bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Self-Learning Control Method of Reusable Launch Vehicle Based on Neural Network Architecture Search
Authors: Xue Shuai , Wang Zhaolei , Bai Hongyang , Yu Chunmei , Li Zian
Published: September 2024

Reusable launch vehicles need to face complex and diverse environments during flight. The design of rocket recovery control law based on traditional deep reinforcement learning (DRL) makes it difficult to obtain a set of network architectures that can adapt to multiple scenarios and multi-parameter uncertainties, and the performance of deep reinforcement learning algorithm depends on manual trial and error of hyperparameters. To solve this problem, this paper proposes a self-learning control method for launch vehicle recovery based on neural architecture search (NAS), which decouples deep network structure search and reinforcement learning hyperparameter optimization. First, using network architecture search technology based on a multi-objective hybrid particle swarm optimization algorithm, the proximal policy optimization algorithm of deep network architecture is automatically designed, and the search space is lightweight design in the process. Secondly, in order to further improve the landing accuracy of the launch vehicle, the Bayesian optimization (BO) method is used to automatically optimize the hyperparameters of reinforcement learning, and the control law of the landing phase in the recovery process of the launch vehicle is obtained through training. Finally, the algorithm is transplanted to the rocket intelligent learning embedded platform for comparative testing to verify its online deployment capability. The simulation results show that the proposed method can satisfy the landing accuracy of the launch vehicle recovery mission, and the control effect is basically the same as the landing accuracy of the trained rocket model under the untrained condition of model parameter deviation and wind field interference, which verifies the generalization of the proposed method.
Study on the Contact Parameter Calibration of the Maize Kernel Polyhedral Discrete Element Model
Authors: Chen Huhu , Lin Haipeng , Song Xuefeng , Zhang Fengwei , Dai Fei , Yang Ting , Li Baicheng
Published: September 2024

During maize production and transportation, maize kernels frequently interact with mechanical components. To accurately simulate the interaction process between maize and mechanical components, it is essential to establish a reliable maize kernel model and input precise contact parameters. This study established polyhedral discrete element models of different maize kernels and calibrated the contact parameters between maize kernels and steel plates using the inclined plane method. The coefficients of restitution, static friction, and dynamic friction between maize and steel sheets were measured to be 0.5, 0.545, and 0.213, respectively. Subsequently, the contact parameters between maize kernels were determined through steepest climb tests and central composite design response surface tests. Then, the above parameters were optimized using Design-Expert software. The coefficients of restitution, static friction, and dynamic friction between maize kernels were measured to be 0.318, 0.182, and 0.232, respectively. Finally, the optimized parameters were validated using the angle of repose experiment, which found that the relative error between the experiment and the simulation was only 1.24%. The results indicated that the obtained contact parameters were accurate and reliable.
Design and Experimentation of Targeted Deep Fertilization Device for Corn Cultivation
Authors: Qi Zhongying , Liu Cunliang , Wang Yao , Zhang Zhiwei , Sun Xiaobo
Published: September 2024

In response to the challenges of low fertilizer utilization rates, excessive application amounts, and difficulties in precise targeted fertilization during the middle tillage and top-dressing period for corn, a targeted deep fertilization device is designed, integrating mechanical structure design and automatic control technology. The device mainly includes a strong discharge fertilization device and a targeted fertilization control system. The fertilization device has been designed, and the main factors affecting the performance of the fertilization wheel have been identified. Based on the structure, a strong discharge fertilization plate mechanism has been added, and a mechanical model for the fertilization wheel during the refilling and discharging processes has been constructed. A targeted fertilization control system for corn has been developed that utilizes a photoelectric sensor to detect the position of the corn plants. A microcontroller combines the plant position information and the device moving speed to adjust the intermittent rotation of the stepper motor in real time, achieving targeted deep fertilization for corn. Coupled simulation analysis was conducted using discrete element software EDEM and dynamic software Adams. Through single-factor and multi-factor experiments, the main factors affecting fertilization performance were analyzed, and the optimal structural parameters for the fertilization wheel were determined. Bench validation tests were conducted, and the results demonstrated that under forward speeds of 0.4 to 1.2 m/s, the coefficient of variation of the fertilizer application rate per hole of the discharge device ranged from 2.02% to 4.46%, the error in fertilizer application rate per hole ranged from 7.12% to 12.18%, the average length of fertilizer application holes ranged from 72.5 mm to 130.2 mm, and the coefficient of variation of hole length stability ranged from 1.94% to 3.54%. These parameters were consistent with the results from the simulation tests, and the operational performance met the requirements. Finally, field tests validated the overall operational performance of the device. When the device’s speed ranged from 0.4 m/s to 1.2 m/s, the coefficient of variation of the fertilizer application rate per hole, the error in fertilizer application rate per hole, the average length of fertilizer application holes, the coefficient of variation of hole length stability, and the qualification rate of fertilization position were 3.63%, 10.46%, 108.8 mm, 2.96%, and 87.16%, respectively. The overall performance of the device is stable and meets the requirements for targeted deep fertilization in corn cultivation.
Reducing Grain Damage in Moist Corn Threshing via Corncob Division
Authors: Wang Gang , Jin Chengqian , Zhang Min , Wu Chongyou , Tang Qing , Yang Yao
Published: September 2024

