bluuebunny/crossref_metadata_2025 huggingface dataset:
An Architecture for Distributed Electronic Documents Storage in Decentralized Blockchain B2B Applications
Authors: Hammoud Obadah , Tarkhanov Ivan , Kosmarski Artyom
Published: November 2021

This paper investigates the problem of distributed storage of electronic documents (both metadata and files) in decentralized blockchain-based b2b systems (DApps). The need to reduce the cost of implementing such systems and the insufficient elaboration of the issue of storing big data in DLT are considered. An approach for building such systems is proposed, which allows optimizing the size of the required storage (by using Erasure coding) and simultaneously providing secure data storage in geographically distributed systems of a company, or within a consortium of companies. The novelty of this solution is that we are the first who combine enterprise DLT with distributed file storage, in which the availability of files is controlled. The results of our experiment demonstrate that the speed of the described DApp is comparable to known b2c torrent projects, and subsequently justify the choice of Hyperledger Fabric and Ethereum Enterprise for its use. Obtained test results show that public blockchain networks are not suitable for creating such a b2b system. The proposed system solves the main challenges of distributed data storage by grouping data into clusters and managing them with a load balancer, while preventing data tempering using a blockchain network. The considered DApps storage methodology easily scales horizontally in terms of distributed file storage and can be deployed on cloud computing technologies, while minimizing the required storage space. We compare this approach with known methods of file storage in distributed systems, including central storage, torrents, IPFS, and Storj. The reliability of this approach is calculated and the result is compared to traditional solutions based on full backup.
Connecting Complex Electronic Pattern Formation to Critical Exponents
Authors: Liu Shuo , Carlson Erica W. , Dahmen Karin A.
Published: November 2021

Scanning probes reveal complex, inhomogeneous patterns on the surface of many condensed matter systems. In some cases, the patterns form self-similar, fractal geometric clusters. In this paper, we advance the theory of criticality as it pertains to those geometric clusters (defined as connected sets of nearest-neighbor aligned spins) in the context of Ising models. We show how data from surface probes can be used to distinguish whether electronic patterns observed at the surface of a material are confined to the surface, or whether the patterns originate in the bulk. Whereas thermodynamic critical exponents are derived from the behavior of Fortuin–Kasteleyn (FK) clusters, critical exponents can be similarly defined for geometric clusters. We find that these geometric critical exponents are not only distinct numerically from the thermodynamic and uncorrelated percolation exponents, but that they separately satisfy scaling relations at the critical fixed points discussed in the text. We furthermore find that the two-dimensional (2D) cross-sections of geometric clusters in the three-dimensional (3D) Ising model display critical scaling behavior at the bulk phase transition temperature. In particular, we show that when considered on a 2D slice of a 3D system, the pair connectivity function familiar from percolation theory displays more robust critical behavior than the spin-spin correlation function, and we calculate the corresponding critical exponent. We discuss the implications of these two distinct length scales in Ising models. We also calculate the pair connectivity exponent in the clean 2D case. These results extend the theory of geometric criticality in the clean Ising universality classes, and facilitate the broad application of geometric cluster analysis techniques to maximize the information that can be extracted from scanning image probe data in condensed matter systems.
High Hole Mobility Polycrystalline GaSb Thin Films
Authors: Curran Anya , Gity Farzan , Gocalinska Agnieszka , Mura Enrica , Nagle Roger E. , Schmidt Michael , Sheehan Brendan , Pelucchi Emanuele , O’Dwyer Colm , Hurley Paul K.
Published: November 2021

