bluuebunny/crossref_metadata_2025 huggingface dataset:
Homology in Sex Determination in Two Distant Spiny Frogs, Nanorana quadranus and Quasipaa yei
Authors: Xiao Yu , Liao Guangjiong , Luo Wei , Xia Yun , Zeng Xiaomao
Published: June 2024

Sex determination is remarkably diverse, with frequent transitions between sex chromosomes, in amphibians. Under these transitions, some chromosomes are more likely to be recurrently co-opted as sex chromosomes, as they are often observed across deeply divergent taxa. However, little is known about the pattern of sex chromosome evolution among closely related groups. Here, we examined sex chromosome and sex determination in two spiny frogs, Nanorana quadranus and Quasipaa yei. We conducted an analysis of genotyping-by-sequencing (GBS) data from a total of 34 individuals to identify sex-specific makers, with the results verified by PCR. The results suggest that chromosome 1 is a homologous sex chromosome with an XY pattern in both species. This chromosome has been evolutionarily conserved across these closely related groups within a period of time. The DMRT1 gene is proposed to be implicated in homology across two distantly related spiny frog species as a putative candidate sex-determining gene. Harboring the DMRT1 gene, chromosome 1 would have been independently co-opted for sex determination in deeply divergent groups of anurans.
Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning
Authors: Ramírez-Coronel Fernando Joaquín , Rodríguez-Elías Oscar Mario , Esquer-Miranda Edgard , Pérez-Patricio Madaín , Pérez-Báez Anna Judith , Hinojosa-Palafox Eduardo Antonio
Published: June 2024

Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.
Development, Optimization and Evaluation of a Sensitive Enzyme-Linked Immunosorbent Assay (ELISA) Prototype for Detection of Chicken-Based IgY Polyclonal Antibodies against Toxins of D. polylepis Venom
Authors: Kpordze Stephen Wilson , Kikuvi Gideon Mutie , Kimotho James Hungo , Mobegi Victor Atunga
Published: June 2024

Life-threatening medical issues can result from snakebite, and hence this is a public health concern. In many tropical and subtropical nations such as Kenya, where a wide variety of poisonous snakes are prevalent, diagnosis of snakebite in health facilities is imperative. Different antivenoms are needed to treat the venom of different snake species. Nonetheless, it might be difficult for medical professionals to identify the exact snake species that envenomated a patient due to the similarities of several snake envenomations’ clinical symptoms. Therefore, the necessity for an assay or technique for identifying venomous species is critical. The current study sought to develop a sensitive ELISA prototype for the detection of D. polylepis venom in Kenya using generated chicken-based IgY polyclonal antibodies. Serum samples containing specific chicken-based IgY antibodies previously raised against D. polylepis venom toxins were used in the assay development. ELISA parameters were optimized, and the developed assay was assessed for applicability. The limit of detection (LoD) of the ELISA for neurotoxic venoms was determined to be 0.01 µg/mL. Successful discrimination between neurotoxic and cytotoxic venoms was achieved by the ensuing inhibition ELISA assay. The developed assay showed the capability of identifying venoms in blood samples (from spiked and venom-challenged blood samples) of BALB/c mice, providing compelling evidence of the strategy’s usefulness. This assay could help physicians diagnose and manage victims of snakebites through the evaluation of clinical samples.
Robinsoniella peoriensis Infections in Humans—A Narrative Review
Authors: Ioannou Petros , Baliou Stella , Kofteridis Diamantis
Published: June 2024

Robinsoniella peoriensis is a Gram-positive, strictly anaerobic, spore-forming, rod-shaped bacterium belonging to the phylum Firmicutes and the family Lachnospiraceae. Until now, R. peoriensis is the only species of its genus. It was first isolated in 2003 during a study into the flora of lagoons and manure pits. Given the rarity of this microorganism and the sparse information in the literature about its way of transmission, the way to diagnose its infections and identify it in the microbiology laboratory, and its public health relevance, the present study aimed to identify all the published cases of Robinsoniella, describe the epidemiological, clinical, and microbiological characteristics, and provide information about its antimicrobial resistance, treatment, and outcomes. A narrative review was performed based on a Pubmed/Medline and Scopus databases search. In total, 14 studies provided data on 17 patients with infections by Robinsoniella. The median age of patients was 63 years and 47% were male. The most common types of infection were bone and joint infections, bacteremia, infective endocarditis, and peritonitis. The only isolated species was R. peoriensis, and antimicrobial resistance to clindamycin was 50%, but was 0% to the combination of piperacillin with tazobactam, aminopenicillin with a beta-lactamase inhibitor, and metronidazole which were the most commonly used antimicrobials for the treatment of these infections. The overall mortality depends on the type of infection and is notable only for bacteremia, while all other infections had an optimal outcome. Future studies should better assess these infections’ clinical and epidemiological characteristics and the mechanisms of the antimicrobial resistance of this microorganism from a mechanistic and genetic perspective.
Implementation of a Multifaceted Program to Improve the Rational Use of Antibiotics in Children under 3 Years of Age in Primary Care
Authors: Alfayate-Miguélez Santiago , Martín-Ayala Gema , Jiménez-Guillén Casimiro , Alcaraz-Quiñonero Manuel , Delicado Rafael Herrero , Arnau-Sánchez José
Published: June 2024