For the prompt planting of subsequent crops, most of China’s corn harvest must occur before full maturity, with a grain moisture content above 25%. Harvesting moist corn presents challenges due to significant grain damage during threshing. We conducted a high-speed photography observation test of moist corn threshing. It demonstrated that corn ears, when passing through the threshing cylinder, often break into pieces. Grains on divided corncobs can be threshed more easily and earlier than those on undivided ones, suggesting that pre-dividing corn ears reduces grain damage. An experiment using the Lianchuang 825 variety examined the effect of moisture content and the divided rate of corncobs (DRC) on grain damage. The results showed that as moisture content increased from 25% to 37%, grain damage to undivided ears increased from 3.75% to 37.71%. Dividing corn ears before threshing significantly reduced damage, with an eight-piece division reducing damage by approximately 70% across all moisture levels. Verification with the Jinyu 1233 variety confirmed that a higher DRC consistently reduced damage. This study provides a new approach to reducing damage in moist corn threshing and aids in the development of low-damage threshing devices.
Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China
Authors: Lin Yinghao , Guo Xiaoyu , Liu Yang , Zhou Liming , Wang Yadi , Ge Qiang , Wang Yuye
Published: September 2024

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.
Effects of Zinc Oxide and Zinc–Silica-Based Nanofertilizers with Yeasts on Selected Components of Soybean in the Central European Agronomic Region: A Short-Term Study
Authors: Ernst Dávid , Kolenčík Marek , Kupec Michal , Šebesta Martin , Qian Yu , Straka Viktor , Černý Ivan , Soulange Joyce Govinden , Ducsay Ladislav
Published: September 2024

The action-to-reaction dynamics of next-generation nanofertilizers (NFs) towards field crops are currently being addressed in precision and sustainable agriculture. Therefore, our aim was to evaluate the effects of foliar application of ZnO nanoparticles (ZnO-NPs) or their combination with hybrid nanoporous biosilica mixed with yeast (ZnSi-bio) for soybean plants’ selected production and physiological indices in comparison to an NF-free control. The experiment was conducted at eco-friendly concentrations in Veľký Krtíš, Slovakia, a location within the Central European agronomical region. The ZnSi-bio variant had an improved number of pods, seed count, and yield, while the ZnO-NPs variant had an enhanced seed bulk density compared to the NF-free control, which had a greater effect on thousand-seed weight (TSW). Significant differences were found in the final quality components of soybeans with respect to phosphorus content without ZnO-NP biofortification. In the case of the ZnSi-bio variant, soybeans were biofortified with zinc. Both leaf-applied NFs markedly improved nutritional and energetic values for soybeans. NFs continued to positively affect seasonal physiology, such as the stomatal conductance (Ig) and crop water stress index (CWSI), compared to the control. The results suggest that the ZnO-NPs, especially when combined with hybrid biosilica and yeast, open new avenues for interdisciplinary research in agro-food science.
Soil-Improving Effect of Sesbania–Sorghum Rotation in a Heavily Saline–Alkaline Coastal Region
Authors: Wu Zhe , Meng Ran , Feng Wei , Li Zhaojia , Lu Xuelin , Chen Yue , Deng Xian , Chen Tiecheng , Xue Zhizhong , Wang Xiuping
Published: September 2024

