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This study reports on the highly simple fabrication of green carbon black (GCB) generated from scrap tires with acetic acid to improve the adsorption efficiency for water purification, which is thoroughly compared with conventional carbon black (CB) obtained from petrochemicals. Unlike traditional modification processes with strong acids or bases, the introduction of a relatively mild acid readily allowed for the effective modification of GCB to increase the uptake capability of metal ions and toxic organic dyes to serve as effective adsorbents. The morphological features and thermal decomposition patterns were examined by electron microscopy and thermogravimetric analysis (TGA). The surface functional groups were characterized by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The structural information (ratio of D-defects/G band-graphitic domains) obtained by Raman spectroscopy clearly suggested the successful fabrication of GCB (ID/IG ratio of 0.74), which was distinctively different from typical CB (ID/IG ratio of 0.91). In the modified GCB, the specific surface area (SBET) gradually increased with the reduction of pore size as a function of acetic acid content (52.97 m2/g for CB, 86.64 m2/g for GCB, 102.10-119.50 m2/g for acid-treated GCB). The uptake capability of the modified GCB (312.5 mg/g) for metal ions and organic dyes was greater than that of the unmodified GCB (161.3 mg/g) and typical CB (181.8 mg/g), presumably due to the presence of adsorbed acid. Upon testing them as adsorbents in an aqueous solution, all these carbon materials followed the Langmuir isotherm over the Freundlich model. In addition, the removal rates of cationic species (>70% removal of Cu2+ and crystal violet in 30 min) were much faster and far greater than those of anionic metanil yellow (<40% removal in 3 h), given the strong electrostatic interactions. Thus, this work demonstrates the possibility of recycling waste tires in the powder form of GCB as a cost-effective and green adsorbent that can potentially substitute traditional CB, and the modification strategy provides a proof of concept for developing simple fabrication guidelines of other carbonaceous materials. [ABSTRACT FROM AUTHOR]

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher–student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models. [ABSTRACT FROM AUTHOR]

Soft hydrated permeable surfaces of hydrogels exhibit unique lubrication behaviors, including frictional hysteresis found in tribo-rheometry measurements. A hydrogel lubrication model that describes the transient behavior was previously developed using the structure kinetics model in the field of rheology and rate-and-state friction model, where the friction change is described as a competition between buildup and breakdown rates. In this study, the model is further modified to include the effect of hydrophilicity of a countersurface. Ultraviolet (UV)/ozone treatment on an aluminum surface significantly removes organic materials, resulting in extremely hydrophilic surface. Friction response of a polyacrylamide hydrogel against untreated and UV/ozone-treated aluminum exhibited noteworthy difference in the trajectory of hysteresis. Model fits were conducted using the modified lubrication model on both hystereses, and the fitting parameters of both hystereses are compared with each other to identify a parameter addressing hydrophilicity. Based on the model fits, we suggest that the hydrophilicity of the countersurface initially prevents the adsorption on the hydrogel surface because it holds water better. However, once water goes out of the contact due to contact pressure, a stronger adsorption occurs, which increases friction and decreases the speed dependence of friction. [ABSTRACT FROM AUTHOR]

