bluuebunny/crossref_metadata_2025 huggingface dataset:
Stable Isotopes Reveal the Dominant Species to Have the Widest Trophic Niche of Three Syntopic Microtus Voles
Authors: Balčiauskas Linas , Skipitytė Raminta , Garbaras Andrius , Stirkė Vitalijus , Balčiauskienė Laima , Remeikis Vidmantas
Published: June 2021

Diets and trophic positions of co-occurring animals are fundamental issues in their ecology, and these issues in syntopic rodents have been studied insufficiently. Using carbon (δ13C) and nitrogen (δ15N) stable isotope ratios from hair samples, we analysed the trophic niches of common (Microtus arvalis), field (M. agrestis), and root (M. oeconomus) voles co-occurring in orchards, berry plantations, and nearby meadows (as control habitat to orchards and plantations). We tested if the niche of the dominant common vole was the widest, whether its width depended on the presence of other vole species, and whether there were intraspecific differences. Results suggest stability in the trophic niches of all three Microtus species, as season explained only 2% of the variance. The widest trophic niche was a characteristic of the dominant common vole, the range of δ13C values exceeding the other two species by 1.6, the range of δ15N values exceeding the other two species by 1.9, and the total area of niche exceeding that of the other voles by 2.3–3 times. In the meadows and apple orchards, co-occurring vole species were separated according to δ13C (highest values in the dominant common vole), but they maintained similar δ15N values. Results give new insights into the trophic ecology small herbivores, showing the impact of species co-occurrence.
Current Status for Controlling the Overlooked Caprine Fasciolosis
Authors: Zerna Gemma , Spithill Terry W. , Beddoe Travis
Published: June 2021

The disease fasciolosis is caused by the liver flukes Fasciola hepatica and F. gigantica, which infect a wide range of mammals and production livestock, including goats. These flatworm parasites are globally distributed and predicted to cost the livestock industry a now conservative USD 3 billion per year in treatment and lowered on-farm productivity. Infection poses a risk to animal welfare and results in lowered fertility rates and reduced production yields of meat, milk and wool. This zoonotic disease is estimated to infect over 600 million animals and up to 2.4 million humans. Current and future control is threatened with the global emergence of flukes resistant to anthelmintics. Drug resistance calls for immediate on-farm parasite management to ensure treatments are effective and re-infection rates are kept low, while a sustainable long-term control method, such as a vaccine, is being developed. Despite the recent expansion of the goat industry, particularly in developing countries, there are limited studies on goat-focused vaccine control studies and the effectiveness of drug treatments. There is a requirement to collate caprine-specific fasciolosis knowledge. This review will present the current status of liver fluke caprine infections and potential control methods for application in goat farming.
Use of Antimicrobials among Suspected COVID-19 Patients at Selected Hospitals, Bangladesh: Findings from the First Wave of COVID-19 Pandemic
Authors: Mah-E-Muneer Syeda , Hassan Md. Zakiul , Biswas Md. Abdullah Al Jubayer , Rahman Fahmida , Akhtar Zubair , Das Pritimoy , Islam Md. Ariful , Chowdhury Fahmida
Published: June 2021

Antimicrobials are empirically used in COVID-19 patients resulting in increased antimicrobial resistance. Our objective was to assess antimicrobial use among suspected COVID-19 in-patients. From March to August 2020, we collected data from in-patients of 12 tertiary-level hospitals across Bangladesh. We identified suspected COVID-19 patients; collected information on antimicrobial received within 24 h before and on hospitalization; tested nasopharyngeal swab for SARS-CoV-2 using rRT-PCR. We used descriptive statistics and a regression model for data analysis. Among 1188 suspected COVID-19 patients, 69% were male, 40% had comorbidities, and 53% required oxygen. Antibiotics were used in 92% of patients, 47% within 24 h before, and 89% on admission. Patients also received antiviral (1%) and antiparasitic drugs (3%). Third-generation cephalosporin use was the highest (708; 60%), followed by macrolide (481; 40%), and the majority (853; 78%) who took antibiotics were SARS-CoV-2 negative. On admission, 77% mild and 94% moderately ill patients received antibiotics. Antibiotic use on admission was higher among severely ill patients (AOR = 11.7; 95% CI: 4.5–30.1) and those who received antibiotics within 24 h before hospital admission (AOR = 1.6; 95% CI: 1.0–2.5). Antimicrobial use was highly prevalent among suspected COVID-19 in-patients in Bangladesh. Initiating treatment with third-generation cephalosporin among mild to moderately ill patients was common. Promoting antimicrobial stewardship with monitoring is essential to prevent blanket antibiotic use, thereby mitigating antimicrobial resistance.
Electron Donor–Acceptor Capacity of Selected Pharmaceuticals against COVID-19
Authors: Martínez Ana
Published: June 2021

