bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
Authors: Ren Huarong , Xu Weikai , Wang Lin
Published: August 2022

Recently, orthogonal time frequency space modulation with index modulation (OTFS-IM) has been proposed to improve the bit-error-rate (BER) performance of the OTFS system. However, only some of the grids in the OTFS-IM system are activated, resulting in low spectral efficiency (SE). In order to solve this problem, a new scheme called multiple-mode OTFS-IM (MM-OTFS-IM) is proposed in this paper. In the proposed scheme, all grids are activated to transmit modulation bits. Each grid in the subblock adopts a different modulation mode, and the index bits are transmitted implicitly by the combination of different constellation modes. At the receiver, a distance-based signal detection algorithm is designed, which uses the distance matrix to find the combination of the minimum sum of elements to recover the index bits. The simulation results demonstrate the enhanced performance of the proposed scheme in the time-varying channels.
UCAV Air Combat Maneuver Decisions Based on a Proximal Policy Optimization Algorithm with Situation Reward Shaping
Authors: Yang Kaibiao , Dong Wenhan , Cai Ming , Jia Shengde , Liu Ri
Published: August 2022

Autonomous maneuver decision by an unmanned combat air vehicle (UCAV) is a critical part of air combat that requires both flight safety and tactical maneuvering. In this paper, an unmanned combat air vehicle air combat maneuver decision method based on a proximal policy optimization algorithm (PPO) is proposed. Firstly, a motion model of the unmanned combat air vehicle and a situation assessment model of air combat was established to describe the motion situation of the unmanned combat air vehicle. An enemy maneuver policy based on a situation assessment with a greedy algorithm was also proposed for air combat confrontation, which aimed to verify the performance of the proximal policy optimization algorithm. Then, an action space based on a basic maneuver library and a state observation space of the proximal policy optimization algorithm were constructed, and a reward function with situation reward shaping was designed for accelerating the convergence rate. Finally, a simulation of air combat confrontation was carried out, which showed that the agent using the proximal policy optimization algorithm learned to combine a series of basic maneuvers, such as diving, climb and circling, into tactical maneuvers and eventually defeated the enemy. The winning rate of the proximal policy optimization algorithm reached 62%, and the corresponding losing rate was only 11%.
Wi-CAL: A Cross-Scene Human Motion Recognition Method Based on Domain Adaptation in a Wi-Fi Environment
Authors: Hao Zhanjun , Niu Juan , Dang Xiaochao , Feng Danyang
Published: August 2022

In recent years, research on Wi-Fi sensing technology has developed rapidly. This technology automatically senses human activities through commercial Wi-Fi devices, such as lying down, falling, walking, waving, sitting down, and standing up. Because the movement of human parts affects the transmission of Wi-Fi signals, resulting in changes in CSI. In the context of indoor monitoring of human health through daily behavior, we propose Wi-CAL. More precisely, CSI fingerprints were collected at six events in two indoor locations, and data enhancement technology Dynamic Time Warping Barycentric Averaging (DBA) was used to expand the data. Then the feature weighting algorithm and convolution layer are combined to select the most representative CSI data features of human action. Finally, a classification model suitable for multiple scenes was obtained by blending the softmax classifier and CORrelation ALignment (CORAL) loss. Experiments are carried out on public data sets and the data sets before and after the expansion collected in this paper. Through comparative experiments, it can be seen that our method can achieve good recognition performance.
Multimodal CT Image Synthesis Using Unsupervised Deep Generative Adversarial Networks for Stroke Lesion Segmentation
Authors: Wang Suzhe , Zhang Xueying , Hui Haisheng , Li Fenglian , Wu Zelin
Published: August 2022

Deep learning-based techniques can obtain high precision for multimodal stroke segmentation tasks. However, the performance often requires a large number of training examples. Additionally, existing data extension approaches for the segmentation are less efficient in creating much more realistic images. To overcome these limitations, an unsupervised adversarial data augmentation mechanism (UTC-GAN) is developed to synthesize multimodal computed tomography (CT) brain scans. In our approach, the CT samples generation and cross-modality translation differentiation are accomplished simultaneously by integrating a Siamesed auto-encoder architecture into the generative adversarial network. In addition, a Gaussian mixture translation module is further proposed, which incorporates a translation loss to learn an intrinsic mapping between the latent space and the multimodal translation function. Finally, qualitative and quantitative experiments show that UTC-GAN significantly improves the generation ability. The stroke dataset enriched by the proposed model also provides a superior improvement in segmentation accuracy, compared with the performance of current competing unsupervised models.
Virtual Training System for Unmanned Aerial Vehicle Control Teaching–Learning Processes
Authors: Ruiz Ricardo J. , Saravia Jorge L. , Andaluz Víctor H. , Sánchez Jorge S.
Published: August 2022

