bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Amending Phosphatic Fertilizers with Clinoptilolite Zeolite on Phosphorus Availability and Its Fractionation in an Acid Soil
Authors: Hasbullah Nur Aainaa , Ahmed Osumanu Haruna , Ab Majid Nik Muhamad
Published: May 2020

Soils of the tropics are highly weathered, acidic, and low in phosphorus (P) because of high contents of Al and Fe. Satisfactory P supply is essential to ensure optimum soil and crop productivity. Thus, there is a need for amending soils with zeolite to improve availability of P in acid soils as this mineral can fix Fe and Al instead of P. This study was undertaken to determine the transformations of P fertilizers in acid soils following application of Clinoptilolite zeolite (CZ) in laboratory (incubation) and pot trials. An acid soil was incubated with a recommended fertilization rate and a reduced amount of the existing recommended fertilization by 25% but substituting this reduction with an equivalent amount of CZ. Triple superphosphate (TSP), Egypt Rock phosphate (ERP), and Christmas Island Rock phosphate (CIRP) were used as P sources. Selected soil chemical properties, inorganic P fractions, available P, and total P of the native soil were determined before and after the laboratory and pot trials. Zea mays L. (test crop) plant dry matter production, P concentration, P uptake, and P use efficiency were also determined using standard procedures. Effects of the treatments with CZ compared to the recommended fertilization on P fixation were similar. In the laboratory study, the treatments with TSP showed lower dominance of Fe–P but more pronounced in Al–P, whereas for the RPs, Ca–P was dominant. In the pot study, Al–P, Ca–P, and Fe–P were rather pronounced in the treatments with TSP, ERP, and CIRP, respectively. There was a decrease in exchangeable Al and soil titratable acidity because of the ability of the CZ to increase soil pH. Although the availability of P was not significant with the inclusion of CZ in the incubation study, dry matter production, P concentration, P uptake, and P use efficiency in the pot trial were comparable with that of the existing/recommended fertilization, suggesting that the CZ is beneficial and could be used to reduce the P fertilizer requirement for Zea mays L. cultivation on acid soils. Regardless of type of P fertilizer, prevalence of the moderately labile P fractions (Al–P, Fe–P, and Ca–P) of the incubation and pot studies acted as slow-release P sources to contribute to long-term P release. Further studies on the potential of CZ to reduce fertilization and its effects on soil and crop productivity are essential. It is also important to determine the economic benefits of including CZ in Zea mays L. cultivation.
A Novel System for Patient Ventilation and Dosing of Hyperpolarized 3He for Magnetic Resonance Imaging of Human Lungs
Authors: Kordulasiński Robert , Królewska Marta , Głowacz Bartosz , Mikowska Lutosława , Olejniczak Zbigniew , Pałasz Tadeusz , Suchanek Mateusz , Filipow Winicjusz , Dohnalik Tomasz
Published: May 2020

A versatile ventilator for controlling a patient’s breath cycle and dosing 3He gas has been designed and constructed. It is compatible with a medical magnetic resonance imaging scanner and can be incorporated into routine human lungs imaging procedure that employs hyperpolarized noble gas as a contrast agent. The system adapts to the patient’s lung volume and their breath cycle rhythm, providing maximum achievable comfort during the medical examination. Good quality magnetic resonance lung images of healthy volunteers were obtained. The system has the capability of recycling the exhaled gas to recover the expensive 3He isotope, and can be also adapted to human lung imaging with hyperpolarized 129Xe.
An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays
Authors: Liu Jiancheng , Shi Feng , Sun Yecheng , Li Peng
Published: May 2020

The Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array still holds some redundancy in sensor spatial sampling, and it means that this sensor array may be further thinned. For this reason, the Almost Different Sets (ADS) method is proposed to further thin the Mills Cross array. First, the original Mills Cross array is divided into several transversal linear arrays and one longitudinal linear array. Secondly, the Peak Side Lobe Level (PSLL) of each virtual linear array is estimated in advance. After the ADS parameters are matched according to the thinned ratio of the expectant array, all linear arrays are thinned in order. In the end, the element locations in the thinned linear array are used to determine which elements are kept or discarded from the original Mills array. Simulations demonstrate that the ADS method can be used to thin the Mills array and to further decrease the complexity of the imaging system while retaining beam performance.
Performance Investigation of Integrated Model of Quarter Car Semi-Active Seat Suspension with Human Model
Authors: Jain Saransh , Saboo Shubham , Pruncu Catalin Iulian , Unune Deepak Rajendra
Published: May 2020