In this paper, we report on the structural and electronic properties of polycrystalline gallium antimonide (poly-GaSb) films (50–250 nm) deposited on p+ Si/SiO2 by metalorganic vapour phase epitaxy at 475 °C. GaSb films grown on semi-insulating GaAs substrates are included as comparative samples. In all cases, the unintentionally doped GaSb is p-type, with a hole concentration in the range of 2 × 1016 to 2 × 1017 cm−3. Exceptional hole mobilities are measured for polycrystalline GaSb on SiO2 in the range of 9–66 cm2/Vs, exceeding the reported values for many other semiconductors grown at low temperatures. A mobility of 9.1 cm2/Vs is recorded for an amorphous GaSb layer in a poly-GaAs/amorphous GaSb heterostructure. Mechanisms limiting the mobility in the GaSb thin films are investigated. It is found that for the GaSb grown directly on GaAs, the mobility is phonon-limited, while the GaSb deposited directly on SiO2 has a Coulomb scattering limited mobility, and the poly-GaAs/amorphous GaSb heterostructure on SiO2 displays a mobility which is consistent with variable-range-hopping. GaSb films grown at low temperatures demonstrate a far greater potential for implementation in p-channel devices than for implementation in ICs.
Investigating Genetic Diversity and Genomic Signatures of Hatchery-Induced Evolution in Gilthead Seabream (Sparus aurata) Populations
Authors: Gkagkavouzis Konstantinos , Papakostas Spiros , Maroso Francesco , Karaiskou Nikoleta , Carr Adrian , Nielsen Einar Eg , Bargelloni Luca , Triantafyllidis Alexandros
Published: November 2021

The identification of the genetic basis of domestication in fish species is of timely importance for the aquaculture industry in order to increase productivity, quality, and the welfare of farmed fish. The goal of this study is to investigate the largely unknown aquaculture-induced evolution in gilthead seabream, which is one of the most important farmed fish in the Mediterranean region. We used a panel of 1159 genome-wide SNPs, and genotyped 956 fish from 23 wild populations of Mediterranean-wide distribution and 362 farmed fish from five Greek hatcheries. We assessed the genetic diversity of the sampled populations and contrasted the results of four different approaches of outlier detection methods. We recognized one very strong candidate and two good candidate SNPs with evidence for aquaculture-induced evolution in gilthead seabream. The annotation of these SNPs revealed neighboring genes with biological roles from stress tolerance and disease resistance to sexual maturation that may explain our observations. In conclusion, we demonstrate that the genome of gilthead seabream, despite the fact that the species is often suggested to be in the early stages of the domestication process, shows evidence of aquaculture-induced evolution. We report on a list of genes that may explain our observations and that may be investigated further. We anticipate that our findings will stimulate additional research with the use of SNP panels of higher density that can elucidate the genomic architecture of domestication in this species of high aquacultural interest.
Computer Vision Estimation of Physical Parameters and Its Application to Power Requirements of Natural and Artificial Swimmers
Authors: Coe Michael , Gutschmidt Stefanie
Published: November 2021

A useful measure of efficiency of transport in aquatic animals and autonomous underwater vehicles is cost of transport. Often, cost of transport data on specific animals or platforms is not readily available or does not fit specific use cases, but images are readily available. In this work, we present a methodology to synthesize such data without the need for a specimen or laboratory tests. We propose a computer vision in a methodology called Ika-Fit to determine important physical characteristics, such as surface area, slenderness ratio, and mass, that are used for a cost of transport model. The Ika-Fit method provides a good estimation of parameters when compared to biological data and robotic platforms. These parameters are estimated for existing engineered systems, and the model is compared to published data; the model is found to demonstrate higher accuracy using fewer parameters in estimating cost of transport over existing methods.
A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
Authors: Ammari Samy , Sallé de Chou Raoul , Balleyguier Corinne , Chouzenoux Emilie , Touat Mehdi , Quillent Arnaud , Dumont Sarah , Bockel Sophie , Garcia Gabriel C. T. E. , Elhaik Mickael , Francois Bidault , Borget Valentin , Lassau Nathalie , Khettab Mohamed , Assi Tarek
Published: November 2021

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.
A Step Closer to the “Fourth 90”: A Practical Narrative Review of Diagnosis and Management of Nutritional Issues of People Living with HIV
Authors: Bavaro Davide Fiore , Laghetti Paola , Poliseno Mariacristina , De Gennaro Nicolò , Di Gennaro Francesco , Saracino Annalisa
Published: November 2021

