bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Forskolin on Body Weight, Glucose Metabolism and Adipocyte Size of Diet-Induced Obesity in Mice
Authors: Chen Jing-Yi , Peng Shao-Yu , Cheng Yeong-Hsiang , Lee I-Ta , Yu Yu-Hsiang
Published: March 2021

The purpose of this study was to investigate the effects of forskolin on body weight, glucose metabolism and fat cell diameter in high-fat diet-induced obese mice. Four-week-old male mice (C57BL/6) were randomly assigned to 1 of 3 treatment groups: a high-fat diet plus 5% dimethyl sulfoxide (vehicle), high-fat diet plus 2 mg/kg of forskolin (dissolved in 5% dimethyl sulfoxide) and high-fat diet plus 4 mg/kg of forskolin (dissolved in 5% dimethyl sulfoxide). Forskolin or dimethyl sulfoxide was administered intraperitoneally every two days. The results indicated that no significant difference was observed in the body weight, feed intake and serum lipid parameters among groups at 20 weeks of age. The blood glucose levels were significantly reduced in the groups treated with 2 mg/kg of forskolin before glucose tolerance test. Forskolin administration linearly decreased blood glucose levels of high-fat diet-fed mice at 90 min and total area under curve (AUC) after insulin tolerance test. The subcutaneous adipocyte diameter was significantly reduced in the groups treated with 2 mg/kg of forskolin. Forskolin administration linearly reduced the gonadal adipocyte diameter of high-fat diet-fed mice. Forskolin significantly reduced the differentiation of murine mesenchymal stem cells into adipocytes and this was accompanied by a decrease in intracellular triglyceride content and an increase in glycerol concentration in the culture medium. The subcutaneous adipocyte diameter, gonadal adipocyte diameter and total AUC of insulin tolerance test were moderately negatively correlated with the concentration of forskolin in the high-fat diet-induced obese model. These results demonstrate that forskolin can regulate glucose metabolism and reduce fat cell diameter of high-fat diet-fed mice and inhibit the adipocyte differentiation of murine mesenchymal stem cells.
Monitoring and Controlling House Mouse, Mus musculus domesticus, Infestations in Low-Income Multi-Family Dwellings
Authors: Sked Shannon , Abbar Salehe , Cooper Richard , Corrigan Robert , Pan Xiaodan , Ranabhat Sabita , Wang Changlu
Published: March 2021

The house mouse, Mus musculus domesticus, is a common pest in multi-family residential apartment buildings. This study was designed to gain insights into residents’ impressions of house mice, develop more effective house mouse detection methods, and evaluate the effectiveness of building-wide house mouse management programs. Two high-rise apartment buildings in New Jersey were selected for this study during 2019–2020. Bait stations with three different non-toxic baits were used to detect house mouse activity. Two rodenticides (FirstStrike®—0.0025% difethialone and Contrac®—0.005% bromadiolone) were applied by researchers over a 63-day period and pest control operations were then returned to pest control contractors for rodent management. There were significant differences in the consumption rates of non-toxic baits and two toxic baits tested. A novel non-toxic bait, chocolate spread, was much more sensitive than the two commercial non-toxic baits for detecting mouse activity. The house mouse management programs resulted in an average 87% reduction in the number of infested apartments after three months. At 12 months, the number of infestations decreased by 94% in one building, but increased by 26% in the second building. Sustainable control of house mouse infestations requires the use of effective monitoring strategies and control programs coupled with preventative measures.
Coping with Tissue Sampling in Suboptimal Conditions: Comparison of Different Tissue Preservation Methods for Histological and Molecular Analysis
Authors: Nicoletti Arturo , Pregel Paola , Starvaggi Cucuzza Laura , Cannizzo Francesca Tiziana , Sereno Alessandra , Scaglione Frine Eleonora
Published: March 2021