The present work is focused on the development of a Virtual Environment as a test system for new advanced control algorithms for an Unmanned Aerial Vehicles. The virtualized environment allows us to visualize the behavior of the UAV by including the mathematical model of it. The mathematical structure of the kinematic and dynamic models is represented in a matrix form in order to be used in different control algorithms proposals. For the dynamic model, the constants are obtained experimentally, using a DJI Matrice 600 Pro UAV. All of this is conducted with the purpose of using the virtualized environment in educational processes in which, due to the excessive cost of the materials, it is not possible to acquire physical equipment; moreover, is it desired to avoid damaging them. Finally, the stability and robustness of the proposed controllers are determined to ensure analytically the compliance with the control criteria and its correct operation.
Multilevel Pyramid Network for Monocular Depth Estimation Based on Feature Refinement and Adaptive Fusion
Authors: Xu Huihui , Li Fei
Published: August 2022

As a traditional computer vision task, monocular depth estimation plays an essential role in novel view 3D reconstruction and augmented reality. Convolutional neural network (CNN)-based models have achieved good performance for this task. However, in the depth map recovered by some existing deep learning-based methods, local details are still lost. To generate convincing depth maps with rich local details, this study proposes an efficient multilevel pyramid network for monocular depth estimation based on feature refinement and adaptive fusion. Specifically, a multilevel spatial feature generation scheme is developed to extract rich features from the spatial branch. Then, a feature refinement module that combines and enhances these multilevel contextual and spatial information is designed to derive detailed information. In addition, we design an adaptive fusion block for improving the capability of fully connected features. The performance evaluation results on public RGBD datasets indicate that the proposed approach can recover reasonable depth outputs with better details and outperform several depth recovery algorithms from a qualitative and quantitative perspective.
Wavelet-Transform-Based Sparse Code Multiple Access for Power Line Communication
Authors: Sarwar Muhammad Sajid , Baig Sobia , Asif Hafiz M. , Raahemifar Kaamran , Al-Busaidi Samir
Published: August 2022

This paper presents Discrete Wavelet Transformed Sparse Code Multiple Access (DWT-SCMA) in Power Line Communication (PLC) systems. In the present internet of things era, PLC provides an established infrastructure for low-cost and reliable indoor connectivity. PLC systems can benefit from the Sparse Code Multiple Access (SCMA) technique, which allows multiple users to access a frequency slot simultaneously to maximize spectrum efficiency. However, interuser interference arises in SCMA when numerous users map their data to the same frequency resource; this, in turn, is likely to be enhanced by the noisy PLC channel. This article adopts the intriguing aspects of DWT to address the interference difficulties. A mathematical model of the proposed technique is also presented and compared with Fast Fourier Transformed SCMA (FFT-SCMA). In the PLC environment, DWT-SCMA is found to outperform FFT-SCMA.
A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
Authors: El-Mottaleb Somia A. Abd , Métwalli Ahmed , Chehri Abdellah , Ahmed Hassan Yousif , Zeghid Medien , Khan Akhtar Nawaz
Published: August 2022

Code classification is essential nowadays, as determining the transmission code at the receiver side is a challenge. A novel algorithm for fixed right shift (FRS) code may be employed in embedded next-generation optical fiber communication (OFC) systems. The code aims to provide various quality of services (QoS): audio, video, and data. The Q-factor, bit error rate (BER), and signal-to-noise ratio (SNR) are studied to be used as predictors for machine learning (ML) and used in the design of an embedded QoS classifier. The hypothesis test is used to prove the ML input data robustness. Pearson’s correlation and variance-inflation factor (VIF) are revealed, as they are typical detectors of a data multicollinearity problem. The hypothesis testing shows that the statistical properties for the samples of Q-factor, BER, and SNR are similar to the population dataset, with p-values of 0.98, 0.99, and 0.97, respectively. Pearson’s correlation matrix shows a highly positive correlation between Q-factor and SNR, with 0.9. The highest VIF value is 4.5, resulting in the Q-factor. In the end, the ML evaluation shows promising results as the decision tree (DT) and the random forest (RF) classifiers achieve 94% and 99% accuracy, respectively. Each case’s receiver operating characteristic (ROC) curves are revealed, showing that the RF outperforms the DT classification performance.
Circulating Current Control of Phase-Shifted Carrier-Based Modular Multilevel Converter Fed by Fuel Cell Employing Fuzzy Logic Control Technique
Authors: Priya Murthy , Ponnambalam Pathipooranam
Published: August 2022

The contribution of the modular multilevel converter (MMC) in integrating non-conventional energy sources into the grid is significant; the integration of fuel cells with distributed energy sources is especially prominent as they provide a constant voltage and current for constant load applications. Still, there is a high demand for a high-quality power conditioning unit since there is an occurrence of frequent power spikes. Further, the circulating current (CC) in phase legs is an inherent phenomenon of MMC that must be mitigated. Hence, this article proposed an MMC incorporating a fuzzy logic controller (FLC)-based technique to control the circulating currents. The fuzzy controller effectively reduced the harmonics of the CC in the dc-link system. In addition, phase-shifted carrier (PSC) modulation was employed for the MMC to improve the capacitor voltage balancing to maintain the constant input voltage. Moreover, a mathematical analysis of PSC modulation for MMC was performed to identify the PWM harmonic characteristics of the output voltage and the CC. The performance analysis of the proposed system was tested using the hardware in loop (HIL) simulation with the help of the real-time simulator OP-5700 to verify the feasibility.
Polymer Membranes for Enthalpy Exchangers
Authors: Križo Kamil , Kapjor Andrej , Holubčík Michal
Published: August 2022

