bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Validation of a Questionnaire to Understand Students’ Perceptions of Bilingual Education in Taiwan
Authors: Mason Seng Chee-Peng , Huang Chia-Kai , Lin Tzu-Bin
Published: October 2024

This study employed a localized bilingual education model developed in Taiwan to design a questionnaire targeted at junior high school students. The questionnaire was validated using exploratory factor analysis. Reliability testing indicated Cronbach’s alpha values for subscales ranging from 0.88 to 0.95, with an overall Cronbach’s alpha of 0.98. A total of 760 junior high school students in Taipei City who have received bilingual instruction completed the questionnaire. The results demonstrate that the questionnaire has high construct validity and internal consistency, making it a practical tool to evaluate students’ perceptions of bilingual education. Future studies should focus on qualitative research such as in-depth interviews, extend the survey to students at the university and primary school levels, and verify the questionnaire’s structure through confirmatory factor analysis.
Podcasts and Informal Learning: Exploring Knowledge Acquisition and Retention
Authors: Meden Ema , Radovan Marko , Štefanc Damijan
Published: October 2024

This study explores how podcasts’ accessibility fosters learning, examining listener engagement, content preferences, and the unique educational opportunities they offer. With the rise of digital audio platforms, podcasts have emerged as a significant medium for acquiring knowledge outside formal educational settings. Using an online survey, data were collected from 605 respondents, who were predominantly young and well educated, to understand their podcast listening habits and the extent of informal learning. The study categorizes informal learning into self-directed, incidental, and tacit learning. Results show that more than half of respondents engage in self-directed learning, while a third participate in incidental learning, indicating that podcasts serve as effective tools for intentional and unintentional knowledge acquisition. ANOVA analysis reveals that self-directed learners exhibit higher information retention and critical evaluation skills compared to tacit learners. Additionally, trust in podcast content is significantly higher among educated listeners. The study concludes that while podcasts are valuable for learning and professional development, there is a need for enhanced digital literacy to improve critical appraisal skills among listeners. These findings provide insights into the cognitive processes involved in podcast-based learning and suggest practical strategies for educators and content creators to enhance the educational impact of podcasts.
FOBism Unveiled: Quantifying Assimilative Racism within Asians in the United States
Authors: Wang Kenneth T. , Kim Seong-Hyeon , Wang Juliet K. , Wang Katelyn J. , Jun Helen H. , Lee Daniel D.
Published: October 2024

FOB (fresh-off-the-boat) is a term used to refer to unassimilated immigrants or sojourners, which has created a divide within the Asian community. In this study, we coined the term FOBism, a form of internalized racism (or appropriated racial oppression) that intersects with assimilation, and we developed a measure of FOBism. We created a 14-item, 3-factor FOBism Scale and evaluated its psychometric properties among a sample of 296 Asians in the United States. Exploratory structural equation modeling (ESEM) was utilized to select items and evaluate the factorial validity. Results yielded a strong factor structure, internal consistency reliability, and construct validity. Construct validity was demonstrated through FOBism scores’ positive correlations with measures of within-group discrimination and internalized racism, and negative associations with an Asian cultural orientation. The FOBism Scale is a promising measure that could be used as an assessment tool and to raise awareness of the phenomenon.
The Influence of Thick Cathode Fabrication Processing on Battery Cell Performance
Authors: Kong Dewen , Liu Haijing , Chen Si , Wu Meiyuan
Published: October 2024

