bluuebunny/crossref_metadata_2025 huggingface dataset:
Ecological Considerations When Designing Mitigation Translocations: An Australian Reptile Case Study
Authors: Bradley Holly S. , Craig Michael D. , Tomlinson Sean , Cross Adam T. , Bamford Michael J. , Bateman Philip W.
Published: August 2023

Translocation science has made considerable progress over the last two decades; however, reptile translocations still frequently fail around the world. Major knowledge gaps surround the basic ecology of reptile species, including basic factors such as habitat preference, which have a critical influence on translocation success. The western spiny-tailed skink (Egernia stokesii badia) is used here as a case study to exemplify how empirical research can directly inform on-ground management and future translocation planning. A combination of studies, including LiDAR scanning of microhabitat structures, camera trapping, plasticine replica model experiments and unbounded point count surveys to assess predation risk, and visual and DNA analysis of dietary requirements, were all used to better understand the ecological requirements of E. s. badia. We found that the skinks have specific log pile requirements, both native and non-native predator management requirements, and a largely herbivorous, broad diet, which all influence translocation site selection and management planning. The use of E. s. badia as an Australian case study provides a clear strategic framework for the targeted research of meaningful ecological factors that influence translocation decision-making. Similar approaches applied to other reptile species are likely to fundamentally increase the capacity for effective management, and the likelihood of future successful translocations.
Behavioral Welfare Research for the Management of Sanctuary Chimpanzees (Pan troglodytes)
Authors: Fultz Amy , Lewis Rebekah , Kelly Liam , Garbarino Jordan
Published: August 2023

Chimp Haven is a sanctuary for chimpanzees retired from biomedical research, rescued from the pet trade, or re-homed after other organizations could no longer care for them. To provide optimal care for over 300 chimpanzees, Chimp Haven’s animal care team includes experts in behavioral science, veterinary treatment, and husbandry practices. To aid these teams in making routine welfare management decisions, a system of behavioral metrics provides objective data to guide decisions and track outcomes. Chimp Haven has built and piloted seven behavioral metric protocols over the past 5 years to provide staff with an objective and comprehensive picture of the chimpanzees’ behavioral welfare. The data from behavioral observations, staff surveys, and routine staff documentation are analyzed and processed through Google Forms, ZooMonitor, Microsoft Power Bi, Microsoft Excel, and R. Each metric assists staff in making data-based decisions regarding the management of captive chimpanzees related to abnormal behavior, hair loss, wounding, social relationships, positive reinforcement training and overall wellness. In this article, we explore examples of each metric and how they have been utilized to monitor and make decisions for both social groups of chimpanzees as well as individuals. These metrics can be collected and shared easily in an understandable format, which may provide an important framework for others to follow to enable the tracking of welfare for other sanctuaries, non-human primates, as well as other species.
Melatonin Supplementation during In Vitro Maturation of Porcine Oocytes Alleviates Oxidative Stress and Endoplasmic Reticulum Stress Induced by Imidacloprid Exposure
Authors: Wang Jing , Wang Xin-Qin , Liu Rong-Ping , Li Ying-Hua , Yao Xue-Rui , Kim Nam-Hyung , Xu Yong-Nan
Published: August 2023

Imidacloprid (IMI) is an endogenous neonicotinoid insecticide widely used in agriculture and has attracted researchers’ attention because of its risks to the environment and human health. Melatonin (MT) is an antioxidant hormone produced by the pineal gland of the brain. Studies have shown that it has a variety of physiological functions and plays a crucial role in the development of animal germ cells and embryos. The potential protective effects of MT against oocyte damage caused by neonicotinoid pesticide toxicity remain unclear. In this study, we report the toxicity of IMI against, and its effects on the quality of, porcine oocytes and the protective effect of MT on IMI-exposed oocytes. The results show that IMI exposure adversely affected oocyte maturation, while MT supplementation ameliorated its toxic effects. Specifically, IMI exposure increased oxidative stress (OS), endoplasmic reticulum stress (ERS), and apoptosis, which may affect polar body expulsion rates and blastocyst formation. Also, IMI exposure reduced oocyte cleavage rates and the number of cells in blastocysts. However, all of these toxic effects can be restored after a melatonin supplementation treatment. In conclusion, these results suggest that melatonin has a protective effect on IMI-induced defects during porcine oocyte maturation.
Correlation between Targeted qPCR Assays and Untargeted DNA Shotgun Metagenomic Sequencing for Assessing the Fecal Microbiota in Dogs
Authors: Sung Chi-Hsuan , Pilla Rachel , Chen Chih-Chun , Ishii Patricia Eri , Toresson Linda , Allenspach-Jorn Karin , Jergens Albert E. , Summers Stacie , Swanson Kelly S. , Volk Holger , Schmidt Teresa , Stuebing Helene , Rieder Johanna , Busch Kathrin , Werner Melanie , Lisjak Anja , Gaschen Frederic P. , Belchik Sara E. , Tolbert M. Katherine , Lidbury Jonathan A. , Steiner Joerg M. , Suchodolski Jan S.
Published: August 2023

