National Central University

Cisplatin (cDDP) is a crucial chemotherapy drug for treating various cancers, including hepatocellular carcinoma (HCC). However, its effectiveness is often hindered by side effects and drug resistance. Selenocystine (SeC) demonstrates potential as an anticancer agent, particularly by inhibiting DNA repair mechanisms. This study explored the synergistic potential of SeC combined with cDDP for treating HCC. Our results show that SeC pretreatment followed by cDDP significantly suppresses HCC cell proliferation more effectively than either treatment alone, with minimal toxicity to normal liver cells. The combination induces significant DNA damage by inhibiting homologous recombination (HR) and non-homologous end joining (NHEJ) pathways. Xenograft experiments confirmed that the combined therapy strongly inhibits tumor growth. SeC boost the effectiveness of cDDP by amplifying DNA damage and inhibiting DNA repair, presenting a promising approach to enhancing liver cancer treatment.

Integrating frameworks of Fermi normalization and fast data density functional transform (fDDFT), we established a new global convolutional self-action module to reduce the computational complexity in modern deep convolutional neural networks (CNNs). The Fermi normalization conflates mathematical properties of sigmoid function and z-score normalization with high efficiency. Global convolutional kernels embedded in the fDDFT simultaneously extract global features from whole input images through long-range dependency. The fDDFT endows the transformed images with a smoothness property, so the images can be substantially down-sampled before the global convolutions and then resized back to the original dimensions without losing accuracy. To inspect the feasibility of the synergy of Fermi normalization and fDDFT and the combinational effect with modern CNNs, we applied the dimension-fusion U-Net as a backbone and utilized the datasets from BraTS 2020. Experimental results exhibited that the model embedded with the module saved 57%–60% computational costs and raised 50%–53% inferencing speeds compared to the naïve D-UNet model. Furthermore, the module enhanced the accuracy of brain tumor image segmentation. The dice scores of the work are 0.9221 for whole tumors, 0.8760 for tumor cores, 0.8659 for enhancing tumors, and 0.8362 for peritumoral edema. These results exhibit comparable performance to the winner of BraTS 2020. Our results also validate that image inputs processed by the module provide aligned and unified bases, establishing a specific space with optimized feature map combinations to reduce computational complexity efficiently. The module significantly boosted the performance of training and inferencing without losing model accuracy.

Robust estimation is essential in computer vision, robotics, and navigation, aiming to minimize the impact of outlier measurements for improved accuracy. We present a fast algorithm for Geman-McClure robust estimation, FracGM, leveraging fractional programming techniques. This solver reformulates the original non-convex fractional problem to a convex dual problem and a linear equation system, iteratively solving them in an alternating optimization pattern. Compared to graduated non-convexity approaches, this strategy exhibits a faster convergence rate and better outlier rejection capability. In addition, the global optimality of the proposed solver can be guaranteed under given conditions. We demonstrate the proposed FracGM solver with Wahba's rotation problem and 3-D point-cloud registration along with relaxation pre-processing and projection post-processing. Compared to state-of-the-art algorithms, when the outlier rates increase from 20% to 80%, FracGM shows 53% and 88% lower rotation and translation increases. In real-world scenarios, FracGM achieves better results in 13 out of 18 outcomes, while having a 19.43% improvement in the computation time.