The lithium-ion battery (LIB) is the key energy storage device for electric transportation. The thick electrode (single-sided areal capacity >4.0 mAh/cm2) design is a straightforward and effective strategy for improving cell energy density by improving the mass proportion of electroactive materials in whole cell components and for reducing cost of the battery cell without involving new chemistries of uncertainties. Thus, selecting a low-cost and environmentally friendly fabrication process to achieve a thick cathode electrode with good electrochemical performance is of strong interest. This study investigated the impact of fabrication processes on the performance of thick LiNi0.75Mn0.25O2 (NM75) cathode electrodes in pouch cells. Two fabrication methods were compared: the conventional polyvinylidene fluoride (PVDF)-based slurry casting method (C-NM75) and the polytetrafluoroethylene (PTFE)-based powder fibrillating process (F-NM75). The pouch cells with F-NM75 electrodes exhibited significantly improved discharge and charge rate capabilities, with a discharge capacity ratio (3 C vs. C/3) > 62% and a charge capacity ratio (2 C vs. C/3) > 81%. Furthermore, F-NM75 cells demonstrated outstanding C/3 cycling performance, retaining 86% of discharge capacity after 2200 cycles. These results strongly indicated that the PTFE-based powder fibrillating process is a promising solution to construct high-performance thick cathode electrodes for electric vehicles (EVs) applications.
Dynamic Assessment-Based Curriculum Learning Method for Chinese Grammatical Error Correction
Authors: Duan Ruixue , Ma Zhiyuan , Zhang Yangsen , Ding Zhigang , Liu Xiulei
Published: October 2024

Current mainstream for Chinese grammatical error correction methods rely on deep neural network models, which require a large amount of high-quality data for training. However, existing Chinese grammatical error correction corpora have a low annotation quality and high noise levels, leading to a low generalization ability of the models and difficulty in handling complex sentences. To address this issue, this paper proposes a dynamic assessment-based curriculum learning method for Chinese grammatical error correction. The proposed approach focuses on two key components: defining the difficulty of training samples and devising an effective training strategy. In the difficulty assessment phase, we enhance the accuracy of the curriculum sequence by dynamically updating the evaluation model. During the training strategy phase, a multi-stage dynamic progressive approach is employed to select training samples of varying difficulty levels, which helps prevent the model from prematurely converging to local optima and enhances the overall training effectiveness. Experimental results on the MuCGEC and NLPCC 2018 Chinese grammatical error correction datasets show that the proposed curriculum learning method significantly improves the model’s error correction performance, with F0.5 scores increasing by 0.9 and 1.05, respectively, validating the method’s effectiveness.
Enhanced Image-Based Malware Classification Using Transformer-Based Convolutional Neural Networks (CNNs)
Authors: Ashawa Moses , Owoh Nsikak , Hosseinzadeh Salaheddin , Osamor Jude
Published: October 2024

As malware samples grow in complexity and employ advanced evasion techniques, traditional detection methods are insufficient for accurately classifying large volumes of sophisticated malware variants. To address this issue, image-based malware classification techniques leveraging machine learning algorithms have been developed as a more optimal solution to this challenge. However, accurately classifying content distribution-based features with unique pixel intensities from grayscale images remains a challenge. This paper proposes an enhanced image-based malware classification system using convolutional neural networks (CNNs) using ResNet-152 and vision transformer (ViT). The two architectures are then compared to determine their classification abilities. A total of 6137 benign files and 9861 malicious executables are converted from text files to unsigned integers and then to images. The ViT examined unsigned integers as pixel values, while ResNet-152 converted the pixel values into floating points for classification. The result of the experiments demonstrates a high-performance accuracy of 99.62% with effective hyperparameters of 10-fold cross-validation. The findings indicate that the proposed model is capable of being implemented in dynamic and complex malware environments, achieving a practical computational efficiency of 47.2 s for the identification and classification of new malware samples.
Offload Shaping for Wearable Cognitive Assistance
Authors: Iyengar Roger , Dong Qifei , Nguyen Chanh , Pillai Padmanabhan , Satyanarayanan Mahadev
Published: October 2024

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance. Our investigation considers the offload shaping strategies of mapping processes to different computing tiers, gating, and decluttering. We find that all three strategies offer a significant bandwidth savings compared to transmitting full camera images to a cloudlet. Two out of the three devices we test are capable of running all offload shaping strategies within a reasonable latency bound.
SPM-FL: A Federated Learning Privacy-Protection Mechanism Based on Local Differential Privacy
Authors: Chen Zhiyan , Zheng Hong
Published: October 2024

Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, information such as model weights can still be analyzed or attacked, leading to potential privacy breaches. Traditional federated learning methods often disturb models by adding Gaussian or Laplacian noise, but under smaller privacy budgets, the large variance of the noise adversely affects model accuracy. To address this issue, this paper proposes a Symmetric Partition Mechanism (SPM), which probabilistically perturbs the sign of local model weight parameters before model aggregation. This mechanism satisfies strict ϵ-differential privacy, while introducing a variance constraint mechanism that effectively reduces the impact of noise interference on model performance. Compared with traditional methods, SPM generates smaller variance under the same privacy budget, thereby improving model accuracy and being applicable to scenarios with varying numbers of clients. Through theoretical analysis and experimental validation on multiple datasets, this paper demonstrates the effectiveness and privacy-protection capabilities of the proposed mechanism.
Advanced Security Auditing Methods for Solidity-Based Smart Contracts
Authors: Xiao Meihua , Xu Yangping , Li Zehuan , Wan Hongbin
Published: October 2024

The development of smart contracts remains in its early stages, with significant differences in underlying programming languages and application platforms resulting in a lack of standardization. This lack of standardization increases the susceptibility to vulnerabilities and associated financial losses. To address security vulnerabilities in smart contracts on the Ethereum blockchain platform, this paper proposes a security audit method based on formal verification. The method integrates an input module, static analysis module, formal verification module, analog execution module, and report and recommendation module, which can accurately discover the security vulnerabilities and logical flaws of smart contracts through formal verification and other analysis techniques, thus realizing correctness detection. During the experiment, the method detects 8 types of common vulnerabilities in 148 smart contracts and marks 21 smart contracts with vulnerabilities. After manual review and analysis, it is found that 17 of these 21 marked smart contracts do have security vulnerabilities. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed, thus significantly improving the security of smart contracts and reducing the economic losses that may be caused by code defects.
Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
Authors: Khan Muhammad Iqbal , da Silva Bruno
Published: October 2024

Over the past decade, wearable medical devices (WMDs) have become the norm for continuous health monitoring, enabling real-time vital sign analysis and preventive healthcare. These battery-powered devices face computational power, size, and energy resource constraints. Traditionally, low-power microcontrollers (MCUs) and application-specific integrated circuits (ASICs) have been used for their energy efficiency. However, the increasing demand for multi-modal sensors and artificial intelligence (AI) requires more computational power than MCUs, and rapidly evolving AI asks for more flexibility, which ASICs lack. Field-programmable gate arrays (FPGAs), which are more efficient than MCUs and more flexible than ASICs, offer a potential solution when optimized for energy consumption. By combining real-time reconfigurability with intelligent energy optimization strategies, FPGAs can provide energy-efficient solutions for handling multimodal sensors and evolving AI requirements. This paper reviews low-power strategies toward FPGA-based WMD for physiological monitoring. It examines low-power FPGA families, highlighting their potential in power-sensitive applications. Future research directions are suggested, including exploring underutilized optimizations like sleep mode, voltage scaling, partial reconfiguration, and compressed learning and investigating underexplored flash and hybrid-based FPGAs. Overall, it provides guidelines for designing energy-efficient FPGA-based WMDs.
Simulation Environment for the Testing of Electrical Arc Fault Detection Algorithms
Authors: Lezama Jinmi , Schweitzer Patrick , Tisserand Etienne , Weber Serge
Published: October 2024

Electrical arc fault detector development requires many tests to develop and validate detection algorithms. The use of artificial intelligence or mathematical transformation requires the use of consequential datasets of current signatures corresponding to as many different situations as possible. In addition, one of the main drawbacks is that these experiments take a great deal of time and are often laborious in the laboratory. To overcome these limitations, a virtual test bench based on the modeling of a modular 230 VAC electrical circuit has been developed. The simulated network is composed of different home appliances (resistor, vacuum cleaner, dimmer, etc.) and its configurations are those of single and combined loads. The fault modeled is an electric arc, modeled by active diode switching, which can be inserted at any point of the circuit. This arc model takes into account the random variations in the restrike and arc voltage. All the appliance models are validated by comparing the frequential (harmonic distortion) and temporal (agreement index) signatures of the measured currents in real situations to those obtained by modeling. The results obtained using the model and experiment network show that the current signatures are comparable in both cases. Further, two detection algorithms are tested on those current signatures obtained by the modeling and experimentation. The results are comparable and provide identical detection thresholds.
Comprehensive Assessment of the Impact of Green Roofs and Walls on Building Energy Performance: A Scientific Review
Authors: Nasr Yara , El Zakhem Henri , Hamami Ameur El Amine , El Bachawati Makram , Belarbi Rafik
Published: October 2024

