bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production
Authors: Gao Xiaotong , Li Songwei , Su Xiaotong , Li Yan , Huang Lingyun , Tang Weidong , Zhang Yuanchen , Dong Min
Published: July 2024

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production.
Evaluation of Grain Moisture Content at Maturity and Screening for Identification Indexes of Maize Inbred Lines
Authors: Gao Yuqian , Li Jianping , Ning Ruiyao , Zheng Yunxiao , Song Weibin , Hou Peng , Zhu Liying , Jia Xiaoyan , Zhao Yongfeng , Song Wei , Guo Rui , Guo Jinjie
Published: July 2024

The grain moisture content of maize inbred lines at maturity is one of the most important indicators for mechanical harvesting of kernels. In this study, 116 maize inbred lines from a wide range of sources were used as research materials and 30 traits of grain moisture content were analyzed using multivariate statistical analysis. The results showed that all 30 traits had some correlations. Principal component analysis downscaled the 30 traits into 10 principal component factors, reflecting 77.674% of the information in the original traits. Cluster analysis categorized the 116 inbred lines into 5 major groups containing 26, 29, 31, 16 and 14 inbred lines. Based on the D value of the overall evaluation, discriminant analysis reclassified the maize inbred lines by principal component scores and 98 maize inbred lines were correctly discriminated with a probability of 84.48%, which can be regarded as a relatively reliable clustering result. The stepwise regression method was further used to screen seven traits: GMC2, GDR1, HMC3, NH, GDR2, CD and EL and to establish a comprehensive evaluation model for the grain moisture content of maize inbred lines. Among 116 maize inbred lines, 14, represented by H21 and MS71, had the lowest grain moisture content at maturity.
Operation Mechanism Analysis and Parameter Optimization of Bean Impurity-Separation Device for Tiger Nut Harvester
Authors: Chen Wenhui , Yang Luoyi , Peng Huijie , Kan Za , Meng Hewei , Qi Jiangtao
Published: July 2024

This study aimed to enhance the efficiency of tiger nut combine harvesters by reducing impurity and loss rates during processing. Scholars focused on analyzing the composition and suspension speed of the bean mixture, leading to the development of a wind-screen impurity-removal method. The wind-screen-type bean-separation device was designed with a cross-flow fan, louver screen, frame, and driving mechanism. Theoretical analysis was employed to discuss the motion characteristics and behavior of the sieve body and material, thereby revealing the screening dynamics of tiger nuts and impurities. Factors such as crank radius, crank speed, and fan speed were identified as crucial for optimizing separation performance. Initial single-factor tests helped narrow down the range of influencing factors. Subsequently, a three-factor, three-level Box–Behnken test was conducted with crank radius, crank speed, and fan speed as variables and impurity rate and loss rate as evaluation indexes. This led to the establishment of a multiple regression equation linking these factors to the evaluation indexes. Through response surface analysis and multi-objective optimization using the regression model, the optimal operational parameters for the device were determined: crank radius of 45 mm, crank speed of 497 r/min, and the fan speed of 1100 r/min. Theoretical calculations predicted an impurity rate of 2.42% and a loss rate of 0.51%. Verification tests confirmed these findings, showing an average impurity rate of 2.53% and a loss rate of 0.56%, which met the mechanized harvesting standards for tiger nuts. Overall, this study introduces a novel method and technical framework for effectively separating tiger nuts from impurities, thereby advancing the mechanization of tiger nut harvesting processes.
Assembly and Annotation of the Complete Genome Sequence of the Paenibacillus Bacteriophage phJNUCC32
Authors: Xu Yang , Liang Xuhui , Hyun Chang-Gu
Published: July 2024

A potential biocontrol agent for American foulbrood (AFB), the Paenibacillus bacteriophage phJNUCC32, was isolated from Baengnokdam in Halla Mountain. This study aimed to investigate its genomic characteristics through whole-genome sequencing. The genome of phJNUCC32 was found to be 62,871 base pairs in length, with a G + C content of 51.98%. Phylogenetic analysis classified phJNUCC32 within the unclassified Caudoviricetes bacteriophage category. The genome prediction confirmed the absence of virulence factors and antibiotic-resistance genes, ensuring its genetic safety. A total of 63 coding DNA sequences were identified, revealing a modular arrangement. Notably, the annotation of gene function indicates that phJNUCC32 harbors the holin/lysin system, suggesting significant potential for controlling bacterial infections in AFB and agriculture.
Exploring Bacteriophage Applications in Medicine and Beyond
Authors: Elfadadny Ahmed , Ragab Rokaia F. , Abou Shehata Manar A. , Elfadadny Medhat R. , Farag Ahmed , Abd El-Aziz Ayman H. , Khalifa Hazim O.
Published: July 2024

