bluuebunny/crossref_metadata_2025 huggingface dataset:
Coping Strategies and Help-Seeking Behaviors of College Students and Postdoctoral Fellows with Disabilities or Pre-Existing Conditions during COVID-19
Authors: Wolfner Caro , Ott Corilyn , Upshaw Kalani , Stowe Angela , Schwiebert Lisa , Lanzi Robin Gaines
Published: February 2023

The COVID-19 pandemic continues to have a global and multifaceted impact on public health. Marginalized and vulnerable populations, such as college students and postdoctoral fellows with disabilities or pre-existing conditions, are being disproportionately affected by the COVID-19 pandemic. Various barriers contribute to an individual’s intentions to seek mental health help, but with COVID-19’s unprecedented permeability, more research is needed to support this student population. This phenomenological study explored the coping strategies and help-seeking behaviors of college students and postdoctoral fellows with disabilities during the COVID-19 pandemic. Using semi-structured, in-depth interviews from fall 2020 (n = 36) and spring 2021 (n = 28), a thematic analysis was conducted. The Transactional Model of Stress and Coping was used to triangulate findings, to better understand the relationship between perceived stressors, coping mechanisms, and psychological outcomes. The findings show that (1) college students with disabilities coped in multiple ways (i.e., behavioral, relational, and mental), with some noting improvements in their coping abilities by spring, and (2) stigma prevented college students with disabilities from seeking help when needed. These findings emphasize the need for higher education to address ableism and use factors beneficial to fostering resiliency (i.e., social support, optimism, and self-advocacy) among college students with disabilities.
Retrospective Longitudinal Study on Changes in Atmospheric Pressure as a Predisposing Factor for Odontogenic Abscess Formation
Authors: Tarle Marko , Zubović Arijan , Kos Boris , Raguž Marina , Lukšić Ivica
Published: February 2023

In our retrospective longitudinal study based on the data from 292 patients, we wanted to investigate whether there was an association between weather conditions and the occurrence of odontogenic abscesses (OA) requiring hospitalization. In the adult group (249 patients), the incidence of severe OA was highest in winter (32.9%) during January (11.6%), with the most common localizations being the perimandibular (35.7%) and submandibular (23.3%) regions. We found that changes in mean daily atmospheric pressure five days before hospitalization showed a positive association with the occurrence of OA, especially pressure variations greater than 12 hPa. Atmospheric pressure changes two and five days before hospitalization were also found to be moderate predictors of complications during treatment. Antibiogram analysis revealed resistance of streptococci to clindamycin in 26.3%. In the pediatric group, OA were also most frequent in winter (30.2%), and the perimandibular region (37.2%) and the canine fossa (20.9%) were the most frequent abscess localizations, while an association with meteorological parameters was not demonstrated. Clinical experience teaches us that weather change influences the occurrence of severe OA requiring hospitalization, which we confirmed in this research. To our knowledge, our study is the first to provide a threshold and precise time frame for atmospheric pressure changes.
Artificial Intelligence in Periodontology: A Scoping Review
Authors: Scott James , Biancardi Alberto M. , Jones Oliver , Andrew David
Published: February 2023

Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.
Uncertainty Relations in the Madelung Picture Including a Dissipative Environment
Authors: Schuch Dieter , Bonilla-Licea Moise
Published: February 2023

In a recent paper, we have shown how in Madelung’s hydrodynamic formulation of quantum mechanics, the uncertainties are related to the phase and amplitude of the complex wave function. Now we include a dissipative environment via a nonlinear modified Schrödinger equation. The effect of the environment is described by a complex logarithmic nonlinearity that vanishes on average. Nevertheless, there are various changes in the dynamics of the uncertainties originating from the nonlinear term. Again, this is illustrated explicitly using generalized coherent states as examples. With particular focus on the quantum mechanical contribution to the energy and the uncertainty product, connections can be made with the thermodynamic properties of the environment.
Live Remote Classroom: A Tool for Coherent Teacher Education
Authors: Ulvik Marit , Eide Liv , Kvam Edel Karin , Roness Dag
Published: February 2023