More than a year ago, the first case of infection by a new coronavirus was identified, which subsequently produced a pandemic causing human deaths throughout the world. Much research has been published on this virus, and discoveries indicate that oxidative stress contributes to the possibility of getting sick from the new SARS-CoV-2. It follows that free radical scavengers may be useful for the treatment of coronavirus 19 disease (COVID-19). This report investigates the antioxidant properties of nine antivirals, two anticancer molecules, one antibiotic, one antioxidant found in orange juice (Hesperidin), one anthelmintic and one antiparasitic (Ivermectin). A molecule that is apt for scavenging free radicals can be either an electron donor or electron acceptor. The results I present here show Valrubicin as the best electron acceptor (an anticancer drug with three F atoms in its structure) and elbasvir as the best electron donor (antiviral for chronic hepatitis C). Most antiviral drugs are good electron donors, meaning that they are molecules capable of reduzing other molecules. Ivermectin and Molnupiravir are two powerful COVID-19 drugs that are not good electron acceptors, and the fact that they are not as effective oxidants as other molecules may be an advantage. Electron acceptor molecules oxidize other molecules and affect the conditions necessary for viral infection, such as the replication and spread of the virus, but they may also oxidize molecules that are essential for life. This means that the weapons used to defend us from COVID-19 may also harm us. This study posits the idea that oxide reduction balance may help explain the toxicity or efficacy of these drugs. These results represent a further advance on the road towards understanding the action mechanisms of drugs used as possible treatments for COVID-19. Looking ahead, clinical studies are needed to define the importance of antioxidants in treating COVID-19.
Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics
Authors: Cruz-Duarte Jorge M. , Ortiz-Bayliss José C. , Amaya Ivan , Pillay Nelishia
Published: June 2021

Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.
Chemical Images on Fingerprints Revealed with Mass Spectrometry
Authors: Carrà Andrea , Falciola Luigi , Cappelletti Giuseppe , Morosi Lavinia , Davoli Enrico
Published: June 2021

Commercially available UV-adsorbent TiO2 nanoparticles were used to assist laser/desorption ionization in the course of matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). Titanium nano-powders proved extremely stable and efficient for small molecule ionization, with negligible background noise in the low mass region (m/z < 500 Da). Validation steps were carried out, assessing detection limits and comparing the results to those of the established DESI/Orbitrap technique. The new analytical method was used to reveal the molecular distribution of endogenous (lipids) and exogenous (analgesics and antipyretics) compounds in latent finger marks (LFMs). The detection limits of endogenous fatty acids and small molecules such as caffeine were in the range of fmol/mm2 on LFMs. The technique separated overlapping latent finger marks, exploiting the differences in lipid expression of human skin. Finally, the method was used to prove contact between skin and objects contaminated by different substances, such as credit cards and paper clips, with chemical images that maintain the shape of the objects on the LFM.
A Sustainable Approach for Extracting Non-Extractable Phenolic Compounds from Mangosteen Peel Using Ultrasound-Assisted Extraction and Natural Deep Eutectic Solvents
Authors: Plaza Merichel , Domínguez-Rodríguez Gloria , Sahelices Cristina , Marina María Luisa
Published: June 2021

Mangosteen (Garcinia mangostana L.) peel is a potential source of phenolic compounds with beneficial properties. Natural deep eutectic solvents (NaDES) have been considered an environmentally friendly and cheap alternative to conventional organic solvents. In this work, a green extraction methodology was developed using ultrasound-assisted extraction (UAE) and NaDES for the extraction of antioxidant non-extractable polyphenols (NEPs) from mangosteen peel. To select the best NaDES to extract NEPs from mangosteen peel, seven NaDES were studied. Antioxidant capacity and total phenolic and proanthocyanidin contents were determined for the extracts. The molecular weights for the NEPs present in those extracts were evaluated by size exclusion chromatography. Experimental results showed that choline chloride–lactic acid (1:2) was the NaDES allowing the highest antioxidant proanthocyanidin content in the extracts. A Box–Behnken experimental design was employed to optimize the main parameters in UAE with NaDES: water percentage, ultrasound amplitude, and extraction time. The optimal extraction conditions were 18.8% (v/v) water, 60% ultrasound amplitude, and 15 min as the extraction time. In addition, the cytotoxicity of the NEP extracts obtained under optimal extraction conditions was evaluated. Results indicated for the first time that the use of NaDES in combination with UAE could be a sustainable alternative for the extraction of antioxidant NEPs from mangosteen peel for important applications in the food, pharmaceutical, agrochemical, and cosmetic fields, as the extracts presented low cytotoxicity.
Microbiota “Fingerprint” of Greek Feta Cheese through Ripening
Authors: Tzora Athina , Nelli Aikaterini , Voidarou Chrysoula , Fthenakis George , Rozos Georgios , Theodorides Georgios , Bonos Eleftherios , Skoufos Ioannis
Published: June 2021

