bluuebunny/crossref_metadata_2025 huggingface dataset:
Breeding Soft Durum Wheat through Introgression of the T5AL·5VS Translocated Chromosome
Authors: Li Wen , Wei Yi , Jin Yinyu , Chen Heyu , Kong Lingna , Liu Xiaoxue , Xing Liping , Cao Aizhong , Zhang Ruiqi
Published: April 2024

The limited culinary utilizations of durum wheat (Triticum turgidum ssp. durum) are partly related to its very hard kernel texture, which is due to the softness genes Puroindoline a (Pina) and Puroindoline b (Pinb) on the Hardness (Ha) locus eliminated during allopolyploid formation. A previous study has reported that the softness genes Dina/Dinb, homologous to Pina/Pinb, were located on the chromosome arm 5VS of wild species Dasypyrum villosum. In the present study, we describe the process of transferring the soft grain texture from D. villosum into durum wheat through homoeologous recombination to develop a Robertsonian translocation. A durum wheat–D. villosum T5AL·5V#5S translocation line, S1286, was developed and characterized by molecular cytogenetic analysis from BC4F2 progeny of durum cv. ZY1286/D. villosum 01I140. The translocation line S1286 exhibited a soft grain texture as evidenced by observation through an electron microscope and a Single Kernel Characterization System (SKCS) hardness value of 5.5. Additionally, a newly developed 5VS/5AS co-dominant InDel marker, LW5VS-1, facilitated the transfer of the T5AL·5V#5S translocated chromosome into diverse durum wheat backgrounds. Subsequently, the T5AL·5V#5S translocated chromosome was transferred into five high-yielding durum wheat backgrounds by backcrossing and traced using marker LW5VS-1. Compared with each recurrent parent, T5AL·5V#5S lines showed good viability, similar development, and no yield penalty. Meanwhile, a significant decrease in plant height of about 6.0% was observed when comparing T5AL·5V#5S translocation lines with their recurrent parents. Accordingly, our results provide an efficient strategy for developing soft kernel durum wheat through the combination of T5AL·5V#5S translocation and the co-dominant marker LW5VS-1, which will be crucial for meeting the future challenges of sustainable agriculture and food security.
Tumor Immune Microenvironment and Its Clinicopathological and Prognostic Associations in Canine Splenic Hemangiosarcoma
Authors: Brachelente Chiara , Torrigiani Filippo , Porcellato Ilaria , Drigo Michele , Brescia Martina , Treggiari Elisabetta , Ferro Silvia , Zappulli Valentina , Sforna Monica
Published: April 2024

Tumor cells can induce important cellular and molecular modifications in the tissue or host where they grow. The idea that the host and tumor interact with each other has led to the concept of a tumor microenvironment, composed of immune cells, stromal cells, blood vessels, and extracellular matrix, representing a unique environment participating and, in some cases, promoting cancer progression. The study of the tumor immune microenvironment, particularly focusing on the role of tumor-infiltrating lymphocytes (TILs), is highly relevant in oncology due to the prognostic and therapeutic significance of TILs in various tumors and their identification as targets for therapeutic intervention. Canine splenic hemangiosarcoma (HSA) is a common tumor; however, its immune microenvironment remains poorly understood. This retrospective study aimed to characterize the histological and immunohistochemical features of 56 cases of canine splenic HSA, focusing particularly on tumor-infiltrating lymphocytes (TILs). We assessed the correlations between the lymphocytic response, the macroscopic and histological characteristics of the tumor, and the survival data. Our study demonstrated that FoxP3 distribution was associated with tumor-related death and survival, while the CD20 count was associated with metastasis. This study provides an in-depth characterization of the tumor immune microenvironment in canine splenic HSA and describes potential prognostic factors.
Fcγ-Receptor-Independent Controlled Activation of CD40 Canonical Signaling by Novel Therapeutic Antibodies for Cancer Therapy
Authors: Beckmann Karsten , Reitinger Carmen , Yan Xianglei , Carle Anna , Blümle Eva , Jurkschat Nicole , Paulmann Claudia , Prassl Sandra , Kazandjian Linda V. , Loré Karin , Nimmerjahn Falk , Fischer Stephan
Published: April 2024

