bluuebunny/crossref_metadata_2025 huggingface dataset:
Irrigation Effects on Volatile Profile and Essential Oil Yield of Guayule During Flowering
Authors: González-Navarro Emilio José , García-Martínez Maria de las Mercedes , Jiménez María Engracia Carrión , Carmona Manuel , Zalacain Amaya
Published: November 2024

Guayule (Parthenium argentatum A. Gray) has the potential to be an alternative source of rubber if its co-products can be exploited on an industrial scale. Among the co-products that have garnered interest are the essential oils (EOs), which can reach relatively high yields. In the present study, the production and profile of EOs from two guayule accessions, AZ-3 and AZ-5, across different flowering stages (5 months) were analyzed under two irrigation regimes (100% and 50% of crop water evapotranspiration) and compared with control plants that received no additional water, (considered as a water-stress condition). The results showed that the extracted EO yield was consistently higher in the AZ-3 accession than in the AZ-5, especially under water-stress conditions, and that the flowering stage significantly affected the yield irrespective of the accession. Furthermore, differences in EO composition were observed between accessions, with AZ-3 containing more monoterpenes and AZ-5 containing more sesquiterpenes. The yields obtained underline the economic potential of guayule EO production, especially under water-stress and flowering conditions, and position it favorably against other aromatic plants. These results provide valuable insights for optimizing guayule cultivation to increase EO yields, with both economic and environmental benefits.
Object Detection for Yellow Maturing Citrus Fruits from Constrained or Biased UAV Images: Performance Comparison of Various Versions of YOLO Models
Authors: Tanimoto Yuu , Zhang Zhen , Yoshida Shinichi
Published: November 2024

Citrus yield estimation using deep learning and unmanned aerial vehicles (UAVs) is an effective method that can potentially achieve high accuracy and labor savings. However, many citrus varieties with different fruit shapes and colors require varietal-specific fruit detection models, making it challenging to acquire a substantial number of images for each variety. Understanding the performance of models on constrained or biased image datasets is crucial for determining methods for improving model performance. In this study, we evaluated the accuracy of the You Only Look Once (YOLO) v8m, YOLOv9c, and YOLOv5mu models using constrained or biased image datasets to obtain fundamental knowledge for estimating the yield from UAV images of yellow maturing citrus (Citrus junos) trees. Our results demonstrate that the YOLOv5mu model performed better than the others based on the constrained 25-image datasets, achieving a higher average precision at an intersection over union of 0.50 (AP@50) (85.1%) than the YOLOv8m (80.3%) and YOLOv9c (81.6%) models in the training dataset. On the other hand, it was revealed that the performance improvement due to data augmentation was high for the YOLOv8m and YOLOv9c models. Moreover, the impact of the bias in the training dataset, such as the light condition and the coloring of the fruit, on the performance of the fruit detection model is demonstrated. These findings provide critical insights for selecting models based on the quantity and quality of the image data collected under actual field conditions.
Industrial Hemp Variety Performance in Latvia Under Baltic Sea Climate
Authors: Stramkale Veneranda , Andze Laura , Cernova Larisa , Teirumnieka Erika , Filipova Inese , Stramkalis Aldis , Teirumnieks Edmunds , Andzs Martins
Published: November 2024

As the world shifts towards more sustainable and eco-friendly practices, industrial hemp (Cannabis sativa L.) is gaining recognition as a versatile crop with numerous applications. The Baltic Sea region is well-suited for hemp cultivation, with its temperate climate and varied soil types. This study evaluates the suitability of various hemp varieties for the region, focusing on their ability to produce high-quality biomass, fibers, seeds, and dual-purpose products. The findings will contribute to the development of a thriving hemp industry in the region. Five years of research was conducted to investigate the productivity of 12 industrial hemp varieties, including 7 varieties mainly developed for seed production and 5 varieties mainly designed for fiber production. The results showed significant differences in yields among the varieties, with ‘Bialobrzeskie’ exhibiting the highest biomass yield (47.2 t ha−1) and ‘Futura 75’ producing the highest fiber yield (10.8 t ha−1). ‘Henola’ demonstrated the highest seed yield (3.5 t ha−1), while ‘KA-2-2011’ and ‘USO-31’ were identified as dual-purpose varieties suitable for fiber (3.4 and 6.4 t ha−1, respectively) and seed production (2.2 and 1.3 t ha−1, respectively). The calorific value of hemp shives is comparable to wood fuels, indicating their potential as a viable fuel source. The results offer farmers a crucial tool for selecting the best-suited varieties for their specific region, promoting sustainable agriculture practices.
Validation of KASP Markers Associated with Hydrogen Cyanide in Fresh Cassava Roots in Uganda Cassava Germplasm
Authors: Kanaabi Michael , Mukasa Settumba B. , Nuwamanya Ephraim , Iragaba Paula , Baguma Julius Karubanga , Nanyonjo Ann Ritah , Wagaba Henry , Muhumuza Nicholas , Namakula Fatumah Babirye , Wembabazi Enoch , Ozimati Alfred , Kayondo Ismail Siraj , Esuma Williams , Kawuki Robert S.
Published: November 2024

