bluuebunny/crossref_metadata_2025 huggingface dataset:
In Silico Analysis of miRNA-Mediated Genes in the Regulation of Dog Testes Development from Immature to Adult Form
Authors: Kasimanickam Vanmathy R. , Kasimanickam Ramanathan K.
Published: May 2023

High-throughput in-silico techniques help us understand the role of individual proteins, protein–protein interaction, and their biological functions by corroborating experimental data as epitomized biological networks. The objective of this investigation was to elucidate the association of miRNA-mediated genes in the regulation of dog testes development from immature to adult form by in-silico analysis. Differentially expressed (DE) canine testis miRNAs between healthy immature (2.2 ± 0.13 months; n = 4) and mature (11 ± 1.0 months; n = 4) dogs were utilized in this investigation. In silico analysis was performed using miRNet, STRING, and ClueGo programs. The determination of mRNA and protein expressions of predicted pivotal genes and their association with miRNA were studied. The results showed protein–protein interaction for the upregulated miRNAs, which revealed 978 enriched biological processes GO terms and 127 KEGG enrichment pathways, and for the down-regulated miRNAs revealed 405 significantly enriched biological processes GO terms and 72 significant KEGG enrichment pathways (False Recovery Rate, p < 0.05). The in-silico analysis of DE-miRNA’s associated genes revealed their involvement in the governing of several key biological functions (cell cycle, cell proliferation, growth, maturation, survival, and apoptosis) in the testis as they evolve from immature to adult forms, mediated by several key signaling pathways (ErbB, p53, PI3K-Akt, VEGF and JAK-STAT), cytokines and hormones (estrogen, GnRH, relaxin, thyroid hormone, and prolactin). Elucidation of DE-miRNA predicted genes’ specific roles, signal transduction pathways, and mechanisms, by mimics and inhibitors, which could perhaps offer diagnostic and therapeutic targets for infertility, cancer, and birth control.
The Antimicrobial Effects of Colistin Encapsulated in Chelating Complex Micelles for the Treatment of Multi-Drug-Resistant Gram-Negative Bacteria: A Pharmacokinetic Study
Authors: Liao Wei-Chuan , Wang Chau-Hui , Sun Tzu-Hui , Su Yu-Cheng , Chen Chia-Hung , Chang Wen-Teng , Chen Po-Lin , Shiue Yow-Ling
Published: May 2023

Background: Infections caused by multi-drug-resistant Gram-negative bacteria (MDR-GNB) are an emerging problem globally. Colistin is the last-sort antibiotic for MDR-GNB, but its toxicity limits its clinical use. We aimed to test the efficacy of colistin-loaded micelles (CCM-CL) against drug-resistant Pseudomonas aeruginosa and compare their safety with that of free colistin in vitro and in vivo. Materials and methods: We incorporated colistin into chelating complex micelles (CCMs), thus producing colistin-loaded micelles (CCM-CL), and conducted both safety and efficacy surveys to elucidate their potential uses. Results: In a murine model, the safe dose of CCM-CL was 62.5%, which is much better than that achieved after the intravenous bolus injection of ‘free’ colistin. With a slow drug infusion, the safe dose of CCM-CL reached 16 mg/kg, which is double the free colistin, 8 mg/kg. The area under the curve (AUC) levels for CCM-CL were 4.09- and 4.95-fold higher than those for free colistin in terms of AUC0-t and AUC0-inf, respectively. The elimination half-lives of CCM-CL and free colistin groups were 12.46 and 102.23 min, respectively. In the neutropenic mice model with carbapenem-resistant Pseudomonas aeruginosa pneumonia, the 14-day survival rate of the mice treated with CCM-CL was 80%, which was significantly higher than the 30% in the free colistin group (p < 0.05). Conclusions: Our results showed that CCM-CL, an encapsulated form of colistin, is safe and effective, and thus may become a drug of choice against MDR-GNB.
Randomized Clinical Trial of the Need for Antibiotic Treatment for Low-Risk Catheter-Related Bloodstream Infection Caused by Coagulase-Negative Staphylococci
Authors: Badia-Cebada Laia , Carmezim João , Pérez-Rodríguez María-Teresa , Bereciartua Elena , López Luis-Eduardo , Montenegro Marta Represa , Pomar Virginia , Andrés Marta , Petkova Elizabet , Sopena Nieves , Lora-Tamayo Jaime , Monsálvez Víctor , Ramirez-Hidalgo Maria Fernanda , Gómez-Zorrilla Silvia , Boix Lucía , Meije Yolanda , Jiménez Emili , Gasch Oriol
Published: May 2023