The activation of CD40-mediated signaling in antigen-presenting cells is a promising therapeutic strategy to promote immune responses against tumors. Most agonistic anti-CD40 antibodies currently in development require the Fcγ-receptor (FcγR)-mediated crosslinking of CD40 molecules for a meaningful activation of CD40 signaling but have limitations due to dose-limiting toxicities. Here we describe the identification of CD40 antibodies which strongly stimulate antigen-presenting cells in an entirely FcγR-independent manner. These Fc-silenced anti-CD40 antibodies induce an efficient upregulation of costimulatory receptors and cytokine release by dendritic cells. Finally, the most active identified anti-CD40 antibody shows activity in humanized mice. More importantly, there are no signs of obvious toxicities. These studies thus demonstrate the potent activation of antigen-presenting cells with anti-CD40 antibodies lacking FcγR-binding activity and open the possibility for an efficacious and safe combination therapy for cancer patients.
Comparison of the Antioxidant Potency of Four Triterpenes of Centella asiatica against Oxidative Stress
Authors: Lim Jinyeong , Lee Hana , Hong Seonghwa , Lee Junsoo , Kim Younghwa
Published: April 2024

We comparatively evaluated the antioxidant properties of key triterpenes from Centella asiatica, including asiatic acid (AA), asiaticoside, madecassic acid, and madecassoside, in several cell types, including skin fibroblasts, macrophages, hepatocytes, and endothelial cells, under conditions promoting oxidative stress. AA conferred the highest viability on Hs68 cells exposed to ultraviolet B (UVB) irradiation. Triterpene pretreatment attenuated the UVB-induced generation of reactive oxygen species (ROS) and malondialdehyde (MDA), as well as the UVB-induced depletion of glutathione (GSH) in skin fibroblasts. AA most potently inhibited UVB-induced MMP generation, resulting in increased intracellular collagen levels. Pretreatment with triterpenes, particularly AA, significantly improved cell viability and attenuated TBHP-induced levels of ROS, alanine aminotransferase, and aspartate aminotransferase in HepG2 cells. Triterpenes attenuated ROS levels and reduced MDA and GSH expression in EA.hy926 cells. In RAW264.7 macrophages, production of nitric oxide, tumor necrosis factor-α, and interleukin-6 (indicators of LPS-induced oxidative damage) was significantly reduced by treatment with any of the triterpenes. Statistical analyses of triterpene biological activities using principal component analysis and hierarchical clustering revealed that AA exerted the greatest overall influence and showed remarkable activity in Hs68 and HepG2 cells.
Unlocking the Therapeutic Potential of Ellagic Acid for Non-Alcoholic Fatty Liver Disease and Non-Alcoholic Steatohepatitis
Authors: Senavirathna Tharani , Shafaei Armaghan , Lareu Ricky , Balmer Lois
Published: April 2024

Obesity is in epidemic proportions in many parts of the world, contributing to increasing rates of non-alcoholic fatty liver disease (NAFLD). NAFLD represents a range of conditions from the initial stage of fatty liver to non-alcoholic steatohepatitis (NASH), which can progress to severe fibrosis, through to hepatocellular carcinoma. There currently exists no treatment for the long-term management of NAFLD/NASH, however, dietary interventions have been investigated for the treatment of NASH, including several polyphenolic compounds. Ellagic acid is one such polyphenolic compound. Nutraceutical food abundant in ellagic acid undergoes initial hydrolysis to free ellagic acid within the stomach and small intestine. The proposed mechanism of action of ellagic acid extends beyond its initial therapeutic potential, as it is further broken down by the gut microbiome into urolithin. Both ellagic acid and urolithin have been found to alleviate oxidative stress, inflammation, and fibrosis, which are associated with NAFLD/NASH. While progress has been made in understanding the pharmacological and biological activity of ellagic acid and its involvement in NAFLD/NASH, it has yet to be fully elucidated. Thus, the aim of this review is to summarise the currently available literature elucidating the therapeutic potential of ellagic acid and its microbial-derived metabolite urolithin in NAFLD/NASH.
Granular Flow Impact on Shed Tunnels and the Buffering Effect of Cushion Layers
Authors: Wei Li , Wang Jianghong , Dai Zili
Published: April 2024

