bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Mixed Planting on Machine Transplanting Adaptability and Grain Yield of Hybrid Rice
Authors: Deng Shiwen , Gao Pinglei , Wang Haoyu , Chen Yinglong , Wei Huanhe , Dai Qigen
Published: February 2023

Because the current hybrid rice machine transplanting sowing quantity is too large and the high rate of missing hills or poor seedling quality during mechanical transplanting, the mechanized planting of hybrid rice is limited, which results in significant hindrance of large-scale planting of hybrid rice in China. In this study, a mixed sowing technology (replacement of a portion of hybrid rice seeds with conventional rice seeds) in seedling cultivation was adopted to determine the appropriate variety combinations and conventional rice sowing quantity using a variety combination experiment and sowing quantity experiments, respectively. The results of the variety combination experiment showed that combinations of Changyou 4 mixed with Nanjing 5055, and Yuanliangyou mixed with Yangdao 6 could reduce the sowing quantity of hybrid rice, improve the quality of machine transplanting, and not reduce the grain yield. The results of the conventional rice sowing quantity experiment showed that with the increase in conventional rice sowing quantity, the seedling quality, spikelet per panicle, and filled kernel percentage decreased, and the mechanical transplanting quality improved. Compared with pure sowing hybrid rice, the grain yield was not significantly different in the japonica rice treatment when the sowing quantity of Nanjing 5055 was more than 75 g/tray and Yangdao 6 was more than 60 g/tray. With the increase in conventional rice sowing quantity, the missing hill rate decreased significantly and the seedling density increased significantly, so that the panicle number was also improved, which might have compensated the reduction in seedling quality, spikelet per panicle, and filled kernel percentage. As a whole, 90 g Nanjing 5055 seeds mixed with 30 g Changyou 4 seeds per tray and 75 g Yangdao 6 seeds mixed with 30 g Yuanliangyou seeds per tray were suitable sowing quantities for mixed planting that could ensure strong seedlings and better mechanical transplanting quality, while reducing the cost (reduce the sowing quantity of hybrid rice and conventional rice) without reducing the grain yield.
The Effect of Parity on the Quality of Colostrum of Holstein Dairy Cows in the Organic Production System
Authors: Grodkowska Kinga , Gołębiewski Marcin , Slósarz Jan , Grodkowski Grzegorz , Kostusiak Piotr , Sakowski Tomasz , Klopčič Marija , Puppel Kamila
Published: February 2023

A special feature of organic production systems is the cows’ significantly lower productivity, which is adapted to environmental conditions. The quantity and quality of colostrum is negatively correlated, high amounts of colostrum are associated with low amounts of immunoglobulins. Cows’ parity is also a modulating factor, and studies have shown an increase in colostral IgG corresponds with increasing parity. This study’s aim was to determine the effect of cows’ parity on colostrum quality, in an organic farm setting. From a basic organic herd of dairy cattle, 40 Polish Holstein–Friesian cows were selected: 10 cows each of primiparous, second lactation, fourth lactation, and fifth lactation. Colostrum and transition milk samples were taken from each cow seven times: twice daily on the 1st and 2nd days after calving (every 12 h), and once daily on the 3rd to 5th days. Multiparous cows’ colostrum had higher levels of total proteins, casein, and non-fat dry matter, versus primiparous. Only cows in the second and fourth lactations produced very good quality colostrum (with immunoglobulins over 50 g/L), meeting standards for immunoglobulin concentration. In conclusion, the production of very good quality colostrum is limited during the first lactation, which may suggest the mammary gland is poorly developed, and, thus, immunoglobulin transport is limited. Variability in the colostrum’s immunoglobulin content from first and second collections post-calving was higher in multiparous versus primiparous cows. Therefore, it should be good practice to freeze colostrum from multiparous cows in case of poor-quality primiparous colostrum.
Slaughterhouse Visual and Palpation Method for Estimating the Economic Damage of Porcine Proliferative Enteropathy (PPE)
Authors: Szabó István , Makkai István , Máté Péter , Molnár Tamás , Swam Hanny , von Berg Stephan , Holtkamp Derald J. , Glávits Róbert , Szabó István , Ózsvári László , Búza László
Published: February 2023