According to clinical guidelines, the management of catheter-related bloodstream infections (CRBSI) due to coagulase-negative staphylococci (CoNS) includes catheter removal and antibiotic treatment for 5 to 7 days. However, in low-risk episodes, it remains uncertain whether antibiotic therapy is necessary. This randomized clinical trial aims to determine whether the non-administration of antibiotic therapy is as safe and effective as the recommended strategy in low-risk episodes of CRBSI caused by CoNS. With this purpose, a randomized, open-label, multicenter, non-inferiority clinical trial was conducted in 14 Spanish hospitals from 1 July 2019 to 31 January 2022. Patients with low-risk CRBSI caused by CoNS were randomized 1:1 after catheter withdrawal to receive/not receive parenteral antibiotics with activity against the isolated strain. The primary endpoint was the presence of any complication related to bacteremia or to antibiotic therapy within 90 days of follow-up. The secondary endpoints were persistent bacteremia, septic embolism, time until microbiological cure, and time until the disappearance of a fever. EudraCT: 2017-003612-39 INF-BACT-2017. A total of 741 patients were assessed for eligibility. Of these, 27 were included in the study; 15 (55.6%) were randomized to the intervention arm (non-antibiotic administration) and 12 (44.4%) to the control arm (antibiotic therapy as per standard practice). The primary endpoint occurred in one of the 15 patients in the intervention group (septic thrombophlebitis) and in no patients in the control group. The median time until microbiological cure was 3 days (IQR 1–3) in the intervention arm and 1.25 days (IQR 0.5–2.62) in the control arm, while the median time until fever resolution was zero days in both arms. The study was stopped due to the insufficient number of recruited patients. These results seem to indicate that low-risk CRBSI caused by CoNS can be managed without antibiotic therapy after catheter removal; efficacy and safety are not affected.
Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction
Authors: Yao Jingxiu , Liu Bin , Wu Yumei , Li Zhibo
Published: May 2023

Heterogeneous defect prediction (HDP) is a significant research topic in cross-project defect prediction (CPDP), due to the inconsistency of metrics used between source and target projects. While most HDP methods aim to improve the performance of models trained on data from one source project, few studies have investigated how the number of source projects affects predictive performance. In this paper, we propose a new multi-source heterogeneous kernel mapping (MSHKM) algorithm to analyze the effects of different numbers of source projects on prediction results. First, we introduce two strategies based on MSHKM for multi-source HDP. To determine the impact of the number of source projects on the predictive performance of the model, we regularly vary the number of source projects in each strategy. Then, we compare the proposed MSHKM with state-of-the-art HDP methods and within-project defect prediction (WPDP) methods, in terms of three common performance measures, using 28 data sets from five widely used projects. Our results demonstrate that, (1) in the multi-source HDP scenario, strategy 2 outperforms strategy 1; (2) for MSHKM, a lower number of source projects leads to better results and performance under strategy 1, while n = 4 is the optimal number under strategy 2; (3) MSHKM performs better than related state-of-the-art HDP methods; and (4) MSHKM outperforms WPDP. In summary, our proposed MSHKM algorithm provides a promising solution for heterogeneous cross-project defect prediction, and our findings suggest that the number of source projects should be carefully selected to achieve optimal predictive performance.
Innovative Surface-Enhanced Raman Spectroscopy Method as a Fast Tool to Assess the Oxidation of Lipids in Ground Pork
Authors: Wrona Magdalena , Lours Juliette , Salafranca Jesús , Joly Catherine , Nerín Cristina
Published: May 2023

A novel method for the determination of lipid oxidation using Raman microscopy was developed. A home-made surface-enhanced Raman spectroscopy (SERS) substrate based on silver nanoparticles deposited on a glass Petri dish was used. The degradation of ground pork stored at 5 °C was monitored for 16 days. Two packages were considered: an active packaging containing an oregano extract with antioxidant properties and a conventional one consisting of a low-density polyethylene (LDPE) film. The lipid fraction of the ground pork was extracted with a mixture of diethyl ether/n-hexane (1:1). A remarkable maximum signal enhancement factor of 1.64 × 107 at 1439 cm−1 shift (and up to 8.58 × 106 at 1655 cm−1, chosen for fat oxidation assessment) was obtained with SERS compared to conventional Raman. In addition, SERS provided better discrimination among samples than the results obtained by the thiobarbituric acid reactive substances (TBARS) method. The experimental conditions for SERS were optimized and discussed.
Study on Compaction Characteristics and Compaction Process of an Unsaturated Silt Based on PFC3D
Authors: Ma Zhitao , Lu Keyu , Song Daifu , Liu Wenhu , Wang Yipeng , Li Shunhai
Published: May 2023