The quality of life of people living with HIV (PLWH) has remarkably increased thanks to the introduction of combined antiretroviral therapy. Still, PLWH are exposed to an increased risk of cardiovascular diseases, diabetes, chronic kidney disease, and liver disease. Hence, the purpose of this review is to summarize the current knowledge about diagnosis and nutritional management with specific indication of macro and micronutrients intake for the main comorbidities of PLWH. In fact, a prompt diagnosis and management of lifestyle behaviors are fundamental steps to reach the “fourth 90”. To achieve an early diagnosis of these comorbidities, clinicians have at their disposal algorithms such as the Framingham Score to assess cardiovascular risk; transient elastography and liver biopsy to detect NAFLD and NASH; and markers such as the oral glucose tolerance test and GFR to identify glucose impairment and renal failure, respectively. Furthermore, maintenance of ideal body weight is the goal for reducing cardiovascular risk and to improve diabetes, steatosis and fibrosis; while Mediterranean and low-carbohydrate diets are the dietetic approaches proposed for cardioprotective effects and for glycemic control, respectively. Conversely, diet management of chronic kidney disease requires different nutritional assessment, especially regarding protein intake, according to disease stage and eventually concomitant diabetes.
Ex Vivo Vibration Spectroscopic Analysis of Colorectal Polyps for the Early Diagnosis of Colorectal Carcinoma
Authors: Synytsya Alla , Vaňková Aneta , Miškovičová Michaela , Petrtýl Jaromír , Petruželka Luboš
Published: November 2021

Colorectal cancer is one of the most common and often fatal cancers in humans, but it has the highest chance of a cure if detected at an early precancerous stage. Carcinogenesis in the colon begins as an uncontrolled growth forming polyps. Some of these polyps can finally be converted to colon cancer. Early diagnosis of adenomatous polyps is the main approach for screening and preventing colorectal cancer, and vibration spectroscopy can be used for this purpose. This work is focused on evaluating FTIR and Raman spectroscopy as a tool in the ex vivo analysis of colorectal polyps, which could be important for the early diagnosis of colorectal carcinoma. Multivariate analyses (PCA and LDA) were used to assist the spectroscopic discrimination of normal colon tissue, as well as benign and malignant colon polyps. The spectra demonstrated evident differences in the characteristic bands of the main tissue constituents, i.e., proteins, nucleic acids, lipids, polysaccharides, etc. Suitable models for discriminating the three mentioned diagnostic groups were proposed based on multivariate analyses of the spectroscopic data. LDA classification was especially successful in the case of a combined set of 55 variables from the FTIR, FT Raman and dispersion Raman spectra. This model can be proposed for ex vivo colorectal cancer diagnostics in combination with the colonoscopic extraction of colon polyps for further testing. This pilot study is a precursor for the further evaluation of the diagnostic potential for the simultaneous in vivo application of colonoscopic Raman probes.
Teenagers’ Smartphone Use during Homework: An Analysis of Beliefs and Behaviors around Digital Multitasking
Authors: Mrazek Alissa J. , Mrazek Michael D. , Ortega Joshua R. , Ji Rosie R. , Karimi Sana S. , Brown Chelsea S. , Alexander Chelsie A. , Khan Maliha , Panahi Rhozhen , Sadoff Madeline , Scott Ashley , Tyszka JimiRose E. , Schooler Jonathan W.
Published: November 2021

Although research suggests that phone usage during academic activities is problematic for learning and performance, little is known about high school students’ digital multitasking during homework. This exploratory descriptive study surveyed 135 students from four public U.S. high schools to investigate teenagers’ attitudes towards digital distraction, smartphone use during homework, cell phone dependence, and motivations for digital multitasking. Our findings suggested that teens were distracted during homework about 38% of the time, and both mind-wandering and the use of digital devices contributed to this distraction. Of the students surveyed, 64% believed that they should focus more during homework than they currently did, and most were willing to try strategies such as silencing their phone or putting it out of sight. However, many were not currently using such strategies, and our data suggested that students may be spending approximately 204 h per year trying to complete homework but unintentionally distracted from it. We explored their current motivations and beliefs as a necessary first step for the creation of future interventions to help teens reduce their digital multitasking during homework.
A New Hybrid Prime Code for OCDMA Network Multimedia Applications
Authors: Morsy Morsy A. , Aly Moustafa H.
Published: November 2021