Sustainability and energy efficiency are now two pivotal goals that society aims towards. Green roofs and facades have gained significant attention in this direction for innovative, sustainable solutions for enhancing building energy performance. With a focus on sustainable urban development and energy-efficient building practices, this study delves into the intricate relationship between these green infrastructure elements and the overall energy dynamics of constructed environments. Furthermore, a range of case studies from diverse geographical locations are presented to provide valuable insights into their practical implications as emerging technologies that contribute to improved insulation, reduced heat transfer, regulating indoor temperatures, and mitigation of urban heat island effects, thus reducing the need for artificial heating and cooling and optimizing overall energy consumption. This comprehensive review serves as a dataset for understanding and highlighting all the research findings of the numerical and experimental investigations invested in the field of greenery systems to encourage their integration, which is crucial for combating climate change and pollution. Previous research is often focused on isolated, short-term, or single-climate analyses of consumption; therefore, by providing an inclusive description of their practical benefits in both temperate and extreme climates, the gap in previous articles is tackled.
Photovoltaic Power Intermittency Mitigating with Battery Storage Using Improved WEEC Generic Models
Authors: Schiochet André Fernando , Duailibe Monteiro Paulo Roberto , Borges Thiago Trezza , Passos Filho João Alberto , de Oliveira Janaína Gonçalves
Published: October 2024

The growing integration of renewable energy sources, such as photovoltaic and wind systems, into energy grids has underscored the need for reliable control mechanisms to mitigate the inherent intermittency of these sources. According to the Brazilian grid operator (ONS), there have been cascading disconnections in renewable energy distributed systems (REDs) in recent years, highlighting the need for robust control models. This article addresses this issue by presenting the validation of an active power ramp rate control (PRRC) function for a PV plant coupled with a Battery Energy Storage System (BESS) using WECC generic models. The proposed model underwent rigorous validation over an extended analysis period, demonstrating good accuracy using the Root Mean Squared Error (RMSE) and an R-squared (R2) metrics for the active power injected at the Point of Connection (POI), PV active power, and BESS State of Charge (SOC), providing valuable insights for medium and long-term analyses. The ramp rate control module was implemented within the plant power controller (PPC), leveraging second-generation Renewable Energy Systems (RES) models developed by the Western Electricity Coordination Council (WECC) as a foundational framework. We conducted simulations using the Anatem software, comparing the results with real-world data collected at 100 ms to 1000 ms intervals from a PV plant equipped with a BESS in Brazil. The proposed model underwent rigorous validation over an extended analysis period, with the presented results based on two days of measurements. The positive sequence model used to represent this control demonstrated good accuracy, as confirmed by metrics such as the Root Mean Squared Error (RMSE) and R-squared (R2). Furthermore, the article underscores the critical role of accurately accounting for the power sampling rate when calculating the ramp rate.
Application of the FDTD Method to Analyze the Influence of Brick Complexity on Electromagnetic Wave Propagation
Authors: Choroszucho Agnieszka , Szczegielniak Tomasz , Kusiak Dariusz
Published: October 2024

This article presents a numerical analysis of the effects related to the propagation of electromagnetic waves in an area containing a non-ideal, non-uniform, and absorbing dielectric. The analysis concerns the influence of electrical parameters, the structure of the building material, and the layering of the wall on the values of the electric field intensity. A multivariate analysis was carried out with different conductivity values. Homogeneous materials (e.g., solid brick) can be analyzed using the analytical method. In the case of complex materials containing, e.g., hollows (brick with hollows, hollow block), it is necessary to use the numerical method. The FDTD (finite difference time domain) method was used to assess the dependence of the electric field intensity on the layering, the length of hollows in bricks, and the material loss. In order to check the correctness of the adopted numerical assumptions, a series of tests related to the discretization of the model was carried out. The article also presents the influence of changing the length of hollows in bricks on the values of the electric field intensity at a frequency of 2.4 GHz. The instantaneous field distributions and maximum values of the electric field intensity are presented. In the model with a two-layer wall, regardless of the conductivity, the field values were the same for the two models, where the difference in the percentage of ceramic mass in the brick was 8%. A 12% decrease in the percentage of ceramic mass in the brick resulted in a 15% increase in the value of the area between a single-layer and a double-layer wall made of clinker bricks. At a conductivity of 0.04 S/m for a single-layer wall, the field values were similar for all brick variants.
Novel Droop-Based Techniques for Dynamic Performance Improvement in a Linear Active Disturbance Rejection Controlled-Dual Active Bridge for Fast Battery Charging of Electric Vehicles
Authors: Nkembi Armel Asongu , Santoro Danilo , Ahmad Fawad , Kortabarria Iñigo , Cova Paolo , Sacchi Emilio , Delmonte Nicola
Published: October 2024