DNA shotgun sequencing is an untargeted approach for identifying changes in relative abundances, while qPCR allows reproducible quantification of specific bacteria. The canine dysbiosis index (DI) assesses the canine fecal microbiota by using a mathematical algorithm based on qPCR results. We evaluated the correlation between qPCR and shotgun sequencing using fecal samples from 296 dogs with different clinical phenotypes. While significant correlations were found between qPCR and sequencing, certain taxa were only detectable by qPCR and not by sequencing. Based on sequencing, less than 2% of bacterial species (17/1190) were consistently present in all healthy dogs (n = 76). Dogs with an abnormal DI had lower alpha-diversity compared to dogs with normal DI. Increases in the DI correctly predicted the gradual shifts in microbiota observed by sequencing: minor changes (R = 0.19, DI < 0 with any targeted taxa outside the reference interval, RI), mild-moderate changes (R = 0.24, 0 < DI < 2), and significant dysbiosis (R = 0.54, 0.73, and 0.91 for DI > 2, DI > 5, and DI > 8, respectively), compared to dogs with a normal DI (DI < 0, all targets within the RI), as higher R-values indicated larger dissimilarities. In conclusion, the qPCR-based DI is an effective indicator of overall microbiota shifts observed by shotgun sequencing in dogs.
Integrated Comparative Transcriptome and circRNA-lncRNA-miRNA-mRNA ceRNA Regulatory Network Analyses Identify Molecular Mechanisms Associated with Intramuscular Fat Content in Beef Cattle
Authors: Dehghanian Reyhan Vahid , Ghafouri Farzad , Sadeghi Mostafa , Miraei-Ashtiani Seyed Reza , Kastelic John P. , Barkema Herman W. , Shirali Masoud
Published: August 2023

Intramuscular fat content (IMF), one of the most important carcass traits in beef cattle, is controlled by complex regulatory factors. At present, molecular mechanisms involved in regulating IMF and fat metabolism in beef cattle are not well understood. Our objective was to integrate comparative transcriptomic and competing endogenous RNA (ceRNA) network analyses to identify candidate messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of longissimus dorsi muscle (LDM) tissue for IMF and fat metabolism of 5 beef cattle breeds (Angus, Chinese Simmental, Luxi, Nanyang, and Shandong Black). In total, 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, 7 key subnets with 16 circRNAs, 43 lncRNAs, 7 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 48, 13, and 28 significantly enriched GO terms related to IMF in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways associated with IMF and fat metabolism that were enriched included metabolic, calcium, cGMP-PKG, thyroid hormone, and oxytocin signaling pathways. Moreover, MCU, CYB5R1, and BAG3 genes were common among the 10 comparative groups defined as important candidate marker genes for fat metabolism in beef cattle. Contributions of transcriptome profiles from various beef breeds and a competing endogenous RNA (ceRNA) regulatory network underlying phenotypic differences in IMF provided novel insights into molecular mechanisms associated with meat quality.
Postmortem Metabolism and Pork Quality Development Are Affected by Electrical Stimulation across Three Genetic Lines
Authors: Spires Matthew D. , Bodmer Jocelyn S. , Beline Mariane , Wicks Jordan C. , Zumbaugh Morgan D. , Shi Tim Hao , Reichert Brian T. , Schinckel Allan P. , Grant Alan L. , Gerrard David E.
Published: August 2023

