bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Advances in Plant Growth Promoting Bacteria Alleviating Salt Stress for Sustainable Agriculture
Authors: Mokrani Slimane , Nabti El-hafid , Cruz Cristina
Published: October 2020

Humanity in the modern world is confronted with diverse problems at several levels. The environmental concern is probably the most important as it threatens different ecosystems, food, and farming as well as humans, animals, and plants. More specifically, salinization of agricultural soils is a global concern because of on one side, the permanent increase of the areas affected, and on the other side, the disastrous damage caused to various plants affecting hugely crop productivity and yields. Currently, great attention is directed towards the use of Plant Growth Promoting Bacteria (PGPB). This alternative method, which is healthy, safe, and ecological, seems to be very promising in terms of simultaneous salinity alleviation and improving crop productivity. This review attempts to deal with different aspects of the current advances concerning the use of PGPBs for saline stress alleviation. The objective is to explain, discuss, and present the current progress in this area of research. We firstly discuss the implication of PGPB on soil desalinization. We present the impacts of salinity on crops. We look for the different salinity origin and its impacts on plants. We discuss the impacts of salinity on soil. Then, we review various recent progress of hemophilic PGPB for sustainable agriculture. We categorize the mechanisms of PGPB toward salinity tolerance. We discuss the use of PGPB inoculants under salinity that can reduce chemical fertilization. Finally, we present some possible directions for future investigation. It seems that PGPBs use for saline stress alleviation gain more importance, investigations, and applications. Regarding the complexity of the mechanisms implicated in this domain, various aspects remain to be elucidated.
Optimization of Soil to Fly-Ash Mix Ratio for Enhanced Engineering Properties of Clayey Sand for Subgrade Use
Authors: Karim M. A. , Sami Hassan Ahmed , Kaplan Adam
Published: October 2020

A laboratory investigation was carried out to determine the optimum soil to fly ash mix ratio to enhance the engineering properties of clayey sand that can potentially be used as a road subgrade. Grain size distribution and Atterberg limits tests were conducted to classify the soil and to study the effects of the fly ash on the soil plasticity. The Proctor test was conducted to determine the optimum moisture content and maximum dry density of soil-fly-ash mixtures with arbitrarily selected 0%, 40%, 50%, and 60% fly ash content. A higher percentage was selected to find the highest optimum fly ash content to maximize the beneficial use. Unconfined compression and consolidation tests were conducted with air-dry arbitrarily selected curing periods of 0, 2, 8, and 28 days to determine the strength and to predict the settlement and the volume change behavior. It can be concluded from the trend analysis that a fly ash content range of 32–50% appeared to be optimum that is expected to perform better as subgrade materials for a curing period range of 16–19 days. However, experimental data showed a fly ash content of 50% was the optimum for a curing period of 8 days. The settlement and the volume change behavior improved at least 44% with increased fly ash content.
Assessment and Optimization of a Clean and Healthier Fusion Welding Procedure for Rebar in Building Structures
Authors: Ferreiro-Cabello Javier , Fraile-Garcia Esteban , Lara-Santillán Pedro María , Mendoza-Villena Montserrat
Published: October 2020

The welding procedure for reinforcing bars used in reinforced concrete takes place at fixed industrial installations. The welders must ensure that the reinforcing bar positions are maintained throughout all stages leading up to concreting. Fusion welding offers an alternative procedure that entails fewer risks for the workers’ health. In this study, we aimed to determine the minimum parameters for the intensity, pressure, and time necessary to ensure the level of performance required by this assembly welding. A total of 2160 joints were manufactured, comprising the different thicknesses. For each thickness measurement, 45 different combinations of parameters were applied. The joints were evaluated based on the resulting dimensions and on the failure force necessary to break the joint. For different thicknesses, the breaking loads were over 7350 N. In addition, the welding cycle times did not exceed three seconds. Based on the results obtained in this study, we concluded that assembling rebar (manufacturing reinforcing bars) by electrical resistance welding is a viable option. This method is fast, safe, and clean, and reduces the risks to the workers’ health.
Recommender Systems and Collaborative Filtering
Authors: Ortega Fernando , González-Prieto Ángel
Published: October 2020

Recommender Systems (RSs) have become an essential tool for the information society [...]
Effect of Harvest Age on Total Phenolic, Total Anthocyanin Content, Bioactive Antioxidant Capacity and Antiproliferation of Black and White Glutinous Rice Sprouts
Authors: So Visessakseth , Pocasap Piman , Sutthanut Khaetthareeya , Sethabouppha Benjabhorn , Thukhammee Wipawee , Wattanathorn Jintanaporn , Weerapreeyakul Natthida
Published: October 2020