This paper presents a new family of spreading code sequences called hybrid prime code (HPC), to be used as source code for the optical code division multiple access (OCDMA) network for large network capacity. The network capacity directly depends on the number of available code sequences provided and their correlation properties. Therefore, the proposed HPC is designed based on combining two or more different code words belonging to two or more different prime numbers. This increases the number of code sequences generated. The code construction method utilized allows the generation of different code sets, each with different code length and weight, according to the number of prime numbers used. In addition, the incoherent pulse position modulation (PPM) OCDMA system is proposed based on the HPC code. Furthermore, the bit error rate (BER) performance analysis is introduced versus the received optical power and the number of active users. Moreover, the error vector magnitude (EVM) is calculated versus the optical signal-to-noise ratio. This work proves that using two prime numbers simultaneously generates far more codes than using prime numbers separately. It also achieved an OCDMA system capacity higher than the system that uses the optical orthogonal codes (OOCs), modified prime codes (MPCs) families, and two code families with separate simultaneously prime numbers, at a BER below 10−9 which is the optimum level.
Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
Authors: Camarena-Martinez Rocio , Lizarraga-Morales Rocio A. , Baeza-Serrato Roberto
Published: November 2021

Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.
A Review of Different Aspects of Off-Earth Drilling
Authors: Knez Dariusz , Khalilidermani Mitra
Published: November 2021

Off-Earth drilling may be assumed as the second phase of space exploration to discover the unrevealed subsurface on the planetary bodies. It accelerates future space objectives such as in-situ propellant production, mineral exploitation, and space tourism. Owing to the rampant progress in modern technology, the new drill tools mounted on the sophisticated robots are capable to drill the planetary regolith dispersed on the celestial objects; however, formidable obstacles such as microgravity, vacuum condition, and temperature fluctuation as well as the weight limitation, lack of real-time drilling analysis, and remote robot-operator communication impose pressing restrictions on the quick development of space drilling tools. In this study, research on the past and present aspects of off-Earth drilling has been implemented to illuminate the horizon of this technology in the near-term future. The context encompasses a detailed description of the limitations, applications and mechanisms of the different drilling techniques adopted for planetary bodies. A particular emphasis is put on the hydraulic power systems which have not been satisfactorily deployed in off-Earth drilling yet. The research strives to glance over the pivotal aspects of off-Earth drilling to contribute to the future drilling programs planned by the national and private space agencies.
Evaluation of Modular Power Converter Integrated with 5G Network
Authors: Baba Sebastian , Bachman Serafin , Jasinski Marek , Li Hong
Published: November 2021

This paper focuses on two key technical concepts, which may have a tremendous impact on future generations of power electronic converters: the Power Electronic Building Block (PEBB) concept, and the 5G/6G wireless data transfer. It is expected that these two trends may induce development of new cognitive of power electronic converters: Power Electronics 4.0. To investigate this concept, a Proof of Concept (PoC) of PEBB-based power converter integrated with a 5G network, was designed and tested. Study confirmed that power converter assembled from PEBB modules can compete with state-of-the-art devices. Moreover, test results indicates that several challenges related to PEBB and integration of power electronic equipment with 5G network has to be resolved, to enable growth of augmented power electronic converters, especially if wireless data transfer is meant for communication between PEBB modules.
Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
Authors: Perez-Villalpando Marlene A. , Gurubel Tun Kelly J. , Arellano-Muro Carlos A. , Fausto Fernando
Published: November 2021

Optimal operation of hydropower plants (HP) is a crucial task for the control of several variables involved in the power generation process, including hydraulic level and power generation rate. In general, there are three main problems that an optimal operation approach must address: (i) maintaining a hydraulic head level which satisfies the energy demand at a given time, (ii) regulating operation to match with certain established conditions, even in the presence of system’s parametric variations, and (iii) managing external disturbances at the system’s input. To address these problems, in this paper we propose an approach for optimal hydraulic level tracking based on an Inverse Optimal Controller (IOC), devised with the purpose of regulating power generation rates on a specific HP infrastructure. The Closed–Loop System (CLS) has been simulated using data collected from the HP through a whole year of operation as a tracking reference. Furthermore, to combat parametric variations, an accumulative action is incorporated into the control scheme. In addition, a Recurrent Neural Network (RNN) based on Feature Engineering (FE) techniques has been implemented to aid the system in the prediction and management of external perturbations. Besides, a landslide is simulated, causing the system’s response to show a deviation in reference tracking, which is corrected through the control action. Afterward, the RNN is including of the aforementioned system, where the trajectories tracking deviation is not perceptible, at the hand of, a better response with respect to use a single scheme. The results show the robustness of the proposed control scheme despite climatic variations and landslides in the reservoir operation process. This proposed combined scheme shows good performance in presence of parametric variations and external perturbations.
Electrochemical Performance of Graphene-Modulated Sulfur Composite Cathodes Using LiBH4 Electrolyte for All-Solid-State Li-S Battery
Authors: Patodia Tarun , Gupta Mukesh Kumar , Singh Rini , Ichikawa Takayuki , Jain Ankur , Tripathi Balram
Published: November 2021