Low hydrogen cyanide (HCN) concentration is a high-priority trait for cassava varieties targeting their fresh and dry product profiles. To be acceptable, varieties bred and developed for these market segments must meet international safety standards for maximum acceptable residual levels of cyanide in cassava food and food products. The discovery of molecular markers that co-segregate with low HCN has not yet resulted in widespread usage in marker-assisted selection (MAS) in breeding programs. To deploy these HCN markers in regular MAS, assessing their reliability in various genetic backgrounds is crucial. The aim of this study was to assess the predictive accuracy of trait specific markers for HCN. The study used six HCN kompetitive allele-specific polymerase chain reaction (KASP) markers that had previously been developed in a Brazilian population and verified in segregating West African cassava populations. For most markers used in the study, the average call rate was more than 91.7%. Three markers—snpME00404, snpME00405 and snpME00406—showed a significant co-segregation of genotypes with the HCN phenotype. On average, genotypes that carried at least one copy of the favourable allele had lower HCN scores. The proportion of phenotypic variance accounted for by the three most important markers was 14% (snpME00406), 17% (snpME00405) and 27% (snpME00404). The validation of identified HCN SNP-markers marks a significant step in their deployment to support selection and advancement decisions in cassava breeding programs.
Genomic Selection in Alfalfa Across Multiple Ploidy Levels: A Comparative Study Using Machine Learning and Bayesian Methods
Authors: Zhu Xiaoyue , Zhang Ruixin , Zhang Tianxiang , Guo Changhong , Shu Yongjun
Published: November 2024

Agronomic traits and quality traits of alfalfa are of great importance to the feed industry. Genomic selection (GS) based on genotyping-by-sequencing (GBS) data, if it achieves moderate to high accuracy, has the potential to significantly shorten breeding cycles for complex traits and accelerate genetic progress. This study aims to investigate the effect of different reference genomes on the prediction accuracy of genomic selection. A total of 11 Bayesian and machine learning models and nine different reference genomes were used to conduct genomic selection on five traits in 385 alfalfa accessions. The accuracy of GS was evaluated using five-fold cross-validation, based on the correlation between genomic estimated breeding values (GEBVs) and estimated breeding values (EBVs). For the five traits, it was found that traits with high heritability exhibited significantly higher prediction accuracy. The prediction accuracy fluctuated minimally across different reference genomes, with the diploid genome showing relatively higher accuracy. For two high-heritability traits, fall dormancy and plant height, predictions were made after SNP density reduction, and it was observed that density had little effect on prediction accuracy. However, for the fall dormancy trait in the diploid genome, more than half of the models showed regular fluctuations, with prediction accuracy increasing as SNP density increased. In conclusion, this study provides a theoretical basis for precision breeding of alfalfa and other polyploid crops by combining different reference genomes and models, and offers important guidance for optimizing future genomic selection strategies.
The Role of Viral Pathogens in Horse Respiratory Diseases: A Cytological and Molecular Approach Using Next-Generation Sequencing
Authors: Mazzei Maurizio , Sorvillo Benedetta , Sgorbini Micaela , Bindi Francesca , Perelli Alice , Laus Fulvio
Published: November 2024

(1) Background: Respiratory problems may be associated with pathogens among which viruses may play an important role in causing or promoting clinical signs. However, traditional diagnostic methods for equine infectious diseases, such as PCR, are limited to known pathogens and often miss rare or novel viruses. Metagenomic next-generation sequencing is a transformative method that allows the sequencing of all genomes present in a sample. Recent studies have used next-generation sequences to identify viral sequences in horses with unexplained respiratory symptoms, revealing potential links between viruses and respiratory problems. The aim of the study was to verify a relationship between respiratory diseases and the presence of viral agents through molecular analyses, applied to bronchoalveolar lavage fluid obtained from horses with history or presence of clinical signs of respiratory diseases. (2) Methods: A cohort of 14 horses was enrolled. All the subjects were submitted to a clinical assessment of the respiratory tract, thoracic ultrasound evaluation, respiratory tract endoscopy, and bronchoalveolar lavage. The latter one was used for cytological analysis, DNA and RNA extraction, and molecular biology analysis. (3) Results: No positive results were obtained in the molecular studies except for a sequence of 753 bp obtained by next-generation sequences, with complete homology to Equid gammaherpesvirus 2 strains. The samples were taken from a thoroughbred female horse aged 2 years old and referred for poor performance and sporadic cough at the beginning of the training session. (4) Conclusions: viral involvement may be not common in horses with respiratory diseases. We cannot exclude that the lack of virus detection may be due to the small sample size of the population included.
Effect of Grazing on the Welfare of Dairy Cows Raised Under Different Housing Conditions in Compost Barns
Authors: Danieli Beatriz , de Vitt Maksuel Gatto , Schogor Ana Luiza Bachmann , Zotti Maria Luísa Appendino Nunes , Ferraz Patrícia Ferreira Ponciano , Zampar Aline
Published: November 2024

