bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Noble Metals on the Microstructure and Properties of Biodegradable Mg-Nd-Zr Alloy
Authors: Greshta Viktor , Shalomeev Vadim , Bovkun Svitlana , Petryshynets Ivan , Efremenko Vasily , Brykov Michail
Published: November 2023

In this work, the approach to improve the mechanical properties of a biodegradable Mg-Nd-Zr alloy through modification with noble metals (Ag and Au) was proposed. The separate and combined influence of silver and gold on the macro- and microstructure of the alloy was studied. A qualitative and quantitative assessment of the structural components of the alloy was carried out. It was shown that when modifying the melt, noble metals form the complex intermetallic phases which served as additional crystallization centers. It has been established that adding 0.05 to 0.1 wt.% of noble metals to Mg-based alloy increase the volume fraction of intermetallic compounds by ~1.5 times, shifting them towards smaller size groups with the simultaneous formation of spherical intermetallic compounds. The latter are located in the center of the grain proving them to be the additional nucleation sites. It was shown that complex modification (0.1% Ag + 0.1% Au) of a Mg-based alloy refined its structural components by ~1.5 times, increasing the strength by ~20%, and ductility by ~2 times due to the formation of the intermetallic compounds. The proposed technology for modifying cast biodegradable Mg-based alloys is feasible to be used for the manufacture of implants for osteosynthesis.
Thetis: A Booster for Building Safer Systems Using the Rust Programming Language
Authors: Jiang Renshuang , Dong Pan , Ding Yan , Wei Ran , Jiang Zhe
Published: November 2023

Rust is a new system-level programming language that prioritizes performance, safety, and productivity. However, as evidenced in many previous works, unsafe code fragments broadly exist in Rust projects. The use of these unsafe fragments can fundamentally violate the safety of systems developed using the programming language. In response to this problem, we propose a novel methodology (Thetis) to enhance the safety capability of Rust. The core idea of Thetis is to reduce unsafe code, encapsulate unsafe code using safety rules, and make it easier to verify unsafe code through formal means. The proposed methodology involves three main components. In the context of Rust itself, Thetis combines replacement and encapsulation for Interior Unsafe segments, minimizing unsafe fragments and reducing unsafe operations and their range. For systems developed using Rust, new ACSL formal statutes are applied to reduce the unsafe potential of the encapsulated Interior Unsafe segments, enhancing the safety of the system. Regarding the development life cycle in Rust, Thetis introduces automatic defect detection and optimization based on feature extraction, improving engineering efficiency. We demonstrate the effectiveness of Thetis by using it to fix defects in BlogOS and ArceOS. The experimental results reveal that Thetis reduces the number of unsafe operations in these OSs by 40% and 45%, respectively. The use of Miri to detect and eliminate defects in ArceOS reduces the likelihood of undefined behavior by about 50%, which effectively demonstrates that the proposed method can improve the safety of the Rust system. In addition, performance test results from LMbench show that the performance loss caused by Thetis is only 1.076%, thereby maintaining the high-performance characteristics of the Rust system.
Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks
Authors: Juszczyk Michał , Hanák Tomáš , Výskala Miloslav , Pacyno Hanna , Siejda Michał
Published: November 2023

This paper presents research results on the development of an original cost prediction model for construction costs in sewerage projects. The focus is placed on fast cost estimates applicable in the early stages of a project, based on fundamental information available during the initial design phase of sanitary sewers prior to the detailed design. The originality and novelty of this research lie in the application of artificial neural network ensembles, which include a combination of several individual neural networks and the use of simple averaging and generalized averaging approaches. The research resulted in the development of two ensemble-based models, including five neural networks that were trained and tested using data collected from 125 sewerage projects completed in the Czech Republic between 2018 and 2022. The data included information relevant to various aspects of projects and contract costs, updated to account for changes in costs over time. The developed models present satisfactory predictive performance, especially the ensemble model based on simple averaging, which offers prediction accuracy within the range of ±30% (in terms of percentage errors) for over 90% of the training and testing samples. The developed models, based on the ensembles of neural networks, outperformed the benchmark model based on the classical approach and the use of multiple linear regression.
A LiDAR Multi-Object Detection Algorithm for Autonomous Driving
Authors: Wang Shuqi , Chen Meng
Published: November 2023

Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and categories to provide reliable operational decisions for achieving autonomous driving. To improve the detection and localization accuracy of multi-object targets such as surrounding vehicles and pedestrians in autonomous driving scenarios, based on PointPillars fast object detection network, a three-dimensional object detection algorithm based on the channel attention mechanism, ECA Modules-PointPillars, is proposed. Firstly, the improved algorithm uses point cloud columnarization features to convert a three-dimensional point cloud into a two-dimensional pseudo-image. Then, combining the 2D backbone network for feature extraction with the Efficient Channel Attention (ECA) modules to achieve the enhancement of the positional feature information in the pseudo-image and the weakening of the irrelevant feature information such as background noise. Finally, the single-shot multibox detector (SSD) algorithm was used to complete the 3D object detection task. The experimental results show that the improved algorithm improves the mAP by 3.84% and 4.04% in BEV mode and 3D mode, respectively, compared to PointPillars, which improves the mAP by 4.64% and 5.89% in BEV mode and 3D mode, respectively, compared to F-PointNet, improves the mAP by 11.78% and 14.19% in BEV mode and 3D mode, respectively, compared to VoxelNet, and improves the mAP by 9.47% and 6.55% in BEV mode and 3D mode, respectively, compared to SECOND, demonstrating the effectiveness and reliability of the improved algorithms in autonomous driving scenarios.
Jerusalem Artichoke (Helianthus tuberosus L.) as a Promising Dietary Feed Ingredient for Monogastric Farm Animals
Authors: Cornescu Gabriela Maria , Panaite Tatiana Dumitra , Soica Cristina , Cismileanu Ana , Matache Cristina Camelia
Published: November 2023

In recent years, there has been significant attention toward the incorporation of alternative functional feed ingredients in monogastric diets. The objective is to improve sustainability and optimize animal performance both under normal conditions and in heat stress situations. Among these alternatives, Jerusalem artichoke (Helianthus tuberosus L.) has emerged as a promising candidate due to its nutritional composition and potential health benefits. This review aims to investigate the potential utilization of Jerusalem artichoke in monogastric diets and the impact on productive performance parameters. Moreover, the potential prebiotic effects of Jerusalem artichoke on the composition and activity of monogastric gut microbiota are revealed, showing its implications for gut health and reduction in pathogenic bacteria. The incorporation of Jerusalem artichoke in monogastric diets poses several challenges, such as limitation of the dietary inclusion rate. However, there are also future perspectives to consider, such as optimizing processing techniques, evaluating the effects of different cultivars, and exploring potential synergies with other dietary feed ingredients. In summary, this study provides a comprehensive overview of the key findings and unique perspectives on the utilization of Jerusalem artichoke in monogastric diets, highlighting its potential as a valuable feed ingredient.
Improving Solar Radiation Forecasting Utilizing Data Augmentation Model Generative Adversarial Networks with Convolutional Support Vector Machine (GAN-CSVR)
Authors: Assaf Abbas Mohammed , Haron Habibollah , Abdull Hamed Haza Nuzly , Ghaleb Fuad A. , Dalam Mhassen Elnour , Elfadil Eisa Taiseer Abdalla
Published: November 2023

The accuracy of solar radiation forecasting depends greatly on the quantity and quality of input data. Although deep learning techniques have robust performance, especially when dealing with temporal and spatial features, they are not sufficient because they do not have enough data for training. Therefore, extending a similar climate dataset using an augmentation process will help overcome the issue. This paper proposed a generative adversarial network model with convolutional support vector regression, which is named (GAN-CSVR) that combines a GAN, convolutional neural network, and SVR to augment training data. The proposed model is trained utilizing the Multi-Objective loss function, which combines the mean squared error and binary cross-entropy. The original solar radiation dataset used in the testing is derived from three locations, and the results are evaluated using two scales, namely standard deviation (STD) and cumulative distribution function (CDF). The STD and the average error value of the CDF between the original dataset and the augmented dataset for these three locations are 0.0208, 0.1603, 0.9393, and 7.443981, 4.968554, and 1.495882, respectively. These values show very significant similarity in these two datasets for all locations. The forecasting accuracy findings show that the GAN-CSVR model produced augmented datasets that improved forecasting from 31.77% to 49.86% with respect to RMSE and MAE over the original datasets. This study revealed that the augmented dataset produced by the GAN-CSVR model is reliable because it provides sufficient data for training deep networks.
Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
Authors: Alharbi Shuaa S. , AlRugaibah Athbah A. , Alhasson Haifa F. , Khan Rehan Ullah
Published: November 2023

Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisionlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images’ binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.
Generalized Partially Functional Linear Model with Unknown Link Function
Authors: Xiao Weiwei , Li Songxuan , Liu Haiyan
Published: November 2023

