bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Effect of Digital Technology Adoption and Farmers’ Cognition on Fertilizer Reduction and Efficiency Improvement Behavior
Authors: Peng Xinhui , Yan Xiaohuan , Wang Hongmei
Published: June 2024

Based on the field survey data for 1216 apple growers in three provinces from China’s Loess Plateau Region, this paper adopted Multivariate Probit (MVP) model and intermediary effect model to analyze the influence mechanisms of digital technology adoption (DTA) on farmers’ choices of Fertilizer-reduction and Efficiency-improving Technologies (FETs) from the perspective of farmers’ cognition (FC). The correlation effects of three typical FETs of soil testing and formula fertilization, integrated water-fertilizer fertilization and slow/controlled release fertilizer, were tested empirically. The results showed that DTA could significantly affect the adoption of FETs by apple growers, and FC played a partial mediating role, and there were complementary effects among the three typical FETs. The results were further confirmed by the propensity score matching (PSM) robustness test and the instrumental variable (IV) endogeneity test conducted in the conditional mixed-process (CMP). The results of our heterogeneity analysis showed that the promotion effect of DTA on FETs in the group with junior high school education or below was more significant than that in the group with higher education, and the promotion effect was more significant in the group with large operation scale. Therefore, the improvement in Internet penetration should not be taken as the ultimate goal, but more attention should be paid to farmers’ mastery and effective use of DTA, promote the improvement in farmers’ cognitive level, and implement the “bundled” publicity and guidance strategy of FETs adoption, so as to help in the green transformation of agriculture.
Interoperability Analysis of Tomato Fruit Detection Models for Images Taken at Different Facilities, Cultivation Methods, and Times of the Day
Authors: Naito Hiroki , Shimomoto Kota , Fukatsu Tokihiro , Hosoi Fumiki , Ota Tomohiko
Published: June 2024

This study investigated the interoperability of a tomato fruit detection model trained using nighttime images from two greenhouses. The goal was to evaluate the performance of the models in different environmets, including different facilities, cultivation methods, and imaging times. An innovative imaging approach is introduced to eliminate the background, highlight the target plants, and test the adaptability of the model under diverse conditions. The results demonstrate that the tomato fruit detection accuracy improves when the domain of the training dataset contains the test environment. The quantitative results showed high interoperability, achieving an average accuracy (AP50) of 0.973 in the same greenhouse and a stable performance of 0.962 in another greenhouse. The imaging approach controlled the lighting conditions, effectively eliminating the domain-shift problem. However, training on a dataset with low diversity or inferring plant appearance images but not on the training dataset decreased the average accuracy to approximately 0.80, revealing the need for new approaches to overcome fruit occlusion. Importantly, these findings have practical implications for the application of automated tomato fruit set monitoring systems in greenhouses to enhance agricultural efficiency and productivity.
Effect of Soil Texture on Soil Nutrient Status and Rice Nutrient Absorption in Paddy Soils
Authors: Ye Chang , Zheng Guangjie , Tao Yi , Xu Yanan , Chu Guang , Xu Chunmei , Chen Song , Liu Yuanhui , Zhang Xiufu , Wang Danying
Published: June 2024

Soil texture affects rice nutrient uptake and yield formation by influencing soil structure, microbial activity, and soil nutrient supply capacity. Analyzing the relationship between soil texture, nutrient content, and rice agronomic traits is of great significance for precise and efficient fertilizer application. The tillage layer (0–20 cm) of 31 paddy fields in China’s main rice-producing areas was collected to perform rice pot experiments, and soil texture characteristics, physicochemical properties, microbial-related indicators, and rice agronomic traits were measured and analyzed. The results showed that these soils could be classified into four types of soil texture: loamy sandy soil, sandy loam soil, silty loam soil, and silty soil. Analysis of variance showed that the available nitrogen (AN), available potassium (AK), and available phosphorus (AP) contents were the highest in silty loam, silty, and sandy loam soils, respectively, and silt loamy soil had the highest CEC. Principal component analysis (PCA) also showed that soil physicochemical properties can be distinguished to a certain extent according to soil texture types. For the relationship of soil texture parameters and soil physicochemical properties, soil organic matter (OM), total nitrogen (TN), AN, ammonium nitrogen (NH4+-N), and microbial carbon (MBC) contents were positively correlated with soil clay content, AK was positively correlated with silt content, and soil phosphorus status was significantly related to pH. Mantel’s test revealed significant correlations between rice N, P, and K nutrient status, dry matter accumulation, and yield, and soil available nutrient content, MBC, pH, and soil texture parameters. Structural equation modeling (SEM) indicated that sand affected soil available nutrients by regulating pH, while clay can positively influence soil available nutrients by affecting soil organic matter mineralization and microbial activity, thus influencing nutrient absorption and yield formation in rice. Overall, in rice production, the silty and silty loam paddy soil with fine texture and higher clay content facilitates the mineralization of soil organic matter and the activity of soil microbes, resulting in more available soil nutrients, which benefits the rice absorption and accumulation of nutrients. Furthermore, a higher content of clay also promotes the distribution of dry matter to the panicle, thereby promoting rice yield formation.
ECWS: Soil Salinity Measurement Method Based on Electrical Conductivity and Moisture Content
Authors: Mu Weiyi , Han Ning , Qu Zhi , Zheng Ming , Shan Yuyang , Guo Xin , Sun Yang , Mu Yujie
Published: June 2024