The structural modification of hole‐transporting materials (HTMs) is an effective strategy for enhancing photovoltaic performance in perovskite solar cells (PSCs). Herein, a series of dithienopyran (DTP)‐based HTMs (Me‐H, Ph‐H, CF3‐H, CF3‐mF, and CF3‐oF) is designed and synthesized by substituting different functional groups on the DTP unit and are used fabricating PSCs. In comparison with Me‐H having two methyl substituents on the dithienopyrano ring, the Ph‐H having two phenyl substituents on the ring exhibits higher PCEs. Notably, the incorporation of trifluoromethyl groups in CF3‐H endows the molecule with a larger dipole moment, deeper HOMO energy level, better film morphology, closer molecular stacking, more efficient defect‐passivation, enhanced hydrophobicity, and better photovoltaic performance when compared with the Ph‐H counterpart. Furthermore, the HTMs of CF3‐mF and CF3‐oF, which feature fluorine‐substituted triphenylamine, demonstrated excellent film‐forming properties, more suitable energy levels, enhanced charge mobility, and improved passivation of the buried interface between HTMs and perovskite. As a result, PSCs employing CF3‐mF and CF3‐oF gave impressive PCEs of 23.41 and 24.13%, respectively. In addition, the large‐area (1.00 cm²) PSCs based on CF3‐oF achieved a PCE of 22.31%. Moreover, the PSCs devices with CF3 series HTMs exhibited excellent long‐term stability under different conditions.

This paper focuses on introducing an optimum control design methodology founded on descriptor form for the polynomial fuzzy model (PFM) with input constraints. The methodology commences by representing the closed-loop system of the PFM with the fuzzy controller based on parallel distributed compensation in a descriptor form. After that, a multiple Lyapunov function is applied to obtain more relaxed results for the stability analysis of the optimum control design. The optimum control design aims to minimize the upper bound of a given objective function. Besides considering practical situations, the study also integrates the input constraints into the optimum control design. The validity of the proposed design is substantiated through the provision of three simulation instances. Through the first two instances, it becomes apparent that the proposed design surpasses conventional optimum control design approaches for the PFM, yielding superior and more relaxed results. In the third instance, applying a bicycle dynamic model with an input constraint underscores the superior applicability of the proposed optimum control design approach in comparison to other existing methods.

Metal-organic frameworks (MOF) are an extraordinarily versatile class of porous nanostructured materials that have gained popularity in several scientific fields. Organic ligands are coupled to the inorganic metal centers or clusters to produce MOFs. This frontier review paper critically summarizes the most recent developments in MOF-based materials for electrochemical (EC) detection of key biomarkers, including glucose, dopamine, lactic acid, L-tryptophan, uric and ascorbic acids, H 2 O 2 , and nicotine. Various electrochemical techniques, such as cyclic voltammetry (CV), chronoamperometry, and differential pulse voltammetry (DPV) have been employed to enhance detection sensitivity and specificity. MOF-based EC sensing systems hold promise in medical diagnostics, particularly for diseases such as diabetes, neurodegenerative and cardiovascular disorders, and cancer. These sensors offer distinctive features like an extensive specific surface area, tunable pore sizes, exceptional catalytic performance, and abundant active sites, enabling sensitive, rapid, and cost-effective biomarker detection. The construction of different nanostructures, such as nanoparticles, nanorods, nanowires, and three-dimensional networks, has further improved the electro-catalytic efficiency of MOF-based materials. We also critically assess the performance of advanced MOF-derived nanostructured EC sensor platforms, and discuss future challenges and potential improvements, particularly for enzyme-free EC sensors in clinical diagnostics. This work underscores the potential of MOF-based EC sensors as versatile and effective tools for detecting a wide range of compounds and biomolecules relevant to human health. Graphical abstract

Extracting meetup events from social network posts or webpage announcements is the core technology to build event search services on the Web. While event extraction in English achieves good performance in sentence-level evaluation [1], the quality of auto-labeled training data via distant supervision is not good enough for word-level event extraction due to long event titles [2]. Additionally, meetup event titles are more complex and diverse than trigger-word-based event extraction. Therefore, the performance of event title extraction is usually worse than that of traditional named entity recognition (NER). In this paper, we propose a context-aware meetup event extraction (CAMEE) framework that incorporates a sentence-level event argument positioning model to locate event fields (i.e., title, venue, dates, etc.) within a message and then perform word-level event title, venue, and date extraction. Experimental results show that adding sentence-level event argument positioning as a filtering step improves the word-level event field extraction performance from 0.726 to 0.743 macro-F1, outperforming large language models like GPT-4-turbo (with 0.549 F1) and SOTA NER model SoftLexicon (with 0.733 F1). Furthermore, when evaluating the main event extraction task, the proposed model achieves 0.784 macro-F1.