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs.
Development of the Separation Column’s Temperature Field Monitoring System
Authors: Kukharova Tatyana , Martirosyan Alexander , Asadulagi Mir-Amal , Ilyushin Yury
Published: October 2024

Oil is one of the main resources used by all countries in the world. The ever-growing demand for oil and oil products forces oil companies to increase production and refining. In order to increase net profit, oil producing companies are constantly upgrading equipment, improving oil production technologies, and preparing oil for further processing. When considering the elements of primary oil refining in difficult conditions, such as hard-to-reach or in remote locations, developers face strict limitations in energy resources and dimensions. Therefore, the use of traditional systems causes a number of difficulties, significantly reducing production efficiency. In this study, the authors solve the problem of improving the characteristics of the oil separation process. In their work, the authors analyzed the separation columns of primary oil distillation, identified the shortcomings of the technological process, and searched for technological solutions. Having identified the lack of technical solutions for monitoring the state of the temperature field of the separation column, the authors developed their own hardware–software complex for monitoring the separation column (RF patents No. 2020665473, No. 2021662752 were received). The complex was tested and successfully implemented into production. The study provides an assessment of the economic efficiency of implementation for a year and a forecast of the economic effect for 10 years.
Comparative Analysis and Integrated Methodology for the Electrical Design and Performance Evaluation of Thermoelectric Generators (TEGs) in Energy Harvesting Applications
Authors: Ando Junior Oswaldo Hideo , Silva Eder Andrade da , Lira Emerson Rodrigues de , Degiorgi Sergio Vladimir Barreiro , Carmo João Paulo Pereira do
Published: October 2024

This study presents a comparative analysis of the accuracy of different methodologies for the design and performance evaluation of thermoelectric generators (TEGs), using analytical, computational numerical, and experimental approaches. TEGs are promising devices for capturing waste energy in industrial processes, converting waste heat into electrical energy and contributing to energy sustainability. However, the efficiency of TEGs is a significant challenge due to their low conversion rates. To address this challenge, three different methodologies were developed and systematically compared. Analytical Model: Developed for the electrical design of thermoelectric micro generators, using theoretical performance data and industrial temperature gradients. This method offers a robust theoretical view but may not capture all practical variables. Computational model in Simulink/MATLAB: Created and validated to consider the variation of the Seebeck coefficient and the internal resistance of thermoelectric modules with temperature. This model provides an accurate simulation of operating conditions but depends on the accuracy of the input parameters. Experimental Multi-string Electrical Arrangement Prototype: This involved the design and construction of a prototype followed by experimental tests to validate its performance. This method provides valuable empirical data but can be limited by the complexity and cost of the experiments. The results show that each methodology has specific advantages and limitations, offering valuable insights for the development of more efficient TEG systems. The comparison of analytical, numerical, and experimental methods revealed differences in accuracy and efficiency, highlighting the importance of an integrated approach to TEG design. This study lays a solid foundation for future research and practical applications in the field of industrial residual energy harvesting.
The Consequences of Economy Servitization for Ensuring Energy Sustainability—The Case of Developed and Developing Countries
Authors: Jankiewicz Mateusz , Szulc Elżbieta
Published: October 2024

