bluuebunny/crossref_metadata_2025 huggingface dataset:
Element Concentrations and Histopathology of Liver and Kidney in West Greenland Ringed Seals (Pusa hispida)
Authors: Andersen-Ranberg Emilie U. , Leifsson Pall S. , Rigét Frank F. , Søndergaard Jens , Andersen Steen , Alstrup Aage Kristian Olsen , Dietz Rune , Sonne Christian
Published: June 2024

Ringed seals are consumed in Greenland and are therefore included as a key biomonitoring species with the focus on pollution exposure and health effects. Ringed seals in Central West Greenland (Qeqertarsuaq) and in North West Greenland (Qaanaaq) were analyzed for metal concentrations in the liver and histological changes in the liver and kidney. The mean liver concentration of mercury in Qaanaaq was 3.73 ± 5.01 µg/g ww (range: 0.28–23.29 µg/g ww), and the mean cadmium concentration was 7.80 ± 8.95 µg/g ww (range: 0.013–38.79 µg/g ww). For Qeqertarsuaq, the liver concentration of mercury was 1.78 ± 1.70 µg/g ww (range: 0.45–8.00 µg/g ww) and the mean cadmium concentration was 11.58 ± 6.32 µg/g ww (range: 0.11–25.45 µg/g ww). Age had a positive effect on the liver concentrations of metals, while no effect was found for sex or histological changes. The prevalence of histological changes in liver tissue decreased in the following order: random pattern mononuclear cell infiltration (92.1%), portal cell infiltration (68.4%), hepatic intracellular fat (18.4%), portal fibrosis (7.9%), focal hepatic fibrosis (7.9%), bile duct hyperplasia/fibrosis (7.9%) and lipid granuloma (2.6%). For kidney tissue, the prevalence of histological changes decreased in the following order: glomerular mesangial deposits (54.1%) > glomerular basement membrane thickening (45.9%) > THD (40%) > tubular hyaline casts (14.0%) > glomerular atrophy (13.5%) > dilated tubules (13.5%) > glomerular hyper-cellularity (10.8%) > mononuclear cell infiltrations (8.1%).
Effects of Dietary Protein Level and Rumen-Protected Methionine and Lysine on Growth Performance, Rumen Fermentation and Serum Indexes for Yaks
Authors: Wang Haibo , Fu Jianhui , Wu Xia , Wang Yadong , Li Wenjie , Huang Yanling , Zhong Jincheng , Peng Zhongli
Published: June 2024

This study investigated the effects of the dietary protein level and rumen-protected methionine and lysine (RPML) on the growth performance, rumen fermentation, and serum indexes of yaks. Thirty-six male yaks were randomly assigned to a two by three factorial experiment with two protein levels, 15.05% and 16.51%, and three RPML levels: 0% RPML; 0.05% RPMet and 0.15% RPLys; and 0.1% RPMet and 0.3% RPLys. The trial lasted for sixty days. The results showed that the low-protein diet increased the DMI and feed conversion ratio of yaks. The diet supplemented with RPML increased the activities of IGF1 and INS and nutrient digestibility. The high-protein diet decreased the rumen butyrate concentration and increased the rumen isovalerate concentration. The low-protein diet supplemented with RPML increased the rumen pH and the concentrations of total volatile fatty acids, butyrate and NH3-N; the high-protein diet supplemented with a high level of RPML decreased the rumen pH and the concentrations of isobutyrate, isovalerate, propionate and NH3-N. The low-protein diet supplemented with RPML increased the total antioxidant capacity and glutathione peroxidase activity, along with the concentrations of malondialdehyde and amino acids such as aspartic acid, lysine, cysteine, etc. In conclusion, a low-protein diet supplemented with RPML is beneficial for rumen and body health, physiological response, and metabolic status in yaks.
Targeting Neoantigens in Cancer: Possibilities and Opportunities in Breast Cancer
Authors: Chaudhry Zuhair , Boyadzhyan Anik , Sasaninia Kayvan , Rai Vikrant
Published: June 2024