Background: Ileitis is a wasting disease of pigs. Clinical symptoms are diarrhea in growing pigs, wasting and reduced performance. Ileitis is ubiquitous in pig producing countries all around the world. It is estimated that the economic losses caused by the disease are USD 4.65 per fattening pig, and American pig farmers lose USD 56.1 million annually. It has become necessary to develop a slaughterhouse ileitis monitoring method that is simple, feasible to perform at modern slaughter lines, leads to immediate results and is cost effective. The practical experiences of applying the method are presented below. Methods: Our studies were performed on pig herds and slaughterhouses in Central European countries (Hungary, Romania, Poland, Croatia, and Slovakia). Experiences were evaluated based on visual and palpation. The results of our investigations were evaluated by a scoring method. Authors made histological and immunohistochemical examinations of investigated ileums. The hypothetic economic losses due to the disease in each farm were determined by estimating the loss of profit according to Holtkamp’s presentation in 2019. Results: The essentials of the method we have developed are: it can be performed during slaughterhouse processing, it does not interfere with or make it impossible to carry out normal technological processes, and the results of the procedure are correlated with the results of other laboratory diagnostic tests for ileitis (histology, immunohistochemistry, herd serology, fecal PCR). It is noteworthy that the results of the method can be used to immediately estimate the impact of Lawsonia intracellularis infection on the performance of the herd from which the slaughter animals come. Conclusion: Using the results of the slaughtered pigs’ visual and tactile examination at the slaughterhouse, the magnitude of the loss caused by Lawsonia intracellularis infection can be estimated quickly and accurately, and the return on investment of the strategy to be applied can be accurately planned.
Genetic Diversity and Fine-Scale Genetic Structure of Spodoptera litura Fabricius (Lepidoptera: Noctuidae) in Southern China Based on Microsatellite Markers
Authors: Hu Zhongwen , Yang Fangyuan , Zhang Deping , Zhang Shimeng , Yu Xiaofei , Yang Maofa
Published: February 2023

Population genetic structure is strongly affected by dispersal events, especially for migratory species. The investigation of population structure is therefore conducive to increasing our understanding of species dispersal. Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) is an important tobacco pest in China causing serious damage to multiple crops. In this study, we explore its dispersal dynamics by clarifying the fine-scale population genetics using 545 S. litura samples collected from tobacco plantations at 24 locations (mainly in Baise, Hechi, and Hezhou, Southern China). We analyzed the genetic diversity, genetic structure, and gene flow of these populations using seven microsatellite loci. Our results revealed high genetic diversity and low population genetic structure among S. litura. The genetic distance was uncorrelated with geographical distance, indicating the complete randomness of dispersal among the local populations. Our results suggest that the movement scope of contemporary S. litura might be much higher than the local-level spatial scale, which will provide a theoretical basis for pest management.
Novel Sulfonylurea Derivatives as Potential Antimicrobial Agents: Chemical Synthesis, Biological Evaluation, and Computational Study
Authors: Meng Fan-Fei , Shang Ming-Hao , Wei Wei , Yu Zhen-Wu , Liu Jun-Lian , Li Zheng-Ming , Wang Zhong-Wen , Wang Jian-Guo , Dai Huan-Qin
Published: February 2023

Methicillin-resistant Staphylococcus aureus (MRSA) is a worldwide health threat and has already tormented humanity during its long history, creating an urgent need for the development of new classes of antibacterial agents. In this study, twenty-one novel sulfonylurea derivatives containing phenyl-5-vinyl and pyrimidinyl-4-aryl moieties were designed and synthesized, among which, nine compounds exhibited inhibitory potencies against Gram-positive bacterial strains: MRSA (Chaoyang clinical isolates), S. aureus ATCC6538, vancomycin-resistant Enterococci-309 (VRE-309), and Bacillus subtilis ATCC 6633. Especially, 9i and 9q demonstrated inhibitory activities against the four bacterial strains with minimum inhibitory concentrations (MICs) of 0.78–1.56 μg/mL, and quite a few of other MRSA clinical strains with MICs of 0.78 μg/mL, superior to those of the positive controls vancomycin (MIC of 1 μg/mL) and methicillin (MIC of >200 μg/mL). This is the very first time that sulfonylurea derivatives have been identified as promising inhibitors against different MRSA clinical isolates. In addition, all the MIC values of the synthesized compounds against Candida albicans were greater than 100 μg/mL. Since the reported anti-Candida activities of sulfonylureas were due to acetohydroxyacid synthase (AHAS) inhibition, the molecular target against MRSA for the target sulfonylureas was thought to be a different mode of action. Density functional theory (DFT) calculations were finally performed to understand the structure–activity relationships, based on which, significant differences were observed between their HOMO maps for compounds with strong antibacterial activities and weak anti-MRSA effects. The present results hence provide valuable guidance for the discovery of novel agents to treat bacterial infections, especially against MRSA.
Physicochemical Characteristics and Bioactive Compounds of Different Types of Honey and Their Biological and Therapeutic Properties: A Comprehensive Review
Authors: Al-Kafaween Mohammad A. , Alwahsh Mohammad , Mohd Hilmi Abu Bakar , Abulebdah Dina H.
Published: February 2023

