bluuebunny/crossref_metadata_2025 huggingface dataset:
Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
Authors: Li Yuanhong , Liao Jiapeng , Wang Jing , Luo Yangfan , Lan Yubin
Published: September 2023

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters.
Genome-Wide Association Study on the Content of Nucleotide-Related Compounds in Korean Native Chicken Breast Meat
Authors: Kim Minjun , Munyaneza Jean Pierre , Cho Eunjin , Jang Aera , Jo Cheorun , Nam Ki-Chang , Choo Hyo Jun , Lee Jun Heon
Published: September 2023

Meat flavor is an important factor that influences the palatability of chicken meat. Inosine 5′-monophosphate (IMP), inosine, and hypoxanthine are nucleic acids that serve as taste-active compounds, mainly enhancing flavor in muscle tissue. For this study, we performed a genome-wide association study (GWAS) using a mixed linear model to identify single-nucleotide polymorphisms (SNPs) that are significantly associated with changes in the contents of the nucleotide-related compounds of breast meat in the Korean native chicken (KNC) population. The genomic region on chicken chromosome 5 containing an SNP (rs316338889) was significantly (p < 0.05) associated with all three traits. The trait-related candidate genes located in this significant genomic region were investigated through performing a functional enrichment analysis and protein–protein interaction (PPI) database search. We found six candidate genes related to the function that possibly affected the content of nucleotide-related compounds in the muscle, namely, the TNNT3 and TNNT2 genes that regulate muscle contractions; the INS, IGF2, and DUSP8 genes associated with insulin sensitivity; and the C5NT1AL gene that is presumably related to the nucleotide metabolism process. This study is the first of its kind to find candidate genes associated with the content of all three types of nucleotide-related compounds in chicken meat using GWAS. The candidate genes identified in this study can be used for genomic selection to breed better-quality chickens in the future.
Effects of Lavender Essential Oil Inhalation on the Welfare and Meat Quality of Fattening Heavy Pigs Intended for Parma Ham Production
Authors: Nannoni Eleonora , Martelli Giovanna , Scozzoli Maurizio , Belperio Simona , Buonaiuto Giovanni , Vannetti Niccolò Ian , Truzzi Eleonora , Rossi Enrico , Benvenuti Stefania , Sardi Luca
Published: September 2023

We assessed the effects of inhalation administration of lavender essential oil (LEO) either once (L1) or twice (L2) a day on animal welfare indicators, carcass and meat quality of Italian heavy pigs. Pigs (n = 108) were allotted to three experimental groups (control -C-, L1 and L2) and lavender was administered, via a vaporizer device, to the treated groups during the entire fattening–finishing period (79–160 kg BW). Tail lesion severity was reduced in L1 at the end of the trial compared to the other groups (p < 0.05). Body lesion counts, however, were higher in L2 than in C (p < 0.05), resulting in a more severe overall damage classification (p < 0.01). At slaughter, no differences were observed in carcass traits or blood stress indicators, only minor differences were observed in meat quality, and no LEO residual was found in fat or lean tissues, highlighting the preserved suitability of thighs for the dry curing process. While it was not possible to conclude on the ability to improve animal welfare of vaporized LEO in this production phase, the absence of adverse effects on meat quality and the discrepancies observed regarding the body lesions in L1 and L2 make further studies on behavioral aspects and the method of administration (route, frequency) of the product desirable.
Effect of Dietary Selenium on the Growth and Immune Systems of Fish
Authors: Sumana Sahr Lamin , Chen Huangen , Shui Yan , Zhang Chengfeng , Yu Fan , Zhu Jian , Su Shengyan
Published: September 2023

Dietary selenium (Se) is an essential component that supports fish growth and the immune system. This review attempts to provide insight into the biological impacts of dietary Se, including immunological responses, infection defense, and fish species growth, and it also identifies the routes via which it enters the aquatic environment. Dietary Se is important in fish feed due to its additive, antioxidant, and enzyme properties, which aid in various biological processes. However, excessive intake of it may harm aquatic ecosystems and potentially disrupt the food chain. This review explores the diverse natures of dietary Se, their impact on fish species, and the biological methods for eliminating excesses in aquatic environments. Soil has a potential role in the distribution of Se through erosion from agricultural, industrial, and mine sites. The research on dietary Se’s effects on fish immune system and growth can provide knowledge regarding fish health, fish farming strategies, and the health of aquatic ecosystems, promoting the feed industry and sustainable aquaculture. This review provides data and references from various research studies on managing Se levels in aquatic ecosystems, promoting fish conservation, and utilizing Se in farmed fish diets.
Primary Feline Tauopathy: Clinical, Morphological, Immunohistochemical, and Genetic Studies
Authors: Vidal-Palencia Laura , Font Cristina , Rebollada-Merino Agustín , Santpere Gabriel , Andrés-Benito Pol , Ferrer Isidro , Pumarola Martí
Published: September 2023