Black (cv. BGR) and white (cv. RD6) glutinous rice sprouts from fertilizer- and pesticide-free farm in Khon Kaen province, Thailand were investigated for antioxidation and antiproliferative activity. Three different ages of rice sprouts were collected and prepared as the extract. BGR exerted higher antioxidant capacity than RD6 based on total phenolic (TPC) and total anthocyanin contents (TAC), DPPH, and FRAP assays. BGR at 10–15 days contained the highest TPC (29.72 ± 1.42 mg gallic acid equivalent/g extract) and reducing power (2.22 ± 0.014 mmole FeSO4/g extract). BGR at 20–25 days contained the highest TAC (0.86 ± 0.096 equivalence of cyanidin-3-glucoside/g extract) and DPPH radical scavenging activity (IC50 = 231.09 ± 12.99 μg/mL). Antiproliferative activity of the extracts was evaluated in the human T-lymphocyte (Jurkat), hepatocellular carcinoma (HepG2), colorectal carcinoma (HCT116), melanoma (SK-MEL-2) and noncancerous cells (Vero) by neutral red assay. BGR showed the most selective antiproliferation against Jurkat cells, by inducing apoptosis, and caspase 3/7 activity. BGR at 200 μg/mL from all ages significantly decreased ROS using DCFH-DA and increased endogenous glutathione levels in Jurkat cells compared to the control (p < 0.05). The higher antiproliferation of BGR than RD6 was via its antioxidation capacity and attributed to its higher phenolic and anthocyanin contents. BGR sprout is a potential source of biologically active substances good for wellness and health benefits.
Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints
Authors: Rodrigues Isabel , Rodrigues Nuno , Marx Ítala M. G. , Veloso Ana C. A. , Ramos Ana Cristina , Pereira José Alberto , Peres António M.
Published: October 2020

Sweet cherry is highly appreciated by its characteristic flavor, which conditions the consumer’s preference. In this study, four sweet cherry cultivars (Durona, Lapins, Summit, and Van cultivars) were characterized according to biometric (fruit and stone weights, length, maximum and minimum diameters, pulp/stone mass ratio), physicochemical (CIELAB color, penetration force, titratable acidity, and total soluble solids), and potentiometric profiles (recorded by a lab-made electronic tongue with lipid polymeric membranes). Biometric and physicochemical data were significantly cultivar-dependent (p-value < 0.0001, one-way ANOVA). Summit cherries had higher masses and dimensions. Lapins cherries had the highest penetration force values having, together with Summit cherries, the highest CIELAB values. Van cherries showed the highest total soluble solids contents. No significant differences were found for fruits’ acidity (similar titratable acidities). The possibility of discriminating cherry cultivars was also evaluated using a linear discriminant analysis/simulated-annealing algorithm. A discriminant model was established based on nine non-redundant biometric-physicochemical parameters (using a low-level data fusion), with low sensitivity (75 ± 15% for the repeated K-fold cross-validation). On the contrary, a discriminant model, based on the potentiometric fingerprints of 11 selected sensors, allowed a better discrimination, with sensitivities of 88 ± 7% for the repeated K-fold cross-validation procedure. Thus, the electronic tongue could be used as a practical tool to discriminate cherry cultivars and, if applied by fruit traders, may reduce the risk of mislabeling, increasing the consumers’ confidence when purchasing this high-value product.
Gamifying Massive Online Courses: Effects on the Social Networks and Course Completion Rates
Authors: de Marcos-Ortega Luis , Garcia-Cabot Antonio , Garcia-Lopez Eva , Ramirez-Velarde Raul , Teixeira Antonio , Martínez-Herráiz José-Javier
Published: October 2020

This paper analyzes the effects of gamification in the social network of a massive online course. An educational social-networking platform gathered information about the contributions of participants and about the social networks that were formed during the course. A gamification layer with three game elements (points, badges, and leaderboard) was then implemented in the online learning platform. Social network analysis (SNA) and principal component analysis (PCA) were used to analyze the differences between a treatment and a comparison group (N = 591 and N = 427), using a set of 20 variables for each participant which quantified contributions to the learning platform as well as position and influence in the social network. The results of SNA show that gamification influences the structure of the social network of the course. The results also suggest that the variables cluster similarly for each group and that the linear combination of variables called the first component (F1) is a good descriptor of students’ work and position in the network. F1 can be used to build predictive models of course completion. The models show that the probability of passing the course increases more rapidly in the treatment (gamified) group.
Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions
Authors: Pham Minh Tuan , Kim Jong-Myon , Kim Cheol Hong
Published: October 2020

