bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty
Authors: Gómez-Guerrero Santiago , Ortiz Inocencio , Sosa-Cabrera Gustavo , García-Torres Miguel , Schaerer Christian E.
Published: December 2021

Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models.
Toward a Logic of the Organism: A Process Philosophical Consideration
Authors: Koutroufinis Spyridon A.
Published: December 2021

Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory.
Log Sequence Anomaly Detection Method Based on Contrastive Adversarial Training and Dual Feature Extraction
Authors: Wang Qiaozheng , Zhang Xiuguo , Wang Xuejie , Cao Zhiying
Published: December 2021

The log messages generated in the system reflect the state of the system at all times. The realization of autonomous detection of abnormalities in log messages can help operators find abnormalities in time and provide a basis for analyzing the causes of abnormalities. First, this paper proposes a log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. This method uses BERT (Bidirectional Encoder Representations from Transformers) and VAE (Variational Auto-Encoder) to extract the semantic features and statistical features of the log sequence, respectively, and the dual features are combined to perform anomaly detection on the log sequence, with a novel contrastive adversarial training method also used to train the model. In addition, this paper introduces the method of obtaining statistical features of log sequence and the method of combining semantic features with statistical features. Furthermore, the specific process of contrastive adversarial training is described. Finally, an experimental comparison is carried out, and the experimental results show that the method in this paper is better than the contrasted log sequence anomaly detection method.
Acute Burn Treatment and History of Drug and Alcohol Addiction: Treatment Outcomes and Opioid Use
Authors: Duraes Eliana F. R. , Hung Ya-Ching , Asif Mohammed , Modica Ashley , Sikorski Giulia , Hultman Charles S. , Caffrey Julie
Published: December 2021

Treating pain in burn patients with a history of opioid or drug abuse is challenging. There is no consensus on pain management for burn patients with a history of drug usage. Our aim was to study the association of previous drug addiction and the treatment of acute burn patients, focusing on daily morphine milligram equivalent (MME) requirements and outcomes. We compared patients with (group 1) and without (group 2) a drug addiction history who were admitted to an American Burn Association verified burn center using the Premier database from 2013 to 2018 (n = 3046). Primary outcome was daily MME usage. Secondary outcomes included mortality, expected mortality rate, length of stay (LOS), and number of surgeries. Linear regression was performed to predict MME usage. In total, 16.6% of patients had history of drug abuse. In unadjusted analysis, group 1 had more males (68.1% vs. 57.3%, p < 0.001) and was younger (median 47 vs. median 53, p < 0.001) compared to group 2. In the adjusted analysis, group 1 required 84.1 additional daily MME usage than group 2 (p < 0.01). Drug addiction was associated with an increased number of surgeries, LOS, and higher daily MME usage. Patients with a history of drug usage required almost 60 mg of additional oxycodone per day.
Higher Order Thinking by Setting and Debriefing Tasks in Dutch Geography Lessons
Authors: Krause Uwe , Béneker Tine , van Tartwijk Jan
Published: December 2021

Tasks are a powerful instrument for geography teachers, as they let students engage with the subject. To advance the cumulative learning of students, teachers have to make sure that students learn how to deal with complex and abstract knowledge structures. In the Netherlands, teachers face a dilemma when it comes to task setting: the intended curriculum aims for a considerable part at (parts of) higher order thinking, whereas the high-stakes exams have a clear focus on the use of thinking strategies. This paper explores the task setting and debriefing of Dutch geography teachers by analyzing twenty-three videotaped lessons in upper secondary education by using the Geography Task Categorization Framework. The results show that Dutch teachers mostly rely on textbooks when setting tasks. The focus lies on reproduction and the use of thinking strategies. Tasks aiming at (parts of) higher order thinking are barely used. Furthermore, teachers use tasks from previous high-stakes exams already used in an early stage of upper secondary education. In the debriefing of tasks, teachers move from simple and concrete to complex and abstract knowledge and vice versa. However, most of these movements aim at simplifying knowledge structures. In the observed lessons, curriculum aims at the level of (parts of) higher order thinking are not achieved. The evaluative rules as set by the high-stakes exams and the type of tasks offered by textbooks seem to be dominant.
Validation of a Platform for the Electrostatic Characterization of Textile
Authors: Tahir Hasan Riaz , Malengier Benny , Daele Didier Van , Langenhove Lieva Van
Published: December 2021