Teacher education has been criticised for a lack of coherence and for not preparing student teachers for teaching. To prepare student teachers for practicum and create connections between theory and practice and between schools and university, this study explores how the practice field can be brought onto the university campus through digital resources. Four teacher educators in secondary school teacher education in Norway collaborated with a schoolteacher and tested Live Remote Classroom. The tool provides student teachers with real-life classroom experiences while they are on campus by providing access to a streamed lesson. Using an action research design, the teacher educators evaluated their facilitation of the arrangement through an interview with the involved schoolteacher, a focus group interview with student teachers and the teacher educators’ own logs. The results show that by presenting authentic practice situations, Live Remote Classroom created opportunities to prepare for teaching. However, certain conditions need to be in place for that to happen. We see it as important to support student teachers in their analysis of the observed lesson and to collaborate with a schoolteacher who is able to make her professional choices explicit.
Development of a Methane-Detection System Using a Distributed Feedback Laser Diode and Hollow-Core Photonic Crystal Fiber
Authors: Li Bin , Wang Qingpeng , Wang Qizheng , Huang Yitong
Published: February 2023

A highly integrated methane-detection system was experimentally established by using a distributed feedback laser diode and hollow-core photonic crystal fiber. The self-developed circuits with a laser diode and essential optical devices were integrated into an instrument that generated a modulated optical signal in a fiber-coupled gas cell that contained the hollow-core photonic crystal fiber. The instrument could also process the return optical signal that contained the gas concentration information. The experiments demonstrated the good performance of the developed system. In the spectrum tests, the center wavelength of the laser diode could be tuned linearly by controlling the laser’s working temperature and driving current. The second harmonic signal could be extracted in order to reflect the gas concentration. According to the Allan deviation method, the low limit of detection of the system was determined to be 29.52 ppm. In addition, a long-term stability test demonstrated that the system has a good stable performance. The proposed system can be further optimized in order to be applied in paddy fields to detect and monitor the methane concentration in a large area by using the optical fibers.
A Hybrid GPU and CPU Parallel Computing Method to Accelerate Millimeter-Wave Imaging
Authors: Ding Li , Dong Zhaomiao , He Huagang , Zheng Qibin
Published: February 2023

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.
A Novel Unidirectional Smart Charging Management Algorithm for Electric Buses
Authors: Darii Nicolae , Turri Roberto , Sunderland Keith , Bignucolo Fabio
Published: February 2023

The difficulty of controlling the charging of electric buses (EBs) and their effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This paper focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances. Additionally, the technique makes it possible to facilitate distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also identifies an appropriate individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis of the DN voltage profile and associated line losses, the ideal node position location of the CS is dependent on PV penetration.
EDaLI: A Public Domain Dataset for Emotional Analysis Using Brain Computer Interfaces during an Interaction with a Second-Language Learning Platform
Authors: Restrepo-Rodríguez Andrés Ovidio , Ariza-Riaño Maddyzeth , Gaona-García Paulo Alonso , Montenegro-Marín Carlos Enrique
Published: February 2023

In recent years, it has been shown that emotions influence what we learn and retain, and second-language learning is no exception to this phenomenon. Currently, a variety of mobile learning applications offer content for language learners, and a wide range of languages are presented. The analysis of emotional data in learning environments has been implemented through various methods, such as the collection of vital signs. This is where brain–computer interfaces (BCIs) play an important role in capturing emotional metrics from brain activity. Accordingly, this paper presents the Emotional Data L2 Interaction (EDaLI) dataset for emotional analysis based on the collection of emotions, such as engagement, stress, excitement, interest, relaxation, and focus, through Emotiv Insight, during the interaction of 19 participants with 4 initial lessons in Portuguese as a second-language, through the Babbel application. A preliminary visualization approach is proposed from the generated dataset. In accordance with this, it is concluded that visualization techniques can clearly be applied to EDaLI to show the emotional behavior exhibited by the participants during their interactions. Additionally, the spectrum of algorithms to be applied is open and includes possibilities such as the use of clustering techniques for time series of variable lengths.
Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models
Authors: Dalal Dhaval , Bilal Muhammad , Shah Hritik , Sifat Anwarul Islam , Pal Anamitra , Augustin Philip
Published: February 2023

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.
Hybrid Gray Wolf Optimization–Proportional Integral Based Speed Controllers for Brush-Less DC Motor
Authors: Younus Shukri Mahmood Younus , Kutbay Uğurhan , Rahebi Javad , Hardalaç Fırat
Published: February 2023