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.
Corrosion Inhibition Performances of Imidazole Derivatives-Based New Ionic Liquids on Carbon Steel in Brackish Water
Authors: Zunita Megawati , Wahyuningrum Deana , Buchari  , Bundjali Bunbun , Wenten I Gede , Boopathy Ramaraj
Published: October 2020

In this study, imidazole derivative-based new ionic liquids were investigated as corrosion inhibitors. These new ionic liquids (ILs) are 1,3-dipropyl-2-(2-propoxyphenyl)-4,5-diphenylimidazole iodide (IL1) and 1,3-dibutyl-2-(2-butoxyphenyl)-4,5-diphenylimidazole iodide (IL2). The corrosion inhibition effects of two new ILs were observed on carbon steel in brackish water media (1% NaCl solution). Carbon steel coupons were exposed to 1% NaCl solution with various concentrations of ILs. Corrosion inhibition effects were tested by the electrochemical impedance spectroscopy (EIS) method and the Tafel method at various temperatures ranging from 25 °C to 55 °C. The results showed that ILs have potential as corrosion inhibitors and the adsorption mechanisms of IL1 and IL2 on carbon steel surfaces were also determined, which followed the Langmuir adsorption isotherm model. Acquisition of ∆Gads values of IL1 and IL2 were −35.04 and −34.04 kJ/mol, respectively. The thermodynamic data of the ILs show that semi-chemical and or physical adsorptions occurred on carbon steel surfaces.
Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances
Authors: Recio-Colmenares Roxana , Gurubel-Tun Kelly Joel , Zúñiga-Grajeda Virgilio
Published: October 2020

In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.
Salmonid Antibacterial Immunity: An Aquaculture Perspective
Authors: Semple Shawna L. , Dixon Brian
Published: October 2020

The aquaculture industry is continuously threatened by infectious diseases, including those of bacterial origin. Regardless of the disease burden, aquaculture is already the main method for producing fish protein, having displaced capture fisheries. One attractive sector within this industry is the culture of salmonids, which are (a) uniquely under pressure due to overfishing and (b) the most valuable finfish per unit of weight. There are still knowledge gaps in the understanding of fish immunity, leading to vaccines that are not as effective as in terrestrial species, thus a common method to combat bacterial disease outbreaks is the use of antibiotics. Though effective, this method increases both the prevalence and risk of generating antibiotic-resistant bacteria. To facilitate vaccine design and/or alternative treatment efforts, a deeper understanding of the teleost immune system is essential. This review highlights the current state of teleost antibacterial immunity in the context of salmonid aquaculture. Additionally, the success of current techniques/methods used to combat bacterial diseases in salmonid aquaculture will be addressed. Filling the immunology knowledge gaps highlighted here will assist in reducing aquaculture losses in the future.
Monitoring the Redox Status in Multiple Sclerosis
Authors: Tanaka Masaru , Vécsei László
Published: October 2020

Worldwide, over 2.2 million people suffer from multiple sclerosis (MS), a multifactorial demyelinating disease of the central nervous system. MS is characterized by a wide range of motor, autonomic, and psychobehavioral symptoms, including depression, anxiety, and dementia. The blood, cerebrospinal fluid, and postmortem brain samples of MS patients provide evidence on the disturbance of reduction-oxidation (redox) homeostasis, such as the alterations of oxidative and antioxidative enzyme activities and the presence of degradation products. This review article discusses the components of redox homeostasis, including reactive chemical species, oxidative enzymes, antioxidative enzymes, and degradation products. The reactive chemical species cover frequently discussed reactive oxygen/nitrogen species, infrequently featured reactive chemicals such as sulfur, carbonyl, halogen, selenium, and nucleophilic species that potentially act as reductive, as well as pro-oxidative stressors. The antioxidative enzyme systems cover the nuclear factor erythroid-2-related factor 2 (NRF2)-Kelch-like ECH-associated protein 1 (KEAP1) signaling pathway. The NRF2 and other transcriptional factors potentially become a biomarker sensitive to the initial phase of oxidative stress. Altered components of the redox homeostasis in MS were discussed in search of a diagnostic, prognostic, predictive, and/or therapeutic biomarker. Finally, monitoring the battery of reactive chemical species, oxidative enzymes, antioxidative enzymes, and degradation products helps to evaluate the redox status of MS patients to expedite the building of personalized treatment plans for the sake of a better quality of life.
A Scalable High-Throughput Deposition and Screening Setup Relevant to Industrial Electrocatalysis
Authors: Becker René , Weber Katharina , Pfeiffer Tobias V. , Kranendonk Jan van , Schouten Klaas Jan
Published: October 2020