Honey is considered to be a functional food with health-promoting properties. However, its potential health benefits can be affected by individual composition that varies between honey types. Although studies describing the health benefits of Tualang honey (TH), Kelulut honey (KH), and Sidr honey (SH) are scarce, these honey types showed a comparable therapeutic efficacy to Manuka honey (MH). The purpose of this review is to characterise the physicochemical, biological, and therapeutic properties of TH, KH, and SH. Findings showed that these honeys have antibacterial, antifungal, antiviral, antioxidant, antidiabetic, antiobesity, anticancer, anti-inflammatory and wound-healing properties and effects on the cardiovascular system, nervous system, and respiratory system. The physicochemical characteristics of TH, KH, and SH were compared with MH and discussed, and results showed that they have high-quality contents and excellent biological activity sources. Flavonoids and polyphenols, which act as antioxidants, are two main bioactive molecules present in honey. The activity of honey depends on the type of bee, sources of nectar, and the geographic region where the bees are established. In conclusion, TH, KH, and SH could be considered as natural therapeutic agents for various medicinal purposes compared with MH. Therefore, TH, KH, and SH have a great potential to be developed for modern medicinal use.
Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
Authors: Miao Wenjun , Huang Rong , Huang Xiaoli , Gao Fei , Leng Xiangpeng , Li Qiu
Published: February 2023

Here, the polysaccharides from grape pomace, a by-product in the wine industry, were characterized and evaluated in vitro and in vivo. The polysaccharides were extracted and studied using spectroscopic and chemical methods. The results revealed that GPPs are rich in arabinose, galactose and glucuronic acid and are heteropolysaccharides without protein and nucleic acid, containing α-glycoside bonds with irregular clusters on the surface. In vitro antioxidant activity assays indicated that GPPs have concentration-dependent antioxidant activity. In vivo, GPPs markedly decreased the levels of TNF-a, IL-6, ALT, AST and MDA in serum and liver tissues and restored the levels of SOD, CAT and GSH. Additionally, further histopathological examination confirmed that GPPs could mitigate the injury of liver induced by CCl4. Our results demonstrate that GPPs had antioxidant and hepatoprotective effects, and they are expected to be a potential ingredient for functional foods or hepatoprotective drugs.
Investigation on the Association of Differential Evolution and Constructal Design for Geometric Optimization of Double Y-Shaped Cooling Cavities Inserted into Walls with Heat Generation
Authors: Gonzales Gill Velleda , Biserni Cesare , da Silva Diaz Estrada Emanuel , Platt Gustavo Mendes , Isoldi Liércio André , Rocha Luiz Alberto Oliveira , da Silva Neto Antônio José , dos Santos Elizaldo Domingues
Published: February 2023

In the constructal design method, the comprehension of the effect of design on the system performance is crucial to understanding the contributions of the degrees of freedom or constraints in the system evolution in direction of optimal configurations. However, problems with many degrees of freedom are prohibitive of optimization with exhaustive search, requiring meta-heuristic strategies. Therefore, the investigation of the optimization algorithms is essential. This work investigates the canonical differential evolution algorithm associated with the constructal design for the geometric optimization of an isothermal double Y-shaped cooling cavity inserted into a wall with internal heat generation. The effect of four degrees of freedom over the thermal performance of the system is investigated using sixteen different combinations of differential evolution algorithms: four variations of mutation parameter, two values of amplification factor (F) and two values of crossover rate (CR). The non-parametric statistical methods of Kruskal–Wallis and Dunn test were used to identify the parameters that improve the meta-heuristic efficiency. Results indicated that the proposed methodology selected the proper combination of DE algorithm parameters (CR, F, and mutation) that led to the best effect of degrees of freedom over the thermal performance in all optimization levels investigated.
Quantitative Calculation of the Most Efficient LED Light Combinations at Specific Growth Stages for Basil Indoor Horticulture: Modeling through Design of Experiments
Authors: Barbi Silvia , Barbieri Francesco , Taurino Claudia , Bertacchini Alessandro , Montorsi Monia
Published: February 2023