A novel method, ECWS, is proposed for measuring soil initial salinity content (b), based on the soil electrical conductivity EC and soil moisture content WS. This pioneering model rigorously establishes and incorporates the inherent potential correlation among soil bulk conductivity (ECa), soil solution conductivity (ECw), volume water content (θc), and soil salinity content (SSC). First of all, in order to delve the deeper relationship between ECa, ECw, θc and SSC, the soil salinity conductivity conversion coefficient ρa and soil leaching solution salinity conductivity conversion coefficient ρw were employed based on the formula of parallel conducting channels of the soil–water system, and a new measurement model of salinity content was constructed. After that, a mathematical analysis method was used to transform the coefficients of multiple sets of regression equations into matrices to solve ρa, ρw and b. Finally, to validate the accuracy of the proposed ECWS method, verification tests were conducted by utilizing TDR and PWMER sensors. The results with different salinity contents showed that the b size obtained by ECWS model were K2SO4 (1.84 g/kg), NaCl (1.91 g/kg), and KCl (1.92 g/kg). The maximum deviation was less than 0.08 g/kg (relative error less than 5%). The results showed that the influence of different anions and cations on the measurement of salinity content Cl− is greater than that of SO42−, and K+ is greater than that of Na+. This study revealed the relationship between soil electrical conductivity and soil salinity content to a certain extent, and realized the transformation between them, which provided a new method for the measurement of soil salinity content, and also provided a reference for related research on the measurement of soil salinization.
Effects of Foliar Ca and Mg Nutrients on the Respiration of ‘Feizixiao’ Litchi Pulp and Identification of Differential Expression Genes Associated with Respiration
Authors: Sajjad Muhammad , Tahir Hassam , Ma Wuqiang , Shaopu Shi , Farooq Muhammad Aamir , Zeeshan Ul Haq Muhammad , Sajad Shoukat , Zhou Kaibing
Published: June 2024

The ‘Feizixiao’ litchi cultivar, predominantly grown in Hainan Province, faces the issue of “sugar receding” during fruit ripening. The application of mixed foliar nutrients containing calcium and magnesium (Ca+Mg) during the fruit pericarp’s full coloring stage was investigated to overcome this issue. Experimental trials unveiled significant alterations in litchi pulp physiochemical properties, including the main nutrient and flavor quality, the total respiration rates of the main respiratory pathways, and the activities of some important enzymes associated with Embden–Meyerhof–Parnas (EMP), the tricarboxylic acid cycle (TCA) and the pentose phosphate pathway (PPP). The Ca+Mg treatment showed higher sugar levels than the control (CK) during ripening. Notably, the application of Ca+Mg in litchi pulp inhibited respiration rates through the EMP, TCA, and PPP pathways, resulting in a strong effect. RNA sequencing analysis revealed the impact of Ca+Mg treatment on respiratory pathways, revealing differentially expressed genes (DEGs) such as pyruvate PK1, PK2 (pyruvate kinase), and PDC (pyruvate dehydrogenase complex), validated through qRT-PCR with a significant correlation to RNA-seq results. In general, Ca+Mg treatment during litchi fruit ripening overcame “sugar receding” by inhibiting the expression of respiration key metabolic pathway genes. These findings provide insights for enhancing cultivation management strategies.
Land Degradation Affects Soil Microbial Properties, Organic Matter Composition, and Maize Yield
Authors: Gao Ming , Li Meng , Wang Shuli , Lu Xinchun
Published: June 2024