Although the importance of the second-order Arrow-Pratt coefficients of risk aversion is well established, the importance of higher-order risk attitudes has only recently begun to be recognized. In this paper, we introduce a nonparametric approach to directly measure higher-order Arrow-Pratt coefficients of risk aversion in an expected utility framework using choices between compound lotteries and show how it can be easily implemented in behavioral studies. Specifically, we provide a theoretical basis for using risk apportionment to reveal the intensity of higher-order risk attitudes, and then draw upon our theoretical results to develop a simple, systematic, and generalizable procedure for eliciting higher-order Arrow-Pratt coefficients. We demonstrate our approach in a laboratory experiment and find that the modal second-order, third-order, and fourth-order Arrow-Pratt coefficients are positive and small. Further, we find that degrees of risk aversion are positively correlated across orders. Additionally, we discuss alternative implementations of our procedure. This paper was accepted by Peng Sun, behavioral economics and decision analysis. Funding: R. J. Huang acknowledges financial support from the National Science and Technology Council of Taiwan [Grants MOST 110-2410-H-008 -017 -MY3]. R. J. Huang and L. Y. Tzeng gratefully acknowledge financial support from E.SUN Bank. L. Zhao acknowledges financial support from the National Natural Science Foundation of China [Grants 72125003 and 72131003]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02300 .

In this study, intrinsic ZnO powder was sintered and intercalated with particles. The resulting powder, along with a commercial p-type product, was consolidated into bulk materials, and their thermal conductivity was measured across a temperature range of 350 K–700 K. The thermal conductivity of the commercial p-type ZnO was found to be lower than that of intrinsic ZnO, attributed to controlled doping. Notably, our demonstration illustrated that the thermal conductivity can be reduced by a factor of 5–10 in the presence of AlZn2O4 and ZnP2 precipitates. This methodology presents a feasible approach for the future design of ZnO-based thermoelectric materials, particularly for thermal heat scavenging applications.

Unlike traditional multi‐step synthetic approaches, we developed a single‐step synthesis of versatile π‐conjugated building blocks bearing post‐functionalizable C−H and C−Br bonds. Direct C−H arylation of 3‐bromothiophene with various iodo(hetero)aryls was successfully carried out with good regio‐ and chemo‐selectivity. Under optimized reaction conditions, 20 new compounds were facilely prepared in yields up to 91 %. One of the obtained compounds was demonstrated to further extend its conjugation length using a succinct synthetic plan to create two symmetrical oligo(hetero)aryls (MLC01 and MLC02) that were fabricated as effective hole‐transporting materials (HTM) for perovskite solar cells (PSC). PSC devices utilizing MLC01 as hole‐transport layer displayed promising power conversion efficiencies of up to 17.01 %.

Frailty, is becoming a more serious issue as the population ages. Numerous studies have shown that exercise can effectively slow the development of frailty. Compared with vigorous exercise, Baduanjin (BDJ), a kind of traditional Chinese qigong with eight simple movements, is more suitable for frailty patients. BDJ has been used to train frailty patients by physical therapists. To provide an enhanced training method, we designed a lightweight family-based frailty training system via a virtual BDJ coach. To achieve a compact system, we use a webcam as the main device. The system also supports the Kinect framework. We use pose estimation and motion recognition methods to analyze the user's movements. In addition, a novel transfer learning method is proposed. We designed a mapping model called "Skeleton Mapnet" to convert skeletal data from different frameworks. This method enables datasets from different frameworks to share classification models. It can also mix skeletal data from different frameworks to solve the lack of webcam datasets. Such a design allows the system to be easily ported into other platforms. In addition, the system is also suitable for the use of the Artificial Intelligence(AI) of Things. Our design ensures that frailty patients can easily learn and operate the system.

