bluuebunny/crossref_metadata_2025 huggingface dataset:
A Unified Numerical Approach to the Dynamics of Beams with Longitudinally Varying Cross-Sections, Materials, Foundations, and Loads Using Chebyshev Spectral Approximation
Authors: Liu Haizhou , Huang Yixin , Zhao Yang
Published: September 2023

Structures with inhomogeneous materials, non-uniform cross-sections, non-uniform supports, and subject to non-uniform loads are increasingly common in aerospace applications. This paper presents a simple and unified numerical dynamics model for all beams with arbitrarily axially varying cross-sections, materials, foundations, loads, and general boundary conditions. These spatially varying properties are all approximated by high-order Chebyshev expansions, and discretized by Gauss–Lobatto sampling. The discrete governing equation of non-uniform axially functionally graded beams resting on variable Winkler–Pasternak foundations subjected to non-uniformly distributed loads is derived based on the Euler–Bernoulli beam theory. A projection matrix method is employed to simultaneously assemble spectral elements and impose general boundary conditions. Numerical experiments are performed to validate the proposed method, considering different inhomogeneous materials, boundary conditions, foundations, cross-sections, and loads. The results are compared with those reported in the literature and obtained by the finite element method, and excellent agreement is observed. The convergence, accuracy, and efficiency of the proposed method are demonstrated.
Information Acquisition Ability and Farmers’ Herd Behavior in Rice–Crayfish Coculture System Adoption
Authors: Huang Weihong , Yang Caiyan , Liu Ke , Min Rui
Published: September 2023

Rice–crayfish coculture systems (RCSs) have been widely promoted in China as an efficient circular agriculture model that can simultaneously improve rice quality, raise the efficiency of utilization of resources, and increase farmers’ incomes. However, the herd behavior of farmers in the adoption of the rice–crayfish coculture system warrants more attention, as the rational adoption of this system by farmers is a prerequisite for achieving the above objectives. This paper analyses the formation mechanism of farmers’ herd behavior based on information cascade theory. Using micro-survey data from 603 farmers in China and a bivariate probit model, we examine the existence of herding effects in the adoption of RCSs by farmers and the inhibitory effect of information acquisition capability on herd behavior. To address possible endogeneity issues, we also conducted a robustness test using the IV-probit model. Furthermore, we tested for between-group differences in agricultural income between farmers with herding adoption and non-herding adoption. The study reveals that the larger the number of prior adopters, the higher the probability of blind adoption behavior by farmers, indicating the existence of a herd effect. Information acquisition ability could significantly inhibit the herd behavior of farmers in the adoption of RCSs. We also found that farmers’ herd behavior in the adoption of these systems was not economically rational. Based on the research findings, we proposed several constructive suggestions for policy perfection.
Perspectives on Effective Long-Term Management of Carbon Stocks in Chernozem under Future Climate Conditions
Authors: Husniev Ilshat , Romanenkov Vladimir , Siptits Stanislav , Pavlova Vera , Pasko Sergey , Yakimenko Olga , Krasilnikov Pavel
Published: September 2023

Arable Chernozems with high SOC contents have the potential to be significant sources of GHGs, and climate change is likely to increase SOC losses, making the issue of carbon sequestration in this region even more important. The prospect of maintaining SOC stock or increasing it by 4‰ annually under planned management practice modifications for the period up to 2090 was evaluated using a long-term experiment on Haplic Chernozem in the Rostov Region, Russia. In this study, we used the RothC model to evaluate SOC dynamics for three treatments with mineral and organic fertilization under two adaptation scenarios vs. business-as-usual scenarios, as well as under two climate change scenarios. The correction of crop rotation and the application of organic fertilizers at high rates are essential tools for maintaining and increasing SOC stocks. These methods can maintain SOC stock at the level of 84–87 Mg∙ha−1 until the middle of the 21st century, as the first half of the century is considered to be the most promising period for the introduction of adaptation measures for the additional accumulation of SOC on Chernozems. Part of the additional accumulated SOC is expected to be lost before 2090.
Model Development of the Phenological Cycle from Flower to Fruit of Strawberries (Fragaria × ananassa)
Authors: Hernández-Martínez Nelda , Salazar-Gutiérrez Melba , Chaves-Córdoba Bernardo , Wells Daniel , Foshee Wheeler , McWhirt Amanda
Published: September 2023

