bluuebunny/crossref_metadata_2025 huggingface dataset:
A Blockchained AutoML Network Traffic Analyzer to Industrial Cyber Defense and Protection
Authors: Papanikolaou Alexandros , Alevizopoulos Aggelos , Ilioudis Christos , Demertzis Konstantinos , Rantos Konstantinos
Published: March 2023

Network traffic analysis can raise privacy concerns due to its ability to reveal sensitive information about individuals and organizations. This paper proposes a privacy-preserving Block-chained AutoML Network Traffic Analyzer (BANTA). The system securely stores network traffic logs in a decentralized manner, providing transparency and security. Differential privacy algorithms protect sensitive information in the network flow logs while allowing administrators to analyze network traffic without the risk of leakages. The BANTA uses blockchain technology, where smart contracts automate the process of network traffic analysis, and a multi-signature system ensures the system’s security, safety, and reliability. The proposed approach was evaluated using a real-world network traffic dataset. The results demonstrate the system’s high accuracy and real-time anomaly detection capabilities, which makes it well-suited for scalable cybersecurity operations. The system’s privacy protection, decentralized storage, automation, multi-signature system, and real-world effectiveness ensure that the organization’s data is private, secure, and effectively protected from cyber threats, which are the most vexing issue of modern cyber-physical systems.
Integration of Farm Financial Accounting and Farm Management Information Systems for Better Sustainability Reporting
Authors: Poppe Krijn , Vrolijk Hans , Bosloper Ivor
Published: March 2023

Farmers face an increasing administrative burden as agricultural policies and certification systems of trade partners ask for more sustainability reporting. Several indicator frameworks have been developed to measure sustainability, but they often lack empirical operationalization and are not always measured at the farm level. The research gap we address in this paper is the empirical link between the data needs for sustainability reporting and the developments in data management at the farm level. Family farms do not collect much data for internal management, but external demand for sustainability data can partly be fulfilled by reorganizing data management in the farm office. The Farm Financial Accounts (FFAs) and Farm Management Information Systems (FMISs) are the main data sources in the farm office. They originate from the same source of note-taking by farmers but became separated when formalized and computerized. Nearly all European farms have a bank account and must keep financial accounts (e.g., for Value-Added Tax or income tax) that can be audited. Financial accounts are not designed for environmental accounting or calculating sustainability metrics but provide a wealth of information to make assessments on these subjects. FMISs are much less frequently used but collect more technical and fine-grained data at crop or enterprise level for different fields. FMISs are also strong in integrating sensor and satellite data. Integrating data availability and workflows of FFAs and FMISs makes sustainability reporting less cumbersome regarding data entry and adds valuable data to environmental accounts. This paper applies a design science approach to design an artifact, a dashboard for sustainability reporting based on the integration of information flows from farm financial accounting systems and farm management information systems. The design developed in this paper illustrates that if invoices were digitized, most data-gathering needed for external sustainability reporting would automatically be done when the invoices is paid by a bank transfer. Data on the use of inputs and production could be added with procedures as in current FMISs, but with less data entry, fewer risks of differences in outcomes, and possibilities of cross-checking the results.
Knowledge-Guided Prompt Learning for Few-Shot Text Classification
Authors: Wang Liangguo , Chen Ruoyu , Li Li
Published: March 2023

Recently, prompt-based learning has shown impressive performance on various natural language processing tasks in few-shot scenarios. The previous study of knowledge probing showed that the success of prompt learning contributes to the implicit knowledge stored in pre-trained language models. However, how this implicit knowledge helps solve downstream tasks remains unclear. In this work, we propose a knowledge-guided prompt learning method that can reveal relevant knowledge for text classification. Specifically, a knowledge prompting template and two multi-task frameworks were designed, respectively. The experiments demonstrated the superiority of combining knowledge and prompt learning in few-shot text classification.
Two-Stage Generator Network for High-Quality Image Inpainting in Future Internet
Authors: Zhao Peng , Zhang Dan , Geng Shengling , Zhou Mingquan
Published: March 2023

Sharpness is an important factor for image inpainting in future Internet, but the massive model parameters involved may produce insufficient edge consistency and reduce image quality. In this paper, we propose a two-stage transformer-based high-resolution image inpainting method to address this issue. This model consists of a coarse and a fine generator network. A self-attention mechanism is introduced to guide the transformation of higher-order semantics across the network layers, accelerate the forward propagation and reduce the computational cost. An adaptive multi-head attention mechanism is applied to the fine network to control the input of the features in order to reduce the redundant computations during training. The pyramid and perception are fused as the loss function of the generator network to improve the efficiency of the model. The comparison with Pennet, GapNet and Partial show the significance of the proposed method in reducing parameter scale and improving the resolution and texture details of the inpainted image.
Electromagnetic Characteristics and Capacity Analysis of a Radial–Axial Hybrid Magnetic Bearing with Two Different Radial Stators
Authors: Wu Mengyao , Zhu Huangqiu
Published: March 2023