Variations in postmortem metabolism in muscle impact pork quality development. Curiously, some genetic lines are more refractile to adverse pork quality development than others and may regulate energy metabolism differently. The aim of this study was to challenge pork carcasses from different genetic populations with electrical stimulation (ES) to determine how postmortem metabolism varies with genetic line and explore control points that reside in glycolysis in dying muscle. Three genetic populations (GP) were subjected to ES (100 V or 200 V, 13 pulses, 2 s on/2 s off) at 15- or 25-min post-exsanguination, or no stimulation (NS). Genetic population affected relative muscle relative abundance of different myosin heavy chains, glycogen, G6P, and lactate concentrations. Genetic lines responded similarly to ES, but a comparison of ES treatment groups revealed a trend for an interaction between voltage, time of ES, and time postmortem. Higher voltage accelerated pH decline at 20 min up to 60 min postmortem. Trends in color and firmness scores and L* values were consistent with pH and metabolite data. These data show that genetic populations respond differently to postmortem perturbation by altering glycolytic flux and suggest differences in postmortem glycolysis may be partially responsible for differences in meat quality between genetic populations, though not entirely.
Genomic Characterization of Salmonella Isangi: A Global Perspective of a Rare Serovar
Authors: dos Santos Anamaria Mota Pereira , Panzenhagen Pedro , Ferrari Rafaela G. , de Jesus Ana Carolina S. , Portes Ana Beatriz , Ochioni Alan Clavelland , Rodrigues Dalia dos Prazeres , Conte-Junior Carlos Adam
Published: August 2023

Salmonella Isangi is an infrequent serovar that has recently been reported in several countries due to nosocomial infections. A considerable number of reports indicate Salmonella Isangi multidrug resistance, especially to cephalosporins, which could potentially pose a risk to public health worldwide. Genomic analysis is an excellent tool for monitoring the emergence of microorganisms and related factors. In this context, the aim of this study was to carry out a genomic analysis of Salmonella Isangi isolated from poultry in Brazil, and to compare it with the available genomes from the Pathogen Detection database and Sequence Read Archive. A total of 142 genomes isolated from 11 different countries were investigated. A broad distribution of extended-spectrum beta-lactamase (ESBL) genes was identified in the Salmonella Isangi genomes examined (blaCTX-M-15, blaCTX-M-2, blaDHA-1, blaNDM-1, blaOXA-10, blaOXA-1, blaOXA-48, blaSCO-1, blaSHV-5, blaTEM-131, blaTEM-1B), primarily in South Africa. Resistome analysis revealed predicted resistance to aminoglycoside, sulfonamide, macrolide, tetracycline, trimethoprim, phenicol, chloramphenicol, and quaternary ammonium. Additionally, PMQR (plasmid-mediated quinolone resistance) genes qnr19, qnrB1, and qnrS1 were identified, along with point mutations in the genes gyrAD87N, gyrAS83F, and gyrBS464F, which confer resistance to ciprofloxacin and nalidixic acid. With regard to plasmids, we identified 17 different incompatibility groups, including IncC, Col(pHAD28), IncHI2, IncHI2A, IncM2, ColpVC, Col(Ye4449), Col156, IncR, IncI1(Alpha), IncFIB (pTU3), Col(B5512), IncQ1, IncL, IncN, IncFIB(pHCM2), and IncFIB (pN55391). Phylogenetic analysis revealed five clusters grouped by sequence type and antimicrobial gene distribution. The study highlights the need for monitoring rare serovars that may become emergent due to multidrug resistance.
Dietary Antioxidants and Cardiovascular Health—Editorial Comments and Summary
Authors: Kwaśniewska Magdalena , Waśkiewicz Anna , Drygas Wojciech
Published: August 2023

The influence of dietary antioxidants on health is supported by a large body of evidence [...]
Sleep Deprivation-Induced Oxidative Stress in Rat Models: A Scoping Systematic Review
Authors: Neculicioiu Vlad Sever , Colosi Ioana Alina , Costache Carmen , Toc Dan Alexandru , Sevastre-Berghian Alexandra , Colosi Horațiu Alexandru , Clichici Simona
Published: August 2023

