bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovation of Pedagogical Practices of Future Teachers
Authors: Konečná Petra , Smolka Pavel , Trčková Kateřina , Václavíková Zuzana , Žáček Martin
Published: March 2023

The basic topic of this article is the undergraduate preparation of future teachers in the Czech Republic, specifically the issue of the pedagogical practices of future teachers. We focused mainly on reflective activities within this preparation. The first part of the article deals with the mapping of this area in the educational systems of other countries or other regions of the Czech Republic. The second part is devoted to a description of the current state of reflection among science teacher education students. It also proposes an innovation of the system of reflection implementation using modern IT tools in the context of full-time and distance education. The third part presents the outputs of the e-questionnaire prepared for both face-to-face and distance learning.
Chiplet Multi-Objective Optimization Algorithm Based on Communication Consumption and Temperature
Authors: Sun Haiyan , Peng Xinwei , Cang Dongqing , Zhao Jicong , Liu Yanhua , Fang Jiaen
Published: March 2023

A chiplet multi-objective optimization algorithm for 2.5-D integrated circuit (IC) based on a passive interposer is discussed in this article. Inspired by the network-on-chip mapping problem, we propose a novel algorithm, called chiplet multi-objective optimization, which minimizes the average temperature and the communication consumption between chiplets at the same time. The algorithm considers the specificities of 2.5-D IC chiplets, such as the spacing and different sizes of chiplets. In addition to the weight factor, α is also introduced to make a balance between temperature and the communication consumption. The designer can change the weight factor according to their own requirement. The multi-window display system is used as an example in this article to demonstrate the algorithm’s efficiency and the accuracy. According to our algorithm, the system temperature of the most ideal solution can be reduced by 8.34 K and the communication consumption reduced by 232.13 μJ.
Digital Image Identification and Verification Using Maximum and Preliminary Score Approach with Watermarking for Security and Validation Enhancement
Authors: Upadhyay Shrikant , Kumar Mohit , Upadhyay Aditi , Verma Sahil , Kavita  , Hosen A. S. M. Sanwar , Ra In-Ho , Kaur Maninder , Singh Satnam
Published: March 2023

Digital face approaches possess currently received awesome attention because of their huge wide variety of digital audio, and visual programs. Digitized snapshots are progressively more communicated using an un-relaxed medium together with cyberspace. Consequently, defence, clinical, medical, and exceptional supervised photographs are essentially blanketed towards trying to employ it; such controls ought to damage such choices constructed totally based on those pictures. So, to shield the originality of digital audio/visual snapshots, several approaches proposed. Such techniques incorporate traditional encoding, breakable and nominal breakable watermarking with virtual impressions which are based upon the material of image content. Over the last few decades, various holistic approaches are proposed for improving image identification and verification. In this paper, a combination of both the feature level and score level of different techniques were used. Image is one of the identities of a person which reflects its emotions, feeling, age etc. which also helps to gather an information about a person without knowing their name, caste, and age and this could be not of much importance when it is used for domestic or framing applications. To secure the originality of digital audio/visual impressions many methods come into pictures and are proposed which include digital signatures, watermarking, cryptography, and fragile depend upon face contents. The objective of this research article is to identify & verify real-time video images using feature and score levels using watermarking that will help to judge the authenticity of any images at the initial stage by extracting the features which are evaluated by following an algorithm known as Viterbi and where input data is changed initially into an embedded treat or state then the matrix is evaluated of achieved transformation and on this basis preliminary score estimation will be generated after many iterations for each image that will help in validation. Finally, the tested image will be verified using several approaches to protect and provide security to the original image being verified. This approach may be useful for different surveillance applications for real-time image identification and verification. Also, measurement of accuracy was done by reconfiguring the HMM to identify the constant segmentation and feature removal of the image was settled by initializing parameters and teaching the image feature using the algorithm “Viterbi”.
A Wide-Range Four-Phase All-Digital DLL with De-Skew Circuit
Authors: Kang Jing , Liu Fei , Hai Ya , Wang Yongshan
Published: March 2023

A four-phase all-digital delay-locked loop (ADDLL) with a de-skew circuit for NAND Flash high-speed interfaces is proposed. The proposed de-skew circuit adopts a fall-edge-judgment phase adjuster and a three-stage digitally controlled delay line to align the system input clock and 0∘ output clock of the four-phase DLL over a wide frequency range, thus solving the four-phase offset caused by clock skew. A parallel-cascade configuration is proposed to solve the variable phase alignment problem caused by mode switching, thus effectively improving the phase-locked accuracy. The proposed circuit is fabricated in the 0.13 μm CMOS process with a 0.072 mm2 core area. The chip testing results show an operating frequency range from 26 MHz to 1.55 GHz and a typical alignment error of approximately 17 ps.
Design and Implementation of CNFET SRAM Cells by Using Multi-Threshold Technique
Authors: Kavitha Shanmugam , Kumar Chandrasekaran , Fayek Hady H. , Rusu Eugen
Published: March 2023

