS) could improve the motor function in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse model of PD. The holographic US wave (fundamental frequency: 3 MHz, pulse repetition frequency: 500 Hz, duty cycle: 20%, tone-burst duration: 0.4 ms, sonication duration: 1 s, interstimulus interval: 4 s, spatial-peak temporal-average intensity: 180 mw/cm 2 ) was delivered to the bilateral DS 20 min per day for consecutive 10 d after the last injection of MPTP. Immunohistochemical c-Fos staining demonstrated that holographic US significantly increased the c-Fos-positive neurons in the bilateral DS compared with the sham group ( P = 0.003). Moreover, our results suggested that holographic US stimulation of the bilateral DS ameliorated motor dysfunction ( P < 0.05) and protected the dopaminergic (DA) neurons ( P < 0.001). The neuroprotective effect of holographic US was associated with the prevention of axon degeneration and the reinforcement of postsynaptic densities [growth associated protein-43 ( P < 0.001), phosphorylated Akt ( P = 0.001), β3-tubulin ( P < 0.001), phosphorylated CRMP2 ( P = 0.037), postsynaptic density ( P = 0.023)]. These data suggested that holographic US-induced acoustic radiation force has the potential to achieve multi-target neuromodulation and could serve as a reliable tool for the treatment of PD., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Hui Zhou et al.)

This paper presented the design and performance analysis of a high-gain balanced antipodal Vivaldi antenna (BAVA) integrated with a parasitic patch and a half-spherical shaped dielectric lens, specifically designed for microwave and millimeter-wave applications. Constructed with a Teflon substrate ( ε r  = 0.0002) and energized by a 50-Ω microstrip feedline, the innovative design incorporated a parasitic patch and three different types of dielectric lenses: half-circular, conical, and half-spherical shaped. The half-spherical dielectric lens significantly enhanced performance, achieving a gain of 20.54 dB, side lobe level (SLL) of -18.15 dB, and a maximum beam squint of 1.59°. Additionally, it exhibited a 98% radiation efficiency, a front-to-back ratio of 31.75 dB, and a half-power beamwidth (HPBW) of 6.7°. The incorporation of a parasitic patch and a half-spherical shaped dielectric lens enhanced the antenna's performance. Comparative analysis with existing antennas underscored the superiority of the proposed design, positioning it as a promising solution for microwave and millimeter-wave communication systems, radar, and sensing applications.δ  = 0.0002) and energized by a 50-Ω microstrip feedline, the innovative design incorporated a parasitic patch and three different types of dielectric lenses: half-circular, conical, and half-spherical shaped. The half-spherical dielectric lens significantly enhanced performance, achieving a gain of 20.54 dB, side lobe level (SLL) of -18.15 dB, and a maximum beam squint of 1.59°. Additionally, it exhibited a 98% radiation efficiency, a front-to-back ratio of 31.75 dB, and a half-power beamwidth (HPBW) of 6.7°. The incorporation of a parasitic patch and a half-spherical shaped dielectric lens enhanced the antenna's performance. Comparative analysis with existing antennas underscored the superiority of the proposed design, positioning it as a promising solution for microwave and millimeter-wave communication systems, radar, and sensing applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

The screw, a critical element in a variety of transmission mechanisms, significantly influences the performance of the transmission process. Accurate measurement of screw lead is crucial for ensuring the quality of transmission equipment. However, the measurement process can be affected by the precision limits of the measuring instruments and the challenges of manual fine adjustments. This can lead to the screw being misplaced, introducing errors due to the off-center positioning of the workpiece. Such errors can hinder the achievement of high-precision measurements. This research aims to reduce the time needed to adjust for workpiece misalignment and to improve the accuracy of screw lead measurement through error compensation. This study starts by examining two specific scenarios that can cause workpiece misalignment in screw lead accuracy measurements: the tilting of the workpiece and the misalignment of the workpiece axis relative to the circular grating axis. Then, a mathematical model to quantify this misalignment and measure the associated parameters is developed. Based on the measured parameters, a computational model is established to compensate for the bias error under these conditions. This method allows for efficient and precise measurement of the screw lead even when the workpiece is not perfectly aligned. Calibrated screws and a digital micrometer are used to conduct experiments on workpiece misalignment. By comparing measurements with and without error compensation, the effectiveness of the compensation method in enhancing measurement accuracy is demonstrated.

Competing Interests: The authors have no conflicts of interest.

Background: Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges., Results: Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results., Conclusions: DrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT ., (© 2024. The Author(s).)

