bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Relationships between Boltzmann Entropy of Images and Building Classification Accuracy in Land Cover Mapping
Authors: Li Zhipeng , Lan Tian , Li Zhilin , Gao Peichao
Published: August 2023

Remote sensing images are important data sources for land cover mapping. As one of the most important artificial features in remote sensing images, buildings play a critical role in many applications, such as population estimation and urban planning. Classifying buildings quickly and accurately ensures the reliability of the above applications. It is known that the classification accuracy of buildings (usually indicated by a comprehensive index called F1) is greatly affected by image quality. However, how image quality affects building classification accuracy is still unclear. In this study, Boltzmann entropy (an index considering both compositional and configurational information, simply called BE) is employed to describe image quality, and the potential relationships between BE and F1 are explored based on images from two open-source building datasets (i.e., the WHU and Inria datasets) in three cities (i.e., Christchurch, Chicago and Austin). Experimental results show that (1) F1 fluctuates greatly in images where building proportions are small (especially in images with building proportions smaller than 1%) and (2) BE has a negative relationship with F1 (i.e., when BE becomes larger, F1 tends to become smaller). The negative relationships are confirmed using Spearman correlation coefficients (SCCs) and various confidence intervals via bootstrapping (i.e., a nonparametric statistical method). Such discoveries are helpful in deepening our understanding of how image quality affects building classification accuracy.
Joint Client Selection and CPU Frequency Control in Wireless Federated Learning Networks with Power Constraints
Authors: Zhou Zhaohui , Shi Shijie , Wang Fasong , Zhang Yanbin , Li Yitong
Published: August 2023

Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples. However, within wireless FL networks, resource heterogeneity introduces straggler clients, thereby decelerating the learning process. Additionally, the learning process is further slowed due to the non-independent and identically distributed (non-IID) nature of local training samples. Coupled with resource constraints during the learning process, there arises an imperative need for optimizing client selection and resource allocation strategies to mitigate these challenges. While numerous studies have made strides in this regard, few have considered the joint optimization of client selection and computational power (i.e., CPU frequency) for both clients and the edge server during each global iteration. In this paper, we initially define a cost function encompassing learning latency and non-IID characteristics. Subsequently, we pose a joint client selection and CPU frequency control problem that minimizes the time-averaged cost function subject to long-term power constraints. By utilizing Lyapunov optimization theory, the long-term optimization problem is transformed into a sequence of short-term problems. Finally, an algorithm is proposed to determine the optimal client selection decision and corresponding optimal CPU frequency for both the selected clients and the server. Theoretical analysis provides performance guarantees and our simulation results substantiate that our proposed algorithm outperforms comparative algorithms in terms of test accuracy while maintaining low power consumption.
An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data
Authors: Li Zihao , Zhang Liumei
Published: August 2023

Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The “curse of dimensionality” phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms.
Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods
Authors: Wang Xianhe , Li Ying , Qiao Qian , Tavares Adriano , Liang Yanchun
Published: August 2023

In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.
A Survey of Universal Quantum von Neumann Architecture
Authors: Liu Yuan-Ting , Wang Kai , Liu Yuan-Dong , Wang Dong-Sheng
Published: August 2023

The existence of universal quantum computers has been theoretically well established. However, building up a real quantum computer system not only relies on the theory of universality, but also needs methods to satisfy requirements on other features, such as programmability, modularity, scalability, etc. To this end, here we study the recently proposed model of quantum von Neumann architecture by putting it in a practical and broader setting, namely, the hierarchical design of a computer system. We analyze the structures of quantum CPU and quantum control units and draw their connections with computational advantages. We also point out that a recent demonstration of our model would require less than 20 qubits.
Feature-Fusion-Based Abnormal-Behavior-Detection Method in Virtualization Environment
Authors: Zheng Luxin , Zhang Jian , Lin Faxin , Wang Xiangyi
Published: August 2023