In this paper, an integrated model of a semi-active seat suspension with a human model over a quarter is presented. The proposed eight-degrees of freedom (8-DOF) integrated model consists of 2-DOF for the quarter car model, 2-DOF for the semi-active seat suspension and 4-DOF for the human model. A magneto-rheological (MR) damper is implemented for the seat suspension. The fuzzy logic-based self-tuning (FLST) proportional–integral–derivative (PID) controller allows to regulate the controlled force on the basis of sprung mass velocity error and its derivative as input. The controlled force is tracked by the Heaviside step function which determines the supply voltage for the MR damper. The performance of the proposed integrated model is analysed, in-terms of human head accelerations, for several road profiles and at different speeds. The performance of the semi-active seat suspension is compared with the traditional passive seat suspension to validate the effectiveness of the proposed integrated model with a semi-active seat suspension. The simulation results show that the semi-active seat suspension improves the ride comfort significantly by reducing the head acceleration effectively compared to the passive seat suspension.
Solution Extrusion Additive Manufacturing of Biodegradable Polycaprolactone
Authors: Chen Jian-Ming , Lee Demei , Yang Jheng-Wei , Lin Sheng-Han , Lin Yu-Ting , Liu Shih-Jung
Published: May 2020

Polycaprolactone (PCL) is a resorbable semicrystalline polymer that degrades slowly via hydrolysis and has applications in medical implants and drug delivery. As a result of its low melting point, PCL can be processed easily by conventional polymer processing techniques. However, the additive manufacturing of PCL remains a challenge, mainly due to the fact that there are no commercially available filaments for traditional fused deposition modeling (FDM). Furthermore, when the materials are fabricated via FDM for drug delivery applications, the high temperature may deactivate the incorporated drugs/biomolecules. This study investigates the solution extrusion additive manufacturing of PCL using a lab-developed solution-type device. The device comprises a solution extrusion feeder, driving stepper motors, a power source, a syringe equipped with a dispensing tip, an accumulation platform, and a control interface. The influences of different manufacturing parameters on part quality were evaluated. The experimental results suggest that the tensile strength of the additively manufactured parts increases with fill density but decreases with the ratio of PCL to dichloromethane (DCM) and moving speed of the dispensing tip. Parts fabricated by 90° print orientation of infill exhibited the greatest mechanical strength. The fabricated parts tend to heal the gaps among strips after additive manufacturing, but tiny pores can still be seen on the surfaces.
A New Guideline for Security Assessment of Power Systems with a High Penetration of Wind Turbines
Authors: Shakerighadi Bahram , Peyghami Saeed , Ebrahimzadeh Esmaeil , Blaabjerg Frede , Leth Back Claus
Published: May 2020

By the increase of the penetration of power-electronic-based (PE-based) units, such as wind turbines and PV systems, many features of those power systems, such as stability, security, and protection, have been changed. In this paper, the security of electrical grids with high wind turbines penetration is discussed. To do so, first, an overview of the power systems’ security assessment is presented. Based on that, stability and security challenges introduced by increasing the penetration of wind turbines in power systems are studied, and a new guideline for the security assessment of the PE-based power systems is proposed. Simulation results for the IEEE 39-bus test system show that the proposed security guideline is necessary for PE-based power systems, as the conventional security assessments may not be able to indicate its security status properly.
Methods for the Characterization of Polyetherimide Based Materials Processed by Fused Deposition Modelling
Authors: Tosto Claudio , Saitta Lorena , Pergolizzi Eugenio , Blanco Ignazio , Celano Giovanni , Cicala Gianluca
Published: May 2020

Fused deposition modelling (FDM™) is one of the most promising additive manufacturing technologies and its application in industrial practice is increasingly spreading. Among its successful applications, FDM™ is used in structural applications thanks to the mechanical performances guaranteed by the printed parts. Currently, a shared international standard specifically developed for the testing of FDM™ printed parts is not available. To overcome this limit, we have considered three different tests aimed at characterizing the mechanical properties of technological materials: tensile test (ASTM D638), flexural test (ISO 178) and short-beam shear test (ASTM D2344M). Two aerospace qualified ULTEMTM 9085 resins (i.e., tan and black grades) have been used for printing all specimens by means of an industrial printer (Fortus 400mc). The aim of this research was to improve the understanding of the efficiency of different mechanical tests to characterize materials used for FDM™. For each type of test, the influence on the mechanical properties of the specimen’s materials and geometry was studied using experimental designs. For each test, 22 screening factorial designs were considered and analyzed. The obtained results demonstrated that the use of statistical analysis is recommended to ascertain the real pivotal effects and that specific test standards for FDM™ components are needed to support the development of materials in the additive manufacturing field.
The Effect of Eugenol and Chitosan Concentration on the Encapsulation of Eugenol Using Whey Protein–Maltodextrin Conjugates
Authors: Agustinisari Iceu , Mulia Kamarza , Nasikin Mohammad
Published: May 2020

Eugenol has many functional properties for food and pharmaceutical purposes, especially as an antimicrobial agent. However, its use is constrained by its volatility and shelf life because it is easily degraded due to temperature, oxidation, and light. Research on encapsulation technology using biopolymers is still required to obtain the appropriate formulation in a eugenol delivery system. The aims of this research were to develop a new formulation of protein and polysaccharides in eugenol encapsulation and to determine the effect of eugenol and chitosan concentration on the characteristics of the emulsions and spray-dried powder produced. In this study, eugenol was encapsulated in whey protein–maltodextrin conjugates and chitosan through the double layer encapsulation method. The emulsions which were prepared with 2.0% eugenol were relatively more stable than those of 1.0% eugenol based on the polydispersity index and zeta potential values. Spray-dried powder which was prepared using an emulsion of 2.0% w/w eugenol and 0.33% w/w chitosan had the highest eugenol loading. The presence of chitosan resulted in more stable emulsions based on their zeta potential values, improved thermal stability of eugenol, increased eugenol loading to become twice as much as the loading obtained without chitosan, and modified release profile of eugenol from the spray-dried powders.
A Soft Computing Framework to Support Consumers in Obtaining Sustainable Appliances from the Market
Authors: Santos Ricardo , Abreu António , Soares José , Mendes Fernanda , Calado João M.F.
Published: May 2020

