bluuebunny/crossref_metadata_2025 huggingface dataset:
The Potential of Understory Production Systems to Improve Laying Hen Welfare
Authors: Yan Shaocong , Yang Chenyujing , Zhu Lei , Xue Yongji
Published: September 2022

The welfare of laying hens in cage systems is of increasing concern. Represented by the European Union’s ‘End the Cage Age’ initiative, more and more countries have advocated cage-free farming. China, an important country for poultry farming and consumption in the world, is highly dependent on cage systems and lacks confidence in alternative (e.g., free-range) systems. In this context, using China’s abundant woodland resources (including natural forests, plantations, and commercial forests) to facilitate the management of laying hens in a free-range environment may provide highly promising welfare improvement programs. On the basis of the Five Freedoms, we assess the welfare status of understory laying hen management systems with reference to the behavioural needs and preferences of laying hens and the EU standards for free-range and organic production (highest animal welfare standards in the world). The results show that the considered systems meet or even exceed these standards, in terms of key indicators such as outdoor and indoor stocking density, outdoor activity time, and food and drug use. Specifically, the systems provide sufficient organic food for laying hens without using antibiotics. They allow laying hens to avoid beak trimming, as well as to express nesting, foraging, perching, reproductive, dustbathing and other priority behaviours. The presence of roosters and higher use of woodland space allow the laying hens to achieve better feather and bone conditions, thus reducing stress and fear damage. Notably, the predation problem is not yet considered significant. Second, there is evidence that understory laying hen systems are profitable and have been welcomed and supported by farmers and governments in the southwest, south, and north of China. However, whether it can be scaled up is uncertain, and further research is needed. In addition, laying hens in this management system face various risks, such as foot injury, parasitism, and high dependence on consumer markets, which must be considered. Overall, agro-forestry, or accurately, understory poultry raising, provides opportunities and possibilities for free-range laying hens and welfare improvement in China and other countries.
Progress in Genomic Mating in Domestic Animals
Authors: Zhang Pengfei , Qiu Xiaotian , Wang Lixian , Zhao Fuping
Published: September 2022

Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry.
Combination Treatment for Inhibition of the Growth of Staphylococcus aureus with Recombinant SAP8 Endolysin and Nisin
Authors: Kim Seon-Gyu , Khan Shehzad Abid , Lee Young-Duck , Park Jong-Hyun , Moon Gi-Seong
Published: September 2022

Staphylococcus aureus, a pathogenic species of genus Staphylococcus involved in foodborne illness always remain among the top priorities of the world major concerns. In the present study, we have used recombinant SAP8 endolysin from the bacteriophage SAP8 and commercial nisin to inhibit the viability of pathogenic S. aureus KCTC 3881 cells; however, the approach was not identified as cost-effective. A gradual decrease in the viable S. aureus KCTC 3881 cell counts was observed with an increase in the concentrations of recombinant SAP8 endolysin and nisin. However, combined treatment with recombinant SAP8 endolysin and nisin decreased the viable S. aureus KCTC 3881 cell counts in a significant manner. The combination of 0.01 µM of recombinant SAP8 endolysin with 9 IU/mL and 18 IU/mL of nisin demonstrated a promising decrease in the viable cell counts of the strain. Under the scanning electron microscope, the combination treatment with 0.01 µM of recombinant SAP8 endolysin and 18 IU/mL of nisin showed complete cellular destruction of S. aureus KCTC 3881. We propose that a combination of recombinant SAP8 endolysin and nisin could be a strong alternative to antibiotics to control the growth of S. aureus including MRSA.
Educational Interventions to Reduce Prescription and Dispensing of Antibiotics in Primary Care: A Systematic Review of Economic Impact
Authors: Rocha Vânia , Estrela Marta , Neto Vanessa , Roque Fátima , Figueiras Adolfo , Herdeiro Maria Teresa
Published: September 2022

Antibiotic resistance remains a crucial global public health problem with excessive and inappropriate antibiotic use representing an important driver of this issue. Strategies to improve antibiotic prescription and dispensing are required in primary health care settings. The main purpose of this review is to identify and synthesize available evidence on the economic impact of educational interventions to reduce prescription and dispensing of antibiotics among primary health care professionals. Information about the clinical impact resulting from the implementation of interventions was also gathered. PubMed, Scopus, Web of Science and EMBASE were the scientific databases used to search and identify relevant studies. Of the thirty-three selected articles, most consisted of a simple intervention, such as a guideline implementation, while the others involved multifaceted interventions, and differed regarding study populations, designs and settings. Main findings were grouped either into clinical or cost outcomes. Twenty of the thirty-three articles included studies reporting a reduction in outcome costs, namely in antibiotic cost and associated prescription costs, in part due to an overall improvement in the appropriateness of antibiotic use. The findings of this study show that the implementation of educational interventions is a cost-effective strategy to reduce antibiotic prescription and dispensing among primary healthcare providers.
Systematic Review with Trial Sequential Analysis of Prophylactic Antibiotics for Acute Pancreatitis
Authors: Poropat Goran , Goričanec Karla , Lacković Alojzije , Kresović Andrea , Lončarić Antun , Marušić Martina
Published: September 2022