A high quality of samples is crucial for the success of the analysis and diagnostic purposes, and therefore the right method of conservation is vitally important for an optimal preservation of tissues. Indeed, the time to deliver the sample to the laboratory could be remarkably long, especially under suboptimal conditions, and the use of specific fixatives or cold storage may not be possible. Moreover, the portability and cost of storage equipment, their toxicity, and their ease of use play a central role when choosing the correct preservation method. The aim of this study was the identification of a reliable and economic method for tissue preservation, to be used in “in-field” sampling, suitable for both histological and molecular analysis. Punch biopsies were collected from six cattle livers. Comparisons among methods of preservation using RNAlater, silica beads, and under-vacuum was carried out. These methods were tested through considering different times and temperatures, assuming three days as a maximum time interval from sampling to laboratory and choosing 4 °C and 24 °C as references for refrigeration temperature and room temperature, respectively. Histologically, the integrity of nucleus, cytoplasm, preservation of liver structure, and easiness of recognition of inflammatory infiltrate were evaluated. The integrity of the extracted DNA and RNA was evaluated through PCR and by means of an automated electrophoresis station, respectively. RNAlater and silica beads poorly preserved the histological parameters evaluated, independently from the temperature. Conversely, the vacuum-sealed samples showed a good grade of preservation until 48 h. DNA quality was acceptable for each sample. RNA integrity showed promising results only for samples preserved with silica beads.
Do Service Dogs for Veterans with PTSD Mount a Cortisol Response in Response to Training?
Authors: van Houtert Emmy A. E. , Endenburg Nienke , Rodenburg T. Bas , Vermetten Eric
Published: March 2021

Only a few studies have investigated the welfare of animals participating in animal-assisted interventions (AAIs). Most of these studies focus on dogs in therapeutic settings. There are, however, also dogs—service dogs—that are employed to continuously support a single human. Because the welfare of these service dogs is important for the sustainability of their role, the aim of this study was to investigate their stress response to service dog training sessions. To do this, we took repeated salivary cortisol samples from dogs who participated in a training session (n = 19). Samples were taken just after arrival at the training ground, before training, after training, and after a period of free play. Our results showed that mean cortisol levels in all samples were relatively low (between 1.55 ± 1.10 and 2.73 ± 1.47 nmol/L) compared to similar studies. Analysis further showed that samples taken before and after participation in the training’s session did not differ from one another. Mean cortisol levels in both situations were additionally lower than those upon arrival at the training site and after a period of free play. This led to the conclusion that the dogs in our study did not seem to experience training as stressful.
A Freedom of Coxiella burnetii Infection Survey in European Bison (Bison bonasus) in Poland
Authors: Krzysiak Michał K. , Puchalska Martyna , Olech Wanda , Anusz Krzysztof
Published: March 2021

Q fever is an important zoonosis caused by the intracellular Gram-negative bacteria Coxiella burnetii. The source of infection are numerous species of mammals, birds, reptiles and amphibians, as well as ticks. The disease is widespread throughout Europe, but the role of wildlife in its epidemiology is poorly understood. The European bison (Bison bonasus) population has been growing European-wide quite dynamically over the last few years. The aim of this study was to determine whether C. burnetii infection occurs in European bison and whether it can be considered an important bacterial reservoir in the natural environment. Five hundred and twenty three samples of European bison sera originating from 14 (out of the 26 existing) Polish populations were examined for the presence of specific antibodies using an ID Screen Q Fever Indirect Multi-species ELISA test. Only one (0.19%) serum sample was positive in ELISA, and two other samples were doubtful. The only seropositive animal found in this study was a free-living bull. It suggests possible transmission from domestic cattle by sharing pastures. The transmission of C. burnetii into the European bison was rather accidental in the country and its role as an important wild reservoir is unlikely. Since no tests are available for wildlife ruminants there is a need for the adaptation of the available tests.
Oral Antibiotic for Empirical Management of Acute Dentoalveolar Infections—A Systematic Review
Authors: Teoh Leanne , Cheung Monique C , Dashper Stuart , James Rodney , McCullough Michael J
Published: March 2021