All-solid-state Li-S batteries (use of solid electrolyte LiBH4) were prepared using cathodes of a homogeneous mixture of graphene oxide (GO) and reduced graphene oxide (rGO) with sulfur (S) and solid electrolyte lithium borohydride (LiBH4), and their electrochemical performance was reported. The use of LiBH4 and its compatibility with Li metal permits the utilization of Li anode that improves the vitality of composite electrodes. The GO-S and rGO-S nanocomposites with different proportions have been synthesized. Their structural and morphological characterizations were performed by X-ray diffraction (XRD) and scanning electron microscopy (SEM), and the results are presented. The electrochemical performance was tested by galvanostatic charge-discharge measurements at a 0.1 C-rate. The results presented here demonstrate the successful implementation of GO-S composites in an all-solid-state battery.
Waste-Based Intermediate Bioenergy Carriers: Syngas Production via Coupling Slow Pyrolysis with Gasification under a Circular Economy Model
Authors: Frantzi Danai , Zabaniotou Anastasia
Published: November 2021

Waste-based feedstocks and bioenergy intermediate carriers are key issues of the whole bioenergy value chain. Towards a circular economy, changing upcycling infra-structure systems takes time, while energy-from-waste (EfW) technologies like waste pyrolysis and gasification could play an integral part. Thus, the aim of this study is to propose a circular economy pathway for the waste to energy (WtE) thermochemical technologies, through which solid biomass waste can be slowly pyrolyzed to biochar (main product), in various regionally distributed small plants, and the pyro-oils, by-products of those plants could be used as an intermediate energy carrier to fuel a central gasification plant for syngas production. Through the performed review, the main parameters of the whole process chain, from waste to syngas, were discussed. The study develops a conceptual model that can be implemented for overcoming barriers to the broad deployment of WtE solutions. The proposed model of WtE facilities is changing the recycling economy into a circular economy, where nothing is wasted, while a carbon-negative energy carrier can be achieved. The downstream side of the process (cleaning of syngas) and the economic feasibility of the dual such system need optimization.
Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data
Authors: Afzal Asif , Bhutto Javed Khan , Alrobaian Abdulrahman , Razak Kaladgi Abdul , Khan Sher Afghan
Published: November 2021

The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function.
Lab-Scale Carbonation of Wood Ash for CO2-Sequestration
Authors: Koch Robin , Sailer Gregor , Paczkowski Sebastian , Pelz Stefan , Poetsch Jens , Müller Joachim
Published: November 2021

This study evaluated the CO2 sequestration potential with combustion ashes in the aqueous phase. The aim was to provide a cost-effective carbon sequestration method for combustion unit operators (flue gas cleaning) or biogas producers (biogas upgrading). Therefore, two separate test series were executed to identify the carbonation efficiency (CE) of bottom wood ash (1) at different mixing ratios with water in batch experiments and (2) under dynamic flow conditions. It was furthermore evaluated whether subsequent use of the carbonated wood ash for soil amendment could be possible and whether the process water could be passed into the sewage. The batch test series showed that different mixing ratios of wood ash and water had an influence on the CE. The flow series showed that the mean CE varied between approximately 14% and 17%. Thus, the ash proved to be suitable for carbonation processes. The process water was dischargeable, and the carbonated wood ash has potential for chalking, as no legal thresholds were exceeded. Therefore, wood ash carbonation could be used as a low-tech CO2 sequestration technology. Compared to existing energy consuming and cost intensive carbon capture and storage technologies, sequestration with ash could be beneficial, as it represents a low-tech approach.
A Comprehensive Review of Small-Signal Stability and Power Oscillation Damping through Photovoltaic Inverters
Authors: Nikolaev Nikolay , Dimitrov Kiril , Rangelov Yulian
Published: November 2021

