In Situ Surface Defect Detection in Polymer Tube Extrusion: AI-Based Real-Time Monitoring Approach.

While striving to optimize overall efficiency, smart manufacturing systems face various problems presented by the aging workforce in modern society. The proportion of aging workers is rapidly increasing worldwide, and visual perception, which plays a key role in quality control, is significantly susceptible to the impact of aging. Thus it is necessary to understand these changes and implement state-of-the-art technologies as solutions. In this study, we conduct research to mitigate the negative effects of aging on visual recognition through the synergistic effects of real-time monitoring technology combining cameras and AI in polymer tube production. Cameras positioned strategically and with sophisticated AI within the manufacturing environment promote real-time defect detection and identification, enabling an immediate response. An immediate response to defects minimizes facility downtime and enhances the productivity of manufacturing industries. With excellent detection performance (approximately 99.24%) and speed (approximately 20 ms), simultaneous defects in a tube can be accurately detected in real time. Finally, real-time monitoring technology with adaptive features and superior performance can mitigate the negative impact of decreased visual perception in aging workers and is expected to improve quality consistency and quality management efficiency.

Design, synthesis, and biological evaluation of HDAC6 inhibitors targeting L1 loop and serine 531 residue.

Histone deacetylases (HDACs) are a group of enzymes that remove acetyl groups from histones, leading to the silencing of genes. Targeting specific isoforms of HDACs has emerged as a promising approach for cancer therapy, as it can overcome drawbacks associated with pan-HDAC inhibitors. HDAC6 is a unique HDAC isoform that deacetylates non-histone proteins and is primarily located in the cytoplasm. It also has two catalytic domains and a zinc-finger ubiquitin binding domain (Zf-UBD) unlike other HDACs. HDAC6 plays a critical role in various cellular processes, including cell motility, protein degradation, cell proliferation, and transcription. Hence, the deregulation of HDAC6 is associated with various malignancies. In this study, we report the design and synthesis of a series of HDAC6 inhibitors. We evaluated the synthesized compounds by HDAC enzyme assay and identified that compound 8g exhibited an IC50 value of 21 nM and 40-fold selective activity towards HDAC6. We also assessed the effect of compound 8g on various cell lines and determined its ability to increase protein acetylation levels by Western blotting. Furthermore, the increased acetylation of α-tubulin resulted in microtubule polymerization and changes in cell morphology. Our molecular docking study supported these findings by demonstrating that compound 8g binds well to the catalytic pocket via L1 loop of HDAC6 enzyme. Altogether, compound 8g represents a preferential HDAC6 inhibitor that could serve as a lead for the development of more potent and specific inhibitors.

Olfactory system-inspired electronic nose system using numerous low-cost homogenous and hetrogenous sensors.

This paper presents an electronic nose system inspired by the biological olfactory system. When comparing the human olfactory system to that of a dog, it's worth noting that dogs have 30 times more olfactory receptors and three times as many types of olfactory receptors. This implies that the number of olfactory receptors could be a more important parameter for classifying chemical compounds than the number of receptor types. Instead of using expensive precision sensors, the proposed electronic nose system relies on numerous low-cost homogeneous and heterogeneous sensors with poor cross-interference characteristics due to their low gas selectivity. Even if the same type of sensor shows a slightly different output for the same chemical compound, this variation becomes a unique signal for the target gas being measured. The electronic nose system comprises 30 sensors, the e-nose had 6 differing sensors with 5 replicates of each type. The characteristics of the electronic nose system are evaluated using three different volatile alcoholic compounds, more than 99% of which are the same. Liquid samples are supplied to the sensor chamber for 60 seconds using an air bubbler, followed by a 60-second cleaning of the chamber. Sensor signals are acquired at a sampling rate of 100 Hz. In this experimental study, the effects of data preprocessing methods and the number of sensors of the same type are investigated. By increasing the number of sensors of the same type, classification accuracy exceeds 99%, regardless of the deep learning model. The proposed electronic nose system, based on low-cost sensors, demonstrates similar results to commercial expensive electronic nose systems.

A study on improving the prediction accuracy of cold forging die life based on quantitative evaluation of phosphate film damage.