Concerns regarding increasing antibiotic resistance raise the question of the most appropriate oral antibiotic for empirical therapy in dentistry. The aim of this systematic review was to investigate the antibiotic choices and regimens used to manage acute dentoalveolar infections and their clinical outcomes. A systematic review was undertaken across three databases. Two authors independently screened and quality-assessed the included studies and extracted the antibiotic regimens used and the clinical outcomes. Searches identified 2994 studies, and after screening and quality assessment, 8 studies were included. In addition to incision and drainage, the antibiotics used to manage dentoalveolar infections included amoxicillin, amoxicillin/clavulanic acid, cefalexin, clindamycin, erythromycin, metronidazole, moxifloxacin, ornidazole and phenoxymethylpenicillin. Regimens varied in dose, frequency and duration. The vast majority of regimens showed clinical success. One study showed that patients who did not receive any antibiotics had the same clinical outcomes as patients who received broad-spectrum antibiotics. The ideal choice, regimen and spectrum of empirical oral antibiotics as adjunctive management of acute dentoalveolar infections are unclear. Given that all regimens showed clinical success, broad-spectrum antibiotics as first-line empirical therapy are unnecessary. Narrow-spectrum agents appear to be as effective in an otherwise healthy individual. This review highlights the effectiveness of dental treatment to address the source of infection as being the primary factor in the successful management of dentoalveolar abscesses. Furthermore, the role of antibiotics is questioned in primary space odontogenic infections, if drainage can be established.
A Putative Amidase Endolysin Encoded by Clostridium perfringens St13 Exhibits Specific Lytic Activity and Synergizes with the Muramidase Endolysin Psm
Authors: Sekiya Hiroshi , Okada Maho , Tamai Eiji , Shimamoto Toshi , Shimamoto Tadashi , Nariya Hirofumi
Published: March 2021

Clostridium perfringens is an often-harmful intestinal bacterium that causes various diseases ranging from food poisoning to life-threatening fulminant disease. Potential treatments include phage-derived endolysins, a promising family of alternative antimicrobial agents. We surveyed the genome of the C. perfringens st13 strain and identified an endolysin gene, psa, in the phage remnant region. Psa has an N-terminal catalytic domain that is homologous to the amidase_2 domain, and a C-terminal domain of unknown function. psa and gene derivatives encoding various Psa subdomains were cloned and expressed in Escherichia coli as N-terminal histidine-tagged proteins. Purified His-tagged full-length Psa protein (Psa-his) showed C. perfringens-specific lytic activity in turbidity reduction assays. In addition, we demonstrated that the uncharacterized C-terminal domain has cell wall-binding activity. Furthermore, cell wall-binding measurements showed that Psa binding was highly specific to C. perfringens. These results indicated that Psa is an amidase endolysin that specifically lyses C. perfringens; the enzyme’s specificity is highly dependent on the binding of the C-terminal domain. Moreover, Psa was shown to have a synergistic effect with another C. perfringens-specific endolysin, Psm, which is a muramidase that cleaves peptidoglycan at a site distinct from that targeted by Psa. The combination of Psa and Psm may be effective in the treatment and prevention of C. perfringens infections.
Protective Effect of Oligonol on Dimethylnitrosamine-Induced Liver Fibrosis in Rats via the JNK/NF-κB and PI3K/Akt/Nrf2 Signaling Pathways
Authors: Lee Changyong , Bak Jeonghyeon , Yoon Sik , Moon Jeon-Ok
Published: March 2021

Oligonol is a low molecular weight polyphenol product derived from lychee fruit by a manufacturing process. We investigated oligonol’s anti-fibrotic effect and the underlying mechanism in dimethylnitrosamine (DMN)-induced chronic liver damage in male Sprague–Dawley rats. Oral administration of oligonol (10 and 20 mg/kg body weight) ameliorated the DMN-induced abnormalities in liver histology and serum parameters in rats. Oligonol prevented the DMN-induced elevations of TNF-α, IL-1β, IL-6, cyclooxygenase-2, and inducible nitric oxide synthase expressions at the mRNA level. NF-κB activation and JNK phosphorylation in DMN-treated rats were ablated by oligonol. Oligonol reduced the enhanced production of hepatic malondialdehyde and reactive oxygen species and recovered protein SH, non-protein SH levels, and catalase activity in the DMN treated liver. Nrf2 translocation into the nucleus was enhanced, and PI3K and phosphorylated Akt levels were increased by administering oligonol. The level of hepatic fibrosis-related factors such as α-smooth muscle actin, transforming growth factor-β1, and type I collagen was reduced in rats treated with oligonol. Histology and immunohistochemistry analysis showed that the accumulation of collagen and activation of hepatic stellate cells (HSCs) in liver tissue were restored by oligonol treatment. Taken together, oligonol showed antioxidative, hepatoprotective, and anti-fibrotic effects via JNK/NF-κB and PI3K/Akt/Nrf2 signaling pathways in DMN-intoxicated rats. These results suggest that antioxidant oligonol is a potentially useful agent for the protection against chronic liver injury.
Beyond Antioxidant Effects: Nature-Based Templates Unveil New Strategies for Neurodegenerative Diseases
Authors: Bacci Andrea , Runfola Massimiliano , Sestito Simona , Rapposelli Simona
Published: March 2021