Compared with the widely used four-pole magnetic bearings, three-pole magnetic bearings are driven by a three-phase power inverter and have advantages pertaining to their small volume, low costs, and low power losses. However, the asymmetric structure of the three-pole bearings presents disadvantages in terms of their strong nonlinearity and couplings among the suspension forces of the control currents and displacements. The radial–axial hybrid magnetic bearing (RAHMB) with six-pole bearings is proposed to solve this problem. Firstly, the structure and working principle of the RAHMB are introduced. Secondly, the mathematical models of the RAHMB are established, and in order to obtain the radial capacity, the maximum suspension forces of the three-pole and six-pole RAHMBs are theoretically analyzed. Thirdly, the nonlinearity and couplings of the suspension forces with the control currents and displacements are analyzed. The radial capacity of the three-pole and six-pole RAHMB is 74.6 N and 83.6 N, respectively, which is an increase of 12.0%. Finally, the experiment results prove that the nonlinearity and couplings of the six-pole RAHMB are smaller than the nonlinearity and couplings of the three-pole RAHMB, and the maximum radial capacity of the three-pole and six-pole RAHMB is 84.1 N and 94.8 N, respectively, which is an increase of 12.7%. The simulation results are basically consistent with the experimental results, indicating the correctness of the theoretical analysis.
DC-Link Ripple Reduction for Parallel Inverter Systems by a Novel Formulation Using Multiple Space Vector-Based Interleaving Schemes
Authors: Khan Akbar Ali , Zaffar Nauman Ahmad , Ikram Muhammad Jahangir
Published: March 2023

This paper proposes an analytical formulation-based minimization of DC link current ripples for interleaved parallel inverter systems. Parallel inverter systems find applications in multiple fields. The interleaved superposition of the DC link currents in these systems can potentially be adjusted to mitigate the overall harmonics consequently reducing the DC link capacitor size. To this end, a widely used approach in the literature is the Fourier analysis based on interleaving focusing on dominant harmonic mitigation. However, it leaves room for a generic analytical mechanism to provide time shifts leading to an optimal reduction in DC-link ripples. The goal of this work is to target this optimal reduction by utilizing an analytical mechanism. The paper presents an alternate way of DC-link formulation in terms of the piece-wise sinusoids of inverter output currents for space vector modulation-based systems. The formulation is then used to numerically optimize the interleaved shifts for minimum ripples. Moreover, in addition to the traditional concept of fixed time interleaving, a contemporary concept of sequence-based interleaving is utilized, which is anticipated to have more flexibility in the implementation and additional switching synchronism with PWM rectifiers for back–back converters. Therefore, the sequence interleaving has also been utilized in conjunction with the proposed ripple reduction methodology. Further, an underexplored area of using the combined impact of sequence and time interleaving has also been applied in this work. These interleaving methods are shown to provide significantly improved DC-link ripple mitigation, as compared to existing methods, using numerical assessment followed by simulations and experimental evaluation.
Asset Ownership Transfer and Inventory Using RFID UHF TAGS and Ethereum Blockchain NFTs
Authors: Munoz-Ausecha Cesar , Gómez Jorge Eliecer Gómez , Ruiz-Rosero Juan , Ramirez-Gonzalez Gustavo
Published: March 2023

In the present, many organizations grow on a daily basis, using many assets to perform their activities and generate profit. In large organizations, all of these assets must be managed, occasionally leading to challenges depending on the organization’s size. For this reason, the role of asset custodian is needed. This role entails assigning the fixed assets to one person for their care, maintenance, and safekeeping. In this process, it is necessary to update information in the central system, leading to further administrative processes, which, in the majority of cases, are carried out through traditional methods. This involves time to obtain wet signatures, a great deal of paperwork, and time for the person or people in charge to update the information. Due to these reasons, the process can be updated partially or entirely to use digital means in order to solve the mentioned inconveniences. This paper presents a proof-of-concept system to offer a modernized and practical solution to this problem using the advantages of blockchain technology, and speeding up the process by using assets identified with UHF RFID technology to permit the reading of many tags that can be embedded and hidden with no need for line-of-sight, allowing fast ownership transfer, using smart contracts in the Ethereum private blockchain.
State Parameter Estimation of Intelligent Vehicles Based on an Adaptive Unscented Kalman Filter
Authors: Wang Yu , Li Yushan , Zhao Ziliang
Published: March 2023