Strawberries are a very important economic crop; thus, a lot of research has been conducted on several production areas. However, phenological performance is still lacking information, especially when it comes to modeling. Therefore, this study aims to develop a phenological model for flower–fruit development under hydroponic conditions to support growers’ decision-making. Two day-neutral cultivars, ‘Albion’ and ‘San Andreas’, were established in a drip hydroponic system in Auburn, Alabama for the 2022–2023 production season. Phenological data were collected daily on 30 flowers per cultivar for three periods (Oct 25–Dec 16, Dec 27–Feb 21, and Feb 28–Apr 16). Weather data were obtained from a weather station placed in the greenhouse. Growing degree days (GDD) accumulation was calculated for each stage and cycle using a base temperature (Tb) of 3 °C. The Gaussian model was adjusted for each stage and cycle using a non-linear procedure to obtain Gaussian curves. Simulations were made for the model assuming temperature would increase or decrease by 1 °C. Six stages were identified, and their cycle ranged from 43–56 days to be accomplished. ‘Albion’ needed more days to reach maturity, with 51, 56, and 47 days, and ‘San Andreas’ took 43, 54, and 46 days for cycles 1, 2, and 3 respectively. In addition, for cycles 1 and 2, not all the buds reached maturity, as expected. Stage 5 (fruit formation) needed more days than the rest of the stages to be completed. Because of the different starting dates for each cycle, the starting GDD was different as well. A sensitivity analysis simulation of the model showed that if temperature decreases by 1 °C, the GDD accumulated to complete the stages would be less (same dates), and it would be more if the temperature increased by 1 °C. The opposite happened with the days, if the temperature increased by 1 °C, the duration of the stage decreased, and it would increase if the temperature decreased by 1 °C, affecting stages 4, 5, and 6. Overall, ‘San Andreas’ performed better than ‘Albion’ under hydroponic conditions during three productive cycles.
Reconstructing the Invasive History and Potential Distribution Prediction of Amaranthus palmeri in China
Authors: Jiao Xinyu , Long Mei , Li Jiayi , Yang Qingyu , Liu Zhixiong
Published: September 2023

Palmer Amaranth (Amaranthus palmeri, Amaranthaceae) is one of the most competitive, troublesome, and noxious weeds causing significant yield reductions in various crops. A. palmeri was also a herbicide-resistant weed causing a serious eco-environmental problem. Given that the process of invasion is dynamic, the A. plamer invasion may already be quite severe where invasive species management and surveys are chronically lacking. Predicting the potential habitat of A. palmeri can help to develop effective measures for early warning and long-term detection. However, the invasive history and distribution patterns of A. palmeri in China remain largely unknown. Here, the invasive history and distribution patterns of A. palmeri from 1985 to 2022 in China were reconstructed, and then the potential geographical distribution of A. palmeri was predicted under current and future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) using the optimal MaxEnt model (V 3.4.4) and ArcGIS 10.8.2. The mean AUC values of A. palmeri were 0.967. Under the current climate conditions, the suitable habitat areas for A. palmeri reached 1,067,000 km2 in China and were mainly distributed in north and central China. Under the future scenarios, the highly suitable habitats were mainly distributed in Beijing, Tianjin, and Hebei. Under SSP2–4.5, the future suitable areas will reach the maximum and expand to 1,411,100 km2 in the 2060s. The centroid distribution would northwestward extend under future climate scenarios. The human footprint index, mean temperature of the warmest quarter (Bio_10), April wind speed (Wind_4), temperature seasonality (standard deviation × 100) (bio_4), topsoil gravel content (T_gravel), and precipitation of warmest quarter (Bio_18) were key environmental variables affecting distribution and growth of A. palmeri. Climate change would increase the risk of A. palmeri expanding to high latitudes. Our results will help in developing effective strategies for the early warning, prevention, control, and management of A. palmeri in China.
Evaluation Method of Potato Storage External Defects Based on Improved U-Net
Authors: Zhang Kaili , Wang Shaoxiang , Hu Yaohua , Yang Huanbo , Guo Taifeng , Yi Xuemei
Published: September 2023

The detection of potato surface defects is the key to ensuring potato storage quality. This research explores a method for detecting surface flaws in potatoes, which can promptly identify storage defects such as dry rot and the shriveling of potatoes. In order to assure the quality and safety of potatoes in storage, we used a closed keying method to obtain the pixel area of the mask image for a potato’s surface. The improved U-Net realizes the segmentation and pixel area measurement of potato surface defects and enhances the feature extraction capability of the network model by adding a convolutional block attention module (CBAM) to the baseline network. Compared with the baseline network, the improved U-Net showed a much better performance with respect to MIoU (mean intersection over union), precision, and Fβ, which were improved by 1.99%, 8.27%, and 7.35%, respectively. The effect and efficiency of the segmentation algorithm were also superior compared to other networks. Calculating the fraction of potato surface faults in potato mask images allows for the quantitative detection of potato surface problems. The experimental results show that the absolute accuracy of the quantitative potato evaluation method proposed in this study was greater than 97.55%, allowing it to quantitatively evaluate potato surface defects, provide methodological references for potato detection in the field of deep processing of potatoes, and provide a theoretical basis and technical references for the evaluation of potato surface defects under complex lighting conditions.
Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification
Authors: Nakrosis Arnas , Paulauskaite-Taraseviciene Agne , Raudonis Vidas , Narusis Ignas , Gruzauskas Valentas , Gruzauskas Romas , Lagzdinyte-Budnike Ingrida
Published: September 2023

The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.
The Energy Contents of Broken Rice for Lactating Dairy Cows
Authors: Gunha Thidarat , Kongphitee Kanokwan , Binsulong Bhoowadol , Sommart Kritapon
Published: September 2023