The complex network of malfunctioning pathways occurring in the pathogenesis of neurodegenerative diseases (NDDs) represents a huge hurdle in the development of new effective drugs to be used in therapy. In this context, redox reactions act as crucial regulators in the maintenance of neuronal microenvironment homeostasis. Particularly, their imbalance results in the severe compromising of organism’s natural defense systems and subsequently, in the instauration of deleterious OS, that plays a fundamental role in the insurgence and progress of NDDs. Despite the huge efforts in drug discovery programs, the identification process of new therapeutic agents able to counteract the relentless progress of neurodegenerative processes has produced low or no effective therapies. Consequently, a paradigm-shift in the drug discovery approach for these diseases is gradually occurring, paving the way for innovative therapeutical approaches, such as polypharmacology. The aim of this review is to provide an overview of the main pharmacological features of most promising nature-based scaffolds for a possible application in drug discovery, especially for NDDs, highlighting their multifaceted effects against OS and neuronal disorders.
Evolution of Flavors in Extra Virgin Olive Oil Shelf-Life
Authors: Garcia-Oliveira Paula , Jimenez-Lopez Cecilia , Lourenço-Lopes Catarina , Chamorro Franklin , Pereira Antia Gonzalez , Carrera-Casais Anxo , Fraga-Corral Maria , Carpena Maria , Simal-Gandara Jesus , Prieto Miguel Angel
Published: March 2021

Extra virgin olive oil (EVOO) is one of the most distinctive ingredients of the Mediterranean diet. There are many properties related to this golden ingredient, from supreme organoleptic characteristics to benefits for human health. EVOO contains in its composition molecules capable of exerting bioactivities such as cardio protection, antioxidant, anti-inflammatory, antidiabetic, and anticancer activity, among others, mainly caused by unsaturated fatty acids and certain minor compounds such as tocopherols or phenolic compounds. EVOO is considered the highest quality vegetable oil, which also implies a high sensory quality. The organoleptic properties related to the flavor of this valued product are also due to the presence of a series of compounds in its composition, mainly some carbonyl compounds found in the volatile fraction, although some minor compounds such as phenolic compounds also contribute. However, these properties are greatly affected by the incidence of certain factors, both intrinsic, such as the olive variety, and extrinsic, such as the growing conditions, so that each EVOO has a particular flavor. Furthermore, these flavors are susceptible to change under the influence of other factors throughout the oil’s shelf-life, such as oxidation or temperature. This work offers a description of some of the most remarkable compounds responsible for EVOO’s unique flavor and aroma, the factors affecting them, the mechanism that lead to the degradation of EVOO, and how flavors can be altered during the shelf-life of the oil, as well as several strategies suggested for the preservation of this flavor, on which the quality of the product also depends.
New Lignanamides with Antioxidant and Anti-Inflammatory Activities Screened Out and Identified from Warburgia ugandensis Combining Affinity Ultrafiltration LC-MS with SOD and XOD Enzymes
Authors: Zhuang Xiao-Cui , Chen Gui-Lin , Liu Ye , Zhang Yong-Li , Guo Ming-Quan
Published: March 2021