Background/Objectives: Prophylactic antibiotics (PAB) are being still widely used for treatment of acute pancreatitis (AP) despite trials showing no firm evidence of efficacy. We aimed to evaluate effects of PAB for AP in a meta-analysis and the need for further research by trial sequential analysis (TSA). Methods: Medline, Scopus and Web of Science were searched for randomized clinical trials. Primary outcomes were all infections and mortality. Secondary outcomes comprised infected pancreatic necrosis (IPN), specific infections, organ failure, surgical interventions, and length of hospital stay. Results: Twenty-one trials with 1383 pts were included. PAB were received by 703 pts, while 680 were controls. Mortality was similar with RR 0.85 (95% CI 0.66–1.10). Infections were significantly reduced (RR 0.60; 95% CI 0.49–0.74), mainly due to decreased risk of sepsis (RR 0.43; 95% CI 0.25–0.73) and urinary tract infections (RR 0.46; 95% CI 0.25–0.86). No significant reduction for IPN was shown (RR 0.81; 95% CI 0.63–1.04). Length of hospital stay was diminished by MD −6.65 (95% CI −8.86 to −4.43) days. TSA for all infections showed that the cumulative Z score crossed both conventional and monitoring boundaries at 526 pts from a heterogeneity-corrected required information size of 1113 pts based on a 40% incidence of infections in the control group, RRR of 30%, alpha 5%, beta 20%, and heterogeneity 56%. Conclusions: PABs decrease the rate of infections in AP, mainly due to RRR of extra-pancreatic infections, requiring no further research. No significant effect is shown on IPN and mortality, although firmer evidence is needed.
Point-of-Care and Rapid Tests for the Etiological Diagnosis of Respiratory Tract Infections in Children: A Systematic Review and Meta-Analysis
Authors: Brigadoi Giulia , Gastaldi Andrea , Moi Marco , Barbieri Elisa , Rossin Sara , Biffi Annalisa , Cantarutti Anna , Giaquinto Carlo , Da Dalt Liviana , Donà Daniele
Published: September 2022

Fever is one of the most common causes of medical evaluation of children, and early discrimination between viral and bacterial infection is essential to reduce inappropriate prescriptions. This study aims to systematically review the effects of point-of-care tests (POCTs) and rapid tests for respiratory tract infections on changing antibiotic prescription rate, length of stay, duration of therapy, and healthcare costs. Embase, MEDLINE, and Cochrane Library databases were systematically searched. All randomized control trials and non-randomized observational studies meeting inclusion criteria were evaluated using the NIH assessment tool. A meta-analysis was performed to assess the effects of rapid influenza diagnostic tests and film-array respiratory panel implementation on selected outcomes. From a total of 6440 studies, 57 were eligible for the review. The analysis was stratified by setting and POCT/rapid test type. The most frequent POCTs or rapid tests implemented were the Rapid Influenza Diagnostic Test and film-array and for those types of test a separate meta-analysis assessed a significant reduction in antibiotic prescription and an improvement in oseltamivir prescription. Implementing POCTs and rapid tests to discriminate between viral and bacterial infections for respiratory pathogens is valuable for improving appropriate antimicrobial prescriptions. However, more studies are needed to assess these findings in pediatric settings.
Mechanism-Based Approach to New Antibiotic Producers Screening among Actinomycetes in the Course of the Citizen Science Project
Authors: Volynkina Inna A. , Zakalyukina Yuliya V. , Alferova Vera A. , Belik Albina R. , Yagoda Daria K. , Nikandrova Arina A. , Buyuklyan Yuliya A. , Udalov Andrei V. , Golovin Evgenii V. , Kryakvin Maxim A. , Lukianov Dmitrii A. , Biryukov Mikhail V. , Sergiev Petr V. , Dontsova Olga A. , Osterman Ilya A.
Published: September 2022