Planting salt-tolerant plants is an efficient method of biological improvement for saline–alkali land. However, few studies have examined the soil improvement effects of the rotation of the green manure plant sesbania and the grain crop sorghum. Thus, we planted sesbania in native soil on heavily saline–alkaline coastal land and subsequently planted sorghum after returning the sesbania straw to the soil. The effect of this sesbania–sorghum rotation on soil improvement was clarified by comparing indicators of soil quality before and after sesbania and sorghum were planted, such as the soil structure, water infiltration, soil salt content, and soil microbial changes. The results showed that the soil bulk density of the plow layer (0–40 cm) after crop harvest decreased by 9.63% compared with that of bare land, and the soil porosity increased by 5.67%. The cumulative infiltration, initial infiltration rate, and stable infiltration rate of saline soil were 3.6 times, 2.8 times, and 3.3 times higher than those of bare land, respectively. With the growth of sesbania and sorghum, the soil salt content in the plow layer of the cultivated land decreased by 37.73%, while that of bare land decreased by 9.1%. A further analysis of desalination showed that the total desalination amount in the plow layer was 15.58 t/ha, of which 5% was due to plant absorption, and the rest was from salt leaching. Moreover, sesbania–sorghum rotation increased the soil organic matter content in the plow layer from 69.1 t/ha to 73.8 t/ha. The quantities of some microorganisms that are mainly found in coastal saline soil decreased, while those of some common soil microorganisms increased, reflecting an improvement in the soil quality. The above results prove that sesbania–sorghum rotation had a significant effect on soil improvement and salt reduction, which is of great significance for the further utilization of saline–alkali land to enhance crop productivity.
Mainstream Planting Systems Influence Spatiotemporal Variations in the Soil Quality of Watershed Sloping Farmland
Authors: Li Hongying , Tang Jun , Zhu Ningyuan , Wang Jing , Qiao Jun
Published: September 2024

Mainstream planting systems of watershed sloping farmland span the globe and are closely related to the variations in the soil quality of watershed sloping farmland. However, little information is available about how mainstream planting systems influence spatiotemporal variations in the soil quality of watershed sloping farmland. The soil of 0–20 cm was collected at fixed points in three mainstream planting systems (a low-altitude citrus orchard system, a mid-altitude double-cropping system, and a high-altitude single-cropping system) at a fixed time each year for 15 years in a typical agricultural watershed of the Three Gorges Reservoir area of China. Fourteen physicochemical properties of the sampled soil were measured. We found that (1) the soil quality indexes of the citrus orchard system, double-cropping system, and single-cropping system decreased from 0.75, 0.71, and 0.67 in 2004 to 0.68, 0.57, and 0.55 in 2019, respectively; (2) the order of the six master control factors influencing soil quality was sand content > bulk density > total nitrogen > clay content > pH > total phosphorus in the citrus orchard system, sand content > bulk density > clay content > pH > total phosphorus > total nitrogen in the double-cropping system, and sand content > clay content > total phosphorus > pH > bulk density > total nitrogen in the single-cropping system; and (3) the total effects of soil erosion and fertilization on soil quality were −0.496 and −0.308 in the citrus orchard system, −1.254 and 0.371 in the double-cropping system, and −0.844 and 0.013 in the single-cropping system, respectively. We suggest that the three mainstream planting systems influence soil quality through variations in their master control factors caused primarily by soil erosion and secondarily by fertilization. These findings are important for controlling soil degradation through controlling soil erosion and rational fertilization in watersheds.
Effects of Tillage and Straw Mulching on Soil Hydrothermal and Nutrient Content in Agricultural Soil
Authors: Feng Zijia , Wang Bai , Wang He , Huang Yan
Published: September 2024

Long-term intensive tillage has led to soil environment degradation, reduced fertility, and difficulty in increasing crop yield in the Mollisol region of northeast China. In order to improve the soil’shydrothermal environment and nutrient content, we conducted field experiments to investigate the effects of different tillage practices and the amount of straw mulching on soil hydrothermal environment and nutrient content in agricultural soils in seasonal permafrost areas. Four treatments were established: no-tillage without straw (NT0), no-tillage with half straw mulching (NT1), no-tillage with full straw mulching (NT2), and rotary tillage without straw (CK) as the control treatment. The results indicate that the no-tillage with straw mulching treatments increased the soil ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) content, accompanied by improvements in the soil’s water content and regulation of soil temperature changes, as compared to the CK treatment. Specifically, the soil’s NH4+-N and NO3−-N content in the NT2 treatment were significantly increased by 25.65% and 38.81%, respectively. Our study indicates that NT2 treatment is the most suitable tillage practice and straw-returning method in the Mollisol region of northeast China. This study can provide a theoretical basis and reference for the efficient utilization of farmland soil in seasonal permafrost areas.
A205V, D376E, W574L, S653T, and S653N Substitutions in Acetohydroxy Acid Synthase from Amaranthus retroflexus L. Show Different Functional Impacts on Herbicide Resistance
Authors: Sun Zhonghua , Cong Jianan , Cao Wenli , Yuan Guang , Meng Zhen , Wang Shen , Li Chunjie , Teng Chunhong
Published: September 2024