Feta is a Greek protected designation of origin (PDO) brined curd white cheese made from small ruminants’ milk. In the present research, Greek Feta cheese bacterial diversity was evaluated via matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS). Analysis of 23 cheese samples, produced in different regions of the country, was performed in two ripening times (three or six months post-production). The identified microbiota were primarily constituted of lactic acid bacteria. A total of 13 different genera were obtained. The dominant species in both ripening times were Lactobacillus plantarum (100.0% and 87.0%, at three or six months post-production, respectively), Lactobacillus brevis (56.5% and 73.9%), Lactobacillus paracasei (56.5% and 39.1%), Lactobacillus rhamnosus (13.0% and 17.4%), Lactobacillus paraplantarum (4.3% and 26.1%), Lactobacillus curvatus (8.7% and 8.7%). Other species included Enterococcus faecalis (47.8% and 43.5%), Enterococcus faecium (34.8% and 17.4%), Enterococcus durans (13.0% and 17.4%), Enterococcus malodoratus (4.3% and 4.3%), and Streptococcus salivarius subsp. thermophilus (21.7% and 30.4%). The increased ripening time was found to be correlated to decreased total solids (r = 0.616; p = 0.002), protein (r = 0.683; p < 0.001), and PH (r = 0.780; p < 0.001). The results of this study contribute to a better understanding of the core microbiota of Feta cheese.
Paragraph Boundary Recognition in Novels for Story Understanding
Authors: Iikura Riku , Okada Makoto , Mori Naoki
Published: June 2021

The understanding of narrative stories by computer is an important task for their automatic generation. To date, high-performance neural-network technologies such as BERT have been applied to tasks such as the Story Cloze Test and Story Completion. In this study, we focus on the text segmentation of novels into paragraphs, which is an important writing technique for readers to deepen their understanding of the texts. This type of segmentation, which we call “paragraph boundary recognition”, can be considered to be a binary classification problem in terms of the presence or absence of a boundary, such as a paragraph between target sentences. However, in this case, the data imbalance becomes a bottleneck because the number of paragraphs is generally smaller than the number of sentences. To deal with this problem, we introduced several cost-sensitive loss functions, namely. focal loss, dice loss, and anchor loss, which were robust for imbalanced classification in BERT. In addition, introducing the threshold-moving technique into the model was effective in estimating paragraph boundaries. As a result of the experiment on three newly created datasets, BERT with dice loss and threshold moving obtained a higher F1 than the original BERT had using cross-entropy loss as its loss function (76% to 80%, 50% to 54%, 59% to 63%).
Experimental and Numerical Investigation of 3D Dam-Break Wave Propagation in an Enclosed Domain with Dry and Wet Bottom
Authors: Kocaman Selahattin , Evangelista Stefania , Guzel Hasan , Dal Kaan , Yilmaz Ada , Viccione Giacomo
Published: June 2021

Dam-break flood waves represent a severe threat to people and properties located in downstream regions. Although dam failure has been among the main subjects investigated in academia, little effort has been made toward investigating wave propagation under the influence of tailwater depth. This work presents three-dimensional (3D) numerical simulations of laboratory experiments of dam-breaks with tailwater performed at the Laboratory of Hydraulics of Iskenderun Technical University, Turkey. The dam-break wave was generated by the instantaneous removal of a sluice gate positioned at the center of a transversal wall forming the reservoir. Specifically, in order to understand the influence of tailwater level on wave propagation, three tests were conducted under the conditions of dry and wet downstream bottom with two different tailwater depths, respectively. The present research analyzes the propagation of the positive and negative wave originated by the dam-break, as well as the wave reflection against the channel’s downstream closed boundary. Digital image processing was used to track water surface patterns, and ultrasonic sensors were positioned at five different locations along the channel in order to obtain water stage hydrographs. Laboratory measurements were compared against the numerical results obtained through FLOW-3D commercial software, solving the 3D Reynolds-Averaged Navier–Stokes (RANS) with the k-ε turbulence model for closure, and Shallow Water Equations (SWEs). The comparison achieved a reasonable agreement with both numerical models, although the RANS showed in general, as expected, a better performance.
Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
Authors: Yeh Chiu-Yu , Tsay Yaw-Shyan
Published: June 2021