In designing the multi-stage cold forging process, an important factor along with formability is the die life cycle. As the die life cycle is closely related to the integrity of the phosphate film coating on the surface of the material, this study presents a method for predicting the die life cycle considering damage to the phosphate film coating. First, the correlation between the phosphate treatment conditions (phosphate treatment solution concentration and temperature) and the film layer weight was discussed. Afterwards, the behavior of the friction coefficient according to the damage of the phosphate film was predicted through repeated frictional tests. The behavior of the coefficient of friction was mainly divided into three areas and correlated with the film weight. By applying these results to the automobile engine bolt forging process, a method for performing precise simulation considering the damage to the phosphate film was presented. In addition, a method for predicting the quantitative limit life of the die based on precise simulation results with high accuracy was presented.

Exploring new histone deacetylase 6 inhibitors and their effects on reversing the α-tubulin deacetylation and cell morphology changes caused by methamphetamine.

Indazole-based HDAC6 inhibitors with novel zinc-binding modifications were synthesized and evaluated to determine their potential to inhibit HDAC6. The analogs were subjected to a histone deacetylase (HDAC) enzyme assay, which led to identification of compounds 3a and 3b. Both compounds demonstrated higher potency and selectivity as HDAC6 inhibitors with IC50 values of 9.1 nM and 9.0 nM, respectively, and highlighted the importance of the hydroxamic acid moiety for binding to Zn2+ inside the catalytic pocket of HDAC enzymes. In the neuroblastoma SH-SY5Y cell line, both compounds efficiently acetylated α-tubulin but not histone H3 at a low concentration of 0.5 µM. Moreover, compounds 3a and 3b effectively reversed the deacetylation of α-tubulin caused by methamphetamine in the SH-SY5Y cell line, suggesting the potential usefulness of HDAC6 selective inhibition in restoring blood brain barrier integrity by reversing methamphetamine-induced deacetylation.

Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression.

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

SPC-180002, a SIRT1/3 dual inhibitor, impairs mitochondrial function and redox homeostasis and represents an antitumor activity.

Since sirtuins (SIRTs) are closely associated with reactive oxygen species (ROS) and antioxidant system, the development of their selective inhibitors is drawing attention for understanding of cellular redox homeostasis. Here, we describe the pharmacological properties of SPC-180002, which incorporates a methyl methacrylate group as a key pharmacophore, along with its comprehensive molecular mechanism as a novel dual inhibitor of SIRT1/3. The dual inhibition of SIRT1/3 by SPC-180002 disturbs redox homeostasis via ROS generation, which leads to an increase in both p21 protein stability and mitochondrial dysfunction. Increased p21 interacts with and inhibits CDK, thereby interfering with cell cycle progression. SPC-180002 leads to mitochondrial dysfunction by inhibiting mitophagy, which is accompanied by a reduction in oxygen consumption rate. Consequently, SPC-180002 strongly suppresses the proliferation of cancer cells and exerts anticancer effect in vivo. Taken together, the novel SIRT1/3 dual inhibitor, SPC-180002, impairs mitochondrial function and redox homeostasis, thereby strongly inhibiting cell cycle progression and cancer cell growth.

Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images.

A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of -7.5°, 0.0°, and +7.5° to the vertical plane, and three cameras are placed at 45° intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm × 100 mm × 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5~35.21 µm. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization.

Dynamic Reconstruction and Mesh Compression of 4D Volumetric Model Using Correspondence-Based Deformation for Streaming Service.

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method's effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%.

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network.

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user's device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user's mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.

Development of smart cold forging die life cycle management system based on real-time forging load monitoring.

Cold forging dies are manufactured through the shrink fit process to withstand high pressure loads, but fatigue failure eventually occurs due to repeated compressive stresses. The life cycle until fatigue failure was defined as the limit life, and attempts were made to predict the die life based on finite element method. However, accurate prediction was impossible owing to uncontrollable environmental variables. Consequently, it is impossible to clearly determine the die replacement cycle, resulting in negative consequences such as poor quality, production delay, and cost increase. Various environmental factors affecting the prediction of die life cycle result in the increase or decrease of the forming load, which is an important variable that determines the die life cycle. In this study, a system for monitoring load data generated from forging facilities was developed based on a piezo sensor. In addition, the die life cycle was more accurately predicted by using the forming load data measured in real time, and a die life management system that can determine the die replacement cycle was applied to the automobile steering parts production line.