There is currently no established information for assessing the general welfare conditions and behavior of dairy cows housed in compost-bedded pack barns (CBPs) that allow access to pasture. Therefore, the objective of this study was to evaluate and classify the welfare and behavior of dairy cows in three different housing conditions within CBPs in southern Brazil. During both the cold and hot seasons, nine farms were divided into three groups: CONV (conventional, large, full-time barns), ADAP (conventionally adapted, full-time barns), and PART (part-time barns). The European Welfare Quality® (WQ®) protocol takes into account the characteristics of the animals, animal housing, and farm management to set an overall score to assess animal welfare, which is why WQ® was used in this study. Daytime behavior was monitored over a period of four consecutive hours on two days. The 29 WQ® measures were grouped into 11 criteria, then into four principles, and finally into the general welfare category. The experimental design employed was a randomized block design in a 2 × 3 factorial scheme (two climatic seasons and three groups), with the means of the measures, principles, and criteria for each group, season, and interaction (group × season) compared using the Tukey test. The diurnal behavior of the cows was described by the average absolute frequency of each observed behavioral measure. There were no differences among the groups in any of the measures assessed by the WQ® protocol. However, there was a significant increase in both the incidence of diarrhea and the duration of lying down during the cold season. Only the principle of appropriate behavior varied among the groups, with the PART group demonstrating superior scores. Regardless of the season, the welfare of dairy cows maintained in CBPs was classified as “improved”. No abnormalities in behavior were observed among cows housed in the different groups or seasons. Cows in the PART group laid down less frequently during the hot season. Overall, the CBP system provided favorable welfare and behavioral conditions for cows in Brazil, and access to grazing further enhanced the welfare of animals housed in the PART group.
Spatial Distribution and Growth Patterns of a Common Bivalve Mollusk (Macoma calcarea) in Svalbard Fjords in Relation to Environmental Factors
Authors: Noskovich Alyona E. , Dvoretsky Alexander G.
Published: November 2024

Ongoing warming in the Arctic has led to significant sea-ice loss and alterations in primary production, affecting all components of the marine food web. The considerable spatial variability of near-bottom environments around the Svalbard Archipelago renders the local fjords promising sites for revealing responses of benthic organisms to different environmental conditions. We investigated spatial variations in abundance, biomass, and growth parameters of the common bivalve Macoma calcarea in waters off western Spitsbergen and identified two distinct groups of this species: one composed mainly of cold-water stations from Storfjorden (Group I) and the other comprising warmer-water stations from Grønfjorden and Coles Bay (Group II). Within these groups, the mean abundance, biomass, production, and mortality accounted for 0.2 and 429 ind. m−2, 20 and 179 g m−2, 18.5 and 314 g m−2 year−1, and 0.22 and 0.10 year−1 respectively. The size–frequency and age–frequency distributions were biased towards smaller and younger specimens in Group I, while Group II displayed more even distributions. The maximum ages were 11 and 21 years, respectively. The mollusks from cold water were significantly smaller than their same-aged counterparts from warmer water. Two groups of Macoma were identified: slow-growing individuals with a rate of 1.4 mm and fast-growing individuals with a growth rate of 1.8 mm. Most population parameters were higher than those observed in the Pechora, Kara, and Greenland Seas. Redundancy analysis indicated water temperature as the main driving factor of abundance and biomass, while the latter was also influenced by the presence of pebbles. Our findings provide new insights into the growth patterns and spatial distribution of Macoma at high latitudes and confirm that this species can serve as a reliable indicator of environmental conditions.
Optimizing Antibiotic Use: Addressing Resistance Through Effective Strategies and Health Policies
Authors: Capuozzo Maurizio , Zovi Andrea , Langella Roberto , Ottaiano Alessandro , Cascella Marco , Scognamiglio Manlio , Ferrara Francesco
Published: November 2024

Background: Antimicrobial resistance (AMR) has emerged as a significant challenge to public health, posing a considerable threat to effective disease management on a global scale. The increasing incidence of infections caused by resistant bacteria has led to heightened morbidity and mortality rates, particularly among vulnerable populations. Main text: This review analyzes current strategies and health policies adopted in the European Union (EU) and Italy to manage AMR, presenting an in-depth examination of approaches for containment and mitigation. Factors such as excessive prescriptions, self-medication, and the misuse of antibiotics in livestock contribute to the selection and spread of resistant strains. Furthermore, this review provides a detailed overview of resistance mechanisms, including enzymatic inactivation, reduced permeability, efflux pump activity, and target site protection, with specific examples provided. The review underscores the urgent need to develop new antibiotics and implement diagnostic testing to ensure targeted prescriptions and effectively combat resistant infections. Current estimates indicate that AMR-related infections cause over 60,000 deaths annually in Europe and the United States, with projections suggesting a potential rise to 10 million deaths per year by 2050 if current trends are not reversed. The review also examines existing public health policies in Europe and Italy, focusing on national and regional strategies to combat AMR. These include promoting responsible antibiotic use, improving surveillance systems, and encouraging research and development of new therapeutic options. Conclusions: Finally, the review presents short- and long-term perspectives from the authors, suggesting actionable steps for policymakers and healthcare providers. Ultimately, a coordinated and multidisciplinary approach involving healthcare professionals, policymakers, and the public is essential to mitigate the impact of AMR and ensure the effectiveness of antibiotics for future generations.
A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
Authors: Zhou Liang , Yang Daming , Chen Lei , Zhuang Wei , Zhang Shiyuan , Xiong Yuanyuan
Published: November 2024