Since the discovery of streptomycin, actinomycetes have been a useful source for new antibiotics, but there have been diminishing rates of new finds since the 1960s. The decreasing probability of identifying new active agents led to reduced interest in soil bacteria as a source for new antibiotics. At the same time, actinomycetes remain a promising reservoir for new active molecules. In this work, we present several reporter plasmids encoding visible fluorescent protein genes. These plasmids provide primary information about the action mechanism of antimicrobial agents at an early stage of screening. The reporters and the pipeline described have been optimized and designed to employ citizen scientists without specialized skills or equipment with the aim of essentially crowdsourcing the search for new antibiotic producers in the vast natural reservoir of soil bacteria. The combination of mechanism-based approaches and citizen science has proved its effectiveness in practice, revealing a significant increase in the screening rate. As a proof of concept, two new strains, Streptomyces sp. KB-1 and BV113, were found to produce the antibiotics pikromycin and chartreusin, respectively, demonstrating the efficiency of the pipeline.
Free and Bound Phenolic Compounds Present in Orange Juice By-Product Powder and Their Contribution to Antioxidant Activity
Authors: Camacho María del Mar , Zago Mónica , García-Martínez Eva , Martínez-Navarrete Nuria
Published: September 2022

Interest in fruit/vegetable consumption is not always linked to a particular diet but rather derives from their high antioxidant activity (AOA), with potential health benefits provided, in part, by polyphenols. Although phenols can be found in free form (FP) or bound to plant tissues (BP), the experimental methodology most frequently used for the quantification of total phenols (TP) is based on the extraction of a portion of FP, which may justify the lack of correlation often found between them and AOA. In this context, four successive extractions were performed to obtain FP and BP of powdered orange juice by-product, and their contribution to the AOA was studied. The first extract (MeOH, 30 °C) can be assumed to be one of the conventional methods for the quantification of TP. Re-extraction with MeOH (60 °C) afforded the FP. Two subsequent basic and acidic extractions yielded the BP. Although the FPs were the most abundant, the AOA (DPPH method) of the last fraction of BP was of the same order found in the first fraction of FP. This highlights the interest in extracting the BP from the by-product of orange juice if its antioxidant capacity is to be exploited.
EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
Authors: Chen Xuxin , Huang Xinli
Published: September 2022

Distant supervision for relation extraction (DSRE) automatically acquires large-scale annotated data by aligning the corpus with the knowledge base, which dramatically reduces the cost of manual annotation. However, this technique is plagued by noisy data, which seriously affects the model’s performance. In this paper, we introduce negative training to filter them out. Specifically, we train the model with the complementary label based on the idea that “the sentence does not express the target relation”. The trained model can discriminate the noisy data from the training set. In addition, we believe that additional entity attributes (such as description, alias, and types) can provide more information for sentence representation. On this basis, we propose a DSRE model with entity attributes via negative training called EANT. While filtering noisy sentences, EANT also relabels some false negative sentences and converts them into useful training data. Our experimental results on the widely used New York Times dataset show that EANT can significantly improve the relation extraction performance over the state-of-the-art baselines.
A Research on Image Semantic Refinement Recognition of Product Surface Defects Based on Causal Knowledge
Authors: Zhuang Weibin , Zhang Taihua , Yao Liguo , Lu Yao , Yuan Panliang
Published: September 2022

The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis for solving the problem of product defects in design and manufacturing. Therefore, in this paper, we propose an image semantic refinement recognition method based on causal knowledge for product surface defects. Firstly, an improved ResNet was designed to improve the image classification effect. Then, the causal knowledge graph of surface defects was constructed and stored in Neo4j. Finally, a visualization platform for causal knowledge analysis was developed to realize the causal visualization of the defects in the causal knowledge graph driven by the output data of the network model. In addition, the method is validated by the surface defects dataset. The experimental results show that the average accuracy, recall, and precision of the improved ResNet are improved by 11%, 8.15%, and 8.3%, respectively. Through the application of the visualization platform, the cause results obtained are correct by related analysis and comparison, which can effectively represent the cause of aluminum profile surface defects, verifying the effectiveness of the method proposed in this paper.
Continuous Fiber-Reinforced Material Extrusion with Hybrid Composites of Carbon and Aramid Fibers
Authors: Heitkamp Tim , Girnth Simon , Kuschmitz Sebastian , Klawitter Günter , Waldt Nils , Vietor Thomas
Published: September 2022

