Search

With the swift advancement of wearable electronics and artificial intelligence, the integration of electronic devices with the human body has advanced significantly, leading to enhanced real-time health monitoring and remote disease diagnosis. Despite progress in developing stretchable materials with skin-like mechanical properties, there remains a need for materials that also exhibit high optical transparency. Supercapacitors, as promising energy storage devices, offer advantages such as portability, long cycle life, and rapid charge/discharge rates, but achieving high capacity, stretchability, and transparency simultaneously remains challenging. This study combines the stretchable, transparent polymer PEDOT:PSS with MnO2 nanoparticles to develop high-performance, stretchable, and transparent supercapacitors. PEDOT:PSS films were deposited on a PDMS substrate using a spin-coating method, followed by electrochemical deposition of MnO2 nanoparticles. This method ensured that the nanosized MnO2 particles were uniformly distributed, maintaining the transparency and stretchability of PEDOT:PSS. The resulting PEDOT:PSS/MnO2 nanoparticle electrodes were gathered into a symmetric device using a LiCl/PVA gel electrolyte, achieving an areal capacitance of 1.14 mF cm−2 at 71.2% transparency and maintaining 89.92% capacitance after 5000 cycles of 20% strain. This work presents a scalable and economical technique to manufacturing supercapacitors that combine high capacity, transparency, and mechanical stretchability, suggesting potential applications in wearable electronics. [ABSTRACT FROM AUTHOR]

In this work, a novel formaldehyde sensor was constructed based on nanoporous, flower-like, Pb-containing Pd–Au nanoparticles deposited on the cathode in a double-cabin galvanic cell (DCGC) with a Cu plate as the anode, a multiwalled carbon nanotube-modified glassy carbon electrode as the cathode, a 0.1 M HClO4 aqueous solution as the anolyte, and a 3.0 mM PdCl2 + 1.0 mM HAuCl4 + 5.0 mM Pb(ClO4)2 + 0.1 M HClO4 aqueous solution as the catholyte, respectively. Electrochemical studies reveal that the stripping of bulk Cu can induce underpotential deposition (UPD) of Pb during the galvanic replacement reaction (GRR) process, which affects the composition and morphology of Pb-containing Pd–Au nanoparticles. The electrocatalytic activity of Pb-containing nanoparticles toward formaldehyde oxidation was examined in an alkaline solution, and the experimental results showed that formaldehyde mainly caused direct oxidation on the surface of Pb-containing Pd–Au nanoparticles while inhibiting the formation of CO poison to a large degree. The proposed formaldehyde sensor exhibits a linear amperometric response to formaldehyde concentrations from 0.01 mM to 5.0 mM, with a sensitivity of 666 μA mM−1 cm−2, a limit of detection (LOD) of 0.89 μM at triple signal-to-noise, rapid response, high anti-interference ability, and good repeatability. [ABSTRACT FROM AUTHOR]

In the evolving landscape of portable electronics, there is a critical demand for components that meld stretchability with optical transparency, especially in supercapacitors. Traditional materials fall short in harmonizing conductivity, stretchability, transparency, and capacity. Although poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) stands out as an exemplary candidate, further performance enhancements are necessary to meet the demands of practical applications. This study presents an innovative and effective method for enhancing electrochemical properties by homogeneously incorporating Ru(III) into PEDOT:PSS. These Ru(III) PEDOT:PSS complexes are readily synthesized by dipping PEDOT:PSS films in RuCl3 solution for no longer than one minute, leveraging the high specific capacitance of Ru(III) while minimizing interference with transmittance. The supercapacitor made with this Ru(III) PEDOT:PSS complex demonstrated an areal capacitance of 1.62 mF cm−2 at a transmittance of 73.5%, which was 155% higher than that of the supercapacitor made with PEDOT:PSS under comparable transparency. Notably, the supercapacitor retained 87.8% of its initial capacitance even under 20% tensile strain across 20,000 cycles. This work presents a blueprint for developing stretchable and transparent supercapacitors, marking a significant stride toward next-generation wearable electronics. [ABSTRACT FROM AUTHOR]

Geochemical maps play an important role in mineral resource exploration. There are three traditional methods for creating geochemical maps: the cumulative frequency method, the logarithmic interval method, and the Avg±k∗Std (where Avg and Std are the abbreviations of average and standard deviation, and k is a multiple of Std) method. However, with the increasing scope of the study area and cumulative data, the limitations of traditional methods, which depend on the amount of data, are exposed. A fixed-value method for Sn geological mapping is proposed to overcome the limitations of traditional methods. In the fixed-value method, Sn concentrations are divided into 19 levels on 18 fixed values ranging from 1 μg/g (corresponding to the detection limit) to 1000 μg/g (corresponding to the cut-off grade of Sn in hard rocks). The 19 levels are mapped in six color tones. The first to fifth levels are the lowest background areas in blue tones, which correspond to Sn concentrations ranging from the minimum to 3.4 μg/g. The sixth to ninth levels are high background areas in yellow tones corresponding to concentrations less than 10 μg/g, the 10th to 12th are low anomaly areas in pink tones less than 28 μg/g, the 13th to 15th are high anomaly areas in red tones less than 200 μg/g (corresponding to the placer cut-off grade), the 16th to 18th in gray tones less than 1000 μg/g, and the 19th level is in black corresponding to Sn ores with Sn concentration not less than 1000 μg/g. The fixed-value method along with three traditional methods was used to contour the Sn geochemical maps in the Gejiu area in Southwest China. The illustration results of the presented fixed-value method and three traditional methods for geochemical mapping of Sn are all feasible for Sn deposit exploration in the Gejiu area, Southwest China. Compared to traditional methods, the presented fixed-value method overcomes the flaws of traditional methods and is also more meaningful in geochemistry. [ABSTRACT FROM AUTHOR]