With the development of system-on-chip (SoC) and chiplet technology in the post-Moore era, an increasing number of chiplets are being integrated into a single chip. Consequently, the functions and complexity that can be realized are growing daily. Simultaneously, the volume of test data required for ultra-large-scale integrated circuits (ULSIs) has risen significantly. However, traditional automatic test equipment (ATE) is constrained by its data storage and bandwidth limitations, and its long technology iteration cycle. These cannot keep pace with the rapid development of design technology. This discrepancy leads to challenges in ULSI testing, such as excessively long test time and difficulties in completing the tests. Test compression technology can effectively address these issues by reducing the performance requirements of the test equipment, which in turn can lower test costs. This paper summarizes the classifications of chip test compression technology and, based on their current development, provides a detailed analysis of key technologies. It includes test compression-oriented coding methods, optimization of scan chain structures, and enhancements in coding for compression efficiency. Finally, a forward-looking perspective on the development of chip test compression technology is presented. The aim is to offer a reference for subsequent research in this field and related areas, as well as to provide technical support for the advancement of ULSI testing in the post-Moore era.
EMF Assessment Utilizing Low-Cost Mobile Applications
Authors: Delidimitriou Spyridon , Babas Dimitrios , Manassas Athanasios , Wiart Joe , Samaras Theodoros
Published: November 2024

This study introduces a low-cost alternative method for mapping the electric field strength from 4G LTE base stations and identifies areas where this mapping is more accurate. A drive test campaign was conducted in the urban environment of Thessaloniki, Greece, using data obtained from three identical smartphones, each connected to a different mobile operator and an exposimeter. The smartphones used a mobile application to record Reference Signal Received Power (RSRP) values, while the exposimeter measured the electric field strength in selected frequency bands. In the first part, the variability of the received power over different periods within certain areas was studied, and the reasons for this variability were identified. In the second part, a linear factor was calculated to convert RSRP values into electric field strength using data from both the application and the exposimeter. The converted RSRP values were subsequently compared with the exposimeter data for validation. The results indicate that in areas where the variability of the received power is lower, the linear relationship between smartphone and exposimeter data is statistically stronger resulting in calculated electric field strength values are closer to the measured.
Robust Segmentation of Partial and Imperfect Dental Arches
Authors: Alsheghri Ammar , Zhang Ying , Hosseinimanesh Golriz , Keren Julia , Cheriet Farida , Guibault François
Published: November 2024

Automatic and accurate dental arch segmentation is a fundamental task in computer-aided dentistry. Recent trends in digital dentistry are tackling the design of 3D crowns using artificial intelligence, which initially requires a proper semantic segmentation of teeth from intraoral scans (IOS). In practice, most IOS are partial with as few as three teeth on the scanned arch, and some of them might have preparations, missing, or incomplete teeth. Existing deep learning-based methods (e.g., MeshSegNet, DArch) were proposed for dental arch segmentation, but they are not as efficient for partial arches that include imperfections such as missing teeth and preparations. In this work, we present the ArchSeg framework that can leverage various deep learning models for semantic segmentation of perfect and imperfect dental arches. The Point Transformer V2 deep learning model is used as the backbone for the ArchSeg framework. We present experiments to demonstrate the efficiency of the proposed framework to segment arches with various types of imperfections. Using a raw dental arch scan with two labels indicating the range of present teeth in the arch (i.e., the first and the last teeth), our ArchSeg can segment a standalone dental arch or a pair of aligned master/antagonist arches with more available information (i.e., die mesh). Two generic models are trained for lower and upper arches; they achieve dice similarity coefficient scores of 0.936±0.008 and 0.948±0.007, respectively, on test sets composed of challenging imperfect arches. Our work also highlights the impact of appropriate data pre-processing and post-processing on the final segmentation performance. Our ablation study shows that the segmentation performance of the Point Transformer V2 model integrated in our framework is improved compared with the original standalone model.
Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
Authors: He Xuewen , Cen Yuhao , Liao Yinsheng , Chen Xin , Yang Chao
Published: November 2024