Granular flow is one of the most destructive geological hazards in mountainous areas, posing a severe threat to the economy and personnel safety in the region. Shed tunnels are widely used for the prevention and mitigation of granular flow hazards. Thus, comprehensively studying the impact mechanisms of granular flows on shed tunnels is significant for disaster prevention and mitigation. This study adopts a combined approach of a physical model experiment and Particle Flow Code (PFC) simulation to investigate the impact force of granular flow on shed tunnels and the buffering effect of cushion layers. The influences of slope angle, cushion layer thickness, and cushion layer particle size are discussed. It is revealed that as the slope angle increases, the velocity of the granular flow and the impact force on the shed rise significantly. When the slope angle increases from 40° to 60°, the peak velocity surges by 25%, while the impact force intensifies by 2–3 times. Moreover, increasing the thickness of the cushion layer can mitigate the interaction between the granular flow and the shed tunnel, thereby enhancing structural safety. With an increase in cushion layer thickness from 0 to 200 mm, the impact force is reduced by approximately 50%. Meanwhile, reducing the particle size of the cushion layer effectively decreases the impact force, resulting in less kinetic energy and providing a stronger cushioning effect.
Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
Authors: Duan Lijuan , Han Shengwen , Jiang Wei , He Meng , Qiao Yuanhua
Published: April 2024

A knowledge graph is a repository that represents a vast amount of information in the form of triplets. In the training process of completing the knowledge graph, the knowledge graph only contains positive examples, which makes reliable link prediction difficult, especially in the setting of complex relations. At the same time, current techniques that rely on distance models encapsulate entities within Euclidean space, limiting their ability to depict nuanced relationships and failing to capture their semantic importance. This research offers a unique strategy based on Gibbs sampling and connection embedding to improve the model’s competency in handling link prediction within complex relationships. Gibbs sampling is initially used to obtain high-quality negative samples. Following that, the triplet entities are mapped onto a hyperplane defined by the connection. This procedure produces complicated relationship embeddings loaded with semantic information. Through metric learning, this process produces complex relationship embeddings imbued with semantic meaning. Finally, the method’s effectiveness is demonstrated on three link prediction benchmark datasets FB15k-237, WN11RR and FB15k.
Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
Authors: Yun Lu , Li Hui , Zhang Ning , Shi Wei , Haider Rizwan
Published: April 2024

Polyvinyl Butyral (PVB) laminated glass (LG) with varying numbers of layers is extensively utilized in building structures, and its complex mechanical properties, due to the presence of PVB, pose significant challenges. While comprehensive research has been conducted on the bending behavior of two-layer PVB laminated glass, studies focusing on three-layer variants remain limited. This study aims to investigate the bending behavior of three-layer PVB laminated glass under out-of-plane forces and to ascertain the effects of increasing the number of layers. Experimental studies were carried out on one-, two-, and three-layer PVB laminated glass plates subjected to such loads. During the loading process, the out-of-plane displacement and surface strain of the glass plates were monitored and analyzed. From these observations, load-deflection curves were constructed, allowing for the determination of deflection and strain distribution across the planes. The impact of the number of glass layers on stiffness and ultimate load capacity was examined. Additionally, another experiment was conducted to analyze the mechanical properties of the laminated material (PVB). The research results indicate that the tensile strain at the bottom layer of the laminated glass plate reaches its threshold under an out-of-plane load, leading to a brittle fracture in that layer. However, the remaining layers of PVB LG do not fail at this stage, enabling the plate to continue bearing the load. The average ultimate loads for three-layer, two-layer, and one-layer PVB laminated glass are 37.8 kN, 36 kN, and 24.7 kN, respectively, with a ratio of 1:0.95:0.65. The bending stiffness values for these are 2.77 kN/mm, 1.71 kN/mm, and 1.21 kN/mm, respectively, corresponding to a ratio of 1:0.62:0.44. The stiffness shows a nonlinear increase with the layer count, an effect attributed to the PVB’s characteristics according to the analytical findings. The tensile behavior of the laminated materials demonstrates a bilinear characteristic when subjected to strain. The out-of-plane bending tests on glass plates reveal that when the strain rate is low, PVB behaves like a quasi-linear elastic material. Therefore, for design calculations of PVB LG, adopting an elastic modulus of 1.34 MPa for PVB is deemed reasonable. The findings of this study provide insights for research on laminated glass plates, which are instrumental in refining the calculation methods specified in relevant design standards. Concurrently, it offers guidance for selecting the appropriate number of PVB LG layers in engineering applications.
Enhancing Robustness of Surface Electromyography Pattern Recognition at Different Arm Positions for Transhumeral Amputees Using Deep Adversarial Inception Domain Adaptation
Authors: Li Sujiao , Sun Wanjing , Li Wei , Yu Hongliu
Published: April 2024