This paper presents a CNFET (Carbon Nano-tube FET) based MT (Multi-Threshold)-SRAM (Static Random Access Memory) design based on the leakage reduction mechanism. A multi-threshold logic is employed for reducing the leakage current during read/write operations. Here, the multi-threshold technique is used to insert the high threshold sleep control to the low threshold circuit. The insertion is performed in a serial manner. The high threshold transistors are very useful for deriving the low sub-threshold current. Meanwhile, the low threshold transistors are promising for improving the circuit performance. The high-low threshold transistor pairs are used to change the channel length by modifying the oxide thickness of the transistors. The overall implementation of the Multi-threshold-based SRAM cells are implemented with the help of CNFET in-order to avoid the short channel effect, mobility degradation which is occurred while considering the channel length below 32 nm in CMOS (Complementary Metal Oxide Semiconductor) devices. The paper clearly represents the performance improvement of the proposed SRAM cells with above-mentioned technologies.
The Impact of Digitalization on Macroeconomic Indicators in the New Industrial Age
Authors: Chenic Alina Ștefania , Burlacu Adrian , Dobrea Răzvan Cătălin , Tescan Laurenţiu , Creţu Alin Ioan , Stanef-Puica Mihaela Roberta , Godeanu Teodor Narcis , Manole Alina Magdalena , Virjan Daniela , Moroianu Nicolae
Published: March 2023

Digitalization has become a watchword in all areas of economic and social activity, and its integration has become a necessity for every state worldwide. If a few years ago we were talking about technological innovation and cutting-edge technologies we are now talking about digital technologies, robotics, big data, and artificial intelligence, and future industrial production will develop in symbiosis with modern information and communication technology. The paper aims to show the impact of digitalization on macroeconomic indicators, including labor productivity, value-added, and value of exports of goods and services, and in this regard, we have built an econometric model to see how digitalization and the evolution of macroeconomic indicators work, and how they will influence the degree of growth and economic development. The research results verify the hypotheses we started from, namely that there is a positive and strong correlation between digitalization and productivity, as well as between digitalization and value-added, and that there is a positive, yet weaker correlation between digitization and exports. The impact of digitalization on macroeconomic indicators extends beyond the theoretical and practical implications, as they influence both the decision-making process of the companies’ management and national and European economic policies.
Supervised Learning Spectrum Sensing Method via Geometric Power Feature
Authors: Hu Qian , Luo Zhongqiang , Xiao Wenshi
Published: March 2023

In order to improve the spectrum sensing (SS) performance under a low Signal Noise Ratio (SNR), this paper proposes a supervised learning spectrum sensing method based on Geometric Power (GP) feature. The GP is used as the feature vector in the supervised learning spectrum sensing method for training and testing based on the actual captured data set. Experimental results show that the detection performance of the GP-based supervised learning spectrum sensing method is better than that of the Energy Statistics (ES) and Differential Entropy (DE)-based supervised learning spectrum sensing methods.
Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model
Authors: Yang Nachuan , Zhao Yongjun , Wang Fuqiang , Chen Jinyang
Published: March 2023

This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequency features by sliding window, the convolution neural network (CNN) further extracts the signal features. It analyzes the temporal relationship of each group of signal features using a bidirectional long short-term memory network (Bi-LSTM). Finally, the connectionist temporal classification (CTC) is used to label the unsegmented sequence data to achieve single detection of multiple vibration targets. Experiments show that using this method to process the collected 8563 data, containing 5 different frequency bands of multi-vibration acoustic sensing signal, the system F1 score is 99.49% with a single detection time of 2.2 ms. The highest frequency response is 1 kHz. It is available to quickly and efficiently identify multiple vibration signals when a single demodulated acoustic sensing signal contains multiple vibration events.
Intersection-Based Unicast Routing Using Ant Colony Optimization in Software-Defined Vehicular Networks
Authors: Zhu Hao , Liu Jingru , Jin Li , Zhang Guoan
Published: March 2023