Sleep deprivation is highly prevalent in the modern world, possibly reaching epidemic proportions. While multiple theories regarding the roles of sleep exist (inactivity, energy conservation, restoration, brain plasticity and antioxidant), multiple unknowns still remain regarding the proposed antioxidant roles of sleep. The existing experimental evidence is often contradicting, with studies pointing both toward and against the presence of oxidative stress after sleep deprivation. The main goals of this review were to analyze the existing experimental data regarding the relationship between sleep deprivation and oxidative stress, to attempt to further clarify multiple aspects surrounding this relationship and to identify current knowledge gaps. Systematic searches were conducted in three major online databases for experimental studies performed on rat models with oxidative stress measurements, published between 2015 and 2022. A total of 54 studies were included in the review. Most results seem to point to changes in oxidative stress parameters after sleep deprivation, further suggesting an antioxidant role of sleep. Alterations in these parameters were observed in both paradoxical and total sleep deprivation protocols and in multiple rat strains. Furthermore, the effects of sleep deprivation seem to extend beyond the central nervous system, affecting multiple other body sites in the periphery. Sleep recovery seems to be characterized by an increased variability, with the presence of both normalizations in some parameters and long-lasting changes after sleep deprivation. Surprisingly, most studies revealed the presence of a stress response following sleep deprivation. However, the origin and the impact of the stress response during sleep deprivation remain somewhat unclear. While a definitive exclusion of the influence of the sleep deprivation protocol on the stress response is not possible, the available data seem to suggest that the observed stress response may be determined by sleep deprivation itself as opposed to the experimental conditions. Due to this fact, the observed oxidative changes could be attributed directly to sleep deprivation.
Synergistic Protective Effect of Fermented Schizandrae Fructus Pomace and Hoveniae Semen cum Fructus Extracts Mixture in the Ethanol-Induced Hepatotoxicity
Authors: Jegal Kyung-Hwan , Park Hye-Rim , Choi Beom-Rak , Kim Jae-Kwang , Ku Sae-Kwang
Published: August 2023

Schizandrae Fructus (SF), fruits of Schisandra chinensis (Turcz.) Baill. and Hoveniae Semen cum Fructus (HSCF), the dried peduncle of Hovenia dulcis Thunb., have long been used for alcohol detoxification in the traditional medicine of Korea and China. In the current study, we aimed to evaluate the potential synergistic hepatoprotective effect of a combination mixture (MSH) comprising fermented SF pomace (fSFP) and HSCF hot water extracts at a 1:1 (w:w) ratio against ethanol-induced liver toxicity. Subacute ethanol-mediated hepatotoxicity was induced by the oral administration of ethanol (5 g/kg) in C57BL/6J mice once daily for 14 consecutive days. One hour after each ethanol administration, MSH (50, 100, and 200 mg/kg) was also orally administered daily. MSH administration significantly reduced the serum activities of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and γ-glutamyl transpeptidase. Histological observation indicated that MSH administration synergistically and significantly decreased the fatty changed region of hepatic parenchyma and the formation of lipid droplet in hepatocytes. Moreover, MSH significantly attenuated the hepatic triglyceride accumulation through reducing lipogenesis genes expression and increasing fatty acid oxidation genes expression. In addition, MSH significantly inhibited protein nitrosylation and lipid peroxidation by lowering cytochrome P450 2E1 enzyme activity and restoring the glutathione level, superoxide dismutase and catalase activity in liver. Furthermore, MSH synergistically decreased the mRNA level of tumor necrosis factor-α in the hepatic tissue. These findings indicate that MSH has potential for preventing alcoholic liver disease through inhibiting hepatic steatosis, oxidative stress, and inflammation.
From Plants to Psycho-Neurology: Unravelling the Therapeutic Benefits of Bioactive Compounds in Brain Disorders
Authors: Grosso Clara , Santos Marlene , Barroso M. Fátima
Published: August 2023

The brain’s sensitivity to oxidative stress and neuronal cell death requires effective pharmacotherapy approaches. Current pharmacological therapies are frequently ineffective and display negative side effects. Bioactive chemicals found in plants may provide a potential alternative due to their antioxidant and neuroprotective properties and can be used in therapy and the management of a variety of neuropsychiatric, neurodevelopmental, and neurodegenerative illnesses. Several natural products, including vitamin C, Cammelia sinensis polyphenols, Hypericum perforatum, and Crocus sativus have shown promise in lowering oxidative stress and treating symptoms of major depressive disorder (MDD). Similarly, bioactive compounds such as curcumin, luteolin, resveratrol, quercetin, and plants like Acorus gramineus, Rhodiola rosea, and Ginkgo biloba are associated with neuroprotective effects and symptom improvement in neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Furthermore, in neurodegenerative diseases, natural compounds from Rhodiola rosea, Morinda lucida, and Glutinous rehmannia provide neurological improvement. Further study in clinical samples is required to thoroughly investigate the therapeutic advantages of these bioactive substances for persons suffering from these illnesses.
Deriving Ground Truth Labels for Regression Problems Using Annotator Precision
Authors: Johnston Benjamin , de Chazal Philip
Published: August 2023