Floor covering samples of different thickness, pile height, pile design, materials, construction methods, and applied finishes were selected for electrostatic characterization with a standard plotter platform and a newly designed digital platform. There is an existing standard ISO 6356 in which the voltage generated by a human walking on the carpet is measured with human involvement under controlled conditions. A walking person performs the original test procedure to generate the electrostatic charge and manually calculates results. In contrast, the newly designed system does not require a person to calculate peaks and valleys for the generated electrostatic charges, which offers advantages in terms of accuracy, consistency, and reproducibility, and eliminates human error. The electronic platform is extended with an automated foot for a fully automated test, called “automatic mode”, that has a fixed capacitive and resistive circuit, in replace of human body resistance, and capacitance that varies from person to person and over time. The procedure includes both the old and new platforms, where the new platform is placed in a “human walking” mode to compare the two and validate the new device. Next, all the floor coverings are tested in automatic mode with the automated foot to compare and validate results. We conclude that the new testing device can fully characterize the electrostatic behavior of textile without the involvement of a human, which offers advantages in terms of accuracy, consistency, and reproducibility.
FPGA in the Loop Implementation for Observer Sliding Mode Control of DFIG-Generators for Wind Turbines
Authors: Alami Houda El , Bossoufi Badre , Motahhir Saad , Alkhammash Eman H. , Masud Mehedi , Karim Mohammed , Taoussi Mohammed , Bouderbala Manale , Lamnadi Mouna , El Mahfoud Mohammed
Published: December 2021

This paper presents a new contribution of the nonlinear control technique of electrical energy in a wind energy system. The nonlinear sliding mode technique used to control the powers of the DFIG-Generator is connected to the power grid by two converters (grid side and machine side). The proposed model is validated using tracking and robustness tests with a real wind speed. The control was developed under Matlab/Simulink, and the FPGA in the Loop technique was used to design the DFIG model. By employing a co-simulation, the purpose is to test the controller for the FPGA simulated model or system in its entirety. The results obtained by the cο-simulation show the efficiency of the proposed model in terms of speed and robustness with a rate THD = 0.95, and the proposed model of the sliding mode controller shows a significant improvement in the quality of energy produced by the wind system.
Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink
Authors: Surya Sumukh , Saldanha Cifha Crecil , Williamson Sheldon
Published: December 2021

The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.
Evaluation of Implementation of Biomass and Solar Resources by Energy Systems in the Coal-Mining Areas of Spain
Authors: Paredes-Sánchez Beatriz M. , Paredes-Sánchez José P. , García-Nieto Paulino José
Published: December 2021

Energy demand is steadily growing as society becomes more industrialised. Renewable energy sources (RES) have long been used for various applications by thermal energy systems in the European Union (EU). Biomass and solar energy represent important RES in the development of energy transition in some regions such as coal-mining areas of Europe. Bioenergy is a key renewable energy storage mechanism for solar energy which, when combined, can tackle many of the barriers to the use of solar energy. Against this background, this study evaluates the potential implementation of both biomass and solar energy for energy production in coal-mining areas in Spain as a direct alternative to coal. The shown methodology relies on a comprehensive analysis of existing resources and their conversion to thermal energy from a multi-parametric point of view. The obtained results show that the solar–biomass combination can be used for thermal energy systems as a challenging option. The theoretical total hybrid-modular systems if implemented in the study area are equivalent to 1165 MWth and supply thermal energy for 170,000 single-family houses.
Design and Implementation of an Intelligent Blade Pitch Control System and Stability Analysis for a Small Darrieus Vertical-Axis Wind Turbine
Authors: Abdalrahman Gebreel , Daoud Mohamed A. , Melek William W. , Lien Fue-Sang , Yee Eugene
Published: December 2021

A few studies have been conducted recently in order to improve the aerodynamic performance of Darrieus vertical-axis wind turbines with straight blades (H-type VAWTs). The blade pitch angle control is proposed to enhance the performance of H-type VAWTs. This paper aims to investigate the performance of an H-type VAWT in terms of its power output and self-starting capability using an intelligent blade pitch control strategy based on a multi-layer perceptron artificial neural network (MLP-ANN) method. The performance of the proposed blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., a hybrid controller). The dynamics of an H-type VAWT is mathematically modeled in a nonlinear state space for the stability analysis in the sense of Lyapunov. The effectiveness of the proposed pitch control system is validated by building an H-type VAWT prototype model that is extensively tested outdoors under different conditions for both fixed and variable pitch angle configurations. Results demonstrated that the blade-pitching technique enhanced the power output of an H-type VAWT by approximately 22%. The hybrid controller that used a high percentage of the MLP-ANN controller achieved a better control performance by reducing the overshoot of the control response at high rotor speeds.
Innovative Construction of the AFPM-Type Electric Machine and the Method for Estimation of Its Performance Parameters on the Basis of the Induction Voltage Shape
Authors: Smoleń Andrzej , Gołębiowski Lesław , Gołębiowski Marek
Published: December 2021