A vehicular ad hoc network (VANET) is a mobile ad hoc network composed of communication between vehicles, between vehicles and roadside units, and between vehicles and pedestrians, in order to achieve traffic safety and entertainment services. The design of the routing protocol is very important for the realization of the service function of VANET. Local optimum and network congestion problems are restraints of traditional geographic routing protocols for VANET. In this paper, a software-defined network (SDN) based unicast routing scheme in an urban traffic environment is proposed, which uses Dijkstra’s algorithm to find a global optimal anchor path. The RSU neighbor discovery protocol is proposed, through which each RSU can discover its neighbor RSUs, and then each RSU periodically sends ant packets to its neighbor RSUs, evaluates the communication connection quality of each street segment according to the statistical data of the ant packets received, and sends the evaluation value to the SDN server in time. The SDN server has the connection quality evaluation values of all street segments in the global scope, from which an optimal anchor path can be calculated. The simulation results show that the proposed scheme has better packet delivery ratio than other related schemes.
Thermal Analysis of a Modular Permanent Magnet Machine under Open-Circuit Fault with Asymmetric Temperature Distribution
Authors: Liu Yunfei , Zhang Bingyi , Zong Ming , Feng Guihong
Published: March 2023

A modular permanent magnet machine is composed of several stator modules, and the three-phase winding of each module can be controlled independently. The novel modular permanent magnet machine has good abilities in terms of fault tolerance when the machine is exposed to fault conditions. The current of each phase is different and will result in uneven loss distribution in each phase. Heat transfer occurs in the circumferential direction and temperature distribution will be asymmetric in the circumferential direction. This paper proposes a 3D finite element thermal model to accurately calculate the rise in temperature under open-circuit conditions for modular permanent magnet machines. When two modules are in operation, the machine can output rated torque. When one module is in operation and the temperature is 150 °C, the output torque is 0.76 times the rated torque. The temperature of the machine under the one-phase open-circuit condition with a zero-temperature-difference control strategy will be 0.8 °C lower than that with a minimum copper loss control strategy. Finally, a prototype with three stator modules is manufactured and the calculation results are validated by experimental test. It holds great significance for the accurate calculation of a machine with asymmetric temperature distribution in the circumferential direction.
Permanent Magnet Synchronous Motor Control Based on Phase Current Reconstruction
Authors: Yao Guozhong , Yang Yun , Wang Zhengjiang , Xiao Yuhan
Published: March 2023

The traditional single current sensor control strategy of a permanent magnet synchronous motor (PMSM) often adopts the DC bus method, which makes it difficult to eliminate the blind area of current reconstruction. Therefore, a current reconstruction method based on a sliding mode observer is proposed. Based on the current equation of the motor, the method takes the α-axis and β-axis currents as the observation objects and shares the same synovial surface, so that the α-axis current observation value and the β-axis current observation value converge to the actual current value at the same time and the unknown β-axis current information is obtained. The control system first tests the performance of the motor under different working conditions when the parameters are matched, and then tests the current reconstruction ability of the parameter mismatch. The results show that the current observer with a matched parameter can accurately and quickly reconstruct the β-axis current under various operating conditions, and the maximum current error does not exceed 4 mA. When the parameters are mismatched, high-performance control of the motor can still be achieved. The proposed method has excellent robustness.
Identifying Queenlessness in Honeybee Hives from Audio Signals Using Machine Learning
Authors: Ruvinga Stenford , Hunter Gordon , Duran Olga , Nebel Jean-Christophe
Published: March 2023

Honeybees are vital to both the agricultural industry and the wider ecological system, most importantly for their role as major pollinators of flowering plants, many of which are food crops. Honeybee colonies are dependent on having a healthy queen for their long-term survival since the queen bee is the only reproductive female in the colony. Thus, as the death or loss of the queen is of great negative impact for the well-being of a honeybee colony, beekeepers need to be aware if a queen has died in any of their hives so that appropriate remedial action can be taken. In this paper, we describe our approaches to using acoustic signals recorded in beehives and machine learning algorithms to identify whether beehives do or do not contain a healthy queen. Our results are extremely positive and should help beekeepers decide whether intervention is needed to preserve the colony in each of their hives.
Proton Radiation Effects of CMOS Image Sensors on Different Star Map Recognition Algorithms for Star Sensors
Authors: Cui Yihao , Feng Jie , Li Yudong , Wen Lin , Guo Qi
Published: March 2023

