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This paper proposes a novel method monitoring network packets to classify anomalies in industrial control systems (ICSs). The proposed method combines different mechanisms. It is flow-based as it obtains new features through aggregating packets of the same flow. It then builds a deep neural network (DNN) with multi-attention blocks for spotting core features, and with residual blocks for avoiding the gradient vanishing problem. The DNN is trained with the Ranger (RAdam + Lookahead) optimizer to prevent the training from being stuck in local minima, and with the focal loss to address the data imbalance problem. The Electra Modbus dataset is used to evaluate the performance impacts of different mechanisms on the proposed method. The proposed method is compared with related methods in terms of the precision, recall, and F1-score to show its superiority.

This study aims to present direct evidence on whether the estimations of Level 3 fair values (L3FVs) are used as an earnings management tool. Researchers argue this possibility but there is a lack of direct evidence. Moreover, this study tests the moderating effect of managerial education level on using the estimation of L3FVs as an earnings management tool. The results show that the firms suspected of earnings management report relatively large abnormal valuation adjustments to L3FVs, suggesting that the estimations of L3FVs are used as an earnings management tool. Furthermore, the results indicate that the graduate education of accounting and financial managers in finance reduces the use of the estimation of L3FVs to manage earnings.

The study utilizes the energy-flux-vector method to analyze the heat transfer characteristics of natural convection in a wavy-wall porous square cavity with a partially-heated bottom surface. The effects of the modified Darcy number, modified Rayleigh number, modified Prandtl number, and length of the partially-heated bottom surface on the energy-flux-vector distribution and mean Nusselt number are examined. The results show that when a low modified Darcy number with any value of modified Rayleigh number is given, the recirculation regions are not formed in the energy-flux-vector distribution within the porous cavity. Therefore, a low mean Nusselt number is presented. The recirculation regions do still not form, and thus the mean Nusselt number has a low value when a low modified Darcy number with a high modified Rayleigh number is given. However, when the values of the modified Darcy number and modified Rayleigh number are high, the energy flux vectors generate recirculation regions, and thus a high mean Nusselt number is obtained. In addition, in a convection-dominated region, the mean Nusselt number increases with an increasing modified Prandtl number. Furthermore, as the length of the partially-heated bottom surface lengthens, a higher mean Nusselt number is presented.

Vanadium redox flow batteries (VRFBs) are of considerable importance in large-scale energy storage systems due to their high efficiency, long cycle life and easy scalability. In this work, chemical vapor deposition (CVD) grown carbon nanotubes (CNTs)-modified electrodes and Nafion 117 membrane are utilised for formulating a vanadium redox flow battery (VRFB). In a CVD chamber, the growth of CNTs is carried out on an acid-treated graphite felt surface. Cyclic voltammetry of CNT-modified electrode and acid-treated electrode revealed that CNTs presence improve the reaction kinetics of V3+/V2+ and VO2+/VO2+ redox pairs. Battery performance is recorded for analysing, the effect of modified electrodes, varying electrolyte flow rates, varying current densities and effect of removing the current collector plates. CNTs presence enhance the battery performance and offered 96.30% of Coulombic efficiency, 79.33% of voltage efficiency and 76.39% of energy efficiency. In comparison with pristine electrodes, a battery consisting CNTs grown electrodes shows a 14% and 15% increase in voltage efficiency and energy efficiency, respectively. Battery configured without current collector plates performs better as compared to with current collector plates which is possibly due to decrease in battery resistance.