The identification and optimization of electrode materials is of great importance in the study of (flow and solid state) batteries, industrial electrocatalysis and analytical devices such as sensors. To identify useful materials from a virtually unbound set of metals, alloys and semiconductors, high-throughput techniques are of vital importance. In this paper we present a high-throughput setup that consists of 64 parallel plate electrochemical flow cells, with the anode and cathode compartments separated by a membrane. These cells can be operated sequentially or batch-wise in parallel, using a matrix-addressing approach that allows for scaling up to larger electrode matrices with minimal instrumentation cost. The setup was validated for the preparation and screening of electrode materials under hydrodynamic conditions at industrially relevant current densities, which showed that it could be used to identify optimal catalysts and the robustness of catalyst preparation. The results of the small scale experiments followed theoretical predictions and were used to optimize larger scale experiments.
Aqueous Miscible Organic LDH Derived Ni-Based Catalysts for Efficient CO2 Methanation
Authors: Wang Ziling , Huang Liang , Reina Tomas , Efstathiou Angelos , Wang Qiang
Published: October 2020

Converting CO2 to methane via catalytic routes is an effective way to control the CO2 content released in the atmosphere while producing value-added fuels and chemicals. In this study, the CO2 methanation performance of highly dispersed Ni-based catalysts derived from aqueous miscible organic layered double hydroxides (AMO-LDHs) was investigated. The activity of the catalyst was found to be largely influenced by the chemical composition of Ni metal precursor and loading. A Ni-based catalyst derived from AMO-Ni3Al1-CO3 LDH exhibited a maximum CO2 conversion of 87.9% and 100% CH4 selectivity ascribed to both the lamellar catalyst structure and the high Ni metal dispersion achieved. Moreover, due to the strong Ni metal–support interactions and abundant oxygen vacancy concentration developed, this catalyst also showed excellent resistance to carbon deposition and metal sintering. In particular, high stability was observed after 19 h in CO2/H2 reaction at 360 °C.
A Bioorthogonally Synthesized and Disulfide-Containing Fluorescence Turn-On Chemical Probe for Measurements of Butyrylcholinesterase Activity and Inhibition in the Presence of Physiological Glutathione
Authors: Gong Ming-Mao , Dai Chia-Yen , Severance Scott , Hwang Chi-Ching , Fang Bo-Kai , Lin Heng-Bo , Huang Chien-Hui , Ong Chi-Wi , Wang Jeh-Jeng , Lee Pei-Lun , Wang Tzu-Pin
Published: October 2020

Butyrylcholinesterase (BChE) is a biomarker in human blood. Aberrant BChE activity has been associated with human diseases. Here we developed a fluorescence resonance energy transfer (FRET) chemical probe to specifically quantify BChE activity in serum, while simultaneously discriminating against glutathione (GSH). The FRET chemical probe 11 was synthesized from a key trifunctional bicyclononyne exo-6 and derivatives of 5-(2-aminoethylamino)-1-naphthalenesulfonic acid (EDANS) and 4-[4-(dimethylamino)phenylazo]benzoic acid (DABCYL). EDANS fluorescence visualization and kinetic analysis of 11 in the presence of diverse compounds confirmed the outstanding reactivity and specificity of 11 with thiols. The thiol-dependent fluorescence turn-on property of 11 was attributed to a general base-catalyzed SN2 nucleophilic substitution mechanism and independent of metal ions. Moreover, all thiols, except GSH, reacted swiftly with 11. Kinetic studies of 11 in the presence of covalently modified GSH derivatives corroborated that the steric hindrance of 11 imposing on GSH was the likely cause of the distinguished reactivity. Since GSH commonly interferes in assays measuring BChE activity in blood samples, the 11-based fluorescent assay was employed to directly quantify BChE activity without GSH interference, and delivered a linear range of 4.3–182.2 U L−1 for BChE activity with detection limit of 4.3 U L−1, and accurately quantified serum BChE activity in the presence of 10 μM GSH. Finally, the 11-based assay was exploited to determine Ki of 5 nM for tacrine inhibition on BChE catalysis. We are harnessing the modulated characteristics of 6 to synthesize advanced chemical probes able to more sensitively screen for BChE inhibitors and quantify BChE activity in serum.
Study on Co-Crystallization of LCZ696 Using In Situ ATR-FTIR and Imaging
Authors: Liu Xiao Juan , Zhang Yang , Wang Xue Zhong
Published: October 2020