As one of the most prevalent forms of cancer worldwide, breast cancer has garnered significant attention within the clinical research setting. While traditional treatment employs a multidisciplinary approach including a variety of therapies such as chemotherapy, hormone therapy, and even surgery, researchers have since directed their attention to the budding role of neoantigens. Neoantigens are defined as tumor-specific antigens that result from a multitude of genetic alterations, the most prevalent of which is the single nucleotide variant. As a result of their foreign nature, neoantigens elicit immune responses upon presentation by Major Histocompatibility Complexes I and II followed by recognition by T cell receptors. Previously, researchers have been able to utilize these immunogenic properties and manufacture neoantigen-specific T-cells and neoantigen vaccines. Within the context of breast cancer, biomarkers such as tumor protein 53 (TP53), Survivin, Partner and Localizer of BRCA2 (PALB2), and protein tyrosine phosphatase receptor T (PTPRT) display exceeding potential to serve as neoantigens. However, despite their seemingly limitless potential, neoantigens must overcome various obstacles if they are to be fairly distributed to patients. For instance, a prolonged period between the identification of a neoantigen and the dispersal of treatment poses a serious risk within the context of breast cancer. Regardless of these current obstacles, it appears highly promising that future research into neoantigens will make an everlasting impact on the health outcomes within the realm of breast cancer. The purpose of this literature review is to comprehensively discuss the etiology of various forms of breast cancer and current treatment modalities followed by the significance of neoantigens in cancer therapeutics and their application to breast cancer. Further, we have discussed the limitations, future directions, and the role of transcriptomics in neoantigen identification and personalized medicine. The concepts discussed in the original and review articles were included in this review article.
Comparative Genomics of an Emerging Multidrug-Resistant blaNDM-Carrying ST182 Lineage in Enterobacter cloacae Complex
Authors: Mavroidi Angeliki , Froukala Elisavet , Tsakris Athanasios
Published: June 2024

Background: Enterobacter cloacae, E. hormaechei and related subspecies remain the most clinically relevant among the Enterobacter cloacae complex (ECC). Carbapenemase-producing ECC strains are increasingly identified in hospital-acquired infections and usually belong to four main multilocus sequence types (MLST STs) named ST114, ST93, ST90 and ST78. Instead, ST182 has been sporadically reported among E. hormaechei strains, and recently, outbreaks of blaNDM-producing ST182 clonal strains have emerged. Herein, we aimed to investigate the presence of ST182 and explore its evolution and modes of blaNDM acquisition. Methods: A phylogenetic analysis of 646 MLST STs identified among 4685 E. hormaechei whole-genome sequencing (WGS) assemblies deposited in public repositories was performed, as well as an in silico comparative and phylogenomic analyses for 55 WGS assemblies of ST182. blaNDM-harboring contigs were also compared to published plasmid sequences. Results: ST182 E. hormaechei strains were recovered from patients on five continents during 2011–2021. They were divided into three major genomic clusters, comprising a separate clonal complex with six other STs. In 30 out of 55 ST182 WGS assemblies, blaNDM-harboring structures were identified that were similar to the plasmids predominant in Gram-negative bacteria, harboring resistance genes to multiple antibiotic classes and virulence genes. No associations between the genomic clusters and the country/continent of isolation or the presence and the plasmid types of the blaNDM-harboring contigs were observed. Conclusions: Our findings show that ST182 E. hormaechei strains have been identified in the past decade worldwide; 54.5% of them carried diverse blaNDM genetic structures, suggesting recent acquisition of the blaNDM alleles. Thus, blaNDM-harboring ST182 is an emerging multidrug-resistant and virulent lineage in ECC strains that requires close monitoring.
Incidence of Antibiotic Exposure for Suspected and Proven Neonatal Early-Onset Sepsis between 2019 and 2021: A Retrospective, Multicentre Study
Authors: van Veen Liesanne , van der Weijden Bo , Achten Niek , van der Lee Lotte , Hol Jeroen , van Rossem Maaike , Rijpert Maarten , Oorthuys Anna , van Beek Ron , Dubbink-Verheij Gerdien , Kornelisse René , van der Meer-Kapelle Laura , Van Mechelen Karen , Broekhuizen Suzanne , Dassel A. , Jacobs J. , van Rijssel Paul , Tramper-Stranders Gerdien , van Rossum Annemarie , Plötz Frans
Published: June 2024