An existing challenge in the use of continuous fiber reinforcements in additively manufactured parts is the limited availability of suitable fiber materials. This leads to a reduced adaptability of the mechanical properties to the load case. The increased design freedom of additive manufacturing allows the flexible deposition of fiber strands at defined positions, so that even different fiber materials can be easily combined in a printed part. In this work, therefore, an approach is taken to combine carbon and aramid fibers in printed composite parts to investigate their effects on mechanical properties. For this purpose, tensile, flexural and impact tests were performed on printed composite parts made of carbon and aramid fibers in a nylon matrix with five different mixing ratios. The tests showed that the use of hybrid composites for additive manufacturing is a reasonable approach to adapt the mechanical properties to the loading case at hand. The experiments showed that increasing the aramid fiber content resulted in an increase in impact strength, but a decrease in tensile and flexural strength and a decrease in stiffness. Microstructural investigations of the fracture surfaces showed that debonding and delamination were the main failure mechanisms. Finally, Rule of Hybrid Mixture equations were applied to predict the mechanical properties at different mixture ratios. This resulted in predicted values that differed from the experimentally determined values by an average of 5.6%.
Preliminary Biomechanical Evaluation of a Novel Exoskeleton Robotic System to Assist Stair Climbing
Authors: Böhme Max , Köhler Hans-Peter , Thiel Robert , Jäkel Jens , Zentner Johannes , Witt Maren
Published: September 2022

A novel exoskeleton robotic system was developed to assist stair climbing. This active demonstrator consists of a motor with a cable system, various sensors, and a control system with a power supply. The objective of this preliminary study is a biomechanical evaluation of the novel system to determine its effectiveness in use. For this purpose, three test persons were biomechanically investigated, who performed stair ascents and descents with and without the exoskeleton. Kinematics, kinetics, and muscle activity of the knee extensors were measured. The measured data were biomechanically simulated in order to evaluate the characteristics of joint angles, moments, and reaction forces. The results show that the new exoskeleton assists both the ascent and the descent according to the measured surface electromyography (sEMG) signals, as the knee extensors are relieved by an average of 19.3%. In addition, differences in the interaction between the test persons and the system were found. This could be due to a slightly different operation of the assisting force or to the different influence of the system on the kinematics of the users.
Analysis of a Special Sulphite-Producing Yeast Starter after Fermentation and during Wine Maturation
Authors: Nyitrai Diána Sárdy , Varga Zsuzsanna , Sólyom-Leskó Annamária , Kállay Miklós , Steckl Szabina , Nagy Balázs , Kocsis Dorottya , Antal Eszter
Published: September 2022

In the present study, we investigated the extent to which specific sulphur dioxide-producing wine yeasts produce SO2 during alcoholic fermentation and whether the SO2 they produce is sufficient to prevent oxidation during wine storage. Fermentation was carried out at 12 °C and 20 °C. After inoculation with yeast, the evolution of free and total sulphurous acid concentrations, acetaldehyde concentrations (11.6–46.9 mg L−1) and total polyphenol concentrations (137.4–244.7 mg L−1), including leucoanthocyanin and catechin concentrations (leucoanthocyanidin: 8.5–75.1 mg L−1; catechin: 70.8–115.4 mg L−1), were investigated during the storage of fermented wines. The amount of free sulphur dioxide was measured between 5 and 10 mg L−1. Total sulphuric acid ranged from 6 to 22 mg L−1, taking into account the results of the three years studied. The aim of our tests was to observe whether the use of sulphur-producing yeasts during the ageing of fermented wines showed any benefit. The results of all three vintages tested showed that neither the 12 °C nor the 20 °C fermented batches showed any advantage in using sulphur dioxide-producing yeasts. Our results show that there is no clear evidence that the yeast produces sufficient sulphur dioxide during fermentation and that there is no clear demonstrable benefit from its use.
Solar Irradiance Forecasting with Transformer Model
Authors: Pospíchal Jiří , Kubovčík Martin , Dirgová Luptáková Iveta
Published: September 2022

Solar energy is one of the most popular sources of renewable energy today. It is therefore essential to be able to predict solar power generation and adapt energy needs to these predictions. This paper uses the Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision problems. Here, it is extended by combining features based on their spatiotemporal properties in solar irradiance prediction. The results were predicted for arbitrary long-time horizons since the prediction is always 1 day ahead, which can be included at the end along the timestep axis of the input data and the first timestep representing the oldest timestep removed. A maximum worst-case mean absolute percentage error of 3.45% for the one-day-ahead prediction was obtained, which gave better results than the directly competing methods.
Benchmarking Deep Learning Models for Instance Segmentation
Authors: Jung Sunguk , Heo Hyeonbeom , Park Sangheon , Jung Sung-Uk , Lee Kyungjae
Published: September 2022

