bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
Authors: Liang Xingwei , Du Jiachen , Niu Taiyu , Zhou Lanjun , Xu Ruifeng
Published: July 2023

In the Knowledge Grounded Dialogue (KGD) generation, the explicit modeling of instance-variety of knowledge specificity and its seamless fusion with the dialogue context remains challenging. This paper presents an innovative approach, the Knowledge Interpolated conditional Variational auto-encoder (KIV), to address these issues. In particular, KIV introduces a novel interpolation mechanism to fuse two latent variables: independently encoding dialogue context and grounded knowledge. This distinct fusion of context and knowledge in the semantic space enables the interpolated latent variable to guide the decoder toward generating more contextually rich and engaging responses. We further explore deterministic and probabilistic methodologies to ascertain the interpolation weight, capturing the level of knowledge specificity. Comprehensive empirical analysis conducted on the Wizard-of-Wikipedia and Holl-E datasets verifies that the responses generated by our model performs better than strong baselines, with notable performance improvements observed in both automatic metrics and manual evaluation.
Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation
Authors: Li Jianming , Wan Lili , Li Jing , Hou Kai
Published: July 2023

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.
Vision Transformers for Anomaly Detection and Localisation in Leather Surface Defect Classification Based on Low-Resolution Images and a Small Dataset
Authors: Smith Antony Douglas , Du Shengzhi , Kurien Anish
Published: July 2023

Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry’s immense scale, there may be numerous unavoidable causes of damages, leading to surface defects that occur during both the manufacturing process and the bovine’s own lifespan. Owing to the heterogenous and manifold nature of leather surface characteristics, great difficulties can arise during the visual inspection of raw materials by human inspectors. To mitigate the industry’s challenges in the quality control process, this paper proposes the application of a modern vision transformer (ViT) architecture for the purposes of low-resolution image-based anomaly detection for defect localisation as a means of leather surface defect classification. Utilising the low-resolution defective and non-defective images found in the opensource Leather Defect detection and Classification dataset and higher-resolution MVTec AD anomaly benchmarking dataset, three configurations of the vision transformer and three deep learning (DL) knowledge transfer methods are compared in terms of performance metrics as well as in leather defect classification and anomaly localisation. Experiments show the proposed ViT method outperforms the light-weight state-of-the-art methods in the field in the aspect of classification accuracy. Besides the classification, the low computation load and low requirements for image resolution and size of training samples are also advantages of the proposed method.
Identical Parallel Machine Scheduling Considering Workload Smoothness Index
Authors: Wang Zhaojie , Zheng Feifeng , Liu Ming
Published: July 2023

Workload balance is significant in the manufacturing industry. However, on the one hand, some existing specific criteria cannot achieve the minimization workload imbalance of parallel machines. On the other hand, there are few algorithms in existing studies that can effectively solve the parallel machine scheduling problem with the objective of minimizing workload imbalance. Inspired by this, we investigate an identical parallel machine scheduling problem with the objective of the minimum workload smoothness index. We first establish a mathematical model for the considered problem and then linearize its objective function. We prove the NP-hardness of the problem by reducing the PARTITION problem to it, and we provide both the upper bound and lower bound of the studied problem. An efficient genetic algorithm and an improved list scheduling algorithm are also proposed to efficiently address the considered problem. The numerical results demonstrate the effectiveness of the proposed methods.
Potential Use of Paper Mill Sludge in Improving Soil Quality for Plant Growth
Authors: Lee Chang-Young , Kim Chul-Hwan , Park Hyeong-Hun , Park Min-Sik , Lee Cheong-Ha , Park Ju-Hyun
Published: July 2023

This study evaluated the impact of soil modifiers on seed germination and soil quality. Mixtures of paper mill sludge (PMS) with camellia oilseed cake (CO) and peat moss (P), as well as with oilseed cake of toothache tree (TO) and peat moss (P), showed promising results as biostimulants for seed germination. The addition of PMS increased the germination index, indicating its beneficial effects on seed performance. The pH levels remained optimal, and the electrical conductivity values stayed within acceptable ranges, suggesting minimal hindrance to plant growth. The cation exchange capacity increased with PMS, enhancing nutrient availability. Composted mixtures exhibited increased phosphorus levels, contributing to soil fertility. While the organic matter content decreased after composting, the carbon-to-nitrogen ratio remained balanced. The bulk density increased after composting, potentially improving soil drainage. Overall, the TO-containing mixture demonstrated superior growth rates compared to the P. These findings support the use of PMS-based soil modifiers for enhancing seed germination and improving soil quality.
Evaluation of the Nutritional Quality of Different Soybean and Pea Varieties: Their Use in Balanced Diets for Different Pathologies
Authors: Moroșan Elena , Lupu Carmen , Mititelu Magdalena , Musuc Adina , Rusu Andreea , Răducan Ionuț , Karampelas Oana , Voinicu Ionuț , Neacșu Sorinel , Licu Monica , Pogan Ana , Cîrnațu Daniela , Ilie Elena , Dărăban Adriana
Published: July 2023

