bluuebunny/crossref_metadata_2025 huggingface dataset:
Case report: An innovative non-invasive technique to manage shell injuries in C. carbonarius
Authors: Horvath-Pereira Bianca de Oliveira , Paulini Fernanda , Sotelo Marco Olívio , Leardini Ettore Giovanni , Tavares Dhiego Cristiano , Almeida Gustavo Henrique Doná Rodrigues , da Silva Júnior Leandro Norberto , Pinho Letícia Beatriz Mazo , Miglino Maria Angelica , Araujo Michelle Silva
Published: August 2022

Shell fractures are one of the most traumatic and recurrent injuries observed in chelonians during clinical practice. The most common causes of fractures are falling, being run over by automobiles, being burned, and wild animal bites. Epoxy, acrylic resin, polyester, fiber-grass blanket, and screw fixation are among the current techniques used to treat fractures. Regarding the difficulty of fracture repair in the carapace, this case report aimed to report a procedure that is effective, less time-consuming, accessible, affordable, and safe for shell fractures in C. carbonarius. During the physical examination, the animal showed two fractures, in the dorsal region of the carapace and right lateral side of the bridge, with subcutaneous tissue exposure and loss of a small piece of dorsocranial carapace. To treat these injuries, the animal was submitted to a resin application. The procedure consists of using ethyl-cyanoacrylate associated with sodium bicarbonate, which produces a more resistant resin that is bactericidal, non-toxic, and easy to apply in a low surgery time compared to the common methods used to fix shell fractures. The resin application was successfully done, and the animal was under care for a month after the fracture reduction. It was observed that the treatment was effective, presenting reduction of the fracture. A month after the procedure, the animal showed no intercurrence. Three years after the procedure, the animal still presents part of the material still fixed to the shell, normal growth, without interference in locomotor capacity. This resin proved to be an innovative and promising alternative way to treat fractures, suggesting the development of new non-invasive approaches for several tissues and different animal species.
The anti-Toxoplasma activity of the plant natural phenolic compound piceatannol
Authors: Jiang Yucong , Shi Yuehong , Hu Dandan , Song Xingju
Published: August 2022

Toxoplasma gondii is an obligate intracellular protozoan that infects the nucleated cells of warm-blooded animals and causes life-threatening disease in immunocompromised patients. Due to the limited effectiveness and prominent side effects of existing drugs, there is an urgent need to develop new therapeutic options against T. gondii. Piceatannol is a natural plant compound with multiple functions such as antibacterial, antileukemic and antiparasitic activities. In the present study, the anti-T. gondii activity of piceatannol was evaluated. Piceatannol potently inhibited Toxoplasma with a half-maximal effective concentration (EC50) of 28.10 μM. Piceatannol showed a significant inhibitory effect on intracellular proliferation, inhibiting intracellular parasites at a rate of 98.9% when treatment with 100 μM piceatannol. However, the invasion ability of tachyzoites was not affected by piceatannol. By immunofluorescence assay, we noted that the parasite showed abnormalities in cell division after exposure to piceatannol. To determine the in vivo effect of piceatannol on acute infection, a model was established by infecting BALB/c mice with the virulent RH strain of T. gondii. Mice infected with 500 tachyzoites showed a significant therapeutic effect when treated with 15 mg/kg of piceatannol. These results suggest that piceatannol is a promising drug for the treatment of T. gondii.
Karmi, żywi, byt zapewnia. O metaforyzacji ziemi i jej plonów w słownictwie mieszkańców powiatu kłobuckiego związanych z rolnictwem – analiza porównawcza.
Authors: Falana-Jafra Anna
Published: August 2022

The aim of the paper is to determine the ways of metaphorising land and
its crops by inhabitants of the Kłobuck county associated with agriculture. The
metaphor is understood according to the assumptions of the cognitive stream
of linguistics, i.e. as a linguistic means of conceptualising reality, and not
only as a stylistic means. The analyses are aimed not only at describing the
lexical resources of the subjects, but above all at determining the forms of their
thinking and acting. One hundred inhabitants of the Kłobuck county took part
in the survey. A half of them were between 20 and 40 years old, while the other
half were at least 65 years old. The research tool was an interview questionnaire
consisting of four questions. The questions were open-ended in order to decode
the metaphor of land and its crops. The research made it possible to draw
unambiguous conclusions about the conceptual tendencies manifested by both
groups of respondents.
Podwórkowa przekąska. Gwarowe i potoczne nazwy tasznika pospolitego
Authors: Osowski Błażej
Published: August 2022