Amaranthus retroflexus L. is a troublesome dicot weed in crop fields and has developed high resistance to nicosulfuron in China. The objective of this study was to determine the effects of specific resistance mutations (A205V, D376E, W574L, S653T, and S653N) of the acetohydroxy acid synthase enzyme (AHAS) on the resistance of A. retroflexus to nicosulfuron. The resistance mutations in A. retroflexus not only conferred 17.17- to 31.70-fold resistance to nicosulfuron but also greatly decreased AHAS sensitivity and increased AHAS binding affinity to substrate pyruvate, which mechanisms were primarily responsible for the observed A. retroflexus resistance. Molecular docking results indicated that these resistance mutations altered AHAS binding free energy with nicosulfuron. All the resistance mutations showed less sensitivity to feedback inhibition by branched-chain amino acids, but the mutations did not necessarily affect biosynthesis in A. retroflexus. This report to compares the various mutations of ArAHAS in vitro and contributes to understanding herbicide resistance in this field weed.
Does the Colonizing Population Exhibit a Reduced Genetic Diversity and Allele Surfing? A Case Study of the Midday Gerbil (Meriones meridianus Pallas) Expanding Its Range
Authors: Batova Olga N. , Markov Nikolay I. , Titov Sergey V. , Tchabovsky Andrey V.
Published: September 2024

Colonizing populations at the leading edge of range expansion are expected to have a reduced genetic diversity and strong genetic structure caused by genetic drift and allele surfing. Until now, few studies have found the genetic signatures of allele surfing in expanding wild populations. Using mtDNA markers, we studied the genetic structure of the population of midday gerbils (Meriones meridianus) expanding their range to the west in Kalmykia (southern Russia) following the new cycle of desertification, re-colonizing areas abandoned in the mid-2010s. In the colonizing population, we found a reduced genetic diversity, the redistribution of haplotype frequencies—in particular, in favor of variants rare in the core population—and strong genetic structure combined with strong differentiation from the core population—patterns suggestive of allele surfing on the wave of expansion. In terms of genetic diversity and spatial structuration, the western edge population sampled in 2008 before its collapse in 2017 occupies the intermediate position between the current colonizing and core population. This suggests that reduced genetic diversity and increased genetic differentiation are general features of marginal populations, enhanced by the founder and allele-surfing effects at the leading edges of expanding ranges.
Fermented Rapeseed Meal as a Dietary Intervention to Improve Mineral Utilization and Bone Health in Weaned Piglets
Authors: Czech Anna , Woś Katarzyna , Pachciński Karol , Muszyński Siemowit , Świetlicki Michał , Tomaszewska Ewa
Published: September 2024

This study examined the effects of incorporating fermented rapeseed meal (FRSM) into the diet of newly weaned piglets on mineral digestibility and bone health. Experimental diets containing varying levels of FRSM (8%, 12%, 15%, and 25%) were introduced to the piglets at 18 days of age, prior to weaning at 28 days. These diets were continued until the piglets were euthanized at 42 days of age. Mineral absorption was assessed using the apparent total tract digestibility (ATTD) method and blood plasma element analysis, while bone mineral content and mechanical properties were evaluated through densitometry and three-point bending tests. The results showed that intermediate levels of FRSM (12–15%) significantly enhanced the digestibility of key minerals, including phosphorus, calcium, magnesium, copper, zinc, and iron. This improvement was linked to increased femoral mineral content and bone stiffness, as well as a higher yield point, likely due to enhanced collagen synthesis. Additionally, there was an increase in bone fracture load and fracture stress, potentially due to changes in the organization of the bone mineral phase, as no changes in bone mid-shaft mineral density or geometry were observed. These findings suggest FRSM as a promising dietary component for improving mineral bioavailability and bone health in piglets.
Comparative Analysis of the Potential Adaptability of Tibetan Dzo and Yellow Cattle Based on Blood Indices, Metabolites, and Fecal Microbiota
Authors: Li Kenan , Zhang Guorui , Sun Mengjiao , Xia Maolin , Shi Ruizhi , Jin Yanmei , Zhang Xiaoqing
Published: September 2024

This study aimed to investigate the differences in environmental adaptability between dzo and Tibetan yellow cattle by using corresponding assay kits to analyze blood indices, utilizing mass spectrometry for blood metabolite profiling, and performing 16S rDNA sequencing of fecal microbiota. Forty female cattle were randomly divided into a dzomo (female dzo) group (MG, n = 20) and a Tibetan-yellow-cattle group (HG, n = 20). After 150 days of uniform feeding, six cattle from each group were randomly picked for jugular blood sampling and collection of fecal microorganisms. The results showed that the serum albumin, creatinine, total protein, superoxide dismutase, IgG, and IgM concentrations in the MG group were higher (p < 0.05), whereas the serum triglyceride concentration was lower, compared to the HG group (p < 0.05). The higher level of phospholipids containing long-chain polyunsaturated fatty acids (PUFAs) (PC (18:5e/2:0), PC (20:5e/2:0), LPC 18:2, LPC 20:5) observed in the serum of the dzo suggests that they have an advantage in adapting to the challenging conditions of the plateau environment. The fecal microbiota analysis showed that Akkermansia was significantly enriched in the MG group; this might be the key bacterial genus leading to the strong adaptability of dzo. Our findings indicated the dzo’s superior adaptation to the Tibetan Plateau’s harsh environment.
Alkyl Pyridinol Compounds Exhibit Antimicrobial Effects against Gram-Positive Bacteria
Authors: Canchola Juan , Donkor Gracious Yoofi Boafo , Tawiah Patrick Ofori , Fasawe Ayoola , Ayim Emmanuel , Engelke Martin F. , Dahl Jan-Ulrik
Published: September 2024