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there is currently no effective method for determining a single ground truth value or landmark from a set of annotations in regression problems. We propose a novel method for deriving ground truth labels in regression problems that considers the performance and precision of individual annotators when identifying each label separately. In contrast to the commonly accepted method of computing the global mean, our method does not assume each annotator to be equally capable of completing the specified task, but rather ensures that higher-performing annotators have a greater contribution to the final result. The ground truth selection method described within this paper provides a means of improving the quality of input data for machine learning model development by removing lower-quality labels. In this study, we objectively demonstrate the improved performance by applying the method to a simulated dataset where a canonical ground truth position can be known, as well as to a sample of data collected from crowd-sourced labels.
An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
Authors: Jing Liuming , Xia Lei , Zhao Tong , Zhou Jinghua
Published: August 2023

The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization.
Numerical Simulation and Field Test of a PDC Bit with Mixed Cutter Arrangement to Break Non-Homogeneous Granite
Authors: Wu Zebing , Yuan Ruofei , Zhang Wenxi , Hu Shiyao , Jiang Wen
Published: August 2023

As the depth of petroleum drilling increases, the strata environment becomes more complex. The efficiency and lifespan of Polycrystalline Diamond Compact (PDC) drill bits fail to meet current drilling demands. However, the structure and arrangement of PDC cutters are valuable determinants of drilling efficiency, although related research still has gaps and deficiencies. This study focuses on PDC cutters in axe, triangular prism, and circular forms. It establishes an inhomogeneous granite model based on the actual measurements of granite and verifies the accuracy of this model through uniaxial compression simulation. Finite element models of three types of cutters in various combination schemes are constructed to examine rock-breaking effects, with the best scheme optimized using Box-Behnken response surface methodology. The rock-breaking process of the optimal PDC drill bit layout has been compared to that of a single cutter bit. Field drilling has demonstrated the effectiveness of a mixed cutter arrangement. The results show that the inhomogeneous granite model can be trusted. The optimal arrangement involves axe cutters in the front row and an alternate arrangement of triangular prism cutters and axe cutters in the back row. The optimal lateral and longitudinal distances for the triangular cutters from the front row of axe cutters are 10 mm and 7 mm, respectively, while those for the back row of axe cutters from the front row are 10.06 mm and 7 mm, respectively. The ROP standard deviation in the drilling process of mixed cutter bits decreases by 53.06% and 43.08% compared to axe and triangular prism cutter bits, respectively. The drilling efficiency increases by 16.8% and 16.6%, respectively, demonstrating higher efficiency and stability. Field drilling results indicate that a mixed cutter bit increases efficiency by 23.5% compared to a bit with only triangular prism cutters. This study posits that research on the combination schemes and parameters of PDC cutters can significantly enhance drilling efficiency, thereby reducing the drilling cycle and costs.
Modeling Dynamic Hysteresis Curves in Amorphous Magnetic Ribbons
Authors: Chwastek Krzysztof , Najgebauer Mariusz , Jabłoński Paweł , Szczegielniak Tomasz , Kusiak Dariusz , Koprivica Branko , Rosić Marko , Divac Srđan
Published: August 2023

A description of magnetic hysteresis is important for the prediction of losses in soft magnetic materials. In this paper, a viscosity-type equation is used to describe dynamic hysteresis loops in an amorphous ring core for symmetric excitation, as prescribed by international standards. The value of the exponent appearing in the viscosity-based equation can be assumed to be constant if the maximum induction is away from the saturation value. The viscosity-type equation is used to describe the shape variation of magnetization curves due to eddy currents in different time and space scales. Modeling is carried out for various excitation frequencies and induction amplitudes. The discrepancies between the experimental and modeled curves (and also losses) are acceptable in the wide range of the frequency and maximum induction. The paper indicates that the viscosity-type effects, mostly due to eddy currents generated in the conductive material, play an important role in energy dissipation at increased excitation frequencies. The modeling results might be interesting to the designers of magnetic circuits.
A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning
Authors: Tong Dawei , Wu Haifeng , Liu Changxin , Guo Zhangchao , Li Pei
Published: August 2023