In existing models with an unknown link function, the issue of predictors containing both multiple functional data and multiple scalar data has not been studied. To fill this gap, we propose a generalized partially functional linear model, which not only models the relationship between multiple scalar and functional predictors and responses, but also automatically estimates the link function. Specifically, we use the functional principal component analysis method to reduce the dimensionality of functional predictors, estimate the regression coefficients using the maximum likelihood estimation method, estimate the link function using the method of local linear regression, iteratively obtain the final estimator, and establish the asymptotic normality of the estimator. The asymptotic normality is illustrated through simulation experiments. Finally, the proposed model is applied to study the influence of environmental, economic, and medical levels on life expectancy in China. In the study, functional predictors are the daily air quality index, temperature, and humidity of 58 cities in 2020, and scalar predictors are GDP and the number of beds in hospitals. The experimental results indicate that the unknown link function model has a smaller prediction error and better performance than both the model with the known link function and the model without a link function.
Association Study of IGF-1 rs35767 and rs6214 Gene Polymorphisms with Cancer Susceptibility and Circulating Levels of IGF-1, IGFBP-2, and IGFBP-3 in Colorectal Cancer Patients
Authors: Alrashid Maryam H. , Al-Serri Ahmad E. , Hussain Rubina F. , Al-Bustan Suzanne A. , Al-Barrak Jasem
Published: November 2023

Early detection of colorectal cancer (CRC) increases the 5-year survival rate by 90%; therefore, non-invasive biomarkers such as measurable circulating proteins for early detection and prognosis are crucial. Insulin-like growth factor-1 (IGF-1) is involved in the regulation of cell proliferation and apoptosis. IGF binding proteins (IGFBPs) bind and inhibit the activity of IGF-1. It was inconsistently reported that high IGF-1 and IGFBP-2 and low IGFBP-3 circulating levels are associated with high cancer risk, poor prognosis, and tumor metastasis in several cancers. A total of 175 patients with CRC and 429 controls were enrolled in this study. We genotyped for IGF-1 rs35767 and rs6214 gene polymorphisms and assessed their association with circulating levels of IGF-1 and/or the risk for CRC. We also determined plasma levels of IGF-1, IGFBP-2, and IGFBP-3. Neither rs35767 nor rs2614 were associated with cancer risk or IGF-1 levels in our study cohort. IGF-1 and IGFBP-3 levels were higher in controls than in patients, whereas IGFBP-2 was higher in patients than in controls. Only IGFBP-2 was associated with increased tumor grade but not stage. Therefore, IGF-1, IGFBP-2, and IGFBP-3 may be useful as early detection and prognostic biomarkers in CRC.
Examining Medical Staff Well-Being through the Application and Extension of the Job Demands–Resources Model: A Cross-Sectional Study
Authors: Jing Tiantian , Li Xiaoyan , Yu Chenhao , Bai Mayangzong , Zhang Zhiruo , Li Sisi
Published: November 2023

For medical staff, job satisfaction is essential for advancement on an individual and organizational level. This study looked into the relationships between challenging job demands, job resources, personal resources, and well-being. Additionally, it examined the potential mediating effects of emotional exhaustion and work motivation within the framework of the job demands–resources (JD–R) model. Results from a cross-sectional study of 267 medical employees at a second-grade comprehensive hospital in Jiangsu, China’s mainland, indicated that challenging job demands and job satisfaction were positively correlated and mediated via (decreasing) emotional exhaustion. The relationship between job resources and job satisfaction was found to be mediated via (decreasing) emotional exhaustion and (increasing) work motivation. The investigation also demonstrated that the two regulatory focuses serve different purposes. It was discovered that promotion focus had a favorable effect on work motivation but a negative effect on emotional exhaustion. Conversely, preventive focus only positively predicted emotional exhaustion. Thus, the JD–R model offers a valuable structure for clarifying the job satisfaction of health personnel. The implications for enhancing individual and job outcomes are discussed.
Conference Tourism: Exploring Economic Prospects in the Post-COVID-19 Era—Qualitative Research on Greek Hotel Executives
Authors: Moloni Pelagia , Metaxas Theodore
Published: November 2023