A multifaceted, participatory, open program based on a qualitative and quantitative approach was developed in the Region of Murcia (Spain) aimed to reduce antibiotic use in children under 3 years of age diagnosed with upper respiratory tract infections (acute otitis media, pharyngitis, and common cold). Antibiotic consumption was measured using the defined daily dose per 1000 inhabitants per day (DHD). Pre-intervention data showed a prevalence of antibiotic prescriptions in the primary care setting of 45.7% and a DHD of 19.05. In 2019, after the first year of implementation of the program, antibiotic consumption was 10.25 DHD with an overall decrease of 48% as compared with 2015. Although antibiotic consumption decreased in all health areas, there was a large variability in the magnitude of decreases across health areas (e.g., 12.97 vs. 4.77 DHD). The intervention program was effective in reducing the use of antibiotics in children under 3 years of age with common upper respiratory diseases, but reductions in antibiotic consumption were not consistent among all health areas involved.
Phylogenetic Analysis of the Genes in D-Ala-D-Lactate Synthesizing Glycopeptide Resistance Operons: The Different Origins of Functional and Regulatory Genes
Authors: Kardos Gábor , Laczkó Levente , Kaszab Eszter , Timmer Bálint , Szarka Krisztina , Prépost Eszter , Bányai Krisztián
Published: June 2024

The phylogenetic relationships of glycopeptide resistance proteins were investigated. The amino acid sequences of vanA, vanB, vanR and vanS were used as queries to search against bacterial genomes in the NCBI RefSeq database. Hits with >60% amino acid identity and >90% query coverage were aligned, and phylogenetic trees were reconstructed. The ligase gene phylogenies were highly similar for both queries, revealing two major clusters. One contained [[vanA:vanM][vanB:vanD]vanF] and related proteins, with proteins from different Bacillaceae, mostly from Paenibacillus spp., in basal positions to all, except vanB. Ligases from streptomycetes formed the other cluster. The relative positions of vanH and vanX differed from those of the associated ligases, but the basal position of the Paenibacillus spp. and the separation of proteins of Streptomyces origin were similar. The accessory genes vanW, vanY and vanZ were associated with vanB, vanA/vanM and vanA, respectively; the basal branches were always proteins from different Bacillaceae but never from streptomycetes. Multiple homologs of the regulatory genes vanR and vanS were found in the genomes; those associated with the different ligases were unique to the ligases. Similarly to the accessory genes, vanRS from Bacillales and Clostridia, but never from streptomycetes, was found in the basal positions. In conclusion, the core genes vanA/B/D/F/M, vanH and vanX originate most probably from glycopeptide-producing streptomycetes, with Paenibacillus spp. (or other Bacillaceae) mediating the transfer, while the accessory genes and the regulatory apparatus probably originate from these Bacillaceae.
Uncovering the Role of Selenite and Selenium Nanoparticles (SeNPs) in Adolescent Rat Adipose Tissue beyond Oxidative Balance: Transcriptomic Analysis
Authors: Nogales Fátima , Pajuelo Eloísa , Romero-Herrera Inés , Carreras Olimpia , Merchán Francisco , Carrasco López José A. , Ojeda María Luisa
Published: June 2024