The article presents an innovative construction of the Axial Flux Permanent Magnet (AFPM) machine designed for generator performance, which provides the shape of induced voltage that enables estimation of the speed and rotational angle of the machine rotor. Design solutions were proposed, the aim of which is to limit energy losses as a result of the occurrence of eddy currents. The method of direct estimation of the value of the rotational speed and rotational angle of the machine rotor was proposed and investigated on the basis of the measurements of induced voltages and machine phase currents. The advantage of the machine is the utilization of simple and easy-to-use computational procedures. The acquired results were compared with the results obtained for estimation performed by using the Unscented Kalman Filter (UKF).
Comparison between Energy Simulation and Monitoring Data in an Office Building
Authors: Martin-Escudero Koldobika , Atxalandabaso Garazi , Erkoreka Aitor , Uriarte Amaia , Porta Matteo
Published: December 2021

One of the most important steps in the retrofitting process of a building is to understand its pre-retrofitting stage energy performance. The best choice for carrying this out is by means of a calibrated building energy simulation (BES) model. Then, the testing of different retrofitting solutions in the validated model allows for quantifying the improvements that may be obtained, in order to choose the most suitable solution. In this work, based on the available detailed building drawings, constructive details, building operational data and the data sets obtained on a minute basis (for a whole year) from a dedicated energy monitoring system, the calibration of an in-use office building energy model has been carried out. It has been possible to construct a detailed white box model based on Design Builder software. Then, comparing the model output for indoor air temperature, lighting consumption and heating consumption against the monitored data, some of the building envelope parameters and inner building inertia of the model were fine tuned to obtain fits fulfilling the ASHRAE criteria. Problems found during this fitting process and how they are solved are explained in detail. The model calibration is firstly performed on an hourly basis for a typical winter and summer week; then, the whole year results of the simulation are compared against the monitored data. The results show a good agreement for indoor temperature, lighting and heating consumption compared with the ASHRAE criteria for the mean bias error (MBE).
A Constant-Pressure Hydraulic PTO System for a Wave Energy Converter Based on a Hydraulic Transformer and Multi-Chamber Cylinder
Authors: Li Chenglong , Zhang Dahai , Zhang Weijie , Liu Xiaodong , Tan Ming , Si Yulin , Qian Peng
Published: December 2021

This paper presents a constant-pressure hydraulic PTO system that can convert stored pressure energy into electrical energy at a stable speed through hydraulic motors and generators. A multi-chamber cylinder can be connected to the main power generation circuit by check valves, and the motor displacement can be controlled by a fuzzy controller to maintain the main power generation circuit under stable pressure. The hydraulic transformer can control the forces applied to the floater. The hydrodynamic parameters of the floater are calculated by AQWA, and the optimal PTO damping of the hydraulic system is analyzed as the target of transformer control. MATLAB/Simulink and AMESim are used to carry out the co-simulation. Three kinds of wave elevation time-series for the specific state are designed for the simulation. In the co-simulation, three approaches are carried out for the simulation including no control strategy, fuzzy control with a fixed transformer ratio, and fuzzy control with a variable transformer ratio. Under the fuzzy control with a fixed transformer ratio, the floater displacement and captured energy do not increase significantly, but the oil pressure fluctuation is very stable, which indicates that the fuzzy controller maintains the stability of the main power circuit. While under fuzzy control with a variable transformer ratio, the power generation is not larger than those under no control strategy or fuzzy control with a fixed transformer ratio, which proves that this hydraulic transformer concept is less efficient.
Optimization-Based Capacitor Balancing Method with Selective DC Current Ripple Reduction for CHB Converters
Authors: Galván Luis , Gómez Pablo Jesús , Galván Eduardo , Carrasco Juan Manuel
Published: December 2021