A membrane-based enthalpy exchanger is a device used for heat and humidity recovery in ventilated buildings. The energy-saving potential of such a device is dependent on the parameters responsible for heat and moisture recovery. The trend is toward composite membranes, which are custom produced, and their parameters can be adjusted for a given application; therefore, the diffusion and sorption characteristics of such membranes are unknown. In order to obtain the values of the water vapor diffusivity of three investigated handmade membranes, a serial resistance model using a Field and Laboratory Emission Cell (FLEC) is proposed. Experiments were conducted to identify the resistance in each step of the moisture transfer process to extract the moisture diffusivity in the membranes. The calculated moisture diffusivities in the membranes were 8.99 × 10−12 (m2/s) for the membranes from cellulose acetate, 1.9 × 10−10 (m2/s) for the microporous PE/PUR membranes, and 1.53 × 10−11 (m2/s) for the PET/PUR microfibrous membranes. The obtained membrane diffusivities were then used in the proposed effectiveness-NTU-based model of an exchanger with a cross-flow arrangement to predict performance under various operating conditions. The results show that the highest latent effectiveness was found for the exchanger core made from the PE/PUR membrane and the lowest was for the one with the PE/PUR membrane core.
What Drives Petrol Price Dispersion across Australian Cities?
Authors: Ghazanfari Arezoo
Published: August 2022

Petrol directly impacts the ability of and extent to which households can engage in day-to-day activities and ultimately directly influences the aggregate economic activity. Petrol price increases can lead to major economic disruption, especially among the most vulnerable, such as low-income families. In Australia, petrol prices differ substantially between metropolitan and regional areas, and regional drivers must pay more for purchasing petrol than those in capital cities. This research explored why retail petrol prices vary from one city to another within and between Australian regions. In this study, clustering methods and panel models will be used to identify factors that cause price differences. The findings revealed that a considerable part of the price differences arose from specific characteristics of cities that influence the demand and supply of petrol, thereby causing variations in price decisions. Petrol prices were substantially influenced by wholesale petrol prices, oil prices, petrol and diesel vehicles, population density, station density, and public transport accessibility. These factors are the main determinants that contribute significantly to price variations between Australian cities. The findings provide critical information for economic agents that interact in this market. From a social welfare perspective, government authorities can consider these factors to improve living standards and consumers’ welfare under price pressure in regional cities.
The Effect of Mining Remnants on Elastic Strain Energy Arising in the Tremor-Inducing Layer
Authors: Burtan Zbigniew , Chlebowski Dariusz
Published: August 2022

A vast majority of hard coal deposits in Poland have a multi-seam structure, hence the presence of mining remnants left from previous operations. The impact of those remnants (exploitation edges or residual pillars) can further intensify geomechanical phenomena occurring in the rock mass, leading to changes in the original state of stress. This applies to all layers within the rock strata, including thick and coherent ones (referred to as tremor-inducing layers) where the impacts of mining remnants are likely to trigger tremors, thus enhancing the rock bursts hazard. In the light of the geomechanical model of rock strata recalled in the study, it is assumed that homogeneous and isotropic elastic layers are found between the considered mining remnant (which is revealed as the stress distribution), and the rock medium modelled as a homogeneous and isotropic half-plane. Development of the state of stress in the bedded medium was brought down to the analysis of interacting elastic layers, where the biharmonic equation is satisfied for each layer and for each respective half-plane. This equation can be solved by the integral Fourrier transform method. The impacts of the exploitation edge and the residual pillar on the elastic strain energy in the tremor-inducing layer is illustrated by recalling the Burzyński’s stress criterion. Strain energy in the tremor-inducing layer was analysed for various deformation properties of the surrounding strata and for various methods of coal extraction from the seam underneath the tremor-inducing layer. The results of the study evidence that a change in deformation properties of strata in the vicinity of the tremor-inducing layer may affect the state of stress and strain energy, which impacts on the tremor hazard levels in the vicinity of mining remnants areas.
A Novel Permeability Model of Coal Considering Gas Slippage and Gas Sorption-Induced Strain
Authors: Wang Man , Zou Junpeng
Published: August 2022