Facial-image-based age estimation is being increasingly used in various fields. Examples include statistical marketing analysis based on age-specific product preferences, medical applications such as beauty products and telemedicine, and age-based suspect tracking in intelligent surveillance camera systems. Masks are increasingly worn for hygiene, personal privacy concerns, and fashion. In particular, the acquisition of mask-occluded facial images has become more frequent due to the COVID-19 pandemic. These images cause a loss of important features and information for age estimation, which reduces the accuracy of age estimation. Existing de-occlusion studies have investigated masquerade masks that do not completely occlude the eyes, nose, and mouth; however, no studies have investigated the de-occlusion of masks that completely occlude the nose and mouth and its use for age estimation, which is the goal of this study. Accordingly, this study proposes a novel low-complexity attention-generative adversarial network (LCA-GAN) for facial age estimation that combines an attention architecture and conditional generative adversarial network (conditional GAN) to de-occlude mask-occluded human facial images. The open databases MORPH and PAL were used to conduct experiments. According to the results, the mean absolution error (MAE) of age estimation with the de-occluded facial images reconstructed using the proposed LCA-GAN is 6.64 and 6.12 years, respectively. Thus, the proposed method yielded higher age estimation accuracy than when using occluded images or images reconstructed using the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Lithium is perceived as an ideal anode for next generation batteries with high‐energy density. However, the critical issue of the intractable growth of Li dendrites, which leads to a poor cycling life, still remains. Herein, a hierarchical surface is designed and constructed on carbon fiber (CF) using binders in fabricated CF paper (CFP). The lightweight CF with high mechanical properties is facilitated to establish a 3D network structure as an alternative to Cu foil. The binders are transformed into oxygen‐containing amorphous carbon and sodium carbonate (Na2CO3) using a low‐temperature carbonization process, leading to uniform Li nucleation and a stable solid electrolyte interphase layer with inorganic components. In the electrochemical test, the CFP with amorphous carbon and Na2CO3 (ANCFP) shows a low Li nucleation overpotential and smooth dendrite‐free Li plating. Furthermore, the ANCFP electrode exhibits good cycling stability in half and symmetrical cells. A full‐cell assembled using a LiFePO4 cathode with high loading (≈13 mg cm−2) achieves a high‐energy density of 428 Wh kg−1 (at 0.1 C) and an excellent capacity retention of 85% at 1 C after 300 cycles. This strategy is expected to help realize highly stable Li metal anodes for practical application by suppressing Li dendrite growth. [ABSTRACT FROM AUTHOR]

Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one's finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Despite extensive studies on structural changes during the carbonization process of pitch-derived fibers, an accurate description of the transformation from liquid crystalline domains into carbon crystallites is still limited to a few depictions based on common analytical tools for carbon fibers. We employed small-angle X-ray scattering (SAXS) with model fits for the unification of such disparate measures. The carbonization process below 1200 °C is divided into three sequential regimes: Regime I - disruption of stacked polyaromatic mesogens with fluctuations in elasticity from 300 to 600 °C; Regime II - full-scale transformation with enhancement in orientation from 600 to 800 °C; and Regime III - development of semi-crystalline carbon structures with elongation of microvoids from 800 to 1200 °C. By examining the viscoelastic properties of pitch-derived fibers during heat treatment below 600 °C (Regime I), we found that the maximum softness of the pitch-derived fibers is achieved at 500 °C. This is due to the decrease in crosslink density between stacking structures, indicating that the crosslink density below 600 °C is a significant contributor to the formation of carbon crystallites. [Display omitted] • The carbonization of pitch fibers below 1200 °C is divided into three regimes. • Microstructural change is depicted with domains, crystalline carbons and microvoids. • The transformation of domains into carbon crystallites occurs from 600 to 800 °C. • SAXS is employed for the unification of common analytical tools for carbon fibers. • Maximum softness of the pitch-derived fibers is achieved at 500 °C. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a method to enhance the segmentation performance of thyroid nodules in ultrasound images based on information fusion of suggestion and enhancement segmentation networks. Experimental results with two open databases of thyroid digital image databases and 3DThyroid databases showed that our method resulted in a higher performance compared to current up-to-date methods. [ABSTRACT FROM AUTHOR]

To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents while operating at low altitude. Even though many studies have been conducted to detect transmission lines, there still remains many challenges due to their thin shapes in diverse backgrounds. Moreover, most previous methods require a significant level of human involvement to generate pixel-level ground truth data. In this paper, we propose a transmission line detection algorithm based on weakly supervised learning and unpaired image-to-image translation. The proposed algorithm only requires image-level labels, and a novel attention module, which is called parallel dilated attention (PDA), improves the detection accuracy by recalibrating channel importance based on the information from various receptive fields. Finally, we construct a refinement network based on unpaired image-to-image translation in order that the prediction map is guided to detect line-shaped objects. The proposed algorithm outperforms the state-of-the-art method by 2.74% in terms of F1-score, and experimental results demonstrate that the proposed method is effective for detecting transmission lines in both quantitative and qualitative aspects. [ABSTRACT FROM AUTHOR]