Indoor farms are a promising way to obtain vegetables in standard quantity and quality. As opposed to previous studies, this study attempts to calculate optimized LED light conditions for different growth stages (five-days time step) of basil (Ocimum basilicum) to enhance its indoor growth through a statistical approach. Design of Experiments (DoE) was used to plan a limited number of experiments (20) and to calculate quantitatively the effect of different light recipes on four responses: the number of plants, their height, the Leaf Area Index, and the amount of water used. Different proportions (from 25% to 77%) of Hyper Red (660 nm) and Deep Blue (451 nm), intensities in terms of LEDs–plant distance (60, 70 and 80 cm), and the addition of Warm White (3000 K) LEDs were considered as independent variables. The obtained models suggest that a light recipe tailored for every growth step in the plant’s life is beneficial. Appropriate LEDs must be carefully chosen at the beginning of growth, whereas distance becomes relevant at the end. This is confirmed by the results analysis carried out at the end of an additional growth test where the optimal light recipe extracted from the DoE’s results were used.
Performance Comparison of Machine Learning Disruption Predictors at JET
Authors: Aymerich Enrico , Cannas Barbara , Pisano Fabio , Sias Giuliana , Sozzi Carlo , Stuart Chris , Carvalho Pedro , Fanni Alessandra ,   the JET Contributors
Published: February 2023

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs.
Cramér–Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
Authors: Srinivas Sharanya , Welker Samuel , Herschfelt Andrew , Bliss Daniel W.
Published: February 2023

As radio frequency (RF) hardware continues to improve, many technologies that were traditionally impractical have suddenly become viable alternatives to legacy systems. Two-way ranging (TWR) is often considered a poor positioning solution for airborne and other vehicular navigation systems due to its low precision, poor angular resolution, and precise timing requirements. With the advent of modern RF hardware and advanced processing techniques, however, modern studies have experimentally demonstrated TWR systems with an unprecedented, sub-centimeter ranging precision with low size, weight, power, and cost (SWaP-C) consumer-grade hardware. This technique enables a new class of positioning, navigation, and timing (PNT) capabilities for urban and commercial aircraft but also instigates new system design challenges such as antenna placement, installation of new electronics, and design of supporting infrastructure. To inform these aircraft design decisions, we derive 2D and 3D Cramér–Rao lower bounds (CRLBs) on position and orientation estimation in a multi-antenna TWR system. We specifically formulate these bounds as a function of the number of antennas, platform geometry, and geometric dilution of precision (GDoP) to inform aircraft design decisions under different mission requirements. We simulate the performance of several classic position and orientation estimators in this context to validate these bounds and to graphically depict the expected performance with respect to these design considerations. To improve the accessibility of these highly theoretical results, we also present a simplified discussion of how these bounds may be applied to common airborne applications and suggest best practices for using them to inform aircraft design decisions.
Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
Authors: Bai Yang , Li Xiaocui , Wu Xinfan , Zhou Zhangbing
Published: February 2023

With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm for the provision of flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy-workload IoT device may not respond on time. EC has sparked a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration of user-requested offloading schemes creates a remarkable challenge regarding the delay in user requests and the energy consumption of IoT devices in edge networks. To solve this challenge, we propose a dynamic computation offloading strategy consisting of the following: (i) we propose the concept of intermediate nodes, which can minimize the delay in user requests and the energy consumption of the current tasks handled by IoT devices by dynamically combining task-offloading and service migration strategies; (ii) based on the workload of the current network, the intermediate node selection problem is modeled as a multi-dimensional Markov Decision Process (MDP) space, and a deep reinforcement learning algorithm is implemented to reduce the large MDP space and make a fast decision. Experimental results show that this strategy is superior to the existing baseline methods to reduce delays in user requests and the energy consumption of IoT devices.
Effect of Feed Rate on the Force and Energy in the Cutting Process Using Planar Technical Blades
Authors: Chaciński Tomasz , Zieliński Bartosz , Nadolny Krzysztof
Published: February 2023

Considering the technological processes taking place in fish processing enterprises (skinning), it is possible to specify various factors affecting the tool life and process efficiency. The paper presents the methodology of the study and the results and analysis of the collected data on the effect of the cutting speed of the technical blade used in the fish skinning process on the forces occurring in the cutting process. A special test stand was used in the study, which allowed obtaining repeatable results at a wide range of cutting speed values in the cutting process (70–400 mm/s). The study also determined the values of work W and power P, as well as the ej index (unit energy intensity index), which helped to select the most favorable blade feed rate parameter. The results indicated the most favorable cutting speed parameter and showed a clear relationship between the feed rate and the force and energy expenditure; as the cutting speed increases, an upward trend in force and energy expenditure was noted.
Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
Authors: Lu Qizhe , Jing Yicheng , Zhao Xuefeng
Published: February 2023