We report an in-depth analysis of the carrier conduction mechanisms in multilayer doped Ba 0.6 Sr 0.4 TiO 3 films, which offers a significant new message for reducing the leakage current. First, Ba 0.6 Sr 0.4 TiO 3 (BST) sol, Ba 0.6 Sr 0.4 Ti 0.99 Zn 0.01 O 3 (ZBST) sol, and Ba 0.6 Sr 0.4 Ti 0.99 Mg 0.01 O 3 (MBST) sol were prepared using the sol-gel method. Then, BST, ZBST, MBST, and binary alternating-structure Ba 0.6 Sr 0.4 Ti 0.99 Zn 0.01 O 3 /Ba 0.6 Sr 0.4 Ti 0.99 Mg 0.01 O 3 /Ba 0.6 Sr 0.4 Ti 0.99 Zn 0.01 O 3 (ZMZ) thin films were designed and prepared. The effects of single-component doping and binary alternating doping on the interface barrier height and trap barrier height of the Ba 0.6 Sr 0.4 TiO 3 thin films were studied. The results showed that the interface barrier height of ZMZ thin films is 0.55 eV, and the interface barrier height of BST thin films is 0.53 eV. Compared with the ZBST and MBST thin films, the change in the interface barrier height of the ZMZ film was not obvious. The trap barrier height of the ZMZ thin film is 0.17 eV, and the trap potential barrier height of the BST thin film is 0.12 eV. The trap barrier heights of ZBST thin films and MBST thin films are 0.15 eV and 0.16 eV, respectively. The enhancement of the trap barrier height may be related to the weakening of the trap and donor effects caused by oxygen vacancy defects. The energy band diagram shows the relationship between the oxygen vacancy defects, interface barrier height, and trap barrier height., Competing Interests: We declare no competing interests exist., (This journal is © The Royal Society of Chemistry.)

In this paper, we demonstrate stable radio frequency (RF) transfer via metropolitan optical fiber link in the Beijing area. The phase variation of the RF signal is compensated by a phase conjugation method incorporating two high-performance phase-locked loops. The wavelength conversion module extends the transmission length to 200 km with only two parallel 50 km dark optical fibers available. We optimize the configuration of dispersion compensation and optical amplification due to the high loss (0.31 dB/km) of the optical fiber link. At the same time, comparative experiments verify the short-term instability limitation that arises from the group velocity dispersion of the optical fiber link. The measured standard Allan deviation of the 2.4 GHz RF transmission system with dispersion compensation is 4.5 × 10-14/1 and 2.6 × 10-17/20 000 s, which is superior to that of the reference rubidium clock. The short-term instability of the system is deteriorated to 2.5 × 10-13/1 s without dispersion compensation., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm -1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

To address the issue of spatial resolution limitations in traditional Brillouin optical time-domain analysis systems due to phonon lifetime constraints, we employed pre-pumped pulse technology. Additionally, to mitigate the double-peak phenomenon observed in pre-pumped Brillouin optical time-domain analysis systems, we implemented a two-sided band interference method to reduce the linewidth of the double-peak fitting. We conducted bending measurements on three eccentric cores and intermediate cores spaced 120° apart. Our results demonstrate that the system described in this paper can achieve a spatial resolution of 30 cm, with bimodal linewidths of 23.1 MHz and 16.0 MHz. Using the parallel transmission frame algorithm, we determined the curvature of a seven-core fiber with a curvature diameter of approximately 10 cm to be 20.67 m -1 , with an error margin of 3.2%.

The abundant defects on the perovskite surface greatly impact the efficiency improvement and long-term stability of carbon-based perovskite solar cells. Molecules with electron-donating or electron-withdrawing functional groups have been cited for passivating various defects. However, few studies have investigated the potential adverse effects arising from the synergistic interactions among functional groups. Herein, we investigate the correlation between functional group configurations and passivation strength as well as the potential adverse impacts of strong electrostatic structures by methodically designing three distinct interface molecules functionalized with different ending groups, which both belong to biguanide derivatives, including 1-(3,4-dichlorophenyl) biguanide hydrochloride (DBGCl), metformin hydrochloride (MFCl), and biguanide hydrochloride (BGCl). The results indicate that DBGCl establishes comparatively mild active sites, not only passivates defects but also aids in forming a surface with a uniform potential. Conversely, MFCl exerts a more pronounced adverse effect on the perovskite surface, which is attributable to the electronic state perturbations induced by its functional groups. Due to the lack of hydrophobic groups, devices treated with BGCl demonstrate insufficient moisture resistance. Devices passivated with DBGCl demonstrate superior average efficiency, showcasing a 12% enhancement relative to the pristine. Furthermore, DBGCl-treated devices exhibit enhanced stability in three different environments, respectively, achieving the highest PCE retention rates under nitrogen conditions (25 °C), room-temperature air conditions (25 °C, RH = 40 ± 2%), and high-temperature air conditions (65 °C, RH = 40 ± 2%).