With the rapidly escalating demand for high real-time performance and data throughput capabilities, the limitations of on-board computing resources have rendered traditional computing services inadequate to meet these burgeoning requirements. Vehicular edge computing offers a viable solution to this challenge, yet the roadside units (RSUs) are prone to overloading in congested traffic conditions. In this paper, we introduce an optimal task offloading strategy under congested conditions, which is facilitated by a mixed coverage scenario with both 5G base stations and RSUs with the aim of enhancing the efficiency of computing resource utilization and reducing the task processing delay. This study employs long short-term memory networks to predict the loading status of base stations. Then, based on the prediction results, we propose an optimized task offloading strategy using the proximal policy optimization algorithm. The main constraint is that the data transmission rates of users should satisfy the quality of service. It effectively alleviates the overload issue of RSUs during congested conditions and improves service quality. The simulation results substantiate the effectiveness of the proposed strategy in reducing the task processing delay and enhancing the quality of service.
Development of Indirect Sandwich ELLA for Detection of Insects in Food
Authors: Pospiech Matej , Pečová Martina , Bartlová Marie , Javůrková Zdeňka , Kopecká Anežka , Šebelová Kateřina , Pospíšil Ondřej , Kulma Martin , Folke Jakub , Tremlová Bohuslava , Kouřimská Lenka , Hajšlová Jana
Published: November 2024

Edible insects have been officially classified as food in the European Union since 2015. Currently, four insect species are approved for food use. However, no official method for detecting insects in food has been recognized to date. To establish a simple analytical method for insect detection in food, we developed an indirect sandwich (enzyme-linked lectin sorbent assay) ELLA specific for N-acetylglucosamine in chitin and chitosan polymers. The validation of the method demonstrated that the ELLA developed in this study is reliable for insect detection. The limit of detection (LOD) and quantification (LOQ) were 0.006 and 0.028 mg/mL, respectively. Intra-day precision ranged from 2.45% to 30.29%, and inter-day precision from 0.36% to 12.87%. Significant differences in the total amount of chitin and chitosan were observed among the insect products, processed insect products, and samples without any insect addition (p < 0.05).
Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen
Authors: Wang Wenjing , Wang Haiyan , Liu Jun , Liu Chengfa , Wang Shipeng , Zhang Yong
Published: November 2024

This paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, and station characteristics variables, eight lines and one hundred sixty-six stations in Shenzhen Railway Transportation are taken as research objects. Based on the automatic fare collection (AFC) system data and the POI data of AMAP, the multiple regression model (OLS) and the geographically weighted regression (GWR) model based on the least squares method are established, respectively. The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. This paper provides a theoretical basis for the synergistic planning of house price regulation and rail transportation in Shenzhen, which helps to develop effective management and planning strategies.
An Integrated Approach to Explore Interlimb Asymmetries, Neuromuscular Parameters, and Injuries in Semiprofessional Soccer Players
Authors: Domínguez-Díez Marta , López-Valenciano Alejandro , Raya-González Javier , Castillo Daniel , Sánchez-Díaz Silvia , Berasategui Oier , Gene-Morales Javier
Published: November 2024

Interlimb asymmetries have been widely studied with controversial results, suggesting that the magnitude of asymmetries is highly task-dependent and could be related to injury risk. This study aimed to establish an optimal method for assessing asymmetries and evaluate interlimb power performance and range of motion asymmetries in injured and non-injured male semiprofessional soccer players. A prospective and descriptive design was applied, and 20 male semiprofessional soccer players participated. The players underwent a battery of screening tests (i.e., countermovement jump [CMJ], half-squat, hip abduction and hip adduction isometric force, knee flexion and extension isokinetic torque, and lower limb range of movement). The healthy players had a greater range of motion in the hip extension and ankle flexion of the dominant leg and hip abduction and knee flexion of the non-dominant leg. However, the injured players exhibited greater asymmetry in hip abduction maximum isometric strength. These findings suggest the importance of establishing a comprehensive method to assess the range of motion interlimb asymmetries related to injury risk in semiprofessional soccer players.
YoloSortC3D: A YOLOv8, DeepSort, and C3D-Based Method for Sheep Rumination Behavior Recognition
Authors: Yu Wenbo , Liu Yongqi , Yang Xiang , Xie Ruoya , Wang Chuanjiu
Published: November 2024

The rumination behavior of sheep is closely linked to their overall health, making its accurate identification and quantification crucial. However, traditional manual observation methods by farm workers are both time-consuming and labor-intensive. Similarly, while wearable sensors can provide automated monitoring, they are expensive and may cause stress to the animals. This study proposes a deep learning and computer-vision-based approach to recognize and quantify sheep rumination behavior by calculating and recording rumination duration. This study presents a method that integrates the YOLOv8 object detection algorithm with the DeepSORT, multiobject tracking algorithm to monitor sheep head movements. Firstly, head images are identified and enclosed within ID-labeled bounding boxes, then cropped from video frames for further processing. The preprocessing steps include pixel padding, resizing, and center cropping. These processed images are subsequently stored for later analysis. Following this, a 3D convolutional neural network (3D-CNN) is employed to recognize rumination behavior, while the frame difference method is utilized to accurately calculate rumination duration. Lastly, to validate the effectiveness of the proposed approach, tests were conducted on multitarget sheep rumination videos, and the results were compared with manual observations. Experimental results demonstrated an average error of 4.403% during the day and 6.156% at night, confirming the feasibility and accuracy of this method for the automated detection and quantification of sheep rumination behavior.
A Study on Sensory Analysis of Memory Places
Authors: Akdağ Nurcihan , Beyhan Şefika Gülin
Published: November 2024

