bluuebunny/crossref_metadata_2025 huggingface dataset:
Robots as addressable non-persons: an analysis of categorial work at the boundaries of the social world
Authors: Muhle Florian
Published: May 2024

Prompted by the material turn in the social sciences and the development of novel interaction technologies, lively debates in social theory have arisen regarding the agency of non-human entities. While these debates primarily involve exchanging theoretical arguments against the background of different theoretical positions, ethnomethodological membership categorization analysis (MCA) provides an empirical approach to questions of non-human agency. The article discusses the debate on non-human agency, demonstrates how MCA can be used to investigate categorial work at the boundaries of the social, and presents the example of an encounter between two museum visitors and a humanoid robot to show how the robot is categorized in a specific way as an ‘addressable non-person.’ In this way, it becomes clear that social-theoretical debates and empirically oriented MCA can mutually inspire each other and how the ‘basic categorization apparatus’ addresses new alterities.
Genomic loci associated with grain yield under well-watered and water-stressed conditions in multiple bi-parental maize populations
Authors: Ndlovu Noel , Gowda Manje , Beyene Yoseph , Chaikam Vijay , Nzuve Felister M. , Makumbi Dan , McKeown Peter C. , Spillane Charles , Prasanna Boddupalli M.
Published: May 2024

Smallholder maize farming systems in sub-Saharan Africa (SSA) are vulnerable to drought-induced yield losses, which significantly impact food security and livelihoods within these communities. Mapping and characterizing genomic regions associated with water stress tolerance in tropical maize is essential for future breeding initiatives targeting this region. In this study, three biparental F3 populations composed of 753 families were evaluated in Kenya and Zimbabwe and genotyped with high-density single nucleotide polymorphism (SNP) markers. Quantitative trait loci maping was performed on these genotypes to dissect the genetic architecture for grain yield (GY), plant height (PH), ear height (EH) and anthesis-silking interval (ASI) under well-watered (WW) and water-stressed (WS) conditions. Across the studied maize populations, mean GY exhibited a range of 4.55–8.55 t/ha under WW and 1.29–5.59 t/ha under WS, reflecting a 31–59% reduction range under WS conditions. Genotypic and genotype-by-environment (G × E) variances were significant for all traits except ASI. Overall broad sense heritabilities for GY were low to high (0.25–0.60). For GY, these genetic parameters were decreased under WS conditions. Linkage mapping revealed a significant difference in the number of QTLs detected, with 93 identified under WW conditions and 41 under WS conditions. These QTLs were distributed across all maize chromosomes. For GY, eight and two major effect QTLs (>10% phenotypic variation explained) were detected under WW and WS conditions, respectively. Under WS conditions, Joint Linkage Association Mapping (JLAM) identified several QTLs with minor effects for GY and revealed genomic region overlaps in the studied populations. Across the studied water regimes, five-fold cross-validation showed moderate to high prediction accuracies (−0.15–0.90) for GY and other agronomic traits. Our findings demonstrate the polygenic nature of WS tolerance and highlights the immense potential of using genomic selection in improving genetic gain in maize breeding.
Drying characteristics and kinetics of bottle gourd using stand-alone indirect solar dryer
Authors: Al-Ghadani Ohood , Al-Khayari Mawada , Jayasuriya Hemanatha , Pathare Pankaj B. , Al-Belushi Mohammed
Published: May 2024

Drying food is an effective method to preserve fruits and vegetables, increasing their shelf life and preventing germination. Bottle gourds, known for their high nutritional value, can rot quickly due to their high moisture content, causing biochemical and microbial changes. To improve shelf life, moisture can be removed to prevent microbial activity. Different drying methods are used to preserve gourd family vegetables for long periods and off-season. Indirect solar drying shields foods from UV rays, dust, insects, and other contamination by avoiding direct exposure. This study aimed to utilize an indirect solar dryer to research the potential for drying bottle gourds and conduct physicochemical analyses for the dried products to compare the drying techniques. In this experiment, different samples of bottle gourd slices were dried using an indirect solar dryer and compared with sun and shade drying. Under physicochemical properties, moisture content (MC), retention of color, thickness, and shrinkage of dried samples were analyzed and compared. The result showed that the time taken to reach 10% moisture was 13 h for samples dried with sun drying. While shade and indirect solar drying took 17 h. The Midilli-Kucuk model outperforms other approaches for drying bottle gourd, with the greatest R2 values (0.9967, 0.9950, 0.9936) and the lowest RMSE values (0.0182, 0.0220, 0.0277) for direct sun, shade, and indirect solar drying, respectively. The indirect sun dryer showed good color retention compared to the fresh bottle gourd samples with L* value of 44.08, b* value of 15.40 and a* value of 7.01. The volumetric shrinkage was 97.64% in the direct sun drying method after 15 h, which is higher compared to sun and shade dryers due to the effect of time and temperature. In addition, there was a significant difference (p-value <0.05) between the thickness of the three drying methods. The use of indirect solar drying methods can be highly recommended to get high-quality dried food with zero energy cost. For future studies it is recommended to enhance the function and design of the indirect solar dryer to speed up drying times and improve the quality of dried samples.
Case Report: Atypical acute compartment syndrome of the forearm in a child following minor trauma with consecutive osteomyelitis
Authors: Everaert J. , Delafontaine A. , Juanos Cabanas J. , Leclercq G. , Jennart H. , Baillon B.
Published: May 2024

