bluuebunny/crossref_metadata_2025 huggingface dataset:
Ethical Concerns about Fashionable Dog Breeding
Authors: Menor-Campos David J.
Published: February 2024

The historical relationship between humans and dogs has involved selective breeding for various purposes, such as hunting, guarding, and service roles. However, over time, there has been a shift in preferences from functionality to aesthetics, which has influenced the diverse sizes, shapes, and coats of dog breeds. This review looks at fashionable dog breeding and questions the ethics of prioritising looks over health and behaviour. It aims to alert potential owners, breeders, and regulators to the importance of considering a dog’s overall well-being, not just its appearance, which has resulted in fad breeding, leading to genetic disorders, health issues, and a loss of biodiversity. Ethical concerns arise from breeding brachycephalic breeds with respiratory conditions, inbreeding causing inherited disorders, and overbreeding popular breeds while shelter dogs remain unadopted. Additionally, the impact of cosmetic surgeries on popular dog breeds, as well as the neglect of behavioural traits in favour of physical characteristics and strict breeding practices are also considered. The current breeding model can have a negative impact on the emotional and cognitive well-being of dogs, resulting in issues such as aggression, anxiety, and other behavioural problems that can significantly reduce their overall quality of life. Unregulated breeding practices and the demand for rare breeds can lead to illegal breeding, compromising animal welfare. Prospective owners, veterinarians, kennel clubs, and legislators all need to play a responsible role in protecting animals.
“Have You Seen This Drivel?” A Comparison of How Common Health Issues Are Discussed within Brachycephalic and Non-Brachycephalic Dog Breed Groups on Social Media
Authors: Phillips Kitty , Stewart Carrie , Johnston Taryn , Mills Daniel S.
Published: February 2024

As brachycephalic dog breed ownership increases, there is a growing concern for the welfare of these breeds due to extreme brachycephalism. Understanding the motivations and behaviours of those choosing to own these breeds is important if we wish to address these concerns. The aim of this study was to investigate how owners of brachycephalic and non-brachycephalic dog breeds use social media dog breed groups to discuss common health issues. The purpose of Facebook posts in relation to common health issues, owner awareness of health issues and the role of Facebook facilitated social support were explored between brachycephalic and non-brachycephalic dog owners. Twelve Facebook breed goups were selected (brachycephalic breed groups, n = 6, non-brachycephalic breed groups, n = 6). Using key word searches we extracted the first twenty posts from each group. Thematic analysis revealed three overarching themes: advice seeking, advice giving and community bonding mechanisms. Whilst the purpose of posting did not differ between groups, non-brachycephalic owners appeared to display greater awareness of breed-specific health issues, whilst social support played a more prominent role in brachycephalic groups. This research highlights that social media groups can act as platforms for knowledge exchange and emotional support. These could be utilised by owners, veterinarians and welfare organisations to more effectively enhance dog health and wellbeing.
Examining Antimicrobial Resistance in Escherichia coli: A Case Study in Central Virginia’s Environment
Authors: Kim Chyer , Riley Allissa , Sriharan Shobha , Nartea Theresa , Ndegwa Eunice , Dhakal Ramesh , Zheng Guolu , Baffaut Claire
Published: February 2024

While environmental factors may contribute to antimicrobial resistance (AMR) in bacteria, many aspects of environmental antibiotic pollution and resistance remain unknown. Furthermore, the level of AMR in Escherichia coli is considered a reliable indicator of the selection pressure exerted by antimicrobial use in the environment. This study aimed to assess AMR variance in E. coli isolated from diverse environmental samples, such as animal feces and water from wastewater treatment plants (WWTPs) and drainage areas of different land use systems in Central Virginia. In total, 450 E. coli isolates obtained between August 2020 and February 2021 were subjected to susceptibility testing against 12 antimicrobial agents approved for clinical use by the U.S. Food and Drug Administration. Approximately 87.8% of the tested isolates were resistant to at least one antimicrobial agent, with 3.1% showing multi-drug resistance. Streptomycin resistance was the most common (73.1%), while susceptibility to chloramphenicol was the highest (97.6%). One isolate obtained from WWTPs exhibited resistance to seven antimicrobials. AMR prevalence was the highest in WWTP isolates, followed by isolates from drainage areas, wild avians, and livestock. Among livestock, horses had the highest AMR prevalence, while cattle had the lowest. No significant AMR difference was found across land use systems. This study identifies potential AMR hotspots, emphasizing the environmental risk for antimicrobial resistant E. coli. The findings will aid policymakers and researchers, highlighting knowledge gaps in AMR–environment links. This nationally relevant research offers a scalable AMR model for understanding E. coli ecology. Further large-scale research is crucial to confirm the environmental impacts on AMR prevalence in bacteria.
New Insights into Cutaneous Asepsis: Synergism between Pfaffia and Rosemary Extracts
Authors: Miranda Diego Garcia , Carrouel Florence , Silva Tatiane Cristina Alberti , Rozzatto Mariana Cafalchio , Hasna Amjad Abu , Santos Carlos Eduardo Rocha , Morais Flavia Villaça , de Oliveira Luciane Dias , de Paula Ramos Lucas
Published: February 2024