A Distributed Denial of Service (DDoS) attack is a prevalent issue in the blockchain network layer, causing significant revenue loss for honest mining pools. This paper introduces a novel method, the Repeated Game-based DDoS attack mitigation (RGD), to address this problem. Unlike traditional methods such as game theory and machine learning-based detection, the RGD method can effectively reflect the changes in mining revenue and strategies under different network-strength environments. In particular, we abstract the problem of DDoS mining pool revenue loss into a game revenue model and propose the subgame perfect equilibrium (SPE) approach to solve the optimal payoffs and pool strategies in various network environments. Furthermore, we address the returns of mining pools in an infinitely repeated game environment using the Two-Stage Repeated Game (TSRG) method, where the strategy varies with different network environments. The Matlab experimental simulation results indicate that as the network environment improves, the optimal mining strategies of mining pools are gradually shifting from honest strategies to launching DDoS attacks against each other. The RGD method can effectively represent the impact of changes in the network environment on the mining pool's strategy selection and optimal revenue. Consequently, with the changing network environment, the optimal revenue of the mining pool only increases by 10% of the revenue loss during a DDoS attack. [ABSTRACT FROM AUTHOR]

Infected bone defects represent a common clinical condition involving bone tissue, often necessitating surgical intervention and antibiotic therapy. However, conventional treatment methods face obstacles such as antibiotic resistance and susceptibility to postoperative infections. Hydrogels show great potential for application in the field of tissue engineering due to their advantageous biocompatibility, unique mechanical properties, exceptional processability, and degradability. Recent interest has surged in employing hydrogels as a novel therapeutic intervention for infected bone repair. This article aims to comprehensively review the existing literature on the anti-microbial and osteogenic approaches utilized by hydrogels in repairing infected bones, encompassing their fabrication techniques, biocompatibility, antimicrobial efficacy, and biological activities. Additionally, the potential opportunities and obstacles in their practical implementation will be explored. Lastly, the limitations presently encountered and the prospective avenues for further investigation in the realm of hydrogel materials for the management of infected bone defects will be deliberated. This review provides a theoretical foundation and advanced design strategies for the application of hydrogel materials in the treatment of infected bone defects. [ABSTRACT FROM AUTHOR]

A physical unclonable function (PUF) is a technology designed to safeguard sensitive information and ensure data security. PUFs generate unique responses for each challenge by leveraging random deviations in the physical microstructures of integrated circuits (ICs), making it incredibly difficult to replicate them. However, traditional silicon PUFs are now susceptible to various attacks, such as modeling attacks using conventional machine learning techniques and reverse engineering strategies. As a result, PUFs based on new materials or methods are being developed to enhance their security. However, in the realm of survey papers, it has come to our attention that there is a notable scarcity of comprehensive summaries and introductions concerning these emerging PUFs. To fill this gap, this article surveys PUFs based on novel technologies in the literature. In particular, we first provide an insightful overview of four types of PUFs that are rooted in advanced technologies: bionic optical PUF, biological PUF, PUF based on printed electronics (PE), and PUF based on memristors. Based on the overview, we further discuss the evaluation results of their performance based on specific metrics and conduct a comparative analysis of their performance. Despite significant progress in areas such as limited entry and regional expertise, it is worth noting that these PUFs still have room for improvement. Therefore, we have identified their potential shortcomings and areas that require further development. Moreover, we outline various applications of PUFs and propose our own future prospects for this technology. To sum up, this article contributes to the understanding of PUFs based on novel technologies by providing an in-depth analysis of their characteristics, performance evaluation, and potential improvements. It also sheds light on the wide range of applications for PUFs and presents enticing prospects for future advancements in this field. [ABSTRACT FROM AUTHOR]