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model's feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model's ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.

Antimony chalcogenides (Sb 2 (S x Se 1- x ) 3 ) have drawn attention as a potential semiconducting substance for heterojunction photovoltaic (PV) devices due to the remarkable optoelectronic properties and wide range of bandgaps spanning from 1.1 to 1.7 eV. In this investigation, SCAPS-1D simulation software is employed to design an earth abundant, non-toxic, and cost-effective antimony sulfide-selenide (Sb 2 (S,Se) 3 )-based thin-film solar cell (TFSC), where tungsten disulfide (WS 2 ) and cuprous oxide (Cu 2 O) are used as an electron transport layer (ETL) and hole transport layer (HTL), respectively. The PV performance parameters such as power conversion efficiency, open-circuit voltage ( V oc ), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/Cu J sc ), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/Cu 2 O/Sb 2 (S,Se) 3 /WS 2 /FTO/Al TFSC. The optimized physical parameters for the Sb 2 (S,Se) 3 TFSC lead to impressive PV outputs with an efficiency of 30.18%, V , and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient ( oc ) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC.J sc of 33.65 mA cm -2 , and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient ( R 2 ) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC., Competing Interests: The authors have no conflicts of interest., (This journal is © The Royal Society of Chemistry.)

In this paper, a novel skipping spatial-spectral-temporal network (S 3 T-Net) is developed to handle intra-individual differences in electroencephalogram (EEG) signals for accurate, robust, and generalized emotion recognition. In particular, aiming at the 4D features extracted from the raw EEG signals, a multi-branch architecture is proposed to learn spatial-spectral cross-domain representations, which benefits enhancing the model generalization ability. Time dependency among different spatial-spectral features is further captured via a bi-directional long-short term memory module, which employs an attention mechanism to integrate context information. Moreover, a skip-change unit is designed to add another auxiliary pathway for updating model parameters, which alleviates the vanishing gradient problem in complex spatial-temporal network. Evaluation results show that the proposed S 3 T-Net outperforms other advanced models in terms of the emotion recognition accuracy, which yields an performance improvement of 0.23% , 0.13%, and 0.43% as compared to the sub-optimal model in three test scenes, respectively. In addition, the effectiveness and superiority of the key components of S 3 T-Net are demonstrated from various experiments. As a reliable and competent emotion recognition model, the proposed S 3 T-Net contributes to the development of intelligent sentiment analysis in human-computer interaction (HCI) realm., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Researchers have been motivated to develop photovoltaic systems that can efficiently convert artificial light into power with the growing use of indoor electrical devices for the Internet of Things. Understanding the impact of molecular design strategies involving morphological optimization through the terminal group of the non-fullerene acceptors (NFAs) is crucial. This is critically important to enhancing the photovoltaic efficiency of organic photovoltaic devices under diverse irradiation conditions. Halogenation of terminal groups proves to be a standout approach for adjusting energy levels, refining light-harvesting capabilities, crystallinity, and bolstering the intermolecular stacking in NFAs. Herein, we have designed two simple NFAs, DICTF-4F and DICTF-4Cl , to explore the dihalogenation (F and Cl) effect on the terminal group on the optical and electrochemical properties. After combining with the BODIPY-thiophene-backboned donor polymer P(BdP-HT), the organic solar cells (OSCs) using an optimized active layer with P(BdP-HT): DICTF-4F and P(BdP-HT): DICTF-4Cl attained a power conversion efficiency (PCE) of about 8.03 and 14.16%, respectively, under 1 sun illumination. Moreover, the OSC-based P(BdP-HT): DICTF-4Cl active layer showed a PCE approaching 24% under 1000 lx indoor conditions.

Analog and digital quantum simulators can efficiently simulate quantum many-body systems that appear in natural phenomena. However, experimental limitations of near-term devices still make it challenging to perform the entire process of quantum simulation. The purification-based quantum simulation methods can alleviate the limitations in experiments such as the cooling temperature and noise from the environment, while this method has the drawback that it requires global entangled measurement with a prohibitively large number of measurements that scales exponentially with the system size. In this Letter, we propose that we can overcome these problems by restricting the entangled measurements to the vicinity of the local observables to be measured, when the locality of the system can be exploited. We provide theoretical guarantees that the global purification operation can be replaced with local operations under some conditions, in particular for the task of cooling and error mitigation. We furthermore give a numerical verification that the localized purification is valid even when conditions are not satisfied. Our method bridges the fundamental concept of locality with quantum simulators, and therefore is expected to open a path to unexplored quantum many-body phenomena.