Star sensors are widely used by satellites for their precise pointing accuracy. However, protons in space will cause cumulative effects and single-event transients in the imaging systems of star sensors. These effects will affect the success rate of star map recognition of star sensors. In this paper, proton irradiation experiments and field tests were carried out in turn, and three typical star recognition algorithms were used to recognize the star maps. The results showed that cumulative effects led to a decrease in the number of identifiable stars, which greatly affected the recognition success rate of the grid algorithm. Hot pixels caused by displacement damage effects increased the star centroid positioning error, leading to a decrease in the recognition success rate of the triangle algorithm and pyramid algorithm. Single-event transients produced by protons hitting the image sensor are similar to the grayscale value and shape of a star, and were recognized as “false stars”, which had a significant impact on the success rate of the three recognition algorithms. In general, the pyramid algorithm was more effective than the other two algorithms in identifying the affected star map, and the recognition success rate of the grid algorithm was significantly reduced.
TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis
Authors: Alshehri Arwa , Algarni Abdulmohsen
Published: March 2023

In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.
Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power
Authors: Matsumoto Takuji , Yamada Yuji
Published: March 2023

Since the future output of wind power generation is uncertain due to weather conditions, there is an increasing need to manage the risks associated with wind power businesses, which have been increasingly implemented in recent years. This study introduces multiple weather derivatives of wind speed and temperature and examines their effectiveness in reducing (hedging) the fluctuation risk of future cash flows attributed to wind power generation. Given the diversification of hedgers and hedging needs, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross-derivatives,” to minimize the cash flow variance and develop a market-trading scheme to practically use these derivatives in wind power businesses. In particular, while demonstrating the importance of standardizing weather derivatives regarding market liquidity and efficiency, we propose a strategy to narrow down the required number (or volume) of traded instruments and improve trading efficiency by utilizing the least absolute shrinkage and selection operator (LASSO) regression. Empirical analysis reveals that higher-order, multivariate standardized derivatives can not only enhance the out-of-sample hedge effect but also help reduce trading volume. The results suggest that diversification of hedging instruments increases transaction flexibility and helps wind power generators find more efficient portfolios, which can be generalized to risk management practices in other businesses.
Impact of Incremental Methylene Groups on the Energetic Properties of Aromatic Nitramines
Authors: Tamuliene Jelena , Sarlauskas Jonas
Published: March 2023

The investigation of a series of secondary aromatic nitramines was performed to reveal the impact of incremental methylene groups on the stability, shock impulse, and energetic properties of these compounds. Becke’s three-parameter hybrid functional approach with non-local correlation provided by Lee, Yang, and Parr and a cc-pVTZ basis set was used to obtain the geometry, total energy, and heat of formation of the most stable conformers of these aromatic nitramines. These parameters were used to evaluate the density, resistance to shock stimuli, detonation pressure, and velocity of the nitramines under study. Referring to the results obtained, we concluded that the thermal stability and resistance to shock stimuli of the compound investigated was directly CH2 chain length-dependent, while their energetic- properties, such as detonation pressure and velocity, were worsened due to this chain increase. We also found that the stability of the compounds increases more significantly than the worsening energetic properties of aromatic nitramines. The results obtained reveal that in some cases the number of CH2 in the chain should be smaller than three so that the explosive properties of the compounds under study would not be worse than TNT.
Experimental Study of the Shear Characteristics of Fault Filled with Different Types of Gouge in Underground Gas Storage
Authors: Ding Guosheng , Liu Hejuan , Xia Debin , Wang Duocai , Huang Famu , Guo Haitao , Xie Lihuan , Guo Yintong , Wu Mingyang , Mao Haijun
Published: March 2023

In the current international situation, energy storage is an important means for countries to stabilize their energy supply, of which underground storage of natural gas is an important part. Depleted gas reservoir type underground gas storage (UGS) has become the key type of gas storage to be built by virtue of safety and environmental protection and low cost. The multi-cycle high injection and production rate of natural gas in the depleted gas reservoir type UGS will cause the in-situ stress disturbance. The slip risk of fault in the geological system increases greatly compared with that before the construction of the storage engineering, which becomes a great threat to the sealing of the gas storage. Reasonable injection and production strategy depend on the reliable assessment of the shear behavior of the fault belt, which can guarantee the sealing characteristics of the UGS geological system and the efficient operation of the UGS. Therefore, the shear behavior of the fault is studied by carrying out experiments, which can provide important parameters for the evaluation of fault stability. However, there is a large gap between the rock samples used in the previous experimental study and the natural faults, and it is difficult to reflect the shear failure characteristics of natural faults. In this paper, similar fault models based on high-precision three-dimensional scanners and engraving machines, filled with three types of fault gouge, are prepared for a batch of representative direct shear tests. The results show that the peak shear strength of the fault rocks with a shear surface is higher than that of the fault rocks with a tensile surface. Compared with the clay mineral content, the roughness of the fault surface is much more significant for the shear strength of the fault rock. For the fault rocks with similar fault surface morphology, the higher the clay content in the fault gouge, the greater the shear strength of the fault rocks. For the fault rocks with different fault surface morphology and the same fault gouge, the cohesion and internal friction angle of the tensile type is generally smaller than that of the shear type.
Impact of Reactive Current and Phase-Locked Loop on Converters in Grid Faults
Authors: Zhang Ziqian , Schuerhuber Robert
Published: March 2023