Studies on adolescent rats, when body composition is changing deeply, reveal that the administration of sodium selenite and selenium nanoparticles (SeNPs), at the same dose, have opposite effects on adipogenesis in white adipose tissue (WAT). To investigate the mechanisms involved in these contrasting effects by means of transcriptomic analysis, three groups of male adolescent rats (n = 18) were used: control (C), selenite supplemented (S), and SeNPs supplemented (NS). Both treated groups received a twofold increase in Se dose compared to the control group through water intake for three weeks. Following treatment, WAT was removed and frozen at −80 °C until subsequent use for RNA extraction, endogenous antioxidant enzymatic activities determination, and quantification of H2O2 and malondialdehyde. NS rats displayed a larger number of differentially expressed genes and cellular processes impacted than S rats. Remarkably, these changes involved upregulation of gene expression associated with the immune system, catabolism, mitochondrial function, and oxidative balance. NS rats presented an increase in antioxidant enzymes activity, alongside an accumulation of H2O2 and malondialdehyde levels. The expression level of 81 genes related to oxidative stress was significantly affected in NS rats. Analyzing the KEGG pathway enrichment revealed that NS rats exhibited increased activity in key catabolic pathways and decreased activity in crucial growth signaling processes. These changes contribute to the mass decrease in WAT found in NS rats. These results suggest a possible application of SeNPs in WAT reduction and induction of the immune response during adolescence.
Potentials of Natural Antioxidants in Reducing Inflammation and Oxidative Stress in Chronic Kidney Disease
Authors: Lee On Ying Angela , Wong Alex Ngai Nick , Ho Ching Yan , Tse Ka Wai , Chan Angela Zaneta , Leung George Pak-Heng , Kwan Yiu Wa , Yeung Martin Ho Yin
Published: June 2024

Chronic kidney disease (CKD) presents a substantial global public health challenge, with high morbidity and mortality. CKD patients often experience dyslipidaemia and poor glycaemic control, further exacerbating inflammation and oxidative stress in the kidney. If left untreated, these metabolic symptoms can progress to end-stage renal disease, necessitating long-term dialysis or kidney transplantation. Alleviating inflammation responses has become the standard approach in CKD management. Medications such as statins, metformin, and GLP-1 agonists, initially developed for treating metabolic dysregulation, demonstrate promising renal therapeutic benefits. The rising popularity of herbal remedies and supplements, perceived as natural antioxidants, has spurred investigations into their potential efficacy. Notably, lactoferrin, Boerhaavia diffusa, Amauroderma rugosum, and Ganoderma lucidum are known for their anti-inflammatory and antioxidant properties and may support kidney function preservation. However, the mechanisms underlying the effectiveness of Western medications and herbal remedies in alleviating inflammation and oxidative stress occurring in renal dysfunction are not completely known. This review aims to provide a comprehensive overview of CKD treatment strategies and renal function preservation and critically discusses the existing literature’s limitations whilst offering insight into the potential antioxidant effects of these interventions. This could provide a useful guide for future clinical trials and facilitate the development of effective treatment strategies for kidney functions.
Mass Yields, Antioxidant and Anti-DU145 Prostate Cancer Cell Proliferation Properties of ProSoy Soymilk as Affected by Extraction Methods and Cooking
Authors: Chang Sam K. C. , Tan Yingying
Published: June 2024

Both the soybean variety and processing method affect the end soybean product’s characteristics. This study’s objective was to characterize the effects of four extraction methods (variations of soaking and grinding) combined with cooking on the content and composition of phenolic substances and the antioxidant and anti-DU145 prostate cancer cell proliferation properties of soymilks prepared from a yellow soybean of the ProSoy variety, which is a high-protein variety. The results showed that the soymilk processing yield was the greatest using method 4, although method 2 gave the highest solid and protein yields by about 14 and 12%, respectively. Method 4, a two-step grinding method, also gave increased yields (8 and 7% for solids and proteins, respectively), and in all but one instance produced higher total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC), and total isoflavone content values in both raw and cooked soymilks as compared to method 1. Cooking the soymilks reduced 14–17% of their total phenolic substances. Cooking reduced the anti-cancer capacity of the phenolic extracts from the soymilk prepared using method 4 by increasing the IC50 value from about 4.9 mg/mL to 6.8 mg/mL. The increases in phenolic compounds and antioxidants produced in the Prosoy soymilks using methods 2 and 4, with simultaneous increases in product and solid yields, are of significant benefit to the soymilk industry and consumer health.
The Impact of a Virtual Educational Cooking Class on the Inflammatory Potential of Diet in Cancer Survivors
Authors: Jackson Mariah Kay , Ehlers Diane K. , Bilek Laura D. , Graeff-Armas Laura , Acquazzino Melissa , Hébert James R. , Price Sherry , Beaudoin Rebecca , Hanson Corrine K.
Published: June 2024