Instance segmentation has gained attention in various computer vision fields, such as autonomous driving, drone control, and sports analysis. Recently, many successful models have been developed, which can be classified into two categories: accuracy- and speed-focused. Accuracy and inference time are important for real-time applications of this task. However, these models just present inference time measured on different hardware, which makes their comparison difficult. This study is the first to evaluate and compare the performances of state-of-the-art instance segmentation models by focusing on their inference time in a fixed experimental environment. For precise comparison, the test hardware and environment should be identical; hence, we present the accuracy and speed of the models in a fixed hardware environment for quantitative and qualitative analyses. Although speed-focused models run in real-time on high-end GPUs, there is a trade-off between speed and accuracy when the computing power is insufficient. The experimental results show that a feature pyramid network structure may be considered when designing a real-time model, and a balance between the speed and accuracy must be achieved for real-time application.
Design of a Digital Twin Training Centre for an Industrial Robot Arm
Authors: Erdei Timotei István , Krakó Rudolf , Husi Géza
Published: September 2022

The Cyber-Physical and Intelligent Robotics Laboratory has been digitally recreated, and it includes all the key elements that allow 6-axis industrial robots to perform PTP, LIN, and CIRC motions. Furthermore, the user can create a program with these motion types. The human–machine interface is also integrated into our system. It can also assist SMEs in developing their in-house training. After all, training on an industrial robot unit does not entail installation costs within the facility. Nor are there any maintenance and servicing costs. Since the lab is digital, additional robot units can be added or removed. Thus, areas for training or production can be pre-configured within each facility. Because of the customizability and virtual education format, there is no room capacity problem, and trainees can participate in the exercises in parallel. Exercises were also conducted to evaluate the program’s impact on teaching, and the results showed that using machine units can improve teaching. Even today’s digital labs cannot physically convey the sense of space or the relative weights of different elements in virtual space. Even with these features, individuals can operate a machine more effectively than relying solely on traditional, non-interactive demonstration materials.
Vibration Power Flow and Transfer Path Analysis of Two-Dimensional Truss Structure by Impedance Synthesis Method
Authors: Zhu Hong-Zhen , Wu Jiang-Hai , Sun Yu-Dong
Published: September 2022

The violent vibration of truss structures may cause fatigue, faults, or even an accident. Aiming to analyze the vibration power flow and transfer path of two-dimensional truss structures in the mid and high-frequency domain, this paper proposed a fast dynamic calculation method—the impedance synthesis method (ISM)—which is based on an analytical equation with litter elements. Firstly, the global coordination vibration impedance of a Timoshenko beam truss is derived; Secondly, a dynamic model of a two-dimensional truss structure is built up with a single truss beam by force balance and geometric continuity; then, real and imaginary parts of dynamic responses and force in simple and periodically truss structures are verified by compared with FEM results, respectively; finally, the transfer path analysis (TPA) method is applied to separate the contribution of different transfer paths of power flow in periodical truss structures. The results show that the TPA method can easily find the line spectrum frequency of power flow, which should be considered in vibration control. This method can also be expanded to three-dimensional, honeycomb, and other truss beam structures.
Application of Transfer Learning and Convolutional Neural Networks for Autonomous Oil Sheen Monitoring
Authors: Dong Jialin , Sitler Katherine , Scalia Joseph , Ge Yunhao , Bireta Paul , Sihota Natasha , Hoelen Thomas P. , Lowry Gregory V.
Published: September 2022

Oil sheen on the water surface can indicate a source of hydrocarbon in underlying subaquatic sediments. Here, we develop and test the accuracy of an algorithm for automated real-time visual monitoring of the water surface for detecting oil sheen. This detection system is part of an automated oil sheen screening system (OS-SS) that disturbs subaquatic sediments and monitors for the formation of sheen. We first created a new near-surface oil sheen image dataset. We then used this dataset to develop an image-based Oil Sheen Prediction Neural Network (OS-Net), a classification machine learning model based on a convolutional neural network (CNN), to predict the existence of oil sheen on the water surface from images. We explored the effectiveness of different strategies of transfer learning to improve the model accuracy. The performance of OS-Net and the oil detection accuracy reached up to 99% on a test dataset. Because the OS-SS uses video to monitor for sheen, we also created a real-time video-based oil sheen prediction algorithm (VOS-Net) to deploy in the OS-SS to autonomously map the spatial distribution of sheening potential of hydrocarbon-impacted subaquatic sediments.
A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals
Authors: Ngo Ba-Viet , Nguyen Thanh-Hai
Published: September 2022

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built.
Is Strain Elastography Useful in Diagnosing Chronic Autoimmune Thyroiditis in Children?
Authors: Cepeha Cristina Mihaela , Paul Corina , Borlea Andreea , Bende Renata , Mihuta Monica Simina , Stoian Dana
Published: September 2022