This study presents an analysis of the relationship between the servitization process and energy sustainability in the years 2015–2020. The research refers to 164 selected countries, also divided into two regimes: developed and developing. The transformation of the manufacturing process, and as a result, the economy’s structure, towards servitization, is observed in most countries worldwide. The positive influence of the servitization of production by individual manufacturers on sustainability is widely known. In this research, this relationship is considered on a macroeconomic scale, which is one of the novelties of the study. Particularly, sustainability in the energy sector, indicated as an achievement of the 7th goal of Sustainable Development, is discussed. Energy sustainability is evaluated using a synthetic measure by Perkal. This part of the research shows the problem of the low level of energy sustainability in developing countries (particularly in Africa) compared with developed ones. Moreover, spatio-temporal sensitivity models are estimated and verified. The sensitivity parameter in these models shows the impact of the progress in the servitization process on energy sustainability. The models have been enriched with the effects of spatial dependence between countries, taking into account two types of proximity matrices based on (1) the common border criterion and (2) the similarity of the development levels measured by the Human Development Index. Additionally, the differences in sensitivity between developed and developing countries are considered. The results of the study show that in both cases, the economic servitization positively influences energy sustainability, but the strength of the relationship is stronger in the group of developed countries. This can be, for example, the result of the individual characteristics of the given countries, where African countries mainly benefit from agricultural development. Only after reaching a certain level of economic growth will they be able to obtain sustainability faster through economic servitization.
Development of a Smart Material Resource Planning System in the Context of Warehouse 4.0
Authors: Sokolov Oleksandr , Iakovets Angelina , Andrusyshyn Vladyslav , Trojanowska Justyna
Published: October 2024

This study explores enhancing decision-making processes in inventory management and production operations by integrating a developed system. The proposed solution improves the decision-making process, managing the material supply of the product and inventory management in general. Based on the researched issues, the shortcomings of modern enterprise resource planning systems (ERPs) were considered in the context of Warehouse 4.0. Based on the problematic areas of material accounting in manufacturing enterprises, a typical workplace was taken as a basis, which creates a gray area for warehouse systems and does not provide the opportunity of quality-managing the company’s inventory. The main tool for collecting and processing data from the workplace was the neural network. A mobile application was proposed for processing and converting the collected data for the decision-maker on material management. The YOLOv8 convolutional neural network was used to identify materials and production parts. A laboratory experiment was conducted using 3D-printed models of commercially available products at the SmartTechLab laboratory of the Technical University of Košice to evaluate the system’s effectiveness. The data from the network evaluation was obtained with the help of the ONNX format of the network for further use in conjunction with the C++ OpenCV library. The results were normalized and illustrated by diagrams. The designed system works on the principle of client–server communication; it can be easily integrated into the enterprise resource planning system. The proposed system has potential for further development, such as the expansion of the product database, facilitating efficient interaction with production systems in accordance with the circular economy, Warehouse 4.0, and lean manufacturing principles.
Tree-Ring-Based Hydroclimatic Variability in the Southeast Coastal Region of China
Authors: Cao Xinguang , Kao Pei-ken , Li Yingjun , Zhao Zheng , Hu Hongbing , Hu Jing , Zhang Di , Fang Keyan
Published: October 2024

The frequency and severity of extreme hydroclimatic events in humid southeastern China have increased in the past half century, which is a serious concern. In this research, we used wood samples from 134 trees growing in the southeast coastal region of China (SECC) to reconstruct the Standardized Precipitation Evapotranspiration Index (SPEI) for the last 173 years (1843–2015 CE). Our reconstruction explained 41.6% of the variance contained in the November SPEI at a 7 month scale for the period 1957–2015. 17 extremely wet and 16 extremely dry events, 8 dry and 9 wet periods have been identified since 1843, and the most severe drought, coinciding with historical records, occurred in 1869 and 1870. The reconstruction reveals. Although the results reveal a modest upward trend in the SPEI and a predominance of extreme wet events over droughts throughout the period, the 20th century accounted for nine of the summers classified as extremely dry. Strong agreement between the current reconstruction and existing hydroclimatic reconstructions in southeastern China implied that our reconstruction exhibited high reliability. The composite anomalies of circulation during the period from May to November (MJJASON) indicate that the temporal variability in the SPEI reconstruction might be modulated by the local Hadley cell. These findings underscore the effectiveness of tree-ring-derived indices for reconstructing hydroclimatic trends in China’s humid regions and enhance our understanding of these changes within a long-term framework.
TCSNet: A New Individual Tree Crown Segmentation Network from Unmanned Aerial Vehicle Images
Authors: Chi Yue , Wang Chenxi , Chen Zhulin , Xu Sheng
Published: October 2024

