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Atherosclerosis involves an inflammatory response due to plaque formation within the arteries, which can lead to ischemic stroke and heart disease. It is one of the leading causes of death worldwide, with various contributing factors such as hyperlipidemia, hypertension, obesity, diabetes, and smoking. Wall shear stress (WSS) is also known as a contributing factor of the formation of atherosclerotic plaques. Since the causes of atherosclerosis cannot be attributed to a single factor, clearly understanding the mechanisms and causes of its occurrence is crucial for preventing the disease and developing effective treatment strategies. To better understand atherosclerosis and define the correlation between various contributing factors, computational fluid dynamics (CFD) analysis is primarily used. CFD simulates WSS, the frictional force caused by blood flow on the vessel wall with various hemodynamic changes. Using apolipoprotein E knockout (ApoE-KO) mice subjected to partial ligation and a high-fat diet at 1-week, 2-week, and 4-week intervals as an atherosclerosis model, CFD analysis was conducted along with the reconstruction of carotid artery blood flow via magnetic resonance imaging (MRI) and compared to the inflammatory factors and pathological staining. In this experiment, a comparative analysis of the effects of high WSS and low WSS was conducted by comparing the standard deviation of time-averaged wall shear stress (TAWSS) at each point within the vessel wall. As a novel approach, the standard deviation of TAWSS within the vessel was analyzed with the staining results and pathological features. Since the onset of atherosclerosis cannot be explained by a single factor, the aim was to find the correlation between the thickness of atherosclerotic plaques and inflammatory factors through standard deviation analysis. As a result, the gap between low WSS and high WSS widened as the interval between weeks in the atherosclerosis mouse model increased. This finding not only linked the occurrence of atherosclerosis to WSS differences but also provided a connection to the causes of vulnerable plaques. [ABSTRACT FROM AUTHOR]

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time‐consuming data transmission to an external processor, and high energy consumption in multi‐stage data processing. In this study, a gas sensing system using on‐chip annealing for fast and power‐efficient gas detection is proposed. By utilizing a micro‐heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET‐type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on‐chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability. [ABSTRACT FROM AUTHOR]

An increase in the demand for artificial intelligence is leading to advanced research in the field of neuromorphic systems, which imitate human brain functions with the hope of increasing computational speed and lowering power consumption. Especially, the development of energy‐efficient and reliable synaptic devices is critical as synapses are fundamental building blocks of neuromorphic systems. In this study, by adjusting the charge injection pathway of conventional flash memory devices, a lateral migration‐based synaptic device is proposed. Using the efficient program/erase method, the proposed device is operable at a significantly low voltage while maintaining formidable retention and endurance characteristics. Furthermore, an efficient hybrid off‐chip/on‐chip training method using the proposed device is presented. The results demonstrate a variation‐robust neuromorphic system, indicating the superiority of the proposed device. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have emerged as a novel approach for reducing computational costs by mimicking the biologically plausible operations of neurons and synapses. In this article, large‐scale analog SNNs are investigated and optimized at the hardware‐level by using SNNSim, the novel simulator for SNNs that employ analog synaptic devices and integrate‐and‐fire (I&F) neuron circuits. SNNSim is a reconfigurable simulator that accurately and very quickly models the behavior of the user‐defined device characteristics and returns key metrics such as area, accuracy, latency, and power consumption as output. Notably, SNNSim exhibits exceptional efficiency, as it can process the entire 10 000 Modified National Institute of Standards and Technology (MNIST) test dataset in a few seconds, whereas SPICE simulations require hours to simulate a single MNIST test data. Using SNNSim, the conversion of artificial neural networks (ANNs) to SNNs is simulated and the performance of the large‐scale analog SNNs is optimized. The results enable the design of accurate, high‐speed, and low‐power operation of large‐scale SNNs. SNNSim code is now available at https://github.com/SMDLGITHUB/SNNSim. [ABSTRACT FROM AUTHOR]