Chiral molecules, a cornerstone of chemical sciences with applications ranging from pharmaceuticals to molecular electronics, come in mirror-image pairs called enantiomers. However, their synthesis often requires complex control of their molecular geometry. We propose a strategy called "electromagnetic enantiomers" for inducing chirality in molecules located within engineered nanocavities using light, eliminating the need for intricate molecular design. This approach works by exploiting the strong coupling between a nonchiral molecule and a chiral mode within a nanocavity. We provide evidence for this strong coupling through angular emission patterns verified by numerical simulations and with complementary evidence provided by luminescence lifetime measurements. In simpler terms, our hypothesis suggests that chiral properties can be conveyed on to a molecule with a suitable chromophore by placing it within a specially designed chiral nanocavity that is significantly larger (hundreds of nanometers) than the molecule itself. To demonstrate this concept, we showcase an application in display technology, achieving efficient emission of circularly polarized light from a nonchiral molecule. The electromagnetic enantiomer concept offers a simpler approach to chiral control, potentially opening doors for asymmetric synthesis.

Background: Nasopharyngeal carcinoma (NPC) is a malignant epithelial carcinoma arising from the nasopharyngeal mucosal lining. Diagnosis of NPC at early stage can improve the outcome of patients and facilitate reduction in cancer mortality. The most significant change between cancer cells and normal cells is the variation of cell nucleus. Therefore, accurately detecting the biochemical changes in nucleus between cancer cells and normal cells has great potential to explore diagnostic molecular markers for NPC. Highly sensitive surface-enhanced Raman scattering (SERS) could reflect the biochemical changes in the process of cell cancerization at the molecular level. However, rapid nuclear targeting SERS detection remains a challenge., Results: A novel and accurate nuclear-targeting SERS detection method based on electroporation was proposed. With the assistance of electric pulses, nuclear-targeting nanoprobes were rapidly introduced into different NPC cells (including CNE1, CNE2, C666 cell lines) and normal nasopharyngeal epithelial cells (NP69 cell line), respectively. Under the action of nuclear localization signaling peptides (NLS), the nanoprobes entering cells were located to the nucleus, providing high-quality nuclear SERS signals. Hematoxylin and eosin (H&E) staining and in situ cell SERS imaging confirmed the excellent nuclear targeting performance of the nanoprobes developed in this study. The comparison of SERS signals indicated that there were subtle differences in the biochemical components between NPC cells and normal nasopharyngeal cells. Furthermore, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples, and high sensitivity, specificity, and accuracy were obtained in the screening of NPC cells from normal nasopharyngeal epithelial cells., Significance: To the best of our knowledge, this is the first study that employing nuclear-targeting SERS testing to screen nasopharyngeal carcinoma cells. Based on the electroporation technology, nanoprobes can be rapidly introduced into living cells for intracellular biochemical detection. Nuclear-targeting SERS detection can analyze the biochemical changes in the nucleus of cancer cells at the molecular level, which has great potential for early cancer screening and cytotoxicity analysis of anticancer drugs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fiber-integrated micro/nanostructures play a crucial role in modern industry, mainly owing to their compact size, high sensitivity, and resistance to electromagnetic interference. However, the three-dimensional manufacturing of fiber-tip functional structures beyond organic polymers remains challenging. It is essential to construct fiber-integrated inorganic silica with designed functional nanostructures for microsystem applications. Here, we develop a strategy for the 3D nanolithography of fiber-integrated silica from hybrid organic-inorganic materials by ultrafast laser-induced multiphoton absorption. Without silica nanoparticles and polymer additives, the acrylate-functionalized precursors can be locally cross-linked through a nonlinear effect. Followed by annealing at low temperature, the as-printed micro/nanostructures are transformed to high-quality silica with sub-100 nm resolution. Silica microcantilever probes and microtoroid resonators are directly integrated onto the optical fiber, showing strong thermal stability and quality factors. This work provides a promising strategy for fabricating desired fiber-tip silica micro/nanostructures, which is helpful for the development of integrated functional device applications.