As the main area for photosynthesis in trees, the canopy absorbs a large amount of carbon dioxide and plays an irreplaceable role in regulating the carbon cycle in the atmosphere and mitigating climate change. Therefore, monitoring the growth of the canopy is crucial. However, traditional field investigation methods are often limited by time-consuming and labor-intensive methods, as well as limitations in coverage, which may result in incomplete and inaccurate assessments. In response to the challenges encountered in the application of tree crown segmentation algorithms, such as adhesion between individual tree crowns and insufficient generalization ability of the algorithm, this study proposes an improved algorithm based on Mask R-CNN (Mask Region-based Convolutional Neural Network), which identifies irregular edges of tree crowns in RGB images obtained from drones. Firstly, it optimizes the backbone network by improving it to ResNeXt and embedding the SENet (Squeeze-and-Excitation Networks) module to enhance the model’s feature extraction capability. Secondly, the BiFPN-CBAM module is introduced to enable the model to learn and utilize features more effectively. Finally, it optimizes the mask loss function to the Boundary-Dice loss function to further improve the tree crown segmentation effect. In this study, TCSNet also incorporated the concept of panoptic segmentation, achieving the coherent and consistent segmentation of tree crowns throughout the entire scene through fine tree crown boundary recognition and integration. TCSNet was tested on three datasets with different geographical environments and forest types, namely artificial forests, natural forests, and urban forests, with artificial forests performing the best. Compared with the original algorithm, on the artificial forest dataset, the precision increased by 6.6%, the recall rate increased by 1.8%, and the F1-score increased by 4.2%, highlighting its potential and robustness in tree detection and segmentation.
Soil Enzyme Activities and Microbial Nutrient Limitation of Various Temperate Forest Types in Northeastern China
Authors: Xiao Ruihan , Duan Beixing , Dai Changlei , Wu Yu
Published: October 2024

Soil enzymes mediate organic matter decomposition and nutrient cycling, and their stoichiometry can indicate microbial nutrient demands. However, research on the variations in soil enzymes and microbial nutrient limitation under different temperate forest types still lacks insight. In this study, we sampled soils under five typical forest types (including Betula platyphylla Suk. forest, Fraxinus mandschurica Rupr forest, Larix gmelinii (Rupr.) Kuzen. forest, Populus davidiana Dode forest, and Pinus koraiensis Siebold et Zucc.forest) in the temperate climatic region of northeast China. Soil enzyme activities and soil microbial community composition and diversity were determined for each, and vector analysis was used to quantify the value of microbial limitation. The results showed that soil enzyme activity, enzyme stoichiometry, and microbial community structure were significantly different among the five temperate forest types. The ratios of soil C:N:P acquiring enzyme activity were close to 1:1:1. All the forests showed prevalent P limitation over N limitation (all vector angles > 45°), and the degree of impact varied among different forest types. Redundancy analysis (RDA) and Pearson’s test demonstrated that soil enzyme activities and microbial nutrient limitation were mainly determined by soil physical properties and microbial community. These results contribute to understanding the mechanisms that link plant composition, soil enzyme activity, and microbial nutrient limitation in temperate forests.
Constructing a Future Green Space Ecological Network Based on Multi-Scenario Urban Expansion: A Case Study of Chengdu, Sichuan, China
Authors: Luo Yushu , Zhou Yuan , Li Bei , Li Pengyao , Zhang Li , Ning Shunbin
Published: October 2024