Management of suspected early-onset sepsis (EOS) is undergoing continuous evolution aiming to limit antibiotic overtreatment, yet current data on the level of overtreatment are only available for a select number of countries. This study aimed to determine antibiotic initiation and continuation rates for suspected EOS, along with the incidence of culture-proven EOS in The Netherlands. In this retrospective study from 2019 to 2021, data were collected from 15 Dutch hospitals, comprising 13 regional hospitals equipped with Level I-II facilities and 2 academic hospitals equipped with Level IV facilities. Data included birth rates, number of neonates started on antibiotics for suspected EOS, number of neonates that continued treatment beyond 48 h and number of neonates with culture-proven EOS. Additionally, blood culture results were documented. Data were analysed both collectively and separately for regional and academic hospitals. A total of 103,492 live-born neonates were included. In 4755 neonates (4.6%, 95% CI 4.5–4.7), antibiotic therapy was started for suspected EOS, and in 2399 neonates (2.3%, 95% CI 2.2–2.4), antibiotic treatment was continued beyond 48 h. Incidence of culture-proven EOS was 1.1 cases per 1000 live births (0.11%, 95% CI 0.09–0.14). Overall, for each culture-proven EOS case, 40.6 neonates were started on antibiotics and in 21.7 neonates therapy was continued. Large variations in treatment rates were observed across all hospitals, with the number of neonates initiated and continued on antibiotics per culture-proven EOS case varying from 4 to 90 and from 4 to 56, respectively. The high number of antibiotic prescriptions compared to the EOS incidence and wide variety in clinical practice among hospitals in The Netherlands underscore both the need and potential for a novel approach to the management of neonates with suspected EOS.
Nanotechnological Approaches to Enhance the Potential of α-Lipoic Acid for Application in the Clinic
Authors: Bellini Chiara , Mancin Fabrizio , Papini Emanuele , Tavano Regina
Published: June 2024

α-lipoic acid is a naturally occurring compound with potent antioxidant properties that helps protect cells and tissues from oxidative stress. Its incorporation into nanoplatforms can affect factors like bioavailability, stability, reactivity, and targeted delivery. Nanoformulations of α-lipoic acid can significantly enhance its solubility and absorption, making it more bioavailable. While α-lipoic acid can be prone to degradation in its free form, encapsulation within nanoparticles ensures its stability over time, and its release in a controlled and sustained manner to the targeted tissues and cells. In addition, α-lipoic acid can be combined with other compounds, such as other antioxidants, drugs, or nanomaterials, to create synergistic effects that enhance their overall therapeutic benefits or hinder their potential cytotoxicity. This review outlines the advantages and drawbacks associated with the use of α-lipoic acid, as well as various nanotechnological approaches employed to enhance its therapeutic effectiveness, whether alone or in combination with other bioactive agents. Furthermore, it describes the engineering of α-lipoic acid to produce poly(α-lipoic acid) nanoparticles, which hold promise as an effective drug delivery system.
Strength and Deformation of Pillars during Mining in the Shaft Pillar
Authors: Šancer Jindřich , Petroš Vladimír , Hudeček Vlastimil , Zapletal Pavel
Published: June 2024

This study of the strength and deformation of coal samples was triggered by the need to define the stress–strain characteristics of pillars during room and pillar mining in the shaft protective pillar at the ČSM Mine. It was probably the world’s deepest deployment of this mining method in a coal mine. In order to solve the bearing capacity of pillars, the dependence of coal strength on the slenderness ratio is used. For this reason, coal samples with different slenderness ratios were investigated. After considering the purpose of this research, slenderness ratios (width/height) of 1 to 7.7 were chosen. At the same time, the modulus of deformation as a function of the slenderness ratio was determined, and the vertical deformation of the pillars and the safety factor were calculated. Attention is also paid to the influence of sampling on the results of measured coal strengths.
Coarse–Fine Combined Bridge Crack Detection Based on Deep Learning
Authors: Ma Kaifeng , Hao Mengshu , Meng Xiang , Liu Jinping , Meng Junzhen , Xuan Yabing
Published: June 2024