Among the cultivated plants of great interest at the planetary level, soy and pea can be highlighted. Soy represents a major source of protein and oil, with exceptional and widely accepted qualities in most cultures and religions. It is also a very good substitute for animal protein, having significant amounts of essential amino acids. Peas, although less cultivated than soybeans, contain large amounts of protein and carbohydrates, and they are also a source of food used in many diets due to their high nutritional content. The present study focuses on the nutrient composition analysis of five soybean varieties and four pea varieties grown in Romania for their use in food in the most efficient way. Protein dosage was carried out using Bradford and Kjeldahl methods, and the amino acids were dosed using gas chromatography. It was demonstrated that the analyzed varieties are rich in nutrients with different content depending on their type. Among the soybean hybrid varieties analyzed, the beans from the early Ovidiu F type and the semi-early Anduța F stood out for their increased content of lipids (23.28%) and proteins with increased biological value, and in the case of the pea beans, those from the Evelina F (22.21%) varieties of the Afila and Spectral F autumn types presented the highest content of proteins (21.06%) and essential amino acids (16.87%). All the obtained results offer a theoretical foundation for the advanced and balanced application of different varieties of soybean and pea bean in a balanced diet.
GLFFNet: A Global and Local Features Fusion Network with Biencoder for Remote Sensing Image Segmentation
Authors: Tian Qing , Zhao Fuhui , Zhang Zheng , Qu Hongquan
Published: July 2023

In recent years, semantic segmentation of high-resolution remote sensing images has been gradually applied to many important scenes. However, with the rapid development of remote sensing data acquisition technology, the existing image data processing methods are facing major challenges. Especially in the accuracy of extraction and the integrity of the edges of objects, there are often problems such as small objects being assimilated by large objects. In order to solve the above problems, based on the excellent performance of Transformer, convolution and its variants, and feature pyramids in the field of deep learning image segmentation, we designed two encoders with excellent performance to extract global high-order interactive features and low-order local feature information. These encoders are then used as the backbone to construct a global and local feature fusion network with a dual encoder (GLFFNet) to effectively complete the segmentation of remote sensing images. Furthermore, a new auxiliary training module is proposed that uses the semantic attention layer to process the extracted feature maps separately, adjust the losses, and more specifically optimize each encoder of the backbone, thus optimizing the training process of the entire network. A large number of experiments show that our model achieves 87.96% mIoU on the Potsdam dataset and 80.42% mIoU on the GID dataset, and it has superior performance compared with some state-of-the-art methods on semantic segmentation tasks in the field of remote sensing.
Nonuniform Deformation Instability Mechanism of Gob-Side Entry Retained in Inclined Coal Seam and Stability Control
Authors: Qiang Xubo , Li Ji , Chen Chaosen , Dong Jihui , Zheng Yingjian , Chen Zhong
Published: July 2023

In this study, the nonuniform deformation and failure of the goaf retaining roadway in an inclined coal seam due to repeated mining have been investigated by field verification, theoretical analysis and numerical simulation. As a case study, 3131 headentry of a coal mine in Sichuan province was considered. The deformation characteristics of the surrounding rock along the gob of inclined coal seam and the distribution characteristics and evolution of the plastic zone and stress field direction of gob-side entry retaining (GER) in 3131 coal faces during the service period were also studied. Based on the mechanical model of the plastic zone of surrounding rock, the stress field direction effect of nonuniform expansion of the plastic zone is explained, and the nonuniform deformation damage mechanism of the inclined coal seam along the empty tunnel is revealed. The results show that the plastic zone of the side always expands along the coal seam towards the side affected by mining during the whole service period of GER in the inclined coal seam, and the plastic zone of the roof and floor expands to the deep surrounding rock; and the expansion degree of the soft coal (rock) seam position of the roadway is the highest. At the same time, the direction of the surrounding rock stress field will be deflected during the service period of GER, and the plastic zone expands unevenly under the action of the coal seam dip angle and stress direction. The nonuniform expansion degree of the plastic zone is the largest when the angle between the maximum principal stress and the coal (rock) layer is 45° (±5°). A collaborative support method with “supporting and reducing span” as the core in GER is also proposed in this work. Field tests were also carried out. During the retaining period, the displacement of the roof and floor was reduced from 250 mm to 125 mm.
Development and Characterization of N/S-Carbon Quantum Dots by Valorizing Greek Crayfish Food Waste
Authors: Kechagias Achilleas , Lykos Christos , Karabagias Vassilios K. , Georgopoulos Stavros , Sakavitsi Viktoria , Leontiou Areti , Salmas Constantinos E. , Giannakas Aris E. , Konstantinou Ioannis
Published: July 2023