Warburgia ugandensis, also known as “green heart,” is widely used for the treatment of various diseases as a traditional ethnomedicinal plant in local communities in Africa. In this work, 9 and 12 potential superoxide dismutase (SOD) and xanthine oxidase (XOD) ligands from W. ugandensis were quickly screened out by combining SOD and XOD affinity ultrafiltration with LC-MS, respectively. In this way, four new lignanamides (compounds 11–14) and one new macrocyclic glycoside (compound 5), along with three known compounds (compounds 1, 3, and 7), were isolated and identified firstly in this species. The structures of the new compounds were elucidated by spectroscopic analysis, including NMR and UPLC-QTOF-MS/MS. Among these compounds, compound 14 showed the highest 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azinobis-(3-ethylbenzthiazoline)-6-sulfonic acid (ABTS) radical scavenging activities, and total ferric-reducing antioxidant power (FRAP) with IC50 values of 6.405 ± 0.362 µM, 5.381 ± 0.092 µM, and 17.488 ± 1.625 mmol TE/g, respectively. Moreover, compound 14 displayed the highest inhibitory activity on cyclooxygenase-2 (COX-2) with IC50 value of 0.123 ± 0.004 µM, and the ranking order of other compounds’ IC50 values was 13 > 11 > 7 > 1 > 12. The present study suggested that lignanamides might represent interesting new characteristic functional components of W. ugandensis to exert remarkable antioxidant and anti-inflammatory activities. Moreover, compound 14, a new arylnaphthalene lignanamide, would be a highly potential natural antioxidant and anti-inflammatory agent from W. ugandensis.
Electrochemical Corrosion of Galvanized Steel in Binary Sustainable Concrete Made with Sugar Cane Bagasse Ash (SCBA) and Silica Fume (SF) Exposed to Sulfates
Authors: Landa-Ruiz Laura , Baltazar-Zamora Miguel Angel , Bosch Juan , Ress Jacob , Santiago-Hurtado Griselda , Moreno-Landeros Victor Manuel , Márquez-Montero Sabino , Méndez Ce Tochtli , Borunda Adan , Juárez-Alvarado César A. , Mendoza-Rangel José M. , Bastidas David. M.
Published: March 2021

This research evaluates the behavior corrosion of galvanized steel (GS) and AISI 1018 carbon steel (CS) embedded in conventional concrete (CC) made with 100% CPC 30R and two binary sustainable concretes (BSC1 and BSC2) made with sugar cane bagasse ash (SCBA) and silica fume (SF), respectively, after 300 days of exposure to 3.5 wt.% MgSO4 solution as aggressive medium. Electrochemical techniques were applied to monitor corrosion potential (Ecorr) according to ASTM C-876-15 and linear polarization resistance (LPR) according to ASTM G59 for determining corrosion current density (icorr). Ecorr and icorr results indicate after more than 300 days of exposure to the sulfate environment (3.5 wt.% MgSO4 solution), that the CS specimens embedded in BSC1 and BSC2 presented greater protection against corrosion in 3.5 wt.% MgSO4 than the specimens embedded in CC. It was also shown that this protection against sulfates is significantly increased when using GS reinforcements. The results indicate a higher resistance to corrosion by exposure to 3.5 wt.% magnesium sulfate two times greater for BSC1 and BSC2 specimens reinforced with GS than the specimens embedding CS. In summary, the combination of binary sustainable concrete with galvanized steel improves durability and lifetime in service, in addition to reducing the environmental impact of the civil engineering structures.
Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
Authors: Sharma Rahul , Ribeiro Bernardete , Pinto Alexandre Miguel , Cardoso Amílcar
Published: March 2021

Abstract concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure.
Properties of Green Mortar Containing Granite Sawmill
Authors: Yagüe Santiago , Rosales-Prieto Víctor , Sánchez-Lite Alberto , González-Gaya Cristina
Published: March 2021

The cement industry is one of the world’s largest CO2 emitters. The need to minimize these emissions, and assimilate by substitution and different types of waste, are challenges faced in the European Union. The use of granite sawmill from the ornamental stone industry allows for the manufacturing of pozzolanic cements, in which 10% and 20% of ordinary Portland cement (OPC) has been replaced by waste. In the present paper, properties of cements and mortars have been tested (when fresh and once set), such as workability, setting, retraction, mechanical resistance to bending and compression, elastic modulus to compression, creep, retraction, and durability. In all cases and substitution proportions, the results have been as satisfactory as those achieved with OPC, even better, allowing a second life to the waste, and participating in the principles of the circular economy. Bot substitutions are very resistant and have great durability for the gelifraction processes from the new green cements. Using this waste—granite sawmill—its volume is minimized and transfer to a landfill is avoided.
Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints
Authors: Wu Tian-Yau , Lin Chi-Chen
Published: March 2021

The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.
Nozzle with a Feedback Channel for Agricultural Drones
Authors: Kim Seong Kuen , Ahmad Hibal , Moon Jong Woon , Jung Sung Yong
Published: March 2021