Sensory analyses carried out for the perception and experience of the city emphasize the importance of trying to understand the city with different senses, beyond seeing it with the naked eye. While we aim to obtain information about how these changes in the field are remembered through verbal interviews, the use of sensory perception analyses as a tool makes the study different and original from other studies conducted on a city scale. Cities are not only physical spaces but also carriers of collective memory. The study, which aims to map the sensory perceptions of individuals who have experienced the city for a long time, seeks to present projections for the present and the future by collecting information about the urban space, social life, and memory areas of the city of Isparta that have continued for centuries. Qualitative and quantitative research methods were used in the study. Verbal interviews and sensory analyses form the basis of the study. Concepts, such as common areas shared by groups, group life stories, family relations, national consciousness, and belonging, came to the fore in the emergence of collective memory. In this context, potentially valuable memory spaces were analyzed to determine the perceptual meaning continuities formed in the minds of the urban dwellers.
Interval Linguistic-Valued Intuitionistic Fuzzy Concept Lattice and Its Application to Linguistic Association Rule Extraction
Authors: Pang Kuo , Fu Chao , Zou Li , Wang Gaoxuan , Lu Mingyu
Published: November 2024

In a world rich with linguistic-valued data, traditional methods often lead to significant information loss when converting such data into other formats. This paper presents a novel approach for constructing an interval linguistic-valued intuitionistic fuzzy concept lattice, which adeptly manages qualitative linguistic information by leveraging the strengths of interval-valued intuitionistic fuzzy sets to represent both fuzziness and uncertainty. First, the interval linguistic-valued intuitionistic fuzzy concept lattice is constructed by integrating interval intuitionistic fuzzy sets, capturing the bidirectional fuzzy linguistic information between objects, which encompasses both positive and negative aspects. Second, by analyzing the expectations of concept extent relative to intent, and considering both the membership and non-membership perspectives of linguistic expressions, we focus on the extraction of linguistic association rules. Finally, comparative analyses and examples demonstrate the effectiveness of the proposed approach, showcasing its potential to advance the management of linguistic data in various domains.
Shock Model of K/N: G Repairable Retrial System Based on Discrete PH Repair Time
Authors: Yu Xiaoyun , Hu Linmin , Hu Zebin
Published: November 2024

A discrete time modeling method is employed in this paper to analyze and evaluate the reliability of a discrete time K/N: G repairable retrial system with Bernoulli shocks and two-stage repair. Lifetime and shocks are two factors that lead to component failure, and both of them can lead to the simultaneous failure of multiple components. When the repairman is busy, the newly failed component enters retrial orbit and retries in accordance with the first-in-first-out (FIFO) rule to obtain the repair. The repairman provides two-stage repair for failed components, all of which require basic repair and some of which require optional repair. The discrete PH distribution controls the repair times for two stages. Based on discrete time stochastic model properties, priority rules are defined when multiple events occur simultaneously. The state transition probability matrix and state set analysis are used to evaluate the system performance indexes. Numerical experiments are used to illustrate the main performance indexes of the developed discrete time model, and the impact of each parameter variation on the system indexes is examined.
Sentiment Analysis Using Amazon Web Services and Microsoft Azure
Authors: Ivan Sergiu C. , Győrödi Robert Ş. , Győrödi Cornelia A.
Published: November 2024

Recently, more and more companies are using machine learning platforms offered by cloud service providers to build sentiment analysis models that can then be used to analyze public opinions via social media. This paper aims to conduct a comparative analysis of two of the most popular cloud computing platforms, namely Amazon Web Services (AWS) and Microsoft Azure, in terms of their sentiment detection services through the complex analysis of multiple texts. The comparative analysis was carried out by implementing an application that integrates both the sentiment analysis (SA) solutions provided by Amazon Web Services and those offered by Microsoft Azure. To evaluate the services offered by the two platforms, different evaluation metrics were analyzed and compared, such as accuracy, precision, recall, and other relevant characteristics. Also, the paper examines the costs and limitations of the two platforms, Amazon Comprehend and Azure AI Language Text, when they are used to implement solutions for analyzing the sentiments of product reviews. The results obtained highlighted the advantages and disadvantages between the two platforms from several perspectives, such as performance, the quality of the answers provided, or their accuracy. All these aspects help to obtain a clear picture of the advantages and limitations of each service offered by the two cloud platforms.
Tailored Combinations of Human Milk Oligosaccharides Modulate the Immune Response in an In Vitro Model of Intestinal Inflammation
Authors: Walsh Clodagh , Lane Jonathan A. , van Sinderen Douwe , Hickey Rita M.
Published: November 2024

