bluuebunny/crossref_metadata_2025 huggingface dataset:
Collaborative Digital Governance for Sustainable Rural Development in China: An Evolutionary Game Approach
Authors: Yin Shuangming , Li Yansong , Chen Xiaojuan , Yamaka Woraphon , Liu Jianxu
Published: September 2024

This paper explores the significance of digital governance for sustainable rural development in China, emphasizing the collaborative efforts of village administrative organizations, new agricultural business entities, and peasant households. Utilizing an evolutionary game approach, we examine the decision-making behaviors and stability points of these three entities within the context of rural digital governance. Our analysis is grounded in a mechanism of interest linkage among the stakeholders, with numerical simulations used to assess the impact of key variables and parameters on their evolutionary outcomes. The paper reveals that village administrative organizations are highly sensitive to changes in performance gains, special subsidies, penalty losses, and benefit distribution coefficients. Enhancing these variables can significantly motivate these organizations to engage in digital governance. In contrast, new agricultural business entities and peasant households demonstrate a stronger and more consistent willingness to collaborate, minimally affected by variable changes, which suggests a solid economic and social foundation for rural digital governance in China. Our paper underscores the need for positive incentives and a robust fault-tolerance mechanism to foster collaboration among village administrative organizations. It also highlights the importance of integrating new agricultural business entities into the digital governance framework to promote sustainable rural development. These insights provide valuable theoretical and practical implications for policymakers aiming to enhance the efficacy and inclusivity of digital governance in rural China.
The Extent of Aflatoxin B1 Contamination in Chili (Capsicum annuum L.) and Consumer Awareness and Knowledge of Aflatoxins in Oman
Authors: Akintola Abdallah , Al-Dairi Mai , Imtiaz Anam , Al-Bulushi Ismail Mohamed , Gibreel Tarig , Al-Sadi Abdullah Mohammed , Velazhahan Rethinasamy
Published: September 2024

Aflatoxin B1 (AFB1), a hepatocarcinogenic metabolite produced by certain strains of Aspergillus section Flavi, is one of the major contaminants in red chili products that affect human health. This study determined the level of AFB1 in chili traded in Oman. In addition, a survey was conducted among 260 respondents to assess consumers’ knowledge and awareness of aflatoxin contamination in chili. A competitive enzyme-linked immunosorbent assay (ELISA) was performed on 58 samples of dried red chili pods, red chili flakes, and red chili powder collected from various markets in Oman. The ELISA results showed the presence of AFB1 in all the samples analyzed. None of the dried red chili pod samples exceeded 10 ppb AFB1, the maximum permissible limit adopted by the Oman Legislation for foods, whereas a few red chili flakes and red chili powder samples exceeded 10 ppb. However, AFB1 content in all the samples was below the maximum tolerance limit of 20 ppb set by the U.S. Food and Drug Administration. The survey results revealed that most respondents were unaware of aflatoxins, as education, gender, and social media were found to be significant determinants of aflatoxin awareness. This study provides valuable insights into the level of AFB1 contamination in red chili products, raises the need for aflatoxin awareness in Oman, and urges for safe culinary practices in the region.
Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning
Authors: Vilela Emerson Ferreira , Silva Cileimar Aparecida da , Botti Jéssica Mayara Coffler , Martins Elem Fialho , Santana Charles Cardoso , Marin Diego Bedin , Freitas Agnaldo Roberto de Jesus , Jaramillo-Giraldo Carolina , Lopes Iza Paula de Carvalho , Corrêdo Lucas de Paula , Queiroz Daniel Marçal de , Rossi Giuseppe , Bambi Gianluca , Conti Leonardo , Venzon Madelaine
Published: September 2024

The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables.
French Bean Production as Influenced by Biochar and Biochar Blended Manure Application in Two Agro-Ecological Zones of Rwanda
Authors: Uwingabire Solange , Chamshama Shaban Athuman Omar , Nduwamungu Jean , Nyberg Gert
Published: September 2024