From general systems to mission-critical systems at financial and government institutions, the application scope of cloud computing services is continuously expanding. Therefore, there is a need for better methods to ensure the stability and security of the cloud data and services. Monitoring the abnormal behavior of virtual machines (VMs) is one of the most-important means to identify the causes of security incidents related to the cloud. However, current traditional abnormal-behavior-detection methods for VMs on cloud platforms face multiple challenges such as privacy protection and the semantic gap. Virtualization technology plays a key role in cloud computing. Meanwhile, virtualization security is the core issue of cloud computing security as well. To address these issues, this paper proposes a feature-fusion-based abnormal-behavior-detection method (FFABD) in a virtualization environment. This method acquires the hardware features and syscalls of the VM at the physical machine level and the virtualization level, respectively. Therefore, this method is not limited by the operating system running on the VM. This makes our method more efficient and universally applicable compared to traditional abnormal-VM-detectionmethods. The ensemble learning model performs the best among all the models, achieving an Accuracy of 99.7%.
A Mass-In-Mass Metamaterial Design for Harvesting Energy at a Broadband Frequency Range
Authors: Ahmed Hossain , Ahmed Riaz
Published: August 2023

A novel deterministic method to harvest energy within a broadband frequency (0~25 kHz) from a mass-in-mass metamaterial is presented herein. Traditional metamaterials are composed of multiple materials (named as resonators and matrix) with different mechanical properties (e.g., stiffness, density). In this work, the stiffnesses of matrix materials are altered systematically to allow diversified property mismatches between the constituent components to introduce local resonance in the unit cell. While local resonance leverages wave energy passing through the acoustic metamaterials trapped within the relatively soft matrix as dynamic strain energy, a strategic and deterministic methodology is investigated to obtain a broadband local resonance frequency. The frequency band can then be utilized to harvest the trapped energy by embedding a smart material inside the matrix which is capable of electromechanical transduction (e.g., lead zirconate titanate). This concept has been proved numerically by harvesting energy at a broadband frequency with a power density of ~10 μW/in2. Finally, an experimental study is performed to prove the hypothesis proposed in this article.
Models for MATLAB Simulation of a University Campus Micro-Grid
Authors: Simonazzi Marco , Delmonte Nicola , Cova Paolo , Menozzi Roberto
Published: August 2023

This work presents a library of microgrid (MG) component models integrated in a complete university campus MG model in the Simulink/MATLAB environment. The model allows simulations on widely varying time scales and evaluation of the electrical, economic, and environmental performance of the MG. The models include photovoltaic (PV) generation (with MPPT control), battery storage (including charge/discharge control), loads (campus buildings and outdoor lighting), electric vehicle charging stations, and interconnections. The campus MG model is shown to be useful for (i) week-long simulations throughout the year, to analyze seasonal differences; (ii) year-long simulations for the estimation of overall economic and environmental impacts; (iii) MG islanded-mode simulations. The model is an agile tool for planning the deployment and penetration of photovoltaic plants and battery storage, with consideration of economic and environmental (carbon footprint) aspects.
Nexus between Energy Consumption, Foreign Direct Investment, Oil Prices, Economic Growth, and Carbon Emissions in Italy: Fresh Evidence from Autoregressive Distributed Lag and Wavelet Coherence Approach
Authors: Javed Aamir , Rapposelli Agnese , Shah Mohsin , Javed Asif
Published: August 2023

The aim of this study is to explore the impact of economic growth (GDP), energy consumption, foreign direct investment, oil price, and exports on carbon emissions by employing yearly time series data for Italy for the period 1971–2019. For this purpose, we employed the autoregressive distributed lag (ARDL) model and wavelet coherence approach to analyze the interconnections among variables. The cointegration results confirm the long-run association between our variables. Our findings show that GDP has a positive impact on carbon emissions, while the square of GDP has a negative impact, thus confirming the presence of the EKC hypothesis. Further, oil prices have a detrimental impact on carbon emissions both in the long- and short-term; on the contrary, foreign direct investment, energy consumption, and exports promote environmental degradation. We propose some important policy recommendations based on these findings to address the environmental constraints.
Mechanistic Approach towards Designing Covalent Organic Frameworks for Photocatalytic Hydrogen Generation
Authors: Khan Niaz , Azad Chandra , Luo Mengying , Chen Jiahui , Kesharwani Tanay , Badshah Amir , Wang Dong
Published: August 2023