(1) Background. Cognitive dysfunction is prevalent among cancer survivors. Inflammation may contribute to impaired cognition, and diet represents a novel strategy to mitigate cognitive decline. The purpose was to (1) assess the impact of an educational cooking class on cancer survivor eating habits and their inflammatory potential and (2) determine the relationship between diet and cognitive function. (2) Methods. This was a non-randomized interventional study of a virtual educational cooking class in post-treatment, adult cancer survivors. Energy-adjusted Dietary Inflammatory Index (E-DII™) scores and subjective cognitive function were assessed at baseline and 1 month post-intervention. (3) Results. Of 22 subjects, all were female, White, and primarily had breast cancer (64%). There was a significant decrease in E-DII scores, which became more anti-inflammatory, one month after intervention (−2.3 vs. −2.7, p = 0.005). There were significant increases in cognition, including perceived cognitive impairment (COG-PCI, p < 0.001), comments from others (COG-OTH, p < 0.001), and quality of life (COG-QOL, p < 0.001). A change in calories was a significant predictor of a change in perceived cognitive ability (COG-PCA) after adjustment (β = 0.007, p = 0.04; 95% CI (0.000, 0.014)). (4) Conclusions. Educational cooking classes may be an effective way to impact diet-derived inflammation; additional research is needed to assess the long-term effects of dietary changes on cognition.
Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
Authors: Kim Seunghyun , Lee Seungkeon , Lee Eui Chul
Published: June 2024

Recent advancements in deep learning have enabled gaze estimation from images of the face and eye areas without the need for precise geometric locations of the eyes and face. This approach eliminates the need for complex user-dependent calibration and the issues associated with extracting and tracking geometric positions, making further exploration of gaze position performance enhancements challenging. Motivated by this, our study focuses on an ensemble loss function that can enhance the performance of existing 2D-based deep learning models for gaze coordinate (x, y) prediction. We propose a new function and demonstrate its effectiveness by applying it to models from prior studies. The results show significant performance improvements across all cases. When applied to ResNet and iTracker models, the average absolute error reduced significantly from 7.5 cm to 1.2 cm and from 7.67 cm to 1.3 cm, respectively. Notably, when implemented on the AFF-Net, which boasts state-of-the-art performance, the average absolute error was reduced from 4.21 cm to 0.81 cm, based on our MPIIFaceGaze dataset. Additionally, predictions for ranges never encountered during the training phase also displayed a very low error of 0.77 cm in terms of MAE without any personalization process. These findings suggest significant potential for accuracy improvements while maintaining computational complexity similar to the existing models without the need for creating additional or more complex models.
Heart Rate Dynamics and Quantifying Physical Fatigue in Canadian Football
Authors: Zafar Abdullah , Guay Samuel , Vinet Sophie-Andrée , Pilon Francine , Martens Géraldine , Prince François , De Beaumont Louis
Published: June 2024

The cardiac response to physical exertion is linked to factors such as age, work intensity, and fitness levels. The primary objective of this study was to characterize within-session changes in cardiac response to running in Canadian football athletes, which may indicate physical fatigue. Performance profiles were collected from GPS and heart rate (HR) sensors worn by 30 male Canadian varsity football players (20–26 years old) over 28 games and practices. Running efforts with 60–180 s of rest were detected, and the maximum HR (HRmax) and peak HR recovery (HRRpk) during rest were extracted. Additionally, a new metric of cardiovascular cost (CVC) was developed to reflect the efficiency of the HR response to physical workload. HRmax was higher in games (p < 0.001) and in linemen (p < 0.001), and it increased over time (p < 0.001). HRRpk was higher in skilled players (p < 0.001) and changed over time (p < 0.001) depending on the rest period. CVC was higher in linemen (p < 0.001) and increased over time (p < 0.001). This study demonstrated the utility of HR response metrics to quantify ongoing fatigue experienced by Canadian football athletes and proposed a novel fatigue metric capable of monitoring an athlete’s fatigue state in real time.
SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
Authors: Cheng Gang , Chao Peizhi , Yang Jie , Ding Huan
Published: June 2024

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.
Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
Authors: Jeong Da Bin , Lee Boeun , Ko Nak Yong
Published: June 2024