In Taiwan, activity centers such as school auditoriums and gymnasiums are common multi-functional spaces that are often used for performances, singing, and speeches. However, most cases are designed using only Sabine’s equation for architectural acoustics. Although that estimation formula is simple and fast, the calculation process ignores many details. Furthermore, while more accurate analysis can be obtained through acoustics simulation software, it is more complicated and time-consuming and thus is rarely used in practical design. The purpose of this study is to use machine learning to propose a predictive model of acoustic indicators as a simple evaluation tool for the architectural design and interior decoration of multi-functional activity centers. We generated 800 spaces using parametric design, adopting Odeon to obtain acoustic indicators. The machine learning model was trained with basic information of the space. We found that through GBDT and ANN algorithms, almost all acoustic indicators could be predicted within JND ± 2, and the JND of C50, C80, STI, and the distribution of SPL could reach within ±1. Through machine learning methods, we established a convenient, fast, and accurate prediction model and were able to obtain various acoustic indicators of the space without 3D-modeling or simulation software.
Research on Testing Method of Oil Characteristic Based on Quartz Tuning Fork Sensor
Authors: Sun Hao , Liu Yingshuai , Tan Jianwei
Published: June 2021

There is increasing demand for the on-board diagnosis of lubricating oils. In this research, we consider various sensor principles for on-board diagnosis of the thermal aging of engine oils. One of the parameters investigated is the viscosity of the lubricating oil, which can be efficiently measured using a microacoustic sensor. Compared with conventional viscometers, these sensors probe a different rheological domain, which needs to be considered in the interpretation of measurement results. This specific behavior is examined by systematically investigating engine oils, with and without additive packages, that were subjected to a defined artificial aging process. This paper presents design strategies for the algorithm developed and applied for direct on-board diagnosis of engine oil conditions with a fluid property sensor; this enables prediction of remaining oil life and optimization of oil change intervals, thereby minimizing the likelihood of dramatic engine failure and reducing maintenance costs. After a general description of the principles of sensor measurement, different engine oil contaminants, aging phenomena, and associated sensor detection and measurement capabilities are discussed.
Deep-Feature-Based Approach to Marine Debris Classification
Authors: Marin Ivana , Mladenović Saša , Gotovac Sven , Zaharija Goran
Published: June 2021

The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of 91.40% and F1-score 92.08%, followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers’ performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data.
Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads
Authors: Hung Cheng-Wei , Tsai Ying-Kuan , Chen Tai-An , Lai Hsin-Hung , Wu Pin-Wen
Published: June 2021

This study used experimental and numerical simulation methods to discuss the attenuation mechanism of a blast inside a tunnel for different forms of a tunnel pressure reduction module under the condition of a tunnel near-field explosion. In terms of the experiment, a small-scale model was used for the explosion experiments of a tunnel pressure reduction module (expansion chamber, one-pressure relief orifice plate, double-pressure relief orifice plate). In the numerical simulation, the pressure transfer effect was evaluated using the ALE fluid–solid coupling and mapping technique. The findings showed that the pressure attenuation model changed the tunnel section to diffuse, reduce, or detour the pressure transfer, indicating the blast attenuation effect. In terms of the effect of blast attenuation, the double-pressure relief orifice plate was better than the one-pressure relief orifice plate, and the single-pressure relief orifice plate was better than the expansion chamber. The expansion chamber attenuated the blast by 30%, the one-pressure relief orifice plate attenuated the blast by 51%, and the double-pressure relief orifice plate attenuated the blast by 82%. The blast attenuation trend of the numerical simulation result generally matched that of the experimental result. The results of this study can provide a reference for future protective designs and reinforce the U.S. Force regulations.
A Novel Coaxial Balance Mechanism for Reciprocating Piston Engines
Authors: Guan Nanxiang , Wang Ao , Gu Yongpeng , Xie Zhifeng , Zhou Ming
Published: June 2021

Vibration is an important issue faced by reciprocating piston engines, and is caused by reciprocating inertia forces of the piston set. To reduce the vibration without changing the main structure and size of the original engine, we proposed a novel coaxial balance mechanism design based on a compact unit body. By introducing a second-order balance mass, this mechanism can significantly increase the efficiency of vibration reduction. The proposed mechanism can effectively balance the first-order and second-order inertia forces with the potential of balancing high-order inertia forces. To accurately determine the second-order balance mass, a closed-form method was developed. Simulation results with a single-cylinder piston DK32 engine demonstrate the effectiveness and advantage of the proposed mechanism. At a crankshaft speed of 2350 r/min, compared with the first-order balance device, the average root mean square velocity of the test points on the engine’s cylinder was reduced by 97.31%, and the support reaction force was reduced by 96.54%.
VSC-HVDC and Its Applications for Black Start Restoration Processes
Authors: Sanchez Garciarivas Rafael , Rasilla Gonzalez Diego , Navarro Javier Agustin , Soriano Luis Arturo , Rubio José de Jesús , Gomez Maria Victoria , Garcia Victor , Pacheco Jaime
Published: June 2021