Bacteriophages, or phages, are microscopic viruses that specifically infect and replicate within bacterial hosts. Their unique ability to target and control bacterial populations makes them valuable tools with applications ranging from human medicine and agriculture and environmental management to biotechnology. In this comprehensive review, we explore the diverse and promising medical and non-medical applications of bacteriophages, highlighting their pivotal role across various niches. From safeguarding food production through pathogen control to their innovative utilization in wastewater treatment, bacteriophages prove to be versatile agents. To achieve applications of phages on a larger scale, it is necessary to make the legal framework more suitable and flexible, create special approval programs (e.g., for novel antimicrobial drugs), and promote targeted research and development activities on phages. Additionally, a more intensive exchange between academia, industry, regulatory authorities, and stakeholders in the health system should be pursued.
Clinical Anatomy of the Lower Extremity Veins—Topography, Embryology, Anatomical Variability, and Undergraduate Educational Challenges
Authors: Simka Marian , Czaja Joanna , Kawalec Agata
Published: July 2024

Veins of the lower extremity can be categorized into three hierarchically ordered groups: the epifascial, the interfascial, and the deep ones. In the past, the interfascial veins, e.g., the great saphenous vein, were categorized as superficial veins. But nowadays, experts recommend regarding these veins as a separate group because of their unique topography and clinical relevance. In order to better understand the venous anatomy of the lower limbs, which is highly variable, one should also comprehend their embryological development. Venous embryogenesis in the lower limb consists of three stages. During the first stage the primitive fibular vein is the main vein of the extremity. During the second stage it is replaced by the axial vein and finally by the femoral vein. In some adult individuals this embryonic or fetal venous anatomy is still present. Unfortunately, current anatomical textbooks and atlases, as well as traditional cadaver dissections, are not very useful regarding these issues. Therefore, undergraduate teaching of anatomy can be challenging. New educational tools, such as ultrasonography, seem indispensable to teach the anatomy of these veins properly.
In the Eye of the Beholder—Visual Search Behavior in Equestrian Dressage Judges
Authors: Wolframm Inga , Reuter Peter , Zaharia Iulia , Vernooij Johannes
Published: July 2024

This study investigated the visual search behavior of equestrian dressage judges at different expertise levels during the assessment of Grand Prix horse-rider combinations. Twenty judges (11 foundational level, 9 advanced level) participated in the study, with their eye movements recorded using Tobii Fusion Eyetracker as they evaluated video recordings of dressage tests. Fixation metrics, namely Total Duration of Fixation (TDF), Average Duration of Fixation (ADF), and Total Number of Fixations (TNF), were analyzed across four Areas of Interest (AOIs): front, back, rider, and horse’s feet. Statistical analysis utilized linear mixed-effects models. Results demonstrated that judges consistently focused more on the front of the horse, with additional differences in fixation duration and frequency based on judge experience and specific movements. Advanced judges focused more on the horses’ feet, suggesting they draw meaning from specific areas indicative of performance quality. Conversely, foundational level judges focused more on the rider, reflecting different evaluative priorities at lower levels of the sport. These findings suggest that judges focus on a limited number of highly relevant areas, differing across movements and expertise levels. The study underscores the necessity of understanding both gaze behavior and subsequent interpretations of visual information to increase judging transparency, fairness, and equine welfare.
Inter-Antibody Variability in the Clinical Pharmacokinetics of Monoclonal Antibodies Characterized Using Population Physiologically Based Pharmacokinetic Modeling
Authors: Kumar Mokshada , Lanke Sravani , Yadav Alka , Ette Mfonabasi , Mager Donald E. , Shah Dhaval K.
Published: July 2024