Design, synthesis, and biological evalution of bifunctional inhibitors against Hsp90-HDAC6 interplay.

HDAC6 and Hsp90, existing as a cytosolic complex play an important role in maintaining the protein homeostasis. The interplay of HDAC6 and Hsp90 has attracted wide attention due to their important role and promise as therapeutic targets in malignant cancers. Therefore, the discovery of dual inhibitors targeting HDAC6 and Hsp90 is of high importance. In the present study, we describe the design, synthesis, and biological evaluation of bifunctional inhibitors against HDAC6 and Hsp90 interplay. In particular, compound 6e shows a significant inhibitory activity against both HDAC6 and Hsp90 with IC50 values of 106 nM and 61 nM, respectively. Compound 6e promotes the acetylation of HDAC6 substrate proteins such as α-tubulin and Hsp90 via HDAC6 inhibition, and also induces the degradation of Hsp90 clients such as Her2, EGFR, Met, Akt, and HDAC6 via Hsp90 inhibition. Compound 6e consequently furnishes potent antiproliferative effect on gefitinib-resistant H1975 non-small cell lung cancer (NSCLC) with a GI50 value of 1.7 µM. In addition, compound 6e successfully achieved significant tumor growth inhibition in H1975 NSCLC xenograft model without noticeable abnormal behavior, body weight changes, and apparent ocular toxicity. We conclude that compound 6e constitutes an excellent tool as well as a valuable lead for assessment of Hsp90 and HDAC6 dual inhibition with a single molecule.

Design, synthesis, and biological evaluation of histone deacetylase inhibitor with novel salicylamide zinc binding group.

INTRODUCTION: Histone deacetylases (HDACs) have emerged as important therapeutic targets for various diseases, such as cancer and neurological disorders. Although a majority of HDAC inhibitors use hydroxamic acids as zinc binding groups, hydroxamic acid zinc-binding groups suffer from poor bioavailability and nonspecific metal-binding properties, necessitating a new zinc-binding group. Salicylic acid and its derivatives, well-known for their therapeutic value, have also been reported to chelate zinc ions in a bidentate fashion. This drew our attention towards replacing hydroxamic acid with salicylamide as a zinc-binding group. METHODS: In this study, for the first time, compound 5 possessing a novel salicylamide zinc-binding group was synthesized and evaluated biologically for its ability to inhibit various HDAC isoforms and induce acetylation upon α-tubulin and histone H3 among MDA-MB-231 cells. RESULTS: Compound 5 exhibits selective inhibition against class I HDAC isoforms (HDAC1, 2, and 3) over class II and IV HDAC isoforms (HDAC4, 6, and 11). The exposure of MDA-MB-231 cells to compound 5 efficiently induced the acetylation of more histone H3 than α-tubulin, suggesting that compound 5 is a class I selective HDAC inhibitor. Moreover, the molecular docking study indicated that the salicylamide zinc-binding group of compound 5 coordinates the active zinc ion of class I HDAC2 in a bidentate fashion. CONCLUSION: Overall, salicylamide represents a novel zinc-binding group for the development of class I selective HDAC inhibitors. GRAPHICAL ABSTRACT: (http://links.lww.com/MD/G668).

Rational Design and Synthesis of HSF1-PROTACs for Anticancer Drug Development.

PROTACs employ the proteosome-mediated proteolysis via E3 ligase and recruit the natural protein degradation machinery to selectively degrade the cancerous proteins. Herein, we have designed and synthesized heterobifunctional small molecules that consist of different linkers tethering KRIBB11, a HSF1 inhibitor, with pomalidomide, a commonly used E3 ligase ligand for anticancer drug development.

3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview.