Object detection has a wide range of applications as the most fundamental and challenging task in computer vision. However, the image quality problems such as low brightness, low contrast, and high noise in low-light scenes cause significant degradation of object detection performance. To address this, this paper focuses on object detection algorithms in low-light scenarios, carries out exploration and research from the aspects of low-light image enhancement and object detection, and proposes low-level image enhancement for low-light object detection based on the FPGA MPSoC method. On the one hand, the low-light dataset is expanded and the YOLOv3 object detection model is trained based on the low-order image enhancement technique, which improves the detection performance of the model in low-light scenarios; on the other hand, the model is deployed on the MPSoC board to achieve an edge object detection system, which improves the detection efficiency. Finally, validation experiments are conducted on the publicly available low-light object detection dataset and the ZU3EG-AXU3EGB MPSoC board, and the results show that the method in this paper can effectively improve the detection accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The integrated circuit (IC) supply chain has become globalized, thereby inevitably introducing hardware Trojan (HT) threats during the design stage. To safeguard the integrity and security of ICs, many machine learning (ML)-based solutions have been proposed. However, most existing methods lack consideration of the integrity of HTs, thereby resulting in lower true negative rates (TNR) and true positive rate (TPRs). Therefore, to solve these problems, this paper presents a HT detection and diagnosis method for gate-level netlists (GLNs) based on ML and graph theory (GT). In this method, to address the issue of nonuniqueness in submodule partition schemes, the concept of "Maximum Single-Output Submodule (MSOS)" and a partition algorithm are introduced. In addition, to enhance the accuracy of HT diagnosis, we design an implant node search method named breadth-first comparison (BFC). With the support of the aforementioned techniques, we have completed experiments on HT detection and diagnosis. The HT implantation examples selected in this article are sourced from Trust-Hub. The experimental results demostrate the following: (1) The detection method proposed in this article, when detecting gate-level hardware trojans (GLHTs), achieves a TPR exceeding 95%, a TNR exceeding 37%, and F1 values exceeding 97%. Compared to existing methods, this method has improved the TNR for GLHTs by at least 25%. (2) The TPR for diagnosing GLHTs is consistently above 93%, and the TNR is 100%. Compared to existing methods, this method has achieved approximately a 4% improvement in the TPR and a 15% improvement in the TNR for GLHT diagnosis. [ABSTRACT FROM AUTHOR]

Bisphenol A is one of the most widely used industrial compounds. Over the years, it has raised severe concern as a potential hazard to the human endocrine system and the environment. Developing robust and easy-to-use sensors for bisphenol A is important in various areas, such as controlling and monitoring water purification and sewage water systems, food safety monitoring, etc. Here, we report an electrochemical method to fabricate a bisphenol A (BPA) sensor based on a modified Au nanoparticles/multiwalled carbon nanotubes composite electrocatalyst electrode (AuCu-UPD/MWCNTs/GCE). Firstly, the Au-Cu alloy was prepared via a convenient and controllable Cu underpotential/bulk Au co-electrodeposition on a multiwalled modified carbon nanotubes glassy carbon electrode (GCE). Then, the AuCu-UPD/MWCNTs/GCE was obtained via the electrochemical anodic stripping of Cu underpotential deposition (UPD). Our novel prepared sensor enables the high-electrocatalytic and high-performance sensing of BPA. Under optimal conditions, the modified electrode showed a two-segment linear response from 0.01 to 1 µM and 1 to 20 µM with a limit of detection (LOD) of 2.43 nM based on differential pulse voltammetry (DPV). Determination of BPA in real water samples using AuCu-UPD/MWCNTs/GCE yielded satisfactory results. The proposed electrochemical sensor is promising for the development of a simple, low-cost water quality monitoring system for the detection of BPA in ambient water samples. [ABSTRACT FROM AUTHOR]

Premature senescence is a common occurrence in rice production, and seriously affects rice plants' nutrient utilization and growth. A total of 120 recombinant inbred lines (RILs) were obtained from successive self-crossing of F12 generations derived from Huazhan and Nekken2. The superoxide dismutase (SOD) activity, malondialdehyde (MDA), content and catalase (CAT) activity related to the anti-senescence traits and enzyme activity index of rice were measured for QTL mapping using 4858 SNPs. Thirteen QTLs related to anti-senescence were found, among which the highest LOD score was 5.70. Eighteen anti-senescence-related genes were found in these regions, and ten of them differed significantly between the parents. It was inferred that LOC_Os01g61500, LOC_Os01g61810, and LOC_Os04g40130 became involved in the regulation of the anti-senescence molecular network upon upregulation of their expression levels. The identified anti-senescence-related QTLs and candidate genes provide a genetic basis for further research on the mechanism of the molecular network that regulates premature senescence. [ABSTRACT FROM AUTHOR]

This paper designs a fast image-based indoor localization method based on an anchor control network (FILNet) to improve localization accuracy and shorten the duration of feature matching. Particularly, two stages are developed for the proposed algorithm. The offline stage is to construct an anchor feature fingerprint database based on the concept of an anchor control network. This introduces detailed surveys to infer anchor features according to the information of control anchors using the visual–inertial odometry (VIO) based on Google ARcore. In addition, an affine invariance enhancement algorithm based on feature multi-angle screening and supplementation is developed to solve the image perspective transformation problem and complete the feature fingerprint database construction. In the online stage, a fast spatial indexing approach is adopted to improve the feature matching speed by searching for active anchors and matching only anchor features around the active anchors. Further, to improve the correct matching rate, a homography matrix filter model is used to verify the correctness of feature matching, and the correct matching points are selected. Extensive experiments in real-world scenarios are performed to evaluate the proposed FILNet. The experimental results show that in terms of affine invariance, compared with the initial local features, FILNet significantly improves the recall of feature matching from 26% to 57% when the angular deviation is less than 60 degrees. In the image feature matching stage, compared with the initial K-D tree algorithm, FILNet significantly improves the efficiency of feature matching, and the average time of the test image dataset is reduced from 30.3 ms to 12.7 ms. In terms of localization accuracy, compared with the benchmark method based on image localization, FILNet significantly improves the localization accuracy, and the percentage of images with a localization error of less than 0.1m increases from 31.61% to 55.89%. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, the environmental problems caused by heavy metals have always been the focus of attention. Strong concern has been recently shown for the heavy metal pollution of soils in southwestern China. The heavy metals of surface soils in the Xinping area of Yunnan province, China are surveyed along with some crop samples. There are 3312 surface soils and 95 crop samples collected in about 370 square kilometers. Heavy metals including As, Cd, Cr, Hg, and Pb and pH are analyzed. New single and integrated pollution indices of heavy metals for soils (PI and PIn) and crops (PIc and PInc) based on Chinese criteria (GB15618-2018 and GB2762-2022) are described and presented here and used to assess the pollution status of heavy metals. The results indicate that the background level of surface soils is about 62.1%, the screening level is about 33.4%, and the intervention level is about 4.5%, which is mainly a result from Cr and spatially coincides with the peridotite rock, indicating a geogenic pollution source. Most crop samples are not contaminated with heavy metals. Comparing the results of the two integrated pollution indices between soils and crops, two inconsistent assessments are observed. One is that some contaminated crops are growing in unpolluted areas (or Type I) and the other is that some uncontaminated crops are growing in polluted areas (or Type II). This indicates a new challenge between the assessment criteria on soils and crops. [ABSTRACT FROM AUTHOR]

Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners' strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves. [ABSTRACT FROM AUTHOR]

Single-nucleotide polymorphisms (SNPs), the most common form of genetic variation in the human genome, are the main cause of individual differences. Furthermore, such attractive genetic markers are emerging as important hallmarks in clinical diagnosis and treatment. A variety of destructive abnormalities, such as malignancy, cardiovascular disease, inherited metabolic disease, and autoimmune disease, are associated with single-nucleotide variants. Therefore, identification of SNPs is necessary for better understanding of the gene function and health of an individual. SNP detection with simple preparation and operational procedures, high affinity and specificity, and cost-effectiveness have been the key challenge for years. Although biosensing methods offer high specificity and sensitivity, as well, they suffer drawbacks, such as complicated designs, complicated optimization procedures, and the use of complicated chemistry designs and expensive reagents, as well as toxic chemical compounds, for signal detection and amplifications. This review aims to provide an overview on improvements for SNP biosensing based on fluorescent and electrochemical methods. Very recently, novel designs in each category have been presented in detail. Furthermore, detection limitations, advantages and disadvantages, and challenges have also been presented for each type. [ABSTRACT FROM AUTHOR]

Injectable hydrogels possess tremendous merits for use in muscle regeneration; however, they still lack intrinsic biological cues (such as the proliferation and differentiation of myogenic cells), thus considerably restricting their potential for therapeutic use. Herein, we developed a double cross-linked injectable hydrogel composed of methacrylamidated oxidized hyaluronic acid (MOHA) and muscular decellularized matrix (MDM). The chemical composition of the hydrogel was confirmed using 1H NMR and Fourier transform infrared spectroscopy. To achieve cross-linking, the aldehyde groups in MOHA were initially reacted with the amino groups in MDM through a Schiff-based reaction. This relatively weak cross-linking provided the MOHA/MDM hydrogel with satisfactory injectability. Furthermore, the methacrylation of MOHA facilitated a second cross-linking mechanism via UV irradiation, resulting in improved gelation ability, biomechanical properties, and swelling performance. When C2C12 myogenic cells were loaded into the hydrogel, our results showed that the addition of MDM significantly enhanced myoblast proliferation compared to the MOHA hydrogel, as demonstrated by live/dead staining and Cell Counting Kit-8 assay after seven days of in vitro cultivation. In addition, gene expression analysis using quantitative polymerase chain reaction indicated that the MOHA/MDM hydrogel promoted myogenic differentiation of C2C12 cells more effectively than the MOHA hydrogel, as evidenced by elevated expression levels of myogenin, troponin T, and MHC in the MOHA/MDM hydrogel group. Moreover, after four to eight weeks of implantation in a full-thickness abdominal wall-defect model, the MOHA/MDM hydrogel could promote the reconstruction and repair of functional skeletal muscle tissue with enhanced tetanic force and tensile strength. This study provides a new double cross-linked injectable hydrogel for use in muscular tissue engineering. [ABSTRACT FROM AUTHOR]

Information retrieval across multiple modes has attracted much attention from academics and practitioners. One key challenge of cross-modal retrieval is to eliminate the heterogeneous gap between different patterns. Most of the existing methods tend to jointly construct a common subspace. However, very little attention has been given to the study of the importance of different fine-grained regions of various modalities. This lack of consideration significantly influences the utilization of the extracted information of multiple modalities. Therefore, this study proposes a novel text-image cross-modal retrieval approach that constructs a dual attention network and an enhanced relation network (DAER). More specifically, the dual attention network tends to precisely extract fine-grained weight information from text and images, while the enhanced relation network is used to expand the differences between different categories of data in order to improve the computational accuracy of similarity. The comprehensive experimental results on three widely-used major datasets (i.e., Wikipedia, Pascal Sentence, and XMediaNet) show that our proposed approach is effective and superior to existing cross-modal retrieval methods. [ABSTRACT FROM AUTHOR]