Introduction

Forearm compartment syndrome (CS) in children is above all a clinical diagnosis whose main cause is traumatic. However, rarer causes such as infection can alter its clinical presentation.

Clinical case

An 8-year-old boy has been seen in the emergency department complaining of severe forearm pain under a splint in a mild traumatic context. The previous radiological imaging examination three days before had not revealed any fractures. On admission, he presented with major signs of skin inflammation, loss of mobility, paresthesia and a significant biological inflammatory syndrome. The acute CS diagnosis has been made and was treated, but its atypical presentation raised a series of etiological hypotheses, in particular infectious, even if it remains rare. Complementary imaging examinations confirmed the presence of osteomyelitis of the distal radius as well as an occult Salter-Harris II fracture.

Discussion

Beyond the classic “five P's of CS” -pain, paresthesia, paralysis, pallor and pulselessness-, CS's clinical presentations are multiple, especially in pediatric patients. In children, severe pain and increasing analgesic requirement must be indicators of a CS. We hypothesize that this patient sustained a nondisplaced Salter-Harris II fracture with a hematoma colonized by hematogenous osteomyelitis explaining its initial clinical presentation.

Conclusion

Hematogenous osteomyelitis complicated by CS is rare and may be accompanied by a traumatic history. It's atypical presentation in pediatric patients requires vigilance and prompt diagnosis given the disastrous and irreversible complications.
An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder
Authors: Zhou Shenghan , He Zhao , Chen Xu , Chang Wenbing
Published: May 2024

The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.
Improved A* Algorithm for Path Planning Based on CubeSats In-Orbit Electromagnetic Transfer System
Authors: Xu Duo , Yue Honghao , Zhao Yong , Yang Fei , Wu Jun , Pan Xueting , Tang Tao , Zhang Yuhao
Published: May 2024

For future large-scale CubeSat applications in orbit, the deployer must accommodate a greater number of CubeSats and facilitate cluster releases. This paper introduces an improved A* algorithm tailored for CubeSat in-orbit transfer path planning. Unlike the traditional A* algorithm, this enhanced version incorporates a path coordination strategy to manage congestion caused by the simultaneous transfer of many CubeSats, ensuring they reach their designated release positions smoothly and thus significantly boosting the efficiency of CubeSat transfers. Additionally, the algorithm develops a cost model for attitude disturbances on the electromagnetic conveying platform and crafts an improved cost function. It strategically balances the reduction in attitude disturbances caused by CubeSat transfers with the efficiency of these transfers. The primary goal is to minimize platform disturbances while optimizing the number of steps CubeSats need to reach their intended positions. The effectiveness of this algorithm is demonstrated through detailed case studies, which confirm that during the CubeSat transfer process, the platform’s attitude remains stable, and the transfer efficiency is well-managed, achieving efficient path planning for the in-orbit transfer of numerous CubeSats.
A High Step-Down SiC-Based T-Type LLC Resonant Converter for Spacecraft Power Processing Unit
Authors: Ma Wenjie , Li Hui
Published: May 2024