Shepherd’s purse is a common weed in Poland. Its various colloquial and
dialectal names can be found in the literature dedicated to herbalism, yet there is
no linguistic study on this subject. This issue is discussed in this paper. Using an
online survey, the contemporary Polish names of the plant have been collected.
The material is composed of 33 names analysed in terms of classifi cation into
variants of Polish, geographical distribution, semantic motivation. A unit used
within a limited geographical area is chleb babajagi, with its centre in Greater
Poland. The names chlebek świętojański, Boży chlebek, babi chlebek, chleb,
chlebek, chlebki are colloquial in turn.
Ikoniczność, metonimia i metafora w znakach polskiego języka migowego oznaczających mówienie
Authors: Linde-Usiekniewicz Jadwiga , Mostowski Piotr
Published: August 2022

This is the fi rst in a planned series of papers dedicated to Polish Sign Language
(PJM) signs denoting speech. It presents an analysis of twenty different signs
attested in the Polish Sign Language Corpus (KPJM) in terms of iconicity as well
as metonymies and metaphors used in their formation. The conducted analysis
showed that two classes of signs can be distinguished in the examined material:
iconic signs making use of metonymy and signs motivated by a conceptual
metaphor. The former refer exclusively to the production of speech as opposed
to signing: the vehicle is the phonetic act (mouth movement or air movement).
Metaphoric signs, in turn, consistently rely on the conduit metaphor, refer to
both spoken and signed utterances, and focus on transmission of a semantic
content.
Impulse Air Jet Action on the Convective Heat Exchange Tubular Surfaces in Boilers
Authors: Kislovsky Valentin , Melnikov Alexey
Published: August 2022

Calculation and experimental investigations were carried out to determine the parameters of the impulse air jet created by the pneumatic generator of the air-impact system of the cleaning of tubular heat exchangers in boilers. Two configurations (lengthwise and checkerboard) of the arrangement of tubular heat exchangers located at distances from 1.0 m to 2.5 m of nozzle exit were experimentally investigated and the forces acting on them were determined. Numerical modeling of the impulse jet impact on heat exchange tubes was carried out. It was shown that an increase in the pressure of the generated impulse jet to 2.35 MPa makes it possible to significantly increase the acting force from 78 N to 1881 N at a large distance from the exhaust nozzle. The results obtained can be used in the development of specific systems for air impact cleaning systems of boiler units with other sizes and configurations of the heat exchange tubes.
A Diameter Measurement Method of Red Jujubes Trunk Based on Improved PSPNet
Authors: Qiao Yichen , Hu Yaohua , Zheng Zhouzhou , Qu Zhanghao , Wang Chao , Guo Taifeng , Hou Juncai
Published: August 2022

A trunk segmentation and a diameter measurement of red jujubes are important steps in harvesting red jujubes using vibration harvesting robots as the results directly affect the effectiveness of the harvesting. A trunk segmentation algorithm of red jujubes, based on improved Pyramid Scene Parsing Network (PSPNet), and a diameter measurement algorithm to realize the segmentation and diameter measurement of the trunk are proposed in this research. To this end, MobilenetV2 was selected as the backbone of PSPNet so that it could be adapted to embedded mobile applications. Meanwhile, the Convolutional Block Attention Module (CBAM) was embedded in the MobilenetV2 to enhance the feature extraction capability of the model. Furthermore, the Refinement Residual Blocks (RRBs) were introduced into the main branch and side branch of PSPNet to enhance the segmentation result. An algorithm to measure trunk diameter was proposed, which used the segmentation results to determine the trunk outline and the normal of the centerline. The Euclidean distance of the intersection point of the normal with the trunk profile was obtained and its average value was regarded as the final trunk diameter. Compared with the original PSPNet, the Intersection-over-Union (IoU) value, PA value and Fps of the improved model increased by 0.67%, 1.95% and 1.13, respectively, and the number of parameters was 5.00% of that of the original model. Compared with other segmentation networks, the improved model had fewer parameters and better segmentation results. Compared with the original network, the trunk diameter measurement algorithm proposed in this research reduced the average absolute error and the average relative error by 3.75 mm and 9.92%, respectively, and improved the average measurement accuracy by 9.92%. To sum up, the improved PSPNet jujube trunk segmentation algorithm and trunk diameter measurement algorithm can accurately segment and measure the diameter in the natural environment, which provides a theoretical basis and technical support for the clamping of jujube harvesting robots.
AI-Assisted Vision for Agricultural Robots
Authors: Fountas Spyros , Malounas Ioannis , Athanasakos Loukas , Avgoustakis Ioannis , Espejo-Garcia Borja
Published: August 2022

Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where such solutions can aid in making farming easier for the farmers, safer, and with greater margins for profit, while at the same time offering higher quality products with minimal environmental impact. This paper focuses on reviewing the existing state of the art for vision-based perception in agricultural robots across a variety of field operations; specifically: weed detection, crop scouting, phenotyping, disease detection, vision-based navigation, harvesting, and spraying. The review revealed a large interest in the uptake of vision-based solutions in agricultural robotics, with RGB cameras being the most popular sensor of choice. It also outlined that AI can achieve promising results and that there is not a single algorithm that outperforms all others; instead, different artificial intelligence techniques offer their unique advantages to address specific agronomic problems.
Does Environmental Enrichment with Music and Strobe Light Affect Broilers’ Welfare? Analyzing Their On-Farm Reaction
Authors: Jacob Flavia Gerbi , Nääs Irenilza de Alencar , Salgado Douglas D’Alessandro , Baracho Marta dos Santos , Lima Nilsa Duarte da Silva , Pereira Danilo Florentino
Published: August 2022

The present study observed whether environmental enrichment (music and strobe light) influenced farm-housed broiler chickens’ behavior. The trial was carried out on a commercial broiler farm from 21 to 35 days of growth. The sound stimulus consisted of playing a classical music track every day for an approximate length of 6 min, played five times a day for six weeks starting from the birds’ first day of age. The light stimuli came from a colored (red and green ground-projected dots) light-emitting diode (LED) strobe projector used after the musical stimulation. The broilers’ reaction was recorded (from day 21 through day 35), and individual bird behaviors were classified into welfare and stress. The birds’ ability to walk was measured using a gait score scale, and the degree of incidence of pododermatitis was verified. Environmental enrichment with light stimulus increased natural behavior in broiler chickens, such as eating, stretching, ground pecking, and flapping wings (p < 0.05). Broiler chickens tended to walk less in the housing with music stimuli (p < 0.05). In general, the environmental stimuli provided the birds with better walking ability but increased the incidence of pododermatitis (p < 0.01). We observed that the light stimulus left the birds more active; they foraged more and lay less when compared to the birds submitted to musical stimuli and the control. However, we also observed an increase in the frequency of stress-indicating behaviors in the environment under light stimulation. It is unclear whether broilers liked the tested stimuli of music and light in the scenarios studied. The enrichment with light or music apparently increased flock stress in 21- and 28-day-old broilers, with some benefit being observed only in 35-day-old broilers.
Efficacy of Eight Anticoagulant Food Baits in House Mouse (Mus musculus): Comparison of Choice and No-Choice Laboratory Testing Approaches
Authors: Frankova Marcela , Aulicky Radek , Stejskal Vaclav
Published: August 2022

House mouse (Mus musculus) is a cosmopolitan pest in agricultural facilities, commodity stores and rural and urban environments. It is regularly controlled by anticoagulant-based baits. Since the number of registered active ingredients is limited, the producers are trying to develop new formulations with increased tamper-resistance, attractiveness and palatability. Reliable and economical methods for laboratory screening/testing are needed. Therefore, this work compared simple no-choice with more economically demanding choice feeding tests for laboratory evaluation of efficacy of rodenticide anticoagulant-based preparations in wild populations of house mouse. We analyzed mice survival and bait intake of the various rodenticide commercial preparations differing in a toxic active substance (warfarin, bromadiolone, brodifacoum, difethialone) and nontoxic food lure components. In most cases, we found insignificant differences in survival of house mice treated with eight test baits within the no-choice and choice feeding tests. We rejected the tested hypothesis that there would be significant differences in all tested preparations for two different testing approaches.
Augmented Air Traffic Control System—Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts
Authors: Ortner Philipp , Steinhöfler Raphael , Leitgeb Erich , Flühr Holger
Published: August 2022

Today’s air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deterioration of flight safety, greater costs, more delays, and higher emissions. Transforming the ATM into the “next generation” requires complex human-integrated systems that provide better abstraction of airspace and create situational awareness, as described in the literature for this problem. This paper makes the following contributions: (a) It outlines the complexity of the problem. (b) It introduces a digital assistance system to detect conflicts in air traffic by systematically analyzing aircraft surveillance data to provide air traffic controllers with better situational awareness. For this purpose, long short-term memory (LSTMs) networks, which are a popular version of recurrent neural networks (RNNs) are used to determine whether their temporal dynamic behavior is capable of reliably monitoring air traffic and classifying error patterns. (c) Large-scale, realistic air traffic models with several thousand flights containing air traffic conflicts are used to create a parameterized airspace abstraction to train several variations of LSTM networks. The applied networks are based on a 20–10–1 architecture while using leaky ReLU and sigmoid activation function. For the learning process, the binary cross-entropy loss function and the adaptive moment estimation (ADAM) optimizer are applied with different learning rates and batch sizes over ten epochs. (d) Numerical results and achievements by using LSTM networks to predict various weather events, cyberattacks, emergency situations and human factors are presented.
Comprehensive Examination of Cu, Pb, Zn, Fe, Mn and Cd in Lackawanna County Waters, Northeastern Pennsylvania: A Brief Report
Authors: Dumancas Gerard G. , Butka Jake R. , Mehall Adam M.
Published: August 2022