Tauopathies are a group of neurodegenerative diseases characterized by the pathological aggregation of hyperphosphorylated tau in neurons and glia. Primary tauopathies are not uncommon in humans but exceptional in other species. We evaluate the clinical, neuropathological, and genetic alterations related to tau pathology in 16 cats aged from 1 to 21 years with different clinical backgrounds. Interestingly, a 10-year-old female cat presented a six-year progressive history of mental status and gait abnormalities. The imaging study revealed generalized cortical atrophy. Due to the poor prognosis, the cat was euthanatized at the age of ten. Neuropathological lesions were characterized by massive neuronal loss with marked spongiosis and associated moderate reactive gliosis in the parietal cortex, being less severe in other areas of the cerebral cortex, and the loss of Purkinje cells of the cerebellum. Immunohistochemical methods revealed a 4R-tauopathy with granular pre-tangles in neurons and coiled bodies in oligodendrocytes. Deposits were recognized with several phospho-site antibodies (4Rtau, tau5, AT8, PFH, tau-P Thr181, tau-P-Ser 262, tau-P Ser 422) and associated with increased granular expression of active tau kinases (p38-P Thr180/Tyr182 and SAPK/JNK-P Thr138/Thr185). The genetic study revealed well-preserved coding regions of MAPT. No similar alterations related to tau pathology were found in the other 15 cats processed in parallel. To our knowledge, this is the first case reporting a primary 4R-tauopathy with severe cerebral and Purkinje cell degeneration in an adult cat with neurological signs starting at a young age.
Comparison between Neutralization Capacity of Antibodies Elicited by COVID-19 Natural Infection and Vaccination in Indonesia: A Prospective Cohort
Authors: Nurisyah Sitti , Iyori Mitsuhiro , Hasyim Ammar Abdurrahman , Sakamoto Akihiko , Hashimoto Hinata , Yamagata Kyouhei , Yamauchi Saya , Amru Khaeriah , Zainal Kartika Hardianti , Idris Irfan , Yoshida Shigeto , Djaharuddin Irawaty , Syafruddin Din , Bukhari Agussalim , Asih Puji Budi Setia , Yusuf Yenni
Published: September 2023

Background: To fight the COVID-19 pandemic, immunity against SARS-CoV-2 should be achieved not only through natural infection but also by vaccination. The effect of COVID-19 vaccination on previously infected persons is debatable. Methods: A prospective cohort was undergone to collect sera from unvaccinated survivors and vaccinated persons—with and without COVID-19 pre-infection. The sera were analyzed for the anti-receptor binding domain (RBD) titers by ELISA and for the capacity to neutralize the pseudovirus of the Wuhan-Hu-1 strain by luciferase assays. Results: Neither the antibody titers nor the neutralization capacity was significantly different between the three groups. However, the correlation between the antibody titers and the percentage of viral neutralization derived from sera of unvaccinated survivors was higher than that from vaccinated persons with pre-infection and vaccinated naïve individuals (Spearman correlation coefficient (r) = −0.8558; 95% CI, −0.9259 to −0.7288), p < 0.0001 vs. −0.7855; 95% CI, −0.8877 to −0.6096, p < 0.0001 and −0.581; 95% CI, −0.7679 to −0.3028, p = 0.0002, respectively), indicating the capacity to neutralize the virus is most superior by infection alone. Conclusions: Vaccines induce anti-RBD titers as high as the natural infection with lower neutralization capacity, and it does not boost immunity in pre-infected persons.
Poor Sensitivity of the MALDI Biotyper® MBT Subtyping Module for Detection of Klebsiella pneumoniae Carbapenemase (KPC) in Klebsiella Species
Authors: Cuello Luz , Alvarez Otero Judith , Greenwood-Quaintance Kerryl E. , Chen Liang , Hanson Blake , Reyes Jinnethe , Komarow Lauren , Ge Lizhao , Lancaster Zane D. , Gordy Garrett G. , Schuetz Audrey N. , Patel Robin
Published: September 2023