A spacecraft power processing unit (PPU) is utilized to convert power from solar arrays or electric batteries to the payload, including electric propulsion, communication equipment, and scientific instruments. Currently, a high-voltage converter is widely applied to the spacecraft PPU to improve power density and save launch weight. However, the high voltage level poses challenges such as high step-down ratios and high power losses. To achieve less conduction loss, a SiC-based T-type three-level (TL) LLC resonant converter is proposed. To further broaden the gain range and achieve high step-down ratios, a variable frequency and adjustable phase-shift (VFAPS) modulation scheme is proposed. Meanwhile, the steady-state time-domain model is established to elaborate the operation principles and boundary conditions for soft switching. Furthermore, the optimal resonant element design considerations have been elaborated to achieve wider gain range and facilitate easier soft switching. Furthermore, the numerical solutions for switching frequency and phase shift (PS) angle under each specific input could be figured out. Finally, the effectiveness of this theoretical analysis is demonstrated via a 500-W experimental prototype with 650∼950-V input and constant output of 48-V/11-A.
Selenium Treatment Regulated the Accumulation of Reactive Oxygen Species and the Expressions of Related Genes in Postharvest Broccoli
Authors: Liu Yaping , Wang Wei , Ren Gang , Cao Yanan , Di Jianbing , Wang Yu , Zhang Lixin
Published: May 2024

This study aimed to investigate the impact of selenium (Se) treatment on the accumulation of reactive oxygen species (ROS) and the expressions of related genes in broccoli. To achieve this, one group of broccoli heads was treated with a selenite solution of 2 mg L−1, while another group was soaked in distilled water, serving as the control. The effects of these treatments were evaluated by analyzing the browning, hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents, enzyme activity, and gene expression levels of WARK and RBOH. Our results show that the Se treatment effectively inhibited H2O2 accumulation in the broccoli and reduced harmful MDA levels. The inhibition of ROS accumulation following the Se treatment was associated with enhanced activity of the CAT and SOD enzymes, increased expression levels of BoCAT and BoSOD, and decreased expression levels of the WRKY and RBOH transcription factors. Our study provides insights into the mechanism of action of selenium and its potential application in vegetable storage.
Enhancing Cotton Production and Sustainability through Multi-Tier Cropping Systems: Growth, Efficiency, and Profitability Analysis
Authors: Thirukumaran Kanthan , Nagarajan Kadapillai , Vadivel Natarajan , Saitheja Vaddi , Manivannan Venkatesan , Prabukumar Gnanasekaran , Parasuraman Panneerselvam , Kalarani Muthusami Karuppasami , Karthikeyan Ramasamy , Sendhilvel Vaithiyanathan
Published: May 2024

Intercropping presents an opportunity to optimise land use and resource efficiency in cotton cultivation, particularly for small and marginal farmers facing climate-related challenges and rising input costs. This study explores the potential of intercropping short-duration vegetables with cotton to transform this production system into a more economically viable and sustainable one. The study was conducted in the Cotton Department of Tamil Nadu Agricultural University in Coimbatore during the winter irrigated season, from August to January, in both 2020 and 2021. The growth, yield parameters, equivalent yield (3645 and 4234 kg ha−1), and net return (Rs. 123,434 ha−1 and Rs. 154,034 ha−1) were higher in the intercropping system with the paired row planting of Bt cotton with two rows of cluster bean. Upon comparing sole cropping and the paired row method of planting, it was found that adopting the paired row system of planting Bt cotton with two rows of cluster bean was highly profitable in all aspects of crop production. Therefore, the adoption of paired row cropping systems with compatible intercrops that promote synergistic effects on the main crop should be considered for enhancing overall productivity, as well as sustainability.
Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery
Authors: Su Xiangxiang , Nian Ying , Yue Hu , Zhu Yongji , Li Jun , Wang Weiqiang , Sheng Yali , Ma Qiang , Liu Jikai , Wang Wenhui , Li Xinwei
Published: May 2024

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.
The Impact of Suspension Fertilizers Based on Waste Phosphorus Salts from Polyol Production on the Yield of Maize Intended for Green Fodder
Authors: Bogusz Paulina , Brodowska Marzena Sylwia , Rusek Piotr
Published: May 2024

The need to import phosphorus raw materials for fertilizer purposes in Europe as well as the need to manage increasing amounts of waste contributed to the search for alternative sources of phosphorus. One of these is waste sodium–potassium phosphate from the production of polyols. Additionally, a current problem is providing an adequate amount of food, where fertilizers play the main role. Due to the increase in meat consumption, the attractiveness of growing corn for feed is increasing due to its high yield potential and rich composition. The article presents the impact of suspension fertilizers based on waste from the production of polyols on the yield of corn intended for green fodder. In a 3-year field study, the effects of a waste phosphorus source were compared with a commercial granulated phosphorus fertilizer—fosdar. In addition, the suspension fertilizers were assessed according to their composition by testing fertilizers containing only basic nutrients (NPK) and ones enriched with secondary ingredients (S and Mg) and microelements (Zn, Mn and B). The research confirmed the effectiveness of the tested suspension fertilizers. Although the yield obtained was lower than in the case of fosdar fertilization, it still remained at a high level of over 70 t∙ha−1 of fresh yield.
Temporal and Spatial Influences on Fawn Summer Survival in Pronghorn Populations: Management Implications from Noninvasive Monitoring
Authors: Bleke Cole A. , Gese Eric M. , Villalba Juan J. , Roberts Shane B. , French Susannah S.
Published: May 2024