Background/Objectives. The rise of antibiotic-resistant pathogens represents a significant global challenge in infectious disease control, which is amplified by the decline in the discovery of novel antibiotics. Staphylococcus aureus continues to be a highly significant pathogen, causing infections in multiple organs and tissues in both healthcare institutions and community settings. The bacterium has become increasingly resistant to all available antibiotics. Consequently, there is an urgent need for novel small molecules that inhibit the growth or impair the survival of bacterial pathogens. Given their large structural and chemical diversity, as well as often unique mechanisms of action, natural products represent an excellent avenue for the discovery and development of novel antimicrobial treatments. Anaephene A and B are two such naturally occurring compounds with significant antimicrobial activity against Gram-positive bacteria. Here, we report the rapid syntheses and biological characterization of five novel anaephene derivatives, which display low cytotoxicity against mammalian cells but potent antibacterial activity against various S. aureus strains, including methicillin-resistant S. aureus (MRSA) and the multi-drug-resistant community-acquired strain USA300LAC. Methods. A Sonogashira cross-coupling reaction served as the key step for the synthesis of the alkyl pyridinol products. Results/Conclusions. Using the compound JC-01-074, which displays bactericidal activity already at low concentrations (MIC: 16 μg/mL), we provide evidence that alkyl pyridinols target actively growing and biofilm-forming cells and show that these compounds cause disruption and deformation of the staphylococcal membrane, indicating a membrane-associated mechanism of action.
Antibacterial and Antibiofilm Potential of Chlorophyllin Against Streptococcus mutans In Vitro and In Silico
Authors: Khan Seemrose , Ul Haq Ihtisham , Ali Imran , Rehman Abdul , Almehmadi Mazen , Alsuwat Meshari A. , Zaman Tariq , Qasim Muhammad
Published: September 2024

Background: Streptococcus mutans is a leading causative agent of dental caries and exerts pathogenicity by forming biofilms. Dental caries continues to be a significant public health issue worldwide, affecting an estimated 2.5 billion people, showing a 14.6% increase over the past decade. Herein, the antibacterial potential of Chlorophyllin extracted from Spinacia oleracea was evaluated against biofilm-forming S. mutans via in vitro and in silico studies. Methodology: The antimicrobial activity of chlorophyllin extract against S. mutans isolates was tested using the agar well diffusion method. Chlorophyllin extract was also tested against biofilm-forming isolates of S. mutans. Chlorophyllin was docked with the antigen I/II (AgI/II) protein of S. mutans to evaluate its antimicrobial mechanism. The chemical structure and canonical SMILES format of Chlorophyllin were obtained from PubChem. Additionally, adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses of Chlorophyllin were performed using ADMETlab 2.0 to assess its pharmacokinetic properties. Results: An agar well diffusion assay revealed that all S. mutans isolates were susceptible to Chlorophyllin extract and showed a variety of inhibition zones ranging from 32 to 41 mm. Chlorophyllin reduces the biofilm strength of four isolates from strong to moderate and six from strong to weak. The antibiofilm potential of Chlorophyllin was measured by a reduction in the number of functional groups observed in the Fourier Transform Infrared Spectrometer (FTIR) spectra of the extracellular polymeric substance (EPS) samples. Chlorophyllin showed binding with AgI/II proteins of S. mutans, which are involved in adherence to the tooth surface and initiating biofilm formation. The ADMET analysis revealed that the safety of Chlorophyllin exhibited favorable pharmacokinetic properties. Conclusions: Chlorophyllin stands out as a promising antibacterial and antibiofilm agent against biofilm-forming S. mutans, and its safety profile highlights its potential suitability for further investigation as a therapeutic agent.
RETRACTED: Zhang et al. Weld Defect Segmentation in X-ray Image with Boundary Label Smoothing. Appl. Sci. 2022, 12, 12818
Authors: Zhang Junhua , Guo Minghao , Chu Pengzhi , Liu Yang , Chen Jun , Liu Huanxi
Published: September 2024