Biochar (B) has low nutrient content and is recalcitrant to biodegradation. Supplementing B with a fast-releasing nutrient source may improve soil fertility and physical conditions and increase crop productivity. A three-season field study was conducted on sandy loam and sandy clay loam textured soils to investigate the effect of B mixed with livestock manure (LM) on soil properties (pH, organic carbon (OC), cation exchange capacity (CEC), total Nitrogen (TN), available Phosphorus (Avail P)), and French bean yield (Phaseolus vulgaris L.) in Rwanda. The study used a factorial randomized block design with four replications. Treatments comprised three levels of B (0, 1, and 3 t/ha) and three levels of LM (0, 1, and 3 t/ha). Biochar was used from S. sesban, G. sepium, A. angustissima, Eucalyptus, and Grevillea sp., prepared using a drum kiln, while LM was prepared using the pit method. The Analysis of Variance (ANOVA), Tukey (HSD) function at p < 0.05, and linear mixed-effects model were performed in R software version 4.3.3 (R Core Team, 2024). The analysis showed that the treated plots significantly increased French bean yield compared to the control plots, with the highest value found in plots treated with 3 t/ha. The combined plots showed an increased yield compared to sole Biochar or manure. The seasonal increase has been observed, with percentage increases recorded as follows: 16%, 33.56%, 173.06% in sole B plots; 40.28%, 14.43%, and 11.76% in sole LM plots and 125%, 156%, and 209.8% in B + LM plots for season 1, 2, and 3, respectively. Furthermore, the results indicated that the application of B alone or combined with LM significantly enhanced soil pH, OC, TN, avail P, and CEC with the pH ranging from 6.77 to 5.43 for B alone, 6.7–5.35 for LM alone, 8.53–6.06 for B-LM plots, and 4.34–3.78 for control plots. Applying Biochar, either alone or in combination with LM, at a low rate demonstrated positive effects on French bean yield and soil nutrients in smallholder farmers. This study encourages using natural materials such as B and LM to improve soil fertility and increase vegetable production while reducing chemical fertilizers that can cause pollution and damage the environment.
Design and Testing of an Inter-Toothed, Self-Propelled Digger for Deep-Rhizome Chinese Herbal Medicines
Authors: Wu Peng , Sun Wei , Wang Hucun , Zhang Luhai , Simionescu Petru A.
Published: September 2024

To address the challenges of digging deep-rhizome Chinese herbal medicines in northwest China’s hilly terrain, including difficulty, incompleteness, and herb damage, a specialized self-propelled digger with interlocking teeth has been developed. Designed for complex topography, small fields, and resistant soil, this digger provides an efficient and precise alternative to traditional methods. The prototype features in-place reverse differential steering, 360-degree digging capability, and minimized root and soil damage to promote future planting. Key components, including the digging mechanism, vibratory system, crawler chassis, hydraulic transmission system, and worm gear rotary hydraulic reducer, were analyzed and optimized through theoretical, graphical, and simulation studies using RecurDyn. Field tests demonstrated the digger’s effectiveness, achieving depths exceeding 600 mm with minimal herb damage and loss. The digger successfully navigated steep slopes and operated within noise regulations, surpassing industry standards, with less than 1.4% herb damage and a loss rate under 3%. The digger was capable of ascending gradients over 20° with driver noise levels below 92 dB. This innovative solution offers a valuable reference for developing specialized diggers for harvesting traditional Chinese medicinal materials in challenging conditions.
Community Diversity of Endophytic Bacteria in the Leaves and Roots of Pea Seedlings
Authors: Hao Junjie , Liu Quanlan , Song Fengjing , Cui Xiao , Liu Lu , Fu Liping , Zhang Shouan , Wu Xingbo , Zhang Xiaoyan
Published: September 2024

Endophytic bacteria from pea (Pisum sativum L.) plants play important roles in regulating plant growth, health, and nutrition. To enhance the understanding of endophytic bacteria in peas, twenty pea cultivars, two chickpeas, and two broad bean cultivars were planted into artificial soils for 4 weeks. Leaves and roots were collected from plants and sterilized. Endophytic bacterial DNAs were isolated from sterilized materials (leaves, roots, and seeds) and used as templates to detect the bacterial diversity by amplifying the 16S V3–V4 region. The Remel Tryptose Soya Agar (TSA) medium, the aluminum sec-butoxide (ASb) medium, and the yeast extract mannitol agar (YMA) medium were used to isolate bacteria from sterilized leaves and roots, respectively. The plant growth-promoting (PGP) properties of these isolated bacteria, such as the solubilization of phosphorus and potassium and the production of Indole-3-acetic acid (IAA), 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, nitrogenase, pectinase, and cellulose, were studied in vitro. Bacterial isolates were processed for 16S rDNA gene sequencing and performed molecular identification by reconstruction of the phylogenetic tree using the neighborhood association approach in the software MEGA X. Results indicated that the majority of the bacterial communities were shared among leaves, roots, and seeds of pea plants. In both the leaves and roots of pea plants, the prominent phyla identified were Pseudomonadota, Bacteroidota, and Bacillota, with dominant genera such as Rhizobium, Bacteroides, Blautia, and Prevotella prevailing at the genus level. The samples from leaves and roots had unique dominant bacterial genera. In total, 48 endophytic bacteria strains were isolated from leaves and roots, of which 16 strains were from roots and 32 strains were from leaves. The majority of the isolates from leaves (78.13%) and roots (75%) had the ability to produce indole-3-acetic acid (IAA). Moreover, isolates from roots also had greater ability to produce 1-amino-cyclopropane-1-carboxylic acid (ACC) deaminase (81.25%) than those from leaves (62.5%). This study demonstrated the unique distribution of endophytes in leaves and roots of pea, which can have great potential in pea production.
Natural Variation and Association Analysis of Melatonin Synthesis Genes with Root-Related Traits in the Maize Seedling Stage
Authors: Fang Shuai , Li Wei , Wang Baoqing , Zhu Xinjie , Tian Huanling , Zhu Tianze , Sun Dan , Yang Aiqing , Duan Yamin , Yan Yuxing , Wang Houmiao , Yang Zefeng , Xu Chenwu , Li Pengcheng , Wang Yunyun
Published: September 2024