The precise control of output power by grid-connected converters relies on the correct identification and tracking of a grid voltage’s phase at the converter terminal. During severe grid faults, large disturbances cause the converter’s operating point to move away from the stable equilibrium point during normal operation. This leads to oscillations of both the active and reactive power fed into the grid. Using large-signal modelling, this study investigated the converter’s dynamic processes during and after such fault situations. The investigation considered the influence of the converter’s phase-locked loop (PLL), responsible for phase tracking, as well as that of the DC link on the converter-grid system, which has a major influence on the active power exchange with the grid. On this basis, this study also focused on the reactive current reference’s influence during and after fault clearing. Furthermore, an easily implementable strategy for reactive current injection, leading to minimum power oscillations, was presented. The results and the optimized strategies were validated via controller hardware-in-the-loop tests.
Evolution and Prospects of Hydrothermal Carbonization
Authors: Romano Pietro , Stampone Nicola , Di Giacomo Gabriele
Published: March 2023

Hydrothermal carbonization enables the valorization of biomass via thermochemical conversion into various products. Today, this technology is experiencing a situation similar to that experienced in the past by other process technologies. Of these technologies, some have become important industrial realities, such as reverse osmosis, while others have never been able to establish themselves fully. This paper presents a brief overview of this technology’s current status, highlighting its strengths and various drawbacks. The primary purpose of the research activity is to identify a possible future scenario toward which this technology is heading. Hydrothermal carbonization has already been established on a laboratory scale for some time, and now it is in a transitional phase between pilot-scale and industrial-scale applications. The interest that HTC has aroused and continues to arouse is evidenced by the growing number of publications and patents published. In particular, the uniform percentage of patents filed in various countries testifies to the worldwide interest. This technology has advantages but also some bottlenecks that have yet to be overcome. Process integration, higher-capacity plants, and the use of Industry 4.0 technologies seem to be the most interesting options to overcome the last limiting factors and make hydrothermal carbonization an established industrial reality.
Selection of a Photovoltaic Carport Power for an Electric Vehicle
Authors: Sokolovskij Edgar , Małek Arkadiusz , Caban Jacek , Dudziak Agnieszka , Matijošius Jonas , Marciniak Andrzej
Published: March 2023

The increasing number of electric vehicles is forcing new solutions in the field of charging infrastructure. One such solution is photovoltaic carports, which have a double task. Firstly, they enable the generation of electricity to charge vehicles, and secondly, they protect the vehicle against the excessive heating of its interior. This article presents the functioning of a small carport for charging an electric vehicle. Attention is drawn to the problems of selecting the peak power of the photovoltaic system for charging an electric vehicle. An economic and energy analysis is carried out for the effective use of photovoltaic carports. In this article, we present the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of probability distribution.
Advances in Hydrogen and Syngas Generation
Authors: Sadykov Vladislav
Published: March 2023

To solve problems related to global warming, environmental pollution, and green energy fields, a lot of research is now devoted to the catalysis of biofuels and biogas transformation into syngas and hydrogen [...]
Changing Relationships between Water Content and Spectral Features in Moso Bamboo Leaves under Pantana phyllostachysae Chao Stress
Authors: Xu Zhanghua , Li Bin , Yu Hui , Zhang Huafeng , Guo Xiaoyu , Li Zenglu , Wang Lin , Liu Zhicai , Li Yifan , He Anqi , Huang Xuying
Published: March 2023

Leaf water content (LWC) is very important in the growth of vegetation. LWC and leaf spectra change when the leaves are under pest stress; exploring the change mechanism between LWC, leaf spectra, and pest stress can lay the foundation for pest detection. In this study, we measured the LWC and leaf spectra of moso bamboo leaves under different damage levels, used the Pearson–Lasso method to screen the features, and established a multiple linear regression (MLR) and random forest regression (RFR) model to estimate the LWC. We analyzed the relationship between LWC and spectral features of moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and their changes. The results showed that: (1) the LWC showed a decreasing trend as the pest level increased. (2) The spectra changed substantially when the leaves were under pest stress. (3) The number and significance of response features associated with the LWC were diverse under different damage levels. (4) The estimation of LWC under different damage levels differed significantly. LWC, leaf spectra, response features, and the model estimation effect were diverse under different damage levels. The correlation between LWC and features was higher for healthy leaves than for damaged and off-year leaves. The two models were more effective in estimating the LWC of healthy leaves but less effective for damaged and off-year leaves. This study provides theoretical support for the prediction of PPC stress and lays the foundation for remote sensing monitoring.
Impact of City Forests on Haze Reduction—Implementation of the National Forest City Policy in China
Authors: Hu Chao , Chen Jian , Dong Jiayun
Published: March 2023