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading situations, and variable set speed situations, speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations and this is mostly because the characteristics of a Brush-less DC motor drive exhibit non linearity. This work proposes a hybrid gray wolf optimization and proportional integral controller for management of the speed in Brush-less DC motors to address this issue. For constant load conditions, varying load situations and varying set speed situations, the proposed controller’s efficiency is evaluated and contrasted with that of PID controller, PSO-PI controller, and ANFIS. In this study, two PI controller are used to get the more stability of the system based on tuning of their coefficients with meta heuristic method. The simulation findings show that Hybrid GWO-PI-based controllers are in every way superior to other controllers under consideration. In this study, four case studies are presented, and the best-case study was obtained 0.18619, 0.01928, 0.00030, and 0.01233 for RMSE, IAE, ITAE, and ISE respectively.
Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network
Authors: Mahjoub Sameh , Labdai Sami , Chrifi-Alaoui Larbi , Marhic Bruno , Delahoche Laurent
Published: February 2023

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches.
Determining of the Bankrupt Contingency as the Level Estimation Method of Western Ukraine Gas Distribution Enterprises’ Competence Capacity
Authors: Sala Dariusz , Pavlov Kostiantyn , Pavlova Olena , Demchuk Anton , Matiichuk Liubomur , Cichoń Dariusz
Published: February 2023

The functioning of Ukrainian national gas sector is directly dependent on the processes of fuel and energy resources consumption and trends in domestic and foreign markets. Nowadays, the majority of approaches and methods are formed with the obligatory use of expert assessment methods, which, in its turn, predetermines relatively subjective judgments and results. In the process of conducting a comprehensive analysis of financial and economic indicators and those reflecting the results of economic activity of gas distribution network operators functioning in the western region of Ukraine, the following approaches have been used in our study with the involvement of: Altman’s two-factor model; Altman’s five-factor model; Lis’s bankruptcy prediction model; Richard Taffler’s model; Beaver’s coefficient; Tereshchenko’s model and Matviychuk’s model; however, the existing models for diagnosing bankruptcy of enterprises are characterized by ambiguity; as for example, if Lis’s model indicates a low bankruptcy level, then other models prove the opposite situation; domestic diagnostic models need to be improved, as they were developed in the early 2000s and disregard current trends in functioning of enterprises. Since the existing models for diagnosing the bankruptcy of enterprises are characterized by ambiguity, the authors proposed and approbate their own approach to determining the level of competitiveness of gas distribution network operators. A feature of the proposed methodology is taking into account modern trends in the functioning of enterprises, taking into account the peculiarities of the activities of gas distribution network operators, and the market stage. A tangible advantage of this approach is the ability to identify the presence or likelihood of critical events at an early stage.
Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection
Authors: Pinto Smitha Joyce , Siano Pierluigi , Parente Mimmo
Published: February 2023

In a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors. This paper comprehensively reviews smart grid challenges on cyber-physical and cyber security systems, standard protocols, communication, and sensor technology. Existing supervised learning-based Machine Learning (ML) methods for identifying cyberattacks in smart grids mostly rely on instances of both normal and attack events for training. Additionally, for supervised learning to be effective, the training dataset must contain representative examples of various attack situations having different patterns, which is challenging. Therefore, we reviewed a novel Data Mining (DM) approach based on unsupervised rules for identifying False Data Injection Cyber Attacks (FDIA) in smart grids using Phasor Measurement Unit (PMU) data. The unsupervised algorithm is excellent for discovering unidentified assault events since it only uses examples of typical events to train the detection models. The datasets used in our study, which looked at some well-known unsupervised detection methods, helped us assess the performances of different methods. The performance comparison with popular unsupervised algorithms is better at finding attack events if compared with supervised and Deep Learning (DL) algorithms.
Multiple Input-Single Output DC-DC Converters Assessment for Low Power Renewable Sources Integration
Authors: Soldado-Guamán Joaquin , Herrera-Perez Victor , Pacheco-Cunduri Mayra , Paredes-Camacho Alejandro , Delgado-Prieto Miguel , Hernandez-Ambato Jorge
Published: February 2023

This paper presents a comparison of Isolated (Flyback) and non-Isolated (Buck) multiple input-single output (MISO) DC-DC converters. The analysis of DC-DC converters is based on pulsed voltage source cells (PVSC). The modeling of both converter types is detailed through their mathematical models and electrical simulations using Matlab/Simulink and PSIM. The comparison focuses on the sizing parameters, non-ideal output characteristics and efficiency. Results show that the output voltage of the MISO Buck converter exhibits a linear dependence on the duty cycles control signal and has slightly higher efficiency than the Flyback converter. To validate the operation of both converters, a scenario with two inputs (low-power hydroelectric and photovoltaic voltage sources) is considered. The modeling and control of both source systems are detailed and the MISO converter performance response is evaluated under sources changes and efficiency point of view.
A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines
Authors: Pandit Ravi Kumar , Astolfi Davide , Durazo Cardenas Isidro
Published: February 2023