From its introduction to the present day, Cascaded H-Bridge multilevel converters were employed on numerous applications. However, their floating capacitor, while advantageous for some applications (such as photovoltaic) requires the usage of balancing methods by design. Over the years, several such methods were proposed and polished. Some of these methods use optimization techniques or inject a zero-sequence voltage to take advantage of the converter redundancies. This paper describes an optimization-based capacitor balancing method with additional features. It can drive each module DC-Link to a different voltage for independent maximum power point tracking in photovoltaic applications. Moreover, the user can specify the independent active power set points to modules connected to batteries or any other energy storage systems. Finally, DC current ripple can be reduced on some modules, which can extend the lifespan of any connected ultra-capacitors. The method as a whole is tested on real hardware and compared with the state-of-the-art. In its simplest configuration, the presented method shows greater speed, robustness, and current wave quality than the state-of-the-art alternative in spite of producing about 1/3 fewer commutations. Its other characteristics provide additional functionalities and improve the adaptability of the converter to other applications.
An Analysis of Electromagnetic Disturbances from an Electric Vehicle Charging Station
Authors: Mazurek Paweł , Chudy Aleksander
Published: December 2021

The electric vehicles (EVs) could potentially have a significant impact on power quality parameters and distribution networks as they are non-linear loads and their charging might result in tremendous power demand. When connected to the utility grid, a large number of EV charging stations from different manufacturers might create significant harmonic current emissions, impact the voltage profile, and eventually affect the power quality. Nevertheless, practical examples of disturbances from charging stations have not been made public. This paper aims to clarify the characteristics of conductive disturbances and levels of current harmonics generated by charging station and their severity on the quality of electric energy. The analysis was based on tests of a prototype station of an EV charging station integrated with a LED street light. The tests concern the determination of current harmonics and the values of conductive electromagnetic disturbances in the 150 kHz–30 MHz range. The test results of the prototype charger with observed exceedances of current harmonics (25th–39th range) and conducted interference exceedances are comprehensively described. After applying filtering circuits to the final version of the station, retesting in an accredited laboratory showed qualitative compliance.
CFD Evaluation of Heat Transfer and NOx Emissions When Converting a Tangentially Fired Coal Boiler to Use Methane
Authors: Kim Kang-Min , Kim Gyu-Bo , Lee Byoung-Hwa , Bae Yoon-Ho , Jeon Chung-Hwan
Published: December 2021

The need to reduce global carbon dioxide (CO2) emissions is driving the conversion of coal-fired power plants to use methane, which can reduce CO2 emissions by >40%. However, conducting gas firing in coal boilers changes the heat transfer profile; therefore, preliminary evaluations using computational fluid dynamics are required prior to conversion. Here, methane was used as a heat input source in the simulation of an existing coal boiler, and combustion, nitrogen oxides (NOx) emission characteristics, and heat transfer profile changes inside the boiler were analyzed. Furthermore, changes in the burner zone stoichiometric ratio (BZSR) were simulated to restore the decreased heat absorption of the furnace waterwall, revealing that air distribution could change the heat absorption of the waterwall and tube bundles. However, this change was smaller than that caused by conversion from coal to methane. Therefore, to implement gas firing in coal boilers, alternatives such as output derating, using an attemperator, or modifying heat transfer surfaces are necessary. Despite these limitations, a 70% reduction in NOx emissions was achieved at a BZSR of 0.76, compared with coal. As the BZSR contributes significantly to NOx emissions, conducting gas firing in existing coal boilers could significantly reduce NOx and CO2 emissions.
Reinforcement Learning Path Planning Method with Error Estimation
Authors: Zhang Feihu , Wang Can , Cheng Chensheng , Yang Dianyu , Pan Guang
Published: December 2021

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.
Self-Inductance Calculation of the Archimedean Spiral Coil
Authors: Hussain Iftikhar , Woo Dong-Kyun
Published: December 2021

In this paper, a new method to calculate the self-inductance of the Archimedean spiral coil is presented. The proposed method is derived by solving Neumann’s integral formula, and the numerical tool is used to calculate the inductance value. The calculation results are verified with several conventional formulas derived from the Wheeler formula or its modified form and 3D finite element analyses. The comparison with simulation results shows that the conventional formula has an error of above 40% compared to the proposed method, which has below 7% when the wire diameter is reduced. To further check the validity, different sizes of the spiral coil are fabricated by changing the geometrical parameters such as the number of turns, turn spacing, inner radius, outer radius, and wire diameter. Litz wire is chosen for making the spiral coil, and bobbins are made using a 3D printer. Finally, the calculation results are compared with the experimental result. The error between them is less than 2%. The comparison with the conventional formulas, simulation, and measurement results shows the accuracy of the proposed method. This method can be used to calculate the self-inductance of wireless power coils, inductors and antenna design.
The Occurrence of Apiognomonia hystrix and Its Pathogenicity towards Acer pseudoplatanus and Fraxinus excelsior under Field Conditions
Authors: Kowalski Tadeusz , Bilański Piotr , Grad Bartłomiej
Published: December 2021