As is widely known, the COVID-19 pandemic has affected tourism and related activities globally. Due to the restrictive measures implemented for gatherings and movements in order to limit the spread of the virus, conferences and conference tourism received a strong shock since the majority of them were canceled or postponed. At the end of the first quarter year, many countries, like Greece, started organizing digital and hybrid conferences. Therefore, there was a reset in the conference industry as the time when travel was limited allowed the organizers, as well as others involved, to work remotely. The present study aims to investigate the effects of the COVID-19 pandemic on conference tourism and, more specifically, how hotels and their conference facilities were affected. In addition, the pursuit of potential opportunities through the ‘New technologies’ adopted, as well as the shaping of the industry in the post-COVID-19 era, are studied. This is achieved through a qualitative methodology using semi-structured interviews with 27 executives of hotels that offer conference facilities in Athens, Thessaloniki, Heraklion and Rhodes in order to examine whether this specific sector has adapted to the new reality. The analysis of data in this form of research revealed that the pandemic had benefited conferences to some extent, but only under certain circumstances.
Carbon-Coated Ni-Fe Nanocatalysts: Bridging the Gap in Cinnamaldehyde Hydrogenation Performance and Durability
Authors: Cai Yuhang , Yin Anping , Zhang Jianxiang , Wang Jiatai , Qin Xiaofei , Yang Yibo , Qin Gaolei , Sun Xiaodong , He Peng , Yang Yong
Published: November 2023

This study focuses on the synthesis and evaluation of carbon−coated Ni−Fe alloy catalysts (NiFex@C, x = 0, 0.3, 0.7, 1.1) for the hydrogenation of cinnamaldehyde. The catalysts were characterized using Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), Raman spectroscopy, and X-ray Photoelectron Spectroscopy (XPS). The introduction of Fe was found to increase the average particle size of the bimetallic catalysts compared to the monometallic Ni@C. Ni existed in both metallic and oxide states, while Fe exhibited multiple oxidation states in the bimetallic catalysts. The carbon layer, approximately 2–3 nm thick, was confirmed to envelop the alloy particles. The catalytic performance of carbon−coated Ni−Fe alloy catalysts indicated that the addition of Fe to Ni@C enhanced the selectivity towards hydrocinnamaldehyde (HCAL), with NiFe0.7@C showing the highest selectivity (~88.6%) but at a reduced conversion rate. The carbon layer played a pivotal role in the stability and reusability of the catalysts. NiFe0.7@C maintained consistent performance over multiple reaction cycles, while NiFe0.7 NPs (without a carbon layer) exhibited significant deactivation. Both catalysts displayed strong magnetism, facilitating easy separation from the reaction mixture. This study sheds light on the significance of the carbon layer in bimetallic catalysts and provides valuable insights for designing efficient catalysts for hydrogenation processes.
Catalysis in Biomass Valorization—Preface to the Special Issue
Authors: Simakova Irinal L. , Murzin Dmitry Yu.
Published: November 2023

Biomass valorization has been the focus of substantial efforts in the industry and academia, addressing the issues of global warming and depletion of fossil resources [...]
Somatic Copy Number Alterations in Colorectal Cancer Lead to a Differentially Expressed ceRNA Network (ceRNet)
Authors: Herrera-Orozco Héctor , García-Castillo Verónica , López-Urrutia Eduardo , Martinez-Gutierrez Antonio Daniel , Pérez-Yepez Eloy , Millán-Catalán Oliver , Cantú de León David , López-Camarillo César , Jacobo-Herrera Nadia J. , Rodríguez-Dorantes Mauricio , Ramos-Payán Rosalío , Pérez-Plasencia Carlos
Published: November 2023

Colorectal cancer (CRC) represents the second deadliest malignancy worldwide. Around 75% of CRC patients exhibit high levels of chromosome instability that result in the accumulation of somatic copy number alterations. These alterations are associated with the amplification of oncogenes and deletion of tumor-ppressor genes and contribute to the tumoral phenotype in different malignancies. Even though this relationship is well known, much remains to be investigated regarding the effect of said alterations in long non-coding RNAs (lncRNAs) and, in turn, the impact these alterations have on the tumor phenotype. The present study aimed to evaluate the role of differentially expressed lncRNAs coded in regions with copy number alterations in colorectal cancer patient samples. We downloaded RNA-seq files of the Colorectal Adenocarcinoma Project from the The Cancer Genome Atlas (TCGA) repository (285 sequenced tumor tissues and 41 non-tumor tissues), evaluated differential expression, and mapped them over genome sequencing data with regions presenting copy number alterations. We obtained 78 differentially expressed (LFC > 1|< −1, padj < 0.05) lncRNAs, 410 miRNAs, and 5028 mRNAs and constructed a competing endogenous RNA (ceRNA) network, predicting significant lncRNA–miRNA–mRNA interactions. Said network consisted of 30 lncRNAs, 19 miRNAs, and 77 mRNAs. To understand the role that our ceRNA network played, we performed KEGG and GO analysis and found several oncogenic and anti-oncogenic processes enriched by the molecular players in our network. Finally, to evaluate the clinical relevance of the lncRNA expression, we performed survival analysis and found that C5orf64, HOTAIR, and RRN3P3 correlated with overall patient survival. Our results showed that lncRNAs coded in regions affected by SCNAs form a complex gene regulatory network in CCR.
The Effect of Temperature on the Structural Build-Up of Cement Pastes
Authors: El Bitouri Youssef
Published: November 2023