Root system architecture is pivotal for the acquisition of water and nutrients in maize, serving as an essential foundation for achieving high and stable yields. Identification of the genetic components and natural variations determining root traits may facilitate molecular breeding of maize varieties with better root traits. Melatonin plays an important role in plant physiology and development. In this study, nine melatonin biosynthesis genes were re-sequenced in 348 inbred lines, 68 landraces, and 32 teosintes to investigate variations related to maize root traits. The analysis of nucleotide diversity suggested that these genes may have undergone selection, particularly within their promoter regions. Marker–trait association analysis identified 26 variants significantly associated with six root traits. Five variations within the ZmTDC4 promoter were significantly correlated with both total root length (TRL) and lateral root length (LRL). Among these, SNP-1784 explained the most phenotypic variation of root traits, including TRL and LRL, that has undergone selection throughout maize domestication and improvement. Furthermore, knockout of ZmTDC4 in maize resulted in a pronounced reduction in root length, underscoring its critical role in root development. Collectively, these findings elucidate the role of melatonin synthesis genes in root development and identified favorable alleles, thus providing key loci for breeding maize varieties with superior root systems.
Generative Models Utilizing Padding Can Efficiently Integrate and Generate Multi-Omics Data
Authors: Lee Hyeon-Su , Hong Seung-Hwan , Kim Gwan-Heon , You Hye-Jin , Lee Eun-Young , Jeong Jae-Hwan , Ahn Jin-Woo , Kim June-Hyuk
Published: September 2024

Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type > 0.95, primary site > 0.92, sample type > 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression > 0.96, mRNA isoform expression > 0.95, DNA methylation > 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine.
Saharan Dust Contributions to PM10 Levels in Hungary
Authors: Tóth Anita , Ferenczi Zita
Published: September 2024

There are meteorological situations when huge amounts of Saharan dust are transported from Africa to Europe. These natural dust events may have a significant impact on particulate matter concentrations at monitoring sites. This phenomenon affects mainly the countries in Southern Europe; however, some strong advections can bring Saharan dust to higher latitudes too. The number of Saharan dust events in the Carpathian Basin is believed to increase due to the changing patterns in the atmospheric circulation over the Northern Hemisphere’s mid-latitudes. The jet stream becomes more meandering if the temperature difference between the Arctic areas and the lower latitudes decreases. This favours the northward transport of the North African dust. The European regulation makes it possible to subtract the concentration of Saharan-originated aerosol from the measured PM10 concentration. This manuscript describes the methodology used by the HungaroMet to calculate the amount of natural dust contributing to measured PM10 concentrations.
Identification and Functional Analysis of E3 Ubiquitin Ligase g2e3 in Chinese Tongue Sole, Cynoglossus semilaevis
Authors: Cui Zhongkai , Luo Jun , Cheng Fangzhou , Xu Wenteng , Wang Jialin , Lin Mengjiao , Sun Yuqi , Chen Songlin
Published: September 2024

Gametogenesis, the intricate developmental process responsible for the generation of germ cells (gametes), serves as a fundamental prerequisite for the perpetuation of the reproductive cycle across diverse organisms. The g2e3 enzyme is a putative ubiquitin E3 ligase implicated in the intricate regulatory mechanisms underlying cellular proliferation and division processes. The present study delves into the function of G2/M phase-specific E3 ubiquitin protein ligase (Cs-g2e3) in gametogenesis in Chinese Tongue Sole (Cynoglossus semilaevis). Sequence analysis shows that the Cs-g2e3 mRNA spans 6479 bp, encoding a 733 amino acid protein characterized by three conserved structural domains: PHD, RING, and HECT—typical of HECT E3 ubiquitin ligases. The predominant expression of Cs-g2e3 in the gonad tissues is further verified by qPCR. The expression profile of Cs-g2e3 in the gonads of the Chinese Tongue Sole is analyzed at different ages, and the results show that its expression peaks at 8 months of age and then begins to decline and stabilize. It is noteworthy that the expression level remains significantly elevated compared to that observed during the juvenile period. In situ hybridization shows that the mRNA of Cs-g2e3 is mainly localized in the germ cells of the ovary and the testis. RNA interference experiments show that the knockdown of Cs-g2e3 in ovarian and testicular germ cell lines significantly downregulates the expression of key genes involved in oogenesis (e.g., sox9 and cyp19a) and spermatogenesis (e.g., tesk1 and piwil2), respectively. Furthermore, the analysis of mutations in the transcription factor binding sites reveals that mutations within the Myogenin, YY1, and JunB binding sites significantly impact the transcriptional activity of the Cs-g2e3 gene, with the mutation in the YY1 binding site exhibiting the most pronounced effect (p < 0.001). This study contributes to a deeper understanding of the tissue-specific expression patterns of Cs-g2e3 across various tissues in Cynoglossus semilaevis, as well as the potential regulatory influences of transcription factors on its promoter activity. These findings may facilitate future research endeavors aimed at elucidating the expression and functional roles of the Cs-g2e3 gene.
Silicone Shoes for the Treatment of Foot Pad Dermatitis (Bumblefoot) in Pet Chickens—A Retrospective Case Series
Authors: Konicek Cornelia , Gumpenberger Michaela , Schramel Johannes Peter
Published: September 2024