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the Kalman filter, especially the standard unscented Kalman filter (UKF) that has been widely used in vehicle state estimation because of its superiority in dealing with nonlinear filtering problems. However, although the UKF assumes that the noise statistics of the system are known, due to the complex and changeable operating conditions, sensor aging and other factors, these noises vary. In order to realize high-precision vehicle state estimation, a noise-adaptive UKF algorithm is proposed in this article. The maximum a posteriori (MAP) algorithm is used to dynamically update the noise of the vehicle system, and it is embedded into the update step of the UKF to form an adaptive unscented Kalman filter (AUKF). The system will dynamically update the noise when noise statistics are unknown and prevent filter divergence by adjusting the mean and covariance of the estimated noise to improve accuracy. On this basis, the proposed method is verified by the joint simulation of CarSim and Matlab/Simulink, confirming that the AUKF performs better than the standard UKF in estimation accuracy and stability under different degrees of noise disturbance, and the estimation accuracy for the yaw rate, side slip angle and longitudinal velocity is improved by 20.08%, 40.98% and 89.91%, respectively.
Long-Distance Person Detection Based on YOLOv7
Authors: Tang Fan , Yang Fang , Tian Xianqing
Published: March 2023

In the research field of small object detection, most object detectors have been successfully used for pedestrian detection, face recognition, lost and found, and automatic driving, among other applications, and have achieved good results. However, when general object detectors encounter challenging low-resolution images from the TinyPerson dataset, they will produce undesirable detection results because of the dense occlusion between people and different body poses. In order to solve these problems, this paper proposes a tiny object detection method TOD-YOLOv7 based on YOLOv7.First, this paper presents a reconstruction of the YOLOv7 network by adding a tiny object detection layer to enhance its detection ability. Then, we use the recursive gated convolution module to realize the interaction with the higher-order space to accelerate the model initialization process and reduce the reasoning time. Secondly, this paper proposes the integration of a coordinate attention mechanism into the YOLOv7 feature extraction network to strengthen the pedestrian object information and weaken the background information.Additionally, we leverage data augmentation techniques to improve the representation learning of the algorithm. The results show that compared with the baseline model YOLOv7, the detection accuracy of this model on the TinyPerson dataset is improved from 7.1% to 9.5%, and the detection speed reaches 208 frames per second (FPS). The algorithm of this paper is shown to achieve better detection results for tiny object detection.
Modeling and Simulation of a Large-Scale Wind Power Plant Considering Grid Code Requirements
Authors: Nomandela Sinawo , Mnguni Mkhululi E. S. , Raji Atanda K.
Published: March 2023

The load demand to a power grid, as well as the interest in clean and low-cost energy resources, has led to the high integration of wind power plants into power system grids. There are grid code standards that are set for the design and integration of these wind power plants. These codes often look at the design operation of the wind power plant in islanded mode, where possible analysis of the most sensitive power system quantities such as voltage, frequency, reactive power, etc. is carried out. Therefore, in this study, attention was paid to the application of these codes to keep the design and integration of wind power plants well standardized as much as possible. The purpose of this paper is to review and discuss the literature and theory about the design of wind turbine generators and model and simulate a large-scale wind power plant. The modeling was successfully carried out on RSCAD, and the results obtained show that the wind power plant can be further used for other studies such as voltage stability improvement in power grids.
A Study on Creep-Fatigue Evaluation of Nuclear Cladded Components by ASME-III Division 5
Authors: Koo Gyeong-Hoi , Lee Sang-Yun , Seo Joo-Hwan , Song Kang-Hyun , Choi Geun-Suk , Sohn Myong-Sung
Published: March 2023

In this paper, a study on creep-fatigue evaluation on the cladded nuclear component subjecting to low pressure and high temperature services is carried out. To do this, the codes and standards presented by ASME-III Division 5 are reviewed, and a detailed evaluation procedure is presented step by step for practical applications. As an example of practical design application, a molten salt reactor vessel with a cladding thickness of 10% of the base material thickness is designed and four representative operation cycle types are established. The stress cycle types based on finite element stress analysis are determined from the operation cycle types having coolant temperature and pressure time history loads, and results of the creep-fatigue evaluation are described step by step according to the evaluation procedure. From the result of the creep-fatigue evaluation, it is found that the creep-fatigue evaluation for reactors such as molten salt reactor, sodium-cooled reactor, and so on, which are operated at low pressure and high temperature, is dominated by thermal loads. In this study, the effects of the cladding material and the thermal stresses on the creep-fatigue evaluation are investigated. In addition, as one of the design options to reduce the thermal stresses, the thickness of the exampled vessel is reduced, and the calculated creep-fatigue values are compared with the acceptance creep-fatigue envelope criteria of the ASME-III Division 5.
Molecular Dynamics Method for Supercritical CO2 Heat Transfer: A Review
Authors: Chen Lin , Zhang Yizhi , Ragui Karim , Hou Chaofeng , Zang Jinguang , Huang Yanping
Published: March 2023