In recent years, small drones have been used in agriculture, for spraying water and pesticides. Although spraying systems affect the efficiency of agricultural drones considerably, research on the spraying system of drones is insufficient. In this paper, a new nozzle with a feedback channel is proposed for agricultural drones. The proposed nozzle was manufactured through 3D printing, and its performance was compared with that of the nozzle used in commercial agricultural drones. Images taken with a high-speed camera were digitally processed, to track the area and location of spray particles, and the spraying characteristics were evaluated based on the size and uniformity of the droplets obtained from the images. The proposed nozzle provided a better performance, as it could spray smaller droplets more uniformly. Commercial nozzle droplets have an average diameter of 1.76 mm, and the proposed nozzle has been reduced to a maximum of 215 μm. In addition, the full width at half maximum (FWHM) of the commercial nozzle is 0.233, but the proposed nozzle is up to 1.519; the proposed nozzle provided better performance, as it could spray smaller droplets more uniformly. Under the condition of 30 kg, the best performance in the proposed nozzle, the minimum value of the average droplet diameter of the nozzle without feedback channel is 595 μm and the maximum value of FWHM is 1.329. Therefore, a comparison of the performance of the proposed nozzle with that of a nozzle with no feedback channel indicates that the feedback channel effectively reduces the droplet diameter and improves the spraying uniformity. It is expected that the proposed nozzle can be useful for designing the spraying systems of agricultural drones.
Influence of Roof Installation of PV Modules on the Microclimate Conditions of Cattle Breeding Objects
Authors: Bilčík Matúš , Božiková Monika , Čimo Ján
Published: March 2021

This paper is focused on the temperature measurements which can detected the influence of temperature changes on the microclimate in animal production building after the installation of photovoltaic (PV) modules. The first series of experiments were performed on a specially designed model cowshed. For the data comparison and verification, the same measurements were realized in real conditions of the animal production object. The temperature balance was identified by measurements of the temperatures in the different parts of roof, PV modules, and the most important were measurements of the ambient temperature and temperatures in three levels of the cowshed interior. For the confirmation of results, measurements were done in two cowsheds, which had the same azimuth orientation and roof slope. The first cowshed was without installation of the PV modules on the roof and the second building had installed PV modules. By the data analyzed from experimentally obtained time-temperature dependencies, it was found that the installation of PV modules on the cowshed roof had a positive influence on the interior temperature balance. The installation of PV also had a positive effect on the cowshed microclimate, which was declared by calculation of the Temperature—Humidity—Index.
Analysis of Sound Power Level Changes Caused by Wheel with Flat Spot Using Numerical and Physical Experiment
Authors: Kukėnas Vladas , Jasevičius Raimondas , Petrenko Viačeslav , Kilikevičius Artūras , Vainorius Darius
Published: March 2021

Damage to the running surfaces of wheels on railways poses a threat to road safety. They can lead to accidents and disasters. Wheels with a flat spot are the biggest threat. The paper reviews problems that arise when wheels with a flat spot come into contact with a rail, and the methods of their detection and diagnosis. However, the known methods for their determination are still very complex and not precise enough. The research presented is based on previous theoretical studies, during which a simplified mathematical model of the normal force arising from the contact of a wheel with a flat spot with a rail, assuming that as a result of this, a change in sound power is caused was developed and theoretical calculations were performed. It theoretically determined wheel damage during rolling caused by wheel-induced changes in associated sound power, i.e., preliminary values of diagnostic parameters and applied methods. Although initial theoretical research already exists, there was a lack of physical experiments to support the validity of the results of the theoretical model. This work presents the original plan and methodology of the physical experiment performed. A physical experiment performed with the ATLAS LG system and sound pressure measuring equipment showed the suitability and applicability of the theoretical model for the determination of wheel damage.
Control of DC Motors to Guide Unmanned Underwater Vehicles
Authors: Sands Timothy
Published: March 2021

Many research manuscripts propose new methodologies, while others compare several state-of-the-art methods to ascertain the best method for a given application. This manuscript does both by introducing deterministic artificial intelligence (D.A.I.) to control direct current motors used by unmanned underwater vehicles (amongst other applications), and directly comparing the performance of three state-of-the-art nonlinear adaptive control techniques. D.A.I. involves the assertion of self-awareness statements and uses optimal (in a 2-norm sense) learning to compensate for the deleterious effects of error sources. This research reveals that deterministic artificial intelligence yields 4.8% lower mean and 211% lower standard deviation of tracking errors as compared to the best modeling method investigated (indirect self-tuner without process zero cancellation and minimum phase plant). The improved performance cannot be attributed to superior estimation. Coefficient estimation was merely on par with the best alternative methods; some coefficients were estimated more accurately, others less. Instead, the superior performance seems to be attributable to the modeling method. One noteworthy feature is that D.A.I. very closely followed a challenging square wave without overshoot—successfully settling at each switch of the square wave—while all of the other state-of-the-art methods were unable to do so.
An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device
Authors: Hashmani Manzoor Ahmed , Jameel Syed Muslim , Rizvi Syed Sajjad Hussain , Shukla Saurabh
Published: March 2021