Monitoring vital rates allows managers to estimate trends in growth rates of ungulate populations. However, connecting the influence of nutrition on ungulate demography is challenging. Noninvasive sampling offers a low-cost, low-effort alternative for measuring nutritional indices, allowing for an increased understanding of the mechanistic relationships between environmental factors, nutrition, and specific population vital rates. We examined the temporal influence of intrinsic and extrinsic factors on pronghorn (Antilocapra americana) fawn recruitment. We collected fresh fecal samples from adult female pronghorn in five subpopulations spanning three sampling periods associated with critical maternal life-history stages (late gestation, early lactation, breeding season) for 2 years to investigate both intra- and interannual influences. Intrinsic factors were fecal glucocorticoid metabolites (FGMs), nutritional indices (fecal nitrogen (FN) and 2,6-diaminopimelic acid (DAPA)), and dietary composition (protein intake of forbs, graminoids, legumes, other, shrubs), while the extrinsic factor was vegetative greenness (normalized difference vegetation index (NDVI)). We found variations in DAPA, protein intake of forbs, variation in forb protein intake, and protein intake of legumes during late gestation positively influenced fawn recruitment. Fecal nitrogen during early lactation showed the strongest positive influence on the recruitment of any measured parameter. Finally, breeding season NDVI and the variation in DAPA values positively influenced the subsequent year’s fawn recruitment. Our longitudinal study enabled us to investigate which parameter was most important to specific periods of fawn development and recruitment. We combined the results across five subpopulations, but interpretation and subsequent management decisions should be made at the subpopulation level such that pronghorn subpopulations with low recruitment can be positively influenced by increasing nitrogen on the landscape available to adult females during the early lactation period. As the use of noninvasive monitoring methods continues to expand, we believe our methodologies and results can be broadly applied to other ungulate monitoring programs.
Potential Surviving Effect of Cleome droserifolia Extract against Systemic Staphylococcus aureus Infection: Investigation of the Chemical Content of the Plant
Authors: Alqahtani Jawaher , Negm Walaa A. , Elekhnawy Engy , Hussein Ismail A. , Hassan Hassan Samy , Alanzi Abdullah R. , Moglad Ehssan , Ahmed Rehab , Ibrahim Sarah , El-Sherbeni Suzy A.
Published: May 2024

The increasing rates of morbidity and mortality owing to bacterial infections, particularly Staphylococcus aureus have necessitated finding solutions to face this issue. Thus, we elucidated the phytochemical constituents and antibacterial potential of Cleome droserifolia extract (CDE). Using LC-ESI-MS/MS, the main phytoconstituents of CDE were explored, which were kaempferol-3,7-O-bis-alpha-L-rhamnoside, isorhamnetin, cyanidin-3-glucoside, kaempferide, kaempferol-3-O-alpha-L-rhamnoside, caffeic acid, isoquercitrin, quinic acid, isocitrate, mannitol, apigenin, acacetin, and naringenin. The CDE exerted an antibacterial action on S. aureus isolates with minimum inhibitory concentrations ranging from 128 to 512 µg/mL. Also, CDE exhibited antibiofilm action using a crystal violet assay. A scanning electron microscope was employed to illuminate the effect of CDE on biofilm formation, and it considerably diminished S. aureus cell number in the biofilm. Moreover, qRT-PCR was performed to study the effect of CDE on biofilm gene expression (cna, fnbA, and icaA). The CDE revealed a downregulating effect on the studied biofilm genes in 43.48% of S. aureus isolates. Regarding the in vivo model, CDE significantly decreased the S. aureus burden in the liver and spleen of CDE-treated mice. Also, it significantly improved the mice’s survival and substantially decreased the inflammatory markers (interleukin one beta and interleukin six) in the studied tissues. Furthermore, CDE has improved the histology and tumor necrosis factor alpha immunohistochemistry in the liver and spleen of the CDE-treated group. Thus, CDE could be considered a promising candidate for future antimicrobial drug discovery studies.
Genetic Alternatives for Experimental Adaptation to Colistin in Three Pseudomonas aeruginosa Lineages
Authors: Chebotar Igor , Savinova Tatiana , Bocharova Julia , Korostin Dmitriy , Evseev Peter , Mayanskiy Nikolay
Published: May 2024