Land degradation severely affects soil functions, thereby weakening crop productivity. However, the quantitative effects of the soil organic matter (SOM) composition and soil microbial properties on maize yield remain unclear under different levels of land degradation. Here, a gradient of land degradation was selected for sampling in the Horqin Sandy Land in northeast China. The results show that the relative abundances of aliphatic and aromatic groups decreased by 21.51% and 86.01% with increasing land degradation, respectively, and the considerable increase in polysaccharide groups led to a decrease in stability and resistance to SOM decomposition. Microbial properties, such as microbial biomass carbon, basic respiration, qCO2, and enzyme activities, decreased as a result of land degradation. The phospholipid fatty acid content and the ratio between bacteria and fungi markedly decreased with an increasing level of land degradation, and the ratio of G+ to G− markedly increased. Correlation analysis confirmed that the microbial properties were significantly related to the SOM composition, and the random forest model indicated that fungi were key factors affecting maize yield (7.15%, p < 0.05). Moreover, the results of the structure equation modelling revealed that land degradation reduced the soil physiochemical properties, resulting in a decrease in microorganisms, causing variations in the SOM composition and directly leading to a decrease in crop yield. The effect of microorganisms (β = 0.84 ***) on maize yield was greater than that of the SOM composition (β = 0.53 **). Our investigation can provide a theoretical basis for the conservation of the cropland in Horqin Sandy Land.
Glomus mongioiense, a New Species of Arbuscular Mycorrhizal Fungi from Italian Alps and the Phylogeny-Spoiling Issue of Ribosomal Variants in the Glomus Genus
Authors: Magurno Franco , Uszok Sylwia , Bierza Karolina , Bakr Jawdat , Kende Zoltan , Bessa de Queiroz Mariana , Casieri Leonardo
Published: June 2024

Glomus mongioiense, a new species of arbuscular mycorrhizal fungi (AMF) in the family Glomeraceae, was isolated from rhizosphere soil collected from a meadow in the Italian Alps. The novelty of the species and its relationship with other species of the same genus were obtained by morphological and phylogenetic (45S nrDNA + RPB1 gene) analyses. Two glomoid spore-producing AMF isolates from a saltmarsh of the Scottish Highlands and maritime sand dunes of the Baltic Sea in Poland, were also included in this study and later found to be conspecific with G. rugosae. Phylogenetic placement analysis using environmental sequences indicated that G. mongioiense sp. nov. seems to be a rare species. Furthermore, the molecular and phylogenetic analysis provided important insights into the presence of highly divergent ribosomal variants in several Glomus species, with potential negative implication in phylogeny and species recognition.
Towards an Objective Measurement Tool for ADHD-like Traits in Family Dogs: A Comprehensive Test Battery
Authors: Csibra Barbara , Reicher Vivien , Csepregi Melitta , Kristóf Kíra , Gácsi Márta
Published: June 2024

Family dogs exhibit neuropsychological deficits similar to attention-deficit/hyperactivity disorder (ADHD) symptoms in humans. Questionnaire methods have mostly been used to assess ADHD-like behaviours in dogs. In addition to our validated questionnaire (Dog ADHD and Functionality Rating Scale–DAFRS; 2024), we developed a simple behavioural test battery covering the ADHD symptom domains (i.e., inattention, hyperactivity, and impulsivity) in dogs. Our main aim was (i) to provide a final external validation step to the DAFRS by examining its associations with the test variables (N = 59); and (ii) to compare owner- and trainer-rated factor scores’ associations with the test variables (n = 38). We developed four tests covering the ADHD symptom domains: the attention test (inattention), the plush dog test (impulsivity), the leash test, and the sit test (hyperactivity). All four behavioural variables correlated with their respective questionnaire scores, i.e., the strongest for hyperactivity, and the least strong for inattention. Both owner- and trainer-rated scores (n = 38) correlated with the relevant test variables in an expected direction. Dogs’ training status was linked only to the sit test results. Test–retest analyses (n = 34) indicated moderate-to-excellent agreement across all behavioural variables. Our findings support the validity of our novel human-analogue questionnaire for dogs as the behavioural tests strongly correlate with the relevant questionnaire scores, indicating that the two constructs together can effectively assess inattention, hyperactivity, and impulsivity in dogs.
Morphological and Genetic Assessment of Invasive Corbicula Lineages in Southern South America: A Case Study in Argentina
Authors: Hünicken Leandro A. , Paolucci Esteban M. , Lavinia Pablo D. , Sylvester Francisco
Published: June 2024

The broad global distribution of freshwater clams belonging to the genus Corbicula is driven by multiple hermaphroditic lineages. These lineages, characterized by shared morphological traits and phenotypic plasticity, pose challenges to morphological identification. Genetic markers, such as the mitochondrial COI gene, play a crucial role in delineating these lineages and their ranges. Morphotypes represent observed phenotypic variations, while lineages are defined based on genetic markers. Here, we comprehensively review Corbicula’s distribution in Argentina, discriminate extant lineages based on both morphological and genetic (COI) data, and describe variations in internal and external morphologies using 15 Argentine populations. Genetic analyses identified two mitochondrial lineages: the AR morphotype (FW5 haplotype) and CS morphotype (FW17 haplotype). Strikingly, despite having similar vectors, origins, and invasive stages, Corbicula lineages exhibit virtually segregated distributions. However, mitochondrial haplotypes are found in sympatry mainly in northeastern Argentina where individuals with intermediate morphotypes exist, suggesting the presence of hybrids due to maternal genome retention. These findings contribute to the clarification of the identity and distribution of Corbicula lineages in Argentina, where the genus has been found for over half a century. Similar studies are needed in other areas to better understand the invasion patterns of this successful and adaptable group.
Supra- and Subgingival Microbiome in Gingivitis and Impact of Biofilm Control: A Comprehensive Review
Authors: Iniesta Margarita , Vasconcelos Viviane , Sanz Mariano , Herrera David
Published: June 2024