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.
Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
Authors: Sarma Moumita Sen , Deb Kaushik , Dhar Pranab Kumar , Koshiba Takeshi
Published: March 2021

Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.
Application of Vibration Signal Processing Methods to Detect and Diagnose Wheel Flats in Railway Vehicles
Authors: Shim JaeSeok , Kim GeoYoung , Cho ByungJin , Koo JeongSeo
Published: March 2021

This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the changes in train speed. The order analysis changes the domain of the vibration signal from time domain to rotating angular domain to consider the train speed change in the cepstrum analysis. Second, the cross correlation analysis method combined with the order analysis was applied to evaluate the flat signal from the vibration signal well containing the severe field noise produced by the vibrations of the rail irregularities and bogie components. Unlike the cepstrum analysis method, it can find out the wheel flat size because the flat signal linearly increases to the wheel flat. Thus, it is more effective when checking the size of the wheel flat. Finally, the data tested in the Korea Railroad Research Institute were used to confirm that the cepstrum analysis and cross correlation analysis methods are appropriate for not only simulation but also test data.
Evaluation on Improvement Zone of Foundation after Dynamic Compaction
Authors: Zhou Chong , Yang Chenjun , Qi Hui , Yao Kai , Yao Zhanyong , Wang Kai , Ji Ping , Li Hui
Published: March 2021

Dynamic compaction (DC) is one of the most popular methods for ground improvement. To solve the problem of the factors affecting the sandy soil improvement effect and estimate the effective improvement range under DC, the influences of drop number, drop energy, tamping distance, tamper radius, and drop momentum on the relative degree of improvement were investigated. Three normalized indicators △δz,i, △δA,i, and △δU,i were derived to evaluate the weak zone and corresponding improvement effect. For multipoint tamping, it is found that the improvement depth and the improvement of the weak zone are highly correlated with drop energy and drop momentum, while the influence of the drop number and tamper radius is relatively smaller. The improvement of the weak zone and the improvement depth decrease with tamping distance, whereas the improvement area increases with tamping distance. The soil compacted by the previous impact point will be improved to a lesser extent with impact at subsequent impact points. It is also noted that drop energy had better not exceed the saturated drop energy in DC design. Based on the parametric study, a formula considering the various factors of DC was put forward, with the validation by two field cases of DC.
Sea Bass Side Streams Valorization Assisted by Ultrasound. LC-MS/MS-IT Determination of Mycotoxins and Evaluation of Protein Yield, Molecular Size Distribution and Antioxidant Recovery
Authors: Al Khawli Fadila , Pallarés Noelia , Martí-Quijal Francisco J. , Ferrer Emilia , Barba Francisco J.
Published: March 2021

Sea bass side streams obtained from the fish industry can be a good source of nutrients such as high-quality protein, lipids, and antioxidants. In this context, it is interesting to develop innovative approaches to extract the added-value compounds from fish side streams. In this study, a strategy to obtain valuable compounds and to minimize the presence of toxins from fish side streams assisted by ultrasound technology is presented. For this purpose, ultrasound-assisted extraction (UAE) conditions have been optimized based on a response surface methodology (RSM) with the dependent variables: time (0.5–30 min), pH (5.5–8.5), and temperature (20–50 °C). After the treatment, protein extraction and antioxidant activity were evaluated in the extracts obtained from sea bass side streams using some spectrophotometric and fluorometric methods. Furthermore, mycotoxin presence was evaluated by LC-MS/MS-QTRAP. The results obtained revealed a high recovery percentage of proteins and antioxidant activity in the UAE extracts, especially those obtained from viscera, when the time and temperature increased to 30 min and 50 °C. Furthermore, none of the analyzed mycotoxins were detected in the sea bass side streams extracts under the studied variables. The experimental values obtained were close to the expected values, confirming the validity of the model employed to establish the optimal UAE conditions.
Global and Local Information Adjustment for Semantic Similarity Evaluation
Authors: Heo Tak-Sung , Kim Jong-Dae , Park Chan-Young , Kim Yu-Seop
Published: March 2021