Covalent organic frameworks (COFs) have unique features, including intrinsic porosity, crystallinity, and tunability, making them desirable materials for diverse applications ranging from environmental remediation to energy harvesting. Among these applications, COFs are extensively studied for their photocatalytic hydrogen evolution by converting solar energy into clean and renewable fuel via water splitting. COFs have several advantages over conventional inorganic catalysts, such as tunable band structures, high surface areas, and low cost. However, the research in this field is still in the early stages, and COFs still face some challenges, such as low charge carrier mobility, high exciton binding energy, and poor stability. To overcome these challenges, various design strategies relying on a mechanistic approach have been developed to design and modify COFs for enhanced photocatalytic performance. These include extending the π-conjugation, incorporating heteroatoms or metal complexes, and donor–acceptor (D–A) configuration, which ultimately improves the light absorption charge separation of COFs. Additionally, blending COFs with other functional materials, such as inorganic-organic semiconductors, can create synergistic effects to boost photocatalytic activity. In this review, the design aspects of the fabrication of COFs as effective photocatalysts have been reported.
Experimental Investigation of the Improvement Potential of a Heat Pump Equipped with a Two-Phase Ejector
Authors: Singmai Wichean , Onthong Kasemsil , Thongtip Tongchana
Published: August 2023

In this paper, an experimental investigation of the performance improvement of a heat pump equipped with a two-phase ejector, called an “ejector–expansion heat pump (EEHP)”, is proposed. The system performance of the EEHP is compared with that of a vapor-compression heat pump (VCHP). The improvement potential is determined and discussed. The heat pump test system based on a water-to-water heat pump that can experiment with both the EEHP and the VCHP is constructed. A two-phase ejector with a cooling load of up to 2500 W is installed for the experiment. The results show that the EEHP always produces a higher heating rate and COPHP than the VCHP under the specified working conditions. The heating COPHP is increased by 5.7–11.6% depending on the working conditions. It is also found that, under the same heat sink and heat source temperature, the EEHP can produce a lower compressor discharge temperature and a lower compressor pressure ratio than the VCHP. This is evidence that the two-phase ejector can provide the compressor with better working characteristics, which yields a longer compressor lifetime. It is demonstrated that the expansion pressure ratio is key to the performance of the EEHP. A larger expansion pressure ratio yields greater improvement potential when compared with the VCHP.
A Self-Healing Strategy for Modern Distribution Networks
Authors: Reiz Cleberton , Pereira Caio E. M. , Leite Jonatas B.
Published: August 2023

Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices’ parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink® allows for verifying the proposed methodology’s behavior and efficiency.
Control of an Offshore Wind Farm Considering Grid-Connected and Stand-Alone Operation of a High-Voltage Direct Current Transmission System Based on Multilevel Modular Converters
Authors: Paulo Matheus , Almeida Andrei , Almeida Pedro , Barbosa Pedro
Published: August 2023