Background: Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes., Methods: We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis., Results: The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model., Conclusions: The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-257/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: The substrate employed in surface-enhanced Raman spectroscopy (SERS) constitutes an essential element in the cancer detection methodology. In this research, we introduce a three-dimensional (3D) structured SERS substrate that integrates a porous membrane with silver nanoparticles to enhance SERS spectral signals through the utilization of the aggregation effect of silver nanoparticles. This enhancement is crucial because accurate detection results strongly depend on the intensity of specific peaks in Raman spectroscopy. A highly sensitive SERS substrate can significantly improve the accuracy of detection results., Results: We collected 66 plasma samples from individuals with kidney cancer and control individuals, including both bladder cancer patients and healthy individuals. Then, we utilized substrates with and without porous membranes to acquire the SERS spectra of the samples, enabling us to evaluate the enhancement effect of our SERS substrate. The spectral analysis demonstrated enhanced peak intensities in the experimental group (with porous substrate) compared to the control group (without porous substrate). The uniformity and reproducibility of the SERS substrate are also significantly enhanced, which is very helpful for improving the accuracy of detection results. Additionally, the Principal Component Analysis-Linear Discriminant Analysis algorithm (PCA-LDA) was employed to classify the SERS spectra of both groups. In the experimental group, the classification accuracy was 98.5 % for kidney cancer, and 83.3 % for kidney and bladder cancer. Compared to the control group, it improved by 3 % and 12.6 % respectively., Significant: This indicates that our 3D structured SERS substrate combined with multivariate statistical algorithms PCA-LDA can not only improve the accuracy of SERS detection technology in single cancer detection, but also has great potential in multiple cancer detection. This 3D structured SERS substrate is expected to become a new auxiliary means for cancer detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All-time-entropy fusion feature improves the final classification results., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 IPEM. Published by Elsevier Ltd. All rights reserved.)

Structured light three-dimensional (3D) imaging technology captures the geometric information on 3D objects by recording waves reflected from the objects' surface. The projection angle and point number of the laser dots directly determine the field-of-view (FOV) and the resolution of the reconstructed image. Conventionally, diffractive optical elements with micrometer-scale pixel size have been used to generate laser dot arrays, leading to limited FOV and point number within the projection optical path. Here, we theoretically put forward and experimentally demonstrate a monocular geometric phase metasurface composed of deep subwavelength meta-atoms to generate a 10 798 dot array within an FOV of 163°. Attributed to the vast number and high-density point cloud generated by the metasurface, the 3D reconstructed results showcase a maximum relative error in depth of 5.3 mm and a reconstruction error of 6.07%. Additionally, we propose a spin-multiplexed metasurface design method capable of doubling the number of lattice points. We demonstrate its application in the field of 3D imaging through experiments, where the 3D reconstructed results show a maximum relative depth error of 0.44 cm and a reconstruction error of 2.78%. Our proposed metasurface featuring advanced point cloud generation holds substantial potential for various applications such as facial recognition, autonomous driving, virtual reality, and beyond.

Background: 3D printing holds great potential of improving examination, diagnosis and treatment planning as well as interprofessional communication in the field of gynecological oncology. In the current manuscript we evaluated five individualized, patient-specific models of cervical cancer FIGO Stage I-III, created with 3D printing, concerning their value for translational oncology., Methods: Magnetic resonance imaging (MRI) of the pelvis was performed on a 3.0 Tesla MRI, including a T2-weighted isotropic 3D sequence. The MRI images were segmented and transferred to virtual 3D models via a custom-built 3D-model generation pipeline and printed by material extrusion. The 3D models were evaluated by all medical specialties involved in patient care of cervical cancer, namely surgeons, radiologists, pathologists and radiation oncologists. Information was obtained from evaluated profession-specific questionnaires which were filled out after inspecting all five models. The questionnaires included multiple-select questions, questions based on Likert scales (1 = "strongly disagree " or "not at all useful " up to 5 = "strongly agree " or "extremely useful ") and dichotomous questions ("Yes" or "No")., Results: Surgeons rated the models as useful during surgery (4.0 out of 5) and for patient communication (4.7 out of 5). Furthermore, they believed that the models had the potential to revise the patients' treatment plan (3.7 out of 5). Pathologists evaluated with mean ratings of 3.0 out of 5 for the usefulness of the models in diagnostic reporting and macroscopic evaluation. Radiologist acknowledged the possibility of providing additional information compared to imaging alone (3.7 out of 5). Radiation oncologists strongly supported the concept by rating the models highly for understanding patient-specific pathological characteristics (4.3 out of 5), assisting interprofessional communication (mean 4.3 out of 5) and communication with patients (4.7 out of 5). They also found the models useful for improving radiotherapy treatment planning (4.3 out of 5)., Conclusion: The study revealed that the 3D printed models were generally well-received by all medical disciplines, with radiation oncologists showing particularly strong support. Addressing the concerns and tailoring the use of 3D models to the specific needs of each medical speciality will be essential for realizing their full potential in clinical practice., (© 2024. The Author(s).)