This study aimed to evaluate (1) the net energy for lactation of broken rice in dairy cows and (2) the effects of broken rice substituting in diets on feed intake, nutrient energy utilization, and milk production. An energy metabolism experiment was conducted using a respiration chamber system in four multiparous Holstein crossbred cows (88.6% Holstein × 11.4% Native Thai; body weight of 438 ± 16.0 kg; 70 ± 31 days in milk) according to a 4 × 4 Latin square design with four 21-d periods. The four dietary treatments included a basal diet substitution with broken rice at 0%, 12%, 24%, and 36%. Increasing the substitution rate of broken rice in the diet resulted in unaffected feed intake, milk yield and composition, and energy balance (p > 0.05); however, a linear increase in the digestibility of dry matter, organic matter, and neutral detergent fiber (p < 0.05). The estimated net energy for lactation of broken rice was 8.68 MJ/kg. The net energy requirement for maintenance was estimated at 504 kJ/kg of metabolic body weight. Our results indicated that broken rice is a good energy-feed resource and that increasing the proportion in the diet up to 36% had no adverse effect on dairy cows’ production performance.
Two Years after Coxiella burnetii Detection: Pathogen Shedding and Phase-Specific Antibody Response in Three Dairy Goat Herds
Authors: Trachsel Christa , Hirsbrunner Gaby , Herms T. Louise , Runge Martin , Kiene Frederik , Ganter Martin , Zanolari Patrik , Bauer Benjamin U.
Published: September 2023

The infection dynamics of Coxiella (C.) burnetii were investigated in three dairy goat herds (A, B, and C) 2 years after the first pathogen detection. A total of 28 and 29 goats from herds A and B, and 35 goats from herd C, were examined. Sera were analyzed on three sampling dates using phase-specific serology. Pathogen shedding was assessed using post-partum vaginal swabs and monthly bulk tank milk (BTM) samples. Dust samples from a barn and milking parlor were also collected monthly. These samples were analyzed with PCR (target IS1111). In herd A, individual animals tested seropositive, while vaginal swabs, BTM, and most dust samples tested negative. Herds B and C exhibited high IgG phase I activity, indicating a past infection. In herd B, approximately two-thirds of the goats shed C. burnetii with vaginal mucus, and irregular positive results were obtained from BTM. Herd C had two positive goats based on vaginal swabs, and BTM tested positive once. Dust samples from herds B and C contained C. burnetii DNA, with higher quantities typically found in samples from the milking parlor. This study highlights the different infection dynamics in three unvaccinated dairy goat herds and the potential use of dust samples as a supportive tool to detect C. burnetii at the herd level.
Imaging Liquid Water in a Polymer Electrolyte Fuel Cell with High-Energy X-ray Compton Scattering
Authors: Miyazawa Tetsuya , Tsuji Naruki , Fujioka Daiki , Kaneko Takuma , Mizuno Yuki , Uchimoto Yoshiharu , Imai Hideto , Sakurai Yoshiharu
Published: September 2023

Compton scattering imaging with intense, high-energy synchrotron X-rays allows us to visualize a light element substance in an operating electrochemical device. In this paper, we report the first experiment of Compton scattering imaging (CSI) on an operating polymer electrolyte fuel cell (PEFC). The novelty of the CSI technique is a non-destructive direct observation of cross-sectional images with a sensitivity to light elements and a capability of simultaneous measurements with fluorescent X-rays of heavy elements. Analyses of the observed images provide the cross-sectional distribution of generated liquid water and its current density dependency. The results show that the amount of generated water increases in the vicinity of the cathode catalyst layer at current densities ranging from 100 to 500 mA/cm2, while it remains constant or slightly decreases from 500 to 900 mA/cm2. In both the gas diffusion layer and the channel, liquid water is observed near the channel and rib interface above 500 mA/cm2, indicating the formation of a liquid water flow path. In addition, simultaneous measurements of fluorescent Pt-Ka X-rays reveal a significant correlation between the generated liquid water and Pt catalysts, using the Pearson correlation coefficient. The result shows that water is dispersed in the catalyst layer without any correlation with the amount of Pt catalysts at low current densities, but water tends to be distributed in the Pt-rich areas at high current densities. This study demonstrates that Compton scattering imaging is one of the unique techniques to characterize the behavior of generated liquid water in an operating PEFC.
Three-Dimensional Printed Liver Models for Surgical Planning and Intraoperative Guidance of Liver Cancer Resection: A Systematic Review
Authors: Rossi Timothy , Williams Ally , Sun Zhonghua
Published: September 2023