Strain elastography (SE) determines the level of tissue stiffness and thus helps in the diagnosis and differentiation of various pathologies. This paper aims to study the elastographic differences between healthy thyroid parenchyma and ones affected by chronic autoimmune thyroiditis (CAT) in children. We included in our study 52 children diagnosed with CAT and 22 children with no thyroid pathology. After clinic examination and biological tests, an ultrasound was performed followed by SE using a Hitachi Preirus machine with a 5–15 multi-frequency linear probe. The mean strain ratio (SR) values were significantly lower in the control group compared with the CAT group (0.68 ± 0.2 vs. 1.19 ± 0.25; p < 0.0001). A mean value above 0.9 was found predictive for CAT with 84.62% Sensitivity(Se), 95.45% Specificity (Sp), 97.8% Positive Predictive Value (PPV), 72.4% Negative Predictive Value (NPV), and area under receiver operating characteristic (AUROC), 0.9. No differences were found between the two thyroid lobes. Also, we found no differences between girls and boys. Out of the 52 children with CAT, 39 were on therapy with levothyroxine. No differences were found between SR values in preexisting hypothyroid cases compared to euthyroid cases (1.24 ± 0.26 vs. 1.18 ± 0.25; p = 0.4615). Our results show that SE is a useful examination technique of children diagnosed with CAT, in accordance with other studies conducted on adults. This study lays the foundation for elastographic examination among children.
Analysis of Water Quality Improvement Effect of Controlled River by Multi-Functional Weir Operation
Authors: Han Jin-Kyoung , Jung Woo-Suk , Kim Jung-Min , Park In-Hwan , Kim Young-Do
Published: September 2022

In this study, the water quality change in a stagnant controlled river containing hard management structures due to pulsed flow was simulated. The Environmental Fluid Dynamics Code (EFDC) model was used for the water quality change simulation, and the water quality improvement effect caused by pulsed flow was simulated for multiple scenarios. Based on Case 1, in which 274.2–291.8 CMS was discharged for five hours, other scenarios, in which the gate was opened by 0.5 m and 1.0 m while the discharge time was doubled, were simulated. On comparing the water level results with the observed data, a relatively positive result of R2 = 0.82 was obtained. The water quality simulation results showed that the concentrations of Chl-a, COD, and T-P were, respectively, approximately 7.7%, 4.5%, and 4.0% higher in Case 2 than in Case 1, 24 h after the start of discharge. Cases 3 and 4, on the other hand, exhibited decreased concentrations of Chl-a, COD, and T-P 24 h after the start of discharge. These results confirm that pulsed flow leads to different water quality improvement effects depending on the discharge amount and duration. Furthermore, the water quality improvement effect was recorded at close to 0% after 72 h in all scenarios. As the water quality improvement effect due to pulsed flow appears within 24 h and almost disappears after 72 h depending on the scenario, a physical solution to this problem is required.
Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
Authors: Ardhianto Peter , Liau Ben-Yi , Jan Yih-Kuen , Tsai Jen-Yung , Akhyar Fityanul , Lin Chih-Yang , Subiakto Raden Bagus Reinaldy , Lung Chi-Wen
Published: September 2022

People with cerebral palsy (CP) suffer primarily from lower-limb impairments. These impairments contribute to the abnormal performance of functional activities and ambulation. Footprints, such as plantar pressure images, are usually used to assess functional performance in people with spastic CP. Detecting left and right feet based on footprints in people with CP is a challenge due to abnormal foot progression angle and abnormal footprint patterns. Identifying left and right foot profiles in people with CP is essential to provide information on the foot orthosis, walking problems, index gait patterns, and determination of the dominant limb. Deep learning with object detection can localize and classify the object more precisely on the abnormal foot progression angle and complex footprints associated with spastic CP. This study proposes a new object detection model to auto-determine left and right footprints. The footprint images successfully represented the left and right feet with high accuracy in object detection. YOLOv4 more successfully detected the left and right feet using footprint images compared to other object detection models. YOLOv4 reached over 99.00% in various metric performances. Furthermore, detection of the right foot (majority of people’s dominant leg) was more accurate than that of the left foot (majority of people’s non-dominant leg) in different object detection models.
Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber
Authors: Gong Jiangkun , Yan Jun , Li Deren , Hu Huiping , Kong Deyong
Published: September 2022

Traditional radar calibration methods often use one standard sphere as the standard calibration object, but researchers seldom discuss the size and the material content of the calibration sphere. We propose using a pair of different-sized spheres to verify the standard method. In a microwave anechoic chamber, the simulated and measured results show that the radar cross-section (RCS) values of two spheres with radii of 0.15 m and 0.05 m in the Mie region differ from their physical cross-sections in the optic region, and only in the optic region, their difference of RCS value (i.e., 9.5424 dB) can be approximately equal to the theoretical one (i.e., 9.6803 dB). Thus, radar calibration should be conducted in the same scattering region for both the calibration object and the calibrated target. The use of two different-sized spheres can aid in three applications: (1) verifying the scattering regions, (2) searching the pure area in the microwave anechoic chamber, and (3) locating the positions of the targets in the range profiles.
Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare
Authors: Razaque Abdul , Abenova Marzhan , Alotaibi Munif , Alotaibi Bandar , Alshammari Hamoud , Hariri Salim , Alotaibi Aziz
Published: September 2022