The structural build-up of cementitious materials is the subject of more and more attention since it conditions several processes such as formwork pressure and multi-layer casting. However, this phenomenon originating from flocculation and chemical changes is complex and its reversibility is not clearly elucidated. The aim of this paper is to examine the effect of temperature on the reversibility of structural build-up. The results show that irreversible structural build-up remains negligible despite a rise in temperature. It represents between 0.5–7.3% of the total structural build-up. The addition of SCMs allows for a decrease in this irreversible structural build-up. Therefore, a large part of the chemical contribution is expected to be reversible. The effect of temperature can be explained by the increase in the dissolution rate leading to an increase in flocculation and to the bridging effect induced by early hydrates. Finally, the results suggest that the interparticle distance could be the key parameter governing the irreversibility of structural build-up.
Assessing Tropical Cyclone Risk in Australia Using Community Exposure–Vulnerability Indices
Authors: Berman Kade , Kuleshov Yuriy
Published: November 2023

Tropical cyclones (TCs) are one of the most destructive natural hazards to impact on Australia’s population, infrastructure, and the environment. To examine potential TC impacts, it is important to understand which assets are exposed to the hazard and of these, which are vulnerable to damage. The aim of this study is to improve TC risk assessments through developing an exposure–vulnerability index, utilising a case study for the six Local Government Areas (LGAs) impacted by the landfall of TC Debbie in 2017: Burdekin Shire, Charters Towers Region, Isaac Region, Mackay Region, City of Townsville, and Whitsunday Region. This study utilised a natural hazard risk assessment methodology, linking exposure and vulnerability indicators related to social factors, infrastructure, and the environment. The two LGAs with the most extreme exposure–vulnerability values were the coastal regions of Mackay Region and the City of Townsville. This is consistent with urbanisation and city development trends, with these LGAs having more people (social) and infrastructure exposed, while the environmental domain was more exposed and vulnerable to TC impacts in rural LGAs. Therefore, further resilience protocols and mitigation strategies are required, particularly for Mackay Region and the City of Townsville, to reduce the damage and ultimate loss of lives and livelihoods from TC impacts. This study serves as a framework for developing a TC risk index based on hazard, exposure, and vulnerability indices, and insight into the improved mitigation strategies for communities to implement in order to build resilience to the impacts of future TCs.
Low-Resolution Steel Surface Defects Classification Network Based on Autocorrelation Semantic Enhancement
Authors: Guo Xiaoe , Gong Ke , Lu Chunyue
Published: November 2023

Aiming at the problems of low-resolution steel surface defects imaging, such as defect type confusion, feature blurring, and low classification accuracy, this paper proposes an autocorrelation semantic enhancement network (ASENet) for the classification of steel surface defects. It mainly consists of a backbone network and an autocorrelation semantic enhancement module (ASE), in which the autocorrelation semantic enhancement module consists of three main learnable modules: the CS attention module, the autocorrelation computation module, and the contextual feature awareness module. Specifically, we first use the backbone network to extract the basic features of the image and then use the designed CS attention module to enhance the basic features. In addition, to capture different aspects of semantic objects, we use the autocorrelation module to compute the correlation between neighborhoods and contextualize the basic and augmented features to enhance the recognizability of the features. Experimental results show that our method produces significant results, and the classification accuracy reaches 96.24% on the NEU-CLS-64 dataset. Compared with ViT-B/16, Swin_t, ResNet50, Mobilenet_v3_small, Densenet121, Efficientnet_b2, and baseline, the accuracy is 9.43%, 5.15%, 4.87%, 3.34%, 3.28%, 3.01%, and 2.72% higher, respectively.
RETRACTED: A Publicly Verifiable E-Voting System Based on Biometrics
Authors: Liu Jinhui , Han Tianyi , Tan Maolin , Tang Bo , Hu Wei , Yu Yong
Published: November 2023