Apiognomonia hystrix is an ascomycetous fungus within Diaporthales that is found on maples and to a lesser extent on other hardwood trees in Europe, Northern America and Asia. To date, varying opinions on the species’ status as a cause of plant diseases have been expressed. In this study, we present the results of analyses conducted from 2012–2017 at forest sites in Poland on the occurrence of A. hystrix on Acer pseudoplatanus and Fraxinus excelsior and the pathogenicity of this fungus towards both tree species. For the sycamore leaves, A. hystrix conidiomata were detected in connection with 19.2% of galls caused by Dasineura vitrina, 20.4% of galls caused by Drisina glutinosa and 67.9% of extensive vein-associated necroses. The A. hystrix colonization of galls caused by both midge species resulted in statistically significantly larger necroses. On European ash leaves, conidiomata of A. hystrix occurred in connection with 0.8% of Dasineura fraxinea galls. Perithecia of A. hystrix were detected on overwintered leaf petioles in 8.1% of A. pseudoplatanus and 1.2% of F. excelsior samples. Twelve representative cultures were characterized molecularly by barcoding three marker genes (ITS, ACT, CAL). Results of phylogenetic analyses indicate that A. hystrix isolates are genetically variable, and three lineages are distinguishable. Eight isolates, including four originating from sycamore and four from European ash, were used to determine A. hystrix pathogenicity. Among the 48 A. pseudoplatanus petioles inoculated with A. hystrix, 41 developed necrotic lesions after 8 weeks, with the average necrosis length caused by particular isolates ranging from 14.5 to 67.2 mm. None of the 48 inoculated F. excelsior petioles developed necrotic lesions. Finally, selected aspects of A. hystrix morphology on natural substrates and in vitro are discussed in this paper, as well as the species’ potential to cause disease symptoms.
Mathematical Modelling of Bioethanol Production from Raw Sugar Beet Cossettes in a Horizontal Rotating Tubular Bioreactor
Authors: Pavlečić Mladen , Novak Mario , Trontel Antonija , Marđetko Nenad , Grubišić Marina , Ljubas Blanka Didak , Tominac Vlatka Petravić , Rakovac Rozelindra Čož , Šantek Božidar
Published: December 2021

Alternative to the use of fossil fuels are biofuels (e.g., bioethanol, biodiesel and biogas), which are more environmentally friendly and which can be produced from different renewable resources. In this investigation, bioethanol production from raw sugar beet cossettes (semi-solid substrate) by yeast Saccharomyces cerevisiae in a horizontal rotating tubular bioreactor (HRTB) was studied. Obtained results show that HRTB rotation mode (constant or interval) and rotation speed have considerable impact on the efficiency of bioethanol production in the HRTB. The main goal of this research was to develop a non-structural mathematical model of bioethanol production from raw sugar beet cossettes in the HRTB. The established mathematical model of bioethanol production in the HRTB describes substrate utilization and product formation (glycerol, ethanol and acetate) and presumes negative impact of high substrate concentration on the working microorganism (substrate inhibition) by using Andrews inhibition kinetics. All simulations of bioethanol production in the HRTB were performed by using Berkeley Madonna software, version 8.3.14 (Berkeley Madonna, Berkeley, CA, USA). The established non-structural bioprocess model describes relatively well the bioethanol production from raw sugar beet cossettes in the HRTB.
The Interactions among Isolates of Lactiplantibacillus plantarum and Dairy Yeast Contaminants: Towards Biocontrol Applications
Authors: Kavková Miloslava , Cihlář Jaromír , Dráb Vladimír , Bazalová Olga , Dlouhá Zuzana
Published: December 2021