Pseudomonas aeruginosa is characterized by a high adaptive potential, developing resistance in response to antimicrobial pressure. We employed a spatiotemporal evolution model to disclose the pathways of adaptation to colistin, a last-resort polymyxin antimicrobial, among three unrelated P. aeruginosa lineages. The P. aeruginosa ATCC-27833 reference strain (Pa_ATCC), an environmental P. aeruginosa isolate (Pa_Environment), and a clinical isolate with multiple drug resistance (Pa_MDR) were grown over an increasing 5-step colistin concentration gradient from 0 to 400 mg/L. Pa_Environment demonstrated the highest growth pace, achieving the 400 mg/L band in 15 days, whereas it took 37 and 60 days for Pa_MDR and Pa_ATCC, respectively. To identify the genome changes that occurred during adaptation to colistin, the isolates selected during the growth of the bacteria (n = 185) were subjected to whole genome sequencing. In total, 17 mutation variants in eight lipopolysaccharide-synthesis-associated genes were detected. phoQ and lpxL/PA0011 were affected in all three lineages, whereas changes in pmrB were found in Pa_Environment and Pa_MDR but not in Pa_ATCC. In addition, mutations were detected in 34 general metabolism genes, and each lineage developed mutations in a unique set of such genes. Thus, the three examined distinct P. aeruginosa strains demonstrated different capabilities and genetic pathways of colistin adaptation.
Numerical Analysis of Outer-Rotor Synchronous Motors for In-Wheel E-Bikes: Impact of Number of Windings, Slot, and Permanent Magnet Shapes
Authors: Han Jaewoong , Jin Chanyoung , Cho Insu , Lee Jinwook
Published: May 2024

This study investigates the design an electric motor propelling hubless wheel mounted on an electric bicycle (e-bike) by numerical analysis. The motor is part of an in-wheel system that uses a permanent magnet synchronous motor (PMSM) to drive the hubless wheel. This paper presents an optimized PMSM design and compares various torque-related parameters, such as cogging torque, cogging torque total harmonic distortion (THD), and ripple torque. The comparison is based on a motor design that examines the effect of different factors, including the number of windings, slot openings, permanent magnet span, and magnet thickness. The motor was modeled and analyzed with variations in the number of windings (21, 23, 25), slot openings (2.4 mm, 3.0 mm, 3.6 mm), permanent magnet span (23 mm, 25 mm, 27 mm), and permanent magnet thickness (3.5 mm, 4.0 mm, 4.5 mm). Our findings suggest that increasing the permanent magnet’s thickness leads to a higher magnetic flux density. Additionally, when the permanent magnet span exceeds 25 mm, there is a sharp increase in cogging torque THD. Based on these results, a slot opening of 3.0 mm and the use of 25 windings are considered suitable for a hubless e-bike.
Evaluation of Alterations That May Occur on Root Surfaces after Root Planing Procedures with a Scanning Electron Microscope
Authors: Aslan İğrek Canan , Çekici Ali
Published: May 2024

Background: The aim of root debridement procedures in non-surgical periodontal treatment is the elimination of etiologic factors that cause periodontal disease, as well as the removal of cement affected by the disease. However, root debridement procedures for the treatment of periodontal diseases produce root surface irregularities that can adversely affect the healing of periodontal tissues. The objective of the present study is to evaluate the surface texture of a root after instrumentation, including an ultrasonic instrument, a Gracey curette, or a double Gracey curette. Methods: A total of 26 single-rooted teeth were used in this study; two specimens were used as controls, and the remaining 24 specimens were equally divided into three groups. Specimens from each group were then subjected to root planing using one of the following instruments: (1) an ultrasonic instrument; (2) a Gracey curette; or (3) a double Gracey curette. The control group was treated with no instrumentation. The extracted teeth were analyzed under a scanning electron microscope and graded in terms of the “roughness and loss of tooth substance index” and the “exposed dentin tubule index.” The SPSS (Statistical Package for Social Sciences) V.21 program was used to conduct a statistical analysis of the data obtained in this study. While evaluating the data, in addition to descriptive statistical methods (mean and standard deviation), a one-way analysis of variance (one-way ANOVA) was carried out to compare the quantitative data and evaluate normally distributed parameters between groups, while a post hoc multiple comparison test was carried out for subgroup comparisons. Results: There was no statistically significant difference between the roughness and material loss on the root surfaces in the samples treated with the ultrasonic instrument or the Gracey curette, and no exposed dentine tubules were observed. Meanwhile, it was observed that the double Gracey curette caused more roughness and material loss on the root surface, and exposed dentine tubules were observed in some samples. The time required for treatment using a double Gracey curette was significantly shorter than that required with the Gracey curette and ultrasonic instruments. Conclusions: The double Gracey curettes produced a relatively rougher root surface than the Gracey curettes or the ultrasonic instrument.
Omics Insights into Epicardial Adipose Tissue: Unravelling Its Molecular Landscape
Authors: Mitu Ivona , Popescu Roxana , Dimitriu Cristina-Daniela , Miftode Radu-Ștefan , Costache Irina-Iuliana , Mitu Ovidiu
Published: May 2024