The valorization of food industry byproducts has become a significant issue worldwide because of the drive towards a circular economy. The “zero waste target” in human activities seems to be a dominant objective in the design of future products by enterprises. In this work, food waste from the crayfish processing industry was converted into useful products (quantum dots), as nowadays, biowaste-derived materials tend to be more attractive than conventionally produced materials with a similar structure due to their lower production costs and environmentally friendly development processes. More specifically, shell waste from the crayfish industry was treated hydrothermally and, after a freeze-drying process, was transformed to useful quantum dots. Instrumental and chemical techniques, such as XRD, SEM-EDS, AFM, XPS, elemental analysis, fluorescence spectroscopy, TG, Microtox bioassay, and DPPH antioxidant activity, were employed to characterize the final product. The results indicated the existence of thermally stable spherical particles, with a diameter of 5–8 nm, which were mainly composed of carbon, oxygen, nitrogen, calcium, and sulfur. Their external surface was rough and rich with various functional groups that further contributed to their overall optical properties. The final product presented low ecotoxicity, as studied by the Microtox assay. The superior antioxidant activity of this product compared to other similar materials reported elsewhere renders it a potential material for, e.g., food packaging applications. In addition, for the first time, N/S-Carbon QDs were applied as an antioxidant/antibacterial agent for strawberry preservation, showing promising results as the coated strawberries maintained their color and weight for three consecutive days with no mold growth observed on their surface.
Study on the Yield Behavior of Closed-Cell Foams under Multiaxial Loads Based on Different Yield Definitions
Authors: Yu Huimin , Fang Qinzhi , Qian Jiong , Hu Yang
Published: July 2023

In this paper, the yield behavior of closed-cell foams under multiaxial load conditions is investigated via finite element analysis on the representative volume element (RVE) scale by using the regular Kelvin of RVE and random models of RVE, respectively. Several different definitions of yield are considered in the study. By benchmarking the simulations with the experimental data in the literature, it is shown that the elastic energy criterion is shown to be suitable for the definition of the yield point of closed-cell foams under different stress states. Based on the micro-scale elastic energy, which can be readily obtained by numerical simulation, a general yield definition is proposed to determine the yield behavior of the closed-cell foam materials at macroscopic scales. To test the adaptability of this general yield definition, we analyze multiple random models with different relative densities and inner structures. The results indicate that this general yield definition method can be used for different models, even applied to continuum materials.
Review of the Main Mechanical Testing Methods for Interlayer Characterization in Laminated Glass
Authors: Zhou Sicheng , Cattaneo Sara , Biolzi Luigi
Published: July 2023

In the last decades, the use of structural glass has increased exponentially. The reliability of brittle structural glass elements is considerably improved if laminated glass elements are chosen because, in this case, a redistribution of internal forces is permitted once a limited breakage occurs. Thus, instead of monolithic glass, composite materials consisting of two or more glass plies bonded together using a polymeric film as an interlayer are used. In the event of glass failure, because of the chemical bond between the different materials, the adhesion to the interlayer prevents glass fragments from scattering. To design structural elements, the definition of the mechanical features of the interlayer is necessary. However, several standards and techniques can be applied, considering the characterization of either the interlayer itself or the laminated glass. The paper reviews the main existing methods and focuses on the standard suggested by the CEN/TS 19100:2021, analyzing in detail the effect of the different parameters involved. A numerical model is presented to account for the effect of the stress level, glass, and interlayer thickness. Although the standard leaves a certain degree of freedom in choosing those parameters, in some cases the results can differ.
Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification
Authors: K. Haritha , V. Judy M. , Papageorgiou Konstantinos , Papageorgiou Elpiniki
Published: July 2023

This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods.
A Natural-Worker Leaves the Colonial Visual Archive: The Art of Vered Nissim
Authors: Shtang Sivan Rajuan
Published: July 2023