The objective of this work was to develop a population physiologically based pharmacokinetic (popPBPK) model to characterize the variability in the clinical PK of monoclonal antibodies (mAbs) following intravenous (IV) and subcutaneous (SC) administration. An extensive literature search was conducted and clinical PK data for FDA-approved as well as non-approved mAbs were collected. Training and validation datasets of 44 and 9 mAbs exhibiting linear pharmacokinetics were used for model development. The variability in antibody PK was captured by accounting for different rate constants of pinocytosis (CLup) and intracellular degradation (kdeg) for different mAbs. Typical values for CLup and kdeg and their respective inter-antibody variabilities (ωClup, ωKdeg) were estimated to be 0.32 L/h/L and 26.1 h−1 (73% and 46%). Varied absorption profiles following SC dosing were characterized by incorporating inter-antibody variability in local degradation (kSC) and rate of lymphatic uptake (S_Lu) of mAbs. Estimates for typical kSC and S_Lu values, and ωKsc,ωS_Lu, were found to be 0.0015 h−1 and 0.54 (193%, and 49%). FDA-approved mAbs showed less local degradation (0.0014 h−1 vs. 0.0038 h−1) compared with other clinically tested mAbs, whereas no substantial differences in physiological processes involved in disposition were observed. To evaluate the generalizability of estimated PK parameters and model validation, the final popPBPK model was used to simulate the range of expected PK for mAbs following SC administration of nine different mAbs that were not used for model-building purposes. The predicted PK of all nine mAbs was within the expected range specified a priori. Thus, the popPBPK model presented here may serve as a tool to predict the clinical PK of mAbs with linear disposition before administering them to humans. The model may also support preclinical-to-clinical translation and ‘first-in-human’ dose determination for mAbs.
Green Synthesis of Metallic Nanoparticles from Quercus Bark Extracts: Characterization and Functional Properties
Authors: Coman Năstaca-Alina , Nicolae-Maranciuc Alexandra , Berța Lavinia , Nicolescu Alexandru , Babotă Mihai , Man Adrian , Chicea Dan , Farczadi Lenard , Jakab-Farkas László , Silva Barbara , Veiga-Matos Jéssica , Tanase Corneliu
Published: July 2024

Quercus species are utilized for their durable wood, providing sustenance for wildlife, conserving biodiversity, and contributing ecological, medicinal, and esthetic benefits to ecosystems and landscapes. In this study, we aimed to use the bark of three Quercus species (Q. dalechampi, Q. fraineto, and Q. petraea) for the synthesis of silver and gold nanoparticles (AgNPs and AuNPs). The aqueous extracts from the bark of Quercus sp. acted both as reducing and stabilizing agent, facilitating the rapid synthesis of AuNPs (AuQD, AuQF, and AuQP) and AgNPs (AgQD, AgQF, and AgQP). The obtained nanoparticles were characterized using UV-vis spectroscopy, TEM, DLS, and FTIR. Characterizations revealed that the nanoparticles exhibited a variety of shapes, such as polygonal, triangular, and spherical forms, with sizes ranging between 14 and 24 nm for AuNPs and 45–70 nm for AgNPs. The total phenolic content was assessed through spectroscopic methods, while several individual phenolic compounds were identified and quantified using UPLC-PDA. Furthermore, we assessed the antioxidant, antibacterial, and antifungal capacities of AuNPs, AgNPs, and raw extracts. The highest antioxidant activity was observed for raw extracts, followed by AgNPs and AuNPs, while the most potent antibacterial and antifungal activity was observed in AgQP. Moreover, cytotoxicity was examined in a human keratinocyte cell line (HaCaT). The results indicated no cytotoxic effects for AuNPs, while AgNPs and the raw extracts exhibited cytotoxic effects after 48 h of incubation. This research underscores the multifaceted utility of Quercus bark extracts in the green synthesis of metallic nanoparticles and their subsequent bioactivity assessment, suggesting promising perspectives for their application in various fields while urging cautious consideration of their cytotoxic implications.
Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models
Authors: Chen Jiayu , Liu Lisang , Guo Kaiqi , Liu Shurui , He Dongwei
Published: July 2024

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model.
Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
Authors: Tache (Ungureanu) Maria Daniela , Păscuțoiu Ovidiu , Borcoci Eugen
Published: July 2024

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.
The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities
Authors: Sakaida Miu , Yoshimura Takaaki , Tang Minghui , Ichikawa Shota , Sugimori Hiroyuki , Hirata Kenji , Kudo Kohsuke
Published: July 2024

Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patches, derived from 169 mammogram images, was used to train a ResNet50-based classifier. The classifier was evaluated using patches generated from 43 mammograms as a test data set. Additionally, 500 more mammogram images were processed into patches and analyzed using the trained ResNet50 model, with semi-supervised learning applied to patches exceeding certain classification probabilities. This process aimed to enhance the classifier’s accuracy and achieve improvements over the initial model. The findings indicated that semi-supervised learning significantly benefits the accuracy of calcification detection in mammography, underscoring its utility in enhancing diagnostic methodologies.
A Standard Penetration Test-Based Step-by-Step Inverse Method for the Constitutive Model Parameters of the Numerical Simulation of Braced Excavation
Authors: Shen Mengfen , Zhong Wencheng , Zhan Hong , Zhang Xuefeng
Published: July 2024