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.
Driving Style and Traffic Prediction with Artificial Neural Networks Using On-Board Diagnostics and Smartphone Sensors
Authors: Al-refai Ghaith , Al-refai Mohammed , Alzu’bi Ahmad
Published: June 2024

Driving style and road traffic play pivotal roles in the development of smart cities, influencing traffic flow, safety, and environmental sustainability. This study presents an innovative approach for detecting road traffic conditions and driving styles using On-Board Diagnostics (OBD) data and smartphone sensors. This approach offers an inexpensive implementation of prediction, as it utilizes existing vehicle data without requiring additional setups. Two Artificial Neural Network (ANN) models were employed: the first utilizes a forward neural network architecture, while the second leverages bootstrapping or bagging neural networks to enhance detection accuracy for low-labeled classes. Support Vector Machine (SVM) is implemented to serve as a baseline for comparison. Experimental results demonstrate that ANNs exhibit significant improvements in detection accuracy compared to SVM. Moreover, the neural network with bagging model showcases enhanced recall values and a substantial improvement in accurately detecting instances belonging to low-labeled classes in both driving style road traffic.
A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates
Authors: Kern John , Urrea Claudio , Cubillos Francisco , Navarrete Ricardo
Published: June 2024

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.
Laminated Steel Fiber-Reinforced Concrete Hingeless Arch: Research on Damage Evolution Laws
Authors: Tian Zhongchu , Dai Ye , Peng Tao , Zhang Zujun , Cai Yue , Xu Binlin
Published: June 2024

In the context of reinforced concrete (RC) arch bridges, while the incorporation of full sections of steel fibers can enhance the bridge’s toughness, cracking resilience, and bearing capacity, achieving an optimal balance between structural performance and economic viability in this manner remains challenging. This article introduces a novel computational approach—the distributed steel fiber concrete (LSFRC) arch—which considers the spatial distribution of damage in RC arches. The static performance of SFRC elements and LSFRC beams was compared and analyzed using the concrete plastic damage model (CDP) in ABAQUS software. This study validated the rationality of the model and investigated the impact of varying steel fiber volume ratios and steel fiber layer heights on the damage evolution of LSFRC arches. The results of this study demonstrate that the cracking load and bearing capacity of an RC arch can be effectively enhanced through the addition of steel fibers to a local area under static loading. Furthermore, the deflection and damage to the arch waist and arch roof can be significantly reduced. Furthermore, the incorporation of steel fibers at an increased volume rate and at a greater height within the doped section can effectively slow the rate of damage evolution within the section. This results in the inhibition of crack extensions and in an improvement in the ductility and reliability of the damage stage. The LSFRC arches offer superior economic and practical advantages over their full cross-section doped steel fiber (FRC) counterparts. This study offers novel insights and methodological guidance for the design and implementation of concrete arch bridges.
Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
Authors: Wan Xing , Johari Juliana , Ruslan Fazlina Ahmat
Published: June 2024

Text-based CAPTCHAs remain the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs), are the mainstream approach for text CAPTCHA recognition and are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment; they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive CAPTCHA with a CNN combined with an RNN (CRNN) module and trainable Adaptive Fusion Filtering Networks (AFFN), which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexities show that, compared with the baseline model Deep CAPTCHA, the number of parameters of our proposed model is reduced by about 70%, and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed. Compared with several of the latest models, the model proposed by the study also has better comprehensive performance.
CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
Authors: Han Jinho
Published: June 2024

Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system.
Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning
Authors: Wang Shan , Wang Hongtao , Lu Yijun , Huang Jiandong
Published: June 2024

By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels.
Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN
Authors: Hu Ting , Xu Jinming
Published: June 2024