As an unconventional natural gas, coalbed methane (CBM) has been recognized as a significant fuel and chemical feedstock that should be recovered. Permeability is a key factor that controls CBM transport in coal. The slippage effect is an influential phenomenon that occurs during gas penetration processes, especially in low-permeable media. Apparent permeability may differ greatly from intrinsic permeability due to gas slippage. However, the gas slippage effect has not been considered in most analytical permeability models. Based on the cubic law, a new analytical model suited for the permeability analysis of coal under different stress conditions is derived, taking into consideration gas slippage and matrix shrinkage/swelling due to gas desorption/adsorption. To enhance its application, the model is derived under constant hydrostatic stress and pore pressure. The new analytical model is then compared with the existing models, and its reliability is verified by the comparison between the analytical prediction and the experimental permeability data under different stress conditions.
The Transmission Mechanisms and Impacts of Oil Price Fluctuations: Evidence from DSGE Model
Authors: Zhang Bei , Ai Xiaoqing , Fang Xingming , Chen Shi
Published: August 2022

This paper constructs an open economy dynamic stochastic general equilibrium (DSGE) model with oil to investigate the transmission mechanism and impact effects of oil price fluctuations driven by different factors on China’s macroeconomy using quarterly data from 1996 to 2019. The results show that the international crude oil supply-driven oil price decline promotes positive output growth in the short run through the positive cost effect of the supply channel, and the production regulation cost will dampen the incentive to invest in the new energy sector in the long run. Domestic economic development demand-driven oil price increases act on the demand channel, driving output and oil prices to fluctuate in the same direction, generating a negative real balance effect on the economy through the interest rate channel. The oil-specific demand driven by foreign nominal interest rate shocks is transmitted through the exchange rate channel, triggering imported inflation, lower aggregate demand, and lower output. Different sources of oil price fluctuations have different transmission mechanisms and thus differential effects. For this reason, based on the root causes of oil price fluctuations, policy recommendations to deal with international oil price fluctuations in the new situation are proposed at the supply level, demand level, and international level.
Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1
Authors: Almteiri Mohannad , Kim Juyoul
Published: August 2022

The United Arab Emirates (UAE) built four nuclear power plants at the Barakah site to supply 25% of the region’s electricity. Among the Barakah Nuclear Power Plants, (BNPPs), their main objectives are to achieve the highest possible safety for the environment, operators, and community members; quality nuclear reactors and energy; and power production efficiency. To meet these objectives, decision-makers must access large amounts of data in the case of a nuclear accident to prevent the release of radioactive materials. Machine learning offers a feasible solution to propose early warnings and help contain accidents. Thus, our study aimed at developing and testing a machine learning model to classify nuclear accidents using the associated release of radioactive materials. We used Radiological Assessment System for Consequence Analysis (RASCAL) software to estimate the concentration of released radioactive materials in the four seasons of the year 2020. We applied these concentrations as predictors in a classification tree model to classify three types of severe accidents at Unit 1 of BNPPs each season. The average accuracy of the classification models in the four seasons was 97.3% for the training data and 96.5% for the test data, indicating a high efficacy. Thus, the generated classification models can distinguish between the three simulated accidents in any season.
Energy-Efficient beyond 5G Multiple Access Technique with Simultaneous Wireless Information and Power Transfer for the Factory of the Future
Authors: Albataineh Zaid , Andrawes Admoon , Abdullah Nor , Nordin Rosdiadee
Published: August 2022

In the Industrial Internet of Things (IIoT), non-orthogonal multiple access (NOMA) has emerged as a viable multiple access method due to its superior efficiency. In this paper, a new power allocation technique for NOMA-enabled IIoT devices is presented with trade-offs between increasing energy efficiency and decreasing power consumption. We present a joint optimization of transmission rate and energy harvesting in simultaneous wireless information and power transfer (SWIPT) NOMA-enabled IIoT devices. With the power splitting (PS) approach, we examine how to improve overall transmission rate and harvested energy, simultaneously, while fulfilling the minimum rate and harvested energy needs of each IIoT device in a SWIPT-enabled NOMA system. An objective function is established by adding transmission rates obtained from information decoding and the transformed throughput from energy harvesting. The combination of management approaches with Industry 4.0 technology provides a viable strategy to decrease industrial production’s energy use. Several performance metrics may be utilized to study manufacturing process optimization. The efficiency of production equipment may be measured by looking at the overall effectiveness (OE) of the equipment in use. We divide the non-convex optimization problem into two sub-problems, based on the Lagrangian duality method, and solve them to find the optimal solution for the non-convex problem. The approach is validated based on physical layer parameter settings that represent potential factory of the future scenarios. Simulation results confirm the effectiveness of the presented method in a SWIPT-enabled NOMA system, provide considerable performance gains over the classic rate maximization strategy, and demonstrate the energy efficiency of the presented method compared with the conventional system. The results show huge potential of our solutions to reduce the future huge energy demand related to factory automation.
A Rapid Solver for the Prediction of Flow-Field of High-Speed Vehicle Moving in a Tube
Authors: Abdulla Mohammed , Juhany Khalid A.
Published: August 2022