In order to reduce the construction cost of excessively wet silt roadbeds and improve the compaction quality, this paper studies the compaction characteristics and compaction technology of excessively wet silts through theoretical derivation, indoor experiments, numerical simulation, engineering verification, and other research methods. Based on the pendulum-type liquid bridge structure, this paper uses the Hertz–Mindlin theory, particle motion equation of state, and other theories to modify the force between particles of an unsaturated soil. Through the contact angle, which is a medium, the matrix suction is linked to the water content and is used to approximate the water content state of soil samples. Using PFC3D (particle flow code 3D) numerical simulation software, an unsaturated silt model was established based on Hill contact, and the model parameters were calibrated through basic geotechnical tests and triaxial compression tests. The correctness of the theory is verified by comparing the simulated triaxial compression test of wet silts with the indoor test. By simulating subgrade rolling, considering factors such as the distribution of contact force chain and the variation of compaction degree, the effects of rolling times, strong vibration, and weak vibration on the compaction effect were compared and analyzed. Finally, the optimal rolling and compaction process under the optimal water content is obtained through on-site engineering tests. The results show that: (1) The Hill contact model is reasonable for simulating the wet silt. (2) During the simulation of the roadbed compaction process using PFC3D software, it was found that the compaction degree change during the entire compaction process can be roughly divided into the initial compaction stage and the re-compaction stage. The reasonable number of compaction times was determined to be five through discrete element simulation. (3) It is found from the numerical simulation results that compared to static compaction, both strong and weak vibration compaction can effectively improve the compaction effect of subgrade compaction. The larger the vibration amplitude, the more obvious the improvement of the compaction effect. The compaction effect of strong vibration followed by weak vibration is stronger than that of weak vibration followed by strong vibration. (4) The optimal compaction process obtained under the optimal moisture content of 15% is static pressure once + strong vibration twice + weak vibration twice.
Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities
Authors: Wu Bing , Liu Lixue
Published: May 2023

In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.
The Effect of Ultrasound Pretreatment on Pectin Extraction from Watermelon Rind Using Microwave-Assisted Extraction
Authors: Forouhar Ali , Hamdami Nasser , Djelveh Gholamreza , Gardarin Christine , Pierre Guillaume , Ursu Alina Violeta , Michaud Philippe
Published: May 2023

Ultrasound pretreatment and microwave-assisted extraction (MAE) techniques were used to extract pectin from watermelon rind. This work investigated the effect of the power and time of pretreatment on the yield and characteristics of the extracted pectin. Imaging studies showed that ultrasonic pretreatment changed the morphology of the raw powder, leading to a greater porosity, and facilitated the extraction process. The highest extraction yield of pectin was 19.08% (w/w), for 5 min of extraction at 500 W ultrasonic pretreatment. Ultrasonic pretreatment affected the degree of esterification and the galacturonic acid content of pectin. The galacturonic acid content of all samples was higher than 69.0%. Extraction pretreatment reduced the surface tension value of the aqueous solution of pectin and affected the foam capacity of pectin and its monosaccharidic composition. The ultrasonic pretreatment caused significant changes in pectin characteristics, such as intrinsic viscosity, viscosity average, molecular weight, ratio of homogalacturonan to rhamnogalacturonan and the water holding capacity. In addition, kinetic studies showed that a kinetic empirical model adequately describes the effect of ultrasonic pretreatment on foam stability and its half-time.
Acoustic Characterization for The Feeding Activities of Haliotis discus Hannai
Authors: Lin Hongyue , Qian Yiyang , Chen Jia , Gao Xiaolong , Zhang Mo , You Weiwei , Zhang Rongxin
Published: May 2023

In order to analyze the sound production mechanism and the acoustic characteristics of Haliotis discus hannai during feeding, this paper proposes a multi-source information fusion approach combining passive acoustics with videos. In the experiments, abalones with a shell length of 60 ± 2.7 mm were divided into two groups: Group A was fed with fresh macro algae Gracilaria lemaneiformis as food once each day; Group B was placed on a small amount of sand as impurities at the bottom of the tank. As control groups, Group C did not have abalone or food and Group D did not have abalones but food was added. The eating acoustic signals of abalone were mainly concentrated in the frequency range between 9.49 kHz and 44.36 kHz, wherein the peak frequency is 37.86 ± 2.55 kHz, with the maximum energy −66.43 ± 5.17 dBm/Hz. Each pulse sequence is with a duration of 119.12 ± 70.51 ms and consists of several sub-pulses. Nearly 70% of the pulse sequences consist of 1~2 sub-pulses and the duration of the pulse containing one sub-pulse is 42.62 ± 19.72 ms. The eating rate was kept at 0.61 ± 0.04 times/min at the beginning and was decreased significantly to 0.48 ± 0.08 times/min after 60 min. Note that the characteristic analysis of abalone acoustic signals during feeding are first reported in this manuscript to the best of our knowledge, and this paper also demonstrates that the sound of abalone is produced by scraping and grinding food with radula. Because the eating rate decreases with the reduction in the abalone’s level of hunger, the results may be used as an acoustic indicator of feeding strategy for the abalone aquaculture industry.
Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
Authors: Yin Lirong , Wang Lei , Ge Lijun , Tian Jiawei , Yin Zhengtong , Liu Mingzhe , Zheng Wenfeng
Published: May 2023

