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IntroductionAcute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm.MethodsUtilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI).ResultsIn the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease.DiscussionThese results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.

Abstract Charge storage synaptic circuits employing InGaZnO thin‐film transistors (IGZO TFTs) and capacitors are a promising candidate for on‐chip trainable neural network hardware accelerators. However, IGZO TFTs often exhibit bias instability. For synaptic memory applications, the programming transistors are predominantly exposed to asymmetric off‐state biases, and a unique field‐dependent on‐current reduction under off‐scenario is observed which may result in programming current variation. Further examination of the phenomenon is conducted with transmission line‐like method and degradation recovery tests, and current reduction can be attributed to contact resistance increase by charge trapping in the source and drain electrode and the channel region. The current decrease is subsequently formulated with a stretched exponential model with bias‐dependent parameters for quantitative circuit analysis under off‐state degradation. A neural network hardware acceleration simulator is utilized to assess the complicated impact the off‐state current degradation could instigate on on‐chip trainable IGZO TFT‐based synapse arrays. The simulation results generally demonstrate deteriorated training accuracy with aggravated off‐state instability, and the accuracy trend is elucidated from the perspective of weight symmetry point.

Abstract Analog in‐memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on‐chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal‐oxide semiconductor (Si‐CMOS) and capacitor‐based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si‐CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n‐type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on‐chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non‐idealities such as drifting references and long‐term retention loss is proposed, demonstrating the importance of device‐algorithm co‐optimization.

Malicious JavaScript code in web applications poses a significant threat as cyber attackers exploit it to perform various malicious activities. Detecting these malicious scripts is challenging, given their diverse nature and the continuous evolution of attack techniques. Most approaches formulate this task as a static or sequential feature of the script, which is insufficient in terms of flexibility to various attack techniques and the ability to capture the script’s semantic meaning. To address this issue, we propose an alternative approach that leverages JavaScript code’s abstract syntax tree (AST) representation, focusing on distinctive syntactic structure features. The proposed approach uses graph neural networks to extract structural features from the AST graph while considering the attribute features of individual nodes, which uses neural message passing with neighborhood aggregation. The proposed method encodes both the local AST graph structure and attributes of the nodes. It enables capturing the source code’s semantic meaning and exploits the signature structure in the AST representations. The proposed method consistently achieved high detection performance in extensive experiments on two different datasets, with accuracy scores of 99.4% and 96.92%. The obtained evaluation metrics demonstrate the effectiveness of our approach in accurately detecting malicious JavaScript code, with our proposed method successfully detecting more than 81% for various attack types and achieving an almost twofold performance improvement on JS-Droppers compared to the sequence-based approach. In addition, we observed that the AST graph structure represented the code’s semantic meaning, exhibiting distinctive patterns and signatures that could be effectively captured using the proposed method.

The Internet of Things (IoT) networks are getting bigger and bigger. In most cases, all IT assets are connected to the network and various resources and services are provisioned proactively as needed. To achieve this, many smart objects are being developed in the field of intelligent devices. However, most of these objects can only enable their smart functionalities only after the user starts interacting with the object, which leads to an absence of intelligence between things. Bridging the gap between these entities to improve network productivity and improve network security is a challenge. Because the logs generated by IoT devices are vast and diverse, it is difficult to detect and defend against cyber-attacks with existing network security technologies. Existing cyber-attack detection systems cannot detect new attacks because they defend by defining known attack patterns as rules. This paper presents relations and security control in inference system to infer with which object a person wishes to interact by observing his behavior. Security control for services in this paper is important, and specialized. To achieve this goal, this inference problem is resolved into a problem of distinguishing, for each object, whether the IoT device has the intention to interact with it. It analyzes human behavior and detects whether there is a cyber-attack intention. Subsequently, for every object, a set of human-device relations, including Relative Distance, Relative Angle, Movement Speed, Approach Efficiency and Movement Efficiency, is extracted from the person’s behavior. These relationships are used to determine whether a person wants to interact with a particular object using a Support Vector Machine (SVM) classifier. And new mechanisms are needed to shrink massive raw logs and detect new attack patterns. Thus, this paper suggests a method for securing a huge network supporting IoT services. The proposed inference systems detect unusual network patterns by calculating correlations between events based on graphs and network measurements. We model ensemble of events based on log graphs interconnected between network devices and IoT gateways. We implement and evaluate an algorithm that detects new attack patterns by estimating the attack probability by clustering the event ensemble in real time. Finally, for the effectiveness of the proposed relations, the experimental application of a real dataset is evaluated, with encouraging results. Using the proposed human-object relations, it is possible to sense users’ interaction intentions in advance and thus to proactively provide user-adaptive services based on safety.