Voters use traditional paper ballots, a method limited by the factors of time and space, to ensure their voting rights are exercised; this method requires a lot of manpower and resources. Duplicate voting problems may also occur, meaning the transparency and reliability of the voting results cannot be guaranteed. With the rapid developments in science and technology, E-voting system technology is being adopted more frequently in election activities. However, E-voting systems still cannot address the verifiability of the election process; the results of a given election and the credibility of the host organization will be questioned if the election’s verifiability cannot be ensured. Elections may also pose a series of problems related to privacy, security, and so on. To address these issues, this paper presents a public, and verifiable E-voting system with hidden statistics; this system is based on commitment, zk-SNARKs, and machine learning. The system can deal with a large number of candidates, complex voting methods, and result functions in counting both hidden and public votes and can satisfy the requirements of verifiability, privacy, security, and intelligence. Our security analysis shows that our scheme achieves privacy, hidden vote counting and verifiability. Our performance evaluation demonstrates that our system has reasonable applications in real scenarios.
Diagnostic Precision in Lyme borreliosis: Assessing VlsE and C6 Antigens in a Pediatric Cohort
Authors: Wozinska Marta , Toczylowski Kacper , Lewandowski Dawid , Bojkiewicz Ewa , Milewski Robert , Sulik Artur
Published: November 2023

(1) Background: Lyme borreliosis (LB) is a tick-borne disease known for its diagnostic challenges. Conventional two-tiered testing (CTTT) for antibodies is time-consuming, has low sensitivity in the early stages of disease, and sometimes generates false-positive IgM immunoblots. To tackle this issue, modified two-tiered testing (MTTT) was introduced, incorporating recombinant VlsE and C6 antigens to enhance diagnostic accuracy. (2) Methods: In this prospective study, we enrolled children exhibiting symptoms indicative of LB. We collected serum samples at various intervals and subjected them to analysis using standard enzyme immunoassays. We then compared these results with the outcomes from the VlsE and C6 assays. (3) Results: In our study, all 33 patients displaying erythema migrans (EM), a characteristic symptom of LB, exhibited positive responses to the C6 antigen. This finding underscores the potential utility of the C6 antigen as a reliable diagnostic tool for LB. Additionally, we observed a significant reduction in anti-VlsE antibody levels following antibiotic treatment in EM patients. (4) Conclusions: The utilization of recombinant VlsE and C6 antigens in LB diagnostics and monitoring has yielded promising results. Nonetheless, it is imperative for clinicians to exercise caution and interpret results in conjunction with clinical findings, considering the dynamic nature of medical guidelines. Even with recombinant antigen tests, some children with EM tested negative, highlighting the importance of clinical diagnosis for treatment decisions. Furthermore, clinicians should be mindful of the possibility of persistently positive VlsE/C6 test results during LB treatment monitoring.
Reply to Kim et al. Acute Hemorrhagic Encephalomyelitis in the Context of MOG Antibody-Associated Disease. Comment on “Chen et al. Rapid Progressive Fatal Acute Hemorrhagic Encephalomyelitis. Diagnostics 2023, 13, 2481”
Authors: Chen Hung-Chieh , Chen Ting-Bin
Published: November 2023

We want to express our appreciation for the insightful comments [...]
Multiple-Stakeholder Perspectives on Accessibility Data and the Use of Socio-Technical Tools to Improve Sidewalk Accessibility
Authors: Labbé Delphine , Eisenberg Yochai , Snyder Devon , Shanley Judy , Hammel Joy M. , Froehlich Jon E.
Published: November 2023

For people with disabilities, accessible pedestrian infrastructure can support independence, mobility, and improved quality of life. Yet, most pedestrian infrastructure presents barriers that impede movement. A major challenge for cities to improve pedestrian accessibility is the lack of reliable data on sidewalk accessibility. Little is known about the type of data needed for cities, as well as how different stakeholders perceive and use sidewalk data. Therefore, this study seeks to explore the perceptions of multiple stakeholders on the use, gathering, and application of sidewalk accessibility data. We conducted a series of workshops with 51 participants, including people with disabilities, caregivers, Americans with Disabilities Act (ADA) coordinators, and urban planners, to discuss sidewalk accessibility data and data collection tools. We used the socio-technological tool Project Sidewalk as an example. Participants identified various uses for the accessibility data such as route planning, barrier removal plans, and advocacy, and discussed issues of usability, trust, access, and accessibility of the data and tools. Our findings contribute to a better understanding of important factors that impact the use and application of accessibility data and how to implement accessibility data collection initiatives that utilize socio-technological approaches such as crowdsourcing.
A Novel Ergonomic Curette Design Reduces Dental Prophylaxis-Induced Muscle Work and Fatigue
Authors: Lin Kairong , Wink Cherie , Dolan Ben , Osann Kathryn , Habib Ali A. , Gehrig Jill , Wilder-Smith Petra
Published: November 2023