Numerical simulation is an essential method for predicting soil deformation caused by foundation pit excavation. Its accuracy relies on the constitutive model and its parameters. However, obtaining these parameters through lab tests has limitations like long durations, high costs, and potential errors. To improve the simplicity and accuracy of the selection of constitutive model parameters, this study proposes a step-by-step inverse analytical method. Using the braced excavation project in Hangzhou City, China as a case study, the proposed method firstly determines the ratio of the important constitutive parameters then the values of the key constitutive parameters. The results show that the reference secant stiffness (E50ref) and shear strain (γ0.7) of the hardening soil (HS) and hardening soil–small soil (HSS) models are the key constitutive parameters. The step-by-step inverse method not only reduces the number of parameters, but also improves the predicting accuracy. The established empirical relationship between the E50ref and standard penetration test (SPT) blow counts exhibits a good linear correlation. The parameter selection method proposed in this study is an accurate, practical, and efficient method, which can effectively predict the horizontal displacement and surface settlement of the retaining structure in multiple excavation stages.
Parameter Design and Optimization of Grass Aerial Seeding Tower Based Computational Fluid Dynamics
Authors: Wang Bingjie , Zhang Yingzi
Published: July 2024

Unstable airflow driven by natural wind forces inside a grass aerial seeding tower is a technical problem that needs to be improved. The authors used FLUENT 2020 R2 to simulate a basic nozzle unit, which is the fundamental component of a grass aerial seeding tower. The interior flow characteristics of the tower were first analyzed. Then, an optimization was undertaken to enhance the internal airflow’s uniform stability, taking the cross-sectional inclination angle as the design parameter. The coefficient of variation, uniformity index, and velocity direction index were considered as constraints. The simulation results indicate that, subsequent to traversing the Laval nozzle unit, the grass seeds undergo a considerable acceleration. At an inclination angle of 15°, while ensuring the attainment of desired exit velocities, a commendable balance of uniformity and directional consistency was achieved.
Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
Authors: Tamai Hiroki , Kishikawa Takuro , Yamamoto Daisuke
Published: July 2024

The alkali–silica reaction (ASR), a major cause of cracks in concrete, is a critical issue in the maintenance of social infrastructure. In this study, a concrete mesoscale model was meticulously developed, and a coupled stress–moisture model was also developed to reproduce ASR degradation. The aim was to investigate the effect of ASR degradation on the bending load-carrying capacity of RC beams. The concrete mesoscale model, specifically designed to reproduce ASR degradation, was modeled from three phases: coarse aggregate, mortar, and ITZ (interfacial transition zone). ASR was considered as the expansion of the coarse aggregate, and the objective was to reproduce expansion cracks with numerical analysis using the mesoscale model. Uniaxial compression tests were carried out on cylindrical specimens with ASR-accelerated deterioration to clarify the relationship between ASR deterioration and compressive properties, and the experimental results were used to identify material parameters in the mesoscale analysis model. The results showed that the model proposed in this study can reproduce the change in compressive properties due to expansion cracking. Finally, RC beams were constructed using the mesoscale model, and the effect of ASR degradation on the bending load-carrying capacity of the RC beams was investigated. The results showed that the presence of expansion cracks caused the initial stiffness of the load-displacement curves to decrease, but the bearing capacity tended to increase. This suggests that factors other than cracks, such as chemical prestress and boundary conditions in this model, have a strong influence on the load-bearing capacity of deteriorated RC beams.
Viscosity of Clayey Soils: Experimental Studies
Authors: Ter-Martirosyan Armen Z. , Ermoshina Lyubov Yu. , Anzhelo George O.
Published: July 2024