Currently, sustainability is considered a priority by society, with the household appliances being one of the economic sectors involved in achieving sustainability. However, the existence of several issues (e.g., energy and water consumption, reliability, initial cost, and illuminance, among others) together with the diversity of brands and models on the market, make the consumer’s decisions regarding sustainable options difficult, according to their concerns and related to each sustainability dimension (economic, environmental, and social). By combining evolutionary algorithms (EA) with multicriteria techniques, it is possible to achieve sustainable solutions for the consumer based on their requirements. In this paper, a method is presented to support the consumer by obtaining a set of sustainable household appliances on the market that suit their preferences, concerns, and needs. By using a case study to apply the approach developed here, a set of sustainable appliances from the market is obtained, where several benefits are achieved (e.g., energy and water consumption savings, avoidance of CO2 emissions) during the lifecycle of each appliance, chosen from the appliance’s industry.
Towards Piston Fine Tuning of Segmented Mirrors through Reinforcement Learning
Authors: Guerra-Ramos Dailos , Trujillo-Sevilla Juan , Rodríguez-Ramos Jose Manuel
Published: May 2020

Unlike supervised machine learning methods, reinforcement learning allows an entity to learn how to deploy a task from experience rather than labeled data. This approach has been used in this paper to correct piston misalignment between segments in a segmented mirror telescope. It was proven in simulations that the algorithm converges to a point where it learns how to move the piston actuators in order to maximize the Strehl ratio of the wavefront at the intersection.
Modelling of Red Blood Cell Morphological and Deformability Changes during In-Vitro Storage
Authors: Geekiyanage Nadeeshani , Sauret Emilie , Saha Suvash , Flower Robert , Gu YuanTong
Published: May 2020

Storage lesion is a critical issue facing transfusion treatments, and it adversely affects the quality and viability of stored red blood cells (RBCs). RBC deformability is a key indicator of cell health. Deformability measurements of each RBC unit are a key challenge in transfusion medicine research and clinical haematology. In this paper, a numerical study, inspired from the previous research for RBC deformability and morphology predictions, is conducted for the first time, to investigate the deformability and morphology characteristics of RBCs undergoing storage lesion. This study investigates the evolution of the cell shape factor, elongation index and membrane spicule details, where applicable, of discocyte, echinocyte I, echinocyte II, echinocyte III and sphero-echinocyte morphologies during 42 days of in-vitro storage at 4 °C in saline-adenine-glucose-mannitol (SAGM). Computer simulations were performed to investigate the influence of storage lesion-induced membrane structural defects on cell deformability and its recoverability during optical tweezers stretching deformations. The predicted morphology and deformability indicate decreasing quality and viability of stored RBCs undergoing storage lesion. The loss of membrane structural integrity due to the storage lesion further degrades the cell deformability and recoverability during mechanical deformations. This numerical approach provides a potential framework to study the RBC deformation characteristics under varying pathophysiological conditions for better diagnostics and treatments.
Hybrid-Recursive Feature Elimination for Efficient Feature Selection
Authors: Jeon Hyelynn , Oh Sejong
Published: May 2020

As datasets continue to increase in size, it is important to select the optimal feature subset from the original dataset to obtain the best performance in machine learning tasks. Highly dimensional datasets that have an excessive number of features can cause low performance in such tasks. Overfitting is a typical problem. In addition, datasets that are of high dimensionality can create shortages in space and require high computing power, and models fitted to such datasets can produce low classification accuracies. Thus, it is necessary to select a representative subset of features by utilizing an efficient selection method. Many feature selection methods have been proposed, including recursive feature elimination. In this paper, a hybrid-recursive feature elimination method is presented which combines the feature-importance-based recursive feature elimination methods of the support vector machine, random forest, and generalized boosted regression algorithms. From the experiments, we confirm that the performance of the proposed method is superior to that of the three single recursive feature elimination methods.
Reflections on Odor Management for Animal Feeding Operations
Authors: Janni Kevin
Published: May 2020