In situ ATR-FTIR spectroscopy and imaging and image analysis were applied to the study of the multicomponent co-crystallization process involving S-valsartan and sacubitril in which LCZ696 crystals were formed. LCZ696 is a combination drug for use in heart failure that was approved by the FDA in 2015 following development by Novartis Pharmaceuticals. Though much work was reported on LCZ696 about its pharmacokinetic and pharmacodynamic effects in the evaluation and clinical testing, less attention was paid to study on the co-crystallization process. LCZ696 crystals have shown difficulties in filtration mainly due to the small particle size. In this work, LCZ696 crystals were prepared successfully by S-valsartan and sacubitril, and characterized by SEM, XRPD, TG-DSC and ATR-FTIR. ATR-FTIR and imaging and image analysis were used to monitoring solution concentration and investigating the co-crystallization mechanism. It revealed that the nucleation process was very slow compared with the transformation process, which is indication that the co-crystallization was controlled by nucleation. LCZ696 crystals are composed of very thin hexagonal plates, which seems indicating that LCZ696 crystals grow mainly in two size dimensions. Stirrer speed and crystal seeds were found to have noticeable effect on the induction time, transformation time and crystal size distribution. The Johnson-Mehl-Avrami equation was found to be able to describe the co-crystallization process.
Invasive Pulmonary Aspergillosis in Patients with SARS-CoV-2 Infection: A Systematic Review of the Literature
Authors: Apostolopoulou Anna , Esquer Garrigos Zerelda , Vijayvargiya Prakhar , Lerner Alexis Hope , Farmakiotis Dimitrios
Published: October 2020

In this systematic review, we investigate the epidemiology, pathogenesis, risk factors, clinical manifestations, diagnosis and treatment of COVID-19-associated pulmonary aspergillosis (CAPA). We identified 85 cases from 22 studies. The frequency of CAPA is currently unknown but ranges between <5% to >30% in different case series; the possibility of colonization rather than invasive disease is the most important confounder. The vast majority of patients with CAPA did not have any of the classic host risk factors, such as immunosuppression from organ transplant or neutropenia, although a significant proportion (46%) had received corticosteroids. Age, pulmonary comorbidities and male sex were associated with higher mortality. Patients treated with voriconazole had numerically lower case-fatality rate. Clinical vigilance for CAPA is advisable in critically ill patients with COVID-19 who are not improving, even those who do not meet classic host criteria for invasive mycoses, especially if they are receiving corticosteroids. A thorough, multi-faceted diagnostic work-up and early initiation of a mold-active triazole may be lifesaving. Further research studies using standardized, uniform definitions of invasive disease and colonization are urgently needed.
Approximate Learning of High Dimensional Bayesian Network Structures via Pruning of Candidate Parent Sets
Authors: Guo Zhigao , Constantinou Anthony C.
Published: October 2020

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for speed, where the aim is to minimise the loss in accuracy and maximise the gain in speed. While some approximate algorithms are optimised to handle thousands of variables, these algorithms may still be unable to learn such high dimensional structures. Some of the most efficient score-based algorithms cast the structure learning problem as a combinatorial optimisation of candidate parent sets. This paper explores a strategy towards pruning the size of candidate parent sets, and which could form part of existing score-based algorithms as an additional pruning phase aimed at high dimensionality problems. The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
Authors: Jiang Bin , Qu Linbo , Huang Yufei , Zheng Yifei , You Li , Wang Wenjin
Published: October 2020

Herein, we focus on energy efficiency optimization for massive multiple-input multiple-output (MIMO) downlink secure multicast transmission exploiting statistical channel state information (CSI). Privacy engineering in the field of communication is a hot issue under study. The common signal transmitted by the base station is multicast transmitted to multiple legitimate user terminals in our system, but an eavesdropper might eavesdrop this signal. To achieve the energy efficiency utility–privacy trade-off of multicast transmission, we set up the problem of maximizing the energy efficiency which is defined as the ratio of the secure transmit rate to the power consumption. To simplify the formulated nonconvex problem, we use a lower bound of the secure multicast rate as the molecule of the design objective. We then obtain the eigenvector of the optimal transmit covariance matrix into a closed-form, simplifying the matrix-valued multicast transmission strategy problem into a power allocation problem in the beam domain. By utilizing the Minorize-Maximize method, an iterative algorithm is proposed to decompose the secure energy efficiency optimization problem into a sequence of iterative fractional programming subproblems. By using Dinkelbach’s transform, each subproblem becomes an iterative problem with the concave objective function, and it can be solved by classical convex optimization. We guarantee the convergence of the two-level iterative algorithm that we propose. Besides, we reduce the computational complexity of the algorithm by substituting the design objective with its deterministic equivalent. The numerical results show that the approach we propose performs well compared with the conventional methods.
DoubleDeck: Decoupling Complex Control Logic of Network Protocols to Facilitate Efficient Hardware Implementation
Authors: Yang Yi , Fu Wenwen , Yan Jinli , Tang Lu , Sun Zhigang
Published: October 2020