The colonial visual archive has occupied in recent decades the work of scholars and artists from indigenous and racial minority communities, who revealed it as a major apparatus of historical meta-narratives. This article aims at pushing forward this preoccupation by revealing an additional scene: the art of Mizrahi women, descendants of Jewish communities of Arab and Muslim countries. Relying on a visual culture approach and focusing on an analysis of artworks by Mizrahi artist Vered Nissim, as well as on photographs of Mizrahi women, fund in Zionist archives, I demonstrate how Nissim’s work challenges the racial category of Mizrahi women as “natural workers”, constructed in the Zionist historical meta-narrative. Nissim does so by re-enacting the category’s paradigmatic visual image—the Mizrahi women cleaning worker—in a different way, visually and discursively. Body, voice, and visual image, three instances of the subjectivity of Mizrahi women cleaning workers that were separated, shaped, and mediated through Zionist colonial visual archives unite in Nissim’s work when embodied by a real Mizrahi woman cleaning worker: her mother, Esther Nissim. By casting her mother to play herself over the past twenty years, Nissim creates political conditions for the appearance of her mother as the author of her own history as she orally, bodily, and visually writes it in front of her daughter’s camera. Thus, Nissim joins a transnational phenomenon of global south artists who create political conditions enabling the self-imaging of colonized peoples, empowering the reading of colonial imagery and the historical meta-narratives attached to it through their situated knowledge and lived experience and, thus, constructing a counter history communicated visually.
Simulation Study of the Lunar Spectral Irradiances and the Earth-Based Moon Observation Geometry
Authors: Lian Yi , Renyang Qianqian , Tang Tianqi , Zhang Hu , Ping Jinsong , Meng Zhiguo , Li Wenxiao , Gao Huichun
Published: July 2023

As a radiant light source within the dynamic range of most spacecraft payloads, the Moon provides an excellent reference for on-orbit radiometric calibration. This research hinges on the precise simulation of lunar spectral irradiances and Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradiances, utilizing datasets obtained from the Lunar Reconnaissance Orbiter Camera (LROC). The research also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit spacecraft calibration procedures, thereby boosting the fidelity of data gathered from satellite observations.
Modal Projection for Quasi-Homogeneous Anisotropic Turbulence
Authors: Zhu Ying , Cambon Claude
Published: July 2023

This article, or essay, addresses the anisotropic structure and the dynamics of quasi-homogeneous, incompressible turbulence. Modal projection and expansions in terms of spherical harmonics in three-dimensional Fourier space are in line with a seminal study by Jack Herring, around the so-called Craya–Herring frame of reference, with a large review of the related approaches to date. The research part is focused on structure and dynamics of rotating sheared turbulence, including a description of both directional and polarization anisotropy with a minimal number of modes. Effort is made to generalize expansions in terms of scalar spherical harmonics (SSHs) to vector spherical harmonics (VSHs). Looking at stochastic fields, for possibly intermittent vector fields, some directions are explored to reconcile modal projection, firstly used for smooth vector fields, and multifractal approaches for internal intermittency but far beyond scalar correlations, such as structure functions. In order to illustrate turbulence from Earth to planets, stars, and galaxies, applications to geophysics and astrophysics are touched upon, with generalization to coupled vector fields (for kinetic, magnetic, and potential energies), possibly dominated by waves (Coriolis, gravity, and Alfvén).
Tsallis Entropy for the Past Lifetime Distribution with Application
Authors: Kayid Mohamed , Alshehri Mashael A.
Published: July 2023

A fundamental factor in relevant applications is the predictability of the life cycle of a coherent system consisting of more than one component. In this context, we examine how entropy can be applied to evaluate the degree of predictability. In particular, in order to calculate the Tsallis entropy of the past life, we consider a scenario in which all components of the system fail at a given time t and use the system signature to calculate the Tsallis entropy of the past life. We examine a number of analytical results, e.g., expressions, thresholds and orders for the measure at issue in our study. The results may provide insights into the predictability of a coherent system’s life cycle.
Solvability, Approximation and Stability of Periodic Boundary Value Problem for a Nonlinear Hadamard Fractional Differential Equation with p-Laplacian
Authors: Zhao Kaihong
Published: July 2023

The fractional order p-Laplacian differential equation model is a powerful tool for describing turbulent problems in porous viscoelastic media. The study of such models helps to reveal the dynamic behavior of turbulence. Therefore, this article is mainly concerned with the periodic boundary value problem (BVP) for a class of nonlinear Hadamard fractional differential equation with p-Laplacian operator. By virtue of an important fixed point theorem on a complete metric space with two distances, we study the solvability and approximation of this BVP. Based on nonlinear analysis methods, we further discuss the generalized Ulam-Hyers (GUH) stability of this problem. Eventually, we supply two example and simulations to verify the correctness and availability of our main results. Compared to many previous studies, our approach enables the solution of the system to exist in metric space rather than normed space. In summary, we obtain some sufficient conditions for the existence, uniqueness, and stability of solutions in the metric space.
Optimal Reinsurance–Investment Strategy Based on Stochastic Volatility and the Stochastic Interest Rate Model
Authors: Bei Honghan , Wang Qian , Wang Yajie , Wang Wenyang , Murcio Roberto
Published: July 2023