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain's fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution. [ABSTRACT FROM AUTHOR]

This paper presents a numerical study on thermal energy mining from hot dry rock (HDR) using an enhanced geothermal system (EGS). In these simulations, the thermal–hydraulic–mechanical (THM) coupling model is employed on the basis of the embedded discrete fracture model. The evolution of physical fields of the fractured reservoir, including temperature field, pressure field, and stress field is studied over time, and the effects of different controllable factors, such as fracture morphology, fluid injection rate, and the distances between the injection well and producing well on the heat recovery capacity are investigated. The results show that the fracture morphology significantly influences heat extraction performance. The working fluid mainly flows along with the fracture networks, which causes locally low temperatures and low mean effective stress near fractures. The porosity and permeability increase due to the decrease in mean effective stress. For reservoir models with inclined fractures, there will be a significant decrease in the extraction temperature. In the 30th year, the decline in the heat recovery rate is 46.6%, which is much higher than the model without inclined fractures. Moreover, the increasing injection temperature barely influences the production temperature, while it significantly decreases the heat recovery of the EGS. When the injection and production well spacing is small, increasing the well spacing is an effective way to improve the thermal extraction performance of the EGS. In the model in the paper, the heat production increases up to 13.7% when the injection-production well spacing is increased from 150 m to 450 m. The results of this work could provide guidance for the optimization and operation of EGS. [ABSTRACT FROM AUTHOR]

With the advantages of small size, low cost, and moderate accuracy, an open-loop fiber-optic gyroscope (FOG) has a wide range of applications around control and automation. For the most cost-sensitive applications, a simple and stable digital algorithm with a reduced control-circuit volume and cost is highly desirable to realize high-precision control of a FOG. In this work, a new algorithm for an open-loop FOG is proposed based on the discrete multi-point demodulation in the sinusoidal modulation period. Utilizing this algorithm, stable control and angular velocity calculation of a gyro are realized with effectively suppressed gyro error. The use of this algorithm greatly reduces the requirements for processing power and simplifies the gyro circuit. Based on this algorithm, a digital FOG with a volume of only 25 × 20 × 40 mm3 achieves a bias instability of less than 0.15°/h, an angle random walk (ARW) of less than 0.015°/√h, a start-up time of less than 1 s, and a 3 dB bandwidth beyond 160 Hz. This low-cost, compact, and high-performance gyro is sufficient to satisfy the requirements of applications in the navigation and control fields such as unmanned driving. [ABSTRACT FROM AUTHOR]

Simple Summary: Reprogramming of glucose metabolism is a hallmark of cancer and can be targeted by therapeutic agents. Some metabolism regulators, such as ivosidenib and enasidenib, have been approved for cancer treatment. Currently, more advanced and effective glucose metabolism enzyme-targeted anticancer drugs have been developed. Furthermore, some natural products have shown efficacy in killing tumor cells by regulating glucose metabolism, offering novel therapeutic opportunities in cancer. However, most of them have failed to be translated into clinical applications due to low selectivity, high toxicity, and side effects. Recent studies suggest that combining glucose metabolism modulators with chemotherapeutic drugs, immunotherapeutic drugs, and other conventional anticancer drugs may be a future direction for cancer treatment. Reprogramming of glucose metabolism provides sufficient energy and raw materials for the proliferation, metastasis, and immune escape of cancer cells, which is enabled by glucose metabolism-related enzymes that are abundantly expressed in a broad range of cancers. Therefore, targeting glucose metabolism enzymes has emerged as a promising strategy for anticancer drug development. Although several glucose metabolism modulators have been approved for cancer treatment in recent years, some limitations exist, such as a short half-life, poor solubility, and numerous adverse effects. With the rapid development of medicinal chemicals, more advanced and effective glucose metabolism enzyme-targeted anticancer drugs have been developed. Additionally, several studies have found that some natural products can suppress cancer progression by regulating glucose metabolism enzymes. In this review, we summarize the mechanisms underlying the reprogramming of glucose metabolism and present enzymes that could serve as therapeutic targets. In addition, we systematically review the existing drugs targeting glucose metabolism enzymes, including small-molecule modulators and natural products. Finally, the opportunities and challenges for glucose metabolism enzyme-targeted anticancer drugs are also discussed. In conclusion, combining glucose metabolism modulators with conventional anticancer drugs may be a promising cancer treatment strategy. [ABSTRACT FROM AUTHOR]