Multiple ducts in the working shaft and main body of tunnels form a combined tee structure. An efficient and accurate prediction method for the local resistance coefficient is the key to the design and optimization of the maintenance ventilation scheme. However, most existing studies use numerical simulations and model experiments to analyze the local resistance characteristics of specific structures and calculate the local resistance coefficient under specific ventilation conditions. Therefore, there are shortcomings of low efficiency and high cost in the ventilation scheme optimization when considering the influence of the local resistance. This paper proposes a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation based on machine learning. The hybrid prediction model introduces the hybrid kernel into a relevance vector machine to build the hybrid kernel relevance vector machine model (HKRVM). The improved artificial jellyfish search algorithm (IAJS), which utilizes Fuch chaotic mapping, lens-imaging reverse learning, and adaptive hybrid mutation strategies to improve the algorithm performance, is applied to the kernel parameter optimization of the HKRVM model. The results of a case study show that the method proposed in this paper can achieve the efficient and accurate prediction of the local resistance coefficient of maintenance ventilation and improve the prediction accuracy and prediction efficiency to a certain extent. The method proposed in this paper provides a new concept for the prediction of the ventilation local resistance coefficient and can further provide an efficient prediction method for the design and optimization of maintenance ventilation schemes.
SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
Authors: Li Yuanrui , Xiao Liping , Liu Zhaopeng , Liu Muhua , Fang Peng , Chen Xiongfei , Yu Jiajia , Liu Junan , Cai Jinping
Published: August 2023

As a highly productive rice, ratoon rice is widely planted worldwide, but the rolling of rice stubble in mechanical harvesting severely limits its total yield; based on this, some scholars have proposed rolled rice stubble righting machines. However, limited by the uncertainty of the field environment, the machine’s localization accuracy of the target needs to be improved. To address this problem, real-time detection of rolled rice stubble rows is a prerequisite. Therefore, this paper introduces a deep learning method for the first time to achieve this. To this end, we presented a novel approach to improve a model that is used for the simplification of Mask R-CNN, which does not require any modules to be added or replaced on the original model. Firstly, two branches in the second stage were deleted, and the region proposals output from the stage was used directly as the mask generation region, and segmentation performance was substantially improved after a simple optimization of the region proposals. Further, the contribution of the feature map was counted, and the backbone network was simplified accordingly. The resulting SMR-RS model was still able to perform instance segmentation and has better segmentation performance than Mask R-CNN and other state-of-the-art models while significantly reducing the average image processing time and hardware consumption.
Implementing a Digital Twin of an Underground Utility Tunnel for Geospatial Feature Extraction Using a Multimodal Image Sensor
Authors: Lee Jaewook , Lee Yonghwan , Park Sangmi , Hong Changhee
Published: August 2023

The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a digital twin of a subterranean utility tunnel was created, and spatial objects were identified using inbuilt image sensors. The novelty lies in the development of a unique algorithm that breaks down the structure of the utility tunnel into points, lines, and planes, identifying objects using a multimodal image sensor that incorporates light detection and ranging (LiDAR) technology. The three main conclusions of this study are the following: First, a digital twin of the utility tunnel was constructed using building information modeling integrated with a geographic information system (BIM-GIS). Second, a method for extracting spatial objects was defined. Third, image-sensor-based segmentation and a random sample consensus (RANSAC) were applied. In this process, the supplementary algorithm for extracting and updating 3D spatial objects was analyzed and improved. The developed algorithm was tested using point cloud data, showing easier object classification with more precise LiDAR data.
Noise Reduction Based on Improved Variational Mode Decomposition for Acoustic Emission Signal of Coal Failure
Authors: Jing Gang , Zhao Yixin , Gao Yirui , Marin Montanari Pedro , Lacidogna Giuseppe
Published: August 2023

Acoustic emission (AE) signal processing and interpretation are essential in mining engineering to acquire source information about AE events. However, AE signals obtained from coal mine monitoring systems often contain nonlinear noise, limiting the effectiveness of conventional analysis methods. To address this issue, a novel denoising approach using enhanced variational mode decomposition (VMD) and fuzzy entropy is proposed in this study. The denoised AE signal’s spectral multifractal features are analyzed. The optimization algorithm based on VMD with a weighted frequency index is introduced to avoid mode mixing and outperform other decomposition methods. The characteristic parameter Δα of the AE spectral multifractal parameter serves as an early warning indicator of coal instability. These findings contribute to the accurate extraction of time–frequency features and provide insights for on-site AE signal processing.
Performance Assessment of Bias Correction Methods for Precipitation and Temperature from CMIP5 Model Simulation
Authors: Londhe Digambar S. , Katpatal Yashwant B. , Bokde Neeraj Dhanraj
Published: August 2023

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov–Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data.
Increasing the Wear Resistance of Structural Alloy Steel 38CrNi3MoV Subjected to Isothermal Hardening and Deep Cryogenic Treatment
Authors: Bobyr Serhii , Krot Pavlo , Parusov Eduard , Golubenko Tetiana , Baranovs’ka Olena
Published: August 2023