Machine vision based on deep learning is gaining more and more applications in structural health monitoring (SHM) due to the rich information that can be achieved in the images. Bolts are widely used in the connection of steel structures, and their loosening can compromise the safety of steel structures and lead to serious accidents. Therefore, this paper proposes a method for the automatic detection of the bolt loosening angle based on the latest key point detection technology using machine vision and deep learning. First, we built a virtual laboratory in Unreal Engine5 that could automatically label and generate synthetic datasets, and the datasets with bolts were collected. Second, the datasets were trained using the YOLOv7-pose framework, and the resulting model was able to accurately detect key points of bolts in images obtained under different angles and lighting conditions. Third, a bolt loosening angle calculation method was proposed according to the detected key points and the position relationship between neighboring bolts. Our results demonstrate that the proposed method is effective at detecting the bolt loosening angle and that the use of synthetic datasets significantly improves the efficiency of datasets establishment while also improving the performance of model training.
Application of Machine Learning to Predict the Mechanical Characteristics of Concrete Containing Recycled Plastic-Based Materials
Authors: Rezvan Sina , Moradi Mohammad Javad , Dabiri Hamed , Daneshvar Kambiz , Karakouzian Moses , Farhangi Visar
Published: February 2023

One of the practical ways to overcome the adverse environmental effects of plastic bottle waste is to implement bottles into concrete, one of the most widely used materials in the construction industry. Plastic bottles are mainly made of polyethylene terephthalate (PET) and can be used as a fiber to reinforce concrete. In recent years, PET fiber-reinforced concrete (PFRC) has attracted researcher attention, and several experimental studies have been conducted. This paper aims to present the benefits of using PET fiber as a reinforcing element in concrete using a machine learning approach. By considering the effect of PET fibers in concrete, engineers and stakeholders may be encouraged to further use these recycled materials. The proposed network was successfully able to capture the response of PFRC with high accuracy (mean squared error (MSE) of 7.11 MPa and R coefficient of 98%). The results of the proposed network show that the amount of PET fiber usage in concrete has a significant effect on the compressive strength of PFRC. Moreover, the PFRC’s response considering the variation of mechanical and geometrical properties of PET fiber mainly depends on the fiber’s shape. The most effective shapes of PET fiber are shapes with deformation, followed by embossed and irregular shapes.
Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
Authors: Osei Richard Sakyi , Lopez Daphne
Published: February 2023

Catastrophic forgetting is a significant challenge in deep reinforcement learning (RL). To address this problem, researchers introduce the experience replay (ER) concept to complement the training of a deep RL agent. However, the buffer size, experience selection, and experience retention strategies adopted for the ER can negatively affect the agent’s performance stability, especially for complex continuous state action problems. This paper investigates how to address the stability problem using an enhanced ER method that combines a replay policy network, a dual memory, and an alternating transition selection control (ATSC) mechanism. Two frameworks were designed: an experience replay optimisation via alternating transition selection control (ERO-ATSC) without a transition storage control (TSC) and an ERO-ATSC with a TSC. The first is a hybrid of experience replay optimisation (ERO) and dual-memory experience replay (DER) and the second, which has two versions of its kind, integrates a transition storage control (TSC) into the first framework. After comprehensive experimental evaluations of the frameworks on the pendulum-v0 environment and across multiple buffer sizes, retention strategies, and sampling ratios, the reward version of ERO-ATSC with a TSC exhibits superior performance over the first framework and other novel methods, such as the deep deterministic policy gradient (DDPG) and ERO.
Shared Logistics—Literature Review
Authors: Matusiewicz Maria , Książkiewicz Dorota
Published: February 2023

In the face of the energy crisis and the ever-increasing earth’s temperature, one of the ways to deal with these two challenges is to make better use of the existing resources, decoupling, and the circular economy. The solution for the full use of resources is in sharing them, and also in logistics. We decided to review the existing research and concepts in the area of shared logistics to summarize where science is on this subject, what is already known, what is missing, and what is worth supplementing. This paper summarizes the scientific achievements in shared logistics in the 2017–2021 period. Our goal was to create a literature review that would serve as a benchmark for future researchers about what has been achieved and what has not been achieved on the topic, which approaches have already been tried, and what the results have been. For readers other than researchers, our review will provide an important context about where science and practice are in the area of shared logistics. Desk analysis, visual analysis, and benchmark datasets of approaches have been reviewed and discussed to show possible aspects of shared logistics and the advantages it offers to supply chains across many industries. Our analysis showed that the most commonly discussed topics in the area of shared logistics are modeling the potential of sharing in logistics, and digitalization of processes. The last popular topic among researchers is fleet sharing and shared packaging, as well as barriers to implementation and data security. Our paper is a summary of current knowledge on shared logistics, and it highlights areas that require further research.
Development of a Control Algorithm for a Semi-Active Mid-Story Isolation System Using Reinforcement Learning
Authors: Kim Hyun-Su , Kim Uksun
Published: February 2023