Author(s): Jiho Choi [1, 2, 3, 4]; Aaron J. Huebner [1, 2, 3, 4]; Kendell Clement [3, 4, 5]; Ryan M. Walsh [1, 2, 3, 4]; Andrej Savol [1]; Kaixuan [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.chb.2004.03.039 Byline: Inwon Kang (a), Kun Chang Lee (b), Sangjae Lee (c), Jiho Choi (d) Keywords: Cognitive map; Factors affecting online community behavior; Community commitment; Loyalty; Social participation; SEM Abstract: It is difficult even for experts in organizational behavior to cognitively predict the causal effect of one factor on the others. A cognitive map is used to describe the inference process for the investigation of online community voluntary behavior. The investigation of online community voluntary behavior demands consideration of the complex causal effect from support for member communication, perceived community value, recognition for contribution, freedom of expression, and interactive communication, to community commitment, loyalty, and social participation. A standardized causal coefficient estimated in structural equation models (SEMs) is used to create a cognitive map illustrating the effect of the status of one component on the status of another component. The cognitive map provides preliminary insights into the direction of online community voluntary behavior toward maximizing community commitment, loyalty, and social participation. Author Affiliation: (a) School of Economics & International Trade, Kyunghee University, Seoul 130-701, Republic of Korea (b) School of Business Administration Sungkyunkwan University, Seoul 110-745, Republic of Korea (c) School of Business Administration Sejong University, Seoul 143-747, Republic of Korea (d) College of Business Administration, Chonnam National University, Qwangju 500-757, Republic of Korea

Even though plastic improved the human standard of living, handling the plastic waste represents an enormous challenge. It takes more than 100 years to decompose discarded or buried waste plastics. Microplastics are one of the causes of significantly pervasive environmental pollutants. The incineration of plastic waste generates toxic gases, underscoring the need for new approaches, in contrast to conventional strategies that are required for recycling plastic waste. Therefore, several studies have attempted to upcycle plastic waste into high value‐added products. Converting plastic waste into carbonaceous materials is an excellent upcycling technique due to their diverse practical applications. This review summarizes various studies dealing with the upcycling of plastic waste into carbonaceous products. Further, this review discusses the applications of carbonaceous products synthesized from plastic waste including carbon fibers, absorbents for water purification, and electrodes for energy storage. Based on the findings, future directions for effective upcycling of plastic waste into carbonaceous materials are suggested. [ABSTRACT FROM AUTHOR]

Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. However, these methods are limited to represent rumor propagation as a static graph, which isn't optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, named Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task. [ABSTRACT FROM AUTHOR]

Despite the wide-spread use and importance of thixotropic materials, accurate theoretical descriptions are still limited to a handful of model transient flow conditions. We employ an iterative series of tests to experimentally probe the complex dynamics exhibited by thixotropic materials. We use flow cessation tests to identify transient elastic stresses during stress jump tests. It is shown that the evolution of the elastic stress closely follows that of total stress in the series of stress jump tests, indicating that elasticity is a significant contributor to thixotropy. [ABSTRACT FROM AUTHOR]

The optimal design of water distribution systems (WDSs) should be economical, consider practical field applicability, and satisfy hydraulic constraints such as nodal pressure and flow velocity. However, the general optimal design of a WDSs approach using a metaheuristic algorithm was difficult to apply for achieving pipe size continuity at the confluence point. Although some studies developed the design approaches considering the pipe continuity, these approaches took many simulation times. For these reasons, this study improves the existing pipe continuity search method by reducing the computation time and enhancing the ability to handle pipe size continuity at complex joints that have more than three nodes. In addition to more practical WDSs designs, the approach considers various system design factors simultaneously in a multi-objective framework. To verify the proposed approach, the three well-known WDSs to apply WDS design problems are applied, and the results are compared with the previous design method, which used a pipe continuity research algorithm. This study can reduce the computation time by 87% and shows an ability to handle complex joints. Finally, the application of this practical design technique, which considers pipe continuity and multiple design factors, can reduce the gap between the theoretical design and the real world because it considers construction conditions and abnormal situations. [ABSTRACT FROM AUTHOR]