The analysis of reliable studies helps to identify the credibility, scope, and limitations of various techniques for condition monitoring of a wind turbine (WT) system’s design and development to reduce the operation and maintenance (O&M) costs of the WT. In this study, recent advancements in data-driven models for condition monitoring and predictive maintenance of wind turbines’ critical components (e.g., bearing, gearbox, generator, blade pitch) are reviewed. We categorize these models according to data-driven procedures, such as data descriptions, data pre-processing, feature extraction and selection, model selection (classification, regression), validation, and decision making. Our findings after reviewing extensive relevant articles suggest that (a) SCADA (supervisory control and data acquisition) data are widely used as they are available at low cost and are extremely practical (due to the 10 min averaging time), but their use is in some sense nonspecific. (b) Unstructured data and pre-processing remain a significant challenge and consume a significant time of whole machine learning model development. (c) The trade-off between the complexity of the vibration analysis and the applicability of the results deserves further development, especially with regards to drivetrain faults. (d) Most of the proposed techniques focus on gearbox and bearings, and there is a need to apply these models to other wind turbine components. We explain these findings in detail and conclude with a discussion of the main areas for future work in this domain.
Semi-Systematic Literature Review on the Contribution of Hydrogen to Universal Access to Energy in the Rationale of Sustainable Development Goal Target 7.1
Authors: Schöne Nikolas , Heinz Boris
Published: February 2023

As part of the United Nations’ (UN) Sustainable Development Goal 7 (SDG7), SDG target 7.1 recognizes universal electrification and the provision of clean cooking fuel as two fundamental challenges for global society. Faltering progress toward SDG target 7.1 calls for innovative technologies to stimulate advancements. Hydrogen has been proposed as a versatile energy carrier to be applied in both pillars of SDG target 7.1: electrification and clean cooking. This paper conducts a semi-systematic literature review to provide the status quo of research on the application of hydrogen in the rationale of SDG 7.1, covering the technical integration pathways, as well as the key economic, environmental, and social aspects of its use. We identify decisive factors for the future development of hydrogen use in the rationale of SDG target 7.1 and, by complementing our analysis with insights from the related literature, propose future avenues of research. The literature on electrification proposes that hydrogen can serve as a backup power supply in rural off-grid communities. While common electrification efforts aim to supply appliances that use lower amounts of electricity, a hydrogen-based power supply can satisfy appliances with higher power demands including electric cook stoves, while simultaneously supporting clean cooking efforts. Alternatively, with the exclusive aim of stimulating clean cooking, hydrogen is proposed to be used as a clean cooking fuel via direct combustion in distribution and utilization infrastructures analogous to Liquid Petroleum Gas (LPG). While expected economic and technical developments are seen as likely to render hydrogen technologies economically competitive with conventional fossil fuels in the future, the potential of renewably produced hydrogen usage to reduce climate-change impacts and point-of-use emissions is already evident today. Social benefits are likely when meeting essential safety standards, as a hydrogen-based power supply offers service on a high tier that might overachieve SDG 7.1 ambitions, while hydrogen cooking via combustion fits into the existing social habits of LPG users. However, the literature lacks clear evidence on the social impact of hydrogen usage. Impact assessments of demonstration projects are required to fill this research gap.
Computational Optimization of a Loosely-Coupled Strategy for Scale-Resolving CHT CFD Simulation of Gas Turbine Combustors
Authors: Amerini Alberto , Paccati Simone , Andreini Antonio
Published: February 2023