Successful liver cancer resection requires a comprehensive pre- and intraoperative understanding of the spatial relationships between a patient’s cancer and intrahepatic anatomy. The recent literature has highlighted that patient-specific 3D-printed liver models (3DPLMs) reconstructed from medical imaging data may enhance the comprehension of patients’ liver anatomy and thereby provide a useful preoperative planning and intraoperative guidance tool for liver cancer resection (LCR). The purpose of this systematic review was to critically examine the utility and feasibility of 3DPLMs for LCR surgical planning and intraoperative guidance and explore whether these applications improve patient outcomes. Articles were retrieved from four electronic databases (Scopus, Embase, PubMed, and Curtin University Database) according to predetermined eligibility criteria. In total, 22 eligible articles were identified, including 11 original research articles and 11 case reports. Key concepts were synthesised using an inductive content analysis approach suitable for this heterogeneous body of literature. There is significant descriptive and case-report evidence that 3DPLMs strengthen pre- and intraoperative comprehension of patient liver and liver tumour anatomy and can enhance pre- and intraoperative surgical decision making for LCR. The analysis of these studies presents large variances in the times and costs necessary to produce 3DPLMs, as studies did not provide the full expenses of materials, software, and equipment. Production times were focused on different aspects of the 3D printing process and were not comparable. The review nonetheless demonstrates the potential value of 3DPLMs as preoperative planning and intraoperative guidance tools for LCR. Future studies should detail these economic data points to ensure 3DPLMs’ viability. Further experimental research and randomised controlled trials are also necessary to examine the relationship between 3DPLMs and patient’s intra- and postoperative outcomes.
Contrast Enhancement-Based Preprocessing Process to Improve Deep Learning Object Task Performance and Results
Authors: Wang Tae-su , Kim Gi Tae , Kim Minyoung , Jang Jongwook
Published: September 2023

Excessive lighting or sunlight can make it difficult to judge visually. The same goes for cameras that function like the human eye. In the field of computer vision, object tasks have a significant impact on performance depending on how much object information is provided. Light presents difficulties in recognizing objects, and recognition is not easy in shadows or dark areas. In this paper, we propose a contrast enhancement-based preprocessing process to obtain improved results in object recognition tasks by solving problems that occur due to light or lighting conditions. The proposed preprocessing process involves the steps of extracting optimal values, generating optimal images, and evaluating quality and similarity, and it can be applied to the generation of training and input data. As a result of an experiment in which the preprocessing process was applied to an object task, the object task results for areas with shadows or low contrast were improved while the existing performance was maintained for datasets that require contrast enhancement technology.
Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT
Authors: Li Zhuyin , Zhu Xu
Published: September 2023

Integrated sensing and communication (ISAC) in the Industrial Internet of Things (IIoT) presents unique challenges in terms of localization techniques. While three-dimensional (3D) environments offer extra challenges to enhanced accuracy and realism, research in this area remains limited. To bridge this gap, we propose a novel localization technique assisted by a single base station (BS) in 3D IIoT scenarios. Our approach employs the MUltiple SIgnal Classification (MUSIC) algorithm to jointly estimate the angle of arrival (AoA) in azimuth and elevation, as well as the time of arrival (ToA). Compared to conventional multi-BS-assisted or MUSIC-based algorithms, our technique offers flexibility, easy implementation, and low computational cost. To improve performance, we integrate the Taylor-series into the iterative process after a MUSIC-based joint azimuth, elevation angle and delay estimation (JAEDE), resulting in a significant 99% reduction in computational complexity compared to a two-step MUSIC-based approach utilizing coarse-fine grid searching. Through numerical simulations, we compare our algorithm with three other MUSIC-based joint or separate estimation approaches, demonstrating its superior performance in azimuth angle of arrival (AoAz), elevation angle of arrival (AoAe), TOA, and overall location estimation across varying signal-to-noise ratio (SNR) conditions.
Classification of Exergames with Different Types: Perceptual and Physiological Responses in College Students
Authors: Aygün Cihan , Çakır-Atabek Hayriye
Published: September 2023

This study aimed to (i) investigate and compare the effects of five different types of active video games (AVGs) on physiological and perceptual variables, (ii) categorize the AVGs’ physical activity (PA) intensity according to ACSM classification, and (iii) investigate the correlation between rating of perceived exertion (RPE) and enjoyment score within each AVG. Twelve college students participated in this study. Each AVG was played randomly for 15 min on non-consecutive days. The physiological variables were measured with a breath-by-breath system continuously during the AVGs and resting conditions. The AVGs significantly increased oxygen consumption, respiratory exchange ratio, energy expenditure, metabolic equivalent, and heart rate compared to rest conditions (p < 0.001). According to ACSM criteria, Fruit Ninja, Just Dance, and Shape-Up Muscle games were defined as moderate PA (MET = 4.07, 5.46, and 5.25; RPE = 10.33, 10.42, and 14.67 respectively) and Shape-Up Cardio, and Fighter Within games were defined as vigorous PA (MET = 6.83, and 7.03; RPE = 13.92, and 13.75 respectively). All games were enjoyable (>27). Young adults can get the recommended PA intensity with AVGs, burning 80–150 kcal in 15 min. The findings suggest that energy expenditure may be similar among different types of AVGs. However, skeletal muscle energy metabolism may differ according to the type of AVG.
Identification of Critical Scenario Components Based on Driving Database Analysis for Safety Assessment of Automated Driving Systems
Authors: Yoshitake Hiroshi , Shino Motoki
Published: September 2023