High-speed vehicles traveling in a tube with pressures similar to those experienced by aircraft at their maximum altitude are presented. Although the concept resembles Hyperloop, the pressure level investigated here is much higher and safer than that suggested by Hyperloop, and, therefore, the system design is markedly different. Calculating a vehicle’s aerodynamic performance in the initial design stages requires low-budget computational tools to enable iterative design processes. This study presents an algorithm for rapid flow-field prediction based on a one-dimensional Reimann solution, including viscosity and heat transfer effects. The flow-field is divided into near- and far-fields, where the near-field represents the solution directly around the vehicle. The far-field demonstrates the impact of the vehicle’s motion on the vehicle’s flow-field upstream and downstream. Two-dimensional URANS models are compared to the current numerical scheme. The developed algorithm analyzes the flow-field and the propagation of pressure waves along the tube to simulate the vehicle’s movement. The one-dimensional model shows the robustness and predictability of the near and far flow-fields. The results from the developed scheme provide good agreement, with less than a few percent deviations, compared to CFD simulations but with significantly lower computational resources.
Getting Flow: The Place of Production Forests in the Rise of Mountain Biking
Authors: Bayne Karen M. , Scott Matthew B. , Yao Richard T.
Published: August 2022

Mountain biking has increased in popularity in many countries over recent decades. Production forests provide not only an outstanding space for mountain bike riding, but also specific characteristics conducive to purpose-built track building. With recreational access to production forests, managers must balance commercial operations and industry environmental obligations with the interests and actions of riders. Production forests in New Zealand have accommodated mountain biking activities for decades. The trend from 1991 to 2022 showed overall increasing opportunities for mountain biking in production forests, particularly in smaller commercial peri-urban plantations and forest parks maintained as dedicated mountain bike parks. Over the same period, public mountain biking access to larger (>1500 ha) forests peaked in 2008. These recreational changes within forests, and the impact these changes have pressed onto forest managers, have not been well documented. This paper explores the rise in popularity of mountain biking in New Zealand’s production plantation forests, and the response of forest owners and managers to increased mountain biking activities in their commercial forest estates. The paper discusses implications for forest planning to accommodate active recreational sports such as mountain biking in production forests, suggesting policies and procedures to help protect commercial interests and forest ecology, while allowing for a contemporary mix of recreational activities.
The Long-Term Capital-Market Performance of the Forestry Sector: An Investors’ Perspective
Authors: Beljan Karlo , Brener Magdalena , Dolinar Denis
Published: August 2022

High risk-adjusted returns, low correlation with financial asset classes and inflation hedging are investment characteristics that make forests a desirable investment opportunity. To examine returns on forestry investments (from 2011 to 2020), we focused solely on 48 forest companies (across the globe) that were listed on stock exchanges. Results indicate the economic justification of investing in publicly traded forestry companies. The positive five-year beta coefficients (β) range from 0.21 to 3.46, amounting to 1.15 on average. Taking the last 10-year comparison of the world’s most common capital market benchmarks, the highest return was achieved by the S&P500 (13.8% on average) followed by forestry companies (9.1%), U.S. Treasury bonds (4.4%), and gold (3.0%). Forestry companies, along with their associated business activities (sawmilling, final products production, and paper production), show the best historical performance from an investor’s point of view (total return of 13.2%).
Study on the Genetic Variation of Triadica sebifera (Linnaeus) Small Populations Based on SSR Markers
Authors: Zhou Pengyan , Zhou Qi , Dong Fengping , Shen Xin , Li Yingang
Published: August 2022

Triadica sebifera (Linnaeus) Small is a tree species native to China. The seeds of T. sebifera are rich in oil and are widely used in industrial fields. To explore the genetic diversity and genetic differentiation of T. sebifera germplasm resources, 10 pairs of microsatellite markers were applied to 203 samples collected from eight populations. Forty-three alleles were detected. The average expected heterozygosity (He = 0.491) revealed a low level of genetic diversity. The genetic differentiation among T. sebifera populations was low (Fst = 0.026), which might be related to high gene flow (average Nm = 11.151). Genetic distance and structure results further confirmed that the genetic compositions of the eight populations were quite similar. One of the possible reasons for this phenomenon is that the early introduction and cultivation of T. sebifera were common, so gene exchange was frequent among populations. However, UPGMA clustering results indicated that the eight T. sebifera populations could still be divided into three categories. The classification was related to their geographical location: the southwestern group (ZY), central group (HG and XY) and eastern group (LS, HS, LX, XZ and LY). The reason for this differentiation might be severe deforestation following the decline in T. sebifera economic status. In addition, the central XY population had the largest number of rare alleles (4). In conclusion, although T. sebifera germplasm resources had a low level of genetic diversity, several rare alleles were detected in the central populations, which are valuable for breeding. These resources should be conserved to maintain genetic diversity in the T. sebifera populations. Moreover, geographical distances were important reasons for the limited genetic variations among the populations.
Morphophysiological Acclimation of Developed and Senescing Beech Leaves to Different Light Conditions
Authors: Kraj Wojciech , Ślepaczuk Artur
Published: August 2022