The semi-active control system is widely used to reduce the seismic response of building structures. Its control performance mainly depends on the applied control algorithms. Various semi-active control algorithms have been developed to date. Recently, machine learning has been applied to various engineering fields and provided successful results. Because reinforcement learning (RL) has shown good performance for real-time decision-making problems, structural control engineers have become interested in RL. In this study, RL was applied to the development of a semi-active control algorithm. Among various RL methods, a Deep Q-network (DQN) was selected because of its successful application to many control problems. A sample building structure was constructed by using a semi-active mid-story isolation system (SMIS) with a magnetorheological damper. Artificial ground motions were generated for numerical simulation. In this study, the sample building structure and seismic excitation were used to make the RL environment. The reward of RL was designed to reduce the peak story drift and the isolation story drift. Skyhook and groundhook control algorithms were applied for comparative study. Based on numerical results, this paper shows that the proposed control algorithm can effectively reduce the seismic responses of building structures with a SMIS.
Antioxidant Activity of Coffee Components Influenced by Roast Degree and Preparation Method
Authors: Wołosiak Rafał , Pakosz Paulina , Drużyńska Beata , Janowicz Monika
Published: February 2023

Coffee is of interest to consumers and researchers, mainly due to its stimulating properties and positive effects on the human body. Properties of coffee infusions depend on many factors and the most important ones include the roasting process and the brew preparation method. In this paper, we analyzed infusions prepared with the use of pressure methods (professional: espresso and consumer: moka pot brew), obtained from beans roasted to the medium and dark degree, with the aim of determining the share of individual groups of ingredients in the antioxidative properties of coffee. Tested infusions showed various characteristics that were partly related to different parameters of their preparation. In general, a greater amount of phenolic compounds was extracted in moka brews. Espresso, on the other hand, exhibited greater antioxidant activity per portion of the brew and a greater ability to quench free radicals. The degree of roasting had an uneven effect on the antioxidant activity of the brews, depending on the method and its mechanism of antioxidant action. The share of activity of the particular groups of antioxidants in overall activity varied between all methods; the most pronounced changes were observed in the espresso brews. The results indicate, however, that the activity of phenols and melanoidins is based on a mixed mechanism of antioxidant action, but in the case of the latter, the mechanism of hydrogen atom transfer may have an advantage.
Special Issue “Advances in Neuroimaging Data Processing”
Authors: Hramov Alexander. E. , Pisarchik Alexander. N.
Published: February 2023

The development of in vivo neuroimaging technology has led to an incredible amount of digital information concerning the brain [...]
Age Assessment through Third Molar Teeth Developmental Stage in Children
Authors: Sadan Noa , Aderet Netta , Tagger-Green Nirit , Ratson Tal , Laviv Amir
Published: February 2023

Age determination is important for forensic evaluation and for determining criminal responsibility. Based on the development of permanent teeth, the Demirjian staging evaluation key is commonly used in research for estimating age using teeth developmental stage. The purpose of the current study was to correlate the dental developmental age of third molar teeth with the chronological age of adolescent patients. This study evaluated 324 panorex images of Israeli patients aged 8–21 years with known birth dates and genders. The dental developmental stage was compared to the patient’s chronological age by gender and side (right/left and upper/lower jaws). This study also attempted to predict the patient’s age using the third molar developmental stage. The study included 179 male and 145 female patients. There was no statistical difference between the right and left sides of the upper or lower teeth. A strong correlation was found between third molar development and chronological age for both the maxilla and mandible. In 100% of cases, patients in developmental stage >5 were over 12 years old in the maxilla and in 97% of cases, were over 12 years old in the mandible. Patients in developmental stage >6 were over 14 years old in 100% of cases in both the maxilla and mandible. Patients in developmental stage >7 were over 16 years old in 100% of cases in both the maxilla and mandible. This study found that third molars can be used to accurately estimate age in a narrow age range, with a strong correlation between third molar development and chronological age. Further research may be needed to understand the utility of this method and its possible usage in different countries around the world.
DeepBreastCancerNet: A Novel Deep Learning Model for Breast Cancer Detection Using Ultrasound Images
Authors: Raza Asaf , Ullah Naeem , Khan Javed Ali , Assam Muhammad , Guzzo Antonella , Aljuaid Hanan
Published: February 2023

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.
Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
Authors: Cheng Yao , Zhang Guichao , Huang Sifei , Wang Zexi , Cheng Xuan , Lin Juncong
Published: February 2023