Byline: Jae Wook Kim, Jiho Choi, Kyesook Han While many practitioners and researchers have recognised the importance of the online community, there has been little empirical research that investigates how online communities effectively sustain their existence and maintain value for their members. The purpose of this study is to suggest online community voluntary behaviour (OCVB) as a critical behavioural outcome that an online community has to pursue from its members to ensure successful growth and existence, and to empirically examine the relationships among relationship building efforts, community commitment, and OCVB. We have tested a model using data collected from a total of 1514 members of an online community and confirmed that online community commitment is a key mediator between OCVB and relationship support activities. The results suggest that an online community's activities to support its members enhance members' commitment to the community and that the community commitment has an important influence on members' voluntary behaviour.

Oscillatory shearing is a popular method to understand transient nonlinear rheology. Various viscoelastic metrics have been used to analyze oscillatory rheology with different perspectives. We present a translation between various viscoelastic metrics for oscillatory rheology, using the framework of sequence of physical processes (SPPs) as a basis. The relation between the SPP metrics and Fourier-based metrics, such as Fourier sine and cosine coefficients, and large and minimum strain and rate metrics is provided. The meaning of the curvature in elastic and viscous Lissajous figures is explained with the sign of the SPP viscoelastic metrics. A low dimensional interpretation of the SPP framework is presented, featuring the center, size, and orientation of a deltoid in a transient Cole-Cole plot. Finally, we show how statistical information regarding the amount of change exhibited by the SPP metrics over a period of oscillation can be used to enhance the presentation and understanding of traditionally performed amplitude sweep experiments. [ABSTRACT FROM AUTHOR]

The equivalence of human induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs) remains controversial. Here we use genetically matched hESC and hiPSC lines to assess the contribution of cellular origin (hESC vs. hiPSC), the Sendai virus (SeV) reprogramming method and genetic background to transcriptional and DNA methylation patterns while controlling for cell line clonality and sex. We find that transcriptional and epigenetic variation originating from genetic background dominates over variation due to cellular origin or SeV infection. Moreover, the 49 differentially expressed genes we detect between genetically matched hESCs and hiPSCs neither predict functional outcome nor distinguish an independently derived, larger set of unmatched hESC and hiPSC lines. We conclude that hESCs and hiPSCs are molecularly and functionally equivalent and cannot be distinguished by a consistent gene expression signature. Our data further imply that genetic background variation is a major confounding factor for transcriptional and epigenetic comparisons of pluripotent cell lines, explaining some of the previously observed differences between genetically unmatched hESCs and hiPSCs. [ABSTRACT FROM AUTHOR]

How embryonic stem cells (ESCs) commit to specific cell lineages and yield all cell types of a fully formed organism remains a major question. ESC differentiation is accompanied by large-scale histone and DNA modifications, but the relations between these epigenetic categories are not understood. Here we demonstrate the interplay between the histone deacetylase sirtuin 6 (SIRT6) and the ten-eleven translocation enzymes (TETs). SIRT6 targets acetylated histone H3 at Lys 9 and 56 (H3K9ac and H3K56ac), while TETs convert 5-methylcytosine into 5-hydroxymethylcytosine (5hmC). ESCs derived from Sirt6 knockout (S6KO) mice are skewed towards neuroectoderm development. This phenotype involves derepression of OCT4, SOX2 and NANOG, which causes an upregulation of TET-dependent production of 5hmC. Genome-wide analysis revealed neural genes marked with 5hmC in S6KO ESCs, thereby implicating TET enzymes in the neuroectoderm-skewed differentiation phenotype. We demonstrate that SIRT6 functions as a chromatin regulator safeguarding the balance between pluripotency and differentiation through Tet-mediated production of 5hmC. [ABSTRACT FROM AUTHOR]