Infants rely on their developing immune system and the protective components of breast milk to defend against bacterial and viral pathogens, as well as immune disorders such as food allergies, prior to the introduction of solid foods. When breastfeeding is not feasible, fortified infant formula will most frequently be offered, usually based on a cow’s milk-based substitute. The current study aimed to explore the immunomodulatory effects of combinations of commercially available human milk oligosaccharides (HMOs). An in vitro co-culture model of Caco-2 intestinal epithelial cells and THP-1 macrophages was established to replicate the hallmarks of intestinal inflammation and to evaluate the direct effects of different synthetic HMO combinations. Notably, a blend of the most prevalent fucosylated and sialylated HMOs, 2′-fucosyllactose (2′-FL) and 6′-siallylactose (6′-SL), respectively, resulted in decreased pro-inflammatory cytokine levels. These effects were dependent on the HMO concentration and on the HMO ratio resembling those in breastmilk. Interestingly, adding additional HMO structures did not enhance the anti-inflammatory effects. This research highlights the importance of carefully selecting HMO combinations in nutritional products, particularly for infant milk formulations, to effectively mimic the benefits associated with breastmilk.
Thyroid Carcinoma Glycoproteins Express Altered N-Glycans with 3-O-Sulfated Galactose Residues
Authors: Broekhuis Jordan M. , Lu Dongli , Aryal Rajindra P. , Matsumoto Yasuyuki , Pepi Lauren E. , Chaves Natalia , Gomez-Mayorga Jorge L. , James Benjamin C. , Cummings Richard D.
Published: November 2024

Aberrant protein glycosylation is a hallmark alteration of cancer and is highly associated with cancer progression. Papillary thyroid cancer (PTC) is the most common type of thyroid cancer, but the N-glycosylation of its glycoproteins has not been well characterized. In this work, we analyzed multiple freshly prepared PTC specimens along with paired normal tissue obtained from thyroidectomies. Glycomic analyses focused on Asn-linked (N)-glycans and employed mass spectrometry (MS), along with Western blot approaches of total solubilized materials that were examined for binding by specific lectins and a monoclonal antibody (mAb) O6, specific for 3-O-sulfated galactose residues. We observed major differences in PTC versus paired normal specimens, as PTC specimens exhibited higher levels of N-glycan branching and bisection with N-acetylglucosamine residues, consistent with RNAseq data. We also found that 3-O-sulfated galactose was present in N-glycans of multiple glycoproteins from both PTC and control specimens, as recognized by the O6 mAb and as confirmed by MS analyses. These results provide new insights into the N-glycans present in glycoproteins of thyroid cancer and context for further studies of these altered glycans as biomarkers and targets for therapeutics.
Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction
Authors: Tian Heqiang , Zhang Xiang , Yin Yurui , Ma Hongqiang
Published: November 2024

In robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomical variations and replicates the surgeon’s cutting technique through a trajectory parameter prediction model. A spatial mapping relationship between artificial and patient vertebrae is first established, enabling the robot to mimic surgeon-defined trajectories with high accuracy. The robotic system’s trajectory planning begins with acquiring point cloud data of the vertebral plate, which undergoes preprocessing, Non-Uniform Rational B-Splines (NURBS) fitting, and parametric discretization. Using the processed data, a spatial mapping method translates the surgeon’s cutting path to the robotic coordinate system, with simulation validating the trajectory’s adherence to surgical requirements. To further enhance the accuracy and stability of trajectory planning, a Backpropagation(BP) neural network is implemented, providing predictive modeling for trajectory parameters. The analysis and training of the neural network confirm its effectiveness in capturing complex cutting trajectories. Finally, experimental validation, involving an artificial vertebral body model and cutting trials on patient vertebrae, demonstrates the proposed method’s capability to deliver enhanced cutting precision and stability. This skill-learning-based, personalized trajectory planning approach offers significant potential for improving the safety and quality of orthopedic robotic surgeries.
Choline Oxidase-Incorporated ATRP-Based Cerium Nanogels as Nanozymes for Colorimetric Detection of Hydrogen Peroxide and Choline
Authors: Vu Trung Hieu , Yu Byung Jo , Kim Moon Il
Published: November 2024