This work presents a control strategy for integrating an offshore wind farm into the onshore electrical grid using a high-voltage dc transmission system based on modular multilevel converters. The proposed algorithm allows the high-voltage DC system to operate in grid-connected or stand-alone modes, with the second case supplying power to local loads. In either mode, the modular multilevel rectifier works as a grid-forming converter, providing the reference voltage to the collector network. During grid-connected operation, the modular multilevel inverter regulates the DC link voltage while the generating units are controlled to maximize power extracted from the wind turbines. Conversely, in the event of grid disconnection, the onshore modular multilevel converter takes over the regulation of the AC voltage at the point of connection to the grid, ensuring energy supply to local loads. Simultaneously, the generator controller transitions from tracking the maximum power of the wind turbines to regulating the DC link voltage, preventing excessive power injection into the transmission DC link. Additionally, the turbine pitch angle control regulates the speed of the generator. Mathematical models in the synchronous reference frame were developed for each operation mode and used to design the converter’s controllers. A digital model of the wind power plant and a high-voltage dc transmission system was implemented and simulated in the PSCAD/EMTDC program. The system modeled includes two groups of wind turbines, generators, and back-to-back converters, in addition to a DC link with a rectifier and an inverter station, both based on modular multilevel converters with 18 submodules per arm, and a 320 kV/50 km DC cable. Aggregate models were used to represent the two groups of wind turbines, where 30 and 15 smaller units operate in parallel, respectively. The performance of the proposed control strategy and the designed controllers was tested under three distinct scenarios: disconnection of the onshore converter from the AC grid, partial loss of a wind generator set, and reconnection of the onshore converter to the AC grid.
Liquid Water Transport Characteristics and Droplet Dynamics of Proton Exchange Membrane Fuel Cells with 3D Wave Channel
Authors: Li Zijun , Wang Jianguo , Wang Shubo , Li Weiwei , Xie Xiaofeng
Published: August 2023

Water management is a crucial aspect in the efficient functioning of proton exchange membrane fuel cells (PEMFCs). The presence of a two-phase flow, consisting of water and reactive gas, in the channel of the PEMFC is of utmost importance for effective water management. This study focuses on investigating the removal of liquid water in 3D wave channels and 2D straight channels using the volume of fluid method. The study analyzes the dynamic behavior of droplets emerging from the gas diffusion layer (GDL) into the channel under the influence of gas flow. The study also explores the effects of droplet growth, deformation, detachment, force, and pore size on critical water behavior and water content in the channel. The results indicate that the 3D wave channel is more effective in removing liquid water compared to the 2D straight channel. It is observed that increasing the velocity facilitates the discharge of liquid water. However, excessively high velocities lead to parasitic power losses. Furthermore, while larger pore sizes in the GDL are not advantageous for PEMFC performance, a moderate increase in pore size aids in the discharge of liquid water. The knowledge gained through this study deepens the understanding of droplet dynamics in PEMFC gas channels and assists in optimizing the design and operational conditions of these channels.
PV Hosting Capacity in LV Networks by Combining Customer Voltage Sensitivity and Reliability Analysis
Authors: Kisuule Mikka , Ndawula Mike Brian , Gu Chenghong , Hernando-Gil Ignacio
Published: August 2023

This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network.
Small-Signal Modeling and Stability Analysis of a Grid-Following Inverter with Inertia Emulation
Authors: Schramm Dall’Asta Matheus , Brunelli Lazzarin Telles
Published: August 2023

Power-converter-based energy-harvesting and storage systems are becoming more prevalent in the electrical grid, replacing conventional synchronous generators. Consequently, grid inertia is diminishing, and to address this, inverter-based energy conversion systems are required by grid codes to provide frequency control support to the main grid. This is undertaken to increase the equivalent inertia of the system and reduce frequency variations. This type of control is necessary and designed for handling large system transients. However, it also impacts the small-signal stability of the grid-connected converters. To investigate this issue, this paper addresses the influence of synthetic inertia control on the output admittance of a grid-following inverter and its interaction with the grid equivalent impedance. A synchronous reference frame dynamic model of the grid-following inverter closed-loop system is obtained and linearized at an operating point to analyze the small-signal stability of the low-switching frequency inverter. The models are validated through numerical simulations. The analysis verifies the interactions of the internal control loops, such as the AC current control with voltage feedforward, DC-link voltage control with power-feedforward, phase-locked loop, and AC voltage control with inertial control. Additionally, the interactions between the output admittance of the inverter and the grid impedance are verified using the generalized Nyquist criterion. The stability regions are validated through simulations, and the results show that the system gain margin is reduced for increasing values of synthetic inertia gain and lower grid short-circuit ratios. Furthermore, there is a limit in the voltage and power-feedforward bandwidth to avoid degrading the system stability when utilizing the synthetic inertia control.
Heating Strategies for Efficient Combined Inductive and Convective Heating of Profiles
Authors: Gergely Raphael , Hochenauer Christoph
Published: August 2023