This paper focuses on the methods that ensure the rotor angle stability of electric power systems, which is most frequently analyzed with small-signal models. Over the past several decades, power system stabilizers (PSSs) for conventional excitation systems were the main tools for improving the small-signal stability of electromechanical oscillatory modes. In the last decade, power oscillation damping (POD) control implemented in photovoltaic (PV) inverters has been considered an alternative to PSSs. As PV generation undergoes massive rollout due to policy directions and renewable energy source integration activities, it could potentially be used as a source of damping, which is crucial for sustaining the rotor angle stability of the remaining in-service synchronous generators. Several studies have already been dedicated to the development of different damping strategies. This paper contributes to the existing research in power system stability by providing a comprehensive review of the effects of PV generation on small-signal stability, as well as the recent evolution of POD control through PV inverters. The features and impacts of the various ways to realize POD controllers are assessed and summarized in this paper. Currently, detailed information and discussions on the practical application of PV inverter PODs are not available. This paper is, thus, intended to initiate a relevant discussion and propose possible implementation approaches concerning the topic under study.
Metal-Air Batteries—A Review
Authors: Olabi Abdul Ghani , Sayed Enas Taha , Wilberforce Tabbi , Jamal Aisha , Alami Abdul Hai , Elsaid Khaled , Rahman Shek Mohammod Atiqure , Shah Sheikh Khaleduzzaman , Abdelkareem Mohammad Ali
Published: November 2021

Metal–air batteries are a promising technology that could be used in several applications, from portable devices to large-scale energy storage applications. This work is a comprehensive review of the recent progress made in metal-air batteries MABs. It covers the theoretical considerations and mechanisms of MABs, electrochemical performance, and the progress made in the development of different structures of MABs. The operational concepts and recent developments in MABs are thoroughly discussed, with a particular focus on innovative materials design and cell structures. The classical research on traditional MABs was chosen and contrasted with metal–air flow systems, demonstrating the merits associated with the latter in terms of achieving higher energy density and efficiency, along with stability. Furthermore, the recent applications of MABs were discussed. Finally, a broad overview of challenges/opportunities and potential directions for commercializing this technology is carefully discussed. The primary focus of this investigation is to present a concise summary and to establish future directions in the development of MABs from traditional static to advanced flow technologies. A systematic analysis of this subject from a material and chemistry standpoint is presented as well.
Formation of Characteristic Polynomials on the Basis of Fractional Powers j of Dynamic Systems and Stability Problems of Such Systems
Authors: Lozynskyy Orest , Mazur Damian , Marushchak Yaroslav , Kwiatkowski Bogdan , Lozynskyy Andriy , Kwater Tadeusz , Kopchak Bohdan , Hawro Przemysław , Kasha Lidiia , Pękala Robert , Ziemba Robert , Twaróg Bogusław
Published: November 2021

The article presents the creation of characteristic polynomials on the basis of fractional powers j of dynamic systems and problems related to the determination of the stability intervals of such systems.
Flattening of the Power Distribution in the HTGR Core with Structured Control Rods
Authors: Górkiewicz Michał , Cetnar Jerzy
Published: November 2021

Control rods (CRs) have a significant influence on reactor performance. Withdrawal of a control rod leaves a region of the core significantly changed due to lack of absorber, leading to increased fission rate and later to Xe135 buildup. In this paper, an innovative concept of structured control rods made of tungsten is studied. It is demonstrated that the radial division of control rods made of tungsten can effectively compensate for the reactivity loss during the irradiation cycle of high-temperature gas-cooled reactors (HTGRs) with a prismatic core while flattening the core power distribution. Implementation of the radial division of control rods enables an operator to reduce this effect in terms of axial power because the absorber is not completely removed from a reactor region, but its amount is reduced. The results obtained from the characteristic evolution of the reactor core for CRs with a structured design in the burnup calculation using the refined timestep scheme show a very stable core evolution with a reasonably low deviation of the power density and Xe135 concentration from the average values. It is very important that all the distributions improve with burnup.
Warm Plasma Application in Tar Conversion and Syngas Valorization: The Fate of Hydrogen Sulfide
Authors: Wnukowski Mateusz , Moroń Wojciech
Published: November 2021