Pattern recognition in myoelectric control that relies on the myoelectric activity associated with arm motions is an effective control method applied to myoelectric prostheses. Individuals with transhumeral amputation face significant challenges in effectively controlling their prosthetics, as muscle activation varies with changes in arm positions, leading to a notable decrease in the accuracy of motion pattern recognition and consequently resulting in a high rejection rate of prosthetic devices. Therefore, to achieve high accuracy and arm position stability in upper-arm motion recognition, we propose a Deep Adversarial Inception Domain Adaptation (DAIDA) based on the Inception feature module to enhance the generalization ability of the model. Surface electromyography (sEMG) signals were collected from 10 healthy subjects and two transhumeral amputees while performing hand, wrist, and elbow motions at three arm positions. The recognition performance of different feature modules was compared, and ultimately, accurate recognition of upper-arm motions was achieved using the Inception C module with a recognition accuracy of 90.70% ± 9.27%. Subsequently, validation was performed using data from different arm positions as source and target domains, and the results showed that compared to the direct use of a convolutional neural network (CNN), the recognition accuracy on untrained arm positions increased by 75.71% (p < 0.05), with a recognition accuracy of 91.25% ± 6.59%. Similarly, in testing scenarios involving multiple arm positions, there was a significant improvement in recognition accuracy, with recognition accuracy exceeding 90% for both healthy subjects and transhumeral amputees.
Influence of Oxygen and Carbon Dioxide Content in Modified Atmosphere Packaging on the Colour and Water-Holding Capacity of Pork Loin
Authors: Zduńczyk Weronika , Modzelewska-Kapituła Monika , Tkacz Katarzyna
Published: April 2024

During storage, raw meat is exposed to many external factors, which cause visible changes on the surface of the meat and which affect its water-holding capacity. This study aimed to determine the effect of oxygen content in modified atmosphere packaging (MAP) used for storing fresh pork on the colour, pH, value and water-holding capacity during refrigerated storage. The study also analysed the dynamics of changes in colour using the colour difference (ΔE) coefficient and sensory quality. In the study, slices of pork loin were packed in MAP using the following gas compositions: 55% O2/40% CO2/5% N2 and 75% O2/20%, CO2/5% N2; they were then stored for 15 days at a temperature of 4 °C. The colour of pork stored in MAP was significantly affected by time, but not by the proportion of oxygen. During storage, the meat’s lightness (L*), yellowness (b*), chroma, and hue angle increased, whereas its redness index (a*/b*) decreased. Significant differences in colour between freshly packed and stored samples were noted after days 7 and 9 in MAP containing 55% and 75% oxygen, respectively. The values of pH, free water, and purge and cooking loss were not affected by gas concentration but changed over time. Lowering the oxygen content from 75% to 55% in MAP opens the possibility of reducing the oxygen demand from the meat industry without compromising the quality of the meat.
Multilevel Distributed Linear State Estimation Integrated with Transmission Network Topology Processing
Authors: Madurasinghe Dulip , Venayagamoorthy Ganesh Kumar
Published: April 2024

State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE.
Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
Authors: Duan Jie , Li Jiyu , Zhu Yundong , Zhang Hongtao , Liu Yuting , Zhao Yanan
Published: April 2024

Addressing the prevalent issues of low accuracy, low efficiency, and poor image quality in online diameter measurement of large-sized stepped shafts, this study introduces a novel method based on a symmetrical dual-telecentric optical path utilizing dual CCDs, specifically designed for step shafts with diameters ranging from 600 mm to 800 mm. By developing and optimizing an imaging system grounded in the object-image dual-telecentric optical path principle and employing Zemax software for comprehensive analysis and optimization, this research achieves significant findings. The system’s Airy disk radius is calculated at 3.204 μm; the modulation transfer function (MTF) remains above 0.6 across various fields of view at a spatial cutoff frequency of 71.4 lp/mm, with smooth MTF curves; the field curvature is confined within 0.1 μm; and the distortion is maintained below 0.1%, fulfilling high-quality imaging requirements. Additionally, a tolerance analysis is conducted to ensure the system’s stability and reliability. Applied to an experimental setup for measuring the diameter of large-sized step shafts, the system demonstrates an improved measurement precision of 0.02 mm. This research offers a robust technical solution for the high-precision online measurement of large stepped shaft diameters, presenting significant practical implications for enhancing productivity and product quality.
Advancing Brain Tumor Segmentation with Spectral–Spatial Graph Neural Networks
Authors: Mohammadi Sina , Allali Mohamed
Published: April 2024