Rapid detection of Klebsiella pneumoniae carbapenemase (KPC) in the Klebsiella species is desirable. The MALDI Biotyper® MBT Subtyping Module (Bruker Daltonics) uses an algorithm that detects a peak at ~11,109 m/z corresponding to a protein encoded by the p019 gene to detect KPC simultaneously with organism identification by a matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-ToF MS). Here, the subtyping module was evaluated using 795 clinical Klebsiella isolates, with whole genome sequences used to assess for blaKPC and p019. For the isolates identified as KPC positive by sequencing, the overall sensitivity of the MALDI-ToF MS subtyping module was 239/574 (42%) with 100% specificity. For the isolates harboring p019, the subtyping module showed a sensitivity of 97% (239/246) and a specificity of 100%. The subtyping module had poor sensitivity for the detection of blaKPC-positive Klebsiella isolates, albeit exhibiting excellent specificity. The poor sensitivity was a result of p019 being present in only 43% of the blaKPC-positive Klebsiella isolates.
Chiisanoside Mediates the Parkin/ZNF746/PGC-1α Axis by Downregulating MiR-181a to Improve Mitochondrial Biogenesis in 6-OHDA-Caused Neurotoxicity Models In Vitro and In Vivo: Suggestions for Prevention of Parkinson’s Disease
Authors: Hsu Yu-Ling , Chen Hui-Jye , Gao Jia-Xin , Yang Ming-Yang , Fu Ru-Huei
Published: September 2023

The degeneration of dopamine (DA) neurons is known to be associated with defects in mitochondrial biogenesis caused by aging, environmental factors, or mutations in genes, leading to Parkinson’s disease (PD). As PD has not yet been successfully cured, the strategy of using small molecule drugs to protect and restore mitochondrial biogenesis is a promising direction. This study evaluated the efficacy of synthetic chiisanoside (CSS) identified in the leaves of Acanthopanax sessiliflorus to prevent PD symptoms. The results show that in the 6-hydroxydopamine (6-OHDA) model, CSS pretreatment can effectively alleviate the reactive oxygen species generation and apoptosis of SH-SY5Y cells, thereby lessening the defects in the C. elegans model including DA neuron degeneration, dopamine-mediated food sensitivity behavioral disorders, and shortened lifespan. Mechanistically, we found that CSS could restore the expression of proliferator-activated receptor gamma coactivator-1-alpha (PGC-1α), a key molecule in mitochondrial biogenesis, and its downstream related genes inhibited by 6-OHDA. We further confirmed that this is due to the enhanced activity of parkin leading to the ubiquitination and degradation of PGC-1α inhibitor protein Zinc finger protein 746 (ZNF746). Parkin siRNA treatment abolished this effect of CSS. Furthermore, we found that CSS inhibited 6-OHDA-induced expression of miR-181a, which targets parkin. The CSS’s ability to reverse the 6-OHDA-induced reduction in mitochondrial biogenesis and activation of apoptosis was abolished after the transfection of anti-miR-181a and miR-181a mimics. Therefore, the neuroprotective effect of CSS mainly promotes mitochondrial biogenesis by regulating the miR-181a/Parkin/ZNF746/PGC-1α axis. CSS potentially has the opportunity to be developed into PD prevention agents.
Antimicrobial, Antioxidant, and Anti-Inflammatory Properties of Monofloral Honeys from Chile
Authors: Poulsen-Silva Erick , Gordillo-Fuenzalida Felipe , Velásquez Patricia , Llancalahuen Felipe M. , Carvajal Rodrigo , Cabaña-Brunod Mauricio , Otero María Carolina
Published: September 2023