The construction and development of China’s forest cities revolves around the goal of building an environmentally friendly society and achieving harmonious development between human beings and nature, which is essentially the implementation of the concept of green development and helps promote the sustainable development of sustainable cities. Based on the panel data of 263 prefecture-level cities in China from 2001 to 2020, the National Forest City Policy (NFCP) is used as a quasi-natural experiment to assess the effect of NFCP on haze pollution management and their heterogeneity using a time-varying DID (difference-in-differences) model, and a mediating effect model is used to analyze the mechanism of the effect of NFCP on haze pollution. The research found that (1) the NFCP can reduce urban haze pollution, and this finding remained robust after placebo tests and the replacement of explanatory variables; (2) the NFCP is more conducive to reducing haze pollution in the Pearl River Delta urban agglomeration, Type II large cities, cities in the eastern region, and cities east of the Hu Huanyong line; (3) the NFCP will improve urban green space coverage, raise residents’ awareness of environmental protection, and promote the development of tertiary industries, thereby promoting urban haze reduction.
An Ensemble Model for Forest Fire Occurrence Mapping in China
Authors: Shao Yakui , Feng Zhongke , Cao Meng , Wang Wenbiao , Sun Linhao , Yang Xuanhan , Ma Tiantian , Guo Zanquan , Fahad Shahzad , Liu Xiaohan , Wang Zhichao
Published: March 2023

Assessing and predicting forest fires has long been an arduous task. Nowadays, the rapid advancement of artificial intelligence and machine learning technologies have provided a novel solution to forest fire occurrence assessment and prediction. In this research, we developed a novel hybrid machine-learning-technique algorithm to improve forest fire prediction based on random forest (RF), gradient-boosting decision tree (GBDT), support vector machine (SVM), and other machine learning models. The dataset we employed was satellite fire point data from 2010 to 2018 from the Chinese Department of Fire Prevention. The efficacy and performance of our methods were examined by validating the model fit and predictive capability. The results showed that the ensemble model LR (logistic regression)-RF-SVM-GBDT outperformed the single RFSVMGBDT model and the LR-RF-GBDT integrated framework, displaying higher accuracy and greater robustness. We believe that our newly developed hybrid machine-learning algorithm has the potential to improve the accuracy of predicting forest fire occurrences, thus enabling more efficient firefighting efforts and saving time and resources.
Recreation Potential Assessment at Tamarix Forest Reserves: A Method Based on Multicriteria Evaluation Approach and Landscape Metrics
Authors: Bayat Mahmoud , Bettinger Pete , Masteali Sahar Heidari , Hamidi Seyedeh Kosar , Masood Awan Hafiz Umair , Abolhasani Azam
Published: March 2023

The purpose of this study was to develop new methods to describe outdoor recreation potential based on landscape indicators and systemic multicriteria evolution in the Tamarix forest reserves of Varamin city, a part of Iranian–Turanian forests of the Tehran province in Iran. First, in conducting a multicriteria evaluation, ecological factors that included slope, aspect, elevation, vegetation density, precipitation, temperature, and soil texture were mapped, classified, and coded according to the degree of desirability for outdoor recreation. All these maps were then intersected and the final map of recreational potential for three regions of the forest reserves was prepared. Results showed that the Shokrabad region had more recreation potential than the other two regions (Fakhrabad and Dolatabad) in terms of the sum of ecological factors potentially affecting tourism potential. Second, in conducting a landscape-based method, six of the most important indicators of the landscape that are effective in outdoor recreational potential were developed for each region. The combination of these landscape features determined the value of a place for recreational activities from a landscape perspective. The results showed that a large part of the Shokrabad region and a smaller number of places in the Fakhrabad and Dolatabad regions have high outdoor recreational potential. The area suitable for recreation in the output of the multicriteria evaluation method turned out to be greater than the area suggested by the landscape method, as more factors were examined in the multicriteria evaluation method. Of the set investigated, the topography and soil factors played an important role in the evaluation.
Feasibility of Low-Cost LiDAR Scanner Implementation in Forest Sampling Techniques
Authors: Brach Michał , Tracz Wiktor , Krok Grzegorz , Gąsior Jakub
Published: March 2023