The accurate prediction of heat fluxes and, thus, metal wall temperatures of gas turbine (GT) combustor liners is a complicated and numerically expensive task. Computational Fluid Dynamics (CFD) support for the design of cooling systems is essential to ensure safe and proper operation of the entire gas turbine engine. Indeed, it is well known how complicated, and, at the same time, expensive it is to carry out experimental campaigns inside combustors operating under working conditions, and, therefore, pressurized and having high temperatures. The correct prediction of thermal fluxes in a CFD simulation depends on the proper modeling of all the involved phenomena and their interactions with each other. For this reason, Conjugate Heat Transfer (CHT) simulations are mandatory in gas turbine cooling system applications. Multiphysics and multiscale simulations, based on loosely-coupled approaches, have emerged as extremely effective numerical tools, providing enormous computational time savings, as compared with standard CHT simulations. The fundamental advantage of such approaches is based on the fact that each heat transfer mechanism is solved with the most suitable numerical setup, which leads to the use of spatial and temporal resolutions following the characteristic time scales of each phenomenon to be solved. For industrial applications, where the availability of numerical resources is limited and, at the same time, the timelines with which to obtain results are rather tight, having robust and easy-to-use loosely-coupled solutions available for the design of combustion chamber cooling systems would be extremely valuable. In this context, the objective of this work was to perform an initial optimization step for the multiphysics and multiscale tool, U-THERM3D, developed at the University of Florence to revise the coupling strategy workflow with a view to making the numerical tool faster and easier to use. The revised methodology was applied to the RSM gas turbine combustor model test case developed with cooperation between the Universities of Darmstadt, Heidelberg, Karlsruhe, and the DLR. In particular, all experimental tests were conducted by the Institute of Reactive Flows and Diagnostics (Reaktive Strömungen und Messtechnik) of the Department of Mechanical Engineering at TU Darmstadt, from which the gas turbine combustor model takes its name. The newly obtained results were compared and analyzed, both qualitatively and in terms of computational time savings, with those previously achieved with the current version of the U-THERM3D tool already studied by the authors and available in the literature. Moreover, an analysis of computing times was carried out relative to the super-computing center used for the different adopted methodologies.
Electromagnetic Torque Analysis and Structure Optimization of Interior Permanent Magnet Synchronous Machine with Air-Gap Eccentricity
Authors: Wang Jing , Wang Yubin
Published: February 2023

Interior permanent magnet synchronous machine with air-gap eccentricity (AGE-IPMSM) has the advantages of low torque ripple and low noise. However, air-gap eccentricity will lower the power density of the machine to a certain extent. In this paper, an 18-slot/8-pole interior permanent magnet synchronous machine with air-gap eccentricity is taken as the research object. According to the magnetic circuit method, the no-load and load air-gap magnetic field analytical models are calculated, respectively. Then, by Maxwell’s tensor method, the variation law of radial and tangential air-gap magnetic density harmonic amplitudes and phase angle difference cosine values are analyzed, and it is concluded that the electromagnetic torque can be improved by increasing phase angle difference cosine values of the magnetic density harmonic, which produces the driving torque after eccentricity. On this basis, in order to improve the output characteristics of the machine, the eccentricity and the angle between the V-type permanent magnets are optimized with the total harmonic distortion (THD), electromagnetic torque, and torque ripple as the optimization goals, and then the optimal structure size parameters of the machine are finally determined. Finally, a prototype is manufactured on the basis of the optimal parameters, and the experimental results of the prototype verify the validity and correctness of the theoretical analysis and finite element analysis (FEA).
Investigating Empirical Mode Decomposition in the Parameter Estimation of a Three-Section Winding Model
Authors: Banks Daniel Marc , Bekker Johannes Cornelius , Vermeulen Hendrik Johannes
Published: February 2023

Parameter estimation represents an important aspect of modeling electromagnetic systems, and a wide range of parameter estimation strategies has been explored in literature. Most parameter estimation methodologies make use of either time-domain or frequency-domain responses as measured from the terminals of the device under test. Very limited research has, however, been conducted into exploring the use of modal decomposition strategies on the time-domain waveforms for parameter estimation applications. In this paper, the use of Empirical Mode Decomposition for estimating the parameters of a three-section lumped parameter transformer model is explored. A novel approach is proposed to define the optimization cost function in terms of the intrinsic modes of simulated time-domain waveforms. The results are compared with results obtained using classical time-domain and frequency-domain approaches. It is shown through an impulse response test that weighting the modes obtained from the Inferred Empirical Mode Decomposition approach presented in this work offers advantages in terms of accurately representing the target model transfer function dynamics and can assist in interpreting the various dynamic modes associated with the target model.
Energy, Exergy, and Economic (3E) Analysis of Transcritical Carbon Dioxide Refrigeration System Based on ORC System
Authors: Hu Kaiyong , Zhang Yumeng , Yang Wei , Liu Zhi , Sun Huan , Sun Zhili
Published: February 2023

This paper used the energy, exergy, and economic analysis of a carbon dioxide (CO2) transcritical two-stage compression system based on organic Rankine cycle (ORC) waste heat recovery technology. When the intermediate pressure and high-pressure compressor outlet pressure were changed, respectively, this study simulated the change in system energy efficiency by adding the ORC for waste heat recovery, calculated the ratio of exergy loss of each component, and performed an economic analysis of the coupled system. The results show that adding waste heat recovery can effectively increase the energy efficiency of the system, and among all components, the heat exchanger had the largest exergy loss, while the evaporator had the highest capital investment and maintenance costs.
Neural-Assisted Synthesis of a Linear Quadratic Controller for Applications in Active Suspension Systems of Wheeled Vehicles
Authors: Kozek Mateusz , Smoter Adam , Lalik Krzysztof
Published: February 2023