The Editorial Office retracts and removes the article, “Weld Defect Segmentation in X-ray Image with Boundary Label Smoothing” [...]
An Editorial for the Special Issue of “Recent Advances in Biomechanics of Human Movement and Its Clinical Applications”
Authors: Wang Weijie
Published: September 2024

When we get up in the morning, a question may occur in our brain [...]
Method for Detecting Underwater Microbubbles Using Dual-Mode Fusion of Laser Polarization
Authors: Zong Siguang , Yang Shaopeng , Liang Shanyong
Published: September 2024

Bubble detection in water plays important roles in human exploration and management of the ocean. This research presents a detection technique based on laser polarization dual-mode fusion, aiming at solving the difficulties of light scattering intensity characteristics that are hard to extract and the small particle size of underwater bubbles that are hard to detect. To increase the precision of bubble identification, an image fusion technique based on bubble polarization degree is first presented. Second, we quantitatively investigate the grayscale undulation of bubbles with different size and number distributions in the image from both statistical and experimental aspects, introduce image grayscale fluctuation (GF) to fuse two modes of laser polarization and the image, establish an a posteriori distribution probability model of discriminating features such as the size and number of bubbles, and realize the bubble small-sample, multi-source data fitting. The findings demonstrate that dynamic bubble detection in the 50–1000 μm and 100–2000 cm−3 ranges can achieve more than 95%, as well as more than a 93%, accuracy in quantity distribution and bubble size change. This technique achieves the continuous perception of bubble features in complicated underwater environments, and offers a possible application scheme for the detection of marine bubble environments.
Single-Shot Super-Resolution Phase Imaging Method Using Band-Limited Image and Its Fourier Transform Constraints via Analytic Extrapolation
Authors: Xiao Kang , Wang Zhongyang
Published: September 2024

The phase imaging system that records the bandlimited image and its Fourier intensity (BIFT) is a single-shot phase retrieval method with the guarantee of uniqueness and global convergence properties. However, the resolution is limited by the bandlimited imaging system and cannot investigate detailed structures under diffraction limitations. Previous efforts to address such issues focused on synthetic aperture techniques but sacrificed time resolution. In this paper, we propose a single-shot super-resolution imaging method based on analytic extrapolation. Through imaging simulations, we have demonstrated that the resolution can be improved by 1.58 in the case of noise-free. Theoretical analysis in the presence of noise is also carried out, indicating that the enhancement of resolution was determined by signal-to-noise ratio, and the resolution can be enhanced by 1.14 to 1.34 at different signal-to-noise ratios. Based on the single-shot capability of BIFT, this method has the potential to achieve fast and high-throughput phase imaging.
Assessment of the Water Quality of WWTPs’ Effluents through the Use of Wastewater Quality Index
Authors: Benkov Ivan , Tsakovski Stefan , Venelinov Tony
Published: September 2024

Evaluating the efficiency of wastewater treatment plants (WWTPs) and their impact on receiving surface water bodies is a complex and highly significant task due to its regulatory implications for both environmental and public health. The monitoring of many water quality parameters related to the compliance of treated wastewater with environmental standards has led to the development of a unitless metric, the Wastewater Quality Index (WWQI), which serves as a practical tool for regulatory authorities. The aim of this research is to propose an appropriate WWQI methodology, incorporating a set of water quality indicators and a weighting approach, to evaluate wastewater effluents under operational monitoring. In this study, WWQI was successfully applied to access the operation of 21 WWTPs’ effluents within a single monitoring campaign, outside the mandatory monitoring schemes. The WWQI was computed for physical-chemical parameters including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), electrical conductivity (EC) and pH, priority substances (Cd, Ni and Pb) and a specific contaminant (Cr) using the weighted approach in the WWQI calculation, based on equal weighting, expert judgement and PCA weighing using factor loadings. The three approaches give similar results for the calculated WWQI. The expert judgment approach is more suitable for evaluating WWTP performance during a single monitoring campaign due to its simplicity compared to the PCA-based approach and its ability to prioritize specific water quality parameters over an equal weightage method.
Enhancing Injection Molding Simulation Accuracy: A Comparative Evaluation of Rheological Model Performance
Authors: Baum Markus , Anders Denis , Reinicke Tamara
Published: September 2024