Recent environmental concerns have increased demand for renewable polymers and sustainable green resource usage, such as biomass-derived components and carbon dioxide (CO2). Herein, we present crosslinked polyurethanes (CPUs) fabricated from CO2- and biomass-derived monomers via a facile solvent-free ball milling process. Furan-containing bis(cyclic carbonate)s were synthesized through CO2 fixation and further transformed to tetraols, denoted FCTs, by aminolysis and utilized in CPU synthesis. Highly dispersed polyurethane-based hybrid composites (CPU–Ag) were also manufactured using a similar ball milling process. Due to the malleability of the CPU matrix, enabled by transcarbamoylation (dynamic covalent chemistry), CPU-based composites are expected to present very low interfacial thermal resistance between the heat sink and heat source. The characteristics of the dynamic covalent bond (i.e., urethane exchange reaction) were confirmed by the results of dynamic mechanical thermal analysis and stress relaxation analysis. Importantly, the high thermal conductivity of the CPU-based hybrid material was confirmed using laser flash analysis (up to 51.1 W/m·K). Our mechanochemical approach enables the facile preparation of sustainable polymers and hybrid composites for functional application. [ABSTRACT FROM AUTHOR]

Herein, dual‐gate field‐effect transistors (DG FETs) fabricated on Si substrate and a corresponding NOR‐type array designed for low‐power on‐chip trainable hardware neural networks (HNNs) are presented. The fabricated DG FET exhibits notable endurance characteristics, with the subthreshold swing remaining consistently within a 2.45% range of change and ΔVth per cycle maintaining stability at 4.5% over repetitive program and erase operations, up to 104 cycles. Furthermore, a multilevel characteristic is achieved through low‐power program/erase operations based on Fowler–Nordheim (FN) tunneling, which exhibit 0.09 and 0.99 fJ per spike, respectively. These characteristics provide the HNN stability, along with high performance and power efficiency. The NOR‐type array in this work demonstrates selective update and bidirectional vector‐by‐matrix multiplication capabilities. This enables on‐chip training based on a gradient descent algorithm, without the need for an additional array for backpropagation. Subsequently, a simulation of the Modified National Institute of Standards and Technology classification is conducted to evaluate the accuracy and training power consumption of the proposed device in comparison to other two‐terminal memristor devices. The results show that the DG FET array achieves superior accuracy while maintaining over 180.4 times higher energy efficiency, demonstrating the potential of the DG FET as a promising candidate for low‐power HNN applications. [ABSTRACT FROM AUTHOR]

A ferroelectric thin‐film transistor (FeTFT)‐based synaptic device with an indium–gallium–zinc oxide (IGZO) channel and a metal–ferroelectric–metal–insulator–semiconductor (MFMIS) structure is reported. The fabricated FeTFT exhibits a highly linear conductance response (|α| = 0.21) with a large dynamic range (Gmax/Gmin ≈ 53.2), although identical program pulses are applied to the device. In addition, because the inner metal layer of the FeTFTs has an MFMIS structure, the electric field is uniformly applied to the entire IGZO channel, which reduces the cycle‐to‐cycle variation (σ = 0.47%) in the conductance responses. In the system simulation with the measured synaptic characteristics, the high classification accuracy of ≈97.0% is achieved in the MNIST image set, verifying the feasibility of FeTFT‐based neuromorphic systems. [ABSTRACT FROM AUTHOR]

Simple Summary: Polo-like kinase 4 (PLK4) is associated with tumorigenesis and prognosis in various types of cancer. Prognostic analysis of PLK4 expression and data analysis of its association with the somatic mutation and drug resistance through clustering were performed using the Cancer Genome Atlas-lung adenocarcinoma (TCGA-LUAD) dataset. According to these results, PLK4 expression is associated with poor prognosis, TP53 mutations, and drug resistance in patients with LUAD. (1) Background: This study investigated whether polo-like kinase 4 (PLK4) is a suitable therapeutic target or biomarker for lung adenocarcinoma (LUAD). (2) Methods: We acquired LUAD data from The Cancer Genome Atlas (TCGA) database through the UCSC Xena data portal. Gene expression, clinical, survival, and mutation data from multiple samples were analyzed. Gene enrichment analysis, unsupervised clustering of PLK4-related pathways, and differential gene expression analyses were performed. Additionally, correlations, t-tests, survival analyses, and statistical analyses were performed. (3) Results: PLK4 expression was higher in LUAD tissues than in normal tissues and was associated with poor prognosis for both overall and progression-free survival in LUAD. PLK4 was highly correlated with cell-proliferation-related pathways using Gene Ontology (GO) biological process terms. PLK4 expression and pathways that were highly correlated with PLK4 expression levels were upregulated in patients with LUAD with the TP53 mutation. (4) Conclusions: PLK4 expression affects the survival of patients with LUAD and is a potential therapeutic target for LUAD with TP53 mutations. [ABSTRACT FROM AUTHOR]