Hardware Trojans (HTs) are malicious hardware components designed to leak confidential information or cause the chip/circuit on which they are integrated to malfunction during operation. When we deploy such hardware platforms for edge computing, FPGA-based implementations of Coarse-Grained Reconfigurable Array (CGRA) are also currently falling victim to HT insertion. However, for CGRA, an evolvable hardware (EHW) platform, which has the ability to dynamically change its configuration and behavioral characteristics based on inputs from the environment, provides us with a new way to mitigate HT attacks. In this regard, we investigate the feasibility of using EHW to mitigate HTs that disrupt normal functionality in CGRA in this paper. When it is determined that HT is inserted into certain processing elements (PEs), the array autonomously reconfigures the circuit structure based on an evolutionary algorithm (EA) to avoid the use of HT-infected (HT-I) PEs. We show that the proposed approach is applicable to: (1) hardware platforms that support coarse-grained reconfiguration; and (2) pure combinatorial circuits. In a simulation environment built in Python, this paper reports experimental results for two target evolutionary circuits and outlines the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) with a 28-nm process have become popular devices for computing systems. Although current generation FPGAs have advantages over previous models, the phenomenon of circuit aging has become more significant with the sharp reduction in the process size of FPGAs. Aging results in FPGA performance degradation over time and, ultimately, hard faults. However, few studies have focused on understanding aging mechanisms or estimating the aging trend of 28-nm FPGAs. For this, we used a ring oscillator (RO)-based test structure to extract data and build a dataset that could be used to predict aging trends and determine the primary aging mechanisms of 28-nm FPGAs. Moreover, we proposed a correction method to correct temperature-induced measurement errors in accelerated tests. Furthermore, we employed four machine learning (ML) technologies that were based on accurate measurement datasets to predict FPGA aging trends. In the experiment, 24 XILINX 7-series FPGAs (28 nm) were evaluated for 10+ years of circuit operation using accelerated tests. The results showed that the aging effects of negative-bias temperature instability (NBTI) was the primary aging mechanism. The correction method proposed in this paper could effectively eliminate measurement errors. In addition, the minimum prediction error rate of the ML model was only 0.292%. [ABSTRACT FROM AUTHOR]

Cancer is characterized by increased oxidative stress, an imbalance between reactive oxygen species (ROS) and antioxidants. Enhanced ROS accumulation, as a result of metabolic disturbances and signaling aberrations, can promote carcinogenesis and malignant progression by inducing gene mutations and activating pro-oncogenic signaling, providing a possible rationale for targeting oxidative stress in cancer treatment. While numerous antioxidants have demonstrated therapeutic potential, their clinical efficacy in cancer remains unproven. Here, we review the rationale for, and recent advances in, pre-clinical and clinical research on antioxidant therapy in cancer, including targeting ROS with nonenzymatic antioxidants, such as NRF2 activators, vitamins, N-acetylcysteine and GSH esters, or targeting ROS with enzymatic antioxidants, such as NOX inhibitors and SOD mimics. In addition, we will offer insights into prospective therapeutic options for improving the effectiveness of antioxidant therapy, which may expand its applications in clinical cancer treatment. [ABSTRACT FROM AUTHOR]

Immunotherapy is an effective treatment option that revolutionizes the management of various cancers. Nevertheless, only a subset of patients receiving immunotherapy exhibit durable responses. Recently, numerous studies have shown that oxidative stress induced by reactive oxygen species (ROS) plays essential regulatory roles in the tumor immune response, thus regulating immunotherapeutic effects. Specifically, studies have revealed key roles of ROS in promoting the release of tumor-associated antigens, manipulating antigen presentation and recognition, regulating immune cell phenotypic differentiation, increasing immune cell tumor infiltration, preventing immune escape and diminishing immune suppression. In the present study, we briefly summarize the main classes of cancer immunotherapeutic strategies and discuss the interplay between oxidative stress and anticancer immunity, with an emphasis on the molecular mechanisms underlying the oxidative stress-regulated treatment response to cancer immunotherapy. Moreover, we highlight the therapeutic opportunities of manipulating oxidative stress to improve the antitumor immune response, which may improve the clinical outcome. [ABSTRACT FROM AUTHOR]

A crystal quartz tuning fork (QTF) was used as a detector to collect and amplify laser-induced photoacoustic and photothermal waves simultaneously for trace chemical analysis. A wavelength modulation technique was applied to the proposed quartz-enhanced photothermal-acoustic spectroscopy (QEPTAS) to improve the detection signal-to-noise ratio. The QTF detector was exposed to the illumination of a near-infrared distributed feedback laser at distances of 1 m and 2 m to evaluate the QEPTAS sensor performance. The QEPTAS sensor performance was determined by detecting water vapor in ambient air using a near-infrared distributed feedback laser with a power of ~10 mW and a wavelength of 1.39 μm. With an optimized modulation depth of 0.47 cm−1, the normalized noise equivalent absorption (NNEA) coefficients of 8.4 × 10−7 W·cm−1·Hz−1/2 and 3.7 × 10−6 W·cm−1·Hz−1/2 were achieved for a distance of 1 m and 2 m, respectively. The developed QEPTAS technique reduces the requirements for laser beam quality, resulting in a simple but robust sensor structure and demonstrates the ability of remote sensing of gas concentrations. [ABSTRACT FROM AUTHOR]