(1) Background: In hospitals, medical and dental clinics, antiseptics or disinfectants play an essential role in the control of nosocomial infections. This study aimed to evaluate R. officinalis and P. paniculata glycolic extracts regarding: (I) their antimicrobial action on planktonic and biofilm (monotypic and cutaneous biofilm model—S. aureus, S. epidermidis and C. acnes); and (II) their cytotoxicity on human keratinocytes (HaCaT). (2) Methods: Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were performed (CLSI protocol M7-A6 and M11-A8). MTT analysis was used to evaluate the antibiofilm activity of the extracts on biofilms and their cytotoxicity on human keratinocytes. (3) Results: The combined glycolic extracts MIX A (75% P. paniculata + 25% R. officinalis); MIX B (50% P. paniculata + 50% R. officinalis); and MIX C (25% P. paniculata + 75% R. officinalis) promoted MBC values by 50 mg/mL on S. aureus, absent on S. epidermidis, and ranged 6.25–50 mg/mL for C. acnes. The cutaneous biofilm model was reduced more than 90%. In addition, it showed biocompatibility with human keratinocytes, resulting in percentages of viability greater than 50%. (4) Conclusions: The combination of extracts promoted antimicrobial action on planktonic cultures, and monotypic and heterotypic biofilms of skin pathogens. Additionally, these extracts are biocompatible against human keratinocytes.
Extract of Gardenia jasminoides Ellis Attenuates High-Fat Diet-Induced Glycolipid Metabolism Disorder in Rats by Targeting Gut Microbiota and TLR4/Myd88/NF-κB Pathway
Authors: Lv Chenghao , Liu Xin , Chen Shiyun , Yi Yuhang , Wen Xinnian , Li Tao , Qin Si
Published: February 2024

Gardenia jasminoides Ellis is abundant in crocin and has a longstanding historical usage both as a dietary and natural ethnic medicine. Enhanced studies have increasingly revealed the intricate interplay between glycolipid metabolism and gut microbiota, wherein their imbalance is regarded as a pivotal indicator of metabolic disorders. Currently, the precise molecular mechanism of the crude extract of crocin from Gardenia jasminoides Ellis (GC) targeting gut microbiota to regulate glycolipid metabolism disorder is still unclear. Firstly, we explored the effect of GC on digestive enzymes (α-amylase and α-glucosidase) in vitro. Secondly, we investigated the effect of GC on the physical and chemical parameters of high-fat diet (HFD) rats, such as body weight change, fasting blood glucose and lipid levels, and liver oxidative stress and injury. Then, 16S rDNA sequencing was used to analyze the effects of GC on the composition and structure of gut microbiota. Finally, the impact of GC on the TLR4/Myd88/NF-κB signaling pathway in the intestine was assessed by Western Blotting. In the present study, GC was found to exhibit a hypoglycemic effect in vitro, by inhibition of digestive enzymes. In animal experiments, we observed that GC significantly reduced fasting blood glucose, TC, and TG levels while increasing HDL-C levels. Additionally, GC demonstrated hepatoprotective properties by enhancing liver antioxidative capacity through the upregulation of SOD, CAT, and GSH-Px, while reducing ROS. 16S rDNA sequencing results showed that GC had a significant effect on the gut microbiota of HFD rats, mainly by reducing the ratio of Firmicutes/Bateroidota, and significantly affected the genera related to glycolipid metabolism, such as Akkermansia, Ligilactobacillus, Lactobacillus, Bacteroides, Prevotellaceae, etc. The Western Blotting results demonstrated that GC effectively downregulated the protein expressions of TLR4, Myd88, and NF-κB in the intestine of HFD rats, indicating that GC could target the TLR4/Myd88/NF-κB pathway to interfere with glycolipid metabolism disorder. Correlation analysis revealed that GC could target the Akkermansia-TLR4/Myd88/NF-κB pathway axis which attenuates glycolipid metabolism disorder. Therefore, this study establishes the foundation for GC as a novel therapeutic agent for glycolipid metabolism disorder chemoprevention, and it introduces a novel methodology for harnessing the potential of natural botanical extracts in the prevention and treatment of metabolic syndrome.
Selenomethionine Supplementation Mitigates Liver Dysfunction, Oxidative Injury and Apoptosis through Enhancing Antioxidant Capacity and Inhibiting JNK MAPK Pathway in Piglets Fed Deoxynivalenol-Contaminated Diets
Authors: Zhong Haopeng , Huang Zhouyin , Li Lin , Chen Xingping , Zou Tiande , Chen Jun , You Jinming
Published: February 2024