Honey is a mixture of compounds produced by bees that has been appreciated by humanity since the creation of the oldest civilizations. It has multiple uses and can be a highly nutritional and healing substance. It has been used in traditional medicine as a natural alternative for the treatment of diverse clinical conditions. This is due to its reported bioactive properties. The objective of this article is to exhibit and analyze the biological properties of different types of honey originating from Chile based on their antimicrobial, antioxidant, and anti-inflammatory activities, focusing primarily on recompiling experimental studies made on monofloral honey of plant species present in the Chilean territory. The result of this bibliographical review shows that Chilean honey possesses remarkable bioactive properties, mainly antimicrobial and antioxidant activities, with a few studies on its anti-inflammatory activity. Most of these results were attributed to monofloral honey belonging to ulmo (Eucryphia cordifolia) and quillay (Quillaja saponaria Molina) plant species. These properties are related to the presence of several bioactive components, such as phenolic components (mainly flavonoids), hydrogen peroxide (H2O2), enzymes, proteins, and carbohydrates. The biodiversity of the flora and the environmental conditions of the Chilean territory are responsible for the wide range of bioactive compounds and biological properties found in Chilean honey. Further studies must be made to uncover the medicinal potential of these native honeys.
Influence of Reactive Oxygen Species on Wound Healing and Tissue Regeneration in Periodontal and Peri-Implant Tissues in Diabetic Patients
Authors: Buranasin Prima , Kominato Hiromi , Mizutani Koji , Mikami Risako , Saito Natsumi , Takeda Kohei , Iwata Takanori
Published: September 2023

Diabetes mellitus (DM) is associated with periodontal disease. Clinically, periodontal treatment is less effective for patients with DM. Oxidative stress is one of the mechanisms that link DM to periodontitis. The production of reactive oxygen species (ROS) is increased in the periodontal tissues of patients with DM and is involved in the development of insulin resistance in periodontal tissues. Insulin resistance decreases Akt activation and inhibits cell proliferation and angiogenesis. This results in the deterioration of wound healing and tissue repair in periodontal tissues. Antioxidants and insulin resistance ameliorants may inhibit ROS production and improve wound healing, which is worsened by DM. This manuscript provides a comprehensive review of the most recent basic and clinical evidence regarding the generation of ROS in periodontal tissues resulting from microbial challenge and DM. This study also delves into the impact of oxidative stress on wound healing in the context of periodontal and dental implant therapies. Furthermore, it discusses the potential benefits of administering antioxidants and anti-insulin resistance medications, which have been shown to counteract ROS production and inflammation. This approach may potentially enhance wound healing, especially in cases exacerbated by hyperglycemic conditions.
Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study
Authors: Kim Kwangseob , Lee Kiwon
Published: September 2023

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.
A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression
Authors: Li Haoxuan , Shen Zhenzhong , Sun Yiqing , Wu Yijun , Xu Liqun , Shu Yongkang , Tan Jiacheng
Published: September 2023

Seepage is the primary cause of dam failures. Conducting regular seepage analysis for dams can effectively prevent accidents from occurring. Accurate and rapid determination of seepage parameters is a prerequisite for seepage calculation in hydraulic engineering. The Whale Optimization Algorithm (WOA) was combined with Support Vector Regression (SVR) to invert the hydraulic conductivity. The good point set initialization method, a cosine-based nonlinear convergence factor, the Levy flight strategy, and the Quasi-oppositional learning strategy were employed to improve WOA. The effectiveness and practicality of Improved Whale Optimization Algorithm (IWOA) were evaluated via numerical experiments. As a case study, the seepage parameters of the Dono Dam located on the Baishui River in China were inversed, adopting the proposed inversion model. The calculated seepage field was reasonable, and the relative error between the simulated head and the measured value at each monitoring point was within 2%. This new inversion method is more feasible and accurate than the existing hydraulic conductivity estimation methods.
Thermal and Acoustic Performance of Gypsum Plasters Mixed with Different Additives: Influence of Bio-Based, Synthetic, and Mineral Fillers
Authors: Merli Francesca , Bouzit Said , Taha Mohamed , Buratti Cinzia
Published: September 2023