African swine fever virus (ASFV) mainly infects the monocyte/macrophage lineage of pigs and regulates the production of cytokines that influence host immune responses. Several studies have reported changes in cytokine production after infection with ASFV, but the regulatory mechanisms have not yet been elucidated. Therefore, the aim of this study was to examine the immune response mechanism of ASFV using transcriptomic and proteomic analyses. Through multi-omics joint analysis, it was found that ASFV infection regulates the expression of the host NF-B signal pathway and related cytokines. Additionally, changes in the NF-κB signaling pathway and IL-1β and IL-8 expression in porcine alveolar macrophages (PAMs) infected with ASFV were examined. Results show that ASFV infection activates the NF-κB signaling pathway and up-regulates the expression of IL-1β and IL-8. The NF-κB inhibitor BAY11-7082 inhibited the expression profiles of phospho-NF-κB p65, p-IκB, and MyD88 proteins, and inhibited ASFV-induced NF-κB signaling pathway activation. Additionally, the results show that the NF-κB inhibitor BAY11-7082 can inhibit the replication of ASFV and can inhibit IL-1β and, IL-8 expression. Overall, the findings of this study indicate that ASFV infection activates the NF-κB signaling pathway and up-regulates the expression of IL-1β and IL-8, and inhibits the replication of ASFV by inhibiting the NF-κB signaling pathway and interleukin-1 beta and interleukin-8 production. These findings not only provide new insights into the molecular mechanism of the association between the NF-κB signaling pathway and ASFV infection, but also indicate that the NF-κB signaling pathway is a potential immunomodulatory pathway that controls ASF. [ABSTRACT FROM AUTHOR]

With the rapid reduction of CMOS process size, the FPGAs with high-silicon accumulation technology are becoming more sensitive to aging effects. This reduces the reliability and service life of the device. The offline aging-aware layout planning based on balance stress is an effective solution. However, the existing methods need to take a long time to solve the floorplanner, and the corresponding layout solutions occupy many on-chip resources. To this end, we proposed an efficient Aging Mitigation and Resource Optimization Floorplanner (AMROFloor) for FPGAs. First, the layout solution is implemented on the Virtual Coarse-Grained Runtime Reconfigurable Architecture, which contributes to avoiding rule constraints for placement and routing. Second, the Maximize Reconfigurable Regions Algorithm (MRRA) is proposed to quickly determine the RRs' number and size to save the solving time and ensure an effective solution. Furthermore, the Resource Combination Algorithm (RCA) is proposed to optimize the on-chip resources, reducing the on-Chip Resource Utilization (CRU) while achieving the same aging relief effect. Experiments were simulated and implemented on Xilinx FPGA. The results demonstrate that the AMROFloor method designed in this paper can extend the Mean Time to Failure (MTTF) by 13.8% and optimize the resource overhead by 19.2% on average compared to the existing aging-aware layout solutions. [ABSTRACT FROM AUTHOR]

With the advancement of the Internet of Things (IoTs) technology, security issues have received an increasing amount of attention. Since IoT devices are typically resource-limited, conventional security solutions, such as classical cryptography, are no longer applicable. A physically unclonable function (PUF) is a hardware-based, low-cost alternative solution to provide security for IoT devices. It utilizes the inherent nature of hardware to generate a random and unpredictable fingerprint to uniquely identify an IoT device. However, despite existing PUFs having exhibited a good performance, they are not suitable for effective application on resource-constrained IoT devices due to the limited number of challenge-response pairs (CRPs) generated per unit area and the large hardware resources overhead. To solve these problems, this article presents an ultra-lightweight reconfigurable PUF solution, which is named RPPUF. Our method is built on pico-PUF (PPUF). By incorporating configurable logics, one single RPPUF can be instantiated into multiple samples through configurable information K. We implement and verify our design on the Xilinx Spartan-6 field programmable gate array (FPGA) microboards. The experimental results demonstrate that, compared to previous work, our method increases the uniqueness, reliability and uniformity by up to 4.13%, 16.98% and 10.5%, respectively, while dramatically reducing the hardware resource overhead by 98.16% when a 128-bit PUF response is generated. Moreover, the bit per cost (BPC) metric of our proposed RPPUF increased by up to 28.5 and 53.37 times than that of PPUF and the improved butterfly PUF, respectively. This confirms that the proposed RPPUF is ultra-lightweight with a good performance, making it more appropriate and efficient to apply in FPGA-based IoT devices with constrained resources. [ABSTRACT FROM AUTHOR]

African swine fever virus (ASFV) poses serious threats to the swine industry. The mortality rate of African swine fever (ASF) is 100%, and there is no effective vaccine currently available. Complex immune escape strategies of ASFV are crucial factors affecting immune prevention and vaccine development. CD2v and MGF360-505R genes have been implicated in the modulation of the immune response. The molecular mechanisms contributing to innate immunity are poorly understood. In this study, we discover the cytopathic effect and apoptosis of ΔCD2v/ΔMGF360-505R-ASFV after infection in porcine alveolar macrophages (PAMs) was significantly less than wild-type ASFV. We demonstrated that CD2v- and MGF360-505R-deficient ASFV decrease the level of apoptosis by inhibiting the NF-κB signaling pathway and IL-1β mRNA transcription. Compared with wild-type ASFV infection, the levels of phospho-NF-κB p65 and p-IκB protein decreased in CD2v- and MGF360-505R-deficient ASFV. Moreover, CD2v- and MGF360-505R-deficient ASFV induced less IL-1β production than wild-type ASFV and was attenuated in replication compared with wild-type ASFV. We further found that MGF360-12L, MGF360-13L, and MGF-505-2R suppress the promoter activity of NF-κB by reporter assays, and CD2v activates the NF-κB signaling pathway. These findings suggested that CD2v- and MGF360-505R-deficient ASFV could reduce the level of ASFV p30 and the apoptosis of PAMs by inhibiting the NF-κB signaling pathway and IL-1β mRNA transcription, which might reveal a novel strategy for ASFV to maintain the replication of the virus in the host. [ABSTRACT FROM AUTHOR]