This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group SE2(3) and follows the computations based on Lie theory. Previously employed DR methods, which have been widely used, suffer from cumulative errors over time due to inaccuracies in the calculated changes from velocity during the motion of the vehicle or small errors in modeling assumptions. Consequently, this results in significant discrepancies between the estimated and actual positions over time. However, by treating the pose and velocity of the vehicle as elements of the Lie group, the proposed method allows for accurate solutions without the errors introduced by linearization. The incremental updates for pose and velocity in the DR computation are represented in the Lie algebra. Experimental results confirm that the proposed method improves the accuracy of DR. In particular, as the motion prediction time interval of the vehicle increases, the proposed method demonstrates a more pronounced improvement in positional accuracy.
Investigating the Impact of Circular Sector Pole Head Structure on the Performance of a Multipole Magnetorheological Brake
Authors: Shiao Yaojung , Bollepelly Manichandra
Published: June 2024

The magnetorheological brake (MRB) epitomized a revolutionary modification in the braking systems because of its extremely efficient and well-controlled performance. To increase the safety and controllability of automotive braking system, researchers have developed a different MRB structures. Although much research on magnetorheological brakes has shown positive results in terms of brake torque, braking time, thermal efficiency, etc., the ability to increase braking force by expanding the disc surface, through which the magnetic field operates in a compact structure, is restricted. To address this issue, a new multipole MRB configuration with a unique pole head design that maintains compactness. Initially, the conceptual design was achieved by leveraging the combined impact of the twin disc-type structure and multipole concept. The model was used in a dynamic simulation to show how the braking torque of a magnetorheological braking system varies with coil current. The effects of circular sector pole head shape on braking performance were investigated using COMSOL Multiphysics software (version 5.5). A three-dimensional electromagnetic model of the proposed MRB was developed to examine the magnetic flux intensity and the impact of magnetic field dispersion on the proposed pole head structure of a magnetorheological brake. Based on simulation results, the circular sector pole head configuration is capable of increasing the active chaining regions for the MR fluid on the rotor surface, allowing for a more effective use of magnetic flux throughout the whole surface of a rotating brake disc, thereby increasing the magnetic field usage rate. The acquired simulation results show an increase in braking torque while keeping a compact and practical design structure.
High Strain Rate Response of Sandstones with Different Porosity under Dynamic Loading Using Split Hopkinson Pressure Bar (SHPB)
Authors: Stopka Grzegorz , Gieleta Roman , Panowicz Robert , Wałach Daniel , Kaczmarczyk Grzegorz Piotr
Published: June 2024

This article presents the results of dynamic tests of sandstone samples differing in strength parameters and porosity, which were carried out with the use of the split Hopkinson pressure bar (SHPB). For this study, three types of sandstones were considered: two from the region of India (Kandla Grey and Apricot Pink) and one from Central Europe (Barwald). The strength parameters of the samples were identified in static tests (UCS, BTS tests), whereas the porosity was measured using computed tomography. The performed scanning allowed the volume of the pores and their distribution in the samples to be identified. Dynamic tests involved loading the cylindrical samples with a diameter of 23 m in the range of high strain rates, i.e., ε˙ = 102 ÷ 103/s, using the SHPB (split Hopkinson pressure bar) method. Samples with three different values of slenderness were used for testing (L/D = 1, 0.75 and 0.5). Based on the dynamic characteristics of the samples, the maximum dynamic stresses, Dynamic Increase Factor (DIF) and the amount of energy absorbed by the samples were determined. The conducted research indicates a significant impact of material porosity on the amount of dissipated energy under conditions of high strain rates. The research indicates that the values of this parameter for Apricot Pink and Kandla Grey sandstones (slenderness L/D = ¾ and L/D = ½) are similar, although the uniaxial compressive strength (UCS) of Kandla Grey sandstone is approximately 60% higher than that of Apricot Pink sandstone. As a result of the sample destruction process, various forms of sample destruction were obtained. The performed grain analysis indicates a significant increase in the smallest fraction (<0.5 mm) in the case of the sandstone with the highest porosity (Apricot Pink—55% of mass outcome) in comparison to the sandstone with the lowest porosity (Kandla Grey—12% of mass outcome).
Lithium Concentrations in Saline Brines of the Shu–Sarysu Depression
Authors: Baratov Refat T. , Seitmuratova Eleonora Y. , Murtazin Ermek Z. , Dautbekov Diyas O. , Kelyukhov Vyacheslav N. , Shadiyev Nurgali S. , Mashrapova Moldir A.
Published: June 2024