Choline is an important molecule in monitoring food safety and infant nutrition. Here, we report Ce nanogels synthesized by atom transfer radical polymerization (ATRP) employing Ce-coordinated acryloyl-lysine polymer brushes (Ce@SiO2 NGs) as highly efficient cascade nanozymes for colorimetric detection of choline. The synthesized Ce@SiO2 NGs demonstrated remarkable peroxidase-like activity with a porous exterior, which are essential to entrap choline oxidase (COx) to yield COx@Ce@SiO2 NGs and construct a cascade reaction system to detect choline. Immobilized COx catalyzed the oxidation of choline in food samples to produce H2O2, which subsequently induced the oxidation of chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) to produce blue color signals. This method enabled the selective and sensitive detection of target choline with a satisfactory linear range of 4–400 μM, which is sufficient to analyze foodborne choline. The practical utility of the COx@Ce@SiO2 NG-based assay was successfully validated to determine choline spiked in commercially available milk and infant formula with high accuracy and precision values. This approach provides a simple and affordable method of choline detection and has the potential to lead to more developments in ATRP-based nanozymes for diverse biosensing applications.
Noninvasive Ultra Low Intensity Light Photodynamic Treatment of Glioblastoma with Drug Augmentation: LoGlo PDT Regimen
Authors: Kast Richard E. , Kast Anton P. , Arnhold Jürgen , Capanni Felix , Sanabria Laura N. Milla , Bader Nicolas , Vieira Bruno Marques , Alfieri Alex , Karpel-Massler Georg , da Silva Erasmo Barros
Published: November 2024

This paper presents the basis for LoGlo PDT, a new treatment for glioblastoma. Glioblastoma is currently treated with maximal safe resection, temozolomide, and ionizing irradiation. Mortality in 2024 remains over 80% within several years from diagnosis. Oral 5-aminolevulinic acid (5-ALA) is an FDA/EMA approved drug that is selectively taken up by malignant cells, including by glioblastoma. In photodynamic treatment of glioblastoma, intense intraoperative light causes glioblastoma tissue that has taken up 5-ALA to generate cytotoxic reactive oxygen species. The requirement for intense light flux has restricted photodynamic treatment to a single one-hour intraoperative session. We analyze here published data showing that external light, illuminating the entire intact scalp, can attain low μW/cm2 flux several cm into intact brain that would be sufficient to mediate 5-ALA photodynamic treatment of glioblastoma if the light and 5-ALA are delivered continuously over 24 h. At the core of LoGlo PDT regimen is the dataset showing that, for a given fluence, as the duration of PDT light delivery goes down, light intensity (flux) delivered must go up to achieve the same glioblastoma cell cytotoxicity as would a weaker light (lower flux) delivered over a longer time. Thus, a repetitive, noninvasive PDT of glioblastoma using an external light source may be possible. We analyze 5-ALA cellular physiology to show that three non-oncology drugs, ciprofloxacin, deferiprone, and telmisartan, can be repurposed to increase light energy capture after 5-ALA, thereby increasing photodynamic treatment’s glioblastoma cell cytotoxicity. The LoGlo PDT approach uses both drug augmentation and prolonged ultra-low noninvasive transcranial light delivery for a repetitive, noninvasive 5-ALA photodynamic treatment of glioblastoma.
The Impact of BIM Technology on the Lifecycle Cost Control of Prefabricated Buildings: Evidence from China
Authors: Sun Jinkun , Yi Man Li Rita , Deeprasert Jirawan
Published: November 2024

Prefabricated construction has become a significant trend in the international building industry, yet its promotion in China faces cost challenges. This study explores the effect of building information modelling (BIM) technology on the various phases of prefabricated buildings, focusing on the entire lifecycle cost to reduce the overall cost. Key factors influencing the lifecycle as the whole cost control of prefabricated buildings are identified via the top 35 highly cited BIM papers; 15 experts were invited to evaluate the factors influencing the lifecycle cost control of prefabricated buildings, and 22 factors were identified to construct the surveys. The results of 364 valid questionnaires were analysed. Research indicates that BIM significantly impacts cost control across various stages of the lifecycle of prefabricated buildings. BIM’s impact on cost control, ranked from highest to lowest, is as follows: construction and installation phase, production and transportation phase, operational maintenance phase, and design phase. By minimising costs at each stage, BIM enhances design efficiency, simulates production and logistics, reduces rework during construction, and, when integrated with artificial intelligence, BIM optimises operation and maintenance management. Leveraging BIM technology to its full potential effectively reduces the lifecycle costs of prefabricated buildings.
Multi-Hazard Assessment of Masonry Buildings: A State-of-the-Art Review
Authors: Zhang Peng , Chen Lan , Wei Tianyuan , Huang Peng , Wang Hongfan , Chen Xudong
Published: November 2024

Masonry buildings are very popular all over the world, and generally, they are assemblages of masonry units and mortar. However, they are prone to damage and even collapse due to the characteristics of masonry structures. The damages are mainly caused by natural disasters (e.g., flooding, earthquake, and landslide) or human activities (e.g., fire, vehicular impact, and insufficient maintenance). In order to assess the damage to masonry buildings, many approaches are commonly employed, such as on-site investigation, lab testing and experiments, and numerical simulations. In addition, retrofitting is always required for these damaged buildings, and resilience can be obtained to some extent by relying on different strengthening strategies. This article presents a state-of-the-art review of the current research on the multi-hazard assessment of masonry buildings, with a focus on three aspects, i.e., (1) natural and anthropic damages to masonry buildings; (2) applicability and reliability of analysis methods; and (3) strengthening technologies. A rapid and beneficial understanding is expected on the damages, analysis, and protection of ancient and modern masonry buildings.