This contribution provides a detailed comparison of the impact of various rheological models on the filling phase of injection molding simulations in order to enhance the accuracy of flow predictions and improve material processing. The challenge of accurately modeling polymer melt flow behavior under different temperature and shear rate conditions is crucial for optimizing injection molding processes. Therefore, the study examines commonly used rheological models, including Power-Law, Second-Order, Herschel-Bulkley, Carreau and Cross models. Using experimental data for validation, the accuracy of each model in predicting the flow front and viscosity distribution for a quadratic molded part with a PA66 polymer is evaluated. The Carreau-WLF Winter model showed the highest accuracy, with the lowest RMSE values, closely followed by the Carreau model. The Second-Order model exhibited significant deviations in the edge region from experimental results, indicating its limitations. Results indicate that models incorporating both shear rate and temperature dependencies, such as Carreau-WLF Winter, provide superior predictions compared to those including only shear rate dependence. These findings suggest that selecting appropriate rheological models can significantly enhance the predictive capability of injection molding simulations, leading to better process optimization and higher quality in manufactured parts. The study emphasizes the significance of comprehensive rheological analysis and identifies potential avenues for future research and industrial applications in polymer processing.
Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
Authors: Chen Ruimin , Cao Ligang , Lu Congde , Liu Lei
Published: September 2024

Deep learning techniques have flourished in recent years and have shown great potential in ground-penetrating radar (GPR) data interpretation. However, obtaining sufficient training data is a great challenge. This paper proposes an intelligent recognition method based on slicing-aided hyper inference (SAHI) for GPR images. Firstly, for the problem of insufficient samples of GPR images with structural loose distresses, data augmentation is carried out based on deep convolutional generative adversarial networks (DCGAN). Since distress features occupy fewer pixels on the original image, to allow the model to pay greater attention to the distress features, it is necessary to crop the original images centered on the distress labeling boxes first, and then input the cropped images into the model for training. Then, the YOLOv5 model is used for distress detection and the SAHI framework is used in the training and inference stages. The experimental results show that the detection accuracy is improved by 5.3% after adding the DCGAN-generated images, which verifies the effectiveness of the DCGAN-generated images. The detection accuracy is improved by 10.8% after using the SAHI framework in the training and inference stages, which indicates that SAHI is a key part of improving detection performance, as it significantly improves the ability to recognize distress.
Thermal Computational Fluid Dynamics Simulation of Two Designs of Direct Dehydrators for Agricultural Products
Authors: Rodríguez-Romero Said Arturo , Toledano-Ayala Manuel , Macías-Bobadilla Gonzalo , Rivas-Araiza Edgar Alejandro , Flores-Aguilar Priscila Sarai , Soto-Zarazúa Genaro Martín
Published: September 2024

The dehydration process modifies the physical and chemical characteristics of certain crops, thereby increasing their shelf life and consequently reducing the organic waste generated. This process is contingent upon maintaining optimal temperature and humidity levels to prevent deterioration of the product. As indirect dehydrators have a high energy demand, new designs are required that facilitate the uniform distribution of air with a high-volume capacity of 100 kg per day. In the present study, computational fluid dynamics (CFD) techniques were employed to assess the drying performance of two dehydrator models. The simulations were executed in Solidworks 2020 and Flow Simulation, and they examined temperature distribution and velocity within the interior of the dehydrators. In Model 1, an inlet volume flow of 0.08 m3 s−1 and a heat source of 3.5 kW are considered, within a volume of 2.11 m3. In Model 2, an inlet volume flow of 0.03 m3 s−1 and two heat source of 2.5 kW are considered, within a volume of 2.02 m3. Model 1 was unable to achieve uniform air distribution within the drying chamber. In contrast, Model 2 demonstrated uniform velocity and temperature across the majority of the drying chamber, making it a superior option.
Interplay Effect in Spot-Scanning Proton Therapy with Rescanning, Breath Holding, and Gating: A Phantom Study
Authors: Belikhin Mikhail , Shemyakov Alexander , Ivanov Dmitry , Zavestovskaya Irina
Published: September 2024

The interplay effect is a challenge when using proton scanning beams for the treatment of thoracic and abdominal cancers. The aim of this study was to evaluate the facility-specific interplay effect in terms of dose distortion and irradiation time for different beam delivery modalities, including free breathing (FB) irradiation, rescanning, deep inspiration breath-hold (DIBH), and respiratory gating. This study was carried out at a synchrotron-based facility with spot-scanning beam delivery. A motion phantom with a radiochromic film was used to measure dose distributions. Regular and irregular motion patterns were studied. Dose homogeneity and the gamma index were calculated to quantify the interplay effect. The interplay effect significantly decreased the homogeneity and gamma passing rate by 12% and 46%, respectively, when FB irradiation without motion mitigation was used for 20 mm peak-to-peak motion. Rescanning and DIBH partially mitigated the distortions but doubled the irradiation time, while gating provided the superior dose distribution with only a 25% increase in time compared to FB irradiation without mitigation. The interplay effect was a function of motion amplitude and varied with the beam delivery modality. Gating may be a more preferable technique for the synchrotron-based facility in terms of minimizing dose distortion and treatment time.
Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?
Authors: Conte Luana , Rizzo Emanuele , Civino Emanuela , Tarantino Paolo , De Nunzio Giorgio , De Matteis Elisabetta
Published: September 2024