This article presents the results of a study on lithium mineralization in salt flats and underground aquifers of the Shu–Sarysu depression. Analysis of brine samples collected from 2022 to 2023, utilizing spectroscopy and X-ray diffraction, reveals elevated concentrations that hold commercial promise. These findings may have significant implications for exploration efforts and estimation regarding the lithium resource potential, which is currently in high demand. This article examines data regarding lithium brine deposits worldwide, focusing on their geology. The research methodology involves delineating regions of salt flat distribution through remote sensing data interpretation, fieldwork, and laboratory analysis, particularly for surface brines. Underground lithium-bearing brines are detected within oil and gas structures. The article presents findings from analytical studies conducted on saline and co-produced formation brines collected during the 2022 field season, with a specific focus on the epiplatform regions of Kazakhstan, encompassing the Shu–Sarysu depression.
Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
Authors: Wang Yuchun , Yi Cancan , Huang Tao , Liu Jun
Published: June 2024

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy.
Kaolin–Fly Ash Composite for Pb2+ and AsO43− Adsorption from Aqueous System
Authors: Doušová Barbora , Bedrnová Eva , Maxová Kateřina , Koloušek David , Lhotka Miloslav , Pilař Lukáš , Angelis Milan
Published: June 2024

The expected benefit of composite adsorbents generally consists in their growing applicability, thanks to the combination of the adsorption properties of individual components. Composite adsorbents were prepared as mixtures of kaolin from a Czech deposit (kaolin Sedlec, SK) and two fly ashes (FAs) from a fluidised bed boiler in Czech operations differing in fuel type. The mixtures of SK with FA in a ratio of 50:50% wt. were prepared at 20 °C, 65 °C, and 110 °C in an autoclave. The source materials and composite adsorbents were tested for the adsorption of lead as Pb2+, and arsenic as AsO43− from model solutions in laboratory conditions. The adsorption of Pb2+ proceeded quantitatively on the source materials except SK, and on both the composites, with an adsorption yield of >97% and a low adsorbent consumption (~2 g.L−1). The AsO43− adsorption proceeded selectively only on both FAs, with an adsorption yield of >97% again. The adsorption of AsO43− on the composite adsorbents achieved a worse yield (˂80%), with about ten times more adsorbent consumption (~20 g.L−1). An increased preparation temperature did not affect the Pb2+ adsorption at all, but it reduced the efficiency of AsO43− adsorption by up to 30%. The SK–FA composites proved to have promising properties, mostly as cation-active adsorbents.
Fault-Tolerant Model Predictive Control Applied to a Sewer Network
Authors: Cembellín Antonio , Fuente María J. , Vega Pastora , Francisco Mario
Published: June 2024

This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.
Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
Authors: Niu Junlin , Pan Jiafang , Qin Zhaohui , Huang Faguo , Qin Haihua
Published: June 2024

In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on diagnosing small-sample bearings remains scarce. Therefore, this study presents an FD method for small-sample bearings, employing variational-mode decomposition and Symmetric Dot Pattern, combined with a pre-trained and fine-tuned Residual Network18 (VSDP-TLResNet18). The approach utilizes variational-mode decomposition (VMD) to break down the signal, determining the k value and the best Intrinsic-Mode Function (IMF) component based on center frequency and kurtosis criteria. Following this, the chosen IMF component is converted into a two-dimensional image using the Symmetric Dot Pattern (SDP) transform. In order to maximize the discrimination between two-dimensional fault images, Pearson correlation analysis is carried out on the parameters of SDP to select the optimal parameters. Finally, we use the pre-trained and fine-tuned method combined with ResNet18 for small-sample FD to improve the diagnosis accuracy of the model. Relative to alternative approaches, the suggested method demonstrates strong performance when dealing with small-sample FD.
Sound-Absorbing, Thermal-Insulating Material Based on Non-Woven Fabrics Mixed with Aerogel Particles
Authors: Katsura Daiji , Ochiai Hiroya , Kawabe Mitsuyoshi , Yamamoto Takashi , Ohshita Joji
Published: June 2024