This paper reviews molecular dynamics (MD) concepts on heat transfer analysis of supercritical CO2, and highlights the major parameters that can affect the accuracy of respective thermal coefficients. Subsequently, the prime aspects of construction, transfer identification, and thermal performance are organized according to their challenges and prospective solutions associated with the mutability of supercritical CO2 properties. Likewise, the characteristics of bound force field schemes and thermal relaxation approaches are discussed on a case-by-case basis. Both convective and diffusive states of trans- and supercritical CO2 are debated, given their magnitude effects on molecular interactions. Following the scarcity of literature on similar enquiries, this paper recommended a future series of studies on molecular dynamics models in a large region of supercriticality and phase-interactions for coupled heat and mass transfer systems. This review recognizes that the foremost undertaking is to ascertain the thermo-hydraulic identity of supercritical CO2 for process feasibility of developed technology.
The Study of a Magnetostrictive-Based Shading Detection Method and Device for the Photovoltaic System
Authors: Fu Xiaolei , Tian Yizhi
Published: March 2023

When the photovoltaic (PV) system suffers shading problems caused by different degrees and areas, the shaded PV cells will consume electricity and generate heat, the corresponding bypass diode operating at a certain current will conduct, and a special magnetic field will be generated in space. In this study, a magnetostrictive-based shading detection method and device for the PV system are developed from theoretical, simulation, and physical experimental aspects. This study aims to detect the special magnetic field using magnetostrictive material with a certain response pattern under the magnetic field to detect and locate the shading problem of each module in the PV system. Theoretically, the analysis is carried out from the on–off situation of the bypass diodes of PV modules under different shading conditions and the response mechanism of magnetostrictive materials under the action of the magnetic field. During simulation, the finite element magnetic field simulations are performed for the diode and the series magnetic field coil, and the structural parameters of the magnetic field coil are designed based on the simulation results. After establishing the validation idea of the detection method in this study, the experimental platform is built and the experimental steps are designed. Finally, the feasibility of the method proposed in this study is verified, the detection range of the method is calculated, and the minimum spacing of adjacent magnetic field coils is determined by experimental validation. This study provides a novel magnetostrictive-based detection method, as well as a theoretical and experimental basis, for identifying and localizing PV system shading problems, and discusses the feasibility of shading detection at the system level.
A Platform for Outdoor Real-Time Characterization of Photovoltaic Technologies
Authors: Martínez-Deusa Sammy J. , Gómez-García Carlos A. , Velasco-Medina Jaime
Published: March 2023

In recent years, thin-film and organic photovoltaic (OPV) technologies have been increasingly used as alternatives to conventional technologies due to their low weight, portability, and ease of installation. Outdoor characterization studies allow knowing the real performances of these photovoltaic (PV) technologies in different environmental conditions. Therefore, to address the above, this article presents the hardware–software design and implementation of an integrated and scalable platform for performing the outdoor real-time characterization of modern PV/OPV technologies located at different altitudes. The platform allows knowing the outdoor performance of PV/OPV technologies in real environmental conditions by acquiring data from different monitoring stations located at different altitudes. The proposed platform allows characterizing solar panels and mini-modules and acquiring relevant information to analyze power generation capacity and efficiency. Furthermore, other devices for new PV technologies characterization can be easily added, achieving a scale-up of the platform. A preliminary study of the outdoor performance of emerging PV/OPV technologies was carried out at three different altitudes in a tropical climate region. From the results, the copper indium gallium selenide (CIGS) technology presents the best outdoor performance with an average PCE of 9.64%; the OPV technology has the best behavior at high temperatures with a voltage loss rate of 0.0206 V/°C; and the cadmium telluride (CdTe) technology is the most affected by temperature, with a voltage loss rate of 0.0803 V/°C.
A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector
Authors: Giannelos Spyros , Moreira Alexandre , Papadaskalopoulos Dimitrios , Borozan Stefan , Pudjianto Danny , Konstantelos Ioannis , Sun Mingyang , Strbac Goran
Published: March 2023