Pressure sensors prepared from sapphire exhibit excellent characteristics, including high-temperature resistance, high hardness, and resistance to electromagnetic interference. A Fast Fourier Transform and Mean Square Error (FFT-MSE) demodulation algorithm was employed to demodulate a sapphire sandwich-structure Fabry-Perot (F-P) pressure sensor. Through simulation analysis, the experimental results indicated that the demodulation error of the air cavity length in the range of 206 μm to 216 μm was less than 0.0008%. Compared to single demodulation methods and combined demodulation methods based on FFT or Minimum Mean Square Error (MMSE), the method proposed in this work reduced the demodulation error by more than three times and increased accuracy by more than six times. The algorithm was utilized to demodulate the sapphire sandwich-structure F-P pressure sensor, and the test results indicated that the fitting error of the sensor was less than 0.025% within the pressure range of 0 MPa to 10 MPa. The repeatability error was less than 0.066%, the zero-point deviation was 1.26%, and the maximum stability deviation was 0.0063% per 30 min. The algorithm effectively demodulated the actual cavity length variation in the sapphire sandwich-structure F-P pressure sensor, providing a solution for the performance evaluation of the sapphire sandwich-structure F-P pressure sensor.

As the traditional silicon-based CMOS technology advances into the nanoscale stage, approaching its physical limits, the Carbon Nanotube Field-effect Transistor (CNTFET) is considered to be the most significant transistor technology beyond Moore's era. The CNTFET has a quasi-one-dimensional structure so that the carrier can realize ballistic transport and has very high mobility. At the same time, a single CNTFET can integrate hundreds of nanowires as the conductive channels, enabling significant current transport capabilities even in low supply voltage, thereby providing a foundational basis for achieving nanoscale ultra-large-scale analog/logic circuits. This paper summarizes the development status of the CNTFET compact model and digital/analog/RF integrated circuits. The challenges faced by SPICE modeling and circuit design are analyzed. Meanwhile, solutions to these challenges and development trends of carbon-based transistors are discussed. Finally, the future application prospects of carbon-based integrated circuits are presented.

Electrical isolation devices are essential components for safeguarding the reliability of electronic systems under harsh conditions. Digital isolators are widely used in low-power circuits due to their high immunity to disturbances. In this paper, a capacitive digital isolator for high-efficiency power supply scenarios is proposed with a high common-mode transient immunity (CMTI) and high data transmission rate. The on-off keying (OOK) modulation technique is used to ensure a high speed and accurate signal transmission. A fully integrated high-voltage level-shift driver with an ns-scale delay is proposed for increasing the drive capacity. Post-simulation results in Cadence IC 6.1.7 with the standard 0.18 μm CMOS process show that the proposed architecture achieves a 25 Mbps data transmission rate and 15 ns typical propagation delay with output peak currents of 2 A/4 A, respectively. Meanwhile, a CMTI of more than 150 kV/μs is realized.

We have experimentally investigated the mechanism of the exchange bias in 2D van der Waals (vdW) ferromagnets by means of the anomalous Hall effect (AHE) together with the dynamical magnetization property. The temperature dependence of the AC susceptibility with its frequency response indicates a glassy transition of the magnetic property for the Te-rich FeGeTe vdW ferromagnet. We also found that the irreversible temperature dependence in the anomalous Hall voltage follows the de Almeida-Thouless line. Moreover, the freezing temperature of the spin-glass-like phase is found to correlate with the disappearance temperature of the exchange bias. These important signatures suggest that the emergence of magnetic exchange bias in the 2D van der Waals ferromagnets is induced by the presence of the spin-glass-like state in FeGeTe. The unprecedented insights gained from these findings shed light on the underlying principles governing exchange bias in vdW ferromagnets, contributing to the advancement of our understanding.

In this work, optical fiber Bragg grating sensors were used to measure water velocity and examine how it was distributed in open channels. Several types of coatings were incorporated into the design of the sensors to examine their effects on the strain that the fibers experienced as a result of the water flow. Due to their low elastic coefficient, which reduced the hysteresis, the results indicated that the aluminum- and acrylate-coated fibers had the best performance. ANSYS-CFX V2020 R2 software was used to model the strain encountered by the fibers under various flow rates to assess the performance of the FBG sensors. The calculations and actual data exhibited good convergence, demonstrating the accuracy of the FBG sensors in determining water velocity. The study illustrated the usability of the proposal in both scenarios by contrasting its application in rivers and channels., (© 2024. The Author(s).)

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low-Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during the reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image's structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5 dB and 0.2, respectively.

Objectives: This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs)., Methods: CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation., Results: There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76)., Conclusions: Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC., Advances in Knowledge: This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer., Results: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set., Significance: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging.