This article presents a neural algorithm based on Reinforcement Learning for optimising Linear Quadratic Regulator (LQR) creation. The proposed method allows designing such a target function that automatically leads to changes in the quality and resource matrix so that the target LQR regulator achieves the desired performance. The solution’s stability and optimality are the target controller’s responsibility. However, the neural mechanism allows obtaining, without expert knowledge, the appropriate Q and R matrices, which will lead to such a gain matrix that will realise the control that will lead to the desired quality. The presented algorithm was tested for the derived quadrant model of the suspension system. Its application improved user comfort by 67% compared to the passive solution and 14% compared to non-optimised LQR.
Design of a Prototypical Mock-Up for the Experimental Investigation of WCLL First-Wall Performances
Authors: Maccari Pietro , Arena Pietro , Marinari Ranieri , Tincani Amelia , Del Nevo Alessandro
Published: February 2023

A large research effort is currently ongoing within the framework of the EUROfusion consortium for the study and design of a water-cooled lithium–lead (WCLL) breeding blanket (BB). This concept will be tested in ITER through the installation of a test blanket module (TBM) and it is one of the two candidates adopted as driver BBs in DEMO. In this framework, at the ENEA research centre of Brasimone, the realization of the experimental platform, W-HYDRA, is envisaged. The platform is dedicated to the support of the development of WCLL BB and ITER TBM and the investigation of the DEMO balance of plants. One of the most important experimental infrastructures is the water-loop facility, the aim of which is to provide water at a high pressure and temperature (PWR conditions), with a sufficient mass-flow rate and power for the experimental testing of BB and TBM components. The facility will be equipped with a vacuum chamber and an electron beam gun for the reproduction of high surface heat flux on plasma-facing components. In the present work, the design of a prototypical mock-up (MU) of the WCLL BB first wall is described. The MU is used to investigate the thermal, hydraulic and structural behavior of the current first-wall design under relevant heat loads at the expected operational conditions. The delineation of the main experimental test’s features and the instrumentation needed is assessed in the paper. A preliminary CFD calculation on the prototypical MU and the computational results are also presented.
Study on Artificial Neural Network for Predicting Gas-Liquid Two-Phase Pressure Drop in Pipeline-Riser System
Authors: Li Xinping , Li Nailiang , Lei Xiang , Liu Ruotong , Fang Qiwei , Chen Bin
Published: February 2023

The pressure drop for air-water two-phase flow in pipeline systems with S-shaped and vertical risers at various inclinations (−1°, −2°, −4°, −5° and −7° from horizontal) was predicted using an artificial neural network (ANN). In the designing of the ANN model, the superficial velocity of gas and liquid as well as the inclination of the downcomer were used as input variables, while pressure drop values of two-phase flows were determined as the output. An ANN network with a hidden layer containing 14 neurons was developed based on a trial-and-error method. A sigmoid function was chosen as the transfer function for the hidden layer, while a linear function was used in the output layer. The Levenberg-Marquardt algorithm was used for the training of the model. A total of 415 experimental data points reported in the literature were collected and used for the creation of the networks. The statistical results showed that the proposed network is capable of calculating the experimental pressure drop dataset with low average absolute percent error (AAPE) of 3.35% and high determination coefficient (R2) of 0.995.
Variation in Leaf Cutin Content and Chemical Composition along One Annual Cycle in the Mediterranean Cork Oak (Quercus suber L.)
Authors: Simões Rita , Miranda Isabel , Pereira Helena
Published: February 2023