The atmospheric density of the thermosphere is a fundamental parameter for spacecraft launch and orbit control. Under magnetic storm conditions, the thermospheric atmospheric density experiences significant fluctuations, which have a negative impact on spacecraft control. Exploring thermospheric density during geomagnetic storms can help to mitigate the effects of such events. Research on the inversion of accelerometer measurements for different satellites and the variations of atmospheric density under extreme conditions is still in its infancy. In this paper, the distribution of atmospheric density during three geomagnetic storms is investigated from the inversion results of the Swarm-C accelerometer. Three major geomagnetic storms and their recovery phases are selected as case studies. The thermospheric density obtained by Swarm-C is separated into day and night regions. The empirical orthogonal function analysis method is used to study the spatiotemporal distribution of thermospheric density during geomagnetic storms. The results indicate that storms have a more significant impact on nighttime thermospheric density. The impact of magnetic storms on the temporal distribution of thermospheric density is considerable. The first-order empirical orthogonal function (EOF) time coefficient value on the day after the storm is the largest, reaching 2–3 times that before the magnetic storm. The impact of magnetic storms on atmospheric density is mainly reflected in the time distribution. The spatial distribution of atmospheric density is less affected by magnetic storms and is relatively stable in the short term. The impact of magnetic storms on the spatial distribution of nighttime thermospheric density is more significant than that of daytime regions, and the response of daytime regions to magnetic storms is slower.
Security Ontology OntoSecRPA for Robotic Process Automation Domain
Authors: Kurylets Anastasiya , Goranin Nikolaj
Published: May 2023

Robotic process automation (RPA)* based on the use of software robots has proven to be one of the most demanded technologies to emerge in recent years used for automating daily IT routines in many sectors, such as banking and finance. As with any new technology, RPA has a number of potential cyber security weaknesses, caused either by fundamental logical mistakes in the approach or by cyber-human mistakes made during the implementation, configuration, and operation phases. It is important to have an extensive understanding of the related risks before RPA integration into enterprise IT infrastructure. The main asset operated by RPA is confidential enterprise data. Data leakage and theft are the two main threats. The wide application of RPA technology in information security-sensitive sectors makes the protection of RPA against cyber-attacks an important task. Still, this topic is not yet adequately investigated in the scientific press and existing articles mainly concentrate on stating the RPA security importance and describing some threats. In this article, we present a flexible tool, security-oriented ontology OntoSecRPA*, which systematically describes RPA-specific assets, risks, security, threats, vulnerabilities, and countermeasures. To the best of our knowledge, there are currently no ontologies available that are specific to the RPA domain, and existing security ontologies lack RPA-related features. In the future, the proposed ontology can be updated and used in different ways, for example, as a checklist for risk management tasks in RPA solutions and a source of information for an expert system or a concentrated domain-specific source of information, which indicates its wide practical application. The proposed ontology was formally verified by applying ontology completeness assessment and used for risk assessment in a sample scenario.
Hybrid Facial Emotion Recognition Using CNN-Based Features
Authors: Shahzad H. M. , Bhatti Sohail Masood , Jaffar Arfan , Akram Sheeraz , Alhajlah Mousa , Mahmood Awais
Published: May 2023

In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two well-known CNN architectures, AlexNet and VGG-16, using a dataset of masked facial expressions (MLF-W-FER dataset). The results of our experiments demonstrate that Support Vector Machine (SVM) and Ensemble classifiers outperform the SoftMax classifier on both AlexNet and VGG-16 architectures. These algorithms were able to achieve improved accuracy of between 7% and 9% on each layer, suggesting that replacing the classifier in each layer of a DCNN with SVM or ensemble classifiers can be an efficient method for enhancing image classification performance. Overall, our research demonstrates the potential for combining the strengths of CNNs and other machine learning (ML) algorithms to achieve better results in emotion recognition tasks. By extracting learned features from pre-trained CNNs and applying a variety of classifiers, we provide a framework for investigating alternative methods to improve the accuracy of image classification.
Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning
Authors: Gong Heng , Feng Xiaocheng , Qin Bing
Published: May 2023