Extracting gait biometrics from videos has been receiving rocketing attention given its applications, such as person re-identification. Although deep learning arises as a promising solution to improve the accuracy of most gait recognition algorithms, the lack of enough training data becomes a bottleneck. One of the solutions to address data deficiency is to generate synthetic data. However, gait data synthesis is particularly challenging as the inter-subject and intra-subject variations of walking style need to be carefully balanced. In this paper, we propose a complete 3D framework to synthesize unlimited, realistic, and diverse motion data. In addition to walking speed and lighting conditions, we emphasize two key factors: 3D gait motion style and character appearance. Benefiting from its 3D nature, our system can provide various gait-related data, such as accelerometer data and depth map, not limited to silhouettes. We conducted various experiments using the off-the-shelf gait recognition algorithm and draw the following conclusions: (1) the real-to-virtual gap can be closed when adding a small portion of real-world data to a synthetically trained recognizer; (2) the amount of real training data needed to train competitive gait recognition systems can be reduced significantly; (3) the rich variations in gait data are helpful for investigating algorithm performance under different conditions. The synthetic data generator, as well as all experiments, will be made publicly available.
A Phased Array Antenna with Novel Composite Right/Left-Handed (CRLH) Phase Shifters for Wi-Fi 6 Communication Systems
Authors: Ayaz Muhammad , Ullah Irfan
Published: February 2023

A linear phased array antenna excited with a novel composite right/left-handed (CRLH) phase shifters structure is proposed. The phase of the conventional CRLH transmission line is controlled with magnetically aligned micron-sized particles embedded inside the unit cell of the CRLH transmission line. The cascading of unit cells produces the desired phase shifts for the main beam scanning of the linear antenna array operating at a 5.5 GHz center frequency for Wi-Fi 6 applications. The proposed phase shifter design has a very low insertion loss (0.5–2 dB), excellent matching characteristics with the antenna array (less than −10 dB) and a small phase error (1–2 degrees). A 1 × 4 linear patch antenna phased array operating at a 5.5 GHz center frequency of the Wi-Fi 6 band is simulated using the Method of Moments (MoM) simulator platform. Then, the array is driven with the proposed novel CRLH phase shifters for the main beam at broadside and the main beam steered at 15- and 30-degree scan angles toward the desired users. For experimental validation, multiple unit cells of the proposed phase shifters are fabricated, and the 1 × 4 patch antenna array is fed with these fabricated unit cells of the phase shifters. The phased array radiation patterns are measured using an in-house fully calibrated anechoic chamber and were compared with simulated phased array patterns. The measured phased array patterns are in good agreement with the simulated patterns. As compared with commercially available phase shifters, the proposed novel CRLH phase shifters do not need external complex biasing circuitry, which is a major advantage in space constraint limitations at the router side of multi-user MIMO-OFDM systems.
Improved Cascade Correlation Neural Network Model Based on Group Intelligence Optimization Algorithm
Authors: Deng Jun , Li Qingxia , Wei Wenhong
Published: February 2023

The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we propose the single-objective optimization algorithm jDE-B and the multi-objective optimization algorithm MOEA-T, and improve the network expansion mode in the learning process of Cascade Correlation neural networks. We investigate the effect of applying the group intelligent optimization algorithm in the Cascade Correlation learning algorithm. Experimental results show that our improved algorithm is able to enhance the ability of the Cascade Correlation neural network to fit problems, reduce the number of hidden units and the depth of the network, and optimize the network structure.
Design and Modeling of Ultra-Compact Wideband Implantable Antenna for Wireless ISM Band
Authors: Zaki Ahmed , Hamad Ehab , Abouelnaga Tamer , Elsadek Hala , Khaleel Sherif , Al-Gburi Ahmed , Zakaria Zahriladha
Published: February 2023

This paper proposes a wideband ultra-compact implantable antenna for a wireless body area network (WBAN). The proposed patch antenna works in the industrial, scientific, and medical (ISM) bands. The proposed patch antenna with an ultra-compact size (5 × 5 × 0.26 mm3) was designed with 29% wide bandwidth (about 670 MHz). This wide bandwidth makes the antenna unaffected by implantation in different human body parts. The miniaturization process passed many steps by adding many slots with different shapes in the radiating element as well as in the ground plane. A 50 Ω coaxial feeding excites the antenna to maintain matching and low power loss. The specific absorption rate (SAR) was calculated for health considerations. The result was within the standard limits of IEEE organizations and the International Commission on Non-Ionizing Radiation Protection (ICNRP). The antenna was tested in tissues with multiple layers (up to seven layers) and at various depths (up to 29 mm). The link margin was calculated, and the proposed antenna enables 100 Kbps of data to be transferred over a distance of 20 m and approximately 1 Mbps over a distance of 7 m. The proposed antenna was fabricated and tested. The measured S11 parameters and the simulated results using the Computer Simulation Technology (CST Studio) simulator were in good agreement.
Engineering In Situ Weldable Vascular Devices
Authors: Cohn Daniel , Widlan Fany , Zarek Matt , Peselev Ziv , Bloom Allan Isaac
Published: February 2023