In the production of critical parts for various machines and mechanisms, expensive structural steels are used alloyed with chromium, nickel, molybdenum, and vanadium. In practice, the wear resistance of parts, especially under severe operating conditions, may be insufficient due to uneven microstructure and the content of retained austenite. Therefore, increasing the operational stability of various products made of alloy steels is an important task. The purpose of this work is to investigate the effect of isothermal hardening from the intermediate (γ+α)-area and the duration of deep cryogenic treatment on the structure formation and frictional wear resistance of 38CrNi3MoV steel. The isothermal hardening promotes the formation of the required multiphase microstructure of 38CrNi3MoV steel. The influence of the duration of deep cryogenic treatment on the microhardness, amount of retained austenite, fine structure parameters, and friction wear of 38CrNi3MoV steel are established. Complex heat treatment of 38CrNi3MoV steel, according to the proposed mode, makes it possible to achieve a significant decomposition of retained austenite to martensite, which leads to an increase in frictional wear resistance of ~58%.
Film Boiling around a Finite Size Cylindrical Specimen—A Transient Conjugate Heat Transfer Approach
Authors: Cukrov Alen , Sato Yohei , Boras Ivanka , Ničeno Bojan
Published: August 2023

The DNS of film boiling requires strong computational resources that are difficult to obtain for daily CFD use by expert practitioners of industrial R&D. On the other hand, film boiling experiments are associated with the usage of expensive and highly sophisticated apparatus, and research to this end is relatively difficult due to high heat flow rates that are present in the process itself. When combined with transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid is proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, turbulent flow was determined as an important parameter in obtaining an accurate solution to temperature field evolution in a solid specimen, via the proper modeling of the turbulent kinetic energy (TKE) value, that was imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin–Helmholtz instability theory in conjunction with boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10% of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient matched the error bands prescribed by the authors of this paper, which presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30% higher. Further inspection revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method shows full capability for further pursuing industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach.
An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application
Authors: Xu Jianliang , Xu Feng , Wang Yulong , Sui Zhen
Published: August 2023

In order to enhance the performance of model-free adaptive control (MFAC) in solving the control problem caused by interference and improve the tracking speed, this paper focuses on the analysis and research of the system affected by interference using the MFAC method. This method is based on dynamic linearization technology, with system data which are represented by a full format dynamic linearization (FFDL) model that is very similar to actual industrial processes. In this work, a control law is derived by incorporating and assigning weights to both the output error and the output error rate (OER) as the performance index. Rigorous proofs are provided to establish convergence and stability. Considering the inherent complexity of actual systems, this paper also presents the MFAC-OER scheme for multiple-input–multiple-output (MIMO) systems. Furthermore, the effectiveness and practicality of the improved control strategy are evaluated through numerical arithmetic examples and control processes involving water level regulation in a circulating fluidized bed (CFB). Comparisons with conventional MFAC and PID control methods show that the enhanced control method is capable of quickly and accurately tracking the desired signal. Additionally, it exhibits superior anti-interference characteristics and is able to respond in a timely manner to changes in the operating conditions of the circulating fluidized bed system. As a result, it ensures the normal operation of the coal saver and water supply pipe without damage.
Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey
Authors: Rauch Lukas , Braml Thomas
Published: August 2023

Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.
Design and Experiment of the Combined Machine for Transplanting Outcrop of Codonopsis with Micro Ridge Covered with Film
Authors: Shi Binghong , Sun Wei , Zhao Zhiwei , Wang Hucun , Zhang Luhai , Zhang Hua , Li Hui , Liu Xiaolong , Liu Pengxia
Published: August 2023

In response to the problem of no supporting equipment for the cultivation of Codonopsis in the hilly and mountainous areas of northwest China, a combined machine for transplanting outcrop of Codonopsis with micro ridges covered with film is designed. The key components of the prototype are analyzed and designed, and the structures and working parameters of the seedbed preparation device, seedling-casting device, rotary tillage soil-covering device, film-covering device, seedling head burial, and film edge soil-covering device are determined. The transmission system scheme is established, and the working mechanism of the core components is analyzed. Field experiments show that when the target seedling spacing is 4.4 cm and the machine moves forward at a speed of 0.1, 0.15, and 0.2 m/s, the variation coefficient of planting spacing and the qualification rate of planting depth meet the standard requirements. The qualified rate of planting posture and film side outcrop are greatly affected by the operating speed of the machine and decrease with the increase in operating speed. When the operating speed reaches 0.1 m/s, the average variation coefficient of planting spacing is 0.08% and the average qualified rate of planting depth, planting posture and film side outcrop is 95.83%, 94.17%, and 93.33%, respectively, which shows that the operating performance is better than that of the operating speeds of 0.15 m/s and 0.2 m/s. This study provides a new reference for the theoretical research and design of mechanized and automated transplanting machinery for Codonopsis seedlings.
Analysis of the Design of Henry Muncaster’s Two-Cylinder Compound Vertical Steam Engine with Speed Control
Authors: Rojas-Sola José Ignacio , Gutiérrez-Antúnez José Francisco
Published: August 2023