Data-to-text generation plays an important role in natural language processing by processing structured data and helping people understand those data by generating user-friendly descriptive text. It can be applied to news generation, financial report generation, customer service, etc. However, in practice, it needs to adapt to different domains that may lack an annotated training corpus. To alleviate this dataset scarcity problem, distantly-supervised data-to-text generation has emerged, which constructs a training corpus automatically and is more practical to apply to new domains when well-aligned data is expensive to obtain. However, this distant supervision method of training induces an over-generation problem since the automatically aligned text includes hallucination. These expressions cannot be inferred from the data, misguiding the model to produce unfaithful text. To exploit the noisy dataset while maintaining faithfulness, we empower the neural data-to-text model by dynamically increasing the weights of those well-aligned training instances and reducing the weights of the low-quality ones via meta learning. To our best knowledge, we are the first to alleviate the noise in distantly-supervised data-to-text generation via meta learning. In addition, we rewrite those low-quality texts to provide better training instances. Finally, we construct a new distantly-supervised dataset, DIST-ToTTo (abbreviation for Distantly-supervised Table-To-Text), and conduct experiments on both the benchmark WITA (abbreviation for the data source Wikipedia and Wikidata) and DIST-ToTTo datasets. The evaluation results show that our model can improve the state-of-the-art DSG (abbreviation for Distant Supervision Generation) model across all automatic evaluation metrics, with an improvement of 3.72% on the WITA dataset and 3.82% on the DIST-ToTTo dataset in terms of the widely used metric BLEU (abbreviation for BiLingual Evaluation Understudy). Furthermore, based on human evaluation, our model can generate more grammatically correct and more faithful text compared to the state-of-the-art DSG model.
Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization
Authors: He Ke , Ding Haitao , Xu Nan , Guo Konghui
Published: May 2023

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization.
Research on Effective Design Methods of Core Beam of Full Bridge Aeroelastic Model
Authors: Qie Kai , Zhang Zhitian , Li Shouying , Wang Yuanyuan
Published: May 2023

The trial-and-error method is complex and tedious, but often adapted to determine the cross-section sizes of core beams in the design of reduced-scale models. In this study, two optimization methods, the optimization methods in ANSYS and the genetic algorithm, are investigated to optimize the cross-section sizes of core beams of reduced-scale models, which centers around two targeted moments of inertia and a targeted torsion constant. Due to the difficulty of obtaining an analytical solution of the torsion constant, a series of numerical solutions are proposed. Then, taking a U-shaped cross section as an example, the four geometric sizes of the section are optimized by the ANSYS optimization method and the genetic algorithm, respectively. The results of both methods are in good agreement with the targeted values, but the ANSYS optimization method is prone to fall into the local optimization zone and hence could be easily affected by the initial values. The shortcomings of the ANSYS optimization method can be easily avoided by the genetic algorithm, and it is easier to reach the global optimal solution. Finally, taking a suspension bridge with a main span of 920 m as a prototype, the full-bridge aeroelastic model is designed and the genetic algorithm is used to optimize the cross-section sizes of core beams in the bridge tower and the deck. Natural frequencies identified from the aeroelastic model agree well with the target ones, indicating the structural stiffness, which is provided by the core beams, has been modelled successfully.
A Multi-Source Data Fusion Method for Assessing the Tunnel Collapse Risk Based on the Improved Dempster–Shafer Theory
Authors: Wu Bo , Zeng Jiajia , Zhu Ruonan , Zheng Weiqiang , Liu Cong
Published: May 2023

Collapse is the main engineering disaster in tunnel construction when using the drilling and blasting method, and risk assessment is one of the important means to significantly reduce engineering disasters. Aiming at the problems of random decision-making and misjudgment of single indices in traditional risk assessment, a multi-source data fusion method with high accuracy based on improved Dempster–Shafer evidence theory (D-S model) is proposed in this study, which can realize the accurate assessment of tunnel collapse risk value. The evidence conflict coefficient K is used as the identification index, and the credibility and importance are introduced. The weight coefficient is determined according to whether the conflicting evidence is divided into two situations. The advanced geological forecast data, on-site inspection data and instrument monitoring data are trained by Cloud Model (CM), Gradient Boosting Decision Tree (GBDT) and Support Vector Classification (SVC), respectively, to obtain the initial BPA value. Combined with the weight coefficient, the identified conflict evidence is adjusted, and then the evidence from different sources is fused to obtain the overall collapse risk value. Finally, the accuracy is selected to verify the proposed method. The proposed method has been successfully applied to Wenbishan Tunnel. The results show that the evaluation accuracy of the proposed multi-source information fusion method can reach 88%, which is 16% higher than that of the traditional D-S model and more than 20% higher than that of the single-source information method. The high-precision multi-source data fusion method proposed in this paper has good universality and effectiveness in tunnel collapse risk assessment.
ATWin: An Improved and Detailed Startup Model of TTP/C
Authors: Yang Tingting , Sun Xudong , Yan Baoyue , Tong Chao
Published: May 2023