Lackawanna, a US county seat in Northeastern Pennsylvania has an estimated population of 216,000 in 2020. Over the years, it has been reported that several bodies of water found within the county have been contaminated with various metals. However, a comprehensive examination of the presence of these metals has not been conducted. The goal of this brief report was to perform a preliminary quantitative determination of the concentration of various metals found in Lackawanna County water sources. The sources analyzed included Lake Scranton, the Lackawanna River, the Griffin Reservoir, and Keyser Creek. Samples were taken from each source and analyses of copper, lead, zinc, iron, manganese, and cadmium using atomic absorption spectroscopy were performed. A copper concentration of 0.100 ppm was found in the Griffin Reservoir, and 0.380 ppm of iron was found in the Lackawanna River. The concentration of copper and iron in the Griffin Reservoir and Lackawanna River, respectively, were determined to be within safe levels according to guidelines set by the US Environmental Protection Agency.
Development and Preliminary Evaluation of a Nanoparticle-Assisted PCR Assay for the Detection of Cryptosporidium parvum in Calves
Authors: Yao Qian , Yang Xin , Wang Yi , Wang Junwei , Huang Shuang , Song Junke , Zhao Guanghui
Published: August 2022

C. parvum is an important diarrheal pathogen in humans and animals, especially in young hosts. To accurately and rapidly detect C. parvum infection in calves, we established a nano-PCR assay targeting the cgd3_330 gene for the specific detection of C. parvum. This nano-PCR assay was ten times more sensitive than that of the normal PCR assay by applying the same primers and did not cross-react with C. andersoni, C. bovis, C. ryanae, Balantidium coli, Enterocytozoon bieneusi, Giardia lamblia, and Blastocystis sp. To further test the nano-PCR in clinical settings, a total of 20 faecal samples from calves were examined by using the nano-PCR, the normal PCR, and the nested PCR assays. The positive rates were 30% (6/20), 30% (6/20), and 25% (5/20) for the nano-PCR, the normal PCR, and the nested PCR assays, respectively, indicating that the nano-PCR and the normal PCR assays had the same positive rate (30%). Taken together, the present study could provide a candidate method for the specific detection of C. parvum infection in calves in clinical settings.
Model-Informed Translation of In Vitro Effects of Short-, Prolonged- and Continuous-Infusion Meropenem against Pseudomonas aeruginosa to Clinical Settings
Authors: Minichmayr Iris , Kappetein Suzanne , Brill Margreke , Friberg Lena
Published: August 2022

Pharmacokinetic-pharmacodynamic (PKPD) models have met increasing interest as tools to identify potential efficacious antibiotic dosing regimens in vitro and in vivo. We sought to investigate the impact of diversely shaped clinical pharmacokinetic profiles of meropenem on the growth/killing patterns of Pseudomonas aeruginosa (ARU552, MIC = 16 mg/L) over time using a semi-mechanistic PKPD model and a PK/PD index-based approach. Bacterial growth/killing were driven by the PK profiles of six patient populations (infected adults, burns, critically ill, neurosurgery, obese patients) given varied pathogen features (e.g., EC50, growth rate, inoculum), patient characteristics (e.g., creatinine clearance), and ten dosing regimens (including two dose levels and 0.5-h, 3-h and continuous-infusion regimens). Conclusions regarding the most favourable dosing regimen depended on the assessment of (i) the total bacterial load or fT>MIC (time that unbound concentrations exceed the minimum inhibitory concentration); (ii) the median or P0.95 profile of the population; and (iii) 8 h or 24 h time points. Continuous infusion plus loading dose as well as 3-h infusions (3-h infusions: e.g., for scenarios associated with low meropenem concentrations, P0.95 profiles, and MIC ≥ 16 mg/L) appeared superior to standard 0.5-h infusions at 24 h. The developed platform can serve to identify promising strategies of efficacious dosing for clinical trials.
A Proof of Concept of the Usefulness of a TDM-Guided Strategy for Optimizing Pharmacokinetic/Pharmacodynamic Target of Continuous Infusion Ampicillin-Based Regimens in a Case Series of Patients with Enterococcal Bloodstream Infections and/or Endocarditis
Authors: Gatti Milo , Tedeschi Sara , Trapani Filippo , Ramirez Stefania , Mancini Rita , Giannella Maddalena , Viale Pierluigi , Pea Federico
Published: August 2022