Common beech is a shade-tolerant tree species that can adapt to varying light intensities at the level of whole plants, crown fragments, and individual leaves. The acclimation abilities of the morphological, physiological, and biochemical characteristics of beech leaves were experimentally determined in tree crowns for different levels of light availability. About 24% higher length, width, and area and about 35% higher thickness were recorded in the sun leaves compared with shade leaves. Lower and earlier maximum leaf pigment levels, a faster degradation of leaf pigments during senescence, and a higher chlorophyll a/b ratio were observed in fully sun-grown leaves compared with leaves growing deeper in the canopy. Changes in the intensity of oxidative stress and the differential ability of developing and senescing leaves to defend against this effect under different light conditions were determined. This resulted in a higher redox imbalance and faster senescence in the outer parts of the tree crowns. Due to higher ascorbic acid and glutathione content and slower activity loss of antioxidative enzymes involved in superoxide anion and hydrogen peroxide decomposition, better control over the redox balance, oxidative stress, and senescence induction was noted in the sun leaves.
The Impact of Indigenous Non-Saccharomyces Yeasts Inoculated Fermentations on ‘Semillon’ Icewine
Authors: Wang Jing , Ma Yuwen , Sam Faisal Eudes , Gao Pingping , Liang Lihong , Peng Shuai , Li Min
Published: August 2022

The emerging low acidity in icewine grapes is becoming a major problem in producing quality icewine. Using non-Saccharomyces cerevisiae yeasts in fermentation can improve wine’s organoleptic characteristics and aromatic quality. This study evaluated two indigenous non-Saccharomyces cerevisiae yeasts, Lachancea thermotolerans (LT-2) and Torulaspora delbrueckii (TD-3), for their ability to improve the acidity and quality of ‘Semillon’ icewine. Five different inoculation schemes were implemented, including a single inoculation of S. cerevisiae (SC), L. thermotolerans (LT), and T. delbrueckii (TD); the sequential inoculation of L. thermotolerans, followed by S. cerevisiae after 6 days (L-S); and the sequential inoculation of L. thermotolerans, followed by T. delbrueckii after 6 days (L-D). The results showed that, during sequential fermentation (L-S and L-D), the presence of S. cerevisiae or T. delbrueckii slightly restrained the growth of L. thermotolerans. Single or sequential inoculation with L. thermotolerans and T. delbrueckii significantly reduced the amount of volatile acidity and increased the glycerol content. Furthermore, fermentations involving L. thermotolerans produced relevant amounts of lactic acid (2.04–2.2 g/L) without excessive deacidification of the icewines. Additionally, sequential fermentations increased the concentration of terpenes, C13-norisoprenoid compounds, and phenethyl compounds. A sensory analysis also revealed that sequentially fermented icewines (L-S and L-D) had more fruity and floral odors and aroma intensity. This study highlights the potential application of L. thermotolerans and T. delbrueckii in sequential fermentation to improve the icewine quality.
A Comparative Study of the Design Frameworks of the Ghanaian and Nigerian Central Banks’ Digital Currencies (CBDC)
Authors: Ahiabenu Kwami
Published: August 2022

This paper discusses critical considerations in the design of central bank digital currency (CBDC) in West Africa through a comparative case study of Ghana’s (eCedi) and Nigeria’s (eNaira) design frameworks. This paper analyses CBDC design options framed through context (digital payment landscape and CBDC objectives), technical aspects (design principles, architecture, risks), use cases, and deployment plans. This study conducted a thematic analysis of official CBDC design documents to identify similarities, differences, and patterns. The results indicate more similarities between the eCedi and eNaira designs than differences. Differences were observed in the CBDC deployment context, risk profiles, and plans. Surprisingly, neither country has articulated the detailed legal and regulatory environments for CBDC. This paper highlights the use of CBDC designs to promote citizens’ welfare by using financially inclusive policy goals within central banking’s welfare functions, thereby extending their traditional role. Policymakers should focus on adaptive legal and policy design outlooks to address uncertainties associated with CBDC. This paper is important because it is one of the first to contribute to a detailed comparison of Ghana and Nigeria’s CBDC design frameworks.
Inhibitory Effect of Guava Leaf Polyphenols on Advanced Glycation End Products of Frozen Chicken Meatballs (−18 °C) and Its Mechanism Analysis
Authors: Zhao Mengna , Li Ying , Bai Xue , Feng Jia , Xia Xiufang , Li Fangfei
Published: August 2022