Warm plasma techniques are considered a promising method of tar removal in biomass-derived syngas. The fate of another problematic syngas impurity—hydrogen sulfide—is studied in this work. It is revealed that processing simulated syngas with a microwave plasma results in hydrogen sulfide conversion. For different gas flow rates (20–40 NLPM) and hydrogen sulfide concentrations ranging from 250 ppm to 750 ppm, the conversion rate varies from ca. 26% to 45%. The main sulfur-containing products are carbon disulfide (ca. 30% of total sulfur) and carbonyl sulfide (ca. 8% of total sulfur). Besides them, significantly smaller quantities of sulfates and benzothiophene are also detected. The main components of syngas have a tremendous impact on the fate of hydrogen sulfide. While the presence of carbon monoxide, methane, carbon dioxide, and tar surrogate (toluene) leads to the formation of carbonyl sulfide, carbon disulfide, sulfur dioxide, and benzothiophene, respectively, the abundance of hydrogen results in the recreation of hydrogen sulfide. Consequently, the presence of hydrogen in the simulated syngas is the main factor that determines the low conversion rate of hydrogen sulfide. Conversion of hydrogen sulfide into various sulfur compounds might be problematic in the context of syngas purification and the application of the right technique for sulfur removal.
A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells
Authors: Leonori Stefano , Baldini Luca , Rizzi Antonello , Frattale Mascioli Fabio Massimo
Published: November 2021

Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.
Significance of Hydrogen as Economic and Environmentally Friendly Fuel
Authors: Sharma Shashi , Agarwal Shivani , Jain Ankur
Published: November 2021

The major demand of energy in today’s world is fulfilled by the fossil fuels which are not renewable in nature and can no longer be used once exhausted. In the beginning of the 21st century, the limitation of the fossil fuels, continually growing energy demand, and growing impact of green-house gas emissions on the environment were identified as the major challenges with current energy infrastructure all over the world. The energy obtained from fossil fuel is cheap due to its established infrastructure; however, these possess serious issues, as mentioned above, and cause bad environmental impact. Therefore, renewable energy resources are looked to as contenders which may fulfil most energy requirements. Among them, hydrogen is considered as the most environmentally friendly fuel. Hydrogen is clean, sustainable fuel and it has promise as a future energy carrier. It also has the ability to substitute the present energy infrastructure which is based on fossil fuel. This is seen and projected as a solution for the above-mentioned problems including rise in global temperature and environmental degradation. Environmental and economic aspects are the important factors to be considered to establish hydrogen infrastructure. This article describes the various aspects of hydrogen including production, storage, and applications with a focus on fuel cell based electric vehicles. Their environmental as well as economic aspects are also discussed herein.
Torque Ripple Minimizing of Uniform Slot Machines with Delta Rotor via Subdomain Analysis
Authors: Lee Minhyeok , Hwang Yunkyung , Nam Kwanghee
Published: November 2021

Since the slot opening is large in the uniform slot machine, the torque ripple generated by overlapping or misaligning with the rotor cavity is remarkably large in the case of interior permanent magnet (IPM) machine. In this work, it is observed that the magnitude of torque ripple depends strongly on the phase difference between air-gap field harmonics: The ripple is minimized when the two dominant harmonic components cancel each other. Based on this fact, a condition is developed to minimize torque ripple by adjusting the q-flux channel width and d-flux barrier width. The torque ripple minimizing solution is found from a level chart made by subdomain time-stepping analysis. Finite element analysis (FEA) also gives a very similar minimizing solution. A prototype machine is manufactured, and its performances are validated through experiments.
Social Preferences of Young Adults Regarding Urban Forest Recreation Management in Warsaw, Poland
Authors: Mandziuk Agnieszka , Fornal-Pieniak Beata , Stangierska Dagmara , Parzych Stanisław , Widera Katarzyna
Published: November 2021

The paper presents results of questionnaire research conducted to determine social preferences regarding leisure in Warsaw municipal forests (WMF) (Poland). The preferences pertained to frequency of visits and willingness to pay for recreational infrastructure in a forest depending on its appearance, level of development, and the respondent’s income. The data were processed using statistical analysis (ANOVA Friedman test, U Mann–Whitney test, logistic regression). The results show that the higher the development level of a forest, the more often young people choose it for leisure. Willingness to pay for a forest depends on its development level, with respondents more willing to fund forests with a higher development level. No such correlation with the respondents’ income was found. Preferences regarding the selection of a specific type of forest for leisure were dependent on per capita income of the respondents only in the case of forests at a lower level of development. A forest’s preference level affected the willingness to pay for it and varied depending on the development level.
Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features
Authors: Pan Xi , Li Kang , Chen Zhangjing , Yang Zhong
Published: November 2021