TTP/C (Time-Triggered Protocol Class C) is a mainstream communication protocol commonly utilized in cyber–physical systems within the aerospace and automotive industry. Unfortunately, when it comes to the startup model, there are three issues in the standard of TTP/C (namely AS6003). Firstly, AS6003 only mentions a high-level specification, which leads to a gap between the standard and its implementation. Secondly, the standard startup model in AS6003 aggressively handles the multi-clique problem by dropping the first valid frame unconditionally without a contention-detecting mechanism, resulting in additional time consumption in some types of contention scenarios. At last, there is lack of the formal verification for the validity of the standard startup model with an arbitrary number of nodes and the formal derivation of its upper bound of startup time. To address these limitations, we propose a detailed and improved startup model named ATWin based on AS6003. It not only bridges the gap between the top-level standard and its implementation by supplementing the undefined details, but it also enhances the efficiency of the startup time by adding a contention-detecting strategy to the standard startup model. The ATWin model is developed as an open-source implementation for TTP/C’s startup. We also formally demonstrate the validity of ATWin and deduce its upper bound of startup time with an arbitrary number of nodes in this paper.
Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging
Authors: Chou Ka Seng , Wong Kei Long , Aguiari Davide , Tse Rita , Tang Su-Kit , Pau Giovanni
Published: May 2023

In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of carbon dioxide (CO2) being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.
Enhanced Acetone Sensing Based on Group-11 Metal (Cu, Ag, and Au) Nanoparticles Embedded in Graphitic Carbon Nitride (gCN)
Authors: Nihal  , Sharma Rahul , Kaur Navjot , Sharma Mamta , Choudhary B. C. , Goswamy J. K.
Published: May 2023

In this work, a group-11 metal nanoparticle-embedded, graphitic carbon nitride-based, resistive-type sensor was developed for room temperature acetone sensing. We synthesized pure and group-11 transition metal (Cu, Ag and Au) nanoparticles embedded in graphitic carbon nitride (gCN) by thermal polycondensation and chemical reduction methods. The synthesized material was characterized using UV/visspectroscopy, FTIRspectroscopy, XRD, HRTEM, FESEM, and EDS techniques. Sensing properties such as response, response/recovery time, selectivity, and stability were calculated. This study confirms that Ag/gCN is the best material for room temperature sensing of acetone compared to Cu/gCN, Au/gCN, and pure gCN. The response of Ag/gCN for 20 ppm acetone at room temperature is 28%. The response/recovery time is 42.05/37.09 s. Moreover, the response of Ag/gCN is stable for 10 days.
Gamma-Nabla Hardy–Hilbert-Type Inequalities on Time Scales
Authors: Almarri Barakah , El-Deeb Ahmed A.
Published: May 2023

We investigated several novel conformable fractional gamma-nabla dynamic Hardy–Hilbert inequalities on time scales in this study. Several continuous inequalities and their corresponding discrete analogues in the literature are combined and expanded by these inequalities. Hölder’s inequality on time scales and a few algebraic inequalities are used to demonstrate our findings.
Cardiac Imaging Biomarkers in Chronic Kidney Disease
Authors: Valbuena-López Silvia C. , Camastra Giovanni , Cacciotti Luca , Nagel Eike , Puntmann Valentina O. , Arcari Luca
Published: May 2023

Uremic cardiomyopathy (UC), the peculiar cardiac remodeling secondary to the systemic effects of renal dysfunction, is characterized by left ventricular (LV) diffuse fibrosis with hypertrophy (LVH) and stiffness and the development of heart failure and increased rates of cardiovascular mortality. Several imaging modalities can be used to obtain a non-invasive assessment of UC by different imaging biomarkers, which is the focus of the present review. Echocardiography has been largely employed in recent decades, especially for the determination of LVH by 2-dimensional imaging and diastolic dysfunction by pulsed-wave and tissue Doppler, where it retains a robust prognostic value; more recent techniques include parametric assessment of cardiac deformation by speckle tracking echocardiography and the use of 3D-imaging. Cardiac magnetic resonance (CMR) imaging allows a more accurate assessment of cardiac dimensions, including the right heart, and deformation by feature-tracking imaging; however, the most evident added value of CMR remains tissue characterization. T1 mapping demonstrated diffuse fibrosis in CKD patients, increasing with the worsening of renal disease and evident even in early stages of the disease, with few, but emerging, prognostic data. Some studies using T2 mapping highlighted the presence of subtle, diffuse myocardial edema. Finally, computed tomography, though rarely used to specifically assess UC, might provide incidental findings carrying prognostic relevance, including information on cardiac and vascular calcification. In summary, non-invasive cardiovascular imaging provides a wealth of imaging biomarkers for the characterization and risk-stratification of UC; integrating results from different imaging techniques can aid a better understanding of the physiopathology of UC and improve the clinical management of patients with CKD.
Unexpected Distribution of Chitin and Chitin Synthase across Soft-Bodied Cnidarians
Authors: Vandepas Lauren E. , Tassia Michael G. , Halanych Kenneth M. , Amemiya Chris T.
Published: May 2023