This comprehensive review aimed (1) to characterize the sub- and supragingival microbiome in patients with biofilm-induced gingivitis (including experimental gingivitis), (2) to assess its stability and evolution over time, and (3) to assess the impact of biofilm control measures on this stability. An electronic search of the MEDLINE®/PubMed® database until December 2023 was conducted. NCBI Taxonomy, eHOMD 16S rRNA Reference Sequence, and Tree Version 15.23 databases were used to standardize taxonomic nomenclature. Out of 89 papers initially retrieved, 14 studies were finally included: 11 using experimental gingivitis as a model and three randomized clinical trials evaluating the impact of biofilm control measures. Among them, five characterized the subgingival microbiome, nine the supragingival microbiome, and one both the sub- and supragingival microbiome. In addition, five studies evaluated the effect of toothpaste, and four studies evaluated the effect of mouth rinses. The diversity and structure of the microbiome differed significantly between patients with periodontal health and those with biofilm-induced gingivitis (including experimental gingivitis). Those differences were not reversed through conventional oral hygiene measures. Specific antiseptic agents, especially if delivered as mouth rinses, may have an impact on the supra- and subgingival microbiome in gingivitis.
Modification and Synergistic Studies of a Novel Frog Antimicrobial Peptide against Pseudomonas aeruginosa Biofilms
Authors: Liu Xinze , Shi Daning , Cheng Shiya , Chen Xiaoling , Ma Chengbang , Jiang Yangyang , Wang Tao , Chen Tianbao , Shaw Chris , Wang Lei , Zhou Mei
Published: June 2024

The overuse of traditional antibiotics has resulted in bacterial resistance and seriously compromised the therapeutic efficacy of traditional antibiotics, making the exploration of new antimicrobials particularly important. Several studies have shown that bioactive peptides have become an important source of new antimicrobial drugs due to their broad-spectrum antibacterial action and lack of susceptibility to resistance. In this study, a novel bioactive peptide Nigrosin-6VL was characterised from the skin secretion of the golden cross band frog, Odorrana andersonii, by using the ‘shotgun’ cloning strategy. Modifications on the Rana Box of Nigrosin-6VL revealed its critical role in antimicrobial functions. The peptide analogue, 2170-2R, designed to preserve the Rana Box structure while enhancing cationicity, exhibited improved therapeutic efficacy, particularly against Gram-negative bacteria, with a therapeutic value of 45.27. Synergistic studies demonstrated that 2170-2R inherits the synergistic antimicrobial activities of the parent peptides and effectively enhances the antimicrobial capacity of cefepime and gentamicin against both planktonic cells and biofilms. Specifically, 2170-2R can synergise effectively with cefepime and gentamicin against different strains of P. aeruginosa biofilms. Consequently, 2170-2R holds promise as a potent antimicrobial agent developed to combat infections induced by Pseudomonas aeruginosa.
Effects of a Two-Year Lifestyle Intervention on Intrahepatic Fat Reduction and Renal Health: Mitigation of Inflammation and Oxidative Stress, a Randomized Trial
Authors: Quetglas-Llabrés Maria Magdalena , Monserrat-Mesquida Margalida , Bouzas Cristina , García Silvia , Mateos David , Casares Miguel , Gómez Cristina , Ugarriza Lucía , Tur Josep A. , Sureda Antoni
Published: June 2024