In this paper, an experimental and numerical study of a novel heating strategy is shown for thin profiled workpieces using induction and convective heating at the same time. A characteristic of induction heating is its potential for achieving efficient local heating due to the direct conversion of electromagnetic fields to thermal energy. One disadvantage of this is the high temperature gradients that occur on the workpiece and, therefore, the uneven distribution. This is even more significant for thin workpieces or workpieces made out of sheet metal due to the lighter mass and, therefore, less conduction away from the heating zone. This paper presents the idea of combining induction heating with convection to obtain a more even distribution of the temperature but with considerable energy savings compared to pure convective heating. The combination of both heating methods has been analysed both experimentally and numerically with different geometries. The multiphysic simulation included both the induction heating and also the convective heat transfer for temperature-dependent material properties. The results of the simulations and the experiments were in good agreement, and both showed that there is a huge potential for energy savings when convective heating is supported by induction heating (up to 53%). This study provides a reference for future industrial applications for heating sheet metal workpieces, e.g., for drying paint.
Asymmetric Risk Connectedness between Crude Oil and Agricultural Commodity Futures in China before and after the COVID-19 Pandemic: Evidence from High-Frequency Data
Authors: Zhang Deyuan , She Wensen , Qu Fang , He Chunyan
Published: August 2023

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China’s crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security.
Co-pelletization of Hemp Residues and Agricultural Biomass: Effect on Pellet Quality and Stability
Authors: Stulpinaite Urte , Tilvikiene Vita , Zvicevicius Egidijus
Published: August 2023

The rising interest in lowering the use of fossil fuels, which influence environmental pollution and global warming, is driving a substantial increase in renewable sources. Agricultural residues are the likely potential source for bioenergy generation. Some of them are already utilized for energy. Nonetheless, their potential is underutilized due to low biomass quality and high concentrations of sulfur and chloride, which induce the corrosion of adjoining equipment. However, their ash content and ash melting point make their utilization as renewable resources essential. Therefore, there is a need to find technologies to enhance biomass utilization for bioenergy processes. With the increase in hemp cultivation to extract phytocannabinoids, the amount of unused biomass has increased. The aim of this research was to investigate the use of hemp biomass for pellets and improve pellet quality by mixing them with lignin and oak sawdust. The results showed that the lowest amount of ash was found in pellets with 80% oak sawdust and 20% hemp residue compared with pellets made from mixtures of hemp residues, lignin, and oak sawdust. The highest calorific value was achieved by mixing hemp residues (20%) with lignin (80%).
Maceration of Waste Cork in Binary Hydrophilic Solvents for the Production of Functional Extracts
Authors: Şen Umut , Viegas Catarina , Duarte Maria Paula , Maurício Elisabete Muchagato , Nobre Catarina , Correia Ricardo , Pereira Helena , Gonçalves Margarida
Published: August 2023