In view of the shortcomings of existing methods for predicting the settlement of surrounding buildings caused by deep foundation pit construction, this study uses the monitoring data of a foundation pit project in Shanghai and divides the construction process of the pit into three working conditions, that is, enclosure construction, earthwork excavation, and basement support construction. The attention mechanism and residual update are integrated into the artificial neural network (ANN) model, and the root-mean-square error, average absolute error, and determination coefficient are used as the evaluation indices of the model. The artificial neural network prediction model LSTM-RA-ANN for building settlements in deep foundation pit construction was then established. The prediction performance of the model was also analysed under different working conditions, and the influences of the main factors (including the soil parameter, monitoring point location, activation function, hyperparameter, and input number) on the evaluation index was further explored. The results indicate that the performances of the established LSTM-RA-ANN model are closely related to the construction conditions, the predicted settlements agree well with the monitored ones in three working conditions with the greatest errors occurring at a later time of the working conditions, and the prediction accuracy of the great–small order corresponds to basement support, enclosure construction, and earthwork excavation respectively. The farther the monitoring point is from the edge of the pit, the better the model performance is. The activation function, initial learning rate, and maximum iteration batch have a great influence on the evaluation indices of the model, while the number of input points has little effect on the evaluation indices. These results may serve as a reference for the safe construction and normal operation of foundation pit engineering.
Model Optimization of Ice Melting of Bridge Pylon Crossbeams with Built-In Carbon Fiber Electric Heating
Authors: Xu Hao , Chen Zhi , Cao Chunchen , Xiao Henglin , Zheng Lifei
Published: June 2024

This paper aims to improve the deicing performance and energy utilization of bridge pylon crossbeams with built-in carbon fiber electric heating (BPB–CFEH). Therefore, a three-dimensional thermal transfer model of BPB–CFEH with one arrangement is established. Two ice-melting regions and two ice-melting stages were set up according to the characteristics of the icing of the crossbeam. The effects of wind speed and ambient temperature on the paving power required to reach the complete melting of the icicles within 8 h were analyzed. The effects of the laying spacing and rated voltage of the carbon fiber heating cable on the melting ice sheet and the thermal exchange of the two regions of the icicle after heating for 8 h were compared. Additionally, its effect on energy utilization of the process from the ice sheet melting stage to the ice column melting stage was analyzed. Ice-melting experiments verified the applicability and reasonableness of the simulated ice-melting calculation formula. The results show that under ambient temperature of −10 °C and wind speed of 4.5–13.5 m/s, the proposed paving power is 817.5–2248.12 W/m2. Increasing the rated voltage and shortening the spacing increases the thermal exchange capacity of the two melting regions. The shortening of the spacing improves the energy utilization rate of the melting stage of the ice sheet to the melting stage of the icicle processes. The difference between the melting time obtained from the formula proposed by numerical simulation and the melting time obtained from indoor tests is about 10 min. This study provides a design basis for the electrothermal ice melting of bridge pylon crossbeams.
A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
Authors: Lin Benxiang , Wei Chao , Feng Fuyong
Published: June 2024

Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition is proposed in this paper. Firstly, the K-means algorithm was used to cluster samples with similar characteristics together, extract kinematic fragment samples in typical driving conditions, calculate their feature parameters, and carry out principal component analysis on the feature parameters to achieve dimensionality reduction transformation of information. Then, the vehicle velocity prediction sub-neural network models based on long short-term memory (LSTM) with the QPSO algorithm optimized were trained under different driving condition datasets. Furthermore, the kinematic segment recognition and traditional vehicle velocity prediction were integrated to form an adaptive vehicle velocity prediction method based on driving condition identification. Finally, the current driving condition type was identified and updated in real-time during vehicle velocity prediction, and then the corresponding sub-LSTM model was used for vehicle velocity prediction. The simulation experiment demonstrated a significant enhancement in both the velocity and accuracy of prediction through the proposed method. The proposed hybrid method has the potential to improve the accuracy and reliability of vehicle velocity prediction, making it applicable in various fields such as autonomous driving, traffic management, and energy management strategies for hybrid electric vehicles.
Enhanced Berth Mapping and Clothoid Trajectory Prediction Aided Intelligent Underground Localization
Authors: Li Fei , Chen Jialiang , Yuan Yuelin , Hu Zhaozheng , Liu Xiaohui
Published: June 2024

In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth’s corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements.
Effect of Subsequent Subgrade on Seismic Response of the High-Speed Railway Track–Bridge System
Authors: Wei Biao , Yuan Shuaijie , Jiang Lizhong , Yu Yujie , Xiao Binqi , Chen Jun , Zhang Ruimin , Yang Zhixing , Li Shuaijun
Published: June 2024