This study presents a structural analysis and optimization for the lightweight design of a buoyant rotor-type permanent magnet (BRPM) generator, which was first presented in Bang (2010), and compares its structural performance to that of a conventional generator with a spoke arm-type rotor and stator. The main benefit of a BRPM generator is that it can be constructed as a bearingless drive system, free from the mechanical failure of rotor bearings, by using a buoyant rotor. Additionally, the deformation of the generator by blade vibration can be effectively suppressed using joint couplings between the blades and the rotor. For design optimization, the objective is set as the mass of the rotor and the stator, and the maximum deformation of the airgap clearance between the rotor and the stator by external forces is constrained below 10% of the gap width. The commercial software OptiStruct is used for the analysis and optimization. In this investigation, the analysis and optimization are conducted for a 10 MW wind turbine generator. However, the proposed methods can be extended to larger generator designs without requiring considerable modification. The mass of the optimized 10 MW BRPM generator is 160.7 tons (19.3 tons for the rotor and 141.4 tons for the stator), while that of an optimized conventional spoke arm-type generator is 325.6 tons. [ABSTRACT FROM AUTHOR]

Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy. [ABSTRACT FROM AUTHOR]

This work proposes positive feedback (PF) device-based synaptic devices for reliable binary neural networks (BNNs). Due to PF operation, the fabricated PF device shows a high on/off current ratio (2.69 × 107). The PF device has a charge-trap layer by which the turn-on voltage (Von) of the device can be adjusted by program/erase operations and a long-term memory function is implemented. Also, due to the steep switching characteristics of the PF device, the conductance becomes tolerant to the retention time and the variation in turn-on voltage. Simulations show that high accuracy (88.44% for CIFAR-10 image classification) can be achieved in hardware-based BNNs using PF devices with these properties as synapses. [ABSTRACT FROM AUTHOR]

This paper presents a synaptic driving circuit design for processing in-memory (PIM) hardware with a thin-film transistor (TFT) embedded flash (eFlash) for a binary/ternary-weight neural network (NN). An eFlash-based synaptic cell capable of programming negative weight values to store binary/ternary weight values (i.e., ±1, 0) and synaptic driving circuits for erase, program, and read operations of synaptic arrays have been proposed. The proposed synaptic driving circuits improve the calculation accuracy of PIM operation by precisely programming the sensing current of the eFlash synaptic cell to the target current (50 nA ± 0.5 nA) using a pulse train. In addition, during PIM operation, the pulse-width modulation (PWM) conversion circuit converts 8-bit input data into one continuous PWM pulse to minimize non-linearity in the synaptic sensing current integration step of the neuron circuit. The prototype chip, including the proposed synaptic driving circuit, PWM conversion circuit, neuron circuit, and digital blocks, is designed and laid out as the accelerator for binary/ternary weighted NN with a size of 324 × 80 × 10 using a 0.35 μm CMOS process. Hybrid bonding technology using bump bonding and wire bonding is used to package the designed CMOS accelerator die and TFT eFlash-based synapse array dies into a single chip package. [ABSTRACT FROM AUTHOR]

Neovascular age-related macular degeneration (nAMD) and central serous chorioretinopathy (CSC) are two of the most common macular diseases. This study proposes a convolutional neural network (CNN)-based deep learning model for classifying the subtypes of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and CSC (chronic CSC and acute CSC) and healthy individuals using single spectral–domain optical coherence tomography (SD–OCT) images. The proposed model was trained and tested using 6063 SD–OCT images from 521 patients and 47 healthy participants. We used three well-known CNN architectures (VGG–16, VGG–19, and ResNet) and two customized classification layers. Additionally, transfer learning and mix–up-based data augmentation were applied to improve robustness and accuracy. Our model demonstrated high accuracies of 99.7% and 91.1% in the nAMD and CSC classification and retinopathy (nAMD and CSC) subtype classification, including normal participants, respectively. Furthermore, we performed an external test to compare the classification accuracy with that of eight ophthalmologists, and our model showed the highest accuracy. The region determined to be important for classification by the model was confirmed using gradient-weighted class activation mapping. The model's clinical criteria were similar to that of the ophthalmologists. [ABSTRACT FROM AUTHOR]

Background: Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)–based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. Objective: This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. Methods: For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. Results: The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. Conclusions: Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists. [ABSTRACT FROM AUTHOR]