A thorough safety assessment of an automated driving system (ADS) is necessary before its introduction into the market and practical application. Scenario-based assessments have received significant attention in research. However, identifying sufficient critical scenarios for ADSs is a major challenge, especially for complex urban environments with a variety of road geometries, traffic rules, and traffic participants. To identify the critical scenarios in this complex environment, it is essential to understand the environmental factors that lead to safety-critical events (e.g., accidents and near-miss incidents). Thus, this study proposes a method for identification of critical scenario components by analyzing near-miss incident data and extracting environmental factors that induce driver errors. In this study, we applied the proposed method to a scenario, in which an ego vehicle makes a right turn at a signalized intersection with an oncoming vehicle approaching the intersection in left-hand traffic, as a case study. The proposed method identified two components (dynamic occlusion caused by oncoming right-turn vehicles and change in traffic lights) that were both critical and challenging for ADSs. The case study results showed the usefulness of the identified components and the validity of the proposed method, which can extract critical scenario components explicitly.
Speed Control of PMSM Based on Fuzzy Active Disturbance Rejection Control under Small Disturbances
Authors: Zhang Qi , Zhang Caiyue
Published: September 2023

Permanent Magnet Synchronous Motors (PMSMs), with their simple design, small size, and high-power factor, are ideally suited for realizing high-power AC drives and are widely used in various industries. In this study, Fuzzy Active Disturbance Rejection Control (Fuzzy-ADRC) is used to control the speed of the PMSM. When a slight external disturbance occurs, this control strategy maintains the suppression characteristics of the self-excited control for the disturbance and enhances its ability to compensate for the disturbance. First, a mathematical model was developed to study the surface mount PMSM. Then, a motor control simulation model was created using PI control, vector control, and other control methods. The verification results indicate that the improved Fuzzy-ADRC system performs well under both internal and external minor disturbances. It exhibits a faster dynamic response and reduced regulation time (0.026 s to 0.017 s) compared to the traditional ADRC system. Furthermore, it shows less overshoot (reduced from 70% to 2.9%) compared to the Sliding Mode Observer (SMO). Taken together, the improved Fuzzy-ADRC system is characterized by small steady-state error, high load capacity, and control accuracy. With the assistance of this control strategy, the system can track speed with high accuracy and possesses stronger anti-interference capability to mitigate load disturbances.
Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs
Authors: Kim Minwook , Song Myoungki , Oh Sea-Ho , Yu Geun-Hye , Choe Seoyeong , Jeon Hajeong , Kim Jin-Ho , Bae Min-Suk
Published: September 2023

In this study, the impact of long-range transport, one of the factors contributing to the presence of PM2.5, was examined, and an analysis of marker compounds associated with its long-range transport was conducted. Aerosol optical depth, wind field, CO/CO2 back-trajectory analysis, and satellite observation results were performed to determine PM2.5, volatile organic compound (VOC), CO, CO2, SO2, O3, NO, NO2, and NH3 levels at an orchard located in Jeollabuk-do, Republic of Korea. The characteristic of long-range transport at the observation area was evaluated during the research period. The concentrations for long-range transport based on concentration changes in gaseous materials and composition changes in PM2.5 were analyzed. A back-trajectory analysis for the ratio of CO to CO2 with satellite observation results was used to identify long-range transport. Furthermore, the proportionality between the ratio of 1,2-dichloroethane to naphthalene in VOCs and the quantity of precursor compounds linked to long-range transport were observed.
Enhancing STEM Education through Interactive Metaverses: A Case Study and Methodological Framework
Authors: Solanes J. Ernesto , Montava-Jordà Sergi , Golf-Laville Emilio , Colomer-Romero Vicente , Gracia Luis , Muñoz Adolfo
Published: September 2023

In recent years, the rapid growth of virtual reality has opened up new possibilities for creating immersive virtual worlds known as metaverses. With advancements in technology, such as virtual reality headsets, and the emergence of applications with social interaction, metaverses offer exciting opportunities for STEM (Science, Technology, Engineering, and Mathematics) education. This article proposes the integration of metaverses into STEM courses, enabling students to forge connections between different subjects within their curriculum, to leverage current technological advancements, and to revolutionize project presentations and audience interaction. Drawing upon a case study conducted with students enrolled in the “Proyectos RII 1: Organización y Escenarios” course at the Escuela Politécnica Superior de Alcoy of the Technical University of Valencia (Spain), this article provides a comprehensive description of the methodology employed by the students and professors. It outlines the process followed, shedding light on the innovative use of metaverses in their projects. Moreover, the article shares valuable insights obtained through surveys and personal opinions gathered from students throughout the course and upon its completion. By exploring the intersection of metaverses and STEM education, this study showcases the transformative potential of integrating interactive virtual environments. Furthermore, this work highlights the benefits of increased student engagement, motivation, and collaboration, as well as the novel ways in which projects can be presented and interactions with audiences can be redefined.
Special Issue on Dental Materials: Latest Advances and Prospects—Volume II
Authors: Checchi Vittorio
Published: September 2023