Due to the high rate of the development of housing, transportation and hydraulic engineering construction in the last hundred years, the study of the phenomenon of creep of clay soils has become a subject of scientific research. In the study, experimental investigations of clay soil were conducted using a simple shear device in kinematic loading mode, aimed at examining the influence of shear rate on the viscosity coefficient of the clay soil and its strength characteristics. The tests were performed at four different shear rates and three different vertical load values. Based on the results of experimental and theoretical studies, the viscosity coefficients of clay soil were obtained, and a new rheological equation was proposed, which simultaneously takes into account the influence of Coulomb friction, structural cohesion, cohesion of water–colloidal bonds and viscous resistance of the soil. It has been shown that the shear rate has a significant impact on the viscosity coefficient of clay soil, and the viscosity coefficient itself is a variable quantity, depending both on the magnitude of the applied load and the duration of its application. The obtained results can be used for further improvement of methods for calculating the settlement of structures over time, as well as for predicting the time until the bearing capacity of foundation soils is exhausted.
A Novel Approach for Meat Quality Assessment Using an Ensemble of Compact Convolutional Neural Networks
Authors: Elangovan Poonguzhali , Dhurairajan Vijayalakshmi , Nath Malaya Kumar , Yogarajah Pratheepan , Condell Joan
Published: July 2024

The rising awareness of nutritional values has led to an increase in the popularity of meat-based diets. Hence, to ensure public health, industries and consumers are focusing more on the quality and freshness of this food. Authentic meat quality assessment methods can indeed be exorbitant and catastrophic. Furthermore, it is subjective and reliant on the knowledge of specialists. Fully automated computer-aided diagnosis systems are required to eradicate the variability among experts. However, evaluating the quality of meat automatically is challenging. Deep convolutional neural networks have shown a tremendous improvement in meat quality assessment. This research intends to utilize an ensemble framework of shallow convolutional neural networks for assessing the quality and freshness of the meat. Two compact CNN architectures (ConvNet-18 and ConvNet-24) are developed, and the efficacy of the models are evaluated using two publicly available databases. Experimental findings reveal that the ConvNet-18 outperforms other state-of-the models in classifying fresh and spoiled meat with an overall accuracy of 99.4%, whereas ConvNet-24 shows a better outcome in categorizing the meat based on its freshness. This model yields an accuracy of 96.6%, which is much better compared with standard models. Furthermore, the suggested models effectively detect the quality and freshness of the meat with less complexity than the existing state-of-the art techniques.
The Influence of Milling Granulation on The Proportion of Fermentable and Non-Fermentable Ingredients in Mash Obtained from Mixed Endosperm Wheat
Authors: Krstanović Vinko , Mastanjević Krešimir , Košir Iztok Jože , Jurić Miroslav , Habschied Kristina
Published: July 2024

The use of purpose-selected wheat in brewing is limited because it is either not suitable for the application of usual agrotechnical measures or, more often, is not adapted to specific climatic conditions. As unmalted wheat in brewing can cause process problems and problems with the quality composition of mash/wort and consequently beer, by using the appropriate type and variety, but also by adjusting the malting process, these problems can be completely avoided or significantly reduced at the start. Two varieties were selected for the research, which in previous research showed good brewing/malting properties and which have significantly different starting values for the share of total protein (TP) in the grain. The goal of this research was to determine the effect of grain milling granulation on the quality of mash obtained from a hard type of wheat, i.e., the changes in the fermentable and non-fermentable extract ratio during mashing. Varieties with higher and lower initial contents of total protein (TP) in the grain were tested. It was found that regardless of the initial difference in TP concentration, the concentrations of total soluble proteins (TSP) and high molecular protein fraction (HMW N) are similar for varieties with higher and lower initial TP and that the difference in initial TP concentrations is shown as a difference in the concentration of insoluble protein fractions, which are eliminated during fermentation. By increasing the milling granulation in both kinds of wheat, there is a significant increase in the medium molecular protein fraction (MMW N), which is more pronounced in wheat with a lower starting concentration of TP, even though the starting concentrations of TSP in fine (Ø 0.2 mm) and the superfine milling (Ø < 0.2 mm) were similar. A significant drop in fermentability at the superfine granulation of the grain was noted.
Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression
Authors: Zhong Jiafeng , Zhan Hongwu , Xu Fang , Zhang Yinwei
Published: July 2024

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.
Experimental Study to Visualize a Methane Leak of 0.25 mL/min by Direct Absorption Spectroscopy and Mid-Infrared Imaging
Authors: Strahl Thomas , Bergau Max , Maier Eric , Herbst Johannes , Rademacher Sven , Wöllenstein Jürgen , Schmitt Katrin
Published: July 2024