This paper studies insurance companies’ optimal reinsurance–investment strategy under the stochastic interest rate and stochastic volatility model, taking the HARA utility function as the optimal criterion. It uses arithmetic Brownian motion as a diffusion approximation of the insurer’s surplus process and the variance premium principle to calculate premiums. In this paper, we assume that insurance companies can invest in risk-free assets, risky assets, and zero-coupon bonds, where the Cox–Ingersoll–Ross model describes the dynamic change in stochastic interest rates and the Heston model describes the price process of risky assets. The analytic solution of the optimal reinsurance–investment strategy is deduced by employing related methods from the stochastic optimal control theory, the stochastic analysis theory, and the dynamic programming principle. Finally, the influence of model parameters on the optimal reinsurance–investment strategy is illustrated using numerical examples.
A Novel Robust Metric Distance Optimization-Driven Manifold Learning Framework for Semi-Supervised Pattern Classification
Authors: Ma Bao , Ma Jun , Yu Guolin
Published: July 2023

In this work, we address the problem of improving the classification performance of machine learning models, especially in the presence of noisy and outlier data. To this end, we first innovatively design a generalized adaptive robust loss function called Vθ(x). Intuitively, Vθ(x) can improve the robustness of the model by selecting different robust loss functions for different learning tasks during the learning process via the adaptive parameter θ. Compared with other robust loss functions, Vθ(x) has some desirable salient properties, such as symmetry, boundedness, robustness, nonconvexity, and adaptivity, making it suitable for a wide range of machine learning applications. Secondly, a new robust semi-supervised learning framework for pattern classification is proposed. In this learning framework, the proposed robust loss function Vθ(x) and capped L2,p-norm robust distance metric are introduced to improve the robustness and generalization performance of the model, especially when the outliers are far from the normal data distributions. Simultaneously, based on this learning framework, the Welsch manifold robust twin bounded support vector machine (WMRTBSVM) and its least-squares version are developed. Finally, two effective iterative optimization algorithms are designed, their convergence is proved, and their complexity is calculated. Experimental results on several datasets with different noise settings and different evaluation criteria show that our methods have better classification performance and robustness. With the Cancer dataset, when there is no noise, the classification accuracy of our proposed methods is 94.17% and 95.62%, respectively. When the Gaussian noise is 50%, the classification accuracy of our proposed methods is 91.76% and 90.59%, respectively, demonstrating that our method has satisfactory classification performance and robustness.
An Interval Type-3 Fuzzy–Fractal Approach for Plant Monitoring
Authors: Melin Patricia , Castillo Oscar
Published: July 2023

In this article, a plant monitoring approach based on a hybrid mixture of type-3 fuzzy logic (T3FL) and the fractal dimension (FD) is presented. The main reason for combining type-3 and the fractal dimension is to take advantage of both their capabilities in solving the problem of monitoring a plant. Basically, T3FL helps in handling the uncertainty in monitoring the variables of a nonlinear system, while the FD helps to capture the signal complexity by finding key or hidden patterns in the data. The FD is utilized to estimate data complexity of the process variables being monitored. We utilize the box counting algorithm to approximate the values of the FD. A set of T3FL rules is utilized to model monitoring knowledge. The proposed approach was tested with a plant studied in previous works, which was solved with type-1 and type-2 fuzzy logic, and now type-3 is able to surpass the performance of previous approaches for this problem. The main contribution is the T3FL and FD hybrid proposal for plant monitoring, which has not been presented before in the literature. Simulation results illustrate the potential advantage of utilizing the T3FL and FD combination in this area.
A Review on Recent Progress Achieved in Boron Carbon Nitride Nanomaterials for Supercapacitor Applications
Authors: Liu Feng , Zhao Xiang , Shi Ping , Li Laishi , Dong Qidi , Tian Mi , Wu Yusheng , Sun Xudong
Published: July 2023

Supercapacitors are regarded as reliable energy storage devices to alleviate the energy crisis and environmental pollution. However, the relatively low capacitance and low energy density limit the practical application of supercapacitors. In this context, boron carbon nitride (BCN) nanomaterials have been extensively studied in the past decade due to their chemical and thermal stability, high mechanical strength, as well as tunable bandgap. The specific capacitance and energy density of supercapacitors can be significantly improved by fabricating nanostructured BCN-based electrode materials. In this review, the recent advances in the application of BCN-based materials in supercapacitors is presented. Strategies such as structure design, porosity/defect engineering, and hybrid nanostructure construction to boost the electrochemical performance of BCN-based materials are provided and, finally, promising research directions for novel energy storage materials are proposed.
Overview of miR-106a Regulatory Roles: from Cancer to Aging
Authors: Daneshpour Maryam , Ghadimi-Daresajini Ali
Published: July 2023