The depth-integrated horizontal momentum equations and continuity equation are employed to develop a new model. The vertical velocity and pressure can be expressed exactly in terms of horizontal velocities and free-surface elevation, which are the only unknowns in the model. Dividing the water column into elements and approximating horizontal velocities using linear shape function in each element, a set of model equations for horizontal velocities at element nodes is derived by adopting the weighted residual method. These model equations can be applied for transient or steady free-surface flows by prescribing appropriate lateral boundary conditions and initial conditions. Here, only the wave–current–bathymetry interaction problems are investigated. Theoretical analyses are conducted to examine various linear wave properties of the new models, which outperform the Green–Naghdi-type models for the range of water depth to wavelength ratios and the Boussinesq-type models as they are capable of simulating vertically sheared currents. One-dimensional horizontal numerical models, using a finite-difference method, are applied to a wide range of wave–current–bathymetry problems. Numerical validations are performed for nonlinear Stokes wave and bichromatic wave group propagation in deep water, sideband instability, regular wave transformation over a submerged shoal and focusing wave group interacting with linearly sheared currents in deep water. Very good agreements are observed between numerical results and laboratory data. Lastly, numerical experiments of wave shoaling from deep to shallow water are conducted to further demonstrate the capability of the new model.

We investigate the inorganic/organic hybrid vertical phototransistor (VPT) by integrating an atomic layer deposition-processed ZnO (ALD-ZnO) transistor with a prototype poly(3-hexylthiophene):[6,6]-phenyl-C61-butyric acid methyl ester (P3HT:PC61BM) blend organic photodiode (OPD) based on an encapsulated source electrode geometry, and discuss the device mechanism. Our preliminary studies on reference P3HT:PC61BM OPDs show non-ohmic electron injection between the ALD-ZnO and P3HT:PC61BM layers. However, the ALD-ZnO layer enables the accumulation of photogenerated holes under negative bias, which facilitates electron injection upon illumination and thereby enhances the external quantum efficiency (EQE). This mechanism underpins the photoresponse in the VPT. Furthermore, we demonstrate that the gate field in the VPT effectively modulates electron injection from the ALD-ZnO layer to the top OPD, resulting in the VPT operating as a non-ohmic OPD in the OFF state and as an ohmic OPD in the ON state. Benefiting from the unique transistor geometry and gate modulation capability, this hybrid VPT can achieve an EQE of 45,917%, a responsivity of 197 A/W, and a specific detectivity of 3.4 × 1012 Jones under 532 nm illumination and low drain-source voltage (Vds = 3 V) conditions. This transistor geometry also facilitates integration with various OPDs and the miniaturization of the ZnO channel area, offering an ideal basis for the development of highly efficient VPTs and high-resolution image sensors.

Diagnosing bearing faults is an important issue in the field of electrical machines, where approximately 40% of faults in electrical machines are caused by bearings. With the development of deep learning, diagnosing bearing faults from vibration signals helps reduce costs and time while increasing diagnostic accuracy. However, traditional deep learning models need to be trained from large and diverse datasets to be able to provide good diagnostic results, which is not suitable for specific data such as bearings because it can be difficult to collect data and require expensive resources. In this letter, a new diagnostic method is proposed based on few-shot learning to overcome the data problem. The proposed method synthesizes information from both spatial-level and channel-level to find information in the condition of only little training data, improving diagnostic accuracy. Besides, selective aggregation feature extraction is proposed to replace the traditional convolution neural network to extract condensed features that carry more information. For instance, with only 30 training samples, the model achieves 86.67% accuracy on the CWRU dataset, this suggested method obtains state-of-the-art results, demonstrating its efficacy