Tunable laser spectroscopy (TLS) with infrared (IR) imaging is a powerful tool for gas leak detection. This study focuses on direct absorption spectroscopy (DAS) that utilizes wavelength modulation to extract gas information. A tunable interband cascade laser (ICL) with an optical power of 5 mW is periodically modulated by a sawtooth injection current at 10 Hz across the methane absorption around 3271 nm. A fast and sensitive thermal imaging camera for the mid-infrared range between 3 and 5.7 µm is operated at a frame rate of 470 Hz. Offline processing of image stacks is performed using different algorithms (DAS-F, DAS-f and DAS-2f) based on the Lambert–Beer law and the HITRAN database. These algorithms analyze various features of gas absorption, such as area (F), peak (f) and second derivative (2f) of the absorbance. The methane concentration in ppm*m is determined on a pixel-by-pixel analysis without calibration. Leak localization for methane leak rates as low as 0.25 mL/min is accurately displayed in a single concentration image with pixelwise sensitivities of approximately 1 ppm*m in a laboratory environment. Concentration image sequences represent the spatiotemporal dynamics of a gas plume with high contrast. The DAS-2f concept demonstrates promising characteristics, including accuracy, precision, 1/f noise rejection, simplicity and computational efficiency, expanding the applications of DAS.
A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection
Authors: Li Gengsong , Zheng Qibin , Liu Yi , Li Xiang , Qin Wei , Diao Xingchun
Published: July 2024

Data missing is a ubiquitous problem in real-world systems that adversely affects the performance of machine learning algorithms. Although many useful imputation methods are available to address this issue, they often fail to consider the information provided by both features and labels. As a result, the performance of these methods might be constrained. Furthermore, feature selection as a data quality improvement technique has been widely used and has demonstrated its efficiency. To overcome the limitation of imputation methods, we propose a novel algorithm that combines data imputation and feature selection to tackle classification problems for mixed data. Based on the mean and standard deviation of quantitative features and the selecting probabilities of unique values of categorical features, our algorithm constructs different imputation models for quantitative and categorical features. Particle swarm optimization is used to optimize the parameters of the imputation models and select feature subsets simultaneously. Additionally, we introduce a legacy learning mechanism to enhance the optimization capability of our method. To evaluate the performance of the proposed method, seven algorithms and twelve datasets are used for comparison. The results show that our algorithm outperforms other algorithms in terms of accuracy and F1 score and has reasonable time overhead.
A Short Review of Second-Generation Isobutanol Production by SHF and SSF
Authors: Akita Hironaga , Matsushika Akinori
Published: July 2024

As isobutanol exhibits higher energy density and lower hygroscopicity than ethanol, it is considered a better candidate biofuel. The sustainable supply of inedible biomass and lack of competition with the food supply have stimulated significant worldwide interest in the production of isobutanol from this resource. Both separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF) have been applied to isobutanol production to effectively utilize inedible biomass as a feedstock. However, both processes have various challenges, including low isobutanol yield and high production costs. This review summarizes the potential of isobutanol as a biofuel, methods for conferring isobutanol productivity, recent experimental studies, and developments in both SHF and SSF with the isobutanol-producing strains. Challenges to increasing the isobutanol yield and various suggestions for improvements to enable commercial production are also discussed.
From Algebro Geometric Solutions of the Toda Equation to Sato Formulas
Authors: Gaillard Pierre
Published: July 2024

We know that the degeneracy of solutions to PDEs, given in terms of theta functions on Riemann surfaces, provides important results about particular solutions, as in the case of the NLS equation. Here, we degenerate the so called finite gap solutions of the Toda lattice equation from the general formulation in terms of abelian functions when the gaps tend to points. This degeneracy allows us to recover the Sato formulas without using inverse scattering theory or geometric or representation theoretic methods.
Can Music Enhance Working Memory and Speech in Noise Perception in Cochlear Implant Users? Design Protocol for a Randomized Controlled Behavioral and Electrophysiological Study
Authors: Mertel Kathrin , Dimitrijevic Andrew , Thaut Michael
Published: July 2024