This paper proposes a new technique for performing 3D static-point cloud registration after calibrating a multi-view RGB-D camera using a 3D (dimensional) joint set. Consistent feature points are required to calibrate a multi-view camera, and accurate feature points are necessary to obtain high-accuracy calibration results. In general, a special tool, such as a chessboard, is used to calibrate a multi-view camera. However, this paper uses joints on a human skeleton as feature points for calibrating a multi-view camera to perform calibration efficiently without special tools. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D joint set obtained through pose estimation as feature points. Since human body information captured by the multi-view camera may be incomplete, a joint set predicted based on image information obtained through this may be incomplete. After efficiently integrating a plurality of incomplete joint sets into one joint set, multi-view cameras can be calibrated by using the combined joint set to obtain extrinsic matrices. To increase the accuracy of calibration, multiple joint sets are used for optimization through temporal iteration. We prove through experiments that it is possible to calibrate a multi-view camera using a large number of incomplete joint sets.

Phase-only hologram video compression using a deep neural network for up-scaling and restoration.

This paper proposes a coding method for compressing a phase-only hologram video (PoHV), which can be directly displayed in a commercial phase-only spatial light modulator. Recently, there has been active research to use a standard codec as an anchor to develop a new video coding for 3D data such as MPEG point cloud compression. The main merit of this approach is that if a new video codec is developed, the performance of relative coding methods can be increased simultaneously. Furthermore, compatibility is increased by the capability to use various anchor codecs, and the developing time is decreased. This paper uses a currently used video codec as an anchor codec and develops a coding method including progressive scaling and a deep neural network to overcome low temporal correlation between frames of a PoHV. Since it is difficult to temporally predict a correlation between frames of a PoHV, this paper adopts a scaling function and a neural network in the encoding and decoding process, not adding complexity to an anchor itself to predict temporal correlation. The proposed coding method shows an enhanced coding gain of an average of 22%, compared with an anchor in all coding conditions. When observing numerical and optical reconstructions, the result images by the proposed show clearer objects and less juddering than the result by the anchor.

Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system.

Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.

Deep-learning-based hologram generation using a generative model.

We propose a new learning and inferring model that generates digital holograms using deep neural networks (DNNs). This DNN uses a generative adversarial network, trained to infer a complex two-dimensional fringe pattern from a single object point. The intensity and fringe patterns inferred for each object point were multiplied, and all the fringe patterns were accumulated to generate a perfect hologram. This method can achieve generality by recording holograms for two spaces (16 Space and 32 Space). The reconstruction results of both spaces proved to be almost the same as numerical computer-generated holograms by showing the performance at 44.56 and 35.11 dB, respectively. Through displaying the generated hologram in the optical equipment, we proved that the holograms generated by the proposed DNN can be optically reconstructed.

Adverse events and preventive measures related to COVID-19 vaccines.

The coronavirus disease 2019 (COVID-19) vaccines are categorized according to the manufacturing technique, including mRNA vaccines and adenovirus vector vaccines. According to previous studies, the reported efficacy of the COVID-19 vaccine is excellent regardless of the type of vaccine, and the majority of studies have shown similar results for safety. Most of the adverse reactions after vaccination were mild or moderate grade, and severe reactions were reported in a very small proportion. However, the adverse reactions that might occur after nationwide vaccinations can contribute to crowding of emergency departments, and this can further lead to significant obstacles to providing necessary treatment for life-threatening conditions. Therefore, as emergency physicians, we would like to present some concerns and suggestions to prevent these predictable problems.

Wet-Spun Composite Filaments from Lignocellulose Nanofibrils/Alginate and Their Physico-Mechanical Properties.

Lignocellulose nanofibrils (LCNFs) with different lignin contents were prepared using choline chloride (ChCl)/lactic acid (LA), deep eutectic solvent (DES) pretreatment, and subsequent mechanical defibrillation. The LCNFs had a diameter of 15.3-18.2 nm, which was similar to the diameter of commercial pure cellulose nanofibrils (PCNFs). The LCNFs and PCNFs were wet-spun in CaCl2 solution for filament fabrication. The addition of sodium alginate (AL) significantly improved the wet-spinnability of the LCNFs. As the AL content increased, the average diameter of the composite filaments increased, and the orientation index decreased. The increase in AL content improved the wet-spinnability of CNFs but deteriorated the tensile properties. The increase in the spinning rate resulted in an increase in the orientation index, which improved the tensile strength and elastic modulus.