The generation of induced pluripotent stem cells (iPSCs) often results in aberrant epigenetic silencing of the imprinted Dlk1-Dio3 gene cluster, compromising the ability to generate entirely iPSC-derived adult mice ('all-iPSC mice'). Here, we show that reprogramming in the presence of ascorbic acid attenuates hypermethylation of Dlk1-Dio3 by enabling a chromatin configuration that interferes with binding of the de novo DNA methyltransferase Dnmt3a. This approach allowed us to generate all-iPSC mice from mature B cells, which have until now failed to support the development of exclusively iPSC-derived postnatal animals. Our data show that transcription factor-mediated reprogramming can endow a defined, terminally differentiated cell type with a developmental potential equivalent to that of embryonic stem cells. More generally, these findings indicate that culture conditions during cellular reprogramming can strongly influence the epigenetic and biological properties of the resultant iPSCs. [ABSTRACT FROM AUTHOR]

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis. [ABSTRACT FROM AUTHOR]

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants. [ABSTRACT FROM AUTHOR]

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method's performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders. [ABSTRACT FROM AUTHOR]

When images are acquired for finger-vein recognition, images with nonuniformity of illumination are often acquired due to varying thickness of fingers or nonuniformity of illumination intensity elements. Accordingly, the recognition performance is significantly reduced as the features being recognized are deformed. To address this issue, previous studies have used image preprocessing methods, such as grayscale normalization or score-level fusion methods for multiple recognition models, which may improve performance in images with a low degree of nonuniformity of illumination. However, the performance cannot be improved drastically when certain parts of images are saturated due to a severe degree of nonuniformity of illumination. To overcome these drawbacks, this study newly proposes a generative adversarial network for the illumination normalization of finger-vein images (INF-GAN). In the INF-GAN, a one-channel image containing texture information is generated through a residual image generation block, and finger-vein texture information deformed by the severe nonuniformity of illumination is restored, thus improving the recognition performance. The proposed method using the INF-GAN exhibited a better performance compared with state-of-the-art methods when the experiment was conducted using two open databases, the Hong Kong Polytechnic University finger-image database version 1, and the Shandong University homologous multimodal traits finger-vein database. [ABSTRACT FROM AUTHOR]

Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Among many available biometrics identification methods, finger-vein recognition has an advantage that is difficult to counterfeit, as finger veins are located under the skin, and high user convenience as a non-invasive image capturing device is used for recognition. However, blurring can occur when acquiring finger-vein images, and such blur can be mainly categorized into three types. First, skin scattering blur due to light scattering in the skin layer; second, optical blur occurs due to lens focus mismatching; and third, motion blur exists due to finger movements. Blurred images generated in these kinds of blur can significantly reduce finger-vein recognition performance. Therefore, restoration of blurred finger-vein images is necessary. Most of the previous studies have addressed the restoration method of skin scattering blurred images and some of the studies have addressed the restoration method of optically blurred images. However, there has been no research on restoration methods of motion blurred finger-vein images that can occur in actual environments. To address this problem, this study proposes a new method for improving the finger-vein recognition performance by restoring motion blurred finger-vein images using a modified deblur generative adversarial network (modified DeblurGAN). Based on an experiment conducted using two open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database version 1, the proposed method demonstrates outstanding performance that is better than those obtained using state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method. [ABSTRACT FROM AUTHOR]

Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction. [ABSTRACT FROM AUTHOR]

Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications. [ABSTRACT FROM AUTHOR]

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%). [ABSTRACT FROM AUTHOR]

Boronic acids, boronate esters, borate salts, and electron-rich arenes are efficiently cyanated with high functional group tolerance using a new oxidative procedure with NH4I (N source) and DMF (C source). [ABSTRACT FROM AUTHOR]