Most animal feeding operation owners recognize that they need to manage odors from their operations as part of their social relationship with their neighbors and local community. That was not always the case. Odors, whether pleasant or unpleasant, can evoke strong emotions and physiological responses. Odors from animal feeding operations are normally considered unpleasant and offensive if strong smelling and smelled often or for long periods of time. Animal feeding operation owners need to be aware of their odor emissions and the impacts the odors have on their neighbors and community. Good neighbor relations and effective communications can help identify odor problems and communicate what is being done to manage them. Odor management research and education includes odor basics, key processes including generation, emissions and dispersion, impacts, community and neighbor relations, and numerous mitigation practices. Animal feeding operation owners considering practices to reduce odor emissions or their impacts need to weigh the costs, expected effectiveness, and how the practice fits into the overall operation. Policymakers need science-based information to make informed decisions that balance the concerns and needs of neighbors and the community and the businesswomen and men that own and operate the animal feeding operations. This paper provides a broad overview of animal feeding operation odors and odor management.
Dispersion Characteristics of PM10 Particles Identified by Numerical Simulation in the Vicinity of Roads Passing through Various Types of Urban Areas
Authors: Pospisil Jiri , Huzlik Jiri , Licbinsky Roman , Spilacek Michal
Published: May 2020

The dispersion of particulate matter emitted by road transport to the vicinity of roads is predominantly influenced by the character of the air velocity field. The air flow depends on factors such as the speed and direction of the blowing wind, the movement of cars, and the geometries of the buildings around a road. Numerical modeling based on the control volume method was used in this study to describe the relevant processes closely. Detailed air velocity fields were identified in the vicinity of a straight road surrounded by various patterns of built-up urban land. The evaluation of the results was generalized to exponential expressions, affecting the decrease of the mass concentration of fine particles with the increasing distance from the road. The obtained characteristics of the mass concentration fields express the impact of the building geometries and configurations on the dispersion of particulate matter into the environment. These characteristics are presented for two wind speeds, namely, 2 m·s−1 and 4 m·s−1. Furthermore, the characteristics are introduced in relation to three wind directions: perpendicularly, obliquely, and in parallel to the road. The results of the numerical simulations are compared with those obtained via the in-situ measurements, for verification of the validity of the linear emission source calculation.
Five Years of Dust Episodes at the Southern Italy GAW Regional Coastal Mediterranean Observatory: Multisensors and Modeling Analysis
Authors: Calidonna Claudia Roberta , Avolio Elenio , Gullì Daniel , Ammoscato Ivano , De Pino Mariafrancesca , Donateo Antonio , Lo Feudo Teresa
Published: May 2020

The Mediterranean area is a climate-change hotspot because of the natural and anthropogenic pollution pressure. The presence of natural aerosols, such as dust, influences solar radiation and contributes to the detection, in storm episodes, of significant concentrations of PM10 in Southern Italy, where generally fresh and clean air is due to local circulation, and particulate matter concentrations are very low. We present the results of medium-term observations (2015–2019) at Lamezia Terme GAW (Global Atmospheric Watch) Regional Observatory, with the purpose of identifying the dust incursion events by studying the aerosol properties in the site. To achieve this goal, the experimental data, collected by several instruments, have been also correlated with the large-scale atmospheric patterns derived by the ERA5 reanalysis dataset, in order to study the meteorological conditions that strongly influence dust outbreaks and their spatio-temporal behavior. An intense dust-outbreak episode, which occurred on 23–27 April 2019, was chosen as a case study; a detailed analysis was carried out considering surface and column optical properties, chemical properties, large-scale pattern circulation, air-quality modeling/satellite products, and back-trajectory analysis, to confirm the capability of the modeled large-scale atmospheric fields to correctly simulate the conditions mainly related to the desert dust-outbreak events.
Time Series Analysis and Forecasting Using a Novel Hybrid LSTM Data-Driven Model Based on Empirical Wavelet Transform Applied to Total Column of Ozone at Buenos Aires, Argentina (1966–2017)
Authors: Mbatha Nkanyiso , Bencherif Hassan
Published: May 2020

Total column of ozone (TCO) time series analysis and accurate forecasting is of great significance in monitoring the status of the Chapman Mechanism in the stratosphere, which prevents harmful UV radiation from reaching the Earth’s surface. In this study, we performed a detailed time series analysis of the TCO data measured in Buenos Aires, Argentina. Moreover, hybrid data-driven forecasting models, based on long short-term memory networks (LSTM) recurrent neural networks (RNNs), are developed. We extracted the updated trend of the TCO time series by utilizing the singular spectrum analysis (SSA), empirical wavelet transform (EWT), empirical mode decomposition (EMD), and Mann-Kendall. In general, the TCO has been stable since the mid-1990s. The trend analysis shows that there is a recovery of ozone during the period from 2010 to 2017, apart from the decline of ozone observed during 2015, which is presumably associated with the Calbuco volcanic event. The EWT trend method seems to have effective power for trend identification, compared with others. In this study, we developed a robust data-driven hybrid time series-forecasting model (named EWT-LSTM) for the TCO time series forecasting. Our model has the advantage of utilizing the EWT technique in the decomposition stage of the LSTM process. We compared our model with (1) an LSTM model that uses EMD, namely EMD-LSTM; (2) an LSTM model that uses wavelet denoising (WD) (WD-LSTM); (3) a wavelet denoising EWT-LSTM (WD-EWT-LSTM); and (4) a wavelet denoising noise-reducing sequence called EMD-LSTM (WD-EMD-LSTM). The model that uses the EWT decomposition process (EWT-LSTM) outperformed the other five models developed here in terms of various forecasting performance evaluation criteria, such as the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R).
Quantifying the Influences of PM2.5 and Relative Humidity on Change of Atmospheric Visibility over Recent Winters in an Urban Area of East China
Authors: Sun Xiaoyun , Zhao Tianliang , Liu Duanyang , Gong Sunling , Xu Jiaping , Ma Xiaodan
Published: May 2020