Backyard chickens often suffer from foot pad dermatitis (FPD), a condition exacerbated by poor husbandry, nutritional deficiencies, and obesity. Pressure-relieving bandages, commonly used in the treatment of FPD, are impractical for outdoor chickens as they quickly become wet and dirty, necessitating daily changes that are often unfeasible. This retrospective study explores the use of custom-fit silicone shoes created via 3D-printed molds as an alternative to traditional bandages. CT scans were used to design shoes adapted from a design used for birds of prey. Over three years, 16 chickens with varying degrees of FPD were treated. The results demonstrated that silicone shoes were an effective treatment for FPD, allowing outdoor activity and facilitating daily lesion care without frequent bandage changes. The median shoe-wearing period was 14 days, and most owners provided positive feedback. Despite some attachment issues, the occurrence of secondary pressure marks, and feather regrowth challenges, the silicone shoes offered better ventilation, reusability, and consistent pressure relief compared to traditional bandages. This study concludes that silicone shoes are a viable solution for managing FPD in backyard chickens, promoting faster healing and improving owner compliance.
Regulatory Effects of Maternal Intake of Microbial-Derived Antioxidants on Colonization of Microbiota in Breastmilk and That of Intestinal Microbiota in Offspring
Authors: Wu Dangjin , An Ran , Wang Di , Jiang Luoxin , Huang Liu , Lu Tenghui , Xu Weina , Xu Jianxiong , Zhang Jing
Published: September 2024

In this study, sixteen Sprague Dawley (SD) female rats and eight SD male rats were co-housed to mate. Pregnant SD female rats were fed with a control diet or an MA diet. Breast milk, maternal ileum, and intestinal samples of the offspring were collected at the day of birth and ten days afterwards. The results showed that the impact of MA was more obvious on the microbiota of mature milk (p = 0.066) than on that of colostrum. In addition, MA additive did not significantly affect maternal ileal microbiota, but affected offsprings’ colonic microbiota significantly ten days after birth (p = 0.035). From the day of giving birth to ten days afterwards, in addition to the increase in microbial richness and diversity, at genus level, the dominant bacteria of breastmilk changed from Pseudomonas veronii to Bacillus and Lactococcus. Different from breastmilk microbiota, ten days after giving birth, the maternal ileal microbiota and the offsprings’ intestinal microbiota were dominated by Lactobacillus. Instead of ileal microbiota, offsprings’ colonic microbiota is a key action site of maternal MA additive. Therefore, the current findings have significant implications for the development of maternal feed aimed at modulating the intestinal microbiota of offspring, ultimately leading to improved health outcomes for both mothers and their offspring.
Nanostructures for Delivery of Flavonoids with Antibacterial Potential against Klebsiella pneumoniae
Authors: Macêdo Hanne Lazla Rafael de Queiroz , de Oliveira Lara Limeira , de Oliveira David Nattan , Lima Karitas Farias Alves , Cavalcanti Isabella Macário Ferro , Campos Luís André de Almeida
Published: September 2024

Flavonoids are secondary metabolites that exhibit remarkable biological activities, including antimicrobial properties against Klebsiella pneumoniae, a pathogen responsible for several serious nosocomial infections. However, oral administration of these compounds faces considerable challenges, such as low bioavailability and chemical instability. Thus, the encapsulation of flavonoids in nanosystems emerges as a promising strategy to mitigate these limitations, offering protection against degradation; greater solubility; and, in some cases, controlled and targeted release. Different types of nanocarriers, such as polymeric nanoparticles, liposomes, and polymeric micelles, among others, have shown potential to increase the antimicrobial efficacy of flavonoids by reducing the therapeutic dose required and minimizing side effects. In addition, advances in nanotechnology enable co-encapsulation with other therapeutic agents and the development of systems responsive to more specific stimuli, optimizing treatment. In this context, the present article provides an updated review of the literature on flavonoids and the main nanocarriers used for delivering flavonoids with antibacterial properties against Klebsiella pneumoniae.
Phenotypic Variation in Staphylococcus aureus during Colonisation Involves Antibiotic-Tolerant Cell Types
Authors: Burford-Gorst Chloe M. , Kidd Stephen P.
Published: September 2024