Yeast diversity in the cheese manufacturing process and in the cheeses themselves includes indispensable species for the production of specific cheeses and undesired species that cause cheese defects and spoilage. The control of yeast contaminants is problematic due to limitations in sanitation methods and chemicals used in the food industry. The utilisation of lactic acid bacteria and their antifungal products is intensively studied. Lactiplantibacillus plantarum is one of the most frequently studied species producing a wide spectrum of bioactive by-products. In the present study, twenty strains of L. plantarum from four sources were tested against 25 species of yeast isolated from cheeses, brines, and dairy environments. The functional traits of L. plantarum strains, such as the presence of class 2a bacteriocin and chitinase genes and in vitro production of organic acids, were evaluated. The extracellular production of bioactive peptides and proteins was tested using proteomic methods. Antifungal activity against yeast was screened using in vitro tests. Testing of antifungal activity on artificial media and reconstituted milk showed significant variability within the strains of L. plantarum and its group of origin. Strains from sourdoughs (CCDM 3018, K19-3) and raw cheese (L12, L24, L32) strongly inhibited the highest number of yeast strains on medium with reconstituted milk. These strains showed a consistent spectrum of genes belonging to class 2a bacteriocins, the gene of chitinase and its extracellular product 9 LACO Chitin-binding protein. Strain CCDM 3018 with the spectrum of class 2a bacteriocin gene, chitinase and significant production of lactic acid in all media performed significant antifungal effects in artificial and reconstituted milk-based media.
The Important Role of Global State for Multi-Agent Reinforcement Learning
Authors: Li Shuailong , Zhang Wei , Leng Yuquan , Wang Xiaohui
Published: December 2021

Environmental information plays an important role in deep reinforcement learning (DRL). However, many algorithms do not pay much attention to environmental information. In multi-agent reinforcement learning decision-making, because agents need to make decisions combined with the information of other agents in the environment, this makes the environmental information more important. To prove the importance of environmental information, we added environmental information to the algorithm. We evaluated many algorithms on a challenging set of StarCraft II micromanagement tasks. Compared with the original algorithm, the standard deviation (except for the VDN algorithm) was smaller than that of the original algorithm, which shows that our algorithm has better stability. The average score of our algorithm was higher than that of the original algorithm (except for VDN and COMA), which shows that our work significantly outperforms existing multi-agent RL methods.
An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
Authors: Dang Shoujiang , Han Rui
Published: December 2021

In scientific domains such as high-energy particle physics and genomics, the quantity of high-speed data traffic generated may far exceed the storage throughput and be unable to be in time stored in the current node. Cooperating and utilizing multiple storage nodes on the forwarding path provides an opportunity for high-speed data storage. This paper proposes the use of flow entries to dynamically split traffic among selected neighbor nodes to sequentially amortize excess traffic. We propose a neighbor selection mechanism based on the Local Name Mapping and Resolution System, in which the node weights are computed by combing the link bandwidth and node storage capability, and determining whether to split traffic by comparing normalized weight values with thresholds. To dynamically offload traffic among multiple targets, the cooperative storage strategy implemented in a programmable data plane is presented using the relative weights and ID suffix matching. Evaluation shows that our proposed schema is more efficient compared with end-to-end transmission and ECMP in terms of bandwidth usage and transfer time, and is beneficial in big science.
Mechanical and Impact Properties of Engineered Cementitious Composites Reinforced with PP Fibers at Elevated Temperatures
Authors: Al-Ameri Raad A. , Abid Sallal Rashid , Özakça Mustafa
Published: December 2021

The repeated impact performance of engineered cementitious composites (ECCs) is not well explored yet, especially after exposure to severe conditions, such as accidental fires. An experimental study was conducted to evaluate the degradation of strength and repeated impact capacity of ECCs reinforced with Polypropylene fibers after high temperature exposure. Compressive strength and flexural strength were tested using cube and beam specimens, while disk specimens were used to conduct repeated impact tests according to the ACI 544-2R procedure. Reference specimens were tested at room temperature, while three other groups were tested after heating to 200 °C, 400 °C and 600 °C and naturally cooled to room temperature. The test results indicated that the reference ECC specimens exhibited a much higher failure impact resistance compared to normal concrete specimens, which was associated with a ductile failure showing a central surface fracture zone and fine surface multi-cracking under repeated impacts. This behavior was also recorded for specimens subjected to 200 °C, while the exposure to 400 °C and 600 °C significantly deteriorated the impact resistance and ductility of ECCs. The recorded failure impact numbers decreased from 259 before heating to 257, 24 and 10 after exposure to 200 °C, 400 °C and 600 °C, respectively. However, after exposure to all temperature levels, the failure impact records of ECCs kept at least four times higher than their corresponding normal concrete ones.
UHPLC-HRMS-Based Untargeted Lipidomics Reveal Mechanism of Antifungal Activity of Carvacrol against Aspergillus flavus
Authors: Qu Chenling , Li Zhuozhen , Wang Xiupin
Published: December 2021