Fine particulate matters (PM2.5) and relative humidity (RH) in the ambient atmosphere are the leading anthropogenic and natural factors changing atmospheric horizontal visibility. Based on the analysis of environmental and meteorological data observed over 2013–2019 in Nanjing, an urban area in East China, this study investigated the influences of PM2.5 and RH on atmospheric visibility changes over recent years. The visibility had significantly negative correlations with the PM2.5 concentrations and RH changes. The nonlinear relationships existed between PM2.5 concentrations and visibility, as well as between RH and visibility, with the inflection points in the atmospheric visibility changes. The PM2.5 inflection concentrations were 81.0 μg m−3, 76.0 μg m−3, 49.0 μg m−3, and 33.0 μg m−3, respectively, for the RH ranges of RH < 60%, 60% ≤ RH < 80%, 80% ≤ RH < 90%, and RH ≥ 90%, indicating that the improvement of visibility with reducing PM2.5 concentrations could be more difficult under the humid meteorological condition. The visibility changes were most sensitive to PM2.5 concentrations in the RH range of 60–80% in this urban area of East China. The relative contributions of natural factor RH and anthropogenic factor PM2.5 to variations of wintertime atmospheric visibility were quantified with 54.3% and 45.7%, respectively, revealing an important role of natural factor RH in the change of atmospheric visibility in the urban area of East Asian monsoon region.
Arsenic Exposure and Methylation Efficiency in Relation to Oxidative Stress in Semiconductor Workers
Authors: Pan Chih-Hong , Lin Ching-Yu , Lai Ching-Huang , Jeng Hueiwang Anna
Published: May 2020

This study examined associations between oxidative stress and arsenic (As) exposure and methylation efficiency in semiconductor workers. An As-exposed group (n = 427) and a control group (n = 91) were included. The As-exposure group (n = 427) included 149 maintenance staff members and 278 production staff members representing high As exposure and low As exposure, respectively. The control group included 91 administrative staff members with no or minimal As exposure. An occupational exposure assessment was conducted to assess personal As exposure by measuring As concentrations in urine, hair, and fingernails of the subjects. Urinary As(III), As(V), monomethylarsonic (MMA), and dimethylarsinic acid (DMA) were quantified to assess an internal dose of inorganic As. Urinary 8-hydroxy-2’-deoxyguanosine (8-OHdG) and malondialdehyde (MDA) were measured to asses oxidative DNA damage and lipid peroxidation, respectively. As concentrations in urine, hair, and fingernails significantly increased (p < 0.05) in the As-exposed group in comparison to the control group. Geometric mean urinary concentrations of As, 8-OHdG, and MDA in the As-exposed group significantly exceeded those in the control group. As exposure to As-exposed workers had increased concentrations of 8-OHdG in contrast to those in control subjects. Moreover, urinary 8-OHdG concentrations in the semiconductor workers were positively correlated with urinary total As metabolite (As(III) + As(V) + MMA + DMA) concentrations. Furthermore, urinary excretion of 8-OHdG concentrations in As-exposed workers were positively associated with urinary excretion of MMA concentrations and primary methylation index values (the ration of MMA/inorganic As). However, fingernail and hair samples did not perform as well as urinary samples to measure oxidative stress induced by As exposure. 8-OHdG could serve as a more reliable biomarker for assessing As methylation than MDA did. Occupational exposure to inorganic As was associated with increased oxidative stress among semiconductor workers.
Modeling Land Surface Fluxes from Uncertain Rainfall: A Case Study in the Sahel with Field-Driven Stochastic Rainfields
Authors: Cappelaere Bernard , Feurer Denis , Vischel Théo , Ottlé Catherine , Issoufou Hassane Bil-Assanou , Saux-Picart Stéphane , Maïnassara Ibrahim , Oï Monique , Chazarin Jean-Philippe , Barral Hélène , Coudert Benoit , Demarty Jérôme
Published: May 2020