System reliability is a measure of an electric grid system’s ability to deliver uninterrupted service at the proper voltage and frequency. This property of the electric system is commonly affected by critical processes, such as a total blackout. The electric system restoration is a complex process which consists of returning generators, transmission system elements, and restoring load following an outage of the electric system. However, the absence of a generator or unit of black start capabilities may worsen the duration and effects of blackouts, having severe consequences. Black start capability is important as it can reduce the interruption time, decrease the economic loss, and restart the power supply fast and efficiently. In recent years, several works have reported advances about the High Voltage Direct Current (HVDC) technology based on the Voltage-Source Converter (VSC) as an attractive and promising technology to increase black start capability. This paper is a review of the current studies of VSC-HVDC as black start power and discusses the advantages and limitations of recent methods. The major points addressed in this paper are as follows: the current theoretical approach of the black start process and the used HVDC technologies, the advantages of VSC-HVDC as black start power, a compressive review of the literature about the black start capabilities using VSC-HVDC technologies, and a description of the main methods recently used to provide an enhancement for restoration processes. Finally, this paper discusses new challenges and perspectives for VSC-HVDC links in order to provide an enhancement for restoration processes.
Prediction of Size Distribution and Mass Concentration of Smoke Particles on Moisture Content and Combustion Period from Para Rubber Wood Burning
Authors: Kalasee Wachara , Dangwilailux Panya
Published: June 2021

The size distribution and total particle mass concentration (TPMC) of smoke particles from para rubber wood (Hevea brasiliensis) combustion in the ribbed smoked sheet (RSS) process were studied. In this experiment, temperature data values of para rubber wood combustion were recorded at 500 mm above the base of the fire by K-type thermocouples. The wood moisture content and wood combustion period were used to find and improve an equation of smoke particle size distribution (SPSD) and TPMC by the response surface method (RSM). An eight-stage Andersen air sampler and a high-volume sampler were used to measure and calculate SPSD and TPMC, respectively. Resulting data in this experiment showed that TPMC ranged from 3.12 to 77.42 mg/m3. SPSD was single mode in which MMAD, mass median aerodynamic diameter, ranged from 0.64 to 1.27 microns for para wood with moisture content ranging from 31.5 to 89.7% dry weight basis. The combustion period and moisture content of para wood have a direct effect on the change of temperature data above the base of the fire and the TPMC and MMAD values. For predicting TPMC and MMAD values by the para wood moisture contents in each combustion period, the results found that the second-degree model was a better plot than the first-degree model, confirmed by higher values of the coefficient of determination (R2).
Detecting Areas Vulnerable to Flooding Using Hydrological-Topographic Factors and Logistic Regression
Authors: Lee Jae-Yeong , Kim Ji-Sung
Published: June 2021

As a result of rapid urbanization and population movement, flooding in urban areas has become one of the most common types of natural disaster, causing huge losses of both life and property. To mitigate and prevent the damage caused by the recent increase in floods, a number of measures are required, such as installing flood prevention facilities, or specially managing areas vulnerable to flooding. In this study, we presented a technique for determining areas susceptible to flooding using hydrological-topographic characteristics for the purpose of managing flood vulnerable areas. To begin, we collected digital topographic maps and stormwater drainage system data regarding the study area. Using the collected data, surface, locational, and resistant factors were analyzed. In addition, the maximum 1-h rainfall data were collected as an inducing factor and assigned to all grids through spatial interpolation. Next, a logistic regression analysis was performed by inputting hydrological-topographic factors and historical inundation trace maps for each grid as independent and dependent variables, respectively, through which a model for calculating the flood vulnerability of the study area was established. The performance of the model was evaluated by analyzing the receiver operating characteristics (ROC) curve of flood vulnerability and inundation trace maps, and it was found to be improved when the rainfall that changes according to flood events was also considered. The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.
Properties of Different Varieties of Durian
Authors: Arsa Supeeraya , Wipatanawin Angkana , Suwapanich Rachit , Makkerdchoo Orachorn , Chatsuwan Niphattha , Kaewthong Pensiri , Pinsirodom Praphan , Taprap Ruchira , Haruenkit Ratiporn , Poovarodom Sumitra , Lubinska-Szczygeł Martyna , Katrich Elena , Gorinstein Shela
Published: June 2021