As an important part of the boundary conditions on both sides of the high-speed railway track–bridge system, the seismic response of the subgrade structure is different from that of the bridge structure. This difference has become increasingly significant with the widespread adoption of continuous welded rail technology in bridge construction. Therefore, investigating the seismic response of the bridge system, with a specific focus on the longitudinal constraint effects of the subsequent subgrade track structure, is of paramount importance. Utilizing finite element software, two distinct bridge models are developed: one incorporating the subsequent subgrade track structure and another excluding it. Through nonlinear time history analysis under varying seismic intensities, it is demonstrated that the longitudinal constraint of the subsequent subgrade track structure mitigates the longitudinal displacements and internal forces in critical components of the high-speed railway track–bridge system. Concurrently, acknowledging the heightened complexity and cost associated with post-earthquake repairs of the bridge structure compared to subgrade structure, this study uses a risk transfer connecting beam device. This device can redirect seismic damage from bridge structure to subgrade structure, thereby potentially reducing post-seismic repair expenses for the bridge.
Sea Buckthorn Leaves as a Potential Source of Antioxidant Substances
Authors: Bośko Paulina , Biel Wioletta , Smetanska Iryna , Witkowicz Robert , Piątkowska Ewa
Published: June 2024

Each year, agro-foods produce thousands of tonnes of by-products that contain high-value, unique nutrients. The application of plant by-products enables agro-food corporations to obtain value from them and avoid using natural resources. The idea of the bio economy protects against environmental pollution and leads to a cheap source of bioactive components, which can be transformed into value-added products for other industries. The numerous publications on the positive impact of sea buckthorn (SBT, Hippophae rhamnoides L., Elaeagnaceae family) and its products on human health mainly concern its fruits and seeds. However, there are few data relating to the properties of SBT leaves. The leaves of SBT can be a rich source of nutrients and biologically active substances. In this investigation, we report the study of the leaves of four SBT cultivars. We determined their antioxidant capacities, measured total polyphenols and total flavonoids, and quantified their major polyphenols and alkaloids. The results show that SBT leaves are a source of antioxidants and alkaloids. Additionally, in this raw material, we identified the presence of individual flavonols (rutoside and quercetin), individual anthocyanidins (cyaniding, delphinidin, and peonidin), and chelerythrine by high-performance thin-layer chromatography (HPTLC) for the first time. Through these types of studies, we aim to revalue this raw material, which is not well known in the world. Considering its nutritional properties, we seek to increase the use of its high-value, unique nutrients in food processing, medicine, and animal nutrition, in accordance with the goals of a closed-loop bioeconomy.
Evaluation of the Technical Condition of Pipes during the Transportation of Hydrogen Mixtures According to the Energy Approach
Authors: Ivanytskyi Yaroslav , Blikharskyy Yaroslav , Sęp Jarosław , Blikharskyy Zinoviy , Selejdak Jacek , Holiian Oleh
Published: June 2024

In this study, a theoretical–experimental methodology for determining the stress–strain state in pipeline systems, taking into account the hydrogen environment, was developed. A complex of theoretical and experimental studies was conducted to determine the specific energy of destruction as an invariant characteristic of the material’s resistance to strain at different hydrogen concentrations. The technique is based on the construction of complete diagrams of the destruction of the material based on the determination of true strains and stresses in the local volume using the method involving the optical–digital correlation of speckle images. A complex of research was carried out, and true diagrams of material destruction were constructed, depending on the previous elastic–plastic strain and the action of the hydrogen environment. The change in the concentration of hydrogen absorbed by the material was estimated, depending on the value of the specific energy of destruction. A study was conducted on tubular samples, and the degree of damage to the material of the inner wall under the action of hydrogen and stress from the internal pressure was evaluated according to the change in specific energy, depending on the value of the true strain established with the help of an optical–digital correlator on the outer surface, and the degree of damage was determined. It has been established that the specific fracture energy of 17G1S steel decreases by 70–90% under the influence of hydrogen. The effect of the change in the amount of strain energy on the thickness of the pipe wall is illustrated.
The Effect of the Temperature–Humidity Coupling Cycle on the Performance of Styrene Butadiene Styrene Polymer-Modified Asphalt Mastic
Authors: Ma Chao , Su Youliang , Tan Bo , Fan Yuzhu , Zhang Wanzhen
Published: June 2024