The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.
Non-Catalytic Partial Oxidation of Hydrocarbon Gases to Syngas and Hydrogen: A Systematic Review
Authors: Makaryan Iren A. , Salgansky Eugene A. , Arutyunov Vladimir S. , Sedov Igor V.
Published: March 2023

The review contains a comparative analysis of studies on the production of hydrogen and syngas based on the processes of partial oxidation of natural gas and other types of gas feedstock. The results presented in the literature show the high potential of non-catalytic autothermal processes of partial oxidation of hydrocarbons for the development of gas chemistry and energetics. The partial oxidation of hydrocarbons makes it possible to overcome such serious shortcomings of traditional syngas production technologies as technological complexity and high energy and capital intensity. The features of non-catalytic partial oxidation of hydrocarbon gases, the obtained experimental results and the results of kinetic modeling of various options for the implementation of the process, which confirm the adequacy of the kinetic mechanisms used for the analysis, are considered in detail. Examples of industrial implementation of processes based on partial oxidation and proposed alternative options for its organization are considered. Designs of reactors used to ensure stable conversion of rich mixtures of hydrocarbons with an oxidizer are presented. The possibility of obtaining other chemical products by partial oxidation of hydrocarbons is discussed.
Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq
Authors: AL-Qaysi Ahmed Mazin Majid , Bozkurt Altug , Ates Yavuz
Published: March 2023

This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.
Energy Security and the Transition toward Green Energy Production
Authors: Bagus Philipp , Peña-Ramos José
Published: March 2023

The topic of this Special Issue, “Energy Security and the Transition toward Green Energy Production”, acquired an uncomfortable timeliness while it was still in progress [...]
Optimal Wireless Power Transfer Circuit without a Capacitor on the Secondary Side
Authors: Akbar Sabriansyah Rizqika , Setiawan Eko , Hirata Takuya , Hodaka Ichijo
Published: March 2023

This study proposes an approach to obtain maximum power via wireless power transfer using a single primary-side capacitor. It is shown that higher power is achieved when compared to the common wireless power transfer circuit under resonance with dual (primary- and secondary-side) capacitors. This approach is divided into three phases. By choosing the capacitor and frequency as freely assignable variables, we symbolically obtain a formula that allows us to determine the optimized capacitance and frequency for maximum power. To verify our method, we used a numerical analysis and compared it with an electronic circuit simulation. The symbolic formula is able to maintain maximum power despite changes in load or in the coupling coefficients.
The Impact of Grassroots Forestry Institutions on Forest Carbon Sequestration: Evidence from China’s Collective Forests
Authors: Li Shuqiang , Gao Li , Hassan Saif Khan , Li Hua
Published: March 2023

Many countries have established grassroots forestry institutions to manage and protect small-scale forestry resources and provide technology and services to private foresters. Since the inception of township forestry workstations (TFWs) in China almost 70 years ago, TFW has supported resource protection and forest property reform. In this paper, we employ fixed effect models to test the effects of TFW on collective forest carbon density and provide evidence for improving the quality of collective forests. Our results demonstrate that TFWs in China improve the carbon density of collective forests by performing forestry management and service functions. However, significant differences in TFWs exist under different management systems, and the dual leadership township forestry workstation (D_TWF) is more effective in increasing the carbon density of collective forests. The management system’s heterogeneity directly affects its performance, with D_TWF performing better management functions and the single leadership township forestry workstation (S_TWF) performing better service functions. These results underscore the importance of reforming the TFW management system in accordance with local conditions. In areas with abundant forest resources, the TFW’s management system should shift to single leadership (jurisdictional or vertical management). In forest resource-scarce regions, the TFW’s management system should change to dual leadership.
Why Healthy Pine Seedlings Die after They Leave the Nursery
Authors: South David B. , Starkey Tom E. , Lyons Al
Published: March 2023

Artificial regeneration is successful when high-performing seedlings are transported with care to the planting site, stored for a short period in an environment without desiccation or fungal growth, and planted in a deep hole, so roots are in contact with moist soil. One of the requirements for success is the ability to avoid common planting mistakes. Due, in part, to the use of container stock plus an increase in rainfall, the average first-year survival of pine seedlings (89%) in the southern United States is about 15% greater now than 45 years ago. However, when survival is less than 50% six months after planting, some landowners seek reimbursement for their loss. Some assume poor seedling quality was the cause without realizing that anaerobic soils or sudden freeze events, shallow planting holes, pruning roots, a lack of rain or underground insects can kill pines. With a focus on pines planted in the southern United States, we list non-nursery factors that have killed seedlings in North America, Africa and Europe.
Bibliometric Analysis of Forestry Research in Mexico Published by Mexican Journals
Authors: Santillán-Fernández Alberto , Vásquez-Bautista Nehemias , Pelcastre-Ruiz Luis Marcelino , Ortigoza-García Carlos Antonio , Padilla-Herrera Edgar , Tadeo-Noble Alfredo Esteban , Carrillo-Ávila Eugenio , Juárez-López José Francisco , Vera-López Javier Enrique , Bautista-Ortega Jaime
Published: March 2023