Many fields of dentistry are firmly connected to innovative materials, and the highest clinical improvements frequently come with the development and creation of original and high-performing equipment, instruments, and biomaterials [...]
Special Issue on Advanced Technology of Intelligent Control and Simulation Evaluation
Authors: Guo Yangming , Long Jiang , Li Qingdong , Liu Zun
Published: September 2023

Control and simulation evaluation have experienced a rapid development during the last few decades [...]
U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution
Authors: Ono Keiko , Tawara Daisuke , Tani Yuki , Yamakawa Sohei , Yakushijin Shoma
Published: September 2023

Bone semantic segmentation is essential for generating a bone simulation model for automatic diagnoses, and a convolution neural network model is often applied to semantic segmentation. However, ground-truth (GT) images, which are generated based on handwriting borderlines, are required to learn this model. It takes a great deal of time to generate accurate GTs from handwriting borderlines, which is the main reason why bone simulation has not been put to practical use for diagnosis. With the above in mind, we propose the U-net-based semi-automatic semantic segmentation method detailed in this paper to tackle the problem. Moreover, bone computed tomography (CT) images are often presented in digital imaging and communications in medicine format, which consists of various parameters and affects the image quality for segmentation. We also propose a novel adaptive input image generator using an adaptive differential evolution. We evaluate the proposed method compared to conventional U-net and DeepLabv3 models using open bone datasets, the spine and the femur, and our artificial bone data. Performance evaluations show that the proposed method outperforms U-net and DeepLabv3 in terms of Dice, IoU, and pairwise accuracy, and DeepLabv3 show the lowest performance, due to a lack of training data. We verify that the U-net-based model is effective for bone segmentation, where a large quantity of training data are available. Moreover, we verify that the proposed method can effectively create proper GTs and input images, resulting in increased performance and reduced computational costs. We believe that the proposed method enhances the wide use of bone simulation based on CT images for practical use.
Usage of Cultured Human Fecal Microbiota for Colonization of Caenorhabditis elegans to Study Host–Microbe Interaction
Authors: Møller Katrine V. , Wesseltoft Jonas Bruhn , Malazarte Richelle , Kousgaard Sabrina J. , Nielsen Hans L. , Yashiro Erika , Olsen Anders
Published: September 2023

The role of the microbiota in health and disease is a research area receiving much attention in academia and industry. A person’s microbiota refers to a community of microorganisms found mainly in the gut. It is estimated that around 39 trillion bacteria can be found on and inside the human body and there is increasing evidence that they influence human health. Advances in sequencing techniques are revolutionizing characterization of the human microbiome. However, causality and underlying molecular mechanisms are still largely unknown due to the complexity of the human microbiome and its interaction with the host. Turning towards simpler host organisms and using well-defined microbiomes are two ways to strengthen studies of causality and mechanism. Here, we show that the nematode Caenorhabditis elegans can be used as host to study sub-microbiomes derived from human feces samples prepared for fecal microbiota transplantation following a simple feeding protocol. Approximately 200 amplicon sequence variants were identified in the worm gut following transplantation with human fecal microbiota samples. We find that the gut microbiome does not simply reflect the bacterial community initially fed to the worms. Hence, our experimental setup can be used to identify and characterize host genetic factors shaping the microbiota and improving our understanding of host–human microbiome interactions.
Defining Semantically Close Words of Kazakh Language with Distributed System Apache Spark
Authors: Ayazbayev Dauren , Bogdanchikov Andrey , Orynbekova Kamila , Varlamis Iraklis
Published: September 2023

This work focuses on determining semantically close words and using semantic similarity in general in order to improve performance in information retrieval tasks. The semantic similarity of words is an important task with many applications from information retrieval to spell checking or even document clustering and classification. Although, in languages with rich linguistic resources, the methods and tools for this task are well established, some languages do not have such tools. The first step in our experiment is to represent the words in a collection in a vector form and then define the semantic similarity of the terms using a vector similarity method. In order to tame the complexity of the task, which relies on the number of word (and, consequently, of the vector) pairs that have to be combined in order to define the semantically closest word pairs, A distributed method that runs on Apache Spark is designed to reduce the calculation time by running comparison tasks in parallel. Three alternative implementations are proposed and tested using a list of target words and seeking the most semantically similar words from a lexicon for each one of them. In a second step, we employ pre-trained multilingual sentence transformers to capture the content semantics at a sentence level and a vector-based semantic index to accelerate the searches. The code is written in MapReduce, and the experiments and results show that the proposed methods can provide an interesting solution for finding similar words or texts in the Kazakh language.
The Variation of Electrical Pulse Duration Elicits Reliable Network-Mediated Responses of Retinal Ganglion Cells in Normal, Not in Degenerate Primate Retinas
Authors: Cha Seongkwang , Ahn Jungryul , Kim Seong-Woo , Choi Kwang-Eon , Yoo Yongseok , Eom Heejong , Shin Donggwan , Goo Yong Sook
Published: September 2023