To study the variation laws and effects of asphalt mastic under the cooperative interaction of different temperatures and humidities, cyclic conditions for different temperature ranges were set to conduct indoor experimental simulations of thermal–humidity coupling cycles. Firstly, the macroscopic performance changes in styrene butadiene styrene polymer (SBS)-modified asphalt mastic were evaluated by the penetration test, softening point test, ductility test, Brookfield rotational viscosity test, and double-edge notched tensile (DENT) test; then, the mechanism of performance changes was explored from the perspective of chemical composition by combining this with Fourier transform infrared spectroscopy (FTIR). The research results show that with the increase in thermal–humidity coupling cycles, SBS-modified asphalt mastic exhibited aging phenomena such as hardening and embrittlement, and its macroscopic performance deteriorated; under the same test conditions, the interval with a higher temperature difference had a greater impact on the performance of the mastic; the sulfoxide index (IS=O) of SBS-modified asphalt mastic increases after thermal–humidity coupling cycles, while the isoprene index (IB) decreases.
Schizophrenia Detection on EEG Signals Using an Ensemble of a Lightweight Convolutional Neural Network
Authors: Hussain Muhammad , Alsalooli Noudha Abdulrahman , Almaghrabi Norah , Qazi Emad-ul-Haq
Published: June 2024

Schizophrenia is a chronic mental disorder that affects millions of people around the world. Neurologists commonly use EEG signals to distinguish schizophrenia patients from normal controls, but their manual analysis is tedious and time-consuming. This has motivated the need for automated methods based on machine learning. However, the methods based on hand-engineered features need human experts to decide which features should be extracted. Though deep learning has recently shown good results for schizophrenia detection, the existing deep models have high parameter complexity, making them prone to overfitting because the available data are limited. To overcome these limitations, we propose a method based on an ensemble-like approach and a lightweight one-dimensional convolutional neural network to discriminate schizophrenia patients from healthy controls. It splits an input EEG signal for analysis into smaller segments, where the same backbone model analyses each segment. In this way, it makes decisions after scanning an EEG signal of any length without increasing the complexity; i.e., it scales well with an EEG signal of any length. The model architecture is simple and involves a small number of parameters, making it easy to implement and train using a limited amount of data. Though the model is lightweight, enough trials are still needed to learn the discriminative features from available data. To tackle this issue, we introduce a simple data augmentation scheme. The proposed method achieved an accuracy of 99.88% on a public benchmark dataset; it outperformed the state-of-the-art methods. It will help neurologists in the rapid and accurate detection of schizophrenia patients.
Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
Authors: Iqbal Muhammad Ali , Jadoon Waqas , Kim Soo Kyun
Published: June 2024

The performance of facial recognition systems significantly decreases when faced with a lack of training images. This issue is exacerbated when there is only one image per subject available. Probe images may contain variations such as illumination, expression, and disguise, which are difficult to recognize accurately. In this work, we present a model that generates six facial variations from a single neutral face image. Our model is based on a CGAN, designed to produce six highly realistic facial expressions from one neutral face image. To evaluate the accuracy of our approach comprehensively, we employed several pre-trained models (VGG-Face, ResNet-50, FaceNet, and DeepFace) along with a custom CNN model. Initially, these models achieved only about 76% accuracy on single-sample neutral images, highlighting the SSPP challenge. However, after fine-tuning on the synthetic expressions generated by our CGAN from these single images, their accuracy increased significantly to around 99%. Our method has proven highly effective in addressing SSPP issues, as evidenced by the significant improvement achieved.
Evaluation of Solid-State Fermentation Conditions from Pineapple Peel Waste for Release of Bioactive Compounds by Aspergillus niger spp.
Authors: Casas-Rodríguez A. Danitza , Ascacio-Valdés Juan A. , Dávila-Medina Miriam Desirée , Medina-Morales Miguel A. , Londoño-Hernández Liliana , Sepúlveda Leonardo
Published: June 2024