Metabolic-associated fatty liver disease (MAFLD) is the most common chronic liver disease observed in clinical practice worldwide. This disorder has been independently associated with an increased risk of developing chronic kidney disease (CKD). The aim of this study was to evaluate whether a 2-year intervention based on a Mediterranean diet (MedDiet) and physical activity focussed on reducing intrahepatic fat contents (IFC) was associated with a decreased risk of CKD. Forty adults (50% women) residing in Mallorca, aged 48 to 60 years, diagnosed with MAFLD were recruited. Participants were divided into two groups based on whether they improved IFC measured by nuclear magnetic resonance. Anthropometric and clinical parameters improved in responders, including reduced weight, body mass index (BMI), and waist circumference. Only responders showed improvements in lipid profile and liver enzymes. Haematological parameters showed favourable changes in both groups. Oxidative stress and inflammatory biomarkers differed between groups. Responders had lower plasma interleukine-18 (IL-18) levels, but higher erythrocyte malonaldehyde (MDA) levels. Non-responders showed increased erythrocyte catalase and superoxide dismutase activity. After 2 years, non-responders had higher serum creatinine, Modification of Diet in Renal Disease (MDRD), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) levels, while responders showed reductions in these parameters together with uric acid and urine albumin-to-creatinine ratio (UACR). Positive correlations were found between changes in IFC and kidney injury biomarkers, including MDRD and serum creatinine levels. In conclusion, a healthy diet based on the Mediterranean dietary pattern and lifestyle promotes significant improvements in parameters related to cardiovascular, hepatic, and renal health.
Antioxidant Response of Maternal and Fetal Rat Liver to Selenium Nanoparticle Supplementation Compared to Sodium Selenite: Sex Differences between Fetuses
Authors: Manojlović-Stojanoski Milica , Borković-Mitić Slavica , Nestorović Nataša , Ristić Nataša , Stefanović Radomir , Stevanović Magdalena , Filipović Nenad , Stojsavljević Aleksandar , Pavlović Slađan
Published: June 2024

To compare the effects of organic selenium nanoparticles (SeNPs, Se0) and inorganic sodium selenite (NaSe, Na2SeO3, Se4+) on the antioxidant response in maternal and fetal rat liver, pregnant females were treated with two forms of selenium (Se) at equivalent doses during gestation (0.5 mg SeNPs or 0.5 mg NaSe/kg body weight/day). Structural parameters of the liver of gravid females and their fetuses were examined in a sex-specific manner. The oxidative stress parameters superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), glutathione reductase (GR), glutathione S-transferase (GST), total glutathione (GSH) and sulfhydryl groups (SH) were established. In addition, the Se concentration was determined in the blood, liver, urine and feces of the gravid females and in the liver of the fetuses. The structure of the liver of gravid females remained histologically the same after supplementation with both forms of Se, while the oxidative stress in the liver was significantly lower after the use of SeNPs compared to NaSe. Immaturity of fetal antioxidant defenses and sex specificity were demonstrated. This study provides a detailed insight into the differences in the bioavailability of the nano form of Se compared to sodium selenite in the livers of pregnant females and fetuses.
Research on Fault Prediction Method for Electric Multiple Unit Gearbox Based on Gated Recurrent Unit–Hidden Markov Model
Authors: Liu Cheng , Zhang Shengfang , Wang Ziguang , Ma Fujian , Sha Zhihua
Published: June 2024

Due to the limited availability of fault samples and the expensive nature of marking fault samples in Electric Multiple Unit (EMU) gearbox monitoring data, a study was conducted to simulate the degradation process of key components in the CRH5 gearbox using rigid–flexible coupling dynamics. Vibration acceleration data from the simulation were utilized to create a six-dimensional hybrid feature domain representing the degradation process. By leveraging the capabilities of the Hidden Markov Model (HMM) for handling hidden transitive probabilities in temporal data and Gated Recurrent Unit (GRU) for addressing long-distance and high-dependence temporal data, a GRU-HMM fault prediction model was developed. This model was validated using monitoring data and the six-dimensional hybrid feature domain from the CRH5 gearbox and compared against actual maintenance records. The findings indicated that the GRU-HMM fault prediction model can effectively recognize the degradation patterns of multiple components, offering higher accuracy in fault prediction compared to traditional models. These research outcomes are expected to optimize EMU maintenance schedules based on usage conditions, enhance EMU utilization rates, and reduce operational and maintenance costs, thereby providing valuable theoretical support.
Design and Control of a Pneumatic Muscle Servo Drive Applied to a 6-DoF Parallel Manipulator
Authors: Pietrala Dawid Sebastian , Laski Pawel Andrzej , Zwierzchowski Jaroslaw
Published: June 2024

This article is a continuation of research on the possibility of using artificial pneumatic muscles to drive a parallel manipulator with six degrees of freedom. The authors present an artificial pneumatic muscle they have developed. The article contains a description of the research methodology and a description of the authors’ research position. The article presents the results of a comparison of the developed pneumatic muscle with an existing commercial solution. Then, the dynamic characteristics of the authors’ muscle are presented. A mathematical model is proposed that contains two inputs—the force generated by the muscle and the position of the muscle—and one output—the value of the signal controlling the pressure valve. The results of verification of the developed model are presented. The model is then presented in two versions. The first one contains the full dynamic model. The second one is its simplified version: the static model. The authors compare both models and present the comparison results. A servo drive is then synthesized and consists of a pair of muscles capable of generating the desired angular position and intended to drive a single arm of a parallel manipulator. The developed servo drive is verified on a test stand, and its behavior is assessed and analyzed in the context of its use in the manipulator.
Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
Authors: Gui Xia , Lu Yao , Wang Qin , Cai Mengdie , Sun Song
Published: June 2024