Epicardial adipose tissue (EAT) is a unique fat depot located around the heart, intimately associated with the myocardium and coronary arteries. The secretion of bioactive molecules and their interaction with neighbouring cardiac tissues makes it an active organ with important implications in cardiovascular health and disease. In recent years, omics technologies have provided valuable insights into the molecular characteristics and functional relevance of EAT in patients with coronary atherosclerosis, myocardial infarction, atrial fibrillation and associated metabolic disorders. This review aims to summarize the current understanding of EAT biology through the lens of genomics, transcriptomics, proteomics and lipidomics approaches. We discuss key findings from omics studies on EAT, including gene expression profiles, metabolic activity, signalling pathways and regulatory network, in order to explore in depth the disease mechanisms, possible therapeutic strategies, and cardiovascular risk assessment. Further advances in this field and integrating data from multiple omics platforms hold promise for unlocking personalized cardiometabolic profiles with EAT as a possible biomarker and therapeutic target in cardiovascular disease.
Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis
Authors: Ek-Chacón Edgar , Molino-Minero-Re Erik , Méndez-Monroy Paul Erick , Neme Antonio , Ángeles-Hernández Hector
Published: May 2024

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.
X-ray Diffraction Study of Metallized Polyethylene for Creating Heat Storage Systems
Authors: Moravskyi Volodymyr , Kucherenko Anastasiia , Kuznetsova Marta , Dulebova Ludmila , Spišák Emil
Published: May 2024

The paper analyzes the prospects of using a heat-accumulating material consisting of metallized polyethylene granules. X-ray diffraction analysis has been used to study the influence of the number of heating and cooling cycles of the heat-accumulating material on the change in the degree of crystallinity of polyethylene. It was shown that the proposed heat-accumulating material showed a low resistance and a significant decrease in the degree of crystallinity of polyethylene under the experimental conditions. The crystal structure of polyethylene did not change during cyclic heating and cooling. Grounded on the obtained experimental results, it was determined that a high degree of polyethylene crystallinity is being kept for 200–300 heating-cooling cycles.
The Influence of Thermocycling Testing on Enamel Microcracks following the Metal Orthodontic Brackets Debonding
Authors: Alsulaimani Lujain , Qali Mohammad , Alghamdi Faisal , Alhazmi Anwar
Published: May 2024

Enamel microcracks (EMCs) arising during the removal of metal orthodontic brackets represent a considerable challenge in dentistry. This in vitro study aims to explore the impacts of thermocycling, the types of orthodontic bonding agent, and curing techniques on the enamel surface of the tooth structure following the debonding of orthodontic metal brackets. It also examines the incidence, number, length, and direction of EMCs on the buccal surface of the tooth. Additionally, the study compares adhesive remnant index (ARI) scores and bracket failure post-debonding. Forty extracted human maxillary canines were divided into ten groups, including intact enamel negative controls (groups 1, 2) and groups (3–8) with metal brackets bonded using two different bonding agents and curing techniques. Following bonding, half of the groups underwent thermocycling testing. EMCs, ARI scores, and bracket failure modes were evaluated. The formation, length, and direction of cracks did not significantly differ among groups, regardless of experimental conditions. Thermocycling had no significant effect on ARI scores or bracket failure modes. However, significant variations were observed among curing technique groups, with seventh-generation bonding agents demonstrating potential effectiveness in achieving complete adhesive removal. The study underscores the importance of considering bonding agent systems and curing protocols to optimize bond outcomes and minimize the risk of metal bracket failure in orthodontic treatment.
Application of the Drosophila melanogaster Research Model to Evaluate the Toxicity Levels between Lead and Copper
Authors: Ioan Sarac , Irina Petrescu , Emilian Onisan , Sorina Popescu , Cerasela Petolescu , Adriana Ciulca , Dorin Camen , Alina-Maria Tenche-Constantinescu , Dacian Lalescu , Ciprian Stroia , Anamaria Aurelia Morna , Laura-Gratiela Vicas , Mariana Ganea
Published: May 2024