In this paper, we report a low-cost printing process of carbon nanotube (CNT)-based, all-organic microelectrode arrays (MEAs) suitable for in vitro neural stimulation and recording. Conventional MEAs have been mainly composed of expensive metals and manufactured through high-cost and complex lithographic processes, which have limited their accessibility for neuroscience experiments and their application in various studies. Here, we demonstrate a printing-based fabrication method for microelectrodes using organic CNT/paraffin ink, coupled with the deposition of an insulating layer featuring single-cell-sized sensing apertures. The simple microfabrication processes utilizing the economic and readily available ink offer potential for cost reduction and improved accessibility of MEAs. Biocompatibility of the fabricated microelectrode was suggested through a live/dead assay of cultured neural cells, and its large electric double layer capacitance was revealed by cyclic voltammetry that was crucial for preventing cytotoxic electrolysis during electric neural stimulation. Furthermore, the electrode exhibited sufficiently low electric impedance of 2.49 Ω·cm 2 for high signal-to-noise ratio neural recording, and successfully captured model electric waves in physiological saline solution. These results suggest the easily producible and low-cost printed all-organic microelectrodes are available for neural stimulation and recording, and we believe that they can expand the application of MEA in various neuroscience research.

The double perovskite structure of Ca 2 GdSbO 6 as a fluorescent phosphor matrix material possesses a stable structure, making it an excellent candidate for a matrix material. In this study, single-doped Ca 2 GdSbO 6 : Eu 3+ fluorescent phosphors and Bi 3+ sensitized Ca 2 GdSbO 6 : Eu 3+ , Bi 3+ fluorescent phosphor materials were synthesized using the high-temperature solid-state method. The luminescence of this phosphor is based on the 5 D 0 → 4 F 2 transition emission of Eu 3+ ions, which occurs at 612 nm. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence (PL) spectra, high-temperature fluorescence spectra, and fluorescence decay lifetimes to study the phase structure, optical properties, crystal structure, and chemical purity of the samples. The performance of the single-doped phosphor was significantly improved by the addition of Bi 3+ sensitizer. The luminescence intensity increased by nearly 100% compared to Ca 2 GdSbO 6 : Eu 3+ phosphor, with a quantum efficiency increase of 124%. The thermal quenching activation energy was found to be 0.299 eV, and the luminescence intensity remained at 70.3% of room temperature at 453 K. These results indicate that the co-doping of Bi 3+ has a modulation and enhancement effect on the luminescence of Ca 2 GdSbO 6 : Eu 3+ red phosphor, showing great potential for application in near-ultraviolet-excited white LED devices., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The integration of flexible electronics with optics can help realize a powerful tool that facilitates the creation of a smart society wherein internal evaluations can be easily performed nondestructively from the surface of various objects that is used or encountered in daily lives. Here, organic-material-based stretchable optical sensors and imagers that possess both bending capability and rubber-like elasticity are reviewed. The latest trends in nondestructive evaluation equipment that enable simple on-site evaluations of health conditions and abnormalities are discussed without subjecting the targeted living bodies and various objects to mechanical stress. Real-time performance under real-life conditions is becoming increasingly important for creating smart societies interwoven with optical technologies. In particular, the terahertz (THz)-wave region offers a substance- and state-specific fingerprint spectrum that enables instantaneous analyses. However, to make THz sensors accessible, the following issues must be addressed: broadband and high-sensitivity at room temperature, stretchability to follow the surface movements of targets, and digital transformation compatibility. The materials, electronics packaging, and remote imaging systems used to overcome these issues are discussed in detail. Ultimately, stretchable optical sensors and imagers with highly sensitive and broadband THz sensors can facilitate the multifaceted on-site evaluation of solids, liquids, and gases., (© 2023 The Authors. Advanced Materials published by Wiley‐VCH GmbH.)

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach's performance.

It is usually difficult to realize high mobility together with a low threshold voltage and good stability for amorphous oxide thin-film transistors (TFTs). In addition, a low fabrication temperature is preferred in terms of enhancing compatibility with the back end of line of the device. In this study, α-IGZO TFTs were prepared by high-power impulse magnetron sputtering (HiPIMS) at room temperature. The channel was prepared under a two-step deposition pressure process to modulate its electrical properties. X-ray photoelectron spectra revealed that the front-channel has a lower Ga content and a higher oxygen vacancy concentration than the back-channel. This process has the advantage of balancing high mobility and a low threshold voltage of the TFT when compared with a conventional homogeneous channel. It also has a simpler fabrication process than that of a dual active layer comprising heterogeneous materials. The HiPIMS process has the advantage of being a low temperature process for oxide TFTs.