Cork oak (Quercus suber L.) has high economic value given by its sustainable production of cork, and ecological importance in the Mediterranean region. The species is well adapted to the dry climate, namely through the sclerophyllous nature of its leaves with a well-developed cuticle, including cutin and cuticular waxes that contribute to protection against drought. Leaves of cork oaks were collected along one annual cycle, starting from the young leaves in May to the one-year-old leaves in March. Leaf cutin content and chemical composition were determined by transesterification subsequently to the determination of cuticular waxes, and leaf features, and were analyzed along the leaf cycle. Cutin is a major component of the cuticle, representing on average 72.4% of the cutin and cuticular waxes. Cutin amounted to 71.0 g/1000 g of dry leaves, without significant seasonal mass proportion variation, while cutin coverage increased from May to December (429.7 μg/cm2 and 575.4 μg/cm2, respectively). In contrast, a clear seasonality was found in cuticular wax mass proportion and coverage (18.4 g/1000 g of dry leaves and 113.5 μg/cm2 in May, and 28.5 g/1000 g and 235.2 μg/cm2 in September). Cutin is a glyceridic polyester composed by long-chain acids, mainly ω-hydroxyacids, followed by fatty acids with a few ω-diacids and alcohols, and by a substantial proportion of aromatics. Cutin composition varied along time with a proportional increase in ω-hydroxyacids (45.8% in May; 50.8% in December), and a significant decrease in aromatics (24.2% in May and 8.5% in March). The cuticle seasonal development in the cork oak contributes to protect the leaves and the trees from the dry summer conditions.
Enhancement of Wood Coating Properties by Adding Silica Sol to UV-Curable Waterborne Acrylics
Authors: Zhu Yuding , Zhu Wenkai , Li Zequn , Feng Yuan , Qi Wei , Li Song , Wang Xiaoyu , Chen Meiling
Published: February 2023

In recent years, with the development of the coating industry and the increasing awareness of environmental protection, the modification of waterborne wood coatings has become the focus of research. Generally, the system composed of silica sol modification and UV curing can make up for the defects of poor mechanical properties, low hardness, and slow curing speeds of waterborne wood coatings. Herein, we used silica sol-reinforced UV-curable waterborne acrylic wood coatings and tested the related physical properties of the coatings. FT-IR analysis showed that the Si-O-Si bond appeared, indicating that the silica sol was successfully grafted onto the waterborne acrylic molecular chain. The results showed that the mechanical properties of the UV-curable waterborne acrylic wood coating film reached their optimum when the content of silica sol was 1 wt%, the number of UV lamps was 3, and the drying time was 20 min. The corresponding values for wear resistance, hardness, adhesion, and impact strength were 0.106 g (high level), grade 3, and 90 kg·cm, respectively. However, when the content of silica sol is greater than 1 wt%, the related physical properties of the coatings will decrease. The results showed that the gloss of the coating decreased with increasing silica sol content. When the silica sol content was 2 wt%–6 wt%, the coating showed a matte gloss. This present work shows that the modification process is simple, controlled, inexpensive, and meets the demand for UV-curable waterborne acrylic wood coatings in daily life.
Repeated-Batch Ethanol Fermentation from Sweet Sorghum Stem Juice under a Very High Gravity Condition Using a Stirred Tank Bioreactor Coupled with a Column Bioreactor by Immobilized Saccharomyces cerevisiae
Authors: Sriputorn Benjaporn , Laopaiboon Lakkana , Laopaiboon Pattana
Published: February 2023

The ethanol fermentation efficiency of sweet sorghum stem juice (SSJ) under a very high gravity (VHG) condition (250 g/L of sugar) was improved by immobilized Saccharomyces cerevisiae SSJKKU01, using a stirred tank bioreactor (STR) coupled with a column bioreactor (CR). Dried rattan pieces (as carriers for cell immobilization) at 50% of the working volume of the CR were suitable for use in a batch ethanol fermentation. The average ethanol concentration (PE) and ethanol productivity (QP) of repeated-batch fermentation in the CR for eight successive cycles were 109.85 g/L and 1.88 g/L⋅h, respectively. Then an STR coupled with a CR was applied for repeated-batch ethanol fermentation in two systems. System I was an STR (1.8 L working volume), and System II was an STR (1 L) coupled with a CR, referred to as a CR-F (0.8 L). Both systems were connected to a new CR, called CR-I, containing sterile dried rattan pieces at 50% of its working volume. Active yeast cells were inoculated only into the STR, and the medium circulation rate between bioreactors was 5.2 mL/min. The results showed that at least eight successive cycles could be operated with an average PE of 108.51 g/L for System I and 109.44 g/L for System II. The average QP and SC values of both systems were also similar, with values of 1.87 to 1.88 g/L⋅h and 93 to 94%, respectively. The morphology of the carriers with and without immobilized cells before and after the fermentation was investigated. The obtained results demonstrated that a repeated-batch fermentation by immobilized cells on rattan pieces, using an STR coupled with a CR, was successfully used to produce high levels of ethanol from SSJ under a VHG condition.
The Establishment of Evaluation Models for the Cooking Suitability of Different Pork Muscles
Authors: Duan Shengnan , Tang Xiaoyan , Zhan Junliang , Liu Suke , Zhang Yuhui
Published: February 2023