A hierarchical method is often used to simplify the design of complex logic. However, when the hierarchical method is used to implement protocol control, there is no suitable decoupling method to divide the sophisticated protocol control into a series of small control logics. For this reason, this paper designs and implements the DoubleDeck model composed of a top state machine and a set of bottom state machines. The model divides the protocol control logic into protocol state conversion logic and processing logic based on hierarchical idea. Simultaneously, DoubleDeck also provides a bottom deck mapping mechanism (BDM) for all protocol control processing logic. Based on the BDM mechanism, developers can quickly implement the bottom state machine array. Next, we present the hardware design of DoubleDeck and prototype the time synchronization protocol on an FPGA array. The results show that DoubleDeck can be used as a design model to guide the implementation of complex protocol control logic. Compared with the finite state machine (FSM), DoubleDeck uses the BDM mechanism to implement three protocols can reduce the amount of code for developers by 30%.
Energy-Saving Research on New Type of LED Sensor Lamp with Low-Light Mode
Authors: Lee Chun-Te , Ho Ping-Tsan
Published: October 2020

In general, the sensor lamps in the corridors, stairwells, or toilets of buildings will change from completely dark to full brightness when someone passes by. It will make the human eyes feel very uncomfortable, and when the sensor lamp is completely dark, the whole corridor and stairwell will be dark, making women and children feel insecure at night. If the lighting is changed to be sensor-less, there is a serious problem of wasted energy. To solve this dilemma, we developed a new type of “LED sensor lamp with low-light mode” that changes the original “full dark mode” to “low-light mode”. As such, when someone approaches the sensor lamp, their eyes will not be uncomfortable with the momentary illumination. Furthermore, when no one passes by, the sensor lamp will stay in low-light mode, so that people returning home at night no longer have to go through dark corridors, thereby achieving safety, aesthetics, and energy-saving purposes. This new sensor lamp’s power consumption in low-light mode is only 1/10 of the high-light mode, but its brightness can be up to half of the high-light mode, making it very suitable for parking lots, corridors, stairways, or toilets of buildings. It only requires the replacement of the lamp but not the original lamp socket, yet the basic brightness can be maintained. Take the general 15W T8 LED lamp (sensor-less) as an example: if it is replaced by this new type of sensor lamp, and the place where it is installed is rarely passed by people, the power saving rate will be as high as 90%. Assuming that there are 12 passers-by per hour, the saving rate is still 81%.
Predictors of Ambient Intelligence: An Empirical Study in Enterprises in Slovakia
Authors: Bolek Vladimír , Romanová Anita
Published: October 2020

Integration of innovative pervasive technologies into the surrounding environment creates ambient intelligence which has become the subject of several scientific and academic considerations. In particular, the pervasive technology is based on Internet of Things (IoT) and Industrial Internet of Things (IIoT). Ambient intelligence may be viewed from several perspectives: a technological, multi-aspect and visionary point of view. Several predictors influence the level of ambient intelligence. The aim is to identify significant predictors influencing ambient intelligence and simultaneously determine their impact on the achieved level of ambient intelligence of the company. In the research methodology, we acknowledge the research presumptions, the research question and research hypotheses, the measurement instrument, obtained data and methods used in accordance with the research model. The scientific article presents the results of an empirical study conducted on 206 enterprises, focusing on the examining of the influence of selected predictors on the level of ambient intelligence in enterprises in the Slovak Republic. It examines not only the influence of selected predictors on the level of ambient intelligence, but also their mutual interaction. We found that these activities even build ambient intelligence on their own, although in interaction with each other, their effect on the level of ambient intelligence begins to fade. Therefore, it is necessary to opt for their combination appropriately. Finally, we present the limits of the research and suggestions on further directions of research in this field.
Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method
Authors: Ahmed Nizar , Dilmaç Fatih , Alpkocak Adil
Published: October 2020

This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.
Simulating Large-Scale 3D Cadastral Dataset Using Procedural Modelling
Authors: Tekavec Jernej , Lisec Anka , Rodrigues Eugénio
Published: October 2020