Background: A cochlear implant (CI) enables deaf people to understand speech but due to technical restrictions, users face great limitations in noisy conditions. Music training has been shown to augment shared auditory and cognitive neural networks for processing speech and music and to improve auditory–motor coupling, which benefits speech perception in noisy listening conditions. These are promising prerequisites for studying multi-modal neurologic music training (NMT) for speech-in-noise (SIN) perception in adult cochlear implant (CI) users. Furthermore, a better understanding of the neurophysiological correlates when performing working memory (WM) and SIN tasks after multi-modal music training with CI users may provide clinicians with a better understanding of optimal rehabilitation. Methods: Within 3 months, 81 post-lingual deafened adult CI recipients will undergo electrophysiological recordings and a four-week neurologic music therapy multi-modal training randomly assigned to one of three training focusses (pitch, rhythm, and timbre). Pre- and post-tests will analyze behavioral outcomes and apply a novel electrophysiological measurement approach that includes neural tracking to speech and alpha oscillation modulations to the sentence-final-word-identification-and-recall test (SWIR-EEG). Expected outcome: Short-term multi-modal music training will enhance WM and SIN performance in post-lingual deafened adult CI recipients and will be reflected in greater neural tracking and alpha oscillation modulations in prefrontal areas. Prospectively, outcomes could contribute to understanding the relationship between cognitive functioning and SIN besides the technical deficits of the CI. Targeted clinical application of music training for post-lingual deafened adult CI carriers to significantly improve SIN and positively impact the quality of life can be realized.
Optimized Hierarchical Sliding Mode Control for the Swing-Up and Stabilization of a Rotary Inverted Pendulum
Authors: Pham Duc-Binh , Dao Quy-Thinh , Nguyen Thi-Van-Anh
Published: July 2024

This paper presents a study on controlling a rotary inverted pendulum (RIP) system using a hierarchical sliding mode control (HSMC) approach. The objective is to swing up and stabilize the pendulum at a desired position. The proposed HSMC controller addresses the underactuation challenge through a hierarchical structure of sliding surfaces. The particle swarm optimization (PSO) algorithm is used to optimize the controller parameters. Simulations were performed to evaluate the performance of the HSMC controller at different initial pendulum angles, demonstrating its effectiveness in achieving swing-up and stabilization. The integration of the PSO algorithm enhances the controller’s adaptability and robustness, emphasizing the benefits of combining optimization algorithms with controller parameter tuning for underactuated systems like the RIP.
Smooth Logistic Real and Complex, Ordinary and Fractional Neural Network Approximations over Infinite Domains
Authors: Anastassiou George A.
Published: July 2024

In this work, we study the univariate quantitative smooth approximations, including both real and complex and ordinary and fractional approximations, under different functions. The approximators presented here are neural network operators activated by Richard’s curve, a parametrized form of logistic sigmoid function. All domains used are obtained from the whole real line. The neural network operators used here are of the quasi-interpolation type: basic ones, Kantorovich-type ones, and those of the quadrature type. We provide pointwise and uniform approximations with rates. We finish with their applications.
Exploring the Surface: Sampling of Potential Skin Cancer Biomarkers Kynurenine and Tryptophan, Studied on 3D Melanocyte and Melanoma Models
Authors: Hasterok Sylwia , Jankovskaja Skaidre , Miletic Dahlström Ruzica , Prgomet Zdenka , Ohlsson Lars , Björklund Sebastian , Gustafsson Anna
Published: July 2024

Early detection of cancer via biomarkers is vital for improving patient survival rates. In the case of skin cancers, low-molecular-weight biomarkers can penetrate the skin barrier, enabling non-invasive sampling at an early stage. This study focuses on detecting tryptophan (Trp) and kynurenine (Kyn) on the surface of reconstructed 3D melanoma and melanocyte models. This is examined in connection with IDO-1 and IL-6 expression in response to IFN-γ or UVB stimulation, both crucial factors of the melanoma tumor microenvironment (TME). Using a polystyrene scaffold, full-thickness human skin equivalents containing fibroblasts, keratinocytes, and melanocytes or melanoma cells were developed. The samples were stimulated with IFN-γ or UVB, and Trp and Kyn secretion was measured using HPLC-PDA and HPLC-MS. The expression of IDO-1 and IL-6 was measured using RT-qPCR. Increased Trp catabolism to Kyn was observed in IFN-γ-stimulated melanoma and melanocyte models, along with higher IDO-1 expression. UVB exposure led to significant changes in Kyn levels but only in the melanoma model. This study demonstrates the potential of skin surface Trp and Kyn monitoring to capture TME metabolic changes. It also lays the groundwork for future in vivo studies, aiding in understanding and monitoring skin cancer progression.
Discovery of Benzisothiazolone Derivatives as Bifunctional Inhibitors of HIV-1 Reverse Transcriptase DNA Polymerase and Ribonuclease H Activities
Authors: Vázquez Rivera Alondra , Donald Heather , Alaoui-El-Azher Mounia , Skoko John J. , Lazo John S. , Parniak Michael A. , Johnston Paul A. , Sluis-Cremer Nicolas
Published: July 2024