Background: To compare fatigue, comfort, and muscle work associated with the use of two periodontal curettes during scaling: one with a novel adaptive design, the other with a conventional non-adaptive design. Methods: Twelve hygienists scaled a typodont using two Universal Barnhart 5/6 curettes: (1) a prototype featuring an adaptive silicone-covered handle (Curette A), and (2) a stainless-steel curette (Curette B). Surface Electromyography (sEMG) traced muscle work. Hand positions, fatigue, comfort, pinch, and grasp strength were recorded. Paired t-tests and a repeated measures ANOVA with covariates were tested for differences. The significance level was set at p < 0.05. Results: Curette A performed significantly better in all categories. Pinch and grasp strength and fatigue were significantly reduced post-instrumentation for Curette B. Curette A required significantly less (i) total muscle work and (ii) work in individual muscles. Comfort, correct grasp, and blade adaptation were significantly better using Curette A. Conclusions: A curette featuring a novel adaptive handle design demonstrated significantly improved ergonomic performance. Additional clinical studies are needed to solidify our understanding of the potential short- and long-term benefits of the novel curette handle design. Practical Implications: A novel adaptive curette handle design that enables the clinician to adapt the instrument across the index finger may reduce musculoskeletal burden and fatigue, as well as improve comfort during periodontal instrumentation.
Theory and Application of Zero Trust Security: A Brief Survey
Authors: Kang Hongzhaoning , Liu Gang , Wang Quan , Meng Lei , Liu Jing
Published: November 2023

As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security risks related to internal threats by eliminating the demarcations between the internal and external network of traditional network perimeters. Nevertheless, research on the theory and application of zero trust is still in its infancy, and more extensive research is necessary to facilitate a deeper understanding of the paradigm in academia and the industry. In this paper, trust in cybersecurity is discussed, following which the origin, concepts, and principles related to zero trust are elaborated on. The characteristics, strengths, and weaknesses of the existing research are analysed in the context of zero trust achievements and their technical applications in Cloud and IoT environments. Finally, to support the development and application of zero trust in the future, the concept and its current challenges are analysed.
Shell Shape Influence on Latent Heat Thermal Energy Storage Performance during Melting and Solidification
Authors: Wołoszyn Jerzy , Szopa Krystian
Published: November 2023

Phase-change materials have various applications across industries from thermal energy storage through automotive battery temperature management systems to thermal stabilisation. Many of these applications are shell and tube structures with different shell shapes. However, it is not yet known how the shape of the shell affects the melting, solidification times, and heat transport processes in such structures. To fill this research gap, seventeen shell shapes/orientations were compared using a simulation study. The well-known and validated enthalpy porosity algorithm implemented in the Fluent 2021R2 software was used. The numerical calculations were preceded by the measurement of thermal conductivity, phase change enthalpy, and specific heat during melting and solidification of the phase-change material. The shortest melting time was achieved for a semi-circular shell shape in the downward position, which was 44% shorter than the reference circular case. The shortest solidification times were recorded for an isosceles trapezium in an upward orientation relative to the reference circular case. Therefore, it is possible to significantly reduce the melting time in shell-and-tube systems as a result of the appropriate selection of the shell shape.
State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm
Authors: Cai Bingzi , Li Mutian , Yang Huawei , Wang Chunsheng , Chen Yougen
Published: November 2023

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.
Study of Impact of Sediment on the Stability of Salt Cavern Underground Gas Storage
Authors: Liang Xiaopeng , Ma Hongling , Cai Rui , Zhao Kai , Wang Xuan , Zheng Zhuyan , Shi Xilin , Yang Chunhe
Published: November 2023