This study aims to investigate the efficacy of electrical stimulation by comparing network-mediated RGC responses in normal and degenerate retinas using a N-methyl-N-nitrosourea (MNU)-induced non-human primate (NHPs) retinitis pigmentosa (RP) model. Adult cynomolgus monkeys were used for normal and outer retinal degeneration (RD) induced by MNU. The network-mediated RGC responses were recorded from the peripheral retina mounted on an 8 × 8 multielectrode array (MEA). The amplitude and duration of biphasic current pulses were modulated from 1 to 50 μA and 500 to 4000 μs, respectively. The threshold charge density for eliciting a network-mediated RGC response was higher in the RD monkeys than in the normal monkeys (1.47 ± 0.13 mC/cm2 vs. 1.06 ± 0.09 mC/cm2, p < 0.05) at a 500 μs pulse duration. The monkeys required a higher charge density than rodents among the RD models (monkeys; 1.47 ± 0.13 mC/cm2, mouse; 1.04 ± 0.09 mC/cm2, and rat; 1.16 ± 0.16 mC/cm2, p < 0.01). Increasing the pulse amplitude and pulse duration elicited more RGC spikes in the normal primate retinas. However, only pulse amplitude variation elicited more RGC spikes in degenerate primate retinas. Therefore, the pulse strategy for primate RD retinas should be optimized, eventually contributing to retinal prosthetics. Given that RD NHP RGCs are not sensitive to pulse duration, using shorter pulses may potentially be a more charge-effective approach for retinal prosthetics.
Compact Flexible Planar Antennas for Biomedical Applications: Insight into Materials and Systems Design
Authors: Venkatachalam Dinesh , Jagadeesan Vijayalakshmi , Ismail Kamal Batcha Mohamed , Arun Kumar Manoharan , Mahalingam Shanmugam , Kim Junghwan
Published: September 2023

Planar antennas have become an integral component in modern biomedical instruments owing to their compact structure, cost effectiveness, and light weight. These antennas are crucial in realizing medical systems such as body area networks, remote health monitoring, and microwave imaging systems. Antennas intended for the above applications should be conformal and fabricated using lightweight materials that are suitable for wear on the human body. Wearable antennas are intended to be placed on the human body to examine its health conditions. Hence, the performance of the antenna, such as its radiation characteristics across the operating frequency bands, should not be affected by human body proximity. This is achieved by selecting appropriate conformal materials whose characteristics remain stable under all environmental conditions. This paper aims to highlight the effects of human body proximity on wearable antenna performance. Additionally, this paper reviews the various types of flexible antennas proposed for biomedical applications. It describes the challenges in designing wearable antennas, the selection of a flexible material that is suitable for fabricating wearable antennas, and the relevant methods of fabrication. This paper also highlights the future directions in this rapidly growing field. Flexible antennas are the keystone for implementing next-generation wireless communication devices for health monitoring and health safety applications.
Mesenchymal Stem/Stromal Cell-Derived Small Extracellular Vesicles (MSC-sEVs): A Promising Treatment Modality for Diabetic Foot Ulcer
Authors: Kouroupis Dimitrios , Kaplan Lee D. , Ricordi Camillo , Best Thomas M.
Published: September 2023

Diabetic foot ulcer (DFU) is associated with neuropathy and/or peripheral artery disease of the lower limb in diabetic patients, affecting quality of life and leading to repeated hospitalizations and infections [...]
Metabolic Basis of Circadian Dysfunction in Parkinson’s Disease
Authors: Rathor Priya , Ch Ratnasekhar
Published: September 2023

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. The management of PD is a challenging aspect for general physicians and neurologists. It is characterized by the progressive loss of dopaminergic neurons. Impaired α-synuclein secretion and dopamine release may cause mitochondrial dysfunction and perturb energy metabolism, subsequently altering the activity and survival of dopaminergic neurons, thus perpetuating the neurodegenerative process in PD. While the etiology of PD remains multifactorial, emerging research indicates a crucial role of circadian dysfunction in its pathogenesis. Researchers have revealed that circadian dysfunction and sleep disorders are common among PD subjects and disruption of circadian rhythms can increase the risk of PD. Hence, understanding the findings of circadian biology from translational research in PD is important for reducing the risk of neurodegeneration and for improving the quality of life. In this review, we discuss the intricate relationship between circadian dysfunction in cellular metabolism and PD by summarizing the evidence from animal models and human studies. Understanding the metabolic basis of circadian dysfunction in PD may shed light on novel therapeutic approaches to restore circadian rhythm, preserve dopaminergic function, and ameliorate disease progression. Further investigation into the complex interplay between circadian rhythm and PD pathogenesis is essential for the development of targeted therapies and interventions to alleviate the burden of this debilitating neurodegenerative disorder.
utPCR: A Strategy for the Highly Specific and Absolutely Quantitative Detection of Single Molecules within Only Minutes
Authors: Wang Rui , Liu Ying , Chen Shuaiwei , Bai Linlin , Guo Kaiming , Pang Yanan , Qian Feng , Li Yongfang , Ding Li , Wang Yongming
Published: September 2023