This research evaluated the impacts of selenomethionine (Se-Met) on hepatic functions, oxidative stress, mitochondrial function, and apoptosis of piglets fed deoxynivalenol (DON)-contaminated diets. Twenty-four piglets were allocated four dietary treatments (n = 6) in a 28-day feeding trial. The four treatments included the control group, which received 0.3 mg/kg of Se (as Se-Met) without DON treatment, and the DON treatment groups received 0, 0.3, or 0.5 mg/kg Se as Se-Met. A dietary addition of 0.5 mg/kg Se improved liver pathology and reduced serum aspartate aminotransferase and lactate dehydrogenase levels in piglets fed DON-contaminated diets. Furthermore, 0.5 mg/kg Se mitigated the oxidative stress and apoptosis of piglets fed DON-contaminated diets, as indicated by the decreased reactive oxygen species level, and the down-regulated mRNA levels of NRF-1, Bax, and CASP9 in the liver. Importantly, 0.5 mg/kg Se enhanced the hepatic antioxidant capacity, as evidenced by increased hepatic total antioxidant capacity, catalase, glutathione peroxidase, and total superoxide dismutase activities, as well as the up-regulated mRNA levels of Nrf2, Gclm, NQO1, SOD1, and GPX1 in the liver. Moreover, 0.5 mg/kg Se down-regulated the p-JNK protein level in the liver of piglets fed DON-contaminated diets. Collectively, Se-Met supplementation mitigated liver dysfunction, oxidative injury, and apoptosis through enhancing antioxidant capacity and inhibiting the JNK MAPK pathway in piglets fed DON-contaminated diets.
A Novel Model for Evaluating the Natural Antioxidant Carnosic Acid to Improve the Stability of Rapeseed Oil in the Thermal Degradation
Authors: Zhu Yingdan , Chai Chengliang , Xue Yalin , Wang Yong , Duan Zhangqun
Published: February 2024

The quality and stability of oil during thermal processing reflect the reactions in vegetable oil. The deterioration of the oil is close to the viscosity, fatty acid composition (FA), total polar compounds (TPC), etc. Carnosic acid (CA) is the main antioxidant component of rosemary extract; it is a natural and clean-label antioxidant that is allowed to be added to prolong oil processing and storage. To achieve a clear correlation of this situation, a novel stability evaluation model was used to predict the thermal degradation of rapeseed oil (RSO) with CA. The RSO with CA (200 mg/kg, 400 mg/kg, and 700 mg/kg), the tert-Butylhydroquinone (TBHQ, 200 mg/kg), and the fresh RSO (without additives) during thermal processing (180 ± 5 °C) were studied. The temperature dependency of viscosity fits well with the Lioumbas model (R2 ≥ 0.999). The parameter b value in the Lioumbas model showed a decrease linearly with the processing time (tP, R2 ≥ 0.965). The multiple linear regression analysis showed that the accuracy of the model in predicting viscosity was less than ±2 mPa·s−1, and the deviation% was less than ±10% in all the samples. After 32 h of thermal degradation, the addition of 700 mg/kg CA showed the lowest degradation rate (13.84%) of polyunsaturated fatty acids (PUFAs), and the TPC content was 26.00 ± 0.50%. The TPC showed a positive relationship with viscosity (r = 0.99, p < 0.01), tP (r = 0.97, p < 0.01), and effective carbon numbers (ECN, r = 0.84, p < 0.05). In conclusion, this study can make a potential prediction for the stability of RSO.
Identification of Oxidative-Stress-Reducing Plant Extracts from a Novel Extract Library—Comparative Analysis of Cell-Free and Cell-Based In Vitro Assays to Quantitate Antioxidant Activity
Authors: Heckmann Mara , Stadlbauer Verena , Drotarova Ivana , Gramatte Theresa , Feichtinger Michaela , Arnaut Verena , Atzmüller Stefanie , Schwarzinger Bettina , Röhrl Clemens , Blank-Landeshammer Bernhard , Weghuber Julian
Published: February 2024