Flexible imagers are currently under intensive development as versatile optical sensor arrays, designed to capture images of surfaces and internals, irrespective of their shape. A significant challenge in developing flexible imagers is extending their detection capabilities to encompass a broad spectrum of infrared light, particularly terahertz (THz) light at room temperature. This advancement is crucial for thermal and biochemical applications. In this study, a flexible infrared imager is designed using uncooled carbon nanotube (CNT) sensors and organic circuits. The CNT sensors, fabricated on ultrathin 2.4 µm substrates, demonstrate enhanced sensitivity across a wide infrared range, spanning from near-infrared to THz wavelengths. Moreover, they retain their characteristics under bending and crumpling. The design incorporates light-shielded organic transistors and circuits, functioning reliably under light irradiation, and amplifies THz detection signals by a factor of 10. The integration of both CNT sensors and shielded organic transistors into an 8 × 8 active-sensor matrix within the imager enables sequential infrared imaging and nondestructive assessment for heat sources and in-liquid chemicals through wireless communication systems. The proposed imager, offering unique functionality, shows promise for applications in biochemical analysis and soft robotics., (© 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH.)

Context: The new equations have been developed for the structural and electronic properties using the plasmon calculations for the first time for 2-D MoX 2 structures. Literature shows still an extensive study is required on the stability and optical properties of MoX2 under different hydrostatic pressures and thermal properties under different temperatures using the first principles, for electronic industrial applications. The stability is analyzed using binding energy and phonon calculations. The phase transition of metallization of MoX 2 is discussed using band structure calculations under different hydrostatic pressures. The calculated work function shows the photoemission starts from the threshold frequency of 4.189×10 4 cm -1 , 3.184×10 4 cm -1 , and 3.651×10 4 cm -1 , respectively, for MoS 2 , MoSe 2 , and MoTe 2 materials. The optical properties such as refractive index n(0), and static dielectric permittivity ε(0) for three successive materials are calculated under different hydrostatic pressures, applicable for optoelectronic applications. The calculated theoretical and computational values agree well with each other and also agree with reported and experimental values. Some of the values are calculated for the first time., Methods: The theoretical equations are derived using the molecular weight, effective valence electrons, and density of molecule of MoX 2 structures. The simulation work is performed using GGA-PBE approximation in the CASTEP simulation package with DFT+D semi-empirical dispersion correction. An ultra-soft pseudopotential representation calculates the electronic and optical properties with a finite basis set kinetic energy cut-off of 381.0 eV. Each geometry has been optimized using Broyden, Fletcher, Goldfarb, and Shanno's (BFGS) algorithm for 100 iterations with a fixed basis quality variable cell method and finite electronic minimization parameters. The phonon calculations were performed using TDFT with a kinetic energy cut of 460 eV in a norm-conserving linear response method. The interpolation with a finite dispersion quality and q-vector grid spacing is performed., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Immunotherapy is an emerging treatment method aimed at activating the human immune system and relying on its own immune function to kill cancer cells and tumor tissues. It has the advantages of wide applicability and minimal side effects. Effective identification of tumor T cell antigens (TTCAs) will help researchers understand their functions and mechanisms and carry out research on anti-tumor vaccine development. Considering that using biological experimental technology to identify TTCAs can be costly and time-consuming, it is necessary to develop a robust bioinformatics computing tool. At present, different machine learning models have been proposed for identifying TTCAs, but there is still room for further improvement in their performance. To establish a TTCA predictor with better prediction performance, we propose a prediction model called iTTCA-MVL in this paper. We extracted three sets of features from the views of physicochemical information and sequence statistics, namely the distribution descriptor of composition, transition, and distribution (CTDD), TF-IDF, and LSA topic. Then, we used least squares support vector machines (LSSVMs) as submodels and Hilbert‒Schmidt independence criteria (HSIC) as constraints to establish an independent and complementary multi-view learning model. The prediction accuracy of iTTCA-MVL on the independent test set is 0.873, and Matthew's correlation coefficient is 0.747, which is significantly better than those of existing methods. Therefore, iTTCA-MVL is an excellent prediction tool that researchers can use to accurately identify TTCAs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work., (© 2023. Australasian College of Physical Scientists and Engineers in Medicine.)

Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Flexible devices have extensive applications in areas including wearable sensors, healthcare, smart packaging, energy, automotive and aerospace sectors, and other related fields. Droplet printing technology can be utilized to print flexible electronic components with micro/nanostructures on various scales, exhibiting good compatibility and wide material applicability for device production. This paper provides a comprehensive review of the current research status of droplet printing technologies and their applications across various domains, aiming to offer a valuable reference for researchers in related areas.

Background: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation., Results: We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification., Conclusions: CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git ., (© 2024. The Author(s).)