Currently, agroindustrial waste can be used to obtain bioactive compounds. The solid-state fermentation is an alternative for the valorization of these waste and to be able to release bioactive compounds that may be of interest to different industrial sectors. The aim of this study was to evaluate solid-state fermentation conditions using pineapple peel waste as the substrate with Aspergillus niger spp., to release bioactive compounds using a Plackett–Burman exploratory design. Temperature, humidity, inoculum, NaNO3, MgSO4, KCl, and KH2PO4 conditions in the fermentation process were evaluated. The antioxidant capacity was determined, and the main compounds of the fermentation extracts were identified. The results revealed that the Aspergillus niger HT3 strain reached a hydrolyzable tannin release of 10.00 mg/g, While Aspergillus niger Aa20 reached a condensed tannin release of 82.59 mg/g. The KH2PO4 affects the release of condensed tannins with A. niger Aa20, and MgSO4 affects the release of hydrolyzable tannins with A. niger HT3. In addition, a positive antioxidant activity was demonstrated for the DPPH, ABTS, and FRAP technique. The main compounds in the fermented pineapple peel were 3-feruloylquinic acid, caffeic acid, lariciresinol, and 3-hydroxyphloretin 2′-O-xylosyl-glucoside, among others. The solid-state fermentation process is a biotechnological alternative for the release of bioactive compounds.
Design Principles and Development Status of Flexible Integrated Thin and Lightweight Zinc-Ion Batteries
Authors: Liu Xuxian , Jiang Yongchang , Wang Yaqun , Pan Lijia
Published: June 2024

The rapid advancement of wearable devices and flexible electronics has spurred an increasing need for high-performance, thin, lightweight, and flexible energy storage devices. In particular, thin and lightweight zinc-ion batteries require battery materials that possess exceptional flexibility and mechanical stability to accommodate complex deformations often encountered in flexible device applications. Moreover, the development of compact and thin battery structures is essential to minimize the overall size and weight while maintaining excellent electrochemical performance, including high energy density, long cycle life, and stable charge/discharge characteristics, to ensure their versatility across various applications. Researchers have made significant strides in enhancing the battery’s performance by optimizing crucial components such as electrode materials, electrolytes, separators, and battery structure. This review provides a comprehensive analysis of the design principles essential for achieving thinness in zinc-ion batteries, along with a summary of the preparation methods and potential applications of these batteries. Moreover, it delves into the challenges associated with achieving thinness in zinc-ion batteries and proposes effective countermeasures to address these hurdles. This review concludes by offering insights into future developments in this field, underscoring the continual advancements and innovations that can be expected.
Mitochondrial Reactive Oxygen Species in Infection and Immunity
Authors: Mukherjee Arunima , Ghosh Krishna Kanta , Chakrabortty Sabyasachi , Gulyás Balázs , Padmanabhan Parasuraman , Ball Writoban Basu
Published: June 2024

Reactive oxygen species (ROS) contain at least one oxygen atom and one or more unpaired electrons and include singlet oxygen, superoxide anion radical, hydroxyl radical, hydroperoxyl radical, and free nitrogen radicals. Intracellular ROS can be formed as a consequence of several factors, including ultra-violet (UV) radiation, electron leakage during aerobic respiration, inflammatory responses mediated by macrophages, and other external stimuli or stress. The enhanced production of ROS is termed oxidative stress and this leads to cellular damage, such as protein carbonylation, lipid peroxidation, deoxyribonucleic acid (DNA) damage, and base modifications. This damage may manifest in various pathological states, including ageing, cancer, neurological diseases, and metabolic disorders like diabetes. On the other hand, the optimum levels of ROS have been implicated in the regulation of many important physiological processes. For example, the ROS generated in the mitochondria (mitochondrial ROS or mt-ROS), as a byproduct of the electron transport chain (ETC), participate in a plethora of physiological functions, which include ageing, cell growth, cell proliferation, and immune response and regulation. In this current review, we will focus on the mechanisms by which mt-ROS regulate different pathways of host immune responses in the context of infection by bacteria, protozoan parasites, viruses, and fungi. We will also discuss how these pathogens, in turn, modulate mt-ROS to evade host immunity. We will conclude by briefly giving an overview of the potential therapeutic approaches involving mt-ROS in infectious diseases.