Background/Aims: Chronic hepatitis B virus (HBV) infection markedly increases the risk of development of hepatocellular carcinoma (HCC). Among the seven viral proteins that HBV encodes, HBV X protein (HBx) appears to have the most oncogenic potential. The mitochondria-associated HBx can induce oxidative stress in hepatocytes, leading to the production of abundant reactive oxygen species (ROS). High levels of ROS usually induce oxidative DNA damage and 8-hydroxy-2-deoxyguanosine (8-OHdG), also known as 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG), which is one of the major products of DNA oxidation and an important biomarker for oxidative stress and carcinogenesis. Cells have evolved a mechanism to prevent oxidized nucleotides from their incorporation into DNA through nucleotide pool sanitization enzymes of MTH1 (NUDT1), MTH2 (NUDT15), MTH3 (NUDT18) and NUDT5. However, little is known as to whether HBx can regulate the expression of those enzymes and modulate the formation and accumulation of 8-oxodG in hepatocytes. Methods: The level of 8-oxodG was assessed by ELISA in stable HBV-producing hepatoma cell lines, an HBV infectious mouse model, HBV and HBx transgenic mice and HBV-infected patients versus their respective controls. Expression of MTH1, MTH2, MTH3 and NUDT5 was determined by a real-time quantitative PCR and western blot analysis. Transcriptional regulation of MTH1 and MTH2 expression by HBx and the effect of HBx on MTH1 and MTH2 promoter hypermethylation were examined using a luciferase reporter assay and bisulfite sequencing analysis. Results: In comparison with controls, significantly higher levels of 8-oxodG were detected in the genome and culture supernatant of stable HBV-producing HepG2.2.15 cells, in the sera and liver tissues of HBV infectious mice and HBV or HBx transgenic mice, and in the sera of HBV-infected patients. Expression of HBx in hepatocytes significantly increased 8-oxodG level and reduced the expression of MTH1 and MTH2 at both mRNA and protein levels. It was also demonstrated that HBx markedly attenuated the MTH1 or MTH2 promoter activities through hypermethylation. Furthermore, enhancement of 8-oxodG production by HBx was reversible by overexpression of MTH1 and MTH2. Conclusion: Our data show that HBx expression results in the accumulation of 8-oxodG in hepatocytes through inhibiting the expression of MTH1 and MTH2. This may implicate that HBx may act as a tumor promoter through facilitating the mutational potential of 8-oxodG thus connecting a possible link between HBV infection and liver carcinogenesis.

Li3VO4 (LVO) is a highly promising anode material for lithium-ion batteries, owing to its high capacity and stable discharge plateau. However, LVO faces a significant challenge due to its poor rate capability, which is mainly attributed to its low electronic conductivity. To enhance the kinetics of lithium ion insertion and extraction in LVO anode materials, a conductive polymer called poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is applied to coat the surface of LVO. This uniform coating of PEDOT:PSS improves the electronic conductivity of LVO, thereby enhancing the corresponding electrochemical properties of the resulting PEDOT:PSS-decorated LVO (P-LVO) half-cell. The charge/discharge curves between 0.2 and 3.0 V (vs. Li+/Li) indicate that the P-LVO electrode displays a capacity of 191.9 mAh/g at 8 C, while the LVO only delivers a capacity of 111.3 mAh/g at the same current density. To evaluate the practical application of P-LVO, lithium-ion capacitors (LICs) are constructed with P-LVO composite as the negative electrode and active carbon (AC) as the positive electrode. The P-LVO//AC LIC demonstrates an energy density of 107.0 Wh/kg at a power density of 125 W/kg, along with superior cycling stability and 97.4% retention after 2000 cycles. These results highlight the great potential of P-LVO for energy storage applications.

The micro-phase segregation of two incompatible components on a nanometer scale results in a unique solvent-induced extended anisotropic arrangement. With the addition of a chiral dopant, lyotropic liquid crystals can be induced to adopt a helical structure, forming lyotropic cholesteric liquid crystals capable of reflecting incident light. In this study, to prevent fluid leakage in lyotropic materials, we encapsulated a series of hydrogel-stabilized lyotropic liquid crystals, presenting tunable structural colors visible in all directions, mimicking the color-changing characteristics of living organisms. Hydrogel scaffolds with controllable swelling behaviors were engineered by incorporating crosslinking monomers. To ensure stable integration of lyotropic liquid crystals, high-boiling-point ethylene glycol was employed as a fluid during the fabrication process. This study extensively explores the relationship between tensile force, temperature, and pressure and the color changes in lyotropic liquid crystals (LC). The results indicate that lyotropic LC membranes, stabilized by ethylene glycol and PDMS encapsulation, exhibit long-term stability, rendering them suitable for applications in temperature and pressure sensing. This approach ensures the continuous presence and stability of lyotropic liquid crystals within the hydrogel matrix.

Vehicle technology development drives economic development but also causes severe mobile pollution sources. Eco-driving is an effective driving strategy for solving air pollution and achieving driving safety. The on-board diagnostics II (OBD-II) module is a common monitoring tool used to acquire sensing data from in-vehicle electronic control units. However, different vehicle models use different controller area network (CAN) standards, resulting in communication difficulties; however, relevant literature has not discussed compatibility problems. The present study researched and developed the universal OBD-II module, adopted deep learning methods to evaluate fuel consumption, and proposed an intuitive driving graphic user interface design. In addition to using the universal module to obtain data on different CAN standards, this study used deep learning methods to analyze the fuel consumption of three vehicles of different brands on various road conditions. The accuracy was over 96%, thus validating the practicability of the developed system. This system will greatly benefit future applications that employ OBD-II to collect various types of driving data from different car models. For example, it can be implemented for achieving eco-driving in bus driver training. The developed system outperforms those proposed by previous research regarding its completeness and universality.

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.