Waste-grade cork samples of Quercus cerris were subjected to maceration extraction using 7 different solvents, including pure water (I), pure acetone (II), 75% aqueous ethanol (III), 75% aqueous methanol (IV), 75% aqueous acetone (V), 50% aqueous acetone (VI), and 25% aqueous acetone (VII). The extract yields, extract compositions, as well as antioxidant and antimicrobial activities of the extracts were analyzed. The results showed that maceration extraction was highly efficient, particularly with binary solvents resulting in up to 6% extract yield and up to 488 mg GAE/g extract total phenolic content. The extracts exhibited a variable antioxidant activity determined by DPPH and FRAP methods as well as antimicrobial activity against gram-positive bacteria and fungus determined by agar diffusion test. The CIELAB color parameters of extracts were correlated with maceration time, and the correlation was highest with pure water extracts. The FT-IR spectra of acetone-extracted cork revealed six key markers of phenolic compounds with the presence of peaks at approximately 2920 cm−1, 2850 cm−1, 1609 cm−1, 1517 cm−1, 1277 cm−1, and 1114 cm−1. The overall results suggest that the maceration of waste cork in binary solvents and pure acetone are green alternatives to conventional Soxhlet extraction for the production of polar extracts.
Using Functional Traits to Improve Estimates of Height–Diameter Allometry in a Temperate Mixed Forest
Authors: Gao Huanran , Cui Keda , von Gadow Klaus , Wang Xinjie
Published: August 2023

Accurate estimates of tree height (H) are critical for forest productivity and carbon stock assessments. Based on an extensive dataset, we developed a set of generalized mixed-effects height–DBH (H–D) models in a typical natural mixed forest in Northeastern China, adding species functional traits to the H–D base model. Functional traits encompass diverse leaf economic spectrum features as well as maximum tree height and wood density, which characterize the ability of a plant to acquire resources and resist external disturbances. Beyond this, we defined expanded variables at different levels and combined them to form a new model, which provided satisfactory estimates. The results show that functional traits can significantly affect the H–D ratio and improve estimations of allometric relationships. Generalized mixed-effects models with multilevel combinations of expanded variables could improve the prediction accuracy of tree height. There was an 82.42% improvement in the accuracy of carbon stock estimates for the studied zone using our model predictions. This study introduces commonly used functional traits into the H–D model, providing an important reference for forest growth and harvest models.
Nutritional Status of Wood Melick (Melica uniflora Retz.) in a Natural Forest Stand in South-Western Poland
Authors: Paszkiewicz-Jasińska Anna , Wróbel Barbara , Stopa Wojciech , Jakubowska Zuzanna , Steinhoff-Wrześniewska Aleksandra , Zielewicz Waldemar
Published: August 2023

Melica uniflora Retz., commonly known as wood melick, is a grass species that is native to most of Europe. Melica uniflora grows in hardwood forests near Fagus species, providing vital food and shelter for forest wildlife. The nutritional status of wood melick is not sufficiently recognized. The study aims to identify the intrapopulation variability of Melica uniflora plants collected from natural forest habitats in Poland in terms of nutritional status variability in relation to stage development and the course of weather conditions. The research was conducted for two consecutive years: 2021 and 2022, in the area of the Ślęża Massif, near the town of Sobótka in Lower Silesia voivodeship (Poland). The material for analyses was collected from 10 natural forest sites twice: in July (I) and October (II). The content of nutritive components: crude protein (CP), crude ash (CA), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and water-soluble carbohydrates (WSC) was evaluated. Relative feed value (RFV), dry matter digestibility (DDM), dry matter intake (DMI), cellulose (CL), and hemicellulose (HCL) content were calculated based on ADF and NDF. Melica uniflora plants contained 75.2 g·kg−1 DM of CP, 290.6 g·kg−1 DM of CF, 120.0 g·kg−1 DM of CA, and 25.9 g·kg−1 DM of WSC. The content of NDF was 637.6 g·kg−1 DM, ADF 407.5 g·kg−1 DM, and ADL 58.0 g·kg−1 DM. The nutritional status of wood melick depended on the course of weather conditions in the following years and its location, which changed during the growing season. Plants collected in the first year of the study contained more CL, NDF, and ADF fractions and less CA, ADL, and WSC. Melica uniflora plants harvested in June were characterized by higher CP (102.1 g·kg−1 DM) and WSC (30.1 g·kg−1 DM) content and lower content of remaining nutrients. It can be concluded that Melica uniflora plants can be a valuable source of these nutrients in the forage of forest animals.
Optimized Ultrasound-Assisted Extraction of Psidium laruotteanum Roots: A Concentrated Source of Piceid from the Brazilian Savanna
Authors: Takao Leandro Kenji , Setyaningsih Widiastuti , Gualtieri Sonia C. J. , Ruíz-Rodríguez Ana , Varela Rosa M. , Palma Miguel
Published: August 2023