MicroRNAs (miRNAs) comprise a class of non-coding RNA with extensive regulatory functions within cells. MiR-106a is recognized for its super-regulatory roles in vital processes. Hence, the analysis of its expression in association with diseases has attracted considerable attention for molecular diagnosis and drug development. Numerous studies have investigated miR-106 target genes and shown that this miRNA regulates the expression of some critical cell cycle and apoptosis factors, suggesting miR-106a as an ideal diagnostic and prognostic biomarker with therapeutic potential. Furthermore, the reported correlation between miR-106a expression level and cancer drug resistance has demonstrated the complexity of its functions within different tissues. In this study, we have conducted a comprehensive review on the expression levels of miR-106a in various cancers and other diseases, emphasizing its target genes. The promising findings surrounding miR-106a suggest its potential as a valuable biomolecule. However, further validation assessments and overcoming existing limitations are crucial steps before its clinical implementation can be realized.
Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis
Authors: Lee Minhyeok
Published: July 2023

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review’s findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.
A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation
Authors: Ananda Swathi , Jain Rahul Kumar , Li Yinhao , Iwamoto Yutaro , Han Xianhua , Kanasaki Shuzo , Hu Hongjie , Chen Yen-Wei
Published: July 2023

Multi-phase computed tomography (CT) images have gained significant popularity in the diagnosis of hepatic disease. There are several challenges in the liver segmentation of multi-phase CT images. (1) Annotation: due to the distinct contrast enhancements observed in different phases (i.e., each phase is considered a different domain), annotating all phase images in multi-phase CT images for liver or tumor segmentation is a task that consumes substantial time and labor resources. (2) Poor contrast: phase images may have poor contrast, making it difficult to distinguish the liver boundary. In this paper, we propose a boundary-enhanced liver segmentation network for multi-phase CT images with unsupervised domain adaptation. The first contribution is that we propose DD-UDA, a dual discriminator-based unsupervised domain adaptation, for liver segmentation on multi-phase images without multi-phase annotations, effectively tackling the annotation problem. To improve accuracy by reducing distribution differences between the source and target domains, we perform domain adaptation at two levels by employing two discriminators, one at the feature level and the other at the output level. The second contribution is that we introduce an additional boundary-enhanced decoder to the encoder–decoder backbone segmentation network to effectively recognize the boundary region, thereby addressing the problem of poor contrast. In our study, we employ the public LiTS dataset as the source domain and our private MPCT-FLLs dataset as the target domain. The experimental findings validate the efficacy of our proposed methods, producing substantially improved results when tested on each phase of the multi-phase CT image even without the multi-phase annotations. As evaluated on the MPCT-FLLs dataset, the existing baseline (UDA) method achieved IoU scores of 0.785, 0.796, and 0.772 for the PV, ART, and NC phases, respectively, while our proposed approach exhibited superior performance, surpassing both the baseline and other state-of-the-art methods. Notably, our method achieved remarkable IoU scores of 0.823, 0.811, and 0.800 for the PV, ART, and NC phases, respectively, emphasizing its effectiveness in achieving accurate image segmentation.
Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder
Authors: Xing Xin , Liang Gongbo , Wang Chris , Jacobs Nathan , Lin Ai-Ling
Published: July 2023

The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imaging. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images. Our approach involved performing imaging reconstruction using a Vision Transformer (ViT) model as the feature encoder, paired with a custom-defined decoder. Additionally, we fine-tuned the pretrained ViT encoder using a labeled medical dataset, serving as the backbone. To evaluate our approach, we conducted a comparative analysis of three distinct training methods: training from scratch, transfer learning, and MAE-based training, all employing COVID-19 chest X-ray images. The results demonstrate that MAE-based training produces superior performance, achieving an accuracy of 0.985 and an AUC of 0.9957. We explored the mask ratio influence on MAE and found ratio = 0.4 shows the best performance. Furthermore, we illustrate that MAE exhibits remarkable efficiency when applied to labeled data, delivering comparable performance to utilizing only 30% of the original training dataset. Overall, our findings highlight the significant performance enhancement achieved by using MAE, particularly when working with limited datasets. This approach holds profound implications for future disease diagnosis, especially in scenarios where imaging information is scarce.
Ex Vivo Model to Evaluate the Antibacterial and Anti-Inflammatory Effects of Gelatin–Tricalcium Phosphate Composite Incorporated with Emodin and Lumbrokinase for Bone Regeneration
Authors: Wang Wen-Ling , Hsu Yuan-Man , Lin Meng-Liang , Chen Shih-Shun , Lai Yi-Hui , Huang Chiung-Hua , Yao Chun-Hsu
Published: July 2023