In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and global information. Our Spectral GNN implementation employs the Laplacian matrix to efficiently map tumor tissue connectivity by capturing the graph’s global structure. Consequently, this enhances the model’s precision in classifying brain tumors into distinct types: necrosis, edema, and enhancing tumor. This model underwent extensive hyper-parameter tuning to ascertain the most effective configuration for optimal segmentation performance. Our Spectral–Spatial GNN model surpasses traditional segmentation methods in accuracy for both whole tumor and sub-regions, validated by metrics such as the dice coefficient and accuracy. For the necrotic core, the Spectral–Spatial GNN model showed a 10.6% improvement over the Spatial GNN and 8% over the Spectral GNN. Enhancing tumor gains were 9.5% and 6.4%, respectively. For edema, improvements were 12.8% over the Spatial GNN and 7.3% over the Spectral GNN, highlighting its segmentation accuracy for each tumor sub-region. This superiority underscores the model’s potential in improving brain tumor segmentation accuracy, precision, and computational efficiency.
A Metaverse Platform for Preserving and Promoting Intangible Cultural Heritage
Authors: Innocente Chiara , Nonis Francesca , Lo Faro Antonio , Ruggieri Rossella , Ulrich Luca , Vezzetti Enrico
Published: April 2024

The metaverse, powered by XR technologies, enables human augmentation by enhancing physical, cognitive, and sensory capabilities. Cultural heritage sees the metaverse as a vehicle for expression and exploration, providing new methods for heritage fruition and preservation. This article proposes a metaverse application, inspired by the events of the Italian Resistance, promoting interactions between multiple users in an immersive VR experience while safeguarding intangible cultural assets according to an edutainment approach. The virtual environment, based on Ivrea’s town hall square, provides in-depth information about the partisan’s life and the historical value of its actions for the city. Furthermore, the application allows users to meet in the same virtual place and engage with one another in real time through the Spatial SDK. Before the public presentation, a heterogeneous group of thirty users underwent usability and engagement tests to assess the experience on both VR headsets and smartphones. Tests revealed statistically significant evidence that there is a genuine difference in users’ perceptions of usability and engagement with different devices and types of interaction. This study highlights the effectiveness of adopting XR as a supporting technology to complement the real experience of cultural heritage valorization.
Variability in Mechanical Properties and Cracking Behavior of Frozen Sandstone Containing En Echelon Flaws under Compression
Authors: Liu Weimin , Han Li , Wu Di , Jia Hailiang , Tang Liyun
Published: April 2024

The mechanical properties of frozen fissured rock masses are crucial considerations for engineering in frozen earth. However, there has been little research on the mechanical properties of frozen fissured sandstone, including its strength, deformation, and geometric parameters. In this study, sandstone samples with three open en echelon fissures were observed using high-speed photography and acoustic emissions during uniaxial compression tests. The aim was to investigate sandstone’s strength, deformability, and failure process in order to elucidate the effects of freezing on its mechanical properties. In the frozen-saturated and dried states, the uniaxial compression strength (UCS) initially decreases and then increases with an increase in fissure inclination angle. Conversely, the UCS of samples in the saturated state continuously increases. The UCS follows a decreasing trend, as follows: frozen-saturated state > dried state > saturated state. The initial crack angle decreases as the fissure inclination increases in all states, irrespective of temperature and moisture conditions. However, the initial crack stress and time show an increasing trend. The uniaxial compression strength (UCS) of frozen fissured sandstone is influenced by four mechanisms: (1) ice provides support to the rock under compression, (2) ice fills microcracks, (3) unfrozen water films act as a cementing agent under tension or shearing loads, and (4) frost damage leads to softening of the rock.
Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
Authors: Zhang Hongtao , Yoshida Shinichi
Published: April 2024

Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model focusing on a singular type of illusion. There exists a need for a more comprehensive explanation of visual illusions in DNNs, as well as an expansion in the variety of illusions studied. This study is pioneering in its application of representational dissimilarity matrices and feature activation visualization techniques for a detailed examination of how five classic visual illusions are processed by DNNs. Our findings uncover the potential of DNNs to mimic human visual illusions, particularly highlighting notable differences in how these networks process illusions pertaining to color, contrast, length, angle, and spatial positioning. Although there are instances of consistency between DNNs and human perception in certain illusions, the performance distribution and focal points of interest within the models diverge from those of human observers. This study significantly advances our comprehension of DNNs’ capabilities in handling complex visual tasks and their potential to emulate the human biological visual system. It also underscores the existing gaps in our understanding and processing of intricate visual information. While DNNs have shown progress in simulating human vision, their grasp of the nuance and intricacy of complex visual data still requires substantial improvement.
Research on Settlement and Section Optimization of Cemented Sand and Gravel (CSG) Dam Based on BP Neural Network
Authors: Wang Shuyan , Yang Haixia , Lin Zhanghuan
Published: April 2024

In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R2 reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams.
Analyzing Data Reduction Techniques: An Experimental Perspective
Authors: Fernandes Vítor , Carvalho Gonçalo , Pereira Vasco , Bernardino Jorge
Published: April 2024

The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.
Evaluation of the Dark Fermentation Process as an Alternative for the Energy Valorization of the Organic Fraction of Municipal Solid Waste (OFMSW) for Bogotá, Colombia
Authors: Becerra-Quiroz Ana-Paola , Rodríguez-Morón Santiago-Andrés , Acevedo-Pabón Paola-Andrea , Rodrigo-Ilarri Javier , Rodrigo-Clavero María-Elena
Published: April 2024

In the context of valorizing the organic fraction of urban solid waste (OFMSW) in megacities, dark fermentation emerges as a central strategy alongside composting and anaerobic digestion. This article focuses on assessing the environmental, technical, and energy viability of dark fermentation using life cycle assessment (LCA) and circular economy principles. Dark fermentation for biohydrogen production is an active and promising research field in the quest for sustainable biofuels. In this context, defining operational parameters such as organic loading and the substrate-inoculum ratio is relevant for achieving better production yields. Laboratory tests were conducted using organic loading values of 5, 10, and 15 g of volatile solids per liter (gVS/L) and with substrate-inoculum ratios (s/x) of 1, 0.75, and 0.5 g of volatile solids of substrate per gram of volatile solids of inoculum (gVSs/gVSi). The combination with the best performance turned out to be an initial organic loading of 10 gVS/L and an s/x of 1 gVSs/gVSi. From this result, it was determined that the s/x had a greater impact on production. Finally, a valorization plant was dimensioned with the scaled-up process, starting from the municipal solid waste generated by Bogotá projected for 2042. The scaling was demonstrated to be energetically sustainable, producing a power of 2,368,358.72 kWh per day.
Research on a Highway Passenger Volume Prediction Model Based on a Multilayer Perceptron Neural Network
Authors: Lu He , Guo Baohua , Zhang Zhezhe , Gu Weifan
Published: April 2024

The accurate prediction of highway passenger volume is very important for China’s transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume data and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020–2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020–2022 due to various factors such as the epidemic situation and policies, which have had an impact on China’s economic development.
Bioprospection of Bacterial Strains from Chromite Process Industry Residues from Mexico for Potential Remediation
Authors: Martínez-Aldape Paola Abigail , Sandoval-Vergara Mario Enrique , Padilla-Hernández Reyna Edith , Caretta César Augusto , Valerdi-Negreros Julio César , Casanova Pablo , Monteiro Magna Maria , Gassie Claire , Goñi-Urriza Marisol , Brito Elcia Margareth Souza , Guyoneaud Remy
Published: April 2024