This article offers an analysis, from the mechanical engineering viewpoint, of an invention by Henry Muncaster from 1912: the two-cylinder compound vertical steam engine with speed control. This is an invention with a large number of components (106) that was used as an engine in boats and railways. The ultimate objective of this investigation was to determine the operating conditions (maximum pressures of water vapor in the admission of high- and low-pressure cylinders) according to the criteria of resistance of materials since there is no information about this. Therefore, two critical operating conditions were simulated that resemble the start-up of the machine (flywheel locked as the most unfavorable situation) in order to determine those operating conditions that ensure both its safety and optimal operation. For this, a static linear analysis based on the finite element method (FEM) of the 3D CAD model was carried out under real operating conditions, according to the criteria of resistance of materials, using the Autodesk Inventor Nastran 2023 software. The results of the static linear analysis (von Mises stress, displacement and safety factor) confirmed the maximum values of the vapor pressure in the admission of the cylinders: 0.3 MPa on the high-pressure piston plunger and 0.15 MPa on the low-pressure piston plunger.
Electrophysical Method for Identifying the Causes of Excessive Hydro Generator Vibration
Authors: Karpov Aleksei S. , Yaroshevich Vera V. , Fastiy Galina P. , Gubskaya Elizaveta I.
Published: August 2023

This article analyzes the operation of hydro generators based on the operation modes of a hydroelectric power plant operating in the Arctic zone of the Russian Federation. The main load of the hydropower plant is an aluminum smelter. The nonlinear load of the smelter is a powerful source of harmonic disturbances. This load produces current and voltage distortions not only in the electrical networks of the smelter but also in those of the city and in generating voltage busbars of the hydropower plant. As a consequence, higher harmonics cause additional losses in the supply networks and have a negative impact on the hydro generators’ condition. The article analyzes the influence of current impacts of the smelter load on the hydro generators under imbalanced conditions by estimating torsional and tangential vibrations emerging in the generators. The tangential forces of double frequency (100 Hz) have been shown to produce practically no significant vibration displacement when detuning the natural frequency of the basket-type end-winding parts. The values of vibration and surge displacement increase significantly in the near-resonance zone. The intense impact of superimposed surge currents has been shown to result in reduction in the hydraulic turbines’ service life and increase in frequency of repairs.
Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control
Authors: Kołota Jakub , Kargin Turhan Can
Published: August 2023

Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact of various reward functions on the performance of the RL algorithm. The RL algorithm utilized in this study is the Deep Deterministic Policy Gradient (DDPG), which can be applied to both continuous-state and continuous-action problems. The study’s findings reveal that the design of the RL environment, including the selection of reward functions, significantly influences the performance of the RL algorithm. The study provides significant information on the design of RL environments for the control of continuum robots, which may be valuable to researchers and practitioners in the field of robotics and control systems.
Fractional Calculus-Based Statistical Damage Model of Unsaturated Soil under the Coupling Effect of Moistening and Stress Fields
Authors: Zhang Hua , Wang Peng
Published: August 2023

Unsaturated soil exhibits extremely complex engineering mechanical properties under the coupling effect of moistening and stress fields. Firstly, the effective stress principle and limit equilibrium conditions of unsaturated soil under the coupling effect of moistening and stress fields were discussed based on the basic principles of unsaturated soil. Secondly, a fractional-order model considering the viscoelasticity and strain hardening of unsaturated soil was established based on the fractional calculus theory. Then, based on the principle of damage mechanics, the damage variable evolution equation under the coupling effect of moistening and stress fields was established, and the fractional calculus-based statistical constitutive damage model of unsaturated soil under the coupling effect of moistening and stress fields was developed. In turn, parameters of the developed model were solved using a triaxial test of unsaturated loess, and the calculated data using the developed model were compared with the experimental data, which demonstrated that the developed model in this paper performed well in describing the whole strain hardening process of unsaturated soil under the coupling effect of moistening and stress fields. Finally, the sensitivity of the main parameters of the developed model was discussed under the coupling effect of moistening and stress fields, which showed that the proposed model performed well in reflecting the main mechanical properties of unsaturated loess.