Applications of the Drosophila melanogaster (D.m.) research model have an important means both for genetic investigations and for the study of metal toxicity, because D.m. has physiological mechanisms comparable to those in human organisms. In this research, the toxic effect of lead (Pb2+) and copper (Cu2+) on four D.m. genotypes—the wild genotype (Oregon-R, used as control) and three mutant genotypes (white, brown, and white-vestigial)—was compared. Five replicates were made to observe the development progress of monitorized genotypes exposed to five different concentrations 0, 0.50, 0.75, 1.00, and 2.00 mM of copper (CuSO4) and lead Pb(C2H3O2)2. Proliferation rates of larvae, pupae, and adults depend on genetic factors, metals used (copper or lead), and their concentrations. The white-vestigial mutant genotype showed the greatest sensitivity at IC50 concentration (inhibition of proliferation of more than 50% compared with the control sample) at doses of 1.00 mM for Cu2+ and 2.00 mM for Pb2+. In contrast, the control genotype (Oregon-R) showed only an inhibition IC50 concentration of 2.00 mM for Cu2+. The white-vestigial mutant genotype showed the greatest sensitivity at IC50 concentration (inhibition of proliferation of more than 50% compared with the control sample) at doses of 1.00 mM for Cu2+ and 2.00 mM for Pb2+. In contrast, the control genotype (Oregon-R) showed an inhibition at the IC50 concentration of 2.00 mM for Cu2+. The results conclude that (i) the dose influences the prolificacy rate in a directly proportional way, (ii) the comparative analyses between Cu2+ and Pb2+ revealed a more acute effect of Cu2+, and (iii) differentiated prolificacy values according to genotypes were recorded. Those reflect the importance of using D.m. as a research model in the comparative studies of the interactions between genetic factors and metal toxicity. Also, this study provides significant information on non-toxic maximum doses for organisms.
Automatically Distinguishing People’s Explicit and Implicit Attitude Bias by Bridging Psychological Measurements with Sentiment Analysis on Large Corpora
Authors: Wang Bo , Zhang Runxin , Xue Baixiang , Zhao Yachao , Yang Li , Liang Hongxiang
Published: May 2024

Social psychological studies show that people’s explicit attitude bias in public expression can differ from their implicit attitude bias in mind. However, the current automatic attitude analysis does not distinguish between explicit and implicit attitude bias. Simulating the psychological measurements of explicit and implicit attitude bias, i.e., self-report assessment (SRA) and implicit association test (IAT), we propose an automatic language-based analysis to distinguish explicit and implicit attitude bias in a large population. By connecting the criteria of SRA and IAT with the statements containing patterns of special words, we derive explicit and implicit attitude bias with the sentiment scores of the statements, which are obtained by pre-trained machine-learning methods. Extensive experiments on four English and Chinese corpora and four pairs of concepts show that the attitude biases obtained by our method on a large population are consistent with those of traditional psychological experiments in the costly small-scale experiments. The maximum gap between the sentiment scores of explicit and implicit biases reaches 0.9329. Furthermore, we achieve new findings on the difference between the evolution of explicit and implicit attitude bias. The maximum variance gap of sentiment scores in the dynamic changes between explicit and implicit biases reaches 0.249.
Enhancing the Storage Stability and Rutting Resistance of Modified Asphalt through Surface Functionalization of Waste Tire Rubber Powder
Authors: Fan Yinghua , Sun Lijun , Duan Danjun , Zhang Bo , Wang Guofeng , Zhang Sitong , Yu Wenwen
Published: May 2024

Waste tire rubber powder-modified asphalt (RMA) has been widely used in road construction, which was traditionally limited by the poor compatibility of RMA, affecting pavement performance. By synthesizing epoxy soybean oil with amide groups (ESO/TETA) and grafting it onto desulfurized rubber powder (DCR) through microwave irradiation, a surface-functionalized rubber powder (MDCR) was produced successfully. The effects of the physical properties, storage stability, thermal stability, and rheological behavior of the modified asphalt were studied. The results show that the MDCR with a polar surface improved the compatibility and adhesive interactions between the modified crumb rubber and the asphalt. The MDCR content could reach 50%, and the phase separation could meet the requirements of 2.2 °C, which has application conditions in engineering for stable storage. Additionally, the inclusion of MDCR in the asphalt formulations significantly mitigated the temperature sensitivity of the modified asphalt. Importantly, when the MDCR constituted from 20% to 50% of the asphalt, there was a noted reduction in the phase angle at temperatures above 70 °C, indicating a significant improvement in the elastic efficiency. The MDCR also led to substantial enhancements in the resistance of the asphalt to high-temperature and high-stress rutting, addressing the crucial limitations in the consumption ability of waste tire rubber powder and improving the overall performance of RMA in pavement applications.
Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
Authors: Makhmudov Fazliddin , Kultimuratov Alpamis , Cho Young-Im
Published: May 2024