Numerous underexplored plant species are believed to possess considerable potential in combating oxidative stress and its associated health impacts, emphasizing the need for a comprehensive methodological screening approach to assess their antioxidant capacity. This study investigated 375 plant extracts, utilizing both cell-free and cellular methods to evaluate their antioxidant properties. Target-based antioxidant capacity was evaluated by the total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) assays. Cell-based assays employed the H2DCF-DA probe to measure reactive oxygen species (ROS) levels and the Griess assay to quantify nitric oxide (NO) levels in stressed Caco-2 and RAW264.7 cells, respectively. The highest TPC and FRAP values were found in extracts of Origanum vulgare and Fragaria × ananassa leaves. Several plant extracts significantly reduced stress-induced ROS or NO levels by at least 30%. Distinctive selectivity was noted in certain extracts, favoring the significant reduction of NO (e.g., Helianthus tuberosus extract), of ROS (e.g., Prunus domestica subsp. Syriaca extract), or of both (e.g., Fragaria × ananassa leaf extract). A strong correlation between TPC and FRAP values and moderate correlations between the results of the cell-free and cell-based assays were evident. These findings highlight the great antioxidant potential of underexplored plant extracts and the diversity of the underlying mechanisms, emphasizing the importance of a multifaceted approach for a comprehensive assessment.
Method for Sparse Representation of Complex Data Based on Overcomplete Basis, l1 Norm, and Neural MFNN-like Network
Authors: Panokin Nikolay V. , Averin Artem V. , Kostin Ivan A. , Karlovskiy Alexander V. , Orelkina Daria I. , Nalivaiko Anton Yu.
Published: February 2024

The article presents the results of research into a method for representing complex data based on an overcomplete basis and l0/l1 norms. The proposed method is an extended modification of the neural-like MFNN (minimum fuel neural network) for the case of complex data. The influence of the choice of activation function on the performance of the method is analyzed. The results of the numerical simulation demonstrate the effectiveness of the proposed method for the case of sparse representation of complex data and can be used to determine the direction of arrival (DOA) for a uniform linear array (ULA).
Time Series Feature Selection Method Based on Mutual Information
Authors: Huang Lin , Zhou Xingqiang , Shi Lianhui , Gong Li
Published: February 2024

Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.
Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
Authors: Biernacki Arkadiusz
Published: February 2024

Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player’s ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into account the similarity of their mean and variance. Once we distinguished the similar trace fragments, we built a separate LSTM predictive model for each cluster. For the experiment, we used traffic captured from 5G networks generated by individual user equipment (UE) in fixed and mobile scenarios. Our results show that the prior grouping of the network traces improved the prediction compared to the global model operating on the whole trace.
Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions
Authors: Baniata Laith H. , Kang Sangwoo , Alsharaiah Mohammad A. , Baniata Mohammad H.
Published: February 2024

Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model’s potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout.
An Innovative Clinical Evaluation Protocol after Total Ankle Arthroplasty: A Pilot Study Using Inertial Sensors and Baropodometric Platforms
Authors: Mazzotti Antonio , Arceri Alberto , Abdi Pejman , Artioli Elena , Zielli Simone Ottavio , Langone Laura , Ramponi Laura , Ridolfi Arianna , Faldini Cesare , Brognara Lorenzo
Published: February 2024