(1) Objective: To describe the usefulness of a real-time therapeutic drug monitoring (TDM)-based strategy for optimizing pharmacokinetic/pharmacodynamic (PK/PD) target attainment of continuous infusion (CI) ampicillin-based regimens in a case series of patients affected by suspected or documented enterococcal bloodstream infections (BSIs) and/or infective endocarditis (IE). (2) Methods: Patients treated with CI ampicillin-based regimens for documented or suspected enterococcal BSI/IE who underwent real-time therapeutic drug monitoring (TDM)-based expert clinical pharmacological advice (ECPA) between June 2021 and May 2022 were retrospectively assessed. Ampicillin concentrations were determined at steady state, and the free fraction (fCss) was calculated according to a plasma protein binding of 20%. The fCss/MIC ratio was selected as the PD parameter for ampicillin efficacy and was defined as optimal for values between 4 and 8. The requirement for TDM-guided ampicillin dosing adjustments was assessed. (3) Results: Data for 12 patients with documented (n = 10) or suspected (n = 2) enterococcal infections (7 with BSIs and 5 with IE) were retrieved. The ampicillin PK/PD target was optimal over time in all of the 10 documented infections. None of the enterococcal BSIs persisted. Following the first real-time TDM-based ECPA, ampicillin dosage was decreased by >50% in 11 out of 12 patients (91.7%). (4) Conclusions: CI may be helpful in attaining aggressive ampicillin PK/PD targets in patients affected by enterococcal BSIs and/or IE. Administration of CI ampicillin after loading coupled with real-time TDM-based ECPA could be a valuable strategy for managing enterococcal infections.
Identification of Novel Inhibitor of Enoyl-Acyl Carrier Protein Reductase (InhA) Enzyme in Mycobacterium tuberculosis from Plant-Derived Metabolites: An In Silico Study
Authors: Singh Kratika , Pandey Niharika , Ahmad Firoz , Upadhyay Tarun Kumar , Islam Mohammad Hayatul , Alshammari Nawaf , Saeed Mohd , Al-Keridis Lamya Ahmed , Sharma Rolee
Published: August 2022

Mycobacterium tuberculosis (M.tb.) enoyl-acyl carrier protein (ACP) reductase (InhA) is validated as a useful target for tuberculosis therapy and is considered an attractive enzyme to drug discovery. This study aimed to identify the novel inhibitor of the InhA enzyme, a potential target of M.tb. involved in the type II fatty acid biosynthesis pathway that controls mycobacterial cell envelope synthesis. We compiled 80 active compounds from Ruta graveolens and citrus plants belonging to the Rutaceae family for pharmacokinetics and molecular docking analyses. The chemical structures of the 80 phytochemicals and the 3D structure of the target protein were retrieved from the PubChem database and RCSB Protein Data Bank, respectively. The evaluation of druglikeness was performed based on Lipinski’s Rule of Five, while the computed phytochemical properties and molecular descriptors were used to predict the ADMET of the compounds. Amongst these, 11 pharmacokinetically-screened compounds were further examined by performing molecular docking analysis with an InhA target using AutoDock 4.2. The docking results showed that gravacridonediol, a major glycosylated natural alkaloid from Ruta graveolens, might possess a promising inhibitory potential against InhA, with a binding energy (B.E.) of −10.80 kcal/mole and inhibition constant (Ki) of 600.24 nM. These contrast those of the known inhibitor triclosan, which has a B.E. of −6.69 kcal/mole and Ki of 12.43 µM. The binding efficiency of gravacridonediol was higher than that of the well-known inhibitor triclosan against the InhA target. The present study shows that the identified natural compound gravacridonediol possesses drug-like properties and also holds promise in inhibiting InhA, a key target enzyme of M.tb.
Structured Data Storage for Data-Driven Process Optimisation in Bioprinting
Authors: Schmieg Barbara , Brandt Nico , Schnepp Vera J. , Radosevic Luka , Gretzinger Sarah , Selzer Michael , Hubbuch Jürgen
Published: August 2022

Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.
Deep Neural Network-Based Prediction and Early Warning of Student Grades and Recommendations for Similar Learning Approaches
Authors: Tao Tao , Sun Chen , Wu Zhaoyang , Yang Jian , Wang Jing
Published: August 2022

Studies reported that if teachers can accurately predict students’ follow-up learning effects via data mining and other means, as per their current performances, and explore the difficulty level of students’ mastery of future-related courses in advance, it will help improve students’ scores in future exams. Although educational data mining and learning analytics have experienced an increase in exploration and use, they are still difficult to precisely define. The usage of deep learning methods to predict academic performances and recommend optimal learning methods has not received considerable attention from researchers. This study aims to predict unknown course grades based on students’ previous learning situations and use clustering algorithms to identify similar learning situations, thereby improving students’ academic performance. In this study, the methods of linear regression, random forest, back-propagation neural network, and deep neural network are compared; the prediction and early warning of students’ academic performances based on deep neural network are proposed, in addition to the improved K-nearest neighbor clustering based on association rules (Pearson correlation coefficient). The algorithm performs a similar category clustering for early-warning students. Using the mean square error, standard deviation, mean absolute percentage error, and prediction of ups-and-downs accuracy as evaluation indicators, the proposed method achieves a steady improvement of 20% in the prediction of ups-and-downs accuracy, and demonstrates improved prediction results when compared under similar conditions.
Manual Wheelchair Equipped with a Planetary Gear-Research Methodology and Preliminary Results
Authors: Kukla Mateusz , Wieczorek Bartosz , Warguła Łukasz , Rybarczyk Dominik , Górecki Jan
Published: August 2022