There is increased interest in the conversion of solar energy into green chemical energy because of the depletion of fossil fuels and their unpleasant environmental effect. Photocatalytic hydrogen generation from water involves the direct conversion of solar energy into H2 fuels, which exhibits significant advantages and immense promise. Nevertheless, photocatalytic efficiency is considerably lower than the standard range of industrial applications. Low light absorption efficiency, the rapid recombination of photogenerated electrons and holes, slow surface redox reaction kinetics and low photostability are well known to be key factors negatively affecting photocatalytic hydrogen production. Therefore, to construct highly efficient and stable photocatalysts is important and necessary for the development of photocatalytic hydrogen generation technology. In this review, quantum dots (QDs)-based photocatalysts have emerged with representative achievements. Due to their excellent light-harvesting ability, low recombination efficiency of photogenerated electrons and holes, and abundant surface active sites, QDs have attracted remarkable interest as photocatalysts and/or cocatalyst for developing highly efficient photocatalysts. In this review, the application of QDs for photocatalytic H2 production is emphatically introduced. First, the special photophysical properties of QDs are briefly described. Then, recent progress into the research on QDs in photocatalytic H2 production is introduced, in three types: semiconductor QDs (e.g., CdS, CdMnS, and InP QDs), metal QDs (e.g., Au, Pt and Ag QDs), and MXene QDs and carbon QDs (CDQs). Finally, the challenges and prospects of photocatalytic H2 evolution with QDs in the future are discussed.
IndustrialNeRF: Accurate 3D Industrial Digital Twin Based on Integrating Neural Radiance Fields Using Unsupervised Learning
Authors: Zhou Hui , Xu Juangui , Lin Hongbin , Nie Zhenguo , Zheng Li
Published: June 2024

In the era of Industry 4.0, digital twin technology is revolutionizing traditional manufacturing paradigms. However, the adoption of this technology in modern manufacturing systems is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.
A Frequency Domain Kernel Function-Based Manifold Dimensionality Reduction and Its Application for Graph-Based Semi-Supervised Classification
Authors: Liang Zexiao , Gong Ruyi , Tan Guoliang , Ji Shiyin , Zhan Ruidian
Published: June 2024

With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various non-sparse, high-energy occlusions in real-world images can lead to erroneous calculations of sample relationships, invalidating the existing distance-based manifold dimensionality reduction techniques. Many types of noise are difficult to capture and filter in the original domain but can be effectively separated in the frequency domain. Inspired by this idea, a novel approach is proposed in this paper, which obtains the high-dimensional manifold structure according to the correlationships between data points in the frequency domain and accurately maps it to a lower-dimensional space, named Frequency domain-based Manifold Dimensionality Reduction (FMDR). In FMDR, samples are first transformed into frequency domains. Then, interference is filtered based on the distribution in the frequency domain, thereby emphasizing discriminative features. Subsequently, an innovative kernel function is proposed for measuring the similarities between samples according to the correlationships in the frequency domain. With the assistance of these correlationships, a graph structure can be constructed and utilized to find the mapping in a low-dimensional space. To further demonstrate the effectiveness of the proposed algorithm, FMDR is employed for the semi-supervised classification problems in this paper. Experiments using public image datasets indicate that, compared to baseline algorithms and state-of-the-art methods, our approach achieves superior recognition performance. Even with very few labeled data, the advantages of FMDR are still maintained. The effectiveness of FMDR in dimensionality reduction and feature extraction of images makes it widely applicable in fields such as image processing and image recognition.
Mechatronic Device Used to Evaluate the Performance of a Compliant Mechanism and Image Processing System in Determining Optometric Parameters
Authors: Constantin Victor , Comeagă Daniel , Grămescu Bogdan , Besnea Daniel , Cartal Adrian , Moraru Edgar
Published: June 2024

The work presented in the paper describes a mechatronic test stand and technique employed to determine the accuracy of a system developed by the authors to assist optometrists in measuring parameters used in the customization of progressive lenses, as well as regular lenses. The system aims to offer information about interpupillary distance, pantoscopic angle, and vertex distance, as well as measurements useful in correctly mounting the lenses in the frames. This is conducted by attaching a marker support system to the user’s frame and determining the user’s dimensions by using image acquisition techniques performed via a custom application built for this purpose. In this paper, a test mannequin is used to determine the accuracy of the system, with measurements being compared to those obtained by using classic methods. This method is used to determine the accuracy of the measurements in a controlled environment. Following the good results obtained in this paper and pending some improvements to the application, clinical tests will be performed on a small scale in selected optometrist offices.
Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition
Authors: Liu Gaoxiang , Yu Xin , Liu Danyang
Published: June 2024

Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupancy prediction model utilizing the Variational Mode Decomposition (VMD) technique. Initially, the Variational Mode Decomposition decomposes the original traffic flow data into multiple smooth subsequences. Subsequently, each subsequence’s autocorrelation and partial correlation coefficients ascertain the presence of seasonal characteristics. Based on these characteristics, the CT-Transformer and TCN-LSTM models process each subsequence for long-term lane occupancy rate prediction, respectively. Finally, predictions from both models are integrated using variable modes to derive the ultimate lane occupancy predictions. The core CT-Transformer model, an enhancement of the GBT (Two-stage Transformer) model, comprises two phases: autoregressive and prediction. The autoregressive phase leverages historical data for initial predictions inputted into the prediction phase. Here, the novel CSPNet-Attention mechanism replaces the conventional attention mechanism in the Encoder, reducing memory usage and computational resource loss, thereby enhancing the model’s accuracy and robustness. Experiments on the PeMS public dataset demonstrate that the proposed model surpasses existing methods in predicting long-term lane occupancy, offering decent reliability and generalizability.
Parallel GPU-Acceleration of Metaphorless Optimization Algorithms: Application for Solving Large-Scale Nonlinear Equation Systems
Authors: Silva Bruno , Lopes Luiz Guerreiro , Mendonça Fábio
Published: June 2024

Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. This paper presents a review and detailed description of the main metaphorless optimization algorithms, including the Jaya and enhanced Jaya (EJAYA) algorithms, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the new max–min greedy interaction (MaGI) algorithm. This article presents improved GPU-based massively parallel versions of these algorithms using a more efficient parallelization strategy. In particular, a novel GPU-accelerated implementation of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphorless algorithms developed were implemented using the Julia programming language. Both high-end professional-grade GPUs and a powerful consumer-oriented GPU were used for testing, along with a set of hard, large-scale nonlinear equation system problems to gauge the speedup gains from the parallelizations. The computational experiments produced substantial speedup gains, ranging from 33.9× to 561.8×, depending on the test parameters and the GPU used for testing. This highlights the efficiency of the proposed GPU-accelerated versions of the metaphorless algorithms considered.
Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening
Authors: Karampinis Ioannis , Iliadis Lazaros , Karabinis Athanasios
Published: June 2024

Seismic vulnerability assessment is one of the most impactful engineering challenges faced by modern societies. Thus, authorities require a reliable tool that has the potential to rank given structures according to their seismic vulnerability. Various countries and organizations over the past decades have developed Rapid Visual Screening (RVS) tools aiming to efficiently estimate vulnerability indices. In general, RVS tools employ a set of structural features and their associated weights to obtain a vulnerability index, which can be used for ranking. In this paper, Machine Learning (ML) models are implemented within this framework. The proposed formulation is used to train binary classifiers in conjunction with ad hoc rules, employing the features of various Codes (e.g., the Federal Emergency Management Agency, New Zealand, and Canada). The efficiency of this modeling effort is evaluated for each Code separately and it is clearly demonstrated that ML-based models are capable of outperforming currently established engineering practices. Furthermore, in the spirit of the aforementioned Codes, a linearization of the fully trained ML model is proposed. ML feature attribution techniques, namely SHapley Additive exPlanations (SHAP) are employed to introduce weights similar to engineering practices. The promising results motivate the potential applicability of this methodology towards the recalibration of the RVS procedures for various types of cases.
Secure IoT Communication: Implementing a One-Time Pad Protocol with True Random Numbers and Secure Multiparty Sums
Authors: Fenner Julio , Galeas Patricio , Escobar Francisco , Neira Rail
Published: June 2024

We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-Ice v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev–Yao threat model, providing a thorough assessment of its defense against potential cyber threats.
Experimental Study on Rock Deformation Localization Using Digital Image Correlation and Acoustic Emission
Authors: Xing Tongzhen , Zhu Haibin , Song Yimin
Published: June 2024

In this study, the digital image correlation (DIC) method and acoustic emission (AE) technology were combined to study the evolution of rock deformation localization in detail. The second-order spatial–temporal subset DIC (STS-DIC) algorithm was proposed and used for measuring strongly heterogeneous deformation fields of red sandstone specimens under uniaxial compression. The evolution of the deformation field was analyzed with a focus on the deformation localization stage. The length and width of the deformation localization band (DLB) were measured, and the relationships between the relative sliding rate of the DLB, the relative opening rate of the DLB, and the AE counts were identified. Deformation localization was found to result from the rapid evolution of the strain concentration before the peak stress. The complete development of the DLB is an inducing factor for catastrophic rock failure, and the failure modes of the rock specimens were consistent with the final state of the DLB. A good correlation was identified between the AE counts and the relative displacement rate of the DLB, and the sliding rate was found to have a significant influence on the AE counts.
Application Study of Distributed Optical Fiber Seepage Monitoring Technology on Embankment Engineering
Authors: Li Hao , Yang Meng
Published: June 2024