Staphylococcus aureus is a bacterial species that is commonly found colonising healthy individuals but that presents a paradoxical nature: simultaneously, it can migrate within the body and cause a range of diseases. Many of these become chronic by resisting immune responses, antimicrobial treatment, and medical intervention. In part, this ability to persist can be attributed to the adoption of multiple cell types within a single cellular population. These dynamics in the S. aureus cell population could be the result of its interplay with host cells or other co-colonising bacteria—often coagulase-negative Staphylococcal (CoNS) species. Further understanding of the unique traits of S. aureus alternative cell types, the drivers for their selection or formation during disease, as well as their presence even during non-pathological colonisation could advance the development of diagnostic tools and drugs tailored to target specific cells that are eventually responsible for chronic infections.
Unveiling a New Antimicrobial Peptide with Efficacy against P. aeruginosa and K. pneumoniae from Mangrove-Derived Paenibacillus thiaminolyticus NNS5-6 and Genomic Analysis
Authors: Sermkaew Namfa , Atipairin Apichart , Krobthong Sucheewin , Aonbangkhen Chanat , Yingchutrakul Yodying , Uchiyama Jumpei , Songnaka Nuttapon
Published: September 2024

This study focused on the discovery of the antimicrobial peptide (AMP) derived from mangrove bacteria. The most promising isolate, NNS5-6, showed the closest taxonomic relation to Paenibacillus thiaminolyticus, with the highest similarity of 74.9%. The AMP produced by Paenibacillus thiaminolyticus NNS5-6 exhibited antibacterial activity against various Gram-negative pathogens, especially Pseudomonas aeruginosa and Klebsiella pneumoniae. The peptide sequence consisted of 13 amino acids and was elucidated as Val-Lys-Gly-Asp-Gly-Gly-Pro-Gly-Thr-Val-Tyr-Thr-Met. The AMP mainly exhibited random coil and antiparallel beta-sheet structures. The stability study indicated that this AMP was tolerant of various conditions, including proteolytic enzymes, pH (1.2–14), surfactants, and temperatures up to 40 °C for 12 h. The AMP demonstrated 4 µg/mL of MIC and 4–8 µg/mL of MBC against both pathogens. Time-kill kinetics showed that the AMP acted in a time- and concentration-dependent manner. A cell permeability assay and scanning electron microscopy revealed that the AMP exerted the mode of action by disrupting bacterial membranes. Additionally, nineteen biosynthetic gene clusters of secondary metabolites were identified in the genome. NNS5-6 was susceptible to various commonly used antibiotics supporting the primary safety requirement. The findings of this research could pave the way for new therapeutic approaches in combating antibiotic-resistant pathogens.
Silver and Gold Compounds as Antibiotics
Authors: Banti Christina N. , Hadjikakou Sotiris K.
Published: September 2024

This Special Issue entitled “Silver and Gold Compounds as Antibiotics” covers a selection of recent research and review articles focused on biological inorganic chemistry [...]
Genomic Characterization of Extended-Spectrum β-Lactamase-Producing and Third-Generation Cephalosporin-Resistant Escherichia coli Isolated from Stools of Primary Healthcare Patients in Ethiopia
Authors: Wolde Deneke , Eguale Tadesse , Medhin Girmay , Haile Aklilu Feleke , Alemayehu Haile , Mihret Adane , Pirs Mateja , Strašek Smrdel Katja , Avberšek Jana , Kušar Darja , Cerar Kišek Tjaša , Janko Tea , Steyer Andrej , Starčič Erjavec Marjanca
Published: September 2024