The development of highly tribopositive materials is crucial for realizing high‐performance triboelectric nanogenerators. In this study, a novel protocol to maximize the number of non‐bonding electrons with local dipoles for designing highly tribopositive materials is introduced, and nitrogen‐based dimethylol urea, diazolidinyl urea, and imidazolidinyl urea as promising tribopositive materials are suggested. The mechanism by which nitrogen‐based materials provide highly tribopositive properties is investigated using calculations based on density functional theory. Highly electronegative atoms, such as nitrogen and oxygen, attract electrons from neighboring atoms, resulting in the formation of negative local dipoles in the highest occupied molecular orbital band composed of non‐bonding electrons, thereby creating an electron‐donating environment. The proposed design protocol is confirmed by quantitatively investigating the triboelectric properties of nitrogen‐based materials, and analyzing the charge transfer characteristics of dimethylol urea based on dipole interactions. [ABSTRACT FROM AUTHOR]

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1475-6803.2009.01254.x Byline: Joon Ho Hwang (*), Bill Hu ([dagger]) Keywords: G34 Abstract: Abstract We empirically decompose private benefits into benefits accruing from ownership and benefits accruing from control. We document that private benefits increase slowly with respect to the ownership level but increase rapidly with respect to the blockholder's likelihood of exercising control. The decomposition of private benefits allows us to quantify the magnitude of nonpecuniary private benefits by examining the block premium when the blockholder's likelihood of exercising control is close to zero. We find that the size of nonpecuniary private benefits ranges from 0.61% to 5.92% of the share price, or 18% to 29% of the total private benefits. Author Affiliation: (*)Korea University Business School ([dagger])Arkansas State University

After the COVID-19 pandemic, the development of an accurate diagnosis and monitoring of diseases became a more important issue. In order to fabricate high-performance and sensitive biosensors, many researchers and scientists have used many kinds of nanomaterials such as metal nanoparticles (NPs), metal oxide NPs, quantum dots (QDs), and carbon nanomaterials including graphene and carbon nanotubes (CNTs). Among them, CNTs have been considered important biosensing channel candidates due to their excellent physical properties such as high electrical conductivity, strong mechanical properties, plasmonic properties, and so on. Thus, in this review, CNT-based biosensing systems are introduced and various sensing approaches such as electrochemical, optical, and electrical methods are reported. Moreover, such biosensing platforms showed excellent sensitivity and high selectivity against not only viruses but also virus DNA structures. So, based on the amazing potential of CNTs-based biosensing systems, healthcare and public health can be significantly improved. [ABSTRACT FROM AUTHOR]

Multi-functionalized carbon nanomaterials have attracted interest owing to their excellent synergic properties, such as plasmon resonance energy transfer and surface-enhanced Raman scattering. Particularly, nanoparticle (NP)-decorated graphene (GRP) has been applied in various fields. In this study, silver NP (AgNP)- and magnetic iron oxide NP (IONP)-decorated GRP were prepared and utilized as biosensing platforms. In this case, AgNPs and GRP exhibit plasmonic properties, whereas IONPs exhibit magnetic properties; therefore, this hybrid nanomaterial could function as a magnetoplasmonic substrate for the magnetofluoro-immunosensing (MFI) system. Conversely, exosomes were recently considered high-potential biomarkers for the diagnosis of diseases. However, exosome diagnostic use requires complex isolation and purification methods. Nevertheless, we successfully detected a prostate-cancer-cell-derived exosome (PC-exosome) from non-purified exosomes in a culture media sample using Ag/IO-GRP and dye-tetraspanin antibodies (Ab). First, the anti-prostate-specific antigen was immobilized on the Ag/IO-GRP and it could isolate the PC-exosome from the sample via an external magnetic force. Dye-tetraspanin Ab was added to the sample to induce the sandwich structure. Based on the number of exosomes, the fluorescence intensity from the dye varied and the system exhibited highly sensitive and selective performance. Consequently, these hybrid materials exhibited excellent potential for biosensing platforms. [ABSTRACT FROM AUTHOR]

We propose a hybrid self-curing method based on the parasitic bipolar junction transistor (PBJT) inherent to metal-oxide-semiconductor field-effect transistor (MOSFET). The PBJT utilizes the positive feedback of impact ionization to flow a large current close to the channel, generating Joule heat. Unlike conventional self-curing methods, the PBJT-based self-curing recovers the damaged gate oxide along the lateral dimension of the entire channel. The effects of the hybrid curing are quantitatively verified by low-frequency noise spectroscopy and charge pumping method. [ABSTRACT FROM AUTHOR]