As pork consumption increases, rapid and accurate determination of porcine carcass grades at abattoirs has become important. Non-destructive, automated inspection methods have improved slaughter efficiency in abattoirs. Furthermore, the development of a calibration equation suitable for non-destructive inspection of domestic pig breeds may lead to rapid determination of pig carcass and more objective pork grading judgement. In order to increase the efficiency of pig slaughter, the correct estimation of the automated-method that can accommodate the existing pig carcass judgement should be made. In this study, the previously developed calibration equation was verified to confirm whether the estimated traits accord with the actual measured traits of pig carcass. A total of 1,069,019 pigs, to which the developed calibration equation, was applied were used in the study and the optimal estimated regression equation for actual measured two traits (backfat thickness and hot carcass weight) was proposed using the estimated traits. The accuracy of backfat thickness and hot carcass weight traits in the estimated regression models through stepwise regression analysis was 0.840 (R2) and 0.980 (R2), respectively. By comparing the actually measured traits with the estimated traits, we proposed optimal estimated regression equation for the two measured traits, which we expect will be a cornerstone for the Korean porcine carcass grading system.

A tremendous commitment of resources is needed to acquire, understand and apply battery data in terms of performance and aging behavior. There are many state of performance (SOP) and state of health (SOH) metrics that are useful to guide alignment of batteries to end-use, yet how these metrics are measured or extracted can make the difference between usable, valuable datasets versus data that lacks the necessary integrity to meet baseline confidence levels for SOP/SOH quantification. This work will speak to 1) types of data that support SOP and SOH evaluations on mechanistic terms, 2) measurement conditions needed to assure high data integrity, 3) equipment limitations that can compromise data high fidelity, and 4) the impact of cell polarization on data quality. A common goal in battery research and field use is to work from a data platform that supports economical paths of data capture while minimizing down-time for battery diagnostics. An ideal situation would be to utilize data obtained during normal daily use (“pulses or cycles of convenience”) without stopping the daily duty cycles to perform dedicated SOP/SOH diagnostic routines. However, difficulties arise in trying to make use of daily duty cycle data (denoted as cycle-by-cycle, CBC) that underscores the need for standardization of conditions: temperature and duty cycles can vary over the course of a day and throughout a week, month and year; polarization can develop within an immediate cycle and throughout successive cycles as a hysteresis. If CBC data is envisioned as a data source to determine performance and aging trends, it should be recognized that polarization is a frequent consequence of CBC and thus makes it difficult to separate reversible and irreversible components to metrics such as capacity loss and resistance increase over aging. Since CBC conditions can have a major impact on data usability, we will devote part of this paper to CBC data conditioning and management. Differential analyses will also be discussed as a means to detect changing trends in data quality. Our target cell chemistries will be lithium-ion types NMC/graphite and LMO/LTO.

Strong piezoelectricity is typically due to reorientation of ferroelectric domains and is not expected in cubic systems. Here, the strong piezoelectricity of pseudo-cubic 0.3BaTiO3–0.1Bi(Mg1/2Ti1/2)O3–0.6BiFeO3 is explained in terms of partial ordering of Bi ions adapted to any applied field direction.

Abstract Porcine reproductive and respiratory syndrome virus (PRRSV) infection is the most important viral disease causing severe economic losses in the swine industry. However, mechanisms underlying gene expression control in immunity-responsible tissues at different time points during PRRSV infection are poorly understood. We constructed an integrated gene co-expression network and identified tissue- and time-dependent biological mechanisms of PRRSV infection through bioinformatics analysis using three tissues (lungs, bronchial lymph nodes [BLNs], and tonsils) via RNA-Seq. Three groups with specific expression patterns (i.e., the 3-dpi, lung, and BLN groups) were discovered. The 3 dpi-specific group showed antiviral and innate-immune signalling similar to the case for influenza A infection. Moreover, we observed adaptive immune responses in the lung-specific group based on various cytokines, while the BLN-specific group showed down-regulated AMPK signalling related to viral replication. Our study may provide comprehensive insights into PRRSV infection, as well as useful information for vaccine development.