In distributed land surface modeling (LSM) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. This is particularly true for applications in the African Sahel region, where weak knowledge of highly time/space-variable convective rainfall in a poorly monitored region is a considerable obstacle to such developments. In this study, we used a field-based stochastic rainfield generator to analyze the propagation of the rainfall uncertainty through a distributed land surface model simulating water and energy fluxes in Sahelian ecosystems. Ensemble time/space rainfields were generated from field observations of the local AMMA-CATCH-Niger recording raingauge network. The rainfields were then used to force the SEtHyS-Savannah LSM, yielding an ensemble of time/space simulated fluxes. Through informative graphical representations and innovative diagnosis metrics, these outputs were analyzed to separate the different components of flux variability, among which was the uncertainty represented by ensemble-wise variability. Scale dependence was analyzed for each flux type in the water and energy budgets, producing a comprehensive picture of uncertainty propagation for the various flux types, with its relationship to intrinsic space/time flux variability. The study was performed over a 2530 km2 domain over six months, covering an entire monsoon season and the subsequent dry-down, using a kilometer/daily base resolution of analysis. The newly introduced dimensionless uncertainty measure, called the uncertainty coefficient, proved to be more effective in describing uncertainty patterns and relationships than a more classical measure based on variance fractions. Results show a clear scaling relationship in uncertainty coefficients between rainfall and the dependent fluxes, specific to each flux type. These results suggest a higher sensitivity to rainfall uncertainty for hydrological than for agro-ecological or meteorological applications, even though eddy fluxes do receive a substantial part of that source uncertainty.
Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics
Authors: Romanov Anton , Voronina Valeria , Guskov Gleb , Moshkina Irina , Yarushkina Nadezhda
Published: May 2020

The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, and the forecasting of the state of processes. The main point of this study is the development of a set of analytical and prognostic methods. The methods described in this article based on fuzzy logic, statistic, and time series data mining, because data extracted from dynamic systems are initially incomplete and have a high degree of uncertainty. The ultimate goal of the study is to improve the quality of data analysis in industrial and economic systems. The advantages of the proposed methods are flexibility and orientation to the high interpretability of dynamic data. The high level of the interpretability and interoperability of dynamic data is achieved due to a combination of time series data mining and knowledge base engineering methods. The merging of a set of rules extracted from the time series and knowledge base rules allow for making a forecast in case of insufficiency of the length and nature of the time series. The proposed methods are also based on the summarization of the results of processes modeling for diagnosing technical systems, forecasting of the economic condition of enterprises, and approaches to the technological preparation of production in a multi-productive production program with the application of type 2 fuzzy sets for time series modeling. Intelligent systems based on the proposed methods demonstrate an increase in the quality and stability of their functioning. This article contains a set of experiments to approve this statement.
A Newly Designed Modular ZnBr2 Single Cell Structure
Authors: Pang Zongqiang , Gong Yutao , Yuan Ming , Li Xin
Published: May 2020

We describe a ZnBr2 single cell which has a highly modular symmetrical structure. With designed polyethylene shell frames, membrane frame and composite titanium-carbon felt electrodes, it has a higher energy density and is more flexible compared with traditional flow batteries. We repeatedly tested its performance, which showed good tightness, high reliability and a high energy efficiency of 75%. Due to the special symmetrical structure and modular design, it is easy to assemble and disassemble, which makes it suitable as a test platform for electrodes, membranes and electrolyte performance testing. The designed modular flow cell has low cost and high energy density, and can provide good guidance for flow battery research.
3-Iodothyronamine Affects Thermogenic Substrates’ Mobilization in Brown Adipocytes
Authors: Gencarelli Manuela , Laurino Annunziatina , Landucci Elisa , Buonvicino Daniela , Mazzantini Costanza , Chiellini Grazia , Raimondi Laura
Published: May 2020

We investigated the effect of 3-iodothyronamine (T1AM) on thermogenic substrates in brown adipocytes (BAs). BAs isolated from the stromal fraction of rat brown adipose tissue were exposed to an adipogenic medium containing insulin in the absence (M) or in the presence of 20 nM T1AM (M+T1AM) for 6 days. At the end of the treatment, the expression of p-PKA/PKA, p-AKT/AKT, p-AMPK/AMPK, p-CREB/CREB, p-P38/P38, type 1 and 3 beta adrenergic receptors (β1–β3AR), GLUT4, type 2 deiodinase (DIO2), and uncoupling protein 1 (UCP-1) were evaluated. The effects of cell conditioning with T1AM on fatty acid mobilization (basal and adrenergic-mediated), glucose uptake (basal and insulin-mediated), and ATP cell content were also analyzed in both cell populations. When compared to cells not exposed, M+T1AM cells showed increased p-PKA/PKA, p-AKT/AKT, p-CREB/CREB, p-P38/P38, and p-AMPK/AMPK, downregulation of DIO2 and β1AR, and upregulation of glycosylated β3AR, GLUT4, and adiponectin. At basal conditions, glycerol release was higher for M+T1AM cells than M cells, without any significant differences in basal glucose uptake. Notably, in M+T1AM cells, adrenergic agonists failed to activate PKA and lipolysis and to increase ATP level, but the glucose uptake in response to insulin exposure was more pronounced than in M cells. In conclusion, our results suggest that BAs conditioning with T1AM promote a catabolic condition promising to fight obesity and insulin resistance.
The Role of IL-6 in Skin Fibrosis and Cutaneous Wound Healing
Authors: Johnson Blair Z. , Stevenson Andrew W. , Prêle Cecilia M. , Fear Mark W. , Wood Fiona M.
Published: May 2020