Aspergillus flavus is a common contaminant in grain, oil and their products. Its metabolite aflatoxin B1 (AFB1) has been proved to be highly carcinogenic. Therefore, it is of great importance to find possible antifungal substances to inhibit the growth and toxin production of Aspergillus flavus. Carvacrol (CV) was reported as a potent antifungal monoterpene derived from plants. In this paper, the antifungal effects and mechanism of CV on Aspergillus flavus were investigated. CV was shown good inhibition on the growth of Aspergillus flavus and the production of AFB1. CV used in concentrations ranging from 0, 50, 100 and 200 μg/mL inhibited the germination of spores, mycelia growth and AFB1 production dose-dependently. To explore the antifungal mechanism of CV on Aspergillus flavus, we also detected the ergosterol content of Aspergillus flavus mycelia, employed Scanning Electron Microscopy (SEM) to observe mycelia morphology and utilized Ultra-High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) to explore the lipidome profiles of Aspergillus flavus. The results showed that the production of ergosterol of mycelia was reduced as the CV treatment concentration increased. SEM photographs demonstrated a rough surface and a reduction in the thickness of hyphae in Aspergillus flavus treated with CV (200 µg/mL). In positive ion mode, 21 lipids of Aspergillus flavus mycelium were downregulated, and 11 lipids were upregulated after treatment with 200-µg/mL CV. In negative ion mode, nine lipids of Aspergillus flavus mycelium were downregulated, and seven lipids upregulated after treatment with 200-µg/mL CV. In addition, the analysis of different lipid metabolic pathways between the control and 200-µg/mL CV-treated groups demonstrated that glycerophospholipid metabolism was the most enriched pathway related to CV treatment.
White List: An Administrative Tool to Contrast Crime
Authors: Barletta Amelia , Capuano Carlo , De Iudicibus Alessandro
Published: December 2021

White Lists are lists of firms set up at each Italian prefecture. It is a relatively young instrument born as an experimental project designed to solve problems for particular conditions of crime and corruption in the Italian territory. This work aims to understand if the “White List” tool can represent a reliable legality tool for the contracting station and representation for firms registered in these lists, an incentive to obtain awards for faster times of public procurement. Through a descriptive survey, we will try to photograph the “White List” phenomenon by comparing firms belonging to these lists with companies not belonging to them but which are similar in terms of size and economic sector. The comparison takes off some differences in the financial structure of the two groups of companies. in particular, the White List’s firms show a better profitability and a lower recourse to third-party capital.
Competition and Innovation in Markets with Technology Leaders
Authors: Tsao Ku-Chu , Mukherjee Arijit , Ray Achintya
Published: December 2021

In this article, we consider technology leaders (which are innovators) and technology followers (which are non-innovators) to provide a new theoretical explanation for the well-cited empirical evidence of an inverted-U relationship between competition and aggregate innovation. We consider a two-stage game with a deterministic Research and Development (R&D) process, where the leaders first determine their R&D investments simultaneously and then all leaders and followers determine their outputs simultaneously. We show that the inverted-U relationship between competition and aggregate innovation occurs if competition is affected by the number of technology followers. However, the presence of more technology leaders decreases individual R&D investments while increasing aggregate R&D investments. If the total number of firms remains the same but the composition of technology leaders and followers changes in favor of leaders (followers), individual R&D investments decrease (increase) but aggregate R&D investments increase (decrease). The relationship between competition and R&D investments can be U-shaped if the intensity of competition is measured by product substitutability. Contrary to the standard expectation, the presence of more firms may reduce welfare.
A Novel GEMIN4 Variant in a Consanguineous Family Leads to Neurodevelopmental Impairment with Severe Microcephaly, Spastic Quadriplegia, Epilepsy, and Cataracts
Authors: Aldhalaan Hesham , AlBakheet Albandary , AlRuways Sarah , AlMutairi Nouf , AlNakiyah Maha , AlGhofaili Reema , Cardona-Londoño Kelly J. , Alahmadi Khalid Omar , AlQudairy Hanan , AlRasheed Maha M. , Colak Dilek , Arold Stefan T. , Kaya Namik
Published: December 2021