The global spread of antimicrobial resistance genes (ARGs) in Escherichia coli is a major public health concern. The aim of this study was to investigate the genomic characteristics of extended-spectrum β-lactamase (ESBL)-producing and third-generation cephalosporin-resistant E. coli from a previously obtained collection of 260 E. coli isolates from fecal samples of patients attending primary healthcare facilities in Addis Ababa and Hossana, Ethiopia. A total of 29 E. coli isolates (19 phenotypically confirmed ESBL-producing and 10 third-generation cephalosporin-resistant isolates) were used. Whole-genome sequencing (NextSeq 2000 system, Illumina) and bioinformatic analysis (using online available tools) were performed to identify ARGs, virulence-associated genes (VAGs), mobile genetic elements (MGEs), serotypes, sequence types (STs), phylogeny and conjugative elements harbored by these isolates. A total of 7 phylogenetic groups, 22 STs, including ST131, and 23 serotypes with different VAGs were identified. A total of 31 different acquired ARGs and 10 chromosomal mutations in quinolone resistance-determining regions (QRDRs) were detected. The isolates harbored diverse types of MGEs, with IncF plasmids being the most prevalent (66.7%). Genetic determinants associated with conjugative transfer were identified in 75.9% of the E. coli isolates studied. In conclusion, the isolates exhibited considerable genetic diversity and showed a high potential for transferability of ARGs and VAGs. Bioinformatic analyses also revealed that the isolates exhibited substantial genetic diversity in phylogenetic groups, sequence types (ST) and serogroups and were harboring a variety of virulence-associated genes (VAGs). Thus, the studied isolates have a high potential for transferability of ARGs and VAGs.
Socioeconomic Impact on Urban Resilience against Flood Damage
Authors: Park Hyung Jun , Song Su Min , Kim Dong Hyun , Lee Seung Oh
Published: September 2024

While urban populations are rapidly increasing around the world, floods have been frequently and seriously occurring due to the climate crisis. As existing disaster prevention facilities have specific limitations in completely protecting against flood damages, the concept of resilience, which emphasizes the ability to recover after becoming injured and harmed by a flood, is necessary to mitigate such damages. However, there is still a scarcity of studies that quantitatively show the relationship between the resilience and the socioeconomic costs, even though a variety of evaluation methods exist in the literature. This study aims to quantitively analyze the socioeconomic impact of flooding on the urban environment based on the concept of resilience. A method of evaluating four properties of resilience (redundancy, rapidity, resourcefulness, and robustness) through damage function and network analysis was used to measure changes in resilience against flood damages. In addition, to determine the socioeconomic impact of flooding, the costs incurred due to transportation delays and the lack of labor participation were evaluated. Differences in structural and social systems have led to variations in resilience and socioeconomic costs. As a future study, if the circumstances after flood events based on risk-based recovery can be evaluated, more effective urban flooding defense decisions would be expected.
Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
Authors: Xiao Bo , Wang Yuchao , Huang Tingsheng , Liu Xuelian , Xie Da , Zhou Xulang , Liu Zhanwen , Wang Chunyang
Published: September 2024

The target is often obstructed by obstacles with the dynamic tracking scene, leading to a loss of target information and a decrease in tracking accuracy or even complete failure. To address these challenges, we leverage the capabilities of Geiger-mode Avalanche Photodiode (GM-APD) LiDAR to acquire both intensity images and point cloud data for researching a target tracking method that combines the fusion of intensity images and point cloud data. Building upon Kernelized correlation filtering (KCF), we introduce Fourier descriptors based on intensity images to enhance the representational capacity of target features, thereby achieving precise target tracking using intensity images. Additionally, an adaptive factor is designed based on peak sidelobe ratio and intrinsic shape signature to accurately detect occlusions. Finally, by fusing the tracking results from Kalman filter and KCF with adaptive factors following occlusion detection, we obtain location information for the central point of the target. The proposed method is validated through simulations using the KITTI tracking dataset, yielding an average position error of 0.1182m for the central point of the target. Moreover, our approach achieves an average tracking accuracy that is 21.67% higher than that obtained by Kalman filtering algorithm and 7.94% higher than extended Kalman filtering algorithm on average.
Vermicomposting Enhances Microbial Detoxification of Sewage Sludge, Enabling Potential Application of the Treated Product in Agroecosystems
Authors: Gómez-Roel Ana , Aira Manuel , Domínguez Jorge
Published: September 2024

Vermicomposting offers an eco-friendly solution to managing the sewage sludge generated in wastewater treatment plants. The objective of this study was to investigate the microbial community composition, structure and functionality during the vermicomposting of sewage sludge. We analyzed samples of sewage sludge, earthworm casts and vermicompost by applying high-throughput sequencing 16S and ITS rRNA. Most of the bacterial (95%) and fungal taxa (99%) were eliminated and subsequently replaced by other microbial taxa originating from earthworms. Further changes resulted in a vermicompost with a more diverse bacterial (but not fungal) community. In addition, the earthworm activity led to an increase in bacterial and a decrease in fungal alpha diversity, resulting in greater differences in beta diversity between sewage sludge, casts and vermicompost. We also found that bacterial pathways associated with amino acid and plant hormone biosynthesis and antibiotic synthesis were enriched. Vermicomposting successfully eliminated most of the 10 human bacterial pathogens found in the sewage sludge. Simultaneously, parasitic and pathogenic fungal taxa were removed. Overall, vermicompost derived from sewage sludge is safer for disposal on land than raw sludge, particularly regarding their respective microbial compositions. This indicates that it could potentially be used as a soil organic amendment and fertilizer.
Asymmetry Propagation in a Pipe Flow Downstream of a 90° Sharp Elbow Bend
Authors: Mikuž Blaž , Cerkovnik Klemen , Tiselj Iztok
Published: September 2024