Due to the high impact of the building sector on the environment, a growing interest focuses on insulating materials able to ensure good thermo-acoustic performance for the building envelope from a sustainable and circular economy perspective. In this context, Moroccan natural gypsum was mixed with local natural waste materials. The thermal and acoustic properties of the samples were measured; they were compared to those of synthetic- and mineral-based gypsum plasters manufactured with the same technique. A Small Hot Box apparatus was used for thermal characterization, whereas acoustic performance was investigated by means of a Kundt’s Tube. Natural and synthetic additives result in a reduction in density and an improvement in thermal performance. Conductivity values in the 0.181–0.238 W/mK range were obtained, depending on the type of natural additive, with respect to 0.275–0.323 W/mK of mineral-based gypsum plasters. The acoustic measurements showed that all the composites have similar performance in terms of acoustic absorption, whereas high transmission loss values were obtained for the natural additives (TL = 35–59 dB). Petiol of Palm and Stipa Tenacissima were found to be materials able to improve both thermal and acoustic properties.
Study of the Ecosystem Service Value Gradient at the Land–Water Interface Zone of the Xijiang River Mainstem
Authors: Huang Yang , Deng Junling , Xiao Min , Huang Yujie , Li Hui , Xiao Yinyin , Huang Yiting
Published: September 2023

The ecosystem service value (ESV) gradient-evolution pattern of a river basin’s land and water-intertwined zones has a variety of ecosystem service values, such as biodiversity conservation, water conservation, water purification, etc. The study of the ecosystem service value (ESV) gradient-evolution pattern of a river basin’s land and water-intertwined zones will provide a scientific basis for the construction and protection of the ecological security pattern of the river basins. In this study, we combined the unit area equivalent factor method and geographically weighted regression (GWR) model to classify and analyze the gradient change pattern of ESV upstream, downstream, and along the river of the Guangdong mainstream section of the Xijiang River in China, and the conclusions are as follows: (1) The corresponding ESV share of each land use type was in the following order: water bodies > broad-leaved forest > artificial wetland > scrub > paddy field > coniferous forest > natural wetland > grassland. The level of each type of ESV does not depend entirely on the size of the area but is determined by the ecosystem service functions it can provide and the level of ESV per unit area; (2) the relationship between land use types along both sides of the river in the Guangdong section of the Xijiang River Basin shows a tendency to shift from water ecosystems to terrestrial ecosystems, and the ESV gradually decreases with the increase in distance from the water. (3) The upstream to the downstream area showed a trend of changing from terrestrial ecosystems to aquatic ecosystems, such as broad-leaved forests, scrublands, water bodies, artificial wetlands, etc., and the mean land ESV showed a general trend of undulating change and decline with the reduction in the distance from the downstream area. (4) Natural factors, such as the topography and geomorphology of the basin and the socio-economic factors of power consumption, influence the spatial distribution characteristics of the ESV in the region; among them, socio-economic factors, such as total power consumption, industrial exhaust gas emissions, industrial wastewater emissions, etc., in the economically developed areas of the Xijiang River Basin are the determinants of the changes in ESV, which are generated by human living and production activities, and these indirectly affect the magnitude of the ESV by influencing the factors of temperature and gas.
Comparison of 210Pb Age Models of Peat Cores Derived from the Arkhangelsk Region
Authors: Yakovlev Evgeny , Kudryavtseva Alina , Orlov Aleksandr
Published: September 2023

Dating young peatlands using the 210Pb dating procedure is a challenging task. The traditional 210Pb age models assume an exponential decline in radioactivity in line with depth in the peat profile. Lead exhibits considerable migratory capacity in Arctic peatlands; hence, to perform precise peat dating, existing models should be enhanced to remove the effects of migration. Independent isotope chronometers, such as 137Cs, can verify this. The Monte Carlo method and IP-CRS were utilised, together with several CA, CF/CS, PF, and CF models, to analyse the peat core samples acquired in the Arkhangelsk region. Data analysis revealed that the height partitioning of 137Cs and 210Pb is associated with physical characteristics, like the peat ash and the bulk density of the bog. Comparison between the natural activity of 210Pb in the peat and the radioactivity of 137Cs measured at depths of 19–21 cm in relation to the global fallout in 1963 indicated that the CF/CS, CF, and IP-CRS models (1965, 1962 and 1964, respectively) gave the closest age to the reference point given. IP-CRS was found to be the preferred model of these three options, as it gave a rather closer correlation with the 137Cs activity specific to the reference layer, allowing the error. The core dating of 210Pb showed an age of 1963 for a depth of 17–19 cm, which was in agreement with the reference horizon 137Cs and ash content, thus validating the accuracy and sufficiency of the selected model turf profile chronology. The maximum content of man-made radioisotopes in the peatlands corresponded to the formulation of the Partial Test Ban Treaty of 1963. The rates of accumulation of peat and atmospheric flux of 210Pb are in good agreement with the values available for the bogs of Northern Europe and those previously estimated by the authors in the subarctic region of European Russia. Although the problems of the complex migration-related distribution of 210Pb in the peat layer were considered, the dating methods used were effective in our study and can be adapted in following studies to perform the age determination of different peat deposits.
Research on the Dynamic Control Method of CFETR Multi-Purpose Overload Robot
Authors: Zuo Congju , Qin Guodong , Pan Hongtao , Xia Liang , Wang Feng , Zhou Pucheng , Qin Xiaoyan , Shi Ning , Lu Leiji , Zhao Ruijuan , Wan Chenhui , Luo Guangnan , Wang Weihua , Cheng Yong
Published: September 2023