There is scarce research assessing the productivity of scientific articles on forestry topics. The objective of this study was to analyze the scientific production on forestry topics that originated in Mexico and were published in Mexican journals from 1996 to 2019 and to identify the causes that determine the impact factor of such publications and the space-time evolution of forestry research in Mexico. In addition, to analyze whether researchers tend to publish in journals published by their affiliation institutions. The study considered 2384 scientific articles from seven journals belonging to category VI of Biotechnology and Agricultural Sciences listed in the Journals Classification System by the National Council of Science and Technology that publishes forestry topics. Bibliometric indicators were generated through text mining and analysis of co-authorship networks. It was found that forestry research in Mexico from 1996 to 2019 presented exponential growth in the number of publications. Forestry scientific production was concentrated in the center of the country. It was dominated by researchers from three of 122 institutions: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (13.88%), Colegio de Postgraduados (12.50%), and Universidad Autonoma Chapingo (10.44%). The journals with the highest number of publications were: Revista Mexicana de Ciencias Forestales (26.51%), Revista Chapingo Serie Ciencias Forestales y del Ambiente (20.34%), and Madera y Bosques (18.88%). Results show that forestry researchers in Mexico published mostly in journals edited by their affiliation institutions, which restricts constructive criticism of peer review and increases academic endogamy. Also showed the need to generate more forestry research for the southeast of the country on topics such as climate change, carbon capture, forest biometry, and remote perception, which are relevant aspects when we consider that no published research evaluated the development of the forestry sector in Mexico.
Quercus suber Allometry in the West Mediterranean Basin
Authors: Jorge Catarina , Tomé Margarida , Ruiz-Peinado Ricardo , Zribi Lobna , Paulo Joana Amaral
Published: March 2023

The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each country develops their own models to be applied at the local scale because it is more convenient. But, for Quercus suber, a joint regional model can be more beneficial, since the species is distributed across the Mediterranean and is challenging to account for due to felling limitations and the nature of mature cork biomass itself. We found that these characteristics are reflected in the biomass datasets and compatibility was, perhaps, the largest impediment to such a model. The use of dummy variables to differentiate between countries, as well as compromises in the limits of biomass compartments, allowed us to develop two joint models to estimate aboveground biomass in Portugal, Spain and Tunisia. One model as a function of diameter and another as a function of diameter and total tree height. In addition, we developed a separate model for roots (modelling efficiency of fitting = 0.89), since it was not possible to assure additivity of the whole tree. All coefficients were estimated using Seemingly Unrelated Regressions (SUR) and model fitting assured additivity in the aboveground compartments—leaves and woody biomass (modelling efficiency of fitting = 0.89 and 0.93, respectively). This work proves that it is possible to have a biologically sound and efficient model for the three countries, despite differences in the observed allometric patterns.
One-Step Process for the Fabrication of Hydrophobic and Dimensional Stable Wood Using Functionalized Silica Nanoparticles
Authors: Bak Miklós , Takács Dávid , Rákosa Rita , Németh Zsolt István , Németh Róbert
Published: March 2023

The aim of this research was to improve the dimensional stability of wood through bulk hydrophobization, as a result of impregnation with fluorinated silica nanoparticles. The wood species European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.) were used. The characterization of the modified wood was performed using analytical methods, including scanning electron microscopy, energy dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. The effect of fluorinated silica nanoparticles on the anti-swelling efficiency, water uptake, equilibrium moisture content, and water contact angle were investigated. The surface of the cell walls was discontinuously covered with fluorinated silica nanoparticles forming a rough surface coating. The presence of the hydrophobic silica nanoparticles improved the dimensional stability by permanently increasing the hydrophobicity of wood, besides a low weight percent gain. Furthermore, the treatment significantly decreased the equilibrium moisture content and water uptake. The modified wood surfaces showed significantly higher water contact angles, which was the main reason of the improved dimensional stability.
Comparative Genomics Analysis of Ganoderma Orthologs Involved in Plant-Pathogenesis
Authors: Ho Chai-Ling
Published: March 2023