Collaborative annotation system allows a remote user to add handwritten annotation directly on local user’s view. However, previous systems cannot present handwritten annotation in real-time until all annotations have been drawn. This paper proposes a technique for collaborative annotation system that enables the immediate presence of handwritten annotation on moving picture during collaboration. This technique extracts coordinates data from handwritten annotation and reorganizes theses data in the order of strokes. Then it transmits these data to local user side in sequence. The local side receives the annotation data and presents the world-stabilized handwritten annotation in the environment. Two major optimizations are carried out in this study. One is the handwritten annotations can accurately and immediately be presented on the local side. The other is the data usage in collaboration is reduced. This research facilitates the communication between geographically dispersed users to achieve efficient collaboration. The proposed system can be applied in various fields.

JavaScript-based attacks injected into a webpage to perpetrate malicious activities are still the main problem in web security. Recent works have leveraged advances in artificial intelligence by considering many feature representations to improve the performance of malicious webpage detection. However, they did not focus on extracting the intention of JavaScript content, which is crucial for detecting the maliciousness of a webpage. In this study, we introduce an additional feature extraction process that can capture the intention of the JavaScript content of the webpage. In particular, we developed a framework for obtaining a JavaScript representation based on the abstract syntax tree for JavaScript (AST-JS), which enriches the webpage features for a better detection model. Moreover, we investigated the influence of our proposed feature on improving the model’s performance by using the Shapley additive explanation method to define the significance of each feature category compared to our proposed feature. The evaluation shows that adding the AST-JS feature can improve the performance for detecting malicious webpage compared to previous work. We also found that AST significantly influences performance, especially for webpages with JavaScript content.

Intramuscular fat (IMF) and fatty acid composition are characteristics that are used as important indicators of evaluating high quality pork and contribute to the economic benefits of the pig farming industry. In this study, quantitative trait loci (QTL) fine mapping of chromosome 12 was performed in a population of F2 intercross between Yorkshire (YS) and Korean native pigs (KNPs) by adopting combined linkage and linkage disequilibrium method using high-density SNP chips. QTLs for IMF (H3GA0034813 to H3GA0034965) and oleic acid (C18:1) (ASGA0054380 to ALGA0066299) were located at 120 cM (54.112-57.610 kb) and 85 cM (36.097-38.601 kb), respectively, within chromosome 12 (Sscrofa11.1 genomic reference). In addition, 31 candidate genes present within the IMF QTL region and 28 candidate genes existing within C18:1 QTL region were chosen. In order to understand the function of these candidate genes at the molecular level, these candidate genes were functionally categorized by studying gene ontology and analyzing network and pathway. Among the 59 candidate genes within the region of IMF QTL and C18:1 QTL, five (MYH1, MYH2, MYH4, ACACA, and RPS6KB1) directly interacting candidate genes were found. Furthermore, the RPS6KB1 gene was assumed to be an important candidate gene that is involved in leptin and insulin signaling pathway and participates in controlling adipogenic differentiation, fat deposition, and fatty acid composition, which is related to obesity of pigs.

Obfuscation is rampant in both benign and malicious JavaScript (JS) codes. It generates an obscure and undetectable code that hinders comprehension and analysis. Therefore, accurate detection of JS codes that masquerade as innocuous scripts is vital. The existing deobfuscation methods assume that a specific tool can recover an original JS code entirely. For a multi-layer obfuscation, general tools realize a formatted JS code, but some sections remain encoded. For the detection of such codes, this study performs Deobfuscation, Unpacking, and Decoding (DUD-preprocessing) by function redefinition using a Virtual Machine (VM), a JS code editor, and a python int_to_str() function to facilitate feature learning by the FastText model. The learned feature vectors are passed to a classifier model that judges the maliciousness of a JS code. In performance evaluation, the authors use the Hynek Petrak's dataset for obfuscated malicious JS codes and the SRILAB dataset and the Majestic Million service top 10,000 websites for obfuscated benign JS codes. They then compare the performance to other models on the detection of DUD-preprocessed obfuscated malicious JS codes. Their experimental results show that the proposed approach enhances feature learning and provides improved accuracy in the detection of obfuscated malicious JS codes.