The CFETR multi-purpose overload robot (CMOR) is a key subsystem of the remote handling system of the China fusion engineering test reactor (CFETR). This paper first establishes the kinematic and dynamic models of CMOR and analyzes the working process in the vacuum chamber. Based on the uncertainty of rigid-flexible coupling, a CMOR adaptive robust sliding mode controller (ARSMC) is designed based on the Hamilton-Jacobi equation to enhance the robustness of the control system. In addition, to compensate the influence of non-geometric factors on position accuracy, an error compensation method is designed. Based on the matrix differentiation method, the CMOR coupling parameter errors are decoupled, and then the gridded workspace principle is used to identify the parameter errors and improve the motion control accuracy. Finally, the CMOR rigid-flexible coupling simulation system is established by ADAMS-MATLAB/Simulink to analyze the dynamic control effect of ARSMC. The simulation results show that the CMOR end position error exceeds 0.1 m for single joint motion. The average value of CMOR end position error is less than 0.025 m after compensation, and the absolute error value is reduced by 4 times, improves the dynamic control accuracy of CMOR.
Time-Dependent, Two-Photon Rabi Frequency Shift Observed in Multi-Frequency Raman Spectra
Authors: Xu Zujun , Jin Zhiyu , Strickland Donna
Published: September 2023

It has been observed that when the multi-frequency Raman scattering process is pumped by two frequency chirped pulses in the transient regime, the anti-Stokes orders are spectrally broadened on the lower-frequency side, causing each of the orders to have a broader spectrum with double-peak pattern. In this article, we employ frequency-resolved optical gating to study the spectral shift of the first anti-Stokes Raman order. A double-pulse model is introduced to determine the electric field of the anti-Stokes orders from the experimental FROG spectrogram. With the help from the double-pulse model, we demonstrate that the double-peaked spectrum comes from linear Raman scattering between manifolds in the two-photon dressed states.
Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
Authors: Ding Yifan , Du Xiaodong , Wang Changrui , Tian Wei , Deng Chao , Li Ke , Wang Zihang
Published: September 2023

The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface detection, the traditional genetic algorithm faces problems such as slow convergence and easily falling into a local optimal solution. To solve this problem, an improved genetic algorithm combining the historical optimal population (CHOP-IGA) is proposed. First, the algorithm uses the probability-based four-nearest-neighbor method to construct an initial population. Subsequently, the probabilities of the crossover and mutation operators are dynamically adjusted. Next, the algorithm applies the crossover and mutation operators to the population and performs mutation operations on each individual of the historical optimal population. Then, the fitness value is calculated and the next generation of individuals is selected from the parent, offspring, and historical optimal populations according to the elite selection strategy. Finally, the current best fitness is checked to determine whether updating the historical optimal population is necessary. When the termination condition is satisfied, the algorithm outputs the optimal result. Experiments showed that the algorithm satisfactorily solved the path planning problem for conformal antenna surface detection, with a 48.44% improvement in detection efficiency.
Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition
Authors: Lee Yejin , Lee Suho , Hwang Sangheum
Published: September 2023