Background: Total ankle arthroplasty (TAA) has grown in popularity and indications, with encouraging results over time. Today, preoperative and postoperative evaluations are mainly performed using clinical test and diagnostic imaging, but there is a deficiency in objectively evaluating the biomechanics of the foot and ankle, which serve as the functional markers for monitoring the effectiveness and outcomes of surgery. Inertial measurement units associated with plantar pressure measurements may provide an accurate and reliable method of evaluating function through the analysis of gait and ankle joint mobility. The aim of this study was to introduce an innovative technology, to assess its accuracy and feasibility compared to standard clinical assessment methods and to objectify kinematic outcomes in patients with end-stage ankle OA before and after TAA surgery. Methods: A consecutive series of eight patients with symptomatic end-stage osteoarthritis and treated with TAA was prospectively evaluated using clinical scores (AOFAS, MOxFQ, VAS, SF-36, 17-IFFI), physical tests (FPI, ALT), plantar pressure measurements with FLEX EPS/R2 Letsense® baropodometric platform, gait analysis and wearable sensors-based ankle motion and kinematic outcomes using Wiva Science inertial sensors by Letsense®. Data were collected preoperatively and 4 months after surgery. Results: All PROMs exhibited statistical significance in improvement from pre- to postoperative periods, except for one. Physical examinations showed no significant changes of the foot shape and alignment. Plantar pressure analyses revealed no significant changes in static and dynamic evaluations, but a more uniform distribution of plantar pressure was observed between the two periods. Inertial sensor parameters demonstrated no significant differences, except for a significant reduction in stride length and step length for the operated foot after surgery. Conclusions: Gait analysis using inertial sensors and plantar pressure measurements offer ease of handling, cost effectiveness, portability and swift data reading, making them highly appealing for widespread clinical use. Integrating these tools into the routine assessments of patients with TAA holds promise for advancing precision of treating this condition and our depth of its understanding, contributing to more comprehensive and insightful patient care.
Optimization of the Process for Obtaining Antioxidant Protein Hydrolysates from Pumpkin Seed Oil Cake Using Response Surface Methodology
Authors: Dyankova Svetla , Doneva Maria , Terziyska Margarita , Metodieva Petya , Nacheva Iliana
Published: February 2024

Pumpkin seed cake, a byproduct of cold-pressed oil production, represents a food waste material with a great potential for valorization. The objective of the present study is to optimize the papain enzymatic hydrolysis process of pumpkin seed cold-pressed oil cake (CPC) to obtain protein hydrolysates with the highest antioxidant activity. Box–Behnken Response Surface Methodology (RSM) was used to optimize the simultaneous effects of an enzyme concentration of papain, a temperature, and a reaction time on the process of enzymatic hydrolysis on pumpkin seed cold-pressed oil cake (CPC). For these three input factors, different values are used—1, 2, and 3% for papain concentration, 20, 30, and 40 °C for temperature, and 60, 120, and 180 min for hydrolysis time. Thus, the design generated a total of 21 experimental runs. The aim is to obtain protein hydrolysates with the highest antioxidant activity. The responses DPPH and ABTS were calculated and the determined regression models were statistically analyzed and validated. The results revealed that optimal conditions included a papain concentration of 1.0%, a temperature of 40 °C, and a hydrolysis time of 60 min to retrieve the highest level of bioactive compounds.
Defect Prediction for Capacitive Equipment in Power System
Authors: Peng Qingjun , Zheng Zezhong , Hu Hao
Published: February 2024

As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions.
A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
Authors: Hwang Hoseong , Kim Donghyun , Kim Hochul
Published: February 2024

Computed tomography (CT) is a rapid and precise medical imaging modality, but it poses the challenge of high radiation exposure to patients. To control this issue, stringent quality control (QC) evaluations are imperative for CT. One crucial aspect of CT QC involves the evaluation of phantom images, utilizing specifically designed phantoms for accuracy management and subsequent objective evaluation. However, CT QC has qualitative evaluation methods, particularly for evaluating spatial and contrast resolutions. To solve this problem, we propose a quality control method based on deep-learning object detection for quantitatively evaluating spatial and contrast resolutions, CT Attention You Only Look Once v8 (CTA-YOLOv8). First, we utilized the YOLOv8 network as the foundational model, optimizing it for enhanced accuracy. Second, we enhanced the network’s capabilities by integrating the Convolutional Block Attention Module (CBAM) and Swin Transformers, tailored for phantom image evaluations. The CBAM module was employed internally to pinpoint the optimal position for achieving peak performance in CT QC data. Similarly, we fine-tuned the code and patch size of the Swin Transformer module to align it with YOLOv8, culminating in the identification of the optimal configuration. Our proposed CTA-YOLOv8 network showed superior agreement with qualitative evaluation methods, achieving accuracies of 92.03% and 97.56% for spatial and contrast resolution evaluations, respectively. Thus, we suggest that our method offers nearly equivalent performance to qualitative methods. The utilization of the CTA-YOLOv8 network in evaluating CT phantom images holds potential for setting a new standard in quantitative assessment methodologies.
Experimental Study on Preparation and Characteristics of Concrete Modified by Construction Waste
Authors: Zhang Jing , Zhu Xuejun , Zhou Mingyuan , Huang Xianwen
Published: February 2024