Tricalcium phosphate (TCP) has gained attention due to its interconnected porous structures which promote fibrovascular invasion and bony replacement. Moreover, when gelatin is added and crosslinked with genipin (GGT), TCP exhibits robust biocompatibility and stability, making it an excellent bone substitute. In this study, we incorporated emodin and lumbrokinase (LK) into GGT to develop an antibacterial biomaterial. Emodin, derived from various plants, possesses antibacterial and anti-inflammatory properties. LK comprises proteolytic enzymes extracted from the earthworm Lumbricus rubellus and exhibits fibrinolytic activity, enabling it to dissolve biofilms. Additionally, LK stimulates osteoblast activity while inhibiting osteoclast differentiation. GGT was combined with emodin and lumbrokinase to produce the GGTELK composite. The biomedical effects of GGTELK were assessed through in vitro assays and an ex vivo bone defect model. The GGTELK composite demonstrated antibacterial properties, inhibiting the growth of S. aureus and reducing biofilm formation. Moreover, it exhibited anti-inflammatory effects by reducing the secretion of IL-6 in both in vivo cell experiments and the ex vivo model. Therefore, the GGTELK composite, with its stability, efficient degradation, biocompatibility, and anti-inflammatory function, is expected to serve as an ideal bone substitute.
Identification and Expressional Analysis of Putative PRDI-BF1 and RIZ Homology Domain-Containing Transcription Factors in Mulinia lateralis
Authors: Zhao Feng , Guo Xiaolin , Li Xixi , Liu Fang , Fu Yifan , Sun Xiaohan , Yang Zujing , Zhang Zhifeng , Qin Zhenkui
Published: July 2023

Mollusca represents one of the ancient bilaterian groups with high morphological diversity, while the formation mechanisms of the precursors of all germ cells, primordial germ cells (PGCs), have not yet been clarified in mollusks. PRDI-BF1 and RIZ homology domain-containing proteins (PRDMs) are a group of transcriptional repressors, and PRDM1 (also known as BLIMP1) and PRDM14 have been reported to be essential for the formation of PGCs. In the present study, we performed a genome-wide retrieval in Mulinia lateralis and identified 11 putative PRDMs, all of which possessed an N-terminal PR domain. Expressional profiles revealed that all these prdm genes showed specifically high expression levels in the given stages, implying that all PRDMs played important roles during early development stages. Specifically, Ml-prdm1 was highly expressed at the gastrula stage, the key period when PGCs arise, and was specifically localized in the cytoplasm of two or three cells of blastula, gastrula, or trochophore larvae, matching the typical characteristics of PGCs. These results suggested that Ml-prdm1-positive cells may be PGCs and that Ml-prdm1 could be a candidate marker for tracing the formation of PGCs in M. lateralis. In addition, the expression profiles of Ml-prdm14 hinted that it may not be associated with PGCs of M. lateralis. The present study provides insights into the evolution of the PRDM family in mollusks and offers a better understanding of the formation of PGCs in mollusks.
Biological and Genetic Characterizations of a Novel Lytic ΦFifi106 against Indigenous Erwinia amylovora and Evaluation of the Control of Fire Blight in Apple Plants
Authors: Choe Jaein , Kim Byeori , Park Mi-Kyung , Roh Eunjung
Published: July 2023

Erwinia amylovora is a devastating phytobacterium causing fire blight in the Rosaceae family. In this study, ΦFifi106, isolated from pear orchard soil, was further purified and characterized, and its efficacy for the control of fire blight in apple plants was evaluated. Its genomic analysis revealed that it consisted of 84,405 bp and forty-six functional ORFs, without any genes encoding antibiotic resistance, virulence, and lysogenicity. The phage was classified into the genus Kolesnikvirus of the subfamily Ounavirinae. ΦFifi106 specifically infected indigenous E. amylovora and E. pyrifoliae. The lytic activity of ΦFifi106 was stable under temperature and pH ranges of 4–50 °C and 4–10, as well as the exposure to ultraviolet irradiation for 6 h. ΦFifi106 had a latent period of 20 min and a burst size of 310 ± 30 PFU/infected cell. ΦFifi106 efficiently inhibited E. amylovora YKB 14808 at a multiplicity of infection (MOI) of 0.1 for 16 h. Finally, the pretreatment of ΦFifi106 at an MOI of 1000 efficiently reduced disease incidence to 37.0% and disease severity to 0.4 in M9 apple plants. This study addressed the use of ΦFifi106 as a novel, safe, efficient, and effective alternative to control fire blight in apple plants.
Adenosine A2A Receptor Up-Regulation Pre-Dates Deficits of Synaptic Plasticity and of Memory in Mice Exposed to Aβ1–42 to Model Early Alzheimer’s Disease
Authors: Lopes Cátia R. , Silva António C. , Silva Henrique B. , Canas Paula M. , Agostinho Paula , Cunha Rodrigo A. , Lopes João Pedro
Published: July 2023