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.
Production, Purification, and Potential Health Applications of Edible Seeds’ Bioactive Peptides: A Concise Review
Authors: Samtiya Mrinal , Acharya Sovon , Pandey Kush Kumar , Aluko Rotimi E. , Udenigwe Chibuike C. , Dhewa Tejpal
Published: November 2021

Edible seeds play a significant role in contributing essential nutritional needs and impart several health benefits to improve the quality of human life. Previous literature evidence has confirmed that edible seed proteins, their enzymatic hydrolysates, and bioactive peptides (BAPs) have proven and potential attributes to ameliorate numerous chronic disorders through the modulation of activities of several molecular markers. Edible seed-derived proteins and peptides have gained much interest from researchers worldwide as ingredients to formulate therapeutic functional foods and nutraceuticals. In this review, four main methods are discussed (enzymatic hydrolysis, gastrointestinal digestion, fermentation, and genetic engineering) that are used for the production of BAPs, including their purification and characterization. This article’s main aim is to provide current knowledge regarding several health-promoting properties of edible seed BAPs in terms of antihypertensive, anti-cancer, antioxidative, anti-inflammatory, and hypoglycemic activities.
Structure Characterization of Polysaccharide from Chinese Yam (Dioscorea opposite Thunb.) and Its Growth-Promoting Effects on Streptococcus thermophilus
Authors: Ouyang Jia , Wang Feng , Li Wenjia , Li Qingming , Su Xiaojun
Published: November 2021

To clarify the mechanisms underlying the growth-promoting effects of yam polysaccharide on Streptococcus thermophilus (S. thermophilus), the yam polysaccharide was extracted using a deep eutectic solvents (DESs) method and separated into four fractions by DEAE-cellulose 52. These fractions were used as the alternative carbon source to substitute lactose to compare their growth-promoting effects on S. thermophilus. Furthermore, their molecular weight, monosaccharide and functional groups’ composition, microscopic forms and other basic structure characterizations were analyzed. The results showed that all the fractions could significantly promote S. thermophilus growth, and fractions exhibited significantly different growth-promoting effects, whose viable count increased by 6.14, 6.03, 11.48 and 11.29%, respectively, relative to those in the M17 broth medium. Structure-activity relationship analysis revealed that the high growth-promoting activity of yam polysaccharide might be more dependent on the higher molecular weight, the higher galacturonic acid content and its complex spatial configuration, and the existence of β-glycosides would make the yam polysaccharide have a better growth-promoting effect on S. thermophilus.
Chemical Characterization, Nutritional and Bioactive Properties of Physalis peruviana Fruit from High Areas of the Atacama Desert
Authors: Muñoz Patricio , Parra Felipe , Simirgiotis Mario J. , Sepúlveda Chavera Germán F. , Parra Claudio
Published: November 2021

Physalis peruviana L. belongs to the Solanaceae family and produces a spherical fruit used to treat various diseases. However, the chemical composition, nutritional characterization, and bioactive properties of the P. peruviana growing in the Andean region of the Atacama Desert have not been conducted so far. The results showed clear differences in the nutritional and bioactive characteristics of the fruits grown in arid environmental conditions, which were comparable to those from countries with a production tradition. The fruits studied showed a higher Ca, Cu, Mn, P, and Zn content and bioactive compounds such as flavonoids and tannins than those reported in the literature. UHPLC was performed to determine the main phenols. Gallic acid was identified as the predominant phenolic compound in this species (303.63 mg/100 g FW), of which to our knowledge no previous study has reported similar concentrations in this species. Moreover, Cape gooseberry extract has antioxidant and antimicrobial activity against Gram-positive and Gram-negative bacteria. Pseudomonas syringae (MIC 0.313 mg/mL and MBC 1.25 mg/mL) was the most susceptible bacterium. Meanwhile, Erwinia rhapontici was the most resistant bacterium (MIC and MIB 5.00 mg/mL). Furthermore, it was found to inhibit α-amylase activity with an IC50 value (39.28 µg/mL) similar to that of acarbose (35.74 µg/mL). These results expand the knowledge of the species cultivated in arid environmental conditions and suggest an alternative for the potential use of this fruit to manage chronic diseases such as diabetes.