Triphenylamine‐based Y‐shaped organic sensitizers, specifically TPA‐MN (1), TPA‐CA (2), TPAT‐MN (3), and TPAT‐CA (4), are synthesized and utilized as p‐type self‐assembled monolayers (SAMs) for tin‐based perovskite solar cells (TPSCs). These SAMs are developed using low‐cost starting materials, primarily from triphenylamine (TPA) components. An extensive analysis is conducted to examine the crystalline, morphological, thermal, optical, electrochemical, and optoelectronic characteristics of SAMs 1–4, and the results are compared. A two‐step method is employed to successfully develop tin perovskite layers on all four SAM surfaces. The resulting devices demonstrates PCE in the following order: TPAT‐CA (8.1%) > TPAT‐MN (6.1%) > TPA‐MN (5.0%) > TPA‐CA (4.2%). The TPAT‐CA molecule, which contains a thiophene spacer, performed better than the other three SAMs in terms of rapid hole extraction rate, high hole mobility, and retarded charge recombination. Consequently, SAM TPAT‐CA exhibited the highest device performance with excellent stability over time, retaining ≈90% from the beginning values after storage for 3000 h. The innovative Y‐shaped SAMs describe in this study, characterized by their simple and efficient design, have the potential to contribute significantly to the advancement of perovskite photovoltaics, particularly in the development of cost‐effective TPSC technology.

Antimicrobial resistance is one of the most urgent global health threats, especially in the post-pandemic era. Antimicrobial peptides (AMPs) offer a promising alternative to traditional antibiotics, driving growing interest in recent years. dbAMP is a comprehensive database offering extensive annotations on AMPs, including sequence information, functional activity data, physicochemical properties and structural annotations. In this update, dbAMP has curated data from over 5200 publications, encompassing 33,065 AMPs and 2453 antimicrobial proteins from 3534 organisms. Additionally, dbAMP utilizes ESMFold to determine the three-dimensional structures of AMPs, providing over 30,000 structural annotations that facilitate structure-based functional insights for clinical drug development. Furthermore, dbAMP employs molecular docking techniques, providing over 100 docked complexes that contribute useful insights into the potential mechanisms of AMPs. The toxicity and stability of AMPs are critical factors in assessing their potential as clinical drugs. The updated dbAMP introduced an efficient tool for evaluating the hemolytic toxicity and half-life of AMPs, alongside an AMP optimization platform for designing AMPs with high antimicrobial activity, reduced toxicity and increased stability. The updated dbAMP is freely accessible at https://awi.cuhk.edu.cn/dbAMP/. Overall, dbAMP represents a comprehensive and essential resource for AMP analysis and design, poised to advance antimicrobial strategies in the post-pandemic era.

Capillary zone electrophoresis–immunosubtraction (CZE-IS) is an essential laboratory test in diagnosing plasma cell neoplasms. However, the current interpretation of the test results is subjective. To evaluate CZE-IS in a more precise manner, this study proposed five key indexes, namely sharpness index, light chain index, immunoglobulin G index, immunoglobulin A index, and immunoglobulin M index. The reference intervals of these indexes were established using CZE-IS curve data from a clinical laboratory of a referral medical center. A total of 1000 cases with normal electrophoretic patterns were sampled for reference intervals establishment, and an additional 20 cases were included for validation. The following reference intervals in the γ zone were established: 1-6 (sharpness index), 1.06-2.71 (light chain index), 37-454 (immunoglobulin G index), (−9)-41 (immunoglobulin A index), and (−16)-46 (immunoglobulin M index). For the β2 zone, the reference intervals were 3-17 (sharpness index), 0.44-1.90 (light chain index), (−7)-61 (immunoglobulin G index), 2-117 (immunoglobulin A index), and (−12)-35 (immunoglobulin M index). The diagnostic performance of reference intervals of the proposed indexes in validation ranged from 95% to 100%. CZE-IS indexes provide the objective quantification of key characteristics of CZE-IS curves and improve the precision of CZE-IS interpretation.