The utilization of sediment voids for natural gas storage represents the future direction of salt cavern underground gas storage (UGS) in China. In this study, we first analyzed the way in which the sediment interacts with the salt caverns and the equilibrium state of the process. Subsequently, a novel approach employing the Discrete Element Method (DEM) for simulating sediment-filled salt cavern UGS was introduced, successfully modeling the operational process of sediment-filled salt cavern UGS. Moreover, deformation, plastic zone behavior, effective volume shrinkage rate, equivalent strain, and safety factor were employed to assess the impact of sediment on salt cavern stability. The findings indicate a positive influence of sediment on salt cavern stability, particularly in regions directly contacting the sediment. Deformation and effective volume shrinkage of the cavern were effectively mitigated, significantly improving the stress state of rock salt. This effect is more pronounced at lower internal gas pressures. In summary, sediment enhances the stability of salt caverns, providing a long-term and stable environment for natural gas storage within sediment voids.
A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level
Authors: Manembu Pinrolinvic D. K. , Kewo Angreine , Bramstoft Rasmus , Nielsen Per Sieverts
Published: November 2023

Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.
Air-Blown Biomass Gasification Process Intensification for Green Hydrogen Production: Modeling and Simulation in Aspen Plus
Authors: Novais Bernardino , Ramos Ana , Rouboa Abel , Monteiro Eliseu
Published: November 2023

Hydrogen produced sustainably has the potential to be an important energy source in the short term. Biomass gasification is one of the fastest-growing technologies to produce green hydrogen. In this work, an air-blown gasification model was developed in Aspen Plus®, integrating a water–gas shift (WGS) reactor to study green hydrogen production. A sensitivity analysis was performed based on two approaches with the objective of optimizing the WGS reaction. The gasifier is optimized for carbon monoxide production (Case A) or hydrogen production (Case B). A CO2 recycling stream is approached as another intensification process. Results suggested that the Case B approach is more favorable for green hydrogen production, allowing for a 52.5% molar fraction. The introduction of CO2 as an additional gasifying agent showed a negative effect on the H2 molar fraction. A general conclusion can be drawn that the combination of a WGS reactor with an air-blown biomass gasification process allows for attaining 52.5% hydrogen content in syngas with lower steam flow rates than a pure steam gasification process. These results are relevant for the hydrogen economy because they represent reference data for further studies towards the implementation of biomass gasification projects for green hydrogen production.
Study on Market-Based Trading Strategies for Biomass Power Generation Participation in Microgrid Systems
Authors: Yu Weiwei , Wang Weiqing , Li Xiaozhu
Published: November 2023

The Chinese government places significant importance on biomass energy due to its renewable and environmentally friendly attributes. However, the high cost of power generation poses a considerable hurdle to its development. This study aims to address the challenges facing the profitability and sustainable development of biomass power generation after the gradual withdrawal of the Chinese government by proposing a day-ahead real-time market-based trading strategy. It is prompted by the incentives offered by the Chinese government for the ongoing power market reform. This strategy is developed for a microgrid system that integrates biomass power generation with other renewable energy sources. The principles followed by the microgrid system include self-generation and consumption, electricity surplus sales, and electricity shortfall procurement. During the day-ahead stage, peak and valley tariffs are decided by the microgrid operator to exert influence on the incentives of capacity providers in accordance with the load trends, while in the intraday stage, the supply-demand imbalance is resolved by the stored electricity. In the trading process, marginal production and marginal pricing are specified to ensure the minimum trading volume and price for capacity traders, ensuring their profitability. It is demonstrated in this study that the trading strategy presented is more effective than conventional fixed-price trading in making biomass power generation profitable and sustainable, even after the Chinese government subsidy is phased out. Moreover, the other participant in the microgrid system can boost their earnings when compared to generating power individually for trading.
Groundwater Recharge from Below under Changing Hydro-Meteorological Conditions in a Forested and Grassland Site of the Great Hungarian Plain
Authors: Szabó András , Gribovszki Zoltán , Szolgay Ján , Kalicz Péter , Balog Kitti , Szalai József , Hlavčová Kamila , Bolla Bence
Published: November 2023

The process of groundwater evapotranspiration and its subsequent recharge are fundamental aspects of the Earth’s natural water cycle and have significant implications for the preservation and functionality of various forested ecosystems. This study presents a case analysis examining the recent fluctuations in groundwater levels and their replenishment in two wells situated at a designated forested experimental area and a control site. The magnitude and temporal fluctuations of groundwater recharge were examined through the utilisation of a novel adaptation of the traditional White method, which was specifically tailored to the local context. We also tested the sensitivity of the White method as an indicator of the system’s behaviour because the signal has changed in relation to the access of the forests to groundwater under the conditions of regionally declining groundwater resources and a warming climate. The novelty of this approach is found in the examination of the temporal fluctuations in groundwater recharge, which are influenced by both a decrease in groundwater levels caused by forest evaporation in response to climate change and a regional reduction in groundwater supplies. As a result, the ongoing decrease in groundwater levels may have significant adverse effects on local forests.