With the advancement of urbanization construction, the proportion of construction waste to the total urban waste continues to increase, especially waste concrete. The treatment and reuse of waste concrete is a major trend that poses enormous pressure on environmental protection. This article focuses on the problems in the preparation of recycled aggregates from waste concrete, which has important practical value. This article presents a new type of recycled concrete prepared through surface modification of recycled coarse aggregate and design experiments to change the replacement rate of coarse aggregate. The physical properties of recycled coarse aggregate, workability of fresh concrete, and mechanical properties of recycled concrete are analyzed. The research results indicate the following: (1) Through surface modification, recycled concrete can improve the workability of fresh concrete at a fixed water cement ratio, which can meet the requirements of mixing, transportation, and pouring of fresh concrete. (2) By modifying the surface of recycled aggregates, the strong water absorption performance of recycled aggregates caused by old mortar and surface defects has been reduced. And the modification effect of recycled aggregate improves the hydration process of recycled concrete, making the surface structure dense and further enhancing the strength of recycled concrete. (3) The compressive strength of recycled concrete specifications from construction waste shows a decreasing trend with the increase in coat aggregate replacement rate. The final ratio is as follows: modular dose of 12%, modification time of 90 min, and 20% recycled aggregate content.
Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
Authors: Zhang Shuguang , Tu Meng , Liu Wenchang , Du Xiaobo , Zhang Hongchao
Published: February 2024

In the course of asphalt pavement usage, exposure to fuel infiltration accelerates particle detachment, leading to the occurrence of problems such as looseness and peeling. The aim of this study was to comprehensively evaluate the impact of a fuel-resistant modifier (FRM; 1%, 3%, 5%, 7%) on the performance of asphalt and asphalt mixture. Conventional physical tests and high–low temperature rheological tests were conducted on the fuel-resistant modified asphalt (FRMA). The results indicate that, with increased FRM content, the penetration and ductility of FRMA decreased, while the softening point increased. The high-temperature performance improved, but the low-temperature ductility declined. High–low temperature rheological test results demonstrate that the addition of FRM significantly enhanced the asphalt’s shear deformation resistance. A moderate amount of FRM modification improved the asphalt’s low-temperature crack resistance, but excessive FRM resulted in reduced flexibility. In addition, fuel-resistant modified asphalt mixture (FRMAM) specimens were prepared and evaluated for performance. In comparison to the base asphalt, FRM modification enhanced the resistance to oil erosion and peeling as well as the Marshall stability of asphalt mixtures. It reduced the scattering loss caused by fuel dissolution and improved both low-temperature indirect tensile (IDT) strength and high-temperature shear strength. A low content of FRM effectively reduced the sensitivity of asphalt mixtures to fuel infiltration, thus enhancing their road performance.
Commissioning of Bunch Compressor to Compress Space Charge-Dominated Electron Beams for THz Applications
Authors: Lueangaramwong Anusorn , Kongmon Ekkachai , Li Xiangkun , Boonpornprasert Prach , Georgiev Georgi , Krasilnikov Mikhail , Aboulbanine Zakaria , Adhikari Gowri , Aftab Namra , Gross Matthias , Niemczyk Raffael , Oppelt Anne , Qian Houjun , Richard Christopher , Vashchenko Grygorii , Weilbach Tobias , Stephan Frank
Published: February 2024

The high peak current of the electron beam was found to be the key parameter for the THz SASE FEL at the Photo Injector Test facility at DESY in Zeuthen (PITZ). A multipurpose bunch compressor was implemented at PITZ to expand the parameter space of proof-of-principle studies on the tunable high-power accelerator-based THz source for pump-probe experiments at the European XFEL. The magnetic chicane, consisting of four rectangular dipole magnets, is designed with a bending angle of 19 degrees, due to limited space in the PITZ original beamline, to compress electron bunches with a beam momentum of 15–20 MeV/c and a charge up to 2 nC. The space charge effect and coherent synchrotron radiation are expected to drastically affect the bunch compressor performance for these parameters, thereby challenging the beam transport throughout the bunch compressor. A staged commissioning strategy was developed in order to achieve optimum bunch compressor operation. The first commissioning procedure establishes electron beam transport throughout the reference path and provides minimum beam momentum dispersion after the bunch compressor. This procedure yielded correlations between dipole magnet currents. As a result, the first bunch compression experiments were performed.
Small Strain Shear Modulus of the Ljubljana Marsh Soil Measured with Resonant Column and Bender Elements under Isotropic and Anisotropic Stress Conditions
Authors: Jurček Timotej , Pulko Boštjan , Maček Matej
Published: February 2024