Psidium laruotteanum is a subshrub Myrtaceae, native to the Brazilian savanna, and distributed in Central and South America. Here, the health beneficial compound piceid (reservatrol-3-β-mono-d-glucoside) is described for the first time in the genus Psidium. The ultrasound-assisted extraction from P. laruotteanum roots was optimized by a response surface methodology. A central composite design was used to assess six extraction variables. Optimal extractions were obtained with 62.5% MeOH in water at 69.1 °C, amplitude of 70%, cycle of 0.5 s−1, pH 5.7 and solvent–solid ratio of 40:1, for 10 min. The method showed excellent recovery (90%–95%) and precision (CV: 0.69% for repeatability and 0.43% for intermediate precision). This species appears as the most concentrated plant source of piceid reported to date (29.15 mg·g−1 in dry roots). Additionally, a very fast extraction (5 min) can afford 96% recovery of piceid from Psidium laruotteanum roots. It could be used as a natural and cheap source of piceid for several applications.
A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy
Authors: Fan Shuang , Qin Chaoqi , Xu Zhuopin , Wang Qi , Yang Yang , Ni Xiaoyu , Cheng Weimin , Zhang Pengfei , Zhan Yue , Tao Liangzhi , Wu Yuejin
Published: August 2023

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models’ predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.
Simultaneous Screening of 322 Residual Pesticides in Fruits and Vegetables Using GC-MS/MS and Deterministic Health Risk Assessments
Authors: Choi Byong-Sun , Lee Dong-Uk , Kim Woo-Seong , Park Chan-Woong , Choe Won-Jo , Moon Myung-Jun
Published: August 2023

The development of efficient methods for evaluating pesticide residues is essential in order to ensure the safety and quality of agricultural products since the Republic of Korea implemented the Positive List System (PLS). The objective of this research was to establish a method for the simultaneous analysis of 322 pesticide residues in fruits and vegetables (such as coffee, potato, corn, and chili pepper), using the quick, easy, cheap, effective, rugged, and safe (QuEChERS) approach in combination with gas chromatography-tandem mass spectrometry (GC-MS/MS). This study introduces a robust, high-throughput GC-MS/MS method for screening the target pesticide residues in agricultural products, achieving the PLS criterion of 0.01 mg/kg LOQ. Despite some compounds not aligning with the CODEX recovery guideline, sufficient reproducibility was confirmed, attesting to the method’s applicability in qualitative analyses. A health risk assessment conducted using estimated daily intake/acceptable daily intake ratios indicated low risks associated with product consumption (<0.035391%), thereby confirming their safety. This efficient method holds significant implications for the safe distribution of agricultural products, including during import inspections.
Recombinant Fusion Protein Containing Plant Nigellothionin Regulates the Growth of Food-Spoiling Fungus (Aspergillus niger)
Authors: Barashkova Anna S. , Ryazantsev Dmitry Yu. , Zhuravleva Anna S. , Sharoyko Vladimir V. , Rogozhin Eugene A.
Published: August 2023

This study aimed to obtain a recombinant chimeric protein named trx-NsW2 via theheterologous expression of the multifunctional antimicrobial peptide nigellothionin from black cumin (Nigella sativa L.) seeds in the Escherichia coli system. The protein was purified using a combination of Ni-NTA affinity chromatography and reversed-phase HPLC. Based on the HPLC calibration, the total yield of the protein was calculated to be 650 mg/L of bacterial culture. The fungistatic activity of trx-NsW2 against the food-spoiling fungus Aspergillus niger was demonstrated as itinhibited the maturation of conidiawithout affecting conidial germination or fungal growth. In contrast to mature nigellothionin NsW2, the fusion protein showeda low level of cytotoxicity towards both normal and tumor cell lines at concentrationsof up to 100–200 µM. Interestingly, at lower concentrations, it even stimulated cytokinesis. These findings are of critical importance for applying chimeric antimicrobial proteins obtained via microbiological synthesis in applied science.
Certain New Reverse Hölder- and Minkowski-Type Inequalities for Modified Unified Generalized Fractional Integral Operators with Extended Unified Mittag–Leffler Functions
Authors: Yang Wengui
Published: August 2023