The inhibitory effect of guava leaf polyphenols (GLP) on advanced glycation end products (AGEs) of frozen chicken meatballs (−18 °C) and its possible inhibitory mechanism was investigated. Compared with control samples after freezing for 6 months, acidic value (AV), lipid peroxides, thiobarbituric acid reactive substance (TBARS), A294, A420, glyoxal (GO), Nε-carboxymethyl-lysine (CML), pentosidine, and fluorescent AGEs of chicken meatballs with GLP decreased by 11.1%, 22.3%, 19.5%, 4.30%, 8.66%, 8.27%, 4.80%, 20.5%, and 7.68%, respectively; while free sulfhydryl groups the content increased by 4.90%. Meanwhile, there was no significant difference between meatballs with GLP and TP in AV, A294, GO, and CML (p > 0.05). Correlation analysis indicated that GO, CML, pentosidine, and fluorescent AGEs positively correlated with AV, TBARS, A294, and A420, while GO, CML, pentosidine, and fluorescent AGEs negatively correlated with free sulfhydryl groups. These results manifested GLP could inhibit AGEs formation by inhibiting lipid oxidation, protein oxidation, and Maillard reaction. The possible inhibitory mechanism of GLP on the AGEs included scavenging free radicals, capturing dicarbonyl compounds, forming polyphenol–protein compounds, and reducing the formation of glucose. Therefore, the work demonstrated that the addition of plant polyphenols may be a promising method to inhibit AGEs formation in food.
Comparative Study of Food-Grade Pickering Stabilizers Obtained from Agri-Food Byproducts: Chemical Characterization and Emulsifying Capacity
Authors: Burgos-Díaz César , Mosi-Roa Yohanna , Opazo-Navarrete Mauricio , Bustamante Mariela , Garrido-Miranda Karla
Published: August 2022

Natural Pickering emulsions are gaining popularity in several industrial fields, especially in the food industry and plant-based alternative sector. Therefore, the objective of this study was to characterize and compare six agri-food wastes/byproducts (lupin hull, canola press-cake, lupin byproduct, camelina press-cake, linseed hull, and linseed press-cake) as potential sources of food-grade Pickering stabilizers. The results showed that all samples contained surface-active agents such as proteins (46.71–17.90 g/100 g) and dietary fiber (67.10–38.58 g/100 g). Canola press-cake, camelina press-cake, and linseed hull exhibited the highest concentrations of polyphenols: 2891, 2549, and 1672 mg GAE/100 g sample, respectively. Moreover, the agri-food byproduct particles presented a partial wettability with a water contact angle (WCA) between 77.5 and 42.2 degrees, and they were effective for stabilizing oil-in-water (O/W) emulsions. The emulsions stabilized by Camelina press-cake, lupin hull, and lupin by-product (≥3.5%, w/w) were highly stable against creaming during 45 days of storage. Furthermore, polarized and confocal microscopy revealed that the particles were anchored to the interfaces of oil droplets, which is a demonstration of the formation of a Pickering emulsion stabilized by solid particles. These results suggest that agri-food wastes/byproducts are good emulsifiers that can be applied to produce stable Pickering emulsions.
Spatiotemporal Differences, Dynamic Evolution and Trend of the Coupled Coordination Relationship between Urbanization and Food Security in China
Authors: Liu Shan , Hou Mengyang
Published: August 2022

Scientific assessment of the coupled coordination degree between urbanization and food security (CDUFS) revealed regional differences and sources. Dynamic evolution and trends are important references for achieving a coordinated interaction between high-quality urbanization and ensuring food security. Specifically, the CDUFS was measured using prefectural panel data in China from 2000 to 2019 and the coupling coordination degree model, which revealed its spatial correlation and differentiation. On this basis, in order to examine the spatiotemporal differences and evolution of the CDUFS, the Dagum–Gini coefficient and Kernel density estimation were innovatively used to analyze its regional differences and evolution distribution. The spatial Markov chain was further employed to examine the evolution trend of the CDUFS. The study found that the CDUFS showed a downward trend in fluctuation within the low coordination interval. There was a positive spatial correlation, with a more stable distribution pattern of high–high and low–low clusters. The regional differences in the CDUFS were obvious and the overall difference has expanded. The main source of regional differences among different food functional areas was inter-regional differences, followed by intra-regional differences. The regional difference between food main producing areas and food main marketing areas was the highest. The CDUFS shows a single-peak distribution; the imbalance between regions was still prominent with a left trailing phenomenon and no convergence. The CDUFS has the stability of maintaining the original state, and the probability of leapfrogging evolution is low in the short term. Finally, the geospatial effect plays an important role in the dynamic evolution of the CDUFS.
Nonlocal Boundary Value Problems for (k,ψ)-Hilfer Fractional Differential Equations and Inclusions
Authors: Ntouyas Sotiris K. , Ahmad Bashir , Tariboon Jessada
Published: August 2022

In the present research, single and multi-valued (k,ψ)-Hilfer type fractional boundary value problems of order in (1,2] involving nonlocal integral boundary conditions were studied. In the single-valued case, the Banach and Krasnosel’skiĭ fixed point theorems as well as the Leray–Schauder nonlinear alternative were used to establish the existence and uniqueness results. In the multi-valued case, when the right-hand side of the inclusion has convex values, we established an existence result via the Leray–Schauder nonlinear alternative method for multi-valued maps, while the second existence result, dealing with the non-convex valued right-hand side of the inclusion, was obtained by applying Covitz-Nadler fixed point theorem for multi-valued contractions. The obtained theoretical results are well illustrated by the numerical examples provided.
Local Stabilization of Delayed Fractional-Order Neural Networks Subject to Actuator Saturation
Authors: Fan Yingjie , Huang Xia , Wang Zhen
Published: August 2022