Emotion detection holds significant importance in facilitating human–computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. The primary research challenge involves developing models that can accurately interpret and analyze emotions from both auditory and textual data, whereby auditory data require optimizing CNNs to detect subtle and intense emotional fluctuations in speech, and textual data necessitate access to large, diverse datasets to effectively capture nuanced emotional cues in written language. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers to enable profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method here undergoes meticulous testing on two distinct datasets: Carnegie Mellon University’s Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field.
Design and Test of Automatic Feeding Device for Substrate Filling
Authors: Pan Haibing , Xu Qinchao , Bao Xiulan , Zhou Ran , Zhang Jian , Shan Cheng , Li Shanjun
Published: May 2024

An automatic feeding device for substrate filling was designed to address the challenges of difficult feeding and low efficiency in the citrus seedling pot filling and transferring machine. The device comprises a framework, tracks, overturning frame, drive system, etc. In order to ensure optimal performance, the frame’s turning angle was set at a minimum of 110° and the angle between the frame’s horizontal plane and slope was determined to be 120°. Following optimization, the number and intensity of sudden changes in velocity, angular velocity, and thrust were reduced, thereby prolonging the device’s service life. The prototype test demonstrated that the device has an average feeding time of 9.86 s, is capable of raising 0.14 m3 of substrate in a single cycle, and has a handcart turning angle of 111°. Furthermore, no residual substrate remained in the handcart, and the handcart fixing mechanism operated correctly. The torque measurement results of the motor output shaft were found to be consistent with the simulation results in ADAMS, with a maximum force difference of only 298 N. The simulation was found to be accurate, with an error rate of only 3.67%. This model can be utilized as a dependable reference for the optimization of the design of the automatic feeding device.
An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
Authors: Damilos Spyridon , Saliakas Stratos , Karasavvas Dimitris , Koumoulos Elias P.
Published: May 2024

Airborne pollutants pose a significant threat in the occupational workplace resulting in adverse health effects. Within the Industry 4.0 environment, new systems and technologies have been investigated for risk management and as health and safety smart tools. The use of predictive algorithms via artificial intelligence (AI) and machine learning (ML) tools, real-time data exchange via the Internet of Things (IoT), cloud computing, and digital twin (DT) simulation provide innovative solutions for accident prevention and risk mitigation. Additionally, the use of smart sensors, wearable devices and virtual (VR) and augmented reality (AR) platforms can support the training of employees in safety practices and signal the alarming concentrations of airborne hazards, providing support in designing safety strategies and hazard control options. Current reviews outline the drawbacks and challenges of these technologies, including the elevated stress levels of employees, cyber-security, data handling, and privacy concerns, while highlighting limitations. Future research should focus on the ethics, policies, and regulatory aspects of these technologies. This perspective puts together the advances and challenges of Industry 4.0 innovations in terms of occupational safety and exposure assessment, aiding in understanding the full potential of these technologies and supporting their application in industrial manufacturing environments.
Tractability of Multivariate Approximation Problem on Euler and Wiener Integrated Processes
Authors: Zhang Jie
Published: May 2024

This paper examines the tractability of multivariate approximation problems under the normalized error criterion for a zero-mean Gaussian measure in an average-case setting. The Gaussian measure is associated with a covariance kernel, which is represented by the tensor product of one-dimensional kernels corresponding to Euler and Wiener integrated processes with non-negative and nondecreasing smoothness parameters {rd}d∈N. We give matching sufficient and necessary conditions for various concepts of tractability in terms of the asymptotic properties of the regularity parameters, except for (s, 0)-WT.
Coercive and Noncoercive Mixed Generalized Complementarity Problems
Authors: Mohapatra Ram N. , Sahu Bijaya K. , Pany Gayatri
Published: May 2024

Impressed with the very recent developments of noncoercive complementarity problems and the use of recession sets in complementarity problems, here, we discuss mixed generalized complementarity problems in Hausdorff topological vector spaces. We used the Tikhonov regularization procedure, as well as arguments from the recession analysis, to establish the existence of solutions for mixed generalized complementarity problems without coercivity assumptions in Banach spaces.