Time series data are significant, and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce an anomaly detection paradigm called novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data in the healthcare. The proposed paradigm inherits the features from two state-of-the-art algorithms: Scalable Time series Anytime Matrix Profile (STAMP) and Scalable Time-series Ordered-search Matrix Profile (STOMP). The proposed NMP caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP can be used on large multivariate data sets and generates approximate solutions of high quality in a reasonable time. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms, i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.
RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
Authors: Tai Haoyu , Xia Yonghua , He Xiangrong , Wu Xuequn , Li Chen , Yan Min , Kong Xiali , Yang Minglong
Published: September 2022

With the rapid development of the geographic information service industry, point cloud data are widely used in various fields, such as architecture, planning, cultural relics protection, mining engineering, etc. Despite that there are many approaches to collecting point clouds, we are facing the problem of point cloud holes caused by the inability of a 3D laser scanner to collect data completely in the narrow space of the mine access shaft. Thus, this paper uses RGB-D cameras to collect data and reconstruct the hole in the point cloud. We used a 3D laser scanner and RGB-D depth camera to collect the 3D point cloud data of the access shaft roadway. The maximum error was 2.617 cm and the minimum error was 0.031 cm by measuring the distance between the feature points, which satisfied the visualization repair of the missing parts of the 3D laser scanner data collection. We used the FPTH + ICP algorithm, ISS + ICP algorithm, SVD + ICP algorithm, and 3D-NDT algorithm to perform registration and fusion of the processed 3D point cloud and the original point cloud and finally repaired the hole. The study results show that the ISS + ICP registration algorithm had the most matching points and the lowest RMSE value of 13.8524 mm. In addition, in the closed and narrow roadway, the RGB-D camera was light and easy to operate and the point data acquired by it had relatively high precision. The three-dimensional point cloud of the repaired access shaft roadway has a good fit and can meet the repair requirements.
Shear Wave Velocity Estimation Based on Deep-Q Network
Authors: Zhu Xiaoyu , Dong Hefeng
Published: September 2022

Geoacoustic inversion is important for seabed geotechnical applications. It can be formulated as a problem that seeks an optimal solution in a high-dimensional parameter space. The conventional inversion approach exploits optimization methods with a pre-defined search strategy whose hyperparameters need to be fine-tuned for a specific scenario. A framework based on the deep-Q network is proposed in this paper and the environment and agent configurations of the framework are specially defined for geoacoustic inversion. Unlike a conventional optimization method with a pre-defined search strategy, the proposed framework determines a flexible strategy by trial and error. The proposed framework is evaluated by two case studies for estimating the shear wave velocity profile. Its performance is compared with three global optimization methods commonly used in underwater geoacoustic inversion. The results demonstrate that the proposed framework performs the inversion more efficiently and accurately.
Target-Oriented High-Resolution and Wide-Swath Imaging with an Adaptive Receiving–Processing–Decision Feedback Framework
Authors: Zhan Xu , Zhang Xiaoling , Zhang Wensi , Xu Yuetonghui , Shi Jun , Wei Shunjun , Zeng Tianjiao
Published: September 2022

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are required to receive the echoes of the whole scene and process them to obtain imaging results. This is a heavy burden for online monitoring on platforms with limited resources. To address these issues, different from the concept of whole-scene-oriented imaging, we propose a target-oriented imaging concept, which is implemented by an adaptive receiving–processing–decision feedback framework with feedback connection. (1) To reduce receiving resource consumption, we propose a two-dimensional adaptive receiving module. It receives sub-band echoes of targets only through dechirping and subaperture decomposition in the range and azimuth directions, respectively. (2) To reduce computation time, we propose a target-oriented processing module. It processes subarea echoes of targets only through parallelly conducting inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. (3) To allocate resources reasonably, we propose a decision module. It decides the necessary receiving window, bandwidth, and image resolution through constant false alarm rate (CFAR) detection. (4) To allocate resources adaptively, we connect three modules with a closed loop to enable feedback. This enables progressive target imaging and detection from rough to fine. Experimental results verify the feasibility of the proposed framework. Compared with the current one, for a typical scenario, at least 30% of the system’s resources and 99% of computation time are saved.
Quodlibet with Meninas
Authors: Gil Ulldemolins Maria
Published: September 2022