Durian (Durio zibethinus Murr.), like many other exotic, tropical, and conventional fruits, is important in the prevention of different diseases. In this study, the characterization of the main bioactive compounds of the most popular cultivars of durian and their properties are described. The changes in the quality indices of the antioxidant status were determined by CUPRAC, ABTS, FRAP, DPPH, and ORAC assays. The profiling of phytochemicals was carried out by Fourier transform infrared (FTIR) spectroscopy and differential scanning calorimetry (DSC). For the first time, in vitro studies were performed by the interaction of extracted durian polyphenols with human serum proteins (HSP) such as human serum albumin (HSAlb), fibrinogen (HSFib) and globulin (HSGlo) as novel biomarkers of coronary artery disease (CAD). The fluorescence measurements of the resulting intensity and calculated binding properties of the interaction of polyphenols with proteins showed that the most reactive was Monthong durian cultivar. This study suggests that durian cultivars have relatively strong antioxidant, binding, and health potentials and could be a significant source of natural antioxidants used in daily fresh consumption and for functional foods.
Vaccinium Species (Ericaceae): From Chemical Composition to Bio-Functional Activities
Authors: Tundis Rosa , Tenuta Maria C. , Loizzo Monica R. , Bonesi Marco , Finetti Federica , Trabalzini Lorenza , Deguin Brigitte
Published: June 2021

The genus Vaccinium L. (Ericaceae) includes more than 450 species, which mainly grow in cooler areas of the northern hemisphere. Vaccinium species have been used in traditional medicine of different cultures and the berries are widely consumed as food. Indeed, Vaccinium supplement-based herbal medicine and functional food, mainly from V. myrtillus and V. macrocarpon, are used in Europe and North America. Biological studies support traditional uses since, for many Vaccinium components, important biological functions have been described, including antioxidant, antitumor, anti-inflammatory, antidiabetic and endothelium protective activities. Vaccinium components, such as polyphenols, anthocyanins and flavonoids, are widely recognized as modulators of cellular pathways involved in pathological conditions, thus indicating that Vaccinium may be an important source of bioactive molecules. This review aims to better describe the bioactivity of Vaccinium species, focusing on anti-inflammatory and endothelial protective cellular pathways, modulated by their components, to better understand their importance for public health.
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection
Authors: Zeng Yufan , Tang Jiashan
Published: June 2021

Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art results on fraud detection tasks by dealing with relation camouflages and feature camouflages. However, stacking multiple layers in a traditional way defined by hop leads to a rapid performance drop. As the single-layer CARE-GNN cannot extract more information to fix the potential mistakes, the performance heavily relies on the only one layer. In order to avoid the case of single-layer learning, in this paper, we consider a multi-layer architecture which can form a complementary relationship with residual structure. We propose an improved algorithm named Residual Layered CARE-GNN (RLC-GNN). The new algorithm learns layer by layer progressively and corrects mistakes continuously. We choose three metrics—recall, AUC, and F1-score—to evaluate proposed algorithm. Numerical experiments are conducted. We obtain up to 5.66%, 7.72%, and 9.09% improvements in recall, AUC, and F1-score, respectively, on Yelp dataset. Moreover, we also obtain up to 3.66%, 4.27%, and 3.25% improvements in the same three metrics on the Amazon dataset.
On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms
Authors: Hodgetts William , Song Qi , Xiang Xinyue , Cummine Jacqueline
Published: June 2021

(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the implications of such approaches towards the development of novel hearing aid features (i.e., voice familiarity detection). To do this, we built and tested a Convolutional Neural Network (CNN) Model for the identification and classification of a series of voices, namely the 10 cast members of the popular television show “Modern Family”. (2) Methods: Representative voice samples were selected from Season 1 of Modern Family (N = 300; 30 samples for each of the classes of the classification in this model, namely Phil, Claire, Hailey, Alex, Luke, Gloria, Jay, Manny, Mitch, Cameron). The audio samples were then cleaned and normalized. Feature extraction was then implemented and used as the input to train a basic CNN model and an advanced CNN model. (3) Results: Accuracy of voice classification for the basic model was 89%. Accuracy of the voice classification for the advanced model was 99%. (4) Conclusions: Greater familiarity with a voice is known to be beneficial for speech recognition. If a hearing aid can eventually be programmed to recognize voices that are familiar or not, perhaps it can also apply familiar voice features to improve hearing performance. Here we discuss how such machine learning, when applied to voice recognition, is a potential technological solution in the coming years.
Skin Antiaging Effects of the Fermented Outer Layers of Leaf Skin of Aloe barbadensis Miller Associated with the Enhancement of Mitochondrial Activities of UVb-Irradiated Human Skin Fibroblasts
Authors: Lee Hyeonwoo , Choi Woonyong , Ro Hyangseon , Kim Gyurae , Lee Hyeonyong
Published: June 2021