Industrial residues with high concentrations of hexavalent chromium [Cr(VI)], characterized by an alkaline pH (between 9 and 13) and high salinity (around 100 psu), were used as a source for extremophilic chromium-resistant and -reducing microorganisms. An investigation of biodiversity through MiSeq showed the presence of 20 bacterial classes, with Bacilli (47%), Negativicutes (15%), Bacteriodia (8%), Gammaproteobacteria (7%) and Clostridia (5%) being the most abundant. The bioprospection allowed the cultivation of 87 heterotrophic bacterial colonies and 17 bacterial isolates at the end of the isolation, and screening procedures were obtained. The isolates were related to Cellulosimicrobium aquatile, C. funkei, Acinetobacter radioresistens, Staphylococcus equorum, S. epidermis, Brachybacterium paraconglometratum, Glutamicibacter creatinolyticus, Pseudomonas songnenensis, Microbacterium algeriense and Pantoea eucalypti, most of them being resistant to Cr(VI). Resistances of up to 400 mg.L−1 of chromate were obtained for four related strains (QReMLB55A, QRePRA55, QReMLB33A and QReMLB44C). The C. aquatile strain QReMLB55A and the P. songnenensis strain QReMLB33A were exposed to K2Cr2O7 (200 mg.L−1) under optimal conditions, diminishing 94% and 24% of the Cr(VI) in 6 days, respectively. These strains exhibited a high potential for chromium remediation biotechnologies.
Some Applications of Fuzzy Sets in Residuated Lattices
Authors: Flaut Cristina , Piciu Dana , Bercea Bianca Liana
Published: April 2024

Many papers have been devoted to applying fuzzy sets to algebraic structures. In this paper, based on ideals, we investigate residuated lattices from fuzzy set theory, lattice theory, and coding theory points of view, and some applications of fuzzy sets in residuated lattices are presented. Since ideals are important concepts in the theory of algebraic structures used for formal fuzzy logic, first, we investigate the lattice of fuzzy ideals in residuated lattices and study some connections between fuzzy sets associated to ideals and Hadamard codes. Finally, we present applications of fuzzy sets in coding theory.
Terracini Loci, Linear Projections, and the Maximal Defect
Authors: Ballico Edoardo
Published: April 2024

We continue the study of Terracini loci formed by x points of a variety embedded in a projective space. Our main results are a refined study of Terracini loci arising from linear projections, the description of the maximal x with a non-empty Terracini locus for Hirzebruch surfaces, and the maximal “weight”, “corank”, or “defect” in several cases. For low x, we even show which defects can occur.
Aging in First and Second Life of G/LFP 18650 Cells: Diagnosis and Evolution of the State of Health of the Cell and the Negative Electrode under Cycling
Authors: Wheeler William , Venet Pascal , Bultel Yann , Sari Ali , Riviere Elie
Published: April 2024

Second-life applications for lithium-ion batteries offer the industry opportunities to defer recycling costs, enhance economic value, and reduce environmental impacts. An accurate prognosis of the remaining useful life (RUL) is essential for ensuring effective second-life operation. Diagnosis is a necessary step for the establishment of a reliable prognosis, based on the aging modes involved in a cell. This paper introduces a method for characterizing specific aging phenomenon in Graphite/Lithium Iron Phosphate (G/LFP) cells. This method aims to identify aging related to the loss of active material at the negative electrode (LAMNE). The identification and tracking of the state of health (SoH) are based on Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) peak-tracking techniques. The remaining capacity of the electrode is thus evaluated based on these diagnostic results, using a model derived from half-cell electrode characterization. The method is used on a G/LFP cell in the format 18650, with a nominal capacity of 1.1 Ah, aged from its pristine state to 40% of state of health.
Knowledge-Enhanced Prompt Learning for Few-Shot Text Classification
Authors: Liu Jinshuo , Yang Lu
Published: April 2024

Classification methods based on fine-tuning pre-trained language models often require a large number of labeled samples; therefore, few-shot text classification has attracted considerable attention. Prompt learning is an effective method for addressing few-shot text classification tasks in low-resource settings. The essence of prompt tuning is to insert tokens into the input, thereby converting a text classification task into a masked language modeling problem. However, constructing appropriate prompt templates and verbalizers remains challenging, as manual prompts often require expert knowledge, while auto-constructing prompts is time-consuming. In addition, the extensive knowledge contained in entities and relations should not be ignored. To address these issues, we propose a structured knowledge prompt tuning (SKPT) method, which is a knowledge-enhanced prompt tuning approach. Specifically, SKPT includes three components: prompt template, prompt verbalizer, and training strategies. First, we insert virtual tokens into the prompt template based on open triples to introduce external knowledge. Second, we use an improved knowledgeable verbalizer to expand and filter the label words. Finally, we use structured knowledge constraints during the training phase to optimize the model. Through extensive experiments on few-shot text classification tasks with different settings, the effectiveness of our model has been demonstrated.
Fortification of White Wines with Antioxidants and Se: Impacts on Browning Development and Phenolic Content
Authors: Chatzistavridi Melina Maria , Christofi Stefania , Kallithraka Stamatina
Published: April 2024