It is very important for embankment engineering to consider the seepage factor. If the potential seepage is not discovered in time and seepage control measures are not appropriate, seepage is very likely to cause damage and deformation, resulting in embankment failure. Based on temperature and seepage fields theories, a temperature–seepage coupled model is established in this paper. It is combined with a distributed temperature sensing (DTS) system to measure the temperature field of the porous media. This approach allows for the inversion of the inner seepage field, realizing the real-time monitoring of embankment health to ensure its safety and long-term operation. According to the coupling analysis on the temperature–seepage fields, for practical engineering, the influence of temperature on the seepage field is small and neglectable. Only the effect of the seepage field on the temperature field is considered. The DTS optical fiber temperature measurement system is widely used in various projects nowadays because of its high stability and efficiency advantages. The optical fiber is sensitive to temperature and can give fast and accurate temperature feedback regarding seepage location. Combined with the Heat Transfer Module in COMSOL, the multi-line heat source method can be used to invert the seepage field according to the temperature field of the porous medium inside the embankment and derive the seepage flow rate of the stable seepage field. For unstable seepage, optical fiber is good at seepage measuring and location detecting. For different practical engineering, a different heating power can be used for different seepage conditions. By monitoring the temperature change, the seepage condition can be inverted which is one of the indicators for evaluating engineering safety.
Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results
Authors: Devillers James , Devillers Hugo
Published: June 2024

Repellents play a fundamental role in vector control and prevention to keep mosquitoes away from humans. Available in limited numbers, it is absolutely necessary to find new repellents for preventing problems of resistance. QSAR (Quantitative Structure–Activity Relationship) methods are particularly suited for designing molecules with potential repellent activity. These models require that the molecules be described by physicochemical properties, topological indices, and/or structural indicators. In the former situation, QSPR (Quantitative Structure–Property Relationship) models are used for calculating physicochemical descriptors. Use of different QSPR models for the same property can lead to different values for the same molecule. In this context, the influence of the 1-octanol/water partition coefficient (log P) calculated according to two different methodologies was statistically evaluated in the modeling of 2171 molecules for which their skin repellent activity against Aedes aegypti was available. The two series of supervised artificial neural networks differed only by their input neuron coding for log P. Although both categories of classification models led to overall good statistics, we clearly showed that differences in log P values calculated for a molecule could result in very different prediction results. This was especially true for repellents. The practical implication of these differences was discussed.
Multi-Objective Optimization for Complex Systems Considering Both Performance Optimality and Robustness
Authors: Chen Yue , Shi Jian
Published: June 2024

As engineering systems become increasingly complex, performance requirements rise, and tolerance for design parameter variations becomes more crucial due to increased uncertainty. Tolerance to parameter variation can be measured by the volume of the solution space. A larger solution space implies a higher tolerance to parameter changes and thus greater robustness. The box-shaped solution space, represented by intervals with respect to each design parameter, has the advantage of showing which design parameters can be decoupled. Therefore, this paper proposes a new multi-objective optimization problem to optimize both the performance and volume of the box-shaped solution space simultaneously. Often, optimal performance and maximum volume are conflicting objectives, indicating a trade-off between performance and robustness. Furthermore, the DIRECT-NSGA-II approach is proposed for solving this multi-objective optimization problem. The DIRECT algorithm evaluates the minimum value of the performance function within the box-shaped solution space, while the NSGA-II algorithm identifies Pareto-optimal solution spaces. Finally, two case studies are implemented to illustrate the effectiveness of the DIRECT-NSGA-II method. We can conclude that (I) the proposed DIRECT-NSGA-II approach is suitable for black-box performance functions, (II) any point within the obtained solution space is a good design point, and (III) the proposed optimization problem considers both performance optimality and robustness, enabling the identification of a representative set of Pareto-optimal solution spaces that balance these two factors.
Water Impermeability of Self-Compacting Fly-Ash-Containing Concrete
Authors: Dvorkin Leonid , Bordiuzhenko Oleh , Mierzwiński Dariusz , Tracz Tomasz , Sitarz Mateusz
Published: June 2024

The impermeability of self-compacting fly-ash-containing concrete at different ash contents and specific surface areas of ash is studied. The experimental and statistical dependences of the water separation of concrete mixtures, as well as the water impermeability and compressive strength of self-compacting concrete, depending on the composition factors, were obtained. A correlation between concrete strength and its water impermeability has been determined, which allows predicting the latter with sufficient accuracy.