Pathogenic variants in GEMIN4 contribute to a hereditary disorder characterized by neurodevelopmental features, microcephaly, cataracts, and renal abnormalities (known as NEDMCR). To date, only two homoallelic variations have been linked to the disease. Moreover, clinical features associated with the variants have not been fully elucidated yet. Here, we identified a novel variant in GEMIN4 (NM_015721:exon2:c.440A>G:p.His147Arg) in two siblings from a consanguineous Saudi family by using whole exome sequencing followed by Sanger sequence verification. We comprehensively investigated the patients’ clinical features, including brain imaging and electroencephalogram findings, and compared their phenotypic characteristics with those of previously reported cases. In silico prediction and structural modeling support that the p.His147Arg variant is pathogenic.
Exploring the Contribution to ADHD of Genes Involved in Mendelian Disorders Presenting with Hyperactivity and/or Inattention
Authors: Fernàndez-Castillo Noèlia , Cabana-Domínguez Judit , Kappel Djenifer B. , Torrico Bàrbara , Weber Heike , Lesch Klaus-Peter , Lao Oscar , Reif Andreas , Cormand Bru
Published: December 2021

Attention-deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder characterized by hyperactivity, impulsivity, and/or inattention, which are symptoms also observed in many rare genetic disorders. We searched for genes involved in Mendelian disorders presenting with ADHD symptoms in the Online Mendelian Inheritance in Man (OMIM) database, to curate a list of new candidate risk genes for ADHD. We explored the enrichment of functions and pathways in this gene list, and tested whether rare or common variants in these genes are associated with ADHD or with its comorbidities. We identified 139 genes, causal for 137 rare disorders, mainly related to neurodevelopmental and brain function. Most of these Mendelian disorders also present with other psychiatric traits that are often comorbid with ADHD. Using whole exome sequencing (WES) data from 668 ADHD cases, we found rare variants associated with the dimension of the severity of inattention symptoms in three genes: KIF11, WAC, and CRBN. Then, we focused on common variants and identified six genes associated with ADHD (in 19,099 cases and 34,194 controls): MANBA, UQCC2, HIVEP2, FOPX1, KANSL1, and AUH. Furthermore, HIVEP2, FOXP1, and KANSL1 were nominally associated with autism spectrum disorder (ASD) (18,382 cases and 27,969 controls), as well as HIVEP2 with anxiety (7016 cases and 14,475 controls), and FOXP1 with aggression (18,988 individuals), which is in line with the symptomatology of the rare disorders they are responsible for. In conclusion, inspecting Mendelian disorders and the genes responsible for them constitutes a valuable approach for identifying new risk genes and the mechanisms of complex disorders.
Single-Cell Analysis of Aneurysmal Aortic Tissue in Patients with Marfan Syndrome Reveals Dysfunctional TGF-β Signaling
Authors: Dawson Ashley , Li Yanming , Li Yang , Ren Pingping , Vasquez Hernan G. , Zhang Chen , Rebello Kimberly R. , Ageedi Waleed , Azares Alon R. , Mattar Aladdein Burchett , Sheppard Mary Burchett , Lu Hong S. , Coselli Joseph S. , Cassis Lisa A. , Daugherty Alan , Shen Ying H. , LeMaire Scott A.
Published: December 2021

The molecular and cellular processes leading to aortic aneurysm development in Marfan syndrome (MFS) remain poorly understood. In this study, we examined the changes of aortic cell populations and gene expression in MFS by performing single-cell RNA sequencing (scRNA seq) on ascending aortic aneurysm tissues from patients with MFS (n = 3) and age-matched non-aneurysmal control tissues from cardiac donors and recipients (n = 4). The expression of key molecules was confirmed by immunostaining. We detected diverse populations of smooth muscle cells (SMCs), fibroblasts, and endothelial cells (ECs) in the aortic wall. Aortic tissues from MFS showed alterations of cell populations with increased de-differentiated proliferative SMCs compared to controls. Furthermore, there was a downregulation of MYOCD and MYH11 in SMCs, and an upregulation of COL1A1/2 in fibroblasts in MFS samples compared to controls. We also examined TGF-β signaling, an important pathway in aortic homeostasis. We found that TGFB1 was significantly upregulated in two fibroblast clusters in MFS tissues. However, TGF-β receptor genes (predominantly TGFBR2) and SMAD genes were downregulated in SMCs, fibroblasts, and ECs in MFS, indicating impairment in TGF-β signaling. In conclusion, despite upregulation of TGFB1, the rest of the canonical TGF-β pathway and mature SMCs were consistently downregulated in MFS, indicating a potential compromise of TGF-β signaling and lack of stimulus for SMC differentiation.