Ganoderma species are producers of bioactive secondary metabolites and lignin degraders. A few Ganoderma species are known to be plant pathogens that attack economically important trees. In this study, comparative genomics analysis was conducted on the proteome of ten Ganoderma species/strains, focusing on the proteins that have been reported to be involved in plant-pathogenesis in other fungi. Fungal trophic lifestyle prediction of these Ganoderma species/strains supported that G. boninense (a potent pathogen to oil palm) is a hemibiotrophic fungus while the other Ganoderma species/strains analyzed were predicted to be saprophytes or a symbiont based on their Carbohydrate-Active Enzyme (CAZyme) contents. Although these Ganoderma species/strains were demonstrated to share many CAZymes and secondary metabolite core gene clusters, individual species may produce unique CAZymes and secondary metabolite core genes that determine their lifestyles, host-specificity, and potential as a producer of bioactive secondary metabolites. Ortholog groups that are related to fungal virulence from seven Ganoderma species/strains including those involved in lignin degradation, mycotoxin, siderophore and ergosterol biosynthesis, and virulence were summarized. Potential effectors were predicted from the proteome of these Ganoderma species/strains, and putative effectors that were being expressed in G. boninense in oil palm roots but not found in other species were identified. The findings provide a useful resource to further analyze plant-pathogenesis and wood degradation activities of these Ganoderma species.
A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands
Authors: Serrano Salvatore , Scarpa Marco
Published: March 2023

Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cognitive radio technology could be an effective methodology for improving QoS requirements. In order to evaluate QoS in the context of a cognitive radio Internet of Things network, we propose a Petri net-based model that evaluates the cognitive radio environment and operates in a 200 kHz GSM/EDGE transponder band. The model is quite flexible as it considers several circuit and packet switching primary user network loads and configurations and several secondary user types of services (that involve semantic transparency or time transparency); furthermore, it is able to take into account mistakes of the spectrum sensing algorithm used by secondary users. Specifically, we derive the distribution of the response time perceived by the secondary users, where it is then possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a secondary user synchronized access to the channel when using the GSM/EDGE frame structure.
Artificial-Intelligence-Based Charger Deployment in Wireless Rechargeable Sensor Networks
Authors: Cho Hsin-Hung , Chien Wei-Che , Tseng Fan-Hsun , Chao Han-Chieh
Published: March 2023

To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price–performance ratio.
Molecular Characterization and Expression Response of Ghrelin, GLP-1 and PYY to Fasting, Dietary Lipid, and Fatty Acids in Silver Pomfret (Pampus argenteus)
Authors: Zhang Lei , Shi Peng , Zhang Lin , Wang Yajun , Xu Jilin , Yan Xiaojun , Liao Kai
Published: March 2023

Ghrelin, glucagon-like peptide-1 (GLP-1), and peptide YY (PYY) are potent hormones mediating food intake according to the nutritional status in fish. However, limited information is available on these genes and their expression in response to nutrition in silver pomfret (Pampus argenteus). A comparison analysis revealed that ghrelin and GLP-1 were relatively conserved in marine fish. PYYa and PYYb shared a low identity and were clustered to different PYY branches. Ghrelin, pyya, and pyyb mRNAs were highly expressed in the brain, while glp-1 was highly expressed in the gills and liver. The ghrelin mRNA expression was relatively high an hour post-feeding and decreased after 3 to 72 h of fasting. The glp-1 mRNA expression was increased after 6 to 24 h of fasting. The pyya mRNA expression was increased after 72 h of fasting, while the pyyb expression remained stable during fasting. The diet with 14% lipid promoted the expressions of glp-1, pyya, and pyyb, but inhibited the ghrelin expression. The ghrelin expression in the intestine was increased after palmitic acid, oleic acid (OA), linoleic acid (LA), α-linolenic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) incubation. OA decreased the expression of glp-1 and increased the expression of pyya. The expression of pyyb was downregulated by LA, DHA, and EPA. These results suggest the importance of ghrelin, glp-1, and pyy in coordinating food intake in response to fasting, dietary lipid concentration, and fatty acids in silver pomfret.
Application of Plant Proteases in Meat Tenderization: Recent Trends and Future Prospects
Authors: Mohd Azmi Syahira , Kumar Pavan , Sharma Neelesh , Sazili Awis , Lee Sung-Jin , Ismail-Fitry Mohammad
Published: March 2023