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. In this study, we propose a self-supervised transfer learning method based on Vision Transformer (ViT) to learn finer representations without human annotations. Interestingly, it is observed that existing self-supervised learning methods using ViT (e.g., DINO) show poor patch-level semantic consistency, which may be detrimental to learning finer representations. Motivated by this observation, we propose a consistency loss function that encourages patch embeddings of the overlapping area between two augmented views to be similar to each other during self-supervised learning on fine-grained datasets. In addition, we explore effective transfer learning strategies to fully leverage existing self-supervised models trained on large-scale labeled datasets. Contrary to the previous literature, our findings indicate that training only the last block of ViT is effective for self-supervised transfer learning. We demonstrate the effectiveness of our proposed approach through extensive experiments using six fine-grained image classification benchmark datasets, including FGVC Aircraft, CUB-200-2011, Food-101, Oxford 102 Flowers, Stanford Cars, and Stanford Dogs. Under the linear evaluation protocol, our method achieves an average accuracy of 78.5%, outperforming the existing transfer learning method, which yields 77.2%.
Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity
Authors: Huang Jian , Gu Yijun
Published: September 2023

Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.
Combined Optimization of Maintenance Works and Crews in Railway Networks
Authors: Gkonou Nikoletta , Nisyrios Emmanouil , Gkiotsalitis Konstantinos
Published: September 2023

This study develops optimal maintenance schedules for train lines, a critical endeavor ensuring the safety, efficiency, and reliability of railway networks. The study addresses the combined scheduling problem of maintenance works and crews on the railway networks. The baseline scheduling model is initially established with the primary objective of allocating maintenance tasks efficiently while adhering to pertinent constraints, encompassing task grouping and cost minimization. Subsequently, this baseline model is enhanced through the integration of crew scheduling, wherein work crews are strategically assigned to execute predefined tasks, thereby facilitating effective workload distribution. The combined maintenance work and crew scheduling problem is mathematically formulated as a binary linear programming model, enabling the attainment of globally optimal solutions. Comparing the outcomes of our enhanced model, which incorporates both maintenance works and crew schedules, with the baseline model that solely addresses maintenance works, we reveal that task grouping in accordance with predefined conditions leads to reduced overall costs by minimizing maintenance duration during various periods. Additionally, the judicious distribution of workload among the crews ensures comprehensive coverage of all essential tasks. These findings underscore the significance of our proposed approach in enhancing the operational efficacy and economic viability of railway maintenance scheduling, thereby offering valuable insights for practical implementation and future research endeavors.
Study on the High Strength Cable Truss System Control of the Surrounding Coal Roadway Excavated in Regions Left Intensively Mining-Induced Stresses
Authors: Xu Zhijun , Li Chong , Cao Yue , Tai Lianhai , Yan Hong
Published: September 2023

In coal mine roadway support, the arrangement of roadways in areas with high-stress concentrations is a challenging issue. It is crucial to solve this problem in order to ensure the safety and productivity of the mine. In this study, we selected a typical roadway in Zhong Xing Coal Mine, Fen Xi, Shanxi Province, as our research focus. We analyzed the deformation and failure characteristics of the surrounding rock under field mining pressure during roadway excavation through theoretical analysis, numerical simulation, and on-site measurements. Using the Flac3D numerical simulation technique, we studied the stress distribution characteristics of the roadway in the stress concentration area. To address the significant deformation of the roadway, we proposed a high-pretensioned top beam high-strength dual-anchor truss support system (HSCTS) and carefully studied its control mechanism and structural characteristics. After simulating and comparing its support effectiveness with the original support scheme and conducting on-site industrial tests, we determined the HSCTS support system as the alternative support system for roadway 1409. The test results showed that the convergence of the rib plates and the roof was no more than 79 mm and 191 mm, respectively. The significant influence of the roadway on the control of the surrounding rock also indicated effective control of the surrounding rock’s deformation along the 1409 goaf. The support control technology system can provide specific reference values for roadways under similar geological production conditions.
Patient Reported Outcome Measures (PROMs) Amongst Lower Extremity Agonist–Antagonist Myoneural Interface (AMI) Amputees
Authors: Chiao Rachael B. , Sullivan Corey L. , Berger Lori , Sparling Tawnee L. , Clites Kendall , Landry Tracy , Carty Matthew J.
Published: September 2023