The timely resolution of wound healing is critical for restoring the skin as a protective barrier. The switch from a proinflammatory to a reparative microenvironment must be tightly regulated. Interleukin (IL)-6 is a key modulator of the inflammatory and reparative process: it is involved in the differentiation, activation, and proliferation of leukocytes, endothelial cells, keratinocytes, and fibroblasts. This review examines the role of IL-6 in the healing of cutaneous wounds, and how dysregulation of IL-6 signaling can lead to either fibrosis or a failure to heal. The role of an IL-6/TGF-β feedback loop is discussed in the context of fibrogenesis, while IL-6 expression and responses in advanced age, diabetes, and obesity is outlined regarding the development of chronic wounds. Current research on therapies that modulate IL-6 is explored. Here, we consider IL-6′s diverse impact on cutaneous wound healing.
Orthosiphon stamineus Standardized Extract Reverses Streptozotocin-Induced Alzheimer’s Disease-Like Condition in a Rat Model
Authors: Retinasamy Thaarvena , Shaikh Mohd. Farooq , Kumari Yatinesh , Zainal Abidin Syafiq Asnawi , Othman Iekhsan
Published: May 2020

Alzheimer’s disease (AD) is a chronic neurodegenerative brain disease that is characterized by impairment in cognitive functioning as well as the presence of intraneuronal neurofibrillary tangles (NFTs) and extracellular senile plaques. There is a growing interest in the potential of phytochemicals to improve memory, learning, and general cognitive abilities. The Malaysian herb Orthosiphon stamineus is a traditional remedy that possesses anti-inflammatory, anti-oxidant, and free-radical scavenging abilities, all of which are known to protect against AD. Previous studies have reported that intracerebroventricular (ICV) administration of streptozotocin (STZ) mimics a condition similar to that observed in AD. This experiment thus aimed to explore if an ethanolic leaf extract of O. stamineus has the potential to be a novel treatment for AD in a rat model and can reverse the STZ- induced learning and memory dysfunction. The results of this study indicate that O. stamineus has the potential to be potentially effective against AD-like condition, as both behavioral models employed in this study was observed to be able to reverse memory impairment. Treatment with the extract was able to decrease the up-regulated expression levels of amyloid precursor protein (APP), microtubule associated protein tau (MAPT), Nuclear factor kappa-light-chain-enhancer of activated B cells (NFᴋB), glycogen synthase kinase 3 alpha (GSK3α), and glycogen synthase kinase 3 beta (GSK3β) genes indicating the extract’s neuroprotective ability. These research findings suggest that the O. stamineus ethanolic extract demonstrated an improved effect on memory, and hence, could serve as a potential therapeutic target for the treatment of neurodegenerative diseases such as AD.
Neuromodulation for Intractable Pain
Authors: Green Alexander L. , Aziz Tipu Z.
Published: May 2020

Over 7% of the Western population suffer from intractable pain and despite pharmacotherapy, many patients’ pain is refractory [...]
The Source of Palm Orientation Errors in the Signing of Children with ASD: Imitative, Motoric, or Both?
Authors: Shield Aaron , Igel Megan , Randall Kristina , Meier Richard P.
Published: May 2020

Palm orientation reversal errors (e.g., producing the ‘bye-bye’ gesture with palm facing inward rather than outward as is customary in American culture) have been documented in the signing of deaf and hearing children with autism spectrum disorder (ASD) and in the imitation of gestures by signing and non-signing children with ASD. However the source of these unusual errors remains opaque. Given that children with ASD have documented difficulties with both imitation and motor skills, it is important to clarify the nature of these errors. Here we present a longitudinal case study of a single child with ASD, a hearing, signing child of Deaf parents. Samples of the child’s signing were analyzed at ages 4;11, 6;2, 10;2, and 14;11. Lexical signs and fingerspelled letters were coded for the four parameters of sign articulation (handshape, location, movement, and palm orientation). Errors decreased for handshape, location, and movement after age 4;11, but increased on palm orientation from 4;11 and remained high, exceeding 55% of signs by 14;11. Fingerspelled letters contained a large proportion of 180-degree reversals, which suggest an origin in imitation differences, as well as midline-facing errors, suggestive of a motor origin. These longitudinal data suggest that palm orientation errors could be rooted in both imitation differences and motoric difficulties.
7,8-Dihydroxyflavone Enhances Cue-Conditioned Alcohol Reinstatement in Rats
Authors: Hogarth Samuel J. , Djouma Elvan , van den Buuse Maarten
Published: May 2020