Pork is the main meat consumed by Chinese people. In this study, the sensory quality of four muscles (loin, shoulder, belly, and ham) under three cooking methods (boiling, scalding, and roasting) was examined, and the edible quality and nutritional quality of fresh meat were determined at the same time. Principal component analysis, cluster analysis, correlation analysis, and analysis of the coefficient of variation were used to determine key quality indicators, from which comprehensive quality evaluation equations were established. The results showed that, when meat was boiled, the comprehensive quality evaluation model was Y=0.1537X1+0.1805X2+0.2145X3+0.2233X4+0.2281X5 (X1~X5 are a*, fat, odor, tenderness, and flavor, respectively) and the most suitable muscle was belly; when meat slices were scalded in a hot pot, the comprehensive quality evaluation model was Y=0.1541X1+0.1787X2+0.2160X3+0.2174X4+0.2337X5 (X1~X5 are a*, fat, odor, tenderness, and flavor, respectively) and the most suitable muscle was belly; when meat was roasted, the comprehensive quality evaluation model was Y=0.1539X1+0.1557X2+0.1572X3+0.1677X4+0.1808X5+0.1845X6 (X1~X6 are flavor, marbling, elasticity, cooked flesh color, tenderness, and flesh color, respectively), and the most suitable muscles were belly and shoulder.
Seed Disinfestation Practices to Control Seed-Borne Fungi and Bacteria in Home Production of Sprouts
Authors: Gilbert Gregory S. , Diaz Alyssa , Bregoff Haylee A.
Published: February 2023

Concern over microbial contamination limits the adoption of home production of sprouts as a nutritious and sustainable food. Simple, accessible approaches to seed disinfection could support safe home seed sprouting. Here, we quantify bacterial and fungal contamination of seeds of 14 plant cultivars sold for home sprout production and test a range of chemical and physical methods for seed disinfestation appropriate for home use. Most seeds are contaminated with a variety of bacteria and fungi, and those microbes are usually limited to the seed surface. Heat treatments are not effective for seed disinfection because the high temperatures needed to effectively reduce microbial contamination also reduce seed germination. Two chlorine-based chemical disinfectants—dilute household bleach (0.6% sodium hypochlorite) and freshly generated hypochlorous acid (800 ppm chlorine)—were the most effective disinfection agents tested (up to a 5-log reduction in bacteria) that also did not harm seed germination.
Lipidomic and Proteomic Profiling of the Milk Fat Globule Membrane from Different Industrial By-Products of the Butter and Butter Oil Manufacturing Process
Authors: Señoráns María , Gallo Veronica , Calvo María V. , Fontecha Javier
Published: February 2023

Recent studies have demonstrated the positive effects of regular intake of milk fat globule membranes (MFGMs) on neural and cognitive development, as well as immune and gastrointestinal health in infants and elders. Dairy products and by-products generated from the butter and butter oil manufacturing process are valuable sources of MFGM. Thus, in view of the growing need to reduce by-products and waste, it is crucial to foster research aimed at the valorization of dairy by-products rich in MFGM. For this purpose, all the by-products coming from butter and butter oil production (from raw milk to the related by-products) were used to study the MFGM isolated fractions, followed by their characterization through a combined lipidomic and proteomic approach. The patterns of polar lipids and proteins indicated that buttermilk (BM), butterserum (BS), and their mix (BM-BS blend) are the most suitable by-products to be employed as starting material for the isolation and purification of MFGMs, thus obtaining MFGM-enriched ingredients for the manufacture of products with high biological activity.
A Critical Review of Extraction Methods for Vanadium from Petcoke Ash
Authors: Jammulamadaka Hari , Pisupati Sarma V.
Published: February 2023

Petcoke is a solid carbon-rich residue produced during petroleum refining. Petcoke mineral matter is rich in vanadium that, when alloyed with other metals, can significantly improve its properties. Vanadium extraction from steel slags is well studied, while extraction from secondary sources such as petcoke is not well understood. Vanadium is one of the 50 critical minerals identified by the United States Department of Interior. Considering the annual production of petcoke, it is a valuable secondary source of vanadium, especially in places with no steel production. This review paper critically examines the compositional differences between steel and petcoke slags and the various extraction methods that apply to vanadium production, particularly from petcoke, considering the environmental issues associated with each technique. Information on the characterization of US petcoke is also included to identify specific extraction methods for vanadium.