The ribonuclease H (RNase H) active site of HIV-1 reverse transcriptase (RT) is the only viral enzyme not targeted by approved antiretroviral drugs. Using a fluorescence-based in vitro assay, we screened 65,239 compounds at a final concentration of 10 µM to identify inhibitors of RT RNase H activity. We identified 41 compounds that exhibited 50% inhibitory concentration (i.e., IC50) values < 1.0 µM. Two of these compounds, 2-(4-methyl-3-(piperidin-1-ylsulfonyl)phenyl)benzo[d]isothiazol-3(2H)-one (1) and ethyl 2-(2-(3-oxobenzo[d]isothiazol-2(3H)-yl)thiazol-4-yl)acetate (2), which both share the same benzisothiazolone pharmacophore, demonstrate robust antiviral activity (50% effective concentrations of 1.68 ± 0.94 µM and 2.68 ± 0.54, respectively) in the absence of cellular toxicity. A limited structure–activity relationship analysis identified two additional benzisothiazolone analogs, 2-methylbenzo[d]isothiazol-3(2H)-one (3) and N,N-diethyl-3-(3-oxobenzo[d]isothiazol-2(3H)-yl)benzenesulfonamide (4), which also resulted in the inhibition of RT RNase H activity and virus replication. Compounds 1, 2 and 4, but not 3, inhibited the DNA polymerase activity of RT (IC50 values~1 to 6 µM). In conclusion, benzisothiazolone derivatives represent a new class of multifunctional RT inhibitors that warrants further assessment for the treatment of HIV-1 infection.
EGFR Mutation and TKI Treatment Promote Secretion of Small Extracellular Vesicle PD-L1 and Contribute to Immunosuppression in NSCLC
Authors: Liu Hai-Ming , Yu Zi-Li , Xia Hou-Fu , Zhang Lin-Zhou , Fu Qiu-Yun , Wang Yi , Gong Hong-Yun , Chen Gang
Published: July 2024

In Asian populations with non-small-cell lung cancer (NSCLC), EGFR mutations are highly prevalent, occurring in roughly half of these patients. Studies have revealed that individuals with EGFR mutation typically fare worse with immunotherapy. In patients who received EGFR tyrosine kinase inhibitor (TKI) treatment followed by anti-PD-1 therapy, poor results were observed. The underlying mechanism remains unclear. We used high-resolution flow cytometry and ELISA to detect the circulating level of small extracellular vesicle (sEV) PD-L1 in NSCLC individuals with EGFR mutations before and after receiving TKIs. The secretion amount of sEV PD-L1 of lung cancer cell lines with EGFR mutations under TKI treatment or not were detected using high-resolution flow cytometry and Western blotting. The results revealed that patients harboring EGFR mutations exhibit increased levels of sEV PD-L1 in circulation, which inversely correlated with the presence of CD8+ T cells in tumor tissues. Furthermore, tumor cells carrying EGFR mutations secrete a higher quantity of PD-L1-positive sEVs. TKI treatment appeared to amplify the levels of PD-L1-positive sEVs in the bloodstream. Mutation-induced and TKI-induced sEVs substantially impaired the functionality of CD8+ T cells. Importantly, our findings indicated that EGFR mutations and TKI therapies promote secretion of PD-L1-positive sEVs via distinct molecular mechanisms, namely the HRS and ALIX pathways, respectively. In conclusion, the increased secretion of PD-L1-positive sEVs, prompted by genetic alterations and TKI administration, may contribute to the limited efficacy of immunotherapy observed in EGFR-mutant patients and patients who have received TKI treatment.
Biomass and Circular Economy: Now and the Future
Authors: Hsiao Chen-Jie , Hu Jin-Li
Published: July 2024

Biomass is inherently organic and renewable, promoting a circular economy ecosystem. As global consumption patterns change, circular economy strategies have turned into sustainable net-zero strategies for developing countries and developed countries, and its value chain is now included in important biomass energy policies. Many countries are actively transforming their economic growth patterns, developing their own circular economy, targeting ecological sustainable development, and adjusting domestic industrial structures. The concept of a circular society, synergistic with the social economy and developed on the basis of the circular economy, has production and consumption at its core. This research aims to verify the important roles that biomass plays in the circular economy and to initiate a virtuous resource circulation model, promote material recycling and reuse, form a “resources-products-renewable resources” model, and promote better resource use efficiency. It discusses the important roles that the bioeconomy plays when achieving a circular economy and also proposes new economic and policy concepts. The key conclusions cover: (1) the biomass energy–circular economy business model; (2) recognizing the co-benefit of consumers and a prosumer circular economy; and (3) challenges to a renewable cycle under economic applications.