(1) Background: The minimally invasive implantation of medical devices is largely limited by their insertion profile, and, therefore, minimizing them constitutes a leading trend in the field. (2) Methods: This study introduces the in situ welding strategy, whereby the components of the stent grafts used to treat abdominal aortic aneurysms were decoupled, deployed sequentially, and welded together at the aneurysmal site, greatly reducing their insertion profile. Polyurethane elastomers were used to produce the graft and to coat the metallic struts of the stent to render it in vivo weldable. Results: The composition of the polyurethanes was fine-tuned, so to minimize the insertion profiles and optimize the welding properties and the clinical performance of the devices assembled. The stent and graft were deployed successively in pigs via a small 8F introducer, in situ welded, and the patency of the bi-component device was confirmed over a three-month post-implantation period. The strength of the stent/graft welded connection was fully retained, with no de-welding observed. Conclusions: The in situ welding strategy resulted in implantations that were easier to perform and markedly less injurious to tissues and organs, largely expanding the applicability of these ultra-minimally invasive procedures to especially frail segments of the population.
Macroanatomical, Histological and Microtomographic Study of the Teeth of the Komodo Dragon (Varanus komodoensis)—Adaptation to Hunting
Authors: Janeczek Maciej , Goździewska-Harłajczuk Karolina , Hrabska Ludwika , Klećkowska-Nawrot Joanna , Kuropka Piotr , Dobrzyński Maciej , Melnyk Oleksii , Nikodem Anna
Published: February 2023

The present study aimed to characterize the macrostructure and microstructure of the mandibular teeth of the Komodo dragon (Varanus komodoensis) and the methods it uses to obtain food. Examinations were performed using a stereoscopic microscope, autofluorescence method, histological method and computed microtomography. A detailed macro- and micro-structural description of V. komodoensis mandibular teeth were made. The mandibular teeth are laterally flattened along their entire length and the dental crown is hooked caudally. The part of the nasal margin of the tooth crown is irregular, while the caudal margin of the tooth is characteristically serrated, except for the tooth base area. There are longitudinal grooves on the lingual and vestibular surfaces up to the lower third of the tooth height. The mandibular tooth is surrounded by a cuff made of the oral mucosa, containing the opening of the venom gland. In the histological structure of the tooth, the enamel covering the tooth crown and the dentin under the enamel are distinguished. The inside of the tooth, except its basal part, is filled with the tooth chamber, while the inside of the lower part of the tooth is filled with plicidentine, which corresponds to external furrows on the enamel. The plicidentine arrangement resembles a honeycomb. A small amount of dentine folds reach up to the tooth apex. Characteristic features of the structure of the mandibular teeth in V. komodoensis may indicate their significant role, in addition to the venom glands, in obtaining food in the natural environment of this species.
A Pathogenic Role of Non-Parenchymal Liver Cells in Alcohol-Associated Liver Disease of Infectious and Non-Infectious Origin
Authors: Kharbanda Kusum K. , Chokshi Shilpa , Tikhanovich Irina , Weinman Steven A. , New-Aaron Moses , Ganesan Murali , Osna Natalia A.
Published: February 2023

Now, much is known regarding the impact of chronic and heavy alcohol consumption on the disruption of physiological liver functions and the induction of structural distortions in the hepatic tissues in alcohol-associated liver disease (ALD). This review deliberates the effects of alcohol on the activity and properties of liver non-parenchymal cells (NPCs), which are either residential or infiltrated into the liver from the general circulation. NPCs play a pivotal role in the regulation of organ inflammation and fibrosis, both in the context of hepatotropic infections and in non-infectious settings. Here, we overview how NPC functions in ALD are regulated by second hits, such as gender and the exposure to bacterial or viral infections. As an example of the virus-mediated trigger of liver injury, we focused on HIV infections potentiated by alcohol exposure, since this combination was only limitedly studied in relation to the role of hepatic stellate cells (HSCs) in the development of liver fibrosis. The review specifically focusses on liver macrophages, HSC, and T-lymphocytes and their regulation of ALD pathogenesis and outcomes. It also illustrates the activation of NPCs by the engulfment of apoptotic bodies, a frequent event observed when hepatocytes are exposed to ethanol metabolites and infections. As an example of such a double-hit-induced apoptotic hepatocyte death, we deliberate on the hepatotoxic accumulation of HIV proteins, which in combination with ethanol metabolites, causes intensive hepatic cell death and pro-fibrotic activation of HSCs engulfing these HIV- and malondialdehyde-expressing apoptotic hepatocytes.