In this article, we obtain certain novel reverse Hölder- and Minkowski-type inequalities for modified unified generalized fractional integral operators (FIOs) with extended unified Mittag–Leffler functions (MLFs). The predominant results of this article generalize and extend the existing fractional Hölder- and Minkowski-type integral inequalities in the literature. As applications, the reverse versions of weighted Radon-, Jensen- and power mean-type inequalities for modified unified generalized FIOs with extended unified MLFs are also investigated.
Rheological Characterization of Three-Dimensional Neuronal Cultures Embedded in PEGylated Fibrin Hydrogels
Authors: López-León Clara F. , Soriano Jordi , Planet Ramon
Published: August 2023

Three-dimensional (3D) neuronal cultures are valuable models for studying brain complexity in vitro, and the choice of the bulk material in which the neurons grow is a crucial factor in establishing successful cultures. Indeed, neuronal development and network functionality are influenced by the mechanical properties of the selected material; in turn, these properties may change due to neuron–matrix interactions that alter the microstructure of the material. To advance our understanding of the interplay between neurons and their environment, here we utilized a PEGylated fibrin hydrogel as a scaffold for mouse primary neuronal cultures and carried out a rheological characterization of the scaffold over a three-week period, both with and without cells. We observed that the hydrogels exhibited an elastic response that could be described in terms of the Young’s modulus E. The hydrogels without neurons procured a stable E≃420 Pa, while the neuron-laden hydrogels showed a higher E≃590 Pa during the early stages of development that decreased to E≃340 Pa at maturer stages. Our results suggest that neurons and their processes dynamically modify the hydrogel structure during development, potentially compromising both the stability of the material and the functional traits of the developing neuronal network.
A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases
Authors: Cuevas-Chávez Alejandra , Hernández Yasmín , Ortiz-Hernandez Javier , Sánchez-Jiménez Eduardo , Ochoa-Ruiz Gilberto , Pérez Joaquín , González-Serna Gabriel
Published: August 2023

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
The Impact of Minimal Intervention Dentistry on Patient-Reported and Observation-Based Outcomes in the Pediatric Population: A Systematic Review and Meta-Analysis
Authors: Chiu Hilton Hiu Chun , Lam Phoebe Pui Ying , Yiu Cynthia Kar Yung
Published: August 2023

This review aimed to systematically investigate the effect of minimal intervention dentistry on patient-reported and observation-based outcomes of anxiety, pain and patient cooperation in the pediatric population. Microinvasive treatments (MITs) were compared to conventional treatments, home-based and professionally applied non-invasive treatments (NITs), and between MITs. Two reviewers independently screened studies from four electronic databases, extracted data, assessed the risk of bias and certainty of evidence with the GRADE approach. Ultimately, 26 studies were included for qualitative synthesis, with the data from 12 studies being incorporated into the meta-analysis. No statistically significantly differences in terms of anxiety were noted between MITs and conventional treatments, or between MITs from the meta-analyses. The treatment durations of MITs were not necessarily shorter than conventional treatments but varied with the use of local anesthesia, behavioral and clinical approach, and other confounding factors. The certainties of evidence were deemed low due to high risk of bias of the included studies. NITs evoked less anxiety and pain compared to MITs. Minimal intervention dentistry is an alternative treatment to manage dental caries among children and does not arouse different levels of dental anxiety and pain compared to other treatment modalities. However, further well-designed studies are required to draw an evidence-based conclusion.