This paper investigates the local stabilization problem of delayed fractional-order neural networks (FNNs) under the influence of actuator saturation. First, the sector condition and dead-zone nonlinear function are specially introduced to characterize the features of the saturation phenomenon. Then, based on the fractional-order Lyapunov method and the estimation technique of the Mittag–Leffler function, an LMIs-based criterion is derived to guarantee the local stability of closed-loop delayed FNNs subject to actuator saturation. Furthermore, two corresponding convex optimization schemes are proposed to minimize the actuator costs and expand the region of admissible initial values, respectively. At last, two simulation examples are developed to demonstrate the feasibility and effectiveness of the derived results.
Asymptotic Behavior on a Linear Self-Attracting Diffusion Driven by Fractional Brownian Motion
Authors: Yan Litan , Wu Xue , Xia Xiaoyu
Published: August 2022

Let BH={BtH,t≥0} be a fractional Brownian motion with Hurst index 12≤H<1. In this paper, we consider the linear self-attracting diffusion: dXtH=dBtH+σXtHdt−θ∫0tXsH−XuHdsdt+νdt with X0H=0, where θ>0 and σ,ν∈R are three parameters. The process is an analogue of the self-attracting diffusion (Cranston and Le Jan, Math. Ann.303 (1995), 87–93). Our main aim is to study the large time behaviors. We show that the solution t−σθHXtH−X∞H converges in distribution to a normal random variable, as t tends to infinity, and obtain two strong laws of large numbers associated with the solution XH.
A Mathematical Investigation of Sex Differences in Alzheimer’s Disease
Authors: Drapaca Corina S.
Published: August 2022

Alzheimer’s disease (AD) is an age-related degenerative disorder of the cerebral neuro-glial-vascular units. Not only are post-menopausal females, especially those who carry the APOE4 gene, at a higher risk of AD than males, but also AD in females appears to progress faster than in aged-matched male patients. Currently, there is no cure for AD. Mathematical models can help us to understand mechanisms of AD onset, progression, and therapies. However, existing models of AD do not account for sex differences. In this paper a mathematical model of AD is proposed that uses variable-order fractional temporal derivatives to describe the temporal evolutions of some relevant cells’ populations and aggregation-prone amyloid-β fibrils. The approach generalizes the model of Puri and Li. The variable fractional order describes variable fading memory due to neuroprotection loss caused by AD progression with age which, in the case of post-menopausal females, is more aggressive because of fast estrogen decrease. Different expressions of the variable fractional order are used for the two sexes and a sharper decreasing function corresponds to the female’s neuroprotection decay. Numerical simulations show that the population of surviving neurons has decreased more in post-menopausal female patients than in males at the same stage of the disease. The results suggest that if a treatment that may include estrogen replacement therapy is applied to female patients, then the loss of neurons slows down at later times. Additionally, the sooner a treatment starts, the better the outcome is.
A Heterogeneous Duopoly Game under an Isoelastic Demand and Diseconomies of Scale
Authors: Li Xiaoliang , Su Li
Published: August 2022

In this paper, we investigate a duopolistic market with heterogeneous firms under the assumptions of an isoelastic demand and quadratic costs. We obtain the sufficient and necessary condition of the local stability of the Cournot–Nash equilibrium and analytically compare it with that of the analogue model under linear rather than quadratic costs. By approaches of symbolic computation, we prove that diseconomies of scale have an effect of stabilizing the game provided that the cost parameters are large enough. Moreover, by means of numerical simulations, we find that our model loses its stability only through a period-doubling bifurcation, which is different from its analogue having two possible routes to chaotic dynamics.
Fractal Analysis of Porous Alumina and Its Relationships with the Pore Structure and Mechanical Properties
Authors: Wang Xufu , Chen Chang , Wang Yubin
Published: August 2022

Porous alumina was prepared by the sacrificial template approach using 30 vol.%, 50 vol.%, and 70 vol.% of carbon fibers and graphite as pore formers. In order to determine the pore size distribution, porosity, most probable pore size, and median pore size, a mercury intrusion porosimeter (MIP) was used. The surface fractal dimensions (Ds) of porous alumina with various pore formers were assessed based on MIP data. The findings revealed that the pore size distribution of the prepared porous alumina was either bimodal or trimodal at 50 vol.% of the pore formers, and unimodal at 30 vol.% and 70 vol.% of the pore formers in the raw materials. The porous alumina’s pore structure and morphology varied depending on the volume content of the pore formers and their shapes. The porosity and pore size of the porous alumina increased with the increase in carbon fiber content because the carbon fiber was unfavorable to the densification of the initial billet before sintering. After sintering, there were no residual pore formers other than alumina in the samples. The pore structure of the porous alumina samples showed prominent fractal characteristics, and its DS decreased with the increase in the pore former content. The samples’ Ds was highly negatively correlated with the pore structure parameters, and was positively correlated with the flexural strength.