Papain, bromelain, and ficin are commonly used plant proteases used for meat tenderization. Other plant proteases explored for meat tenderization are actinidin, zingibain, and cucumin. The application of plant crude extracts or powders containing higher levels of compounds exerting tenderizing effects is also gaining popularity due to lower cost, improved sensory attributes of meat, and the presence of bioactive compounds exerting additional benefits in addition to tenderization, such as antioxidants and antimicrobial effects. The uncontrolled plant protease action could cause excessive tenderization (mushy texture) and poor quality due to an indiscriminate breakdown of proteins. The higher cost of separation and the purification of enzymes, unstable structure, and poor stability of these enzymes due to autolysis are some major challenges faced by the food industry. The meat industry is targeting the recycling of enzymes and improving their stability and shelf-life by immobilization, encapsulation, protein engineering, medium engineering, and stabilization during tenderization. The present review critically analyzed recent trends and the prospects of the application of plant proteases in meat tenderization.
Activity of Binary Combinations of Natural Phenolics and Synthetic Food Preservatives against Food Spoilage Yeasts
Authors: Kimani Bernard Gitura , Takó Miklós , Veres Csilla , Krisch Judit , Papp Tamás , Kerekes Erika Beáta , Vágvölgyi Csaba
Published: March 2023

Natural compounds are a suitable alternative to synthetic food preservatives due to their natural origin and health-promoting properties. In the current study, phenolic–phenolic and phenolic–synthetic combinations were tested for their antibiofilm formation, anti-planktonic growth, and anti-adhesion properties against Debaryomyces hansenii, Wickerhamomyces anomalus (formerly Pichia anomala), Schizosaccharomyces pombe, and Saccharomyces cerevisiae. The phenolics were vanillin and cinnamic acid, while the synthetic preservatives were sodium benzoate, potassium sorbate, and sodium diacetate. The vanillin–cinnamic acid combination had synergistic effect in all the tested yeasts for the biofilm inhibition with a fractional inhibitory concentration index (FICI) of ≤0.19 for W. anomalus, 0.25 for S. pombe, 0.31 for S. cerevisiae, and 0.5 for D. hansenii. Most of the phenolic–synthetic combinations had indifferent interaction regarding biofilm formation. The vanillin–cinnamic acid combination also had higher activity against spoilage yeasts adhesion on the abiotic surface and planktonic growth compared to the phenolic–synthetic combinations. For the phenolic–synthetic anti-planktonic activity, synergistic interaction was present in all the vanillin–synthetic combinations in S. pombe, vanillin–sodium benzoate and vanillin–potassium sorbate in S. cerevisiae, vanillin–sodium benzoate in W. anomalus, and cinnamic acid–sodium diacetate in S. pombe. These results suggest a novel antimicrobial strategy that may broaden the antimicrobial spectrum and reduce compound toxicity against food spoilage yeasts.
Correction: Ye et al. Evaluation of the Brewing Characteristics, Digestion Profiles, and Neuroprotective Effects of Two Typical Se-Enriched Green Teas. Foods 2022, 11, 2159
Authors: Ye Yuanyuan , He Jiangling , He Zhijun , Zhang Na , Liu Xiaoqing , Zhou Jiaojiao , Cheng Shuiyuan , Cai Jie
Published: March 2023

The authors wish to make the following corrections to their published paper [...]
Effect of Composite Edible Coatings Combined with Modified Atmosphere Packaging on the Storage Quality and Microbiological Properties of Fresh-Cut Pineapple
Authors: Liao Xingmei , Xing Yage , Fan Xiangfeng , Qiu Ye , Xu Qinglian , Liu Xiaocui
Published: March 2023

This study investigated the effect of edible coating (EC), modified atmosphere packaging (MAP), and edible coating + modified atmosphere packaging (EC + MAP) treatments on the quality of fresh-cut pineapples during storage at 4 °C. The quality differences were analyzed by measuring the quality, physiological indicators, and total microbial counts. After 8 d of storage, the brightness (L*) values of the EC + MAP and control samples were 72.76 and 60.83, respectively. The water loss and respiratory rate of the EC + MAP were significantly inhibited from 0% and 29.33 mg CO2 kg−1 h−1 to 4.13% and 43.84 mg CO2 kg−1 h−1, respectively. Furthermore, the fresh-cut pineapples treated with EC + MAP presented a good appearance, with lower total soluble solids (TSS) and relative conductivity and higher titratable acid (TA), ascorbic acid (AA), total phenol content, and firmness compared to the other treatment groups. At the end of storage, the EC + MAP samples exhibited the lowest polyphenol oxidase (PPO) activity, peroxidase (POD) activity, and malondialdehyde (MDA) content at 28.53 U, 60.37 U, and 1.47 nmol·g−1, respectively. Furthermore, the efficiency of EC + MAP treatment exceeded that of EC or MAP alone, preventing key problems involving the surface browning and microbiological safety of the fresh-cut pineapples. The results showed that EC + MAP treatment was more successful in maintaining the storage quality and extending the shelf life of fresh-cut pineapples.