Despite the growing impact of remote sensing technology in forest inventories globally, there is a continuous need for ground measurements on sample plots. Even though the newest volume assessment methodology requires fewer sample plots, the accuracy of ground-recorded data influences the final accuracy of forest stand modeling. Therefore, effective and economically justified tools are in the continuous interest of foresters. In the presented research, a consumer-grade light detection and ranging (LiDAR) sensor mounted on iPad was used for forest inventory sample plot data collection—including tree location and diameter breast height. In contrast to other similar research, feasibility and user-friendliness were also documented and emphasized. The study was conducted in 63 real sample plots used for the inventory of Polish forests. In total, 776 trees were scanned in 3 types of forest stands: pine, birch, and oak. The root mean square error was 0.28 m for tree locations and 0.06 m for diameter breast height. Various additional analyses were performed to describe the usage of an iPad in tree inventories. It was contended that low-cost LiDAR scanners might be successfully used in real forest conditions and can be considered a reliable and easy-to-implement tool in forest inventory measurements.
Monitoring of Respiratory Health Risks Caused by Biomass Storage in Urban-Type Heating Plants
Authors: Lieskovský Martin , Gejdoš Miloš
Published: March 2023

The aim of this work was to carry out long-term monitoring on the concentration and identification of phytopathogens in wood chip storage areas in urban-type heating plants. Three municipal heating plants in the central part of Slovakia were selected. The plants store biomass in large-capacity piles with a volume of 4 to 5000 m3. Samples were obtained every year in the 2017–2022 period from the surface of the piles and from a depth of 0.5 m. Their moisture content was determined in the laboratory and the microbial analysis was performed by an accredited laboratory. The average number of colonies of phytopathogens did not differ significantly in individual years. The highest number of colony-forming units per gram was achieved by the species of the genera Penicillium and Aspergillus. In terms of occurrence in individual years and the frequency of occurrence in individual samples, the most frequently recorded species were Mycelia Sterilia, Aspergillus brasiliensis, Aspergillus unguis, and Yeasts. Based on the results achieved, in the future it will be necessary to establish legislative frameworks for these risks and, at least at the national level, work procedures for individual work activities, so that the health and life of the workers of the plants, as well as residents in the vicinity of this type of plant, are not endangered.
Mixed-Species Plantation of Pinus massoniana Lamb. and Quercus variabilis Bl. and High Soil Nutrient Increase Litter Decomposition Rate
Authors: Zeng Lixiong , Zhou Changjian , He Wei , Lei Lei , Wang Ben , Teng Mingjun , Wang Jin , Yan Zhaogui , Wang Pengcheng , Xiao Wenfa
Published: March 2023

Changes in land use and forest planting have led to substantial changes in soil fertility and leaf litter input. The effects of mixed planting on the leaf litter decomposition rate in contrasting soil nutrient conditions are poorly understood. To elucidate the effects of litter composition and soil fertility on litter decomposition, we conducted a field litterbag-decomposition experiment with single (Pinus massoniana Lamb. or Quercus variabilis Bl.) and mixed (P. massoniana and Q. variabilis) litter treatments on soils of three nutrient levels (high, medium, and low). During the 3-year decomposition, at each decomposition stage and soil nutrient level, the mass-loss rate (MLR) was higher in mixed-litter than in the two single-litter treatments, with the exception of Q. variabilis, which recorded a higher MLR at 724 d in medium and high soil substrates. Between the two single-litter treatments, the MLR of Q. variabilis litter was higher than that of the P. massoniana litter; the MLR of the component litter of P. massoniana and Q. variabilis was higher than that of the corresponding two single-litter treatments. The k values over the 3-year-experiment period increased with the soil nutrient level for all litter treatments, as did microbial biomass carbon and nitrogen content. These findings suggest that mixed planting and high level of soil nutrient can accelerate litter decomposition.
Genome-Wide Identification and Expression Analysis of the R2R3-MYB Gene Family in Rubber Trees
Authors: Liu Mingyang , Yang Hong , Fan Songle , Guo Bingbing , Dai Longjun , Wang Lifeng , Wang Meng
Published: March 2023