The automotive industry is rapidly advancing toward the electrification of vehicles. Battery electric vehicles present unique challenges in heat and noise control due to the absence of an internal combustion engine. These challenges arise from the stringent operating temperature requirements of batteries and the distinct characteristics of their power sources, such as differences in rpm and mounting positions compared to traditional engines. To address these issues, porous sound-absorbing materials and porous insulation materials are commonly employed. Conversely, there is an increasing demand for materials that are both lightweight and compact yet capable of providing excellent sound absorption and thermal insulation. Although porous sound absorbers and insulators are similar, they differ in the microstructure required to achieve high performance, specifically in the size and connectivity of their fluid phases. This increases the challenge of integrating superior sound absorption and insulation properties within the same material. In this study, computational microstructure modeling was employed to develop a non-woven fabric composed of flattened ellipsoidal particles with nanoporosity. This innovative material demonstrates exceptional thermal insulation and sound absorption characteristics attributable to its nanoporosity and high tortuosity.
A Benchmark for Morphological Segmentation in Uyghur and Kazakh
Authors: Abudouwaili Gulinigeer , Ruzmamat Sirajahmat , Abiderexiti Kahaerjiang , Wu Binghong , Wumaier Aishan
Published: June 2024

Morphological segmentation and stemming are foundational tasks in natural language processing. They have become effective ways to alleviate data sparsity in agglutinative languages because of the nature of agglutinative language word formation. Uyghur and Kazakh, as typical agglutinative languages, have made significant progress in morphological segmentation and stemming in recent years. However, the evaluation metrics used in previous work are character-level based, which may not comprehensively reflect the performance of models in morphological segmentation or stemming. Moreover, existing methods avoid manual feature extraction, but the model’s ability to learn features is inadequate in complex scenarios, and the correlation between different features has not been considered. Consequently, these models lack representation in complex contexts, affecting their effective generalization in practical scenarios. To address these issues, this paper redefines the morphological-level evaluation metrics: F1-score and accuracy (ACC) for morphological segmentation and stemming tasks. In addition, two models are proposed for morpheme segmentation and stem extraction tasks: supervised model and unsupervised model. The supervised model learns character and contextual features simultaneously, then feature embeddings are input into a Transformer encoder to study the correlation between character and context embeddings. The last layer of the model uses a CRF or softmax layer to determine morphological boundaries. In unsupervised learning, an encoder–decoder structure introduces n-gram correlation assumptions and masked attention mechanisms, enhancing the correlation between characters within n-grams and reducing the impact of characters outside n-grams on boundaries. Finally, comprehensive comparative analyses of the performance of different models are conducted from various points of view. Experimental results demonstrate that: (1) The proposed evaluation method effectively reflects the differences in morphological segmentation and stemming for Uyghur and Kazakh; (2) Learning different features and their correlation can enhance the model’s generalization ability in complex contexts. The proposed models achieve state-of-the-art performance on Uyghur and Kazakh datasets.
IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
Authors: Xing Zhihuan , Lan Yuqing , Sun Zhongjie , Yang Xiaoyi , Zheng Han , Yu Yichun , Yu Dan
Published: June 2024

With the rapid development of the Internet of Things (IoT), the growth of the Industrial Internet of Things (IIoT) applied in the industrial sector has also been swift. However, in practical applications, there are still issues such as the misalignment between theory and application, the lack of a unified standardization framework, and the frequent occurrence of data silos. These issues limit the maintainability and scalability of IIoT systems and increase the digitalization costs for enterprises. Based on this, drawing from the design principles of classic general-purpose operating systems, we propose the concept of an IoT operating system platform. As a software infrastructure aimed at the next generation of IIoT, the IoT OS platform consists of general-purpose computer operating systems and the platform software running on them. It manages computing resources and entities such as sensors, networks, and ubiquitous artificial intelligence applications or systems, and provides service support for IIoT applications upwards, aiming to improve existing issues and enhance the specificity and scalability of IIoT systems. This paper presents the current status of IIoT systems, the definition and architecture of the IoT OS platform, and validates the theoretical architecture through specific cases.
Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
Authors: Herman Przemyslaw
Published: June 2024

This paper addresses the issue of trajectory tracking control for an autonomous underwater vehicle in the presence of parameter perturbations and disturbances in three-dimensional space. The control scheme is based on a combination of the backstepping method, the adaptive integral sliding mode control scheme, and velocity transformation resulting from the decomposition of the inertia matrix, which is symmetric. In addition, adaptive laws were applied to eliminate the effects of parameter perturbations and external disturbances. The main feature of the proposed approach is that the vehicle model is not fully symmetric but contains quantities due to the shift of the center of mass. Another important feature of the control scheme is the ability to detect some of the consequences caused by reducing the vehicle model by neglecting dynamic couplings. Numerical results on the five degrees of freedom (DOF) vehicle model show the efficiency, effectiveness, and robustness of the developed controller.
Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
Authors: Quirino Matteo , Lavagna Michèle Roberta
Published: June 2024