In Diagrammatic Writing (2013), Johanna Drucker discusses the power dynamics between texts interacting on a page. So-called autotheoretical texts often engage in similar types of performative and relational lay-outs, and yet, not much has been written about this formal phenomenon. Bearing this in mind, I propose an experiment that performs relations by thinking with, and through, Las Meninas, a self-portrait that is not strictly about the self. All that surrounds Velázquez in the painting (the work-in-progress we do not see, the ensemble of courtly characters, the framed reproductions of masters’ works, the much-discussed mirror reflection) informs and contextualises the portrait, but also explodes it into much more. This paper thus attempts to ask whether autotheory can, by being aware of performative and diagrammatic writing, together with the use of images as citations, decentralise the auto- and become a more choral scene, a cluster, a textual quodlibet or medley. Can a form of writing make space for a multitude, or even, a multitude into a space? Can the autotheoretical self be only one more of many characters, present, with agency, but off-centred?
Simultaneous Monitoring of Outdoor PAHs and Particles in a French Peri-Urban Site during COVID Restrictions and the Winter Saharan Dust Event
Authors: Nursanto Farhan Ramadzan , Vaz-Ramos Joana , Delhomme Olivier , Bégin-Colin Sylvie , Le Calvé Stéphane
Published: September 2022

The presence of polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM) in air is known to provoke deleterious effects on human health. This work focused on the monitoring of PM and PAHs in the air over four weeks in a peri-urban site in Strasbourg (France), using a three-stage cascade impactor and a particle analyser allowing PM1, PM2.5 and PM10 discrimination. Meteorological conditions were monitored to study their influence on the pollutant levels. The average PM10 concentration of the cascade impactor and particle analyser varied from 11.8 to 80.2 µg/m3 and 10.6 to 220.2 µg/m3, respectively. The PAH total concentration ranged in 1.1–7.6 ng/m3 and a predominance of 5- and 6-ring PAHs was observed. PAHs were also more abundant in finer particles (PM1). Specifically, identified PAHs are traffic tracers suggesting that vehicular emission was one of its main sources. Two pollution episodes, associated with either a Saharan dust wind episode or traffic pollution, were observed, and led to PM10 and PM2.5 surpassing the daily limit values established by the European Union despite the traffic limitations according to the COVID restrictions. The total PAH concentrations were the highest during these periods suggesting PAHs might be bound to and transported via dust particles.
A Swarming Approach for the Novel Second Order Perturbed Pantograph Lane–Emden Model Arising in Astrophysics
Authors: Al Nuwairan Muneerah , Sabir Zulqurnain
Published: September 2022

The purpose of this study is to provide a mathematical construction based on the novel singular perturbed model of the second kind (NSPM-SK) using the standard form of the Lane–Emden. The singular Lane–Emden types of the models have abundant applications in astrophysics. The inclusive features of this model are provided using the perturbed, pantograph, singular point together and the shape factor based on the NSPM-SK. These models become more complicated by using these factors through the artificial neural networks (ANNs) together with the optimization procedures of the swarming particle swarm optimization (PSO) paradigms and the local sequential quadratic programming (SQP). An objective function is provided based on the differential form of the NSPM-SK and then optimization is performed through the hybridization of the PSOSQP. The exactness of the model is attained to solve three different variations of the mathematical NSPM-SK by using the overlapping of the obtained and true results. The stability, robustness, and convergence of the designed numerical approach are perceived by using different statistical performances of the ANNs together with the optimization of the PSOSQP for 30 independent executions.
A Fuzzy Decision-Making Soft Model for Family Financial Planning in the Post-COVID-19 World
Authors: Sun Chia-Chi
Published: September 2022

With COVID-19 still making headlines around the world, many people currently feel uncertain about many aspects of life, including family financial planning and wealth management. Financial planning is important at all times, but it becomes essential during a crisis such as the coronavirus pandemic, which has disrupted people’s finances. Some economic consequences are already apparent, but the financially-induced stress caused by the uncertainty is less visible. With the increase in family wealth and size of organizations, there is a comparable increase in their assets. There is considerable demand for professionals to manage these assets and coordinate investment activities in order to maintain growth. This raises the issue of how to increase a wealth management bank’s competitive advantages. This study approached the issue by using experts and the application of fuzzy logic and decision-making trial and evaluation laboratory and multiple criteria decision-making to segment a set of the selection criteria used by prospective customers, to select a wealth management bank that can effectively manage personal wealth. The results showed that the management’s learning and growth perspective was the most important factor in respondents’ selection of a wealth management bank. This paper also provides managerial practice implications.