The purpose of the study was to create a research methodology for testing the newly developed wheelchair drive, which allows the operator to choose the gear ratio and, thus, makes it possible to change the propulsion torque value. The aim was to choose such conditions in the experiment, that would result in great enough changes in the participant’s muscle load and body kinematics for it to be possible to register them with applied measuring methods. Surface electromyography was used to assess the effort that was required for the propulsion of a wheelchair under different conditions. Additionally, upper limb motion capture measurements were also performed. The preliminary results show that the muscular effort of the participant propelling the wheelchair increases with the load—resulting from both the gear ratio and the inclination angle. At the same time, the position of the motion range of upper limb individual segments changes significantly. Simultaneously, the mean value of the shoulder displacement and its angle of rotation decreases.
Fibonacci Wavelet Method for the Solution of the Non-Linear Hunter–Saxton Equation
Authors: Srivastava H. M. , Shah Firdous A. , Nayied Naied A.
Published: August 2022

In this article, a novel and efficient collocation method based on Fibonacci wavelets is proposed for the numerical solution of the non-linear Hunter–Saxton equation. Firstly, the operational matrices of integration associated with the Fibonacci wavelets are constructed by following the strategy of Chen and Hsiao. The operational matrices merged with the collocation method are used to convert the given problem into a system of algebraic equations that can be solved by any classical method, such as Newton’s method. Moreover, the non-linearity arising in the Hunter–Saxton equation is handled by invoking the quasi-linearization technique. To show the efficiency and accuracy of the Fibonacci-wavelet-based numerical technique, the approximate solutions of the non-linear Hunter–Saxton equation with other numerical methods including the Haar wavelet, trigonometric B-spline, and Laguerre wavelet methods are compared. The numerical outcomes demonstrate that the proposed method yields a much more stable solution and a better approximation than the existing ones.
Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
Authors: Li Gang , Sun Chengjie , Xu Chuanyun , Zheng Yu , Wang Keya
Published: August 2022

The refined segmentation of nuclei and the cytoplasm is the most challenging task in the automation of cervical cell screening. The U-Shape network structure has demonstrated great superiority in the field of biomedical imaging. However, the classical U-Net network cannot effectively utilize mixed domain information and contextual information, and fails to achieve satisfactory results in this task. To address the above problems, a module based on global dependency and local attention (GDLA) for contextual information modeling and features refinement, is proposed in this study. It consists of three components computed in parallel, which are the global dependency module, the spatial attention module, and the channel attention module. The global dependency module models global contextual information to capture a priori knowledge of cervical cells, such as the positional dependence of the nuclei and cytoplasm, and the closure and uniqueness of the nuclei. The spatial attention module combines contextual information to extract cell boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information, and make it easy to identify subtle but dominant differences of similar objects. Comparative and ablation experiments are conducted on the Herlev dataset, and the experimental results demonstrate the effectiveness of the proposed method, which surpasses the most popular existing channel attention, hybrid attention, and context networks in terms of the nuclei and cytoplasm segmentation metrics, achieving better segmentation performance than most previous advanced methods.
Quantitative Characterization of Oxygen-Containing Groups on the Surface of Carbon Materials: XPS and NEXAFS Study
Authors: Sivkov Danil V. , Petrova Olga V. , Nekipelov Sergey V. , Vinogradov Alexander S. , Skandakov Roman N. , Bakina Ksenia A. , Isaenko Sergey I. , Ob’edkov Anatoly M. , Kaverin Boris S. , Vilkov Ilya V. , Sivkov Viktor N.
Published: August 2022

The results of the comparative quantitative study of oxygen-containing groups adsorbed on the surface of carbonized sponge scaffold (CSS), highly oriented pyrolytic graphite (HOPG), fullerite C60 and multi-walled carbon nanotubes (MWCNTs) introduced into a high vacuum from the atmosphere without any pre-treatment of the surface are discussed. The studied materials are first tested by XRD and Raman spectroscopy, and then quantitatively characterized by XPS and NEXAFS. The research results showed the presence of carbon oxides and water-dissociation products on the surfaces of materials. It was shown that main source of oxygen content (~2%) on the surface of HOPG, MWCNTs, and C60 powder is water condensed from the atmosphere in the form of an adsorbed water molecule and hydroxyl group. On the CSS surface, oxygen atoms are present in the forms of carbon oxides (4–5%) and adsorbed water molecules and hydroxyl groups (5–6%). The high content of adsorbed water on the CSS surface is due to the strong roughness and high porosity of the surface.
Revisiting the Detection of Lateral Movement through Sysmon
Authors: Smiliotopoulos Christos , Barmpatsalou  Konstantia , Kambourakis Georgios
Published: August 2022