The industry-mimicking continuous stabilization of polyacrylonitrile (PAN) fibers was conducted in the temperature range of 180–270 °C to investigate microstructural evolutions of the fibers with comprehensive analysis at various intermediate stages. The chemical reactions during stabilization induce elongational stress along the fiber axis. In the low-temperature regime (below 240 °C), the cyclization reaction primarily occurs in the amorphous phase of the PAN structure, which possesses low thermal stability. The further stabilization including dehydrogenation and oxidation occurs actively even in the crystalline phase beyond 240 °C. The small-angle X-ray scattering (SAXS) observation confirms that the stabilized PAN fibers have two kinds of microvoids: Major large microvoids with a diameter and a length of ∼6 and ∼17 nm, respectively, located between the microfibrils are formed during the spinning of precursors by the solvent-nonsolvent exchange, and small microvoids with a diameter and a length of ∼1 and ∼2 nm, respectively, which are presumably located at the ends of molecules, are generated by gas evolution from inside the fibers during stabilization. We believe that the integrated studies on stabilization with physical and chemical structural evolution of the microvoids provide significant information for industrial manufacturing of high-performance carbon fibers. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The restricted supply of oocytes in the domestic dog limits the development of reproductive biotechnologies in this species. Inter-species somatic cell nuclear transfer could be an alternative for cloning animals whose oocytes are difficult to obtain. In this study, the possibility of cloning dog embryos using pig oocytes was investigated by evaluating nuclear remodeling. Chromatin remodeling, assessed by premature chromosome condensation, pseudo-pronuclei formation, DNA methylation and histone acetylation, along with the developmental ability was compared between intra- and inter-species cloned embryos. The incidence of premature chromosome condensation was significantly higher in intra-species cloned embryos relative to inter-species cloned embryos (87.2% vs. 61.7%; P <0.05), but comparable pseudo-pronuclei formation was observed in both (85.3% vs. 75.8%). None of the inter-species cloned embryos developed beyond the 8-cell stage while 18.3% of intra-species cloned embryos developed to the blastocyst stage. The relative level of both DNA methylation and histone acetylation was similar between intra- and inter-species cloned embryos at all times examined. These results suggest that although partial chromatin remodeling occurs, further investigation is needed to be able to use pig oocytes as recipient oocytes in dog cloning. [Copyright &y& Elsevier]

Purpose: An increasing number of older patients now undergo liver transplantation (LT). Although the overall outcomes in older patients are not different from those of younger patients, there is no tool to predict LT prognosis in older patients. We hypothesized that a modified Charlson comorbidity index (mCCI) and 5-factor modified frailty index (mFI-5) can predict outcomes in older patients after LT., Methods: This retrospective study included 155 patients (aged >65 years) who underwent LT at Seoul National University Hospital. The recipients were subcategorized into 2 groups based on the mCCI score and mFI-5: the low (0-1) and high (2-5) mCCI groups, and low (≤0.4) and high (>0.4) mFI-5 groups. The independent effect of each variable on post-LT survival was determined using the mCCI subgroup, age at transplantation, sex, Child-Turcotte-Pugh score, model for end-stage liver disease (MELD) score, and mFI-5 subgroup., Results: The high-mCCI group (41 patients) showed significantly lower 1- and 3-month and 1-, 3-, and 5-year survival than the low-mCCI group. Using the Cox regression model, the mCCI, sex, and MELD score remained significant. The mFI-5 was not a significant factor to predict patients' survival., Conclusion: The mCCI and MELD scores could be used to predict post-LT survival in older patients., Competing Interests: Conflict of Interest: No potential conflict of interest relevant to this article was reported., (Copyright © 2023, the Korean Surgical Society.)

The present protocol describes the creation of four-dimensional (4D), time-dependent, shape-changeable, stimuli-responsive soft robots using a three-dimensional (3D) bio-printing method. Recently, 4D printing techniques have been extensively proposed as innovative new methods for developing shape-transformable soft robots. In particular, 4D time-dependent shape transformation is an essential factor in soft robotics because it allows effective functions to occur at the right time and place when triggered by external cues, such as heat, pH, and light. In line with this perspective, stimuli-responsive materials, including hydrogels, polymers, and hybrids, can be printed to realize smart shape-transformable soft robotic systems. The current protocol can be used to fabricate thermally responsive soft grippers composed of N-isopropylacrylamide (NIPAM)-based hydrogels, with overall sizes ranging from millimeters to centimeters in length. It is expected that this study will provide new directions for realizing intelligent soft robotic systems for various applications in smart manipulators (e.g., grippers, actuators, and pick-and-place machines), healthcare systems (e.g., drug capsules, biopsy tools, and microsurgeries), and electronics (e.g., wearable sensors and fluidics).