Bloodstream infection is a major health problem worldwide, with extremely high mortality. Detecting infection in the early stage is challenging due to the extremely low concentration of bacteria in the blood. Digital PCR provides unparalleled sensitivity and can achieve absolute quantification, but it is time-consuming. Moreover, the presence of unavoidable background signals in negative controls poses a significant challenge for single-molecule detection. Here, we propose a novel strategy called “Ultrafast flexible thin tube-based droplet digital PCR (utPCR)” that can shorten the digital PCR process from 2 h to only 5 min, with primer annealing/extension time reduced from minutes to only 5 s. Importantly, the ultrafast PCR eliminates nonspecific amplification and thus enables single-molecule detection. The utPCR enabled the sensitive detection and digital quantification of E. coli O157 in the high background of a 106-fold excess of E. coli K12 cells. Moreover, this method also displayed the potential to detect rare pathogens in blood samples, and the limit of detection (LOD) could be as low as 10 CFU per mL of blood without false positive results. Considered ultrafast (<5 min) and highly sensitive (single-molecule detection), the utPCR holds excellent prospects in the next generation of molecular diagnosis.
Investigation of Real Estate Tax Leakage Loss Rates with ANNs
Authors: Yılmaz Mehmet , Bostancı Bülent
Published: September 2023

In Türkiye, many changes have been made in the law within the past fifty years to determine the real estate tax value close to the real market value. However, the changes did not establish a fair valuation system for determining real estate tax. Despite the regulations and records of immovable properties with a geographic information system (GIS)-based inventory in recent years, the problem of leakage loss in real estate tax was still not resolved. Within the scope of this study, a mass appraisal model was created with a dataset of 499 independent sections including trading values from the last year in the district of Kayseri to determine the real estate tax leakage loss rates. Multiple regression analysis (MRA) and artificial neural network (ANN) methods, widely used in mass appraisal, were used in the analysis. Considering the analysis of the test data and the model performances, the ANN model was found to give better results than the MRA model. To conclude this study, the housing values obtained with the mass appraisal methods and the real estate tax values obtained with the existing system were compared, and a 3.7-fold difference was found between them.
Structural Health Monitoring of Fiber-Reinforced Concrete Prisms with Polyolefin Macro-Fibers Using a Piezoelectric Materials Network under Various Load-Induced Stress
Authors: Naoum Maria C. , Papadopoulos Nikos A. , Voutetaki Maristella E. , Chalioris Constantin E.
Published: September 2023

This experimental study investigates the influence of synthetic macro-fibers added in fiber-reinforced concrete (FRC) prismatic specimens on their flexural response and overall cracking performance. Application of a novel structural health monitoring (SHM) system that implements the electromechanical impedance (EMI) technique and the use of piezoelectric lead zirconate titanate (PZT) transducers installed in the FRC prisms are also included. The applied PZT-enabled EMI-based monitoring system was developed to diagnose damage and the overall performance in reinforced concrete (RC) structural members subjected to cyclic repeated loading, simulating seismic excitations in existing RC buildings. The paper also aims to determine the sensitivity of the real-time, wireless, and portable monitoring technique corresponding to the location, the distance, the direction of polarization of the PZT transducers and the location and magnitude of damage due to flexural cracking. Further, the influence of the effect of stresses corresponding at various loading levels and the observed changes in the ΕΜΙ frequency response of the PZT transducers are also examined. Test results indicated that cracking detection is achieved using this SHM system by prompt damage level assessment due to the FRC’s flexural load at early seismic loading stages in existing RC buildings.
Effect of Sarcopenia on Pneumonia after Endoscopic Submucosal Resection in Patients Aged ≥65 Years: A Retrospective Study
Authors: Kim Min-Yu , Kim So Yeon , Shin Hye Jung , Kweon Ki Hong , Park Jooeun , Kim Na Young
Published: September 2023

We aimed to investigate the association between sarcopenia and incidence of pneumonia after endoscopic submucosal dissection (ESD) in patients aged ≥65 years. Patients with (n = 1571) and without sarcopenia (n = 1718) who underwent ESD for gastric neoplasm were included. Propensity score matching (PSM) was performed between the groups (n = 785) at a 1:1 ratio. The primary endpoint was the effect of sarcopenia on the incidence of pneumonia after ESD. Among the included patients, 2.2% (n = 71) developed pneumonia after ESD. After PSM, the incidence rate of pneumonia was significantly higher in patients with sarcopenia than that in patients without sarcopenia (p = 0.024). Sarcopenia and age ≥73 years were significantly associated with the incidence of pneumonia (sarcopenia and age <73 years, odd ratio (OR) = 1.22 [95% confidence interval (CI): 0.46–3.22]; sarcopenia and age ≥73 years, OR = 3.92 [95% CI: 1.79–8.74]). Patients with sarcopenia had an increased risk of developing pneumonia after ESD, even after adjusting for other factors, resulting in a higher incidence of leukocytosis and a longer duration of post-ESD hospitalization. The combination of sarcopenia and age ≥73 years could be an effective predictive factor for screening high-risk groups for pneumonia after ESD.