The association between genetics and lifestyle factors is crucial when determining breast cancer susceptibility, a leading cause of deaths globally. This research aimed to compare the body mass index, smoking behavior, hormonal influences, and BRCA gene mutations between affected patients and healthy individuals, all with a family history of cancer. All these factors were then utilized as features to train a machine learning (ML) model to predict the risk of breast cancer development. Between 2020 and 2023, a total of 1389 women provided detailed lifestyle and risk factor data during visits to a familial cancer center in Italy. Descriptive and inferential statistics were assessed to explore the differences between the groups. Among the various classifiers used, the ensemble of decision trees was the best performer, with a 10-fold cross-validation scheme for training after normalizing the features. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), alongside the accuracy, sensitivity, specificity, precision, and F1 score. Analysis revealed that individuals in the tumor group exhibited a higher risk profile when compared to their healthy counterparts, particularly in terms of the lifestyle and genetic markers. The ML model demonstrated predictive power, with an AUC of 81%, 88% sensitivity, 57% specificity, 78% accuracy, 80% precision, and an F1 score of 0.84. These metrics significantly outperformed traditional statistical prediction models, including the BOADICEA and BCRAT, which showed an AUC below 0.65. This study demonstrated the efficacy of an ML approach in identifying women at higher risk of breast cancer, leveraging lifestyle and genetic factors, with an improved predictive performance over traditional methods.
A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits
Authors: Balcázar-Zumaeta César R. , Maicelo-Quintana Jorge L. , Salón-Llanos Geidy , Barrena Miguel , Muñoz-Astecker Lucas D. , Cayo-Colca Ilse S. , Torrejón-Valqui Llisela , Castro-Alayo Efraín M.
Published: September 2024

Tropical fruits such as cherimoya, soursop, and pineapple share sugars (glucose, fructose, and sucrose) in common but may differ in the content of other phytochemicals. In the present work, confocal Raman spectroscopy and partial least squares discriminant analysis (PLS-DA) were used to establish a classification model among the three fruits and to evaluate the effect of pre-processing methods on the model’s performance. The Raman spectra showed that glucose was present in the fruits in the 800–900 cm−1 band and the 1100–1200 cm−1 band. While sucrose was present in the bands of 1131.22 cm−1, 1134.44 cm−1, and 1133.37 cm−1 in the three fruits, fructose was present in the bands of 1464.22 cm−1, 1467.44 cm−1, and 1464.22 cm−1 in cherimoya, soursop, and pineapple. The accuracy of the PLS-DA model varied according to the pre-processing methods used. The Savitzky–Golay first derivative method produced a model with 98.69–100% and 100% precision on the training and prediction data, respectively.
A Comprehensive Review of Silymarin Extraction and Liposomal Encapsulation Techniques for Potential Applications in Food
Authors: Makouie Sina , Bryś Joanna , Małajowicz Jolanta , Koczoń Piotr , Siol Marta , Palani Bharani K. , Bryś Andrzej , Obranović Marko , Mikolčević Sanja , Gruczyńska-Sękowska Eliza
Published: September 2024

This review explores advances in silymarin extraction and liposomal encapsulation techniques, highlighting their potential applications in the food, pharmaceutical, and cosmetic industries. The study evaluates a range of extraction techniques, including supercritical fluid extraction, microwave-assisted extraction, ultrasound-assisted extraction, and enzyme-assisted extraction, emphasising their efficiency and reduced environmental impact. Furthermore, it examines conventional and innovative liposomal encapsulation techniques, including supercritical carbon dioxide encapsulation and microfluidics, which enhance silymarin’s bioavailability and therapeutic effects. The integration of these methods promises more effective, safe, and eco-friendly silymarin products. This comprehensive review aims to inform readers of the latest research and future industrial applications, with a particular focus on the refinement and scaling up of these processes to meet commercial demands.
A Deep Reinforcement Learning-Based Algorithm for Multi-Objective Agricultural Site Selection and Logistics Optimization Problem
Authors: Liu Huan , Zhang Jizhe , Zhou Zhao , Dai Yongqiang , Qin Lijing
Published: September 2024

The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRPTWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indicate that the suggested MODRL-SIA algorithm outperforms other algorithms in every computational instance, further confirming its efficacy in lowering overall distribution expenses, carbon emissions, and the depletion of fresh produce in the supply chain of fresh agricultural products.