The plant MYB transcription factor family featured as highly conserved DNA-binding domains consisting of 1 to 4 imperfect repeats (R). Increasing evidence indicates that MYB genes participates in growth, differentiation, metabolism, and biotic and abiotic stress responses. However, the functions of MYB genes in the rubber tree remain to be deeply elucidated, especially R2R3-MYB gene family. In this study, molecular biology, bioinformatics, and qRT-PCR were used to identify and analyze HbR2R3-MYB gene family members in the rubber tree. A total of 132 members of the R2R3-MYB gene family were identified in the rubber tree based on genome-wide level. Most of the HbR2R3-MYBs were mapped to 17 rubber tree chromosomes except four genes. A phylogenetic analysis divided all the HbR2R3-MYBs into 20 subgroups with Arabidopsis thaliana. MEME analysis showed that the protein of HbR2R3-MYBs was characterized by 9 conserved motifs. Twenty-six representative R2R3 HbMYBs from different subgroups were selected for expression profiles analysis and the results revealed that the HbR2R3-MYBs members showed various expression patterns in different tissues, powdery mildew-infected and ethylene treatment, implying the diversity of their functions in rubber trees. These results provide fundamental knowledge for further studying the response of the HbR2R3-MYB family to stress and regulation latex flow in rubber tree.
Influential Factors in the Design and Development of a Sustainable Web3/Metaverse and Its Applications
Authors: Aria Reza , Archer Norm , Khanlari Moein , Shah Bharat
Published: March 2023

This paper summarizes the work of many different authors, industries, and countries by introducing important and influential factors that will help in the development, successful adoption, and sustainable use of the Web3/metaverse and its applications. We introduce a few important factors derived from the current state-of-the-art literature, including four essential elements including (1) appropriate decentralization, (2) good user experience, (3) appropriate translation and synchronization to the real world, and (4) a viable economy, which are required for appropriate implementation of a metaverse and its applications. The future of Web3 is all about decentralization, and blockchain can play a significant part in the development of the Metaverse. This paper also sheds light on some of the most relevant open issues and challenges currently facing the Web3/metaverse and its applications, with the hope that this discourse will help to encourage the development of appropriate solutions.
Automated Identification of Morphological Characteristics of Three Thunnus Species Based on Different Machine Learning Algorithms
Authors: Ou Liguo , Liu Bilin , Chen Xinjun , He Qi , Qian Weiguo , Zou Leilei
Published: March 2023

Tuna are economically important fish species. The automated identification of tuna species is of importance in fishery production and resource assessment in that it would facilitate the informed monitoring of tuna fishing vessels and the establishment of electronic observer systems. As morphological characteristics are important for tuna identification, this study aims to verify the performance of the automated identification of three Thunnus species through morphological characteristics based on different machine learning algorithms. Firstly, morphological outlines were visually analyzed using EFT (elliptic Fourier transform) and CNN (convolutional neural network). Then, the EFT feature data and deep feature data of the tuna outline images were extracted, and principal component analysis of the two different morphological characteristics was performed. Finally, different machine learning algorithms were used to analyze the identification performance of tuna of the same genus and different species. The experimental results showed that EFT features had the highest identification accuracy in KNN (K-nearest neighbor), with 90% for T. obesus, 90% for T. albacores, and 85% for T. alalunga. Deep features had the best identification performance in SVM (support vector machine), with 80% for T. obesus, 90% for T. albacores, and 100% for T. alalunga. Deep features were better than EFT features in identification performance. The biodiversity and intergeneric differences among tuna species can be well analyzed using these two different morphological characteristics. Machine learning algorithms open up the way for rapid near-real-time electronic observer systems in these important international fisheries.
Strategies for the Valorization of Date Fruit and Its Co-Products: A New Ingredient in the Development of Value-Added Foods
Authors: Muñoz-Tebar Nuria , Viuda-Martos Manuel , Lorenzo Jose Manuel , Fernandez-Lopez Juana , Perez-Alvarez Jose Angel
Published: March 2023

Date palm trees (Phoenix dactylifera L.) are traditionally cultivated in South-West Asia and North Africa for date fruit consumption, although in recent years, its consumption has increased worldwide, and its cultivation has spread to other areas of America, sub-Saharan Africa, Oceania, and Southern Europe. During date fruit processing, several types of by-products are generated, such as low-quality dates or seeds, which along with date fruit, represent an excellent source of dietary fiber and bioactive compounds such as flavonoids, tannins, carotenoids, tocopherols, and tocotrienols. Therefore, this review provides information on the processing of dates fruit and the value-added by-products generated from them as well as their applications in different types of foods for the development of foods with an enhanced nutritional and functional profile. The incorporation of date fruit and their co-products in food formulations will help to cover the current consumer demands for foods made with ingredients of natural origin and with health properties beyond the merely nutritional.