Semantic similarity evaluation is used in various fields such as question-and-answering and plagiarism testing, and many studies have been conducted into this problem. In previous studies using neural networks to evaluate semantic similarity, similarity has been measured using global information of sentence pairs. However, since sentences do not only have one meaning but a variety of meanings, using only global information can have a negative effect on performance improvement. Therefore, in this study, we propose a model that uses global information and local information simultaneously to evaluate the semantic similarity of sentence pairs. The proposed model can adjust whether to focus more on global information or local information through a weight parameter. As a result of the experiment, the proposed model can show that the accuracy is higher than existing models that use only global information.
Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
Authors: Munaye Yirga Yayeh , Juang Rong-Terng , Lin Hsin-Piao , Tarekegn Getaneh Berie , Lin Ding-Bing
Published: March 2021

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users’ resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.
Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations
Authors: Alarifi Sulaiman A.
Published: March 2021

A comprehensive overview and analysis of the productivity of 1216 recently abandoned multi-stage hydraulically fractured horizontal wells from five shale formations in the United States (US) is presented in this study. In this study, two decline curve analysis (DCA) methods were used to match actual production history data using least-squares fitting to find the best fit production parameters to reliably forecast production. The production history matching conducted resulted in very accurate matches (correlation coefficient of 0.99) between actual production data and the two DCA methods (Arps hyperbolic decline and stretched exponential production decline (SEPD) models). Using the outcomes from production history matching, universal averages of decline parameters for Arps hyperbolic decline and SEPD models were developed for each of the five formations. Furthermore, hindcasting was performed by matching a portion of the known production history and comparing the remaining portion of the known production history to the forecast. The Arps hyperbolic decline and SEPD methods were used to match production using only limited early production data (three months, six months, one year and two years). The main goals for fitting the DCA model to early production data was to estimate the optimum decline parameters that are then used to forecast production and estimate ultimate recovery. Production history matching using limited early production periods produced accurate production forecasts using as few as six months of production history (correlation coefficients between 0.85 and 0.94 using Arps hyperbolic decline). The main outcome of this study was a production analysis conducted on the production data of more than 1000 wells from five different shale formations to present the expected production behaviors of similar wells. Different production key performance indicators (KPIs) such as average well life, cumulative production volumes at different periods, average drop in production rate within the first year of production, average time to reach maximum flow rate, and the maximum flow rate were measured on all the wells from the five formations to provide an overview of the production performance of each formation.
Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
Authors: Bastos Porsani Gabriela , Del Valle de Lersundi Kattalin , Sánchez-Ostiz Gutiérrez Ana , Fernández Bandera Carlos
Published: March 2021

Building information modelling (BIM) is the first step towards the implementation of the industrial revolution 4.0, in which virtual reality and digital twins are key elements. At present, buildings are responsible for 40% of the energy consumption in Europe and, so, there is a growing interest in reducing their energy use. In this context, proper interoperability between BIM and building energy model (BEM) is paramount for integrating the digital world into the construction sector and, therefore, increasing competitiveness by saving costs. This paper evaluates whether there is an automated or semi-automated BIM to BEM workflow that could improve the building design process. For this purpose, a residential building and a warehouse are constructed using the same BIM authoring tool (Revit), where two open schemas were used: green building extensible markup language (gbXML) and industry foundation classes (IFC). These transfer files were imported into software compatible with the EnergyPlus engine—Design Builder, Open Studio, and CYPETHERM HE—in which simulations were performed. Our results showed that the energy models were built up to 7.50% smaller than in the BIM and with missing elements in their thermal envelope. Nevertheless, the materials were properly transferred to gbXML and IFC formats. Moreover, the simulation results revealed a huge difference in values between the models generated by the open schemas, in the range of 6 to 900 times. Overall, we conclude that there exists a semi-automated workflow from BIM to BEM which does not work well for big and complex buildings, as they present major problems when creating the energy model. Furthermore, most of the issues encountered in BEM were errors in the transfer of BIM data to gbXML and IFC files. Therefore, we emphasise the need to improve compatibility between BIM and model exchange formats by their developers, in order to promote BIM–BEM interoperability.