On-orbit autonomous relative navigation performance strongly depends on both sensor suite and state reconstruction selection. Whenever that suite relies on image-based sensors working in the visible spectral band, the illumination conditions strongly affect the accuracy and robustness of the state reconstruction outputs. To cope with that limitation, we investigate the effectiveness of exploiting image sensors active in the IR spectral band, not limited by the lighting conditions. To run effective and comprehensive testing and validation campaigns on navigation algorithms, a large dataset of images is required, either available or easy to obtain in the visible band, not trivial and not accessible for the thermal band. The paper presents an open-source tool that exploits accurate finite volume thermal models of celestial objects and artificial satellites to create thermal images based on the camera dynamic. The thermal model relies on open CFD code (OpenFOAM), pushed to catch the finest details of the terrain or of the target geometries, and then the temperature field is processed to compute the view factors between the camera and each face of the mesh; thus, the radiative flux emitted by each face is extracted. Such data feed the rendering engine (Blender) that, together with the camera position and attitude, outputs the thermal image. The complete pipeline, fed by the orbiting target and the imaging sensor kinematic, outputs a proper synthetic thermal image dataset, exploitable either by a relative navigation block or any other scope of research. Furthermore, in the same framework, the article proposes two different thermal sensor models but any sensor model can be applied, providing full customization of the output. The tool performance is critically discussed and applied for two typical proximity scenarios, asteroid and artificial satellite; for both cases, the challenges and capabilities of the implemented tool for synthetic thermal images are highlighted. In the end, the tool is applied in a phase B mission design sponsored by ESA and in related research works; for such cases, the results are reported in the article.
Digital Inequalities in China in 2020: Spatial and Multivariate Analysis
Authors: Pick James , Ren Fang , Sarkar Avijit
Published: June 2024

China’s digital divide is explored through spatial and multivariate analysis. The dependent variables include general uses of information and communication technologies (ICTs) and mobile devices, measures of ICT infrastructure, purposeful uses for software services, and purposeful uses of e-commerce. Independent variables include a variety of demographic, economic, educational, ethnic, innovation, and knowledge production indicators. Data come from the China Yearbook. Theoretically, the study draws on the Spatially Aware Technology Utilization Model (SATUM). Digital disparities in Chinese provinces are analyzed using regression analysis, spatial autocorrelation, and k-means cluster analysis. The important correlates associated with digital inequality are expenditure for science and technology, income, R&D investment, full-time teachers, non-Han (minority) population, and proportion of urbanization. Longitudinal comparison reveals factors influencing ICT changes to be expenditure on science and technology, the unemployment rate, and college degree. Among the cluster findings are geographic concentrations of digital variables in Shanghai and Beijing and marked spatial pattern differences in central and central–east China between variable groups. Provincial and national policy implications, which are compared to China’s five-year plans, include an emphasis on science and technology, technology education in all provinces, support for higher provincial incomes, and ICT training for the non-Han population. These steps are especially important in ICT-deprived provinces.
Production of Complex Sentences in the Semantic Variant of Primary Progressive Aphasia: Neuropsychological, Clinical, Neuroanatomical and Demographic Correlates
Authors: Koukoulioti Vasiliki , Stavrakaki Stavroula , Mamouli Dimitra , Ioannidis Panagiotis
Published: June 2024

Syntactic deficits are not a core symptom of the semantic variant of primary progressive aphasia (svPPA). However, some studies indicate syntactic impairments that deteriorate with progress of the disease. The study addresses the presence of a syntactic deficit and its association with clinical, neuropsychological, neuroanatomical and demographic variables. Nine individuals with svPPA were tested with a cognitive screening test (Addenbrooke’s Cognitive Examination, ACE-R), a language screening test (Boston Diagnostic Aphasia Examination—BDAE) and a syntactic test (object question elicitation). Additionally, the regional cerebral flow was measured with brain single-photon emission computed tomography (SPECT). The results indicated significant impairment in the production of object questions for some patients. Performance on these clauses was not predicted by the ACE-R or by hypoperfusion in the language areas. While there was no evidence for an effect of disease duration on syntactic performance, the results reveal a potential effect of the age of symptom onset. Furthermore, the results reveal a significant association between the performance in BDAE and the years of education and production of object questions. Overall, these results indicate a significant specific deficit in syntax for some patients with svPPA and highlight the importance of the premorbid educational status for the progress of the disease.