Cnidarians are commonly recognized as sea jellies, corals, or complex colonies such as the Portuguese man-of-war. While some cnidarians possess rigid internal calcareous skeletons (e.g., corals), many are soft-bodied. Intriguingly, genes coding for the chitin-biosynthetic enzyme, chitin synthase (CHS), were recently identified in the model anemone Nematostella vectensis, a species lacking hard structures. Here we report the prevalence and diversity of CHS across Cnidaria and show that cnidarian chitin synthase genes display diverse protein domain organizations. We found that CHS is expressed in cnidarian species and/or developmental stages with no reported chitinous or rigid morphological structures. Chitin affinity histochemistry indicates that chitin is present in soft tissues of some scyphozoan and hydrozoan medusae. To further elucidate the biology of chitin in cnidarian soft tissues, we focused on CHS expression in N. vectensis. Spatial expression data show that three CHS orthologs are differentially expressed in Nematostella embryos and larvae during development, suggesting that chitin has an integral role in the biology of this species. Understanding how a non-bilaterian lineage such as Cnidaria employs chitin may provide new insight into hitherto unknown functions of polysaccharides in animals, as well as their role in the evolution of biological novelty.
High Fructose Corn Syrup Accelerates Kidney Disease and Mortality in Obese Mice with Metabolic Syndrome
Authors: Andres-Hernando Ana , Orlicky David J. , Cicerchi Christina , Kuwabara Masanari , Garcia Gabriela E. , Nakagawa Takahiko , Sanchez-Lozada Laura Gabriela , Johnson Richard J. , Lanaspa Miguel A.
Published: May 2023

The presence of obesity and metabolic syndrome is strongly linked with chronic kidney disease (CKD), but the mechanisms responsible for the association are poorly understood. Here, we tested the hypothesis that mice with obesity and metabolic syndrome might have increased susceptibility to CKD from liquid high fructose corn syrup (HFCS) by favoring the absorption and utilization of fructose. We evaluated the pound mouse model of metabolic syndrome to determine if it showed baseline differences in fructose transport and metabolism and whether it was more susceptible to chronic kidney disease when administered HFCS. Pound mice have increased expression of fructose transporter (Glut5) and fructokinase (the limiting enzyme driving fructose metabolism) associated with enhanced fructose absorption. Pound mice receiving HFCS rapidly develop CKD with increased mortality rates associated with intrarenal mitochondria loss and oxidative stress. In pound mice lacking fructokinase, the effect of HFCS to cause CKD and early mortality was aborted, associated with reductions in oxidative stress and fewer mitochondria loss. Obesity and metabolic syndrome show increased susceptibility to fructose-containing sugars and increased risk for CKD and mortality. Lowering added sugar intake may be beneficial in reducing the risk for CKD in subjects with metabolic syndrome.
Fluorescent Sensing Platforms for Detecting and Imaging the Biomarkers of Alzheimer’s Disease
Authors: Liu Xingyun , Liu Yibiao , Liu Qiong
Published: May 2023

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease with clinical symptoms of memory loss and cognitive impairment. Currently, no effective drug or therapeutic method is available for curing this disease. The major strategy used is to identify and block AD at its initial stage. Thus, early diagnosis is very important for intervention of the disease and assessment of drug efficacy. The gold standards of clinical diagnosis include the measurement of AD biomarkers in cerebrospinal fluid and positron emission tomography imaging of the brain for amyloid-β (Aβ) deposits. However, these methods are difficult to apply to the general screening of a large aging population because of their high cost, radioactivity and inaccessibility. Comparatively, blood sample detection is less invasive and more accessible for the diagnosis of AD. Hence, a variety of assays based on fluorescence analysis, surface-enhanced Raman scattering, electrochemistry, etc., were developed for the detection of AD biomarkers in blood. These methods play significant roles in recognizing asymptomatic AD and predicting the course of the disease. In a clinical setting, the combination of blood biomarker detection with brain imaging may enhance the accuracy of early diagnosis. Fluorescence-sensing techniques can be used not only to detect the levels of biomarkers in blood but also to image biomarkers in the brain in real time due to their low toxicity, high sensitivity and good biocompatibility. In this review, we summarize the newly developed fluorescent sensing platforms and their application in detecting and imaging biomarkers of AD, such as Aβ and tau in the last five years, and discuss their prospects for clinical applications.
Inhibitory Learning during Exposure Treatment in Anorexia Nervosa: A Practical Guide
Authors: Melles Hanna , Duijvis Stefanie , Jansen Anita
Published: May 2023