This study is the first to show that increased mitochondrial activities improved the antiaging effects of Aloe vera leaf skin fermented by Lactobacillus plantarum on UVb-irradiated skin fibroblasts. The fermented extract (AF) increased the activities of mitochondrial reductase and the complex II and significantly reduced reactive oxygen species (ROS) production, even under UVb stress conditions, and also increased DPPH free radical scavenging activities compared with the hot water extract of outer layers of aloe leaf (AW) and quercetin itself. AF exerted a synergistic effect with quercetin and bioactive substances derived from the fermentation process. Moreover, mitochondrial activation of UVb-irradiated human skin fibroblasts by 0.3% (w/v) of the AF plays important roles in increasing collagen production up to 125 ± 5.45% and decreasing MMP-1 secretion down to 69.41 ± 2.63% of the control levels. The AF enhanced the upregulation of collagen gene expression, and this change was also greater than those induced by the AW and quercetin. Therefore, this study concludes that fermentation of the skin of aloe leaves increases the activation of mitochondria and inhibits the photo-aging of UVb-irradiated skin fibroblasts.
Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
Authors: Hur Sung-ho , Reddy Yiza-srikanth
Published: June 2021

The estimation of variables that are normally not measured or are unmeasurable could improve control and condition monitoring of wind turbines. A cost-effective estimation method that exploits machine learning is introduced in this paper. The proposed method allows a potentially expensive sensor, for example, a LiDAR sensor, to be shared between multiple turbines in a cluster. One turbine in a cluster is equipped with a sensor and the remaining turbines are equipped with a nonlinear estimator that acts as a sensor, which significantly reduces the cost of sensors. The turbine with a sensor is used to train the estimator, which is based on an artificial neural network. The proposed method could be used to train the estimator to estimate various different variables; however, this study focuses on wind speed and aerodynamic torque. A new controller is also introduced that uses aerodynamic torque estimated by the neural network-based estimator and is compared with the original controller, which uses aerodynamic torque estimated by a conventional aerodynamic torque estimator, demonstrating improved results.
Usage of IoT Framework in Water Supply Management for Smart City in Nepal
Authors: Gautam Ganesh , Sharma Gajendra , Magar Bipin Thapa , Shrestha Bhanu , Cho Seongsoo , Seo Changho
Published: June 2021

An efficient water supply management system can be one of the applications of the Internet of Things (IoT). Water is a basic physiological need, and smart management of water plays a significant role in a smart city. This paper focuses on a mathematical model and IoT framework that aid in developing a smart city. A framework is developed for water supply management. The efficiency of the water supply can be measured by monitoring leakage conditions, overflow of water, automatic meter reading and online bill payments, and water consumption status of households, community, state, and eventually the whole country as well as the automatic water supply line cut-off. The system where the IoT is being deployed consists of embedded hardware in which sensors and microcontrollers provide messages and gain feedback from each other with the help of the internet, and this process can not only be monitored but also can be controlled from a remote location. The developed framework addresses all these aspects and mathematical equations are used and formulated while developing the IoT application. The mathematical equations are concentrated on consumption level (CL), leakage reporting (LR), and bill amount (BA) based on consumption. These become the point of contact for deploying IoT and eventually a framework is developed. This framework can be useful not only in water supply management but also in the management of road traffic, pollution, garbage, home automation and so on. In a nutshell, this paper illustrates the usage of the IoT framework in water supply management which contributes to developing the smart city.
Seismic Performance of Deposit Slopes with Underlying Bedrock before and after Reinforcement by Stabilizing Piles
Authors: Sun Zhiliang , Kong Lingwei , Bai Wei , Wang Yong
Published: June 2021

The seismic performance of stabilizing piles used to reinforce underlying bedrock in a deposit slope is a complex soil–structure interaction problem. Two centrifuge shaking table model testswere conducted to ascertain the dynamic responses of the underlying bedrock deposit slopes without and with the use of stabilizing piles during an earthquake. Multi-stage seismic waves with various peak accelerations were applied from the bottom of each model. The differencesin the response accelerations between the deposit and bedrock increase significantly with the increase in amplitude of the input seismic waves. The presence of the rock-socketed stabilizing piles can bridge the uncoordinated movement of the bedrock and the overlying deposit to some extent. The resultant force arising from a distributed load increment on the piles caused by an earthquake is mainly concentrated in the upper part. With increases in the peak ground acceleration of the input motion, the resistance of the bedrock in front of the stabilizing piles increases and the peak resistance under the bedrock surface of the stabilizing piles gradually moves downwards.This finding indicates that the strong seismic motion significantly changes the embedded working state of the stabilizing pile.
Fatigue Design of Steel Bridge Deck Asphalt Pavement Based on Nonlinear Damage Accumulation Theory
Authors: Xu Xunqian , Li Yu , Huang Wei , Chen Dakai , Zhang Chen , Shi Wenkang
Published: June 2021