This work attempts to answer in a clear way the following key questions regarding the optimal initialization of the Sysmon tool for the identification of Lateral Movement in the MS Windows ecosystem. First, from an expert’s standpoint and with reference to the relevant literature, what are the criteria for determining the possibly optimal initialization features of the Sysmon event monitoring tool, which are also applicable as custom rules within the config.xml configuration file? Second, based on the identified features, how can a functional configuration file, able to identify as many LM variants as possible, be generated? To answer these questions, we relied on the MITRE ATT and CK knowledge base of adversary tactics and techniques and focused on the execution of the nine commonest LM methods. The conducted experiments, performed on a properly configured testbed, suggested a great number of interrelated networking features that were implemented as custom rules in the Sysmon’s config.xml file. Moreover, by capitalizing on the rich corpus of the 870K Sysmon logs collected, we created and evaluated, in terms of TP and FP rates, an extensible Python .evtx file analyzer, dubbed PeX, which can be used towards automatizing the parsing and scrutiny of such voluminous files. Both the .evtx logs dataset and the developed PeX tool are provided publicly for further propelling future research in this interesting and rapidly evolving field.
Research on Normal Behavior Models for Status Monitoring and Fault Early Warning of Pitch Motors
Authors: Yuan Liang , Qiu Lirong , Zhang Chunxia
Published: August 2022

Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based on the normal behavior modeling technique, this paper studies the status monitoring of the pitch motors. Based on the fact that the state of the motor varies with time, we propose to train an echo state network with the SCADA data to predict the temperature of the pitch motor. Subsequently, the EWMA (exponentially weighted moving average) technique is used to set the alarm limit lines of each parameter. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and early warn the faults of the pitch motor.
The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses
Authors: Chien Jong-Chih , Lee Jiann-Der , Hu Ching-Shu , Wu Chieh-Tsai
Published: August 2022

In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion or tumor not visible to the human eye, and may also help provide additional recessive information by using modern data analysis methods. These methods, including Artificial Intelligence (AI) technologies, are based on deep learning architectures, and have shown remarkable results in recent studies. However, the lack of explanatory ability of connection-based, instead of algorithm-based, deep learning technologies is one of the main reasons for the delay in the acceptance of these technologies in the mainstream medical field. One of the recent methods that may offer the explanatory ability for the CNN classes of deep learning neural networks is the gradient-weighted class activation mapping (Grad-CAM) method, which produces heat-maps that may offer explanations of the classification results. There are already many studies in the literature that compare the objective metrics of Grad-CAM-generated heat-maps against other methods. However, the subjective evaluation of AI-based classification/prediction results using medical images by qualified personnel could potentially contribute more to the acceptance of AI than objective metrics. The purpose of this paper is to investigate whether and how the Grad-CAM heat-maps can help physicians and radiologists in making diagnoses by presenting the results from AI-based classifications as well as their associated Grad-CAM-generated heat-maps to a qualified radiologist. The results of this study show that the radiologist considers Grad-CAM-generated heat-maps to be generally helpful toward diagnosis.
An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
Authors: Ghasemi Mohamad Amin , Zarei Seyed Fariborz , Sohrabi Zahra , Blaabjerg Frede , Peyghami Saeed
Published: August 2022

Distributed energy resources based on grid-following inverters are the dominant part of future modern power systems. To achieve a higher performance, the reliability enhancements and cost reductions of such inverters are the most important demands. In this respect, the sensors are one of the elements which greatly determine the cost and reliability of the inverters, and minimizing the number of sensors may achieve both lower costs and higher reliability. In addition, sensor data are prone to cyber-attacks, and sensor-less control techniques would improve the cyber-physical tolerance capabilities of the system. This paper proposes a grid-side current sensor-less control technique for grid-following inverters. In the proposed scheme, an observer is suggested which estimates the grid-side currents by processing the measured DC-link voltage. The estimated grid-side currents are fed to the control loops of the inverter. To verify the effectiveness of the proposed scheme, the stability of the overall control structure is analyzed, including the nonlinear dynamics of the DC-link voltage in the various operating points, using eigen value analysis and time domain simulations. The results demonstrate that the proposed scheme preserves the proper stability margin and performance of the GF-VSC, even in the presence of uncertainty in output filter inductance.
Theoretical and Numerical Study on Buongiorno’s Model with a Couette Flow of a Nanofluid in a Channel with an Embedded Cavity
Authors: Rossi di Schio Eugenia , Impiombato Andrea Natale , Mokhefi Abderrahim , Biserni Cesare
Published: August 2022