Geospatial data and information within contemporary land administration systems are fundamental to manage the territory adequately. 3D land administration systems, often addressed as 3D cadastre, promise several benefits, particularly in managing today’s complex built environment, but these are currently still non-existent in their full capacity. The development of any complex information and administration system, such as a land administration system, is time-consuming and costly, particularly during the phase of evaluation and testing. In this regard, the process of implementing such systems may benefit from using synthetic data. In this study, the method for simulating the 3D cadastral dataset is presented and discussed. The dataset is generated using a procedural modelling method, referenced to real cadastral data for the Slovenian territory and stored in a spatial database management system (DBMS) that supports storage of 3D spatial data. Spatial queries, related to 3D cadastral data management, are used to evaluate the database performance and storage characteristics, and 3D visualisation options. The results of the study show that the method is feasible for the simulation of large-scale 3D cadastral datasets. Using the developed spatial queries and their performance analysis, we demonstrate the importance of the simulated dataset for developing efficient 3D cadastral data management processes.
Assessing Quality of Life Inequalities. A Geographical Approach
Authors: Faka Antigoni
Published: October 2020

This study proposes an integrated methodology for evaluating and mapping quality of life (QoL) and the quality of a place as residence area, at local level. The QoL assessment was based on the development of composite criteria, using geographical variables that evaluate QoL, and geographic information systems. The composite criteria are related to the natural and the socioeconomic environment, the housing conditions, the infrastructure and services, and the cultural and recreational facilities. Each criterion was evaluated by a set of variables and each variable was weighted based on the residents’ preferences and the analytical hierarchy process. The criteria were also weighted and combined to assess overall QoL. The methodology was implemented in the Municipality of Katerini, Greece, and QoL mapping led to the zoning of the study area and the identification of areas with low and high QoL. The results revealed the highest level of overall QoL in three out of twenty-nine communities, which provide better housing conditions and access to public services and infrastructures, combining also qualitative natural environment, whereas five mountainous and remote communities scored the lowest level. Mapping QoL may support decision making strategies that target to improve human well-being, increase QoL levels and upgrade living conditions.
Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets
Authors: Song Ahram , Kim Yongil
Published: October 2020

Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets.
Characterization of the Complete Mitochondrial Genomes of Two Species with Preliminary Investigation on Phylogenetic Status of Zyginellini (Hemiptera: Cicadellidae: Typhlocybinae)
Authors: Zhou Xian , H. Dietrich Christopher H. , Huang Min
Published: October 2020

To explore the characteristics of mitogenomes and reveal phylogenetic relationships of the tribes of Zyginellini and Typhlocybini in Typhlocybinae, mitogenomes of two species of the Zyginellini, Parazyginella tiani and Limassolla sp., were sequenced. Mitogenomes of both species contain 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), two ribosomal RNA genes (rRNAs) and a large non-coding region (A + T-rich region). These characteristics are similar to other Membracoidea mitogenomes. All PCGs initiate with the standard start codon of ATN and terminate with the complete stop codon of TAA/G or with an incomplete T codon. All tRNAs have the typical clover-leaf structure, except trnS1 which has a reduced DHU arm and the acceptor stem of trnR is 5 or 6 bp in some species, an unusual feature here reported for the first time in Typhlocybinae. The A + T-rich region is highly variable in length and in numbers of tandem repeats present. Our analyses indicate that nad6 and atp6 exhibit higher evolutionary rates compared to other PCGs. Phylogenetic analyses by both maximum likelihood and Bayesian methods based on 13 protein-coding genes of 12 species of Typhlocybinae suggest that Zyginellini are paraphyletic with respect to Typhlocybini.
Development of Radiation-Tolerant HTS Magnet for Muon Production Solenoid
Authors: Ogitsu Toru , Iio Masami , Kawamura Naritoshi , Yoshida Makoto
Published: October 2020

Superconducting magnets are widely used in accelerator science applications. Muon production solenoids are applications that have recently attracted considerable public attention, after the approval of muon-related physics projects such as coherent muon to electron transition or muon-to-electron-conversion experiments. Based on its characteristics, muon production solenoids tend to be subjected to high radiation exposure, which results in a high heat load being applied to the solenoid magnet, thus limiting the superconducting magnet operation, especially for low-temperature superconductors such as niobium titanium alloy. However, the use of high-temperature superconductors may extend the operation capabilities owing to their functionality at higher temperatures. This study reviews the characteristics of high temperature superconductor magnets in high-radiation environments and their potential for application to muon production solenoids.
Inactivation of Dermatophytes Causing Onychomycosis and Its Therapy Using Non-Thermal Plasma
Authors: Lux Jaroslav , Dobiáš Radim , Kuklová Ivana , Litvik Radek , Scholtz Vladimír , Soušková Hana , Khun Josef , Mrázek Jakub , Kantorová Michaela , Jaworská Pavla , Prejdová Táňa , Šnupárková Jana , Hamal Petr , Julák Jaroslav
Published: October 2020