The intracerebroventricular (icv) injection of amyloid peptides (Aβ) models Alzheimer’s disease (AD) in mice, as typified by the onset within 15 days of deficits of memory and of hippocampal long-term potentiation (LTP) that are prevented by the blockade of adenosine A2A receptors (A2AR). Since A2AR overfunction is sufficient to trigger memory deficits, we tested if A2AR were upregulated in hippocampal synapses before the onset of memory deficits to support the hypothesis that A2AR overfunction could be a trigger of AD. Six to eight days after Aβ-icv injection, mice displayed no alterations of hippocampal dependent memory; however, they presented an increased excitability of hippocampal synapses, a slight increase in LTP magnitude in Schaffer fiber-CA1 pyramid synapses and an increased density of A2AR in hippocampal synapses. A2AR blockade with SCH58261 (50 nM) normalized excitability and LTP in hippocampal slices from mice sacrificed 7–8 days after Aβ-icv injection. Fifteen days after Aβ-icv injection, mice displayed evident deficits of hippocampal-dependent memory deterioration, with reduced hippocampal CA1 LTP but no hyperexcitability and a sustained increase in synaptic A2AR, which blockade restored LTP magnitude. This shows that the upregulation of synaptic A2AR precedes the onset of deterioration of memory and of hippocampal synaptic plasticity, supporting the hypothesis that the overfunction of synaptic A2AR could be a trigger of memory deterioration in AD.
Risks after Gestational Diabetes Mellitus in Taiwanese Women: A Nationwide Retrospective Cohort Study
Authors: Tseng Shih-Ting , Lee Ming-Chang , Tsai Yi-Ting , Lu Mei-Chun , Yu Su-Chen , Tsai I-Ju , Lee I-Te , Yan Yuan-Horng
Published: July 2023

Objective: An increasing trend in the prevalence of gestational diabetes mellitus (GDM) has been reported in Taiwan. GDM has been linked to various adverse maternal outcomes over a long period, including cardiovascular disease (CVD) and chronic kidney disease (CKD). However, evidence implies that the effects of GDM on the mid-term surrogate risk factors for these diseases are limited. Furthermore, data from nationwide cohort studies are limited. The primary aim of this study was to investigate the risk of developing type 2 diabetes mellitus (T2DM), arterial hypertension (aHTN), and hyperlipidemia (HL) through a 5-year follow-up post-delivery of women with GDM in a nationwide cohort study in Taiwan. The second objective was to investigate the risk of developing insulin resistance syndrome (IRS)-related diseases, including CVD, acute myocardial infarction (AMI), peripheral artery occlusive disease (PAOD), non-alcoholic fatty liver diseases (NAFLD), and CKD. Methods: This was a retrospective, population-based nationwide cohort study. The data source comprises a merge of the Birth Certificate Application Database (BCA) and the National Health Insurance Research Database in Taiwan. Women aged between 15 and 45 years who gave birth in Taiwan between 2004 and 2011 were included. Women who were enrolled and had a GDM diagnosis were assigned to the exposure group. Women who were enrolled without a GDM diagnosis were assigned to the comparison group. The relative risk of developing T2DM, aHTN, HL, and IRS-related diseases, including CVD, AMI, PAOD, NAFLD, and CKD, were analyzed and presented as hazard ratio (HR) through Cox regression and log-rank regression analyses. Results: A total of 1,180,477 women were identified through the BCA database between 2004 and 2011. Of those, 71,611 GDM-diagnosed women and 286,444 women without GDM were included in the final analysis. After adjusting for age, pre-existing cancer, and parity, developing T2DM, aHTN, and HL were still significantly increased in the GDM group (HR and interquartile range (IQR): 2.83 (2.59, 3.08), 1.09 (1.01, 1.06), and 1.29 (1.20, 1.38), accordingly). CVD, NAFLD, and CKD had a very low incidence and showed insignificant results. Conclusion: Our findings provide nationwide cohort data showing that GDM increased the risk of developing T2DM, aHTN, and HL 5 years after delivery within the same group. The GDM complications and risk of CVD, AMI, PAOD, NAFLD, and CKD need further investigation.