(1) Background: The standard surgical approach to amputation has failed to evolve significantly over the past century. Consequently, standard amputations often fall short with regard to improving the quality of life (QoL) for patients. A modified lower extremity amputation technique incorporating agonist–antagonist myoneural interface (AMI) constructs provides patients with a novel alternative to standard amputation and, to-date, has demonstrated overall significant improvements in their physical and mental wellbeing. (2) Methods: Five PROMs surveys, (1) EQ-5D-3L, (2) Lower Extremity Functional Scale (LEFS), (3) PROMIS-57, (4) Short Form-36 (SF-36), and (5) Sickness Impact Profile (SIP), were administered to our research cohort pre-operatively (baseline) and at various timepoints post-operatively. (3) Results: The cohort’s baseline and 12-month post-operative responses were compared to determine score improvement. Significant improvements were demonstrated across all survey domains (p < 0.05). (4) Conclusions: Modified lower extremity amputation with AMI construction has the potential to provide amputees with increased quality of life when compared to the pre-operative state. However, further investigation is necessary to determine whether the patient-reported outcome measures of the AMI amputee cohort are superior to those who receive a standard amputation.
Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper
Authors: Lin Shanying , Xu Jialu , Liu Shengnan , Ong Muk Chen , Li Wenhua
Published: September 2023

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.
Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models
Authors: Carbo-Bustinza Natalí , Iftikhar Hasnain , Belmonte Marisol , Cabello-Torres Rita Jaqueline , De La Cruz Alex Rubén Huamán , López-Gonzales Javier Linkolk
Published: September 2023

In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.
Seismic Elastic Parameter Inversion via a FCRN and GRU Hybrid Network with Multi-Task Learning
Authors: Zheng Qiqi , Wei Chao , Yan Xinfei , Ruan Housong , Wu Bangyu
Published: September 2023

Seismic elastic parameter inversion translates seismic data into subsurface structures and physical properties of formations. Traditional model-based inversion methods have limitations in retrieving complex geological structures. In recent years, deep learning methods have emerged as preferable alternatives. Nevertheless, inverting multiple elastic parameters using neural networks individually is computationally intensive and can lead to overfitting due to a shortage of labeled data in field applications. Multi-task learning can be employed to invert elastic parameters simultaneously. In this work, a hybrid network that leverages the fully convolutional residual network (FCRN) and the gated recurrent unit network (GRU) is designed for the simultaneous inversion of P-wave velocity and density from post-stack seismic data. The FCRN efficiently extracts local information from seismic data, while the GRU captures global dependency over time. To further improve the horizontal continuity and inversion stability, we use a multi-trace to single-trace (M2S) inversion strategy. Consequently, we name our proposed method the M2S multi-task FCRN and GRU hybrid network (M2S-MFCRGRU). Through anti-noise experiments and blind well tests, M2S-MFCRGRU exhibits superior anti-noise performance and generalization ability. Comprehensive experimental inversion results also showcase the excellent lateral continuity, vertical resolution, and stability of the M2S-MFCRGRU inversion results.
Evaluation of the Performance of CMIP6 Climate Models in Simulating Rainfall over the Philippines
Authors: Ignacio-Reardon Shelly Jo Igpuara , Luo Jing-jia
Published: September 2023

The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to simulate the observed rainfall using the latest state-of-the-art model is essential for reliable forecasting. Based on this background, this paper objectively aims at assessing and ranking the capabilities of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed rainfall over the Philippines. The Global Precipitation Climatology Project (GPCP) v2.3 was used as a proxy to gauge the performance of 11 CMIP6 models in simulating the annual and rainy-season rainfall during 1980–2014. Several statistical metrics (mean, standard deviation, normalized root means square error, percentage bias, Pearson correlation coefficient, Mann–Kendall test, Theil–Sen slope estimator, and skill score) and geospatial measures were assessed. The results show that that CMIP6 historical simulations exhibit satisfactory effectiveness in simulating the annual cycle, though some models display wet/dry biases. The CMIP6 models generally underestimate rainfall on the land but overestimate it over the ocean. The trend analysis shows that rainfall over the country is insignificantly increasing both annually and during the rainy seasons. Notably, most of the models could correctly simulate the trend sign but over/underestimate the magnitude. The CMIP6 historical rainfall simulating models significantly agree on simulating the mean annual cycle but diverge in temporal ability simulation. The performance of the models remarkably differs from one metric to another and among different time scales. Nevertheless, the models may be ranked from the best to the least best at simulating the Philippines’ rainfall in the order GFDL, NOR, ACCESS, ENS, MRI, CMCC, NESM, FIO, MIROC, CESM, TAI, and CAN. The findings of this study form a good basis for the selection of models to be used in robust future climate projection and impact studies regarding the Philippines. The climate model developers may use the documented shortcoming of these models and improve their physical parametrization for better performance in the future.