Pipe bends disrupt the flow, resulting in an asymmetric velocity field across the pipe diameter (D). We examined the recovery length required for the flow to return to a symmetric velocity profile downstream of a sharp elbow. The wall-resolved Large Eddy Simulation (LES) approach was applied to reproduce turbulent fluid flow at Reynolds numbers (Re) of 5600 and 10,000. An additional case in the transitional laminar-turbulent-laminar regime was analyzed at Re=1400. This analysis explored the behavior of the Dean vortices downstream of the elbow and revealed that, in turbulent cases, these vortices reverse their vorticity direction in the region between 8 D and 10 D. However, they eventually decay in structure as far as 25 D from the elbow. Flow asymmetry was analyzed in a 100 D long pipe section downstream of the elbow using four different criteria: wall shear stress (WSS), streamwise velocity, its fluctuations, and vorticity fields. This study found that in turbulent flows, the distance required for flow recovery is a few tens of D and decreases with increasing Re. However, in the transitional case, the flow separation within the elbow induces instabilities that gradually diminish downstream, and flow asymmetry persists even longer than the 100 D length of our outlet pipe section. WSS proved sensitive for detecting asymmetry near walls, whereas flow profiles better revealed bulk asymmetry. It was also shown that asymmetry indicators derived from velocity fluctuations and vorticity were less sensitive than those obtained from streamwise velocity.
Auction-Based Behavior Tree Evolution for Heterogeneous Multi-Agent Systems
Authors: Wen Shanghua , Wu Wendi , Li Ning , Wang Ji , Yang Shaowu , Ben Chi , Yang Wenjing
Published: September 2024

Collaboration in Multi-Agent Systems (MASs) is crucial but challenging in robotics, especially in heterogeneous MASs where robots have different capabilities. Nowadays, the key issue in research on collaboration in MASs is to fully utilize the capabilities of heterogeneous agents. To address this issue, we propose Auction-Based Behavior Tree Evolution (ABTE), a novel two-layer framework designed to learn BTs for heterogeneous MASs. In the first layer, we call it the command layer, and robots receive their tasks through the auction algorithm, enhanced by our innovative three-way handshaking communication protocol embedded in BT implementation, ensuring more efficient task allocation. The second layer of ABTE defines the specific execution behaviors of agents and is, therefore, named the execution layer. The behaviors in this layer are automatically generated by Grammatical Evolution (GE), which has been proven to be a general and effective method for generating swarm BTs. Our experiments are conducted within a Disaster Rescue Scenario, which requires intricate collaboration among multiple robots with diverse capabilities. The results indicate that ABTE outperforms the baseline algorithm, GEESE, in terms of resource utilization. Moreover, it demonstrates robust effectiveness in covering high-priority tasks, thereby validating the efficacy of employing an auction algorithm for generating BTs tailored for heterogeneous MAS.
Influence of Wood and Thickness of Back Wall of Wooden Organ Pipe and Air Pressure in Windchest on Sound
Authors: Danihelová Anna , Štafura Andrej , Čulík Martin , Gergeľ Tomáš
Published: September 2024

In the past, organ builders used many tree species for the production of wooden organ pipes, and the thicknesses of the pipe walls were also different. From the point of view of the sound of wooden organ pipes, it appears that the type of wood, as well as the thickness of the pipe wall, affect the resulting organ sound. Therefore, this research focused on how the wood species and the thickness of the pipe’s wall affect the sound of the organ. Two wood species were selected for our experiment: maple wood, representing hardwood; and spruce wood, representing softwood. The physical and acoustic characteristics (PACHs)—density (ρ), dynamic modulus of elasticity along the wood grain (EL), specific modulus (Esp), speed of sound along the grain (cL), resonant frequency (fr), acoustic constant (A), logarithmic decrement (ϑ), loss coefficient (η), acoustic conversion efficiency (ACE) and sound quality factor (Q)—were determined. Subsequently, the boards were used for making the back wall of the experimental wooden organ pipe with a replaceable back wall. The boards used for the back wall had an initial thickness of 7 mm. The boards were gradually thinned in 1 mm decrements to a final thickness of 1 mm. For each board thickness, the frequency spectrum was recorded at four different air pressures in the windchest, namely, 588 Pa, 716 Pa, 814 Pa and 941 Pa. The results of the experiment showed that at a given back wall thickness, the fundamental tone frequency increases with increasing air pressure. The decrease in the back wall thickness was manifested by a decrease in the fundamental frequency. With increasing air pressure, the intensity of higher harmonic frequencies also increased.
A Study on Performance Enhancement by Integrating Neural Topic Attention with Transformer-Based Language Model
Authors: Um Taehum , Kim Namhyoung
Published: September 2024