The present study explores the efficiency of selenomethionine (Semeth), an organic form of Se, as an antioxidant compared with commonly used antioxidants (ascorbic acid, glutathione, and potassium metabisulfite) in preventing oxidative browning in Greek white wines (Malagouzia and Retsina). The experimental procedure involved an accelerated browning test conducted over 12 days at 55 °C, measurement of antioxidant activity values (using the Folin—Ciocalteau and the free radical diphenylpicrylhydrazyl (DPPH) methods), determination of free sulfhydryl groups using the Ellman’s method, and High-Performance Liquid Chromatographic analysis of selected phenolic compounds. Semeth consistently exhibited a preserving effect on total and free SO2 content and antioxidant activity values of Malagouzia wines. Semeth also showed a protecting effect on free sulfhydryl groups (-SH), even higher than that of SO2 suggesting that its role in maintaining wine color involves more mechanisms than just the prevention of SO2 reduction. Moreover, Semeth demonstrated promising effects in preserving individual phenolic content, in particular (+)-catechin and fertaric acid, compared to the other antioxidant additions. Both browning rate constants and percentage color change values of Retsina where higher than the corresponding values of Malagouzia wines indicating greater susceptibility to browning. Browning development was dependent on the particular antioxidant added, with ascorbic acid being the least effective. The results of this study suggest that Semeth could be an important candidate for enhancing the oxidative stability of white wines, offering at the same time valuable information for optimizing antioxidant strategies in winemaking practices.
Functional and Molecular Analysis of Human Osteoarthritic Chondrocytes Treated with Bone Marrow-Derived MSC-EVs
Authors: Scalzone Annachiara , Sanjurjo-Rodríguez Clara , Berlinguer-Palmini Rolando , Dickinson Anne M. , Jones Elena , Wang Xiao-Nong , Crossland Rachel E.
Published: April 2024

Osteoarthritis (OA) is a degenerative joint disease, causing impaired mobility. There are currently no effective therapies other than palliative treatment. Mesenchymal stromal cells (MSCs) and their secreted extracellular vesicles (MSC-EVs) have shown promise in attenuating OA progression, promoting chondral regeneration, and modulating joint inflammation. However, the precise molecular mechanism of action driving their beneficial effects has not been fully elucidated. In this study, we analyzed MSC-EV-treated human OA chondrocytes (OACs) to assess viability, proliferation, migration, cytokine and catabolic protein expression, and microRNA and mRNA profiles. We observed that MSC-EV-treated OACs displayed increased metabolic activity, proliferation, and migration compared to the controls. They produced decreased proinflammatory (Il-8 and IFN-γ) and increased anti-inflammatory (IL-13) cytokines, and lower levels of MMP13 protein coupled with reduced expression of MMP13 mRNA, as well as negative microRNA regulators of chondrogenesis (miR-145-5p and miR-21-5p). In 3D models, MSC-EV-treated OACs exhibited enhanced chondrogenesis-promoting features (elevated sGAG, ACAN, and aggrecan). MSC-EV treatment also reversed the pathological impact of IL-1β on chondrogenic gene expression and extracellular matrix component (ECM) production. Finally, MSC-EV-treated OACs demonstrated the enhanced expression of genes associated with cartilage function, collagen biosynthesis, and ECM organization and exhibited a signature of 24 differentially expressed microRNAs, associated with chondrogenesis-associated pathways and ECM interactions. In conclusion, our data provide new insights on the potential mechanism of action of MSC-EVs as a treatment option for early-stage OA, including transcriptomic analysis of MSC-EV-treated OA, which may pave the way for more targeted novel therapeutics.
Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study
Authors: Xiao Fang , Liu Minghui , Wang Yalin , Zhou Ligang , Luo Jingchun , Chen Chen , Chen Wei
Published: April 2024

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann–Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.