Alcohol use disorder (AUD) is a detrimental disease that develops through chronic ethanol exposure. Reduced brain-derived neurotrophic factor (BDNF) expression has been associated with AUD and alcohol addiction, however the effects of activation of BDNF signalling in the brain on voluntary alcohol intake reinstatement and relapse are unknown. We therefore trained male and female Sprague Dawley rats in operant chambers to self-administer a 10% ethanol solution. Following baseline acquisition and progressive ratio (PR) analysis, rats were split into drug and vehicle groups during alcohol lever extinction. The animals received two weeks of daily IP injection of either the BDNF receptor, TrkB, agonist, 7,8-dihydroxyflavone (7,8-DHF), or vehicle. During acquisition of alcohol self-administration, males had significantly higher absolute numbers of alcohol-paired lever presses and a higher PR breakpoint. However, after adjusting for body weight, the amount of ethanol was not different between the sexes and the PR breakpoint was higher in females than males. Following extinction, alcohol-primed reinstatement in male rats was not altered by pretreatment with 7,8-DHF when adjusted for body weight. In contrast, in female rats, the weight-adjusted potential amount of ethanol, but not absolute numbers of active lever presses, was significantly enhanced by 7,8-DHF treatment during reinstatement. Analysis of spontaneous locomotor activity in automated photocell cages suggested that the effect of 7,8-DHF was not associated with hyperactivity. These results suggest that stimulation of the TrkB receptor may contribute to reward craving and relapse in AUD, particularly in females.
Postnatal Ethanol-Induced Neurodegeneration Involves CB1R-Mediated β-Catenin Degradation in Neonatal Mice
Authors: Subbanna Shivakumar , Basavarajappa Balapal S.
Published: May 2020

Alcohol consumption by pregnant women may produce neurological abnormalities that affect cognitive processes in children and are together defined as fetal alcohol spectrum disorders (FASDs). However, the molecular underpinnings are still poorly defined. In our earlier studies, we found that ethanol exposure of postnatal day 7 (P7) mice significantly induced widespread neurodegeneration mediated via endocannabinoids (eCBs)/cannabinoid receptor type 1 (CB1R). In the current study, we examined changes in the β-catenin protein levels that are involved in the regulation of neuronal function including neuronal death and survival. We found that moderate- and high-dose postnatal ethanol exposure (PEE) significantly reduced active-β-catenin (ABC) (non-phosphorylated form) protein levels in the hippocampus (HP) and neocortex (NC). In addition, we found that moderate- and high-dose PEE significantly increased the phosphorylated-β-catenin (p-β-catenin)/ABC ratios in the HP and NC. Antagonism/null mutation of CB1R before PEE to inhibit CC3 production mitigated the loss of ABC protein levels. Collectively, these findings demonstrated that the CB1R/β-catenin signaling mechanism causes neurodegeneration in neonatal mouse brains following PEE.
Bioenergy Crisis in Coronavirus Diseases?
Authors: Dutta Anirban , Das Abhijit , Kondziella Daniel , Stachowiak Michal
Published: May 2020

Coronavirus disease (COVID-19) has been declared as a pandemic by the World Health Organization (WHO) [...]
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing
Authors: Klados Manousos A. , Konstantinidi Panagiota , Dacosta-Aguayo Rosalia , Kostaridou Vasiliki-Despoina , Vinciarelli Alessandro , Zervakis Michalis
Published: May 2020

Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.
Comparing Psychopathological Symptoms in Portuguese Football Fans and Non-fans
Authors: Leite Ângela , Ramires Ana , Costa Rui , Castro Filipa , Sousa Hélder Fernando Pedrosa e , Vidal Diogo Guedes , Dinis Maria Alzira Pimenta
Published: May 2020

The present study aims to characterize football fans and non-fans and to compare their psychopathological symptoms with the latest normative values for the Portuguese population from Canavarro in 2007. Results showed that football fans and non-fans are mostly male, have an affective relationship, are childless, have secondary education or a high degree, and are employed or students; fans are more likely to be male, dating, unemployed, to have elementary education and be younger than non-fans. Football fans present significantly higher psychopathological symptoms than non-fans in somatization, interpersonal sensitivity, anxiety, hostility, paranoid ideation and psychoticism and all psychopathological indexes. Football fans present values very close to those of populations with emotional distress in hostility and are above the mean of the general population in obsession–compulsion, hostility, paranoid ideation and psychoticism.
Expendable Graphite as an Efficient Flame-Retardant for Novel Partial Bio-Based Rigid Polyurethane Foams
Authors: M. de Souza Felipe , Choi Jonghyun , Bhoyate Sanket , Kahol Pawan K. , Gupta Ram K.
Published: May 2020

The rigid polyurethane foam (PU) is a versatile material, used especially for construction and household applications. The current situation demands a facile, cost-efficient, and greener approach for developing the polyurethanes from bio-derived materials. In this study, we present a novel bio-polyol synthesized using carvone, an extract from caraway, spearmint, or dill seeds via facile thiol-ene reaction. Our one-step reaction uses a UV irradiation to allow the room temperature conversion of the carvone to a high purity bio-polyol, as confirmed from the standard analytical characterizations. The hydroxyl number of 365 mg KOH/g close to its theoretical limit confirms the high conversion yield of the polyol for rigid PU synthesis. To overcome the flammability issues in PU, expandable graphite (EG) powder was used as an additive flame-retardant during the synthesis step. The resulting foams with EG maintained the uniform closed cell structure (>95%) with a high compression strength of 175 kPa. The addition of EG in PU results in the formation of a protective char layer during the flammability test and reduces the weight loss from 40.70% to 3.55% and burning time from 87 to 11 s. Our results confirm that the carvone-based polyol can be a novel alternative to the petroleum polyols for an industrial-scale application.