Attacks using Uniform Resource Locators (URLs) and their JavaScript (JS) code content to perpetrate malicious activities on the Internet are rampant and continuously evolving. Methods such as blocklisting, client honeypots, domain reputation inspection, and heuristic and signature-based systems are used to detect these malicious activities. Recently, machine learning approaches have been proposed; however, challenges still exist. First, blocklist systems are easily evaded by new URLs and JS code content, obfuscation, fast-flux, cloaking, and URL shortening. Second, heuristic and signature-based systems do not generalize well to zero-day attacks. Third, the Domain Name System allows cybercriminals to easily migrate their malicious servers to hide their Internet protocol addresses behind domain names. Finally, crafting fully representative features is challenging, even for domain experts. This study proposes a feature selection and classification approach for malicious JS code content using Shapley additive explanations and tree ensemble methods. The JS code features are obtained from the Abstract Syntax Tree form of the JS code, sample JS attack codes, and association rule mining. The malicious and benign JS code datasets obtained from Hynek Petrak and the Majestic Million Service were used for performance evaluation. We compared the performance of the proposed method to those of other feature selection methods in the task of malicious JS code content detection. With a recall of 0.9989, our experimental results show that the proposed approach is a better prediction model.

We aimed to comprehensively understand the functional mechanisms of immunity, especially of the CD8+/− subsets of gamma delta (γδ) T cells, using an RNA-sequencing analysis. Herein, γδ T cells were obtained from bronchial lymph node tissues of 38-day-old (after weaning 10-day: D10) and 56-day-old (after weaning 28-day: D28) weaned pigs and sorted into CD8+ and CD8− groups. Differentially expressed genes (DEGs) were identified based on the CD8 groups at D10 and D28 time points. We confirmed 1699 DEGs between D10 CD8+ versus D10 CD8− groups and 1784 DEGs between D28 CD8+ versus D28 CD8− groups; 646 upregulated and 561 downregulated DEGs were common. The common upregulated DEGs were enriched in the cytokine–cytokine receptor interaction and T cell receptor (TCR) signaling pathway, and the common downregulated DEGs were enriched in the B cell receptor signaling pathway. Further, chemokine-related genes, interferon gamma, and CD40 ligand were involved in the cytokine–cytokine receptor interaction and TCR signaling pathway, which are associated with inter-regulation in immunity. We expect our results to form the basic data required for understanding the mechanisms of γδ T cells in pigs; however, further studies are required in order to reveal the dynamic changes in γδ T cells under pathogenic infections, such as those by viruses.

We performed a genome-wide association study and fine mapping using two methods (single marker regression: frequentist approach and Bayesian C (BayesC): fitting selected single nucleotide polymorphisms (SNPs) in a Bayesian framework) through three high-density SNP chip platforms to analyze milk production phenotypes in Korean Holstein cattle (n = 2780). We identified four significant SNPs for each phenotype in the single marker regression model: AX-311625843 and AX-115099068 on Bos taurus autosome (BTA) 14 for milk yield (MY) and adjusted 305-d fat yield (FY), respectively, AX-428357234 on BTA 18 for adjusted 305-d protein yield (PY), and AX-185120896 on BTA 5 for somatic cell score (SCS). Using the BayesC model, we discovered significant 1-Mb window regions that harbored over 0.5% of the additive genetic variance effects for four milk production phenotypes. The concordant significant SNPs and 1-Mb window regions were characterized into quantitative trait loci (QTL). Among the QTL regions, we focused on a well-known gene (diacylglycerol O-acyltransferase 1 (DGAT1)) and newly identified genes (phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) for MY and FY, and observed that DGAT1 is involved in glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism, and PDE4B is involved in cAMP signaling. Our findings suggest that the candidate genes in QTL are strongly related to physiological mechanisms related to the fat production and consequent total MY in Korean Holstein cattle.

In this paper, we study flag structures of matroid base polytopes. We describe faces of matroid base polytopes in terms of matroid data, and give conditions for hyperplane splits of matroid base polytopes. Also, we show how the $\textbf{cd}$-index of a polytope can be expressed when a polytope is cut by a hyperplane, and apply these to the $\textbf{cd}$-index of a matroid base polytope of a rank $2$ matroid.