In the present paper, the fluid flow and heat transfer of a nanofluid are numerically investigated. More specifically, reference is made to a nanofluid, described by means of Buongiorno’s model, subjected to Couette flow. The considered domain consists of a channel that displays a cavity shortly after the inlet section. The transport model for the nanofluid, that is the mass conservation, momentum, and nanoparticles equation, is written in a dimensionless form and solved by employing the software package Comsol Multiphysics. Many ideas emerged from this work: the visualization of the velocity stream function, the dimensionless temperature, and nanoparticle concentration fields are provided, as a function of the governing parameters: Reynolds, Peclet, Lewis, Brownian diffusivity number, and thermophoretic diffusivity number. Concerning the nanofluid typical effects, the thermophoretic diffusion seems to affect the solution much more than the Brownian diffusion. The Nusselt number on the upper wall is calculated as well, and the results show that it proves to be, in most of the considered cases, an increasing function of the Reynolds number. Moreover, concerning the Nusselt number, the Brownian diffusion effects are shown to be negligible.
A Robust Countermeasures for Poisoning Attacks on Deep Neural Networks of Computer Interaction Systems
Authors: Liu I-Hsien , Li Jung-Shian , Peng Yen-Chu , Liu Chuan-Gang
Published: August 2022

In recent years, human–computer interactions have begun to apply deep neural networks (DNNs), known as deep learning, to make them work more friendly. Nowadays, adversarial example attacks, poisoning attacks, and backdoor attacks are the typical attack examples for DNNs. In this paper, we focus on poisoning attacks and analyze three poisoning attacks on DNNs. We develop a countermeasure for poisoning attacks, which is Data Washing, an algorithm based on a denoising autoencoder. It can effectively alleviate the damages inflicted upon datasets caused by poisoning attacks. Furthermore, we also propose the Integrated Detection Algorithm (IDA) to detect various types of attacks. In our experiments, for Paralysis Attacks, Data Washing represents a significant improvement (0.5384) over accuracy increment, and can help IDA detect those attacks, while for Target Attacks, Data Washing makes it so that the false positive rate is reduced to just 1% and IDA can have a high accuracy detection rate of greater than 99%.
Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
Authors: Gouda Mohammed Fathy , Abdul Latiff Abdul Halim , Moussavi Alashloo Seyed Yasser
Published: August 2022

Seismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seismic data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling and a multilayer feed-forward neural network (MLFN). The resulting impedance volumes are compared to those obtained from isotropic simultaneous inversion by using impedance logs. The best impedance volumes are applied to Thomsen’s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. Finally, the shear and acoustic impedances are transformed into elastic properties from which the facies and fluid distributions are predicted by using the logistic regression and decision tree algorithms. The results obtained from the MLFN show better matching with the impedance and facies logs compared to those obtained from isotropic inversion and statistical modeling.
Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning: A Comparative Study
Authors: Kačur Ján , Flegner Patrik , Durdán Milan , Laciak Marek
Published: August 2022

The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests.
The Characteristics of PM2.5 and PM10 and Elemental Carbon Air Pollution in Sevastopol, Crimean Peninsula
Authors: Varenik Alla V.
Published: August 2022

In most cities of the world, air pollution reaches critical levels. The air masses circulating over the Crimean Peninsula bring a significant amount of mineral dust, which contains soil particles, emissions from industrial enterprises, gases, etc. The purpose of this research is to study the processes and the factors influencing atmospheric pollution in Sevastopol (Crimea). Air pollutant concentration data, including elemental carbon, nutrients (inorganic fixed nitrogen, inorganic fixed phosphorus and silicon), PM10, and PM2.5, were collected during this research. Samples were collected at the station that is located at a distance from sources of pollution (background station). Our study has shown that even at the background site the daily-averaged concentrations of PM10 and PM2.5 particles in the atmosphere of Sevastopol reach and even exceed the maximum permissible concentrations in the case of dust transported from deserts. Values of the daily-averaged concentrations of microparticles have exceeded the European maximum permissible concentration (MPC) values in 17 cases for PM2.5 particles and in 6 cases for PM10. The impact of both local sources and long-distance atmospheric transport depends on weather conditions. Concentrations of elemental carbon in air samples have never exceeded the maximum allowed by regulations concentration limits during our research. However, the elemental carbon concentration in air samples collected near highways with a traffic intensity of approximately 500–1000 cars per hour has exceeded the background values by 30–50 times.