The increasing use of finite element analysis in modern infrastructure design emphasizes the importance of determining soil stiffness at small strains. This is usually represented by the normalized shear modulus degradation curve, which is crucial for accurate design. In the absence of specific measurements on the local soil, engineers often rely on empirical correlations and assume comparable behavior of soils with similar intrinsic properties. However, the application of this approach leads to uncertainties, especially for unique geological formations such as the soft cohesive soils of the Ljubljana Marsh. The main objective of this study was to determine the small strain shear modulus of Ljubljana Marsh soil with a plasticity index between 11 and 35%. Isotropic and anisotropic stress conditions were investigated as part of an extensive laboratory test program that included 45 bender element and 89 resonant column tests on 20 soil samples. By emphasizing the importance of measuring soil stiffness at small strains, this study not only provides reliable data for the development of the built environment in the Ljubljana Marsh and similar areas, but also underlines its necessity.
Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
Authors: Xu Haiqing , Yu Mingyang , Zhou Fangliang , Yin Hongling
Published: February 2024

Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model’s real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global–local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model’s real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 × 1024 input on a single 3090 GPU.
Visual–Inertial Odometry of Structured and Unstructured Lines Based on Vanishing Points in Indoor Environments
Authors: He Xiaojing , Li Baoquan , Qiu Shulei , Liu Kexin
Published: February 2024

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method.
A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results
Authors: Münster Sander , Maiwald Ferdinand , Bruschke Jonas , Kröber Cindy , Sun Ying , Dworak Daniel , Komorowicz Dávid , Munir Iqra , Beck Clemens , Münster Dora Luise
Published: February 2024

Numerous digital media repositories have been set up during recent decades, each containing plenty of data about historic cityscapes. In contrast, digital 3D reconstructions of no longer extant cityscapes have so far almost solely been performed for specific cases and via manual modelling techniques. Within the Jena4D research group, we are investigating and developing methods and technologies for transferring historical media and their contextual information into 4D models. The next step will be to automate this process and extend it to the world scale. Our team are working on different facets of that endeavor: retrieving images, text, and object data from public sources; crowdsourcing and collecting qualitative data from contests and pupil labs; processing historical plans and photographs to extract spatial data; location-based mobile visualization; and collection-browsing interfaces. This article is intended to highlight (1) current challenges, (2) the state of the art, (3) our approach to 4D modelling and visualization on the world scale, and (4) results from testing demo cases in Dresden, Jena, and Amsterdam.
Influence of Applied Loads on Free Vibrations of Functionally Graded Material Plate–Shell Panels
Authors: Moita José Simões , Correia Victor Franco , Soares Cristóvão Mota
Published: February 2024

An analysis of the influence of applied loads on free vibrations of plate and shell panels made of functionally graded materials is analyzed in the present work. Formulations for the static analysis considering geometrically nonlinear behavior, as well as linear buckling and free vibrations analyses are considered. A calculation of the through-thickness stress distribution is also performed. The finite element model is based on a higher order shear deformation theory using a non-conforming flat triangular plate/shell element with three nodes, and eight degrees of freedom per node is used in the numerical implementation. The results obtained with this numerical model are presented, discussed, and compared with alternative solutions published by other authors in some benchmark applications.
Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
Authors: Zhang Xiaoli , Xu Nuo , Dai Wei , Zhu Guifu , Wen Jun
Published: February 2024

Predicting the health index of turbofan engines is critical in reducing downtime and ensuring aircraft safety. This study introduces the elite snake optimizer-back propagation (ESO-BP) model to address the challenges of low accuracy and poor stability in predicting the health index of turbofan engines through neural networks. Firstly, the snake optimizer (SO) was improved into the elite snake optimizer (ESO) through an elite-guided strategy and a reverse learning mechanism. The performance improvement was validated using benchmark functions. Additionally, feature importance was introduced as a feature selection method. Finally, the optimization results of the ESO were employed to set the initial weights and biases of the BP neural network, preventing convergence to local optima. The prediction performance of the ESO-BP model was validated using the C-MAPSS datasets. The ESO-BP model was compared with the CNN, RNN, LSTM, baseline BP, and unimproved SO-BP models. The results demonstrated that the ESO-BP model has a superior accuracy with an impressive R-squared (R2) value of 0.931 and a root mean square error (RMSE) of 0.060 on the FD001 sub-dataset. Furthermore, the ESO-BP model exhibited lower standard deviations of evaluation metrics on 100 trials. According to the study, ESO-BP demonstrated a greater prediction accuracy and stability when compared to commonly used models such as CNN, RNN, LSTM, and BP.
Signed Fingermark Liveness Detection Method Based on Deep Residual Networks and Multimodal Decision Fusion
Authors: Zhang Yongliang , Zhou Zihan , Wang Jiahang , Chen Zipeng
Published: February 2024

Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the difficulty of detection. A signed fingermark detection method based on deep residual networks and a decision-level fusion strategy was proposed to defend against spoofing attacks from fake fingermarks. Firstly, the multi-scale structure was introduced in the residual module, which improved the network’s depth and breadth without increasing the parameters. Then, the multi-probability label strategy was refined and employed to enhance the local encoding ability of the feature extraction. A score fusion strategy was designed, with weights allocated based on the difference in signed interference levels of local image blocks. Finally, a model fusion strategy based on evidence theory was suggested, which improved detection accuracy by leveraging complementarity between models. A large-scale fingermark database was established, which included real fingermarks made from real fingers and fake fingermarks made from various materials, and this was divided into two sub databases: signed and unsigned. The experimental results show that the proposed method achieves 96.16% accuracy based on the fingerprint dataset of the global liveness detection competition called LivDet2017 and achieves 99.30% accuracy based on the signed fingermark database, while it has good resistance to spoofing attacks from unknown materials.
CenterInst: Center-Based Real-Time Instance Segmentation
Authors: Tian Shu , Ren Liang
Published: February 2024

Instance segmentation is a computer vision task that aims to give each pixel in an image an instance-specific label. Recently, researchers have shown growing interest in real-time instance segmentation. In this paper, we propose a novel center-based real-time instance segmentation method (CenterInst), which follows the FastInst meta-architecture. Key design aspects include a center-guided query selector, a center-guided sampling-based query decoder, and a lightweight dual-path decoder. The center-guided query selector selects queries via the per-pixel prediction of center point probabilities, avoiding excessive query proposals for single instances. The center-guided sampling-based query decoder adaptively generates local sampling points based on center positions, employing adaptive mixing to update queries without irrelevant sampling disturbances. The lightweight dual-path decoder enhances inference speed and maintains accuracy via pixel decoding on every layer during training but only utilizing the final layer’s decoder during inference. The experimental results show CenterInst achieves superior accuracy and speed compared to state-of-the-art real-time instance segmentation methods.
Evaluation of the Nutritional Value of Prunus dulcis Blossoms and the Antioxidant Compounds of Their Extracted Oil Using Green Extraction Method
Authors: Chatzimitakos Theodoros , Athanasiadis Vassilis , Kotsou Konstantina , Makrygiannis Ioannis , Bozinou Eleni , Lalas Stavros I.
Published: February 2024

Edible blossoms and extracted oils from various parts of plants have gained the interest of researchers in recent years due to their strong antioxidant activity and their high content of vitamins. In addition, they contain a plethora of polyphenols, and they do not have high caloric content. The blossoms of Prunus dulcis (i.e., almond tree) are edible; however, they have not been examined in terms of nutritional value. The present study aimed to examine the nutritional value of almond blossoms, as well as their extracted oil. The fat content of the blossoms was 1.75 g/100 g dry weight (dw), while the defatted blossoms were found to contain 1.34 g/100 g dw of crude protein and 29.97 g/100 g dw of carbohydrates. In addition, the blossom oil was tested for its composition of fatty acids, polyphenols, and total carotenoids. According to the results, several important fatty acids for human health were identified, such as oleic (25.17%), linoleic (15.64%), and linolenic (10.15%). Simultaneously, a low oxidation index (COX), i.e., 4.05, and many monounsaturated (25.17%) and unsaturated (67.56%) fats were detected, while both polyphenols (51.86 mg GAE/kg) and carotenoids were in abundance. Finally, the combination of simple stirring with ultrasound (a green extraction method) was found to be the most appropriate method to ensure maximum amounts of various antioxidant compounds in the blossom extracts (i.e., polyphenols and L-ascorbic acid). After optimization, the total polyphenol content increased by 23.98% and L-ascorbic acid content by 6.96%. In addition, antioxidant activity was tested by different antioxidant assays and specifically FRAP, DPPH, and H2O2, which showed a corresponding increase (14.46, 17.23, and 8.79%, respectively). Therefore, it can be concluded that Prunus dulcis blossoms, besides being edible, are also highly nutritious, and their oil has nutritional value and deserves further exploration.