Onychomycosis is one of the most common nail disorders. Its current treatment is not satisfactorily effective and often causes adverse side effects. This study aims to determine the optimal conditions for non-thermal plasma (NTP) inactivation of the most common dermatophytes in vitro and to apply it in patient`s therapy. The in vitro exposure to NTP produced by negative DC corona discharge caused full inactivation of Trichophyton spp. if applied during the early growth phases. This effect decreased to negligible inactivation with the exposure applied six days after inoculation. In a group of 40 patients with onychomycosis, NTP therapy was combined with nail plate abrasion and refreshment (NPAR) or treatment with antimycotics. The cohort included 17 patients treated with NPAR combined with NTP, 11 patients treated with antimycotics and NTP, and 12 patients treated with NPAR alone. The combination of NPAR and NTP resulted in clinical cure in more than 70% of patients. The synergistic effect of NPAR and NTP caused 85.7% improvement of mycological cure confirmed by negative microscopy and culture of the affected nail plate. We conclude that NTP can significantly improve the treatment of onychomycosis.
Molecular and Biological Properties of Snakins: The Foremost Cysteine-Rich Plant Host Defense Peptides
Authors: Su Tao , Han Mei , Cao Dan , Xu Mingyue
Published: October 2020

Plant host defense peptides (HDPs), also known as antimicrobial peptides (AMPs), are regarded as one of the most prevalent barriers elaborated by plants to combat various infective agents. Among the multiple classes of HDPs, the Snakin class attracts special concern, as they carry 12 cysteine residues, being the foremost cysteine-rich peptides of the plant HDPs. Also, their cysteines are present at very highly conserved positions and arranged in an extremely similar way among different members. Like other plant HDPs, Snakins have been shown to exhibit strong antifungal and antibacterial activity against a wide range of plant pathogens. Moreover, they display diversified biological activities in many aspects of plant growth and the development process. This review is devoted to present the general characters of the Snakin class of plant HDPs, as well as the individual features of different Snakin family members. Specifically, the sequence properties, spatial structures, distributions, expression patterns and biological activities of Snakins are described. In addition, further detailed classification of the Snakin family members, along with their possible mode of action and potential applications in the field of agronomy and pathology are discussed.
Evolutionary Algorithms in Intelligent Systems
Authors: Milani Alfredo
Published: October 2020

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally difficult optimization problems [...]
Space–Time Radial Basis Function–Based Meshless Approach for Solving Convection–Diffusion Equations
Authors: Ku Cheng-Yu , Xiao Jing-En , Liu Chih-Yu
Published: October 2020

This article proposes a space–time meshless approach based on the transient radial polynomial series function (TRPSF) for solving convection–diffusion equations. We adopted the TRPSF as the basis function for the spatial and temporal discretization of the convection–diffusion equation. The TRPSF is constructed in the space–time domain, which is a combination of n–dimensional Euclidean space and time into an n + 1–dimensional manifold. Because the initial and boundary conditions were applied on the space–time domain boundaries, we converted the transient problem into an inverse boundary value problem. Additionally, all partial derivatives of the proposed TRPSF are a series of continuous functions, which are nonsingular and smooth. Solutions were approximated by solving the system of simultaneous equations formulated from the boundary, source, and internal collocation points. Numerical examples including stationary and nonstationary convection–diffusion problems were employed. The numerical solutions revealed that the proposed space–time meshless approach may achieve more accurate numerical solutions than those obtained by using the conventional radial basis function (RBF) with the time-marching scheme. Furthermore, the numerical examples indicated that the TRPSF is more stable and accurate than other RBFs for solving the convection–diffusion equation.
Martingale Approach to Derive Lundberg-Type Inequalities
Authors: Kuras Tautvydas , Sprindys Jonas , Šiaulys Jonas
Published: October 2020

In this paper, we find the upper bound for the tail probability Psupn⩾0∑I=1nξI>x with random summands ξ1,ξ2,… having light-tailed distributions. We find conditions under which the tail probability of supremum of sums can be estimated by quantity ϱ1exp{−ϱ2x} with some positive constants ϱ1 and ϱ2. For the proof we use the martingale approach together with the fundamental Wald’s identity. As the application we derive a few Lundberg-type inequalities for the ultimate ruin probability of the inhomogeneous renewal risk model.