As an extension of the transformer architecture, the BERT model has introduced a new paradigm for natural language processing, achieving impressive results in various downstream tasks. However, high-performance BERT-based models—such as ELECTRA, ALBERT, and RoBERTa—suffer from limitations such as poor continuous learning capability and insufficient understanding of domain-specific documents. To address these issues, we propose the use of an attention mechanism to combine BERT-based models with neural topic models. Unlike traditional stochastic topic modeling, neural topic modeling employs artificial neural networks to learn topic representations. Furthermore, neural topic models can be integrated with other neural models and trained to identify latent variables in documents, thereby enabling BERT-based models to sufficiently comprehend the contexts of specific fields. We conducted experiments on three datasets—Movie Review Dataset (MRD), 20Newsgroups, and YELP—to evaluate our model’s performance. Compared to the vanilla model, the proposed model achieved an accuracy improvement of 1–2% for the ALBERT model in multiclassification tasks across all three datasets, while the ELECTRA model showed an accuracy improvement of less than 1%.
Characterization and Finite Element Modeling of Microperforated Titanium Grade 2
Authors: Marquez-Monje David , Escribano-Garcia Ruben , Zubiri Oier
Published: September 2024

Hybrid Laminar Flow Control (HLFC) is a promising technology for reducing aircraft drag and, therefore, emissions and fuel consumption. The integration of HLFC systems within the small space of the wing leading edge, together with de-icing and high lift systems, is one of the main challenges of this technology. This challenge can be tackled by using microholes along the outer skin panels to control suction without the need for an internal chamber. However, microperforations modify the mechanical properties of titanium sheets, which bring new challenges in terms of wing manufacturability. These modified properties create uncertainty that must be investigated. The present paper studies the mechanical properties of micro-drilled titanium grade 2 sheets and their modeling using the Finite Element Method (FEM). First, an experimental campaign consisting of tensile and Nakajima tests is conducted. Then, an FEM model is developed to understand the role of the anisotropy in sheet formability. The anisotropy ratios are found by combination of Design of Experiments (DoE) and the Response Surface Method (RSM); these ratios are as follows: 1.050, 1.320, and 0.975 in the directions Y, Z, and XY, respectively. Some mechanical properties are affected by the presence of microholes, especially the elongation and formability that are significantly reduced. The reduction in elongation depends on the orientation: 20% in longitudinal, 17% in diagonal, and 31% in transversal.
Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
Authors: Meepokgit Teerapun , Wisayataksin Sumek
Published: September 2024

Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments.
A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
Authors: Chen Jinyi , Fu Zhongzhi , Chen Shengshui , Shi Beixiao
Published: September 2024

The liquefaction of saturated sand-gravel material samples from the Xinjiang valley were investigated under cyclic loading. A series of large-scale dynamic triaxial tests were used to determine the dynamic characteristics of the sand-gravel sample under different confining pressures, consolidation stress ratios, and stress levels. A suitable pore water pressure growth model is proposed for the sand-gravel and sand materials. The number of cycles required to cause liquefaction was an important parameter in the dynamic pore water pressure growth model. A method to determine the number of cycles was proposed and verified by a large number of experimental data. The presentation of the pore water pressure simulation results demonstrates that the proposed pore water pressure growth model accurately characterizes the dynamic pore water pressure development in sand-gravel under cyclic loading and is also applicable to sand. The proposed pore water pressure growth model can be used to study the anti-liquefaction characteristics of foundation and dam materials of high earth-rock dams and high sand-gravel dams on deep overburdens.
Visual Censorship: A Deep Learning-Based Approach to Preventing the Leakage of Confidential Content in Images
Authors: Paradise Vit Abigail , Aronson Yarden , Fraidenberg Raz , Puzis Rami
Published: September 2024

Online social networks (OSNs) are fertile ground for information sharing and public relationships. However, the uncontrolled dissemination of information poses a significant risk of the inadvertent disclosure of sensitive information. This poses a notable challenge to the information security of many organizations. Improving organizations’ ability to automatically identify data leaked within image-based content requires specialized techniques. In contrast to traditional vision-based tasks, detecting data leaked within images presents a unique challenge due to the context-dependent nature and sparsity of the target objects, as well as the possibility that these objects may appear in an image inadvertently as background or small elements rather than as the central focus of the image. In this paper, we investigated the ability of multiple state-of-the-art deep learning methods to detect censored objects in an image. We conducted a case study utilizing Instagram images published by members of a large organization. Six types of objects that were not intended for public exposure were detected with an average accuracy of 0.9454 and an average macro F1-score of 0.658. A further analysis of relevant OSN images revealed that many contained confidential information, exposing the organization and its members to security risks.