Exposure therapy is known to be an effective intervention in the treatment of anxiety-related disorders. In eating disorders, such as anorexia nervosa, anxiety and avoidance are identified as maintenance factors. Therefore, they may constitute an important treatment target, suitable for the use of exposure therapy. Remarkably, exposure techniques to target fears and avoidance behaviors are not commonly used in the treatment of anorexia nervosa. We present a practical guide for the implementation of exposure therapy in the treatment of anorexia nervosa. We outline how exposure therapy is supposed to work according to the inhibitory learning model and how the exposure intervention can be designed for individuals with anorexia nervosa. Practical examples are provided through the case presentation of a patient with anorexia nervosa who completed 31 exposure sessions that focused on her fears of food, eating, weight, weight gain, their feared social consequences and the associated safety behaviors.
An Analysis of Potential Uses, Limitations and Barriers to Implementation of 3D Scan Data for Construction Management-Related Use—Are the Industry and the Technical Solutions Mature Enough for Adoption?
Authors: Mihić Matej , Sigmund Zvonko , Završki Ivica , Butković Lana Lovrenčić
Published: May 2023

The potential uses of 3D scan data in the construction industry have been extensively researched in the last 20 years, with many benefits over traditional methods proclaimed by researchers. However, despite their advocated benefits, their implementation in actual construction sites remains low. This research aims to discover the potential uses of 3D scan data for construction management purposes and the limitations and barriers to their implementation and widespread adoption. Previous research into the topic was analysed to discover what technologies were used for generating 3D scan data, for what purpose and what issues were identified. These discoveries were then used to specify the potential uses of 3D scan data for, primarily, progress monitoring and quality control, which were then cross-referenced with all known limitations and barriers from the literature and the researchers’ own experience. Research has shown that, currently, there are numerous issues with both the capabilities of current technical solutions and with the construction industry’s readiness, which hinder mass adoption. Potential for breakthroughs, fortunately, does exist; however, greater impetus from the construction industry is needed to drive forward the demand for better technical solutions, which would resolve current issues and lead to the widespread adoption of 3D scan data for construction management-related uses.
Machine-Learning-Based Consumption Estimation of Prestressed Steel for Prestressed Concrete Bridge Construction
Authors: Kovačević Miljan , Antoniou Fani
Published: May 2023

Accurate prediction of the prestressed steel amount is essential for a concrete-road bridge’s successful design, construction, and long-term performance. Predicting the amount of steel required can help optimize the design and construction process, and also help project managers and engineers estimate the overall cost of the project more accurately. The prediction model was developed using data from 74 constructed bridges along Serbia’s Corridor X. The study examined operationally applicable models that do not require indepth modeling expertise to be used in practice. Neural networks (NN) models based on regression trees (RT) and genetic programming (GP) models were analyzed. In this work, for the first time, the method of multicriteria compromise ranking was applied to find the optimal model for the prediction of prestressed steel in prestressed concrete bridges. The optival model based on GP was determined using the VIKOR method of multicriteria optimization; the accuracy of which is expressed through the MAPE criterion is 9.16%. A significant average share of 46.11% of the costs related to steelworks, in relation to the total costs, indicates that the model developed in the paper can also be used for the implicit estimation of construction costs.
Exploring the Self-Healing Capability and Fatigue Performance of Modified Bitumen Incorporating Waste Cooking Oil and Polyphosphoric Acid
Authors: Wang Wentong , Guo Dedong , Liu Congcong , Falchetto Augusto Cannone , Li Xinzhou , Wang Teng
Published: May 2023

Bitumen’s self-healing capability is critical to the bitumen industry’s sustainable development. This work attempts to examine the self-healing property and fatigue behavior of bitumen using waste cooking oil (WCO) and polyphosphoric acid (PPA) in bitumen modification. Different components of WCO were mixed with PPA and bitumen for the initial modification. The linear amplitude sweep (LAS) test was used to evaluate the fatigue behavior of the modified bitumen. To assess the extent of bitumen healing after a fatigue-healing test, evaluation indicators, including fatigue life recovery (FLR), modulus recovery (MR), and dissipated energy recovery (DER), were selected. Meanwhile, a radar chart was used to analyze the integrated performance of WCO/PPA (WP)-modified bitumen. Lastly, the SARA fractions were separated from the bitumen to evaluate the modification mechanism. It was observed that the inclusion of PPA and WCO enhanced fatigue behavior. For 2% PPA and an intermediate component (IC) of WCO, the fatigue life of the LAS prediction model showed extreme values, with an increase of 669% over virgin bitumen. Regardless of the PPA concentration, a considerable increase in FLR, MR, and DER was seen in the bitumen processed with IC compared to virgin bitumen. Additionally, as the healing time was extended, the increment in the virgin bitumen’s healing indicators was higher than that of the modified bitumen. SARA results indicated that adding PPA changes the bitumen from a soluble state to a gel state. An innovative approach has been proposed to promote sustainable development within the bitumen industry.