Issuing vouchers is a means of implementing social welfare. There are some security concerns about paper-based vouchers, such as counterfeiting, reproducing, their low operating efficiency and so on. This study explores how to apply blockchain technology and cryptography to build a secure e-voucher system in order to solve various shortcomings of paper vouchers. A feasible application model is proposed that integrates blockchain technology in the context of vouchers to support the field of social welfare. In this study, we conducted an analysis to prove that the security requirements of the e-voucher system are met when applying this model. Subsequently, we used the Hyperledger Fabric blockchain platform and Kafka ordering services to implement the campus welfare meal voucher system. A large number of experimental data were obtained to show that the system has a satisfactory performance. In the conclusion section, we discussed the theoretical and management implications of this study, and how this study meets UN Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

In this investigation, metallocene polyethylene (m-PE) was melt blended with poly(ethylene terephthalate) (PET) with an effort to achieve improved mechanical properties using a novel eccentric rotor extruder (ERE) without the addition of any compatibilizers. The phase morphology, rheological properties, crystallization behavior, and mechanical properties of the fabricated blends were carefully studied to explore the effect of the elongational flow field on the dispersion and mixing of PET in the m-PE matrix and the interface of the two immiscible polymers. For comparison, a conventional twin-screw extruder (TSE) was used to prepare the same blends as references using the same processing condition. It shows that the elongational flow field in ERE is much more effective to disperse the immiscible PET in the m-PE matrix with a smaller particle size and a narrower particle size distribution, compared to the shear flow field in TSE. A compatibilizer, ethylene-co-methyl acrylate-co-glycidyl methacrylate (E-MA-GMA), was added to the m-PE/PET blends during the processing using TSE and ERE. It was observed that the improvement of the tensile property by adding the compatibilizer is marginal for the m-PE/PET blends processed using ERE, which indirectly proves the high effectiveness of the elongational flow field on the enhancement of the dispersion and mixing of PET in the m-PE matrix and the interface interaction. [ABSTRACT FROM AUTHOR]

A novel quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor based on a micro quartz tuning fork (QTF) is reported. As a photoacoustic transducer, a novel micro QTF was 3.7 times smaller than the usually used standard QTF, resulting in a gas sampling volume of ~0.1 mm3. As a proof of concept, water vapor in the air was detected by using 1.39 μm distributed feedback (DFB) laser. A detailed analysis of the performance of a QEPAS sensor based on the micro QTF was performed by detecting atmosphere H2O. The laser focus position and the laser modulation depth were optimized to improve the QEPAS excitation efficiency. A pair of acoustic micro resonators (AmRs) was assembled with the micro QTF in an on-beam configuration to enhance the photoacoustic signal. The AmRs geometry was optimized to amplify the acoustic resonance. With a 1 s integration time, a normalized noise equivalent absorption coefficient (NNEA) of 1.97 × 10−8 W·cm−1·Hz−1/2 was achieved when detecting H2O at less than 1 atm. [ABSTRACT FROM AUTHOR]

Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses' GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error ( M A P E ¯ ) and root mean square error ( R M S E ¯ ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles. [ABSTRACT FROM AUTHOR]

Phosphate minerals of apatite from three mines with different amounts of gangue minerals were activated by milling to increase their microcrystallinity and subsequent dissolution in a citric acid solution to serve as slow-release fertilizers for agricultural production. XRD (X-ray diffraction), FTIR (Fourier Transform Infrared), and SEM (Scanning electron microscope) were employed to characterize the properties of the prepared samples, such as changes in microcrystallinity, mineral composition, lattice structure, and granule morphology during the milling. With prolonged milling time, accompanied with the progress in microcrystallinity, the ratio of the formed amorphous compositions increased, resulting in higher dissolution in citric acid solution. In the case of carrot plants, the addition of the microcrystalline phosphate rock together with phosphorus bacteria allowed an efficient nutrient (P, K, and N) uptake as high as 77.0%, 36.7%, and 32.2%, which increased by 91.3%, 123.0%, and 105.2%, respectively, from the growth on an original soil without any addition, demonstrating clear contribution of the activated apatites in agricultural production. [ABSTRACT FROM AUTHOR]

Recently, microcrystallization technology has gained much interest because of the enhanced dissolution of the target sample and promotion of the sustainable development of agriculture. Phosphorus (P) is one of the most important nutrients for increasing crop yield; the increase in effective P ratio directly from raw phosphate rock (PR) powder by mechanical grinding to increase its microcrystallinity is believed to be the best choice for this purpose. This study reports the improvement in the activation property of PR powder with different lignite ratios (1%, 2%, 3%, and 5%), particularly the relationship between particle-size distribution, specific surface area, granule morphology, and the citric acid-soluble P. It was found that a 3% lignite addition was the optimal treatment for increasing the release of citric acid-soluble P. The maximum total amount of dry matter from rapeseed cultivation and the available P after the test increased by 56.1% and 89.6%, respectively, with direct use of PR and microcrystallized PR powder (PR2), compared with the control test without any addition of phosphate minerals. [ABSTRACT FROM AUTHOR]