Based on the nonlinear damage theory, this paper aims to explore the fatigue performance of steel bridge deck asphalt pavement under multistage fatigue load. Manson–Halford cumulative damage model and the modified model were introduced to describe loading sequence effects, and interactions between multiple loads were represented in stress ratio. The fatigue life prediction method of steel bridge deck asphalt pavement was put forward, considering loading sequence effects and load interactions. The fatigue design of steel bridge deck asphalt pavement was investigated with the fatigue life prediction model. The effects of different load levels and loading sequence on the fatigue design parameters stress ratio of steel bridge deck asphalt pavement were studied. The design results were compared with experimental results, and the prediction results were based on traditional Miner’s theory. The analysis results showed that the fatigue life prediction method based on the nonlinear cumulative damage theory can effectively design and analyze the fatigue characteristics of asphalt pavement of steel bridge deck with high accuracy and reliability. The fatigue life prediction model of steel bridge deck asphalt pavement can well reflect loading sequence effects and load interactions. In addition, the design model has relatively few parameters; therefore, it can be applied to practical engineering design.
Failure Rate, Marginal Bone Loss, and Pink Esthetic with Socket-Shield Technique for Immediate Dental Implant Placement in the Esthetic Zone. A Systematic Review and Meta-Analysis
Authors: Velasco Bohórquez Pilar , Rucco Roberta , Zubizarreta-Macho Álvaro , Montiel-Company José María , de la Vega Buró Susana , Madroño Esther Cáceres , Marín Lara San Hipólito , Hernández Montero Sofía
Published: June 2021

Aim: To compare the failure rate, marginal bone loss, and pink esthetic for the socket-shield technique and the conventional technique for immediate dental implant placement in the esthetic zone. Material and methods: A systematic literature review and meta-analysis, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, of clinical studies that evaluated the failure rate, marginal bone loss, and pink esthetic with the socket-shield technique for immediate dental implant placement in the esthetic zone was performed. A total of 4 databases were consulted in the literature search: PubMed-MEDLINE, Scopus, Embase, and Web of Science. After eliminating duplicated articles and applying the inclusion criteria, 16 articles were selected for the qualitative and quantitative analysis. Results: Four randomized controlled trials, five prospective clinical studies, four retrospective studies, and three case series were included in the meta-analysis. The dental implant failure rate for the socket-shield technique for immediate dental implant placement was 1.37% (95% CI, 0.21–2.54%); however, no statistically significant differences between the conventional and socket-shield technique were found. The estimated mean difference in the marginal bone loss for the socket-shield technique was −0.5 mm (95% CI, −0.82 to −0.18) and statistically significant (p < 0.01), with a high heterogeneity (I2 = 99%). The mean pink esthetic score was 12.27 (Q test = 4.47; p-value = 0.61; I2 = 0%). The difference in pink esthetic between the conventional (n = 55) and socket-shield techniques (n = 55) for immediate dental implant placement was 1.15 (95% CI, 0.73–1.58; Q test = 8.88; p value = 0.11; I2 = 44%). The follow-up time was found to be significant (beta coefficient = 0.023; R2 = 85.6%; QM = 3.82; p = 0.049) for the PES for the socket-shield technique. Conclusions: Within the limitations of this systematic review with meta-analysis, the dental implant failure rate did not differ between the socket-shield technique and conventional technique for immediate implant placement in the esthetic zone. However, a lower marginal bone loss and higher pink esthetic scores were found for the socket-shield technique compared to the conventional technique.
Omics Technologies to Decipher Regulatory Networks in Granulocytic Cell Differentiation
Authors: Novikova Svetlana , Tikhonova Olga , Kurbatov Leonid , Farafonova Tatiana , Vakhrushev Igor , Lupatov Alexey , Yarygin Konstantin , Zgoda Victor
Published: June 2021

Induced granulocytic differentiation of human leukemic cells under all-trans-retinoid acid (ATRA) treatment underlies differentiation therapy of acute myeloid leukemia. Knowing the regulation of this process it is possible to identify potential targets for antileukemic drugs and develop novel approaches to differentiation therapy. In this study, we have performed transcriptomic and proteomic profiling to reveal up- and down-regulated transcripts and proteins during time-course experiments. Using data on differentially expressed transcripts and proteins we have applied upstream regulator search and obtained transcriptome- and proteome-based regulatory networks of induced granulocytic differentiation that cover both up-regulated (HIC1, NFKBIA, and CASP9) and down-regulated (PARP1, VDR, and RXRA) elements. To verify the designed network we measured HIC1 and PARP1 protein abundance during granulocytic differentiation by selected reaction monitoring (SRM) using stable isotopically labeled peptide standards. We also revealed that transcription factor CEBPB and LYN kinase were involved in differentiation onset, and evaluated their protein levels by SRM technique. Obtained results indicate that the omics data reflect involvement of the DNA repair system and the MAPK kinase cascade as well as show the balance between the processes of the cell survival and apoptosis in a p53-independent manner. The differentially expressed transcripts and proteins, predicted transcriptional factors, and key molecules such as HIC1, CEBPB, LYN, and PARP1 may be considered as potential targets for differentiation therapy of acute myeloid leukemia.