As urban spaces expand, changes in land use significantly affect the structure and function of urban ecosystems, particularly with challenges such as green space reduction and uneven distribution. This study focused on the central urban area of Chengdu, China, simulating and forecasting various urban development scenarios for 2035, including cultivated land protection (CP), economic development (ED), ecological priority (EP), and natural development (ND). The construction of green space ecological networks followed a systematic process, incorporating key methods such as ecological source identification, landscape resistance surface construction, and ecological corridor extraction. The connectivity of these ecological networks was assessed using the space syntax. The results indicated that: (1) Construction land expanded across all scenarios, with the ED scenario having the largest area, while the EP scenario resulted in a significant increase in green space. (2) Ecological corridors were established under every scenario, with the EP scenario featuring the most extensive and well-connected network, linking urban green patches with surrounding natural areas. (3) The EP scenario’s ecological network displayed integration, choice, connectivity, and depth values that indicate the most complete and stable network structure. This study provides a comprehensive analysis of green space ecological network changes under different urban development strategies, offering valuable insights for optimizing urban green space planning and management.
Parameter Estimations on Measurement Accuracy for Thermal Conductivity of Wood Using the Transient Plane Source Method
Authors: Meng Hongxu , Yu Xinxin , Chen Bonan , Ren Pengyuan , Zhao Jingyao
Published: October 2024

In order to enhance the reliability and accuracy of the results from the transient plane source (TPS) method for measuring the thermal conductivity of wood, this paper investigates setting parameters and measurement methods to improve measurement accuracy. Criteria are proposed to determine the optimal parameters such as the power output, heating time, and time window. The measurement results of the TPS method and the HFM method are compared. The results show that the total to characteristic time, temperature increase in the probe, mean deviation, and temperature drift graph are valid indicators for evaluating the detection reliability of the TPS method. The optimal parameters for measuring the thermal conductivity of wood using the TPS method are as follows: power output of 0.05 or 0.1 W, heating time of 120 s, and time window covering 60% to 80% of the heating time. The thermal conductivity measured with the TPS method was higher than that measured by the steady-state method in all grain angle directions. The standard uncertainties after optimization were 18.9% to 59.5% lower than before optimization. The optimized TPS measurement method can be applied to other tree species as well.
Exploration of the Bioactivity of Pigmented Extracts from Streptomyces Strains Isolated Along the Banks of the Guaviare and Arauca Rivers (Colombia)
Authors: Sarmiento-Tovar Aixa A. , Prada-Rubio Sara J. , Gonzalez-Ronseria Juliana , Coy-Barrera Ericsson , Diaz Luis
Published: October 2024

Pigments are chemical compounds that impart color through mechanisms such as absorption, reflection, and refraction. While traditional natural pigments are derived from plant and insect tissues, microorganisms, including bacteria, yeasts, algae, and filamentous fungi, have emerged as promising sources for pigment production. In this study, we focused on pigment production by 20 Streptomyces isolates from our in-house actinobacteria strain collection, sourced from the Guaviare and Arauca Rivers in Colombia. The isolates were identified via 16S rRNA gene sequencing, and the bioactivities—including antioxidant, antibacterial, and cytotoxic properties—of their extracts obtained across four different culture media were assessed. Promising pigmented hydroalcoholic extracts demonstrating these bioactivities were further analyzed using LC-MS, leading to the annotation of a variety of pigment-related compounds. This study revealed that culture media significantly influenced both pigment production and bioactivity outcomes. Notably, anthraquinones, phenazines, and naphthoquinones were predominant pigment classes associated with cytotoxic and antimicrobial activities, while carotenoids were linked to antioxidant effects. For instance, S. murinus 4C171 produced various compounds exhibiting both cytotoxic and antioxidant activities. These findings highlighted a growth medium-dependent effect, as pigment production, coloration, and bioactivity outcomes were influenced by growth media. These results demonstrate the significant potential of Streptomyces isolates as sources of bioactive pigments for diverse applications.
Financial Stability and Innovation: The Role of Non-Performing Loans
Authors: Arnone Massimo , Costantiello Alberto , Leogrande Angelo , Naqvi Syed Kafait Hussain , Magazzino Cosimo
Published: October 2024

This study analyses the relationship between non-performing loans (NPLs) and innovation systems at a global level. The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. Since k-means is an unsupervised machine-learning algorithm, we compared the Silhouette coefficient with the Elbow method to find an optimization. The results show that the optimal number of clusters is three, as suggested using the Elbow Method. Furthermore, a panel data analysis was conducted. Results show that the level of NPLs is positively associated with cultural and creative services exports as a percentage of total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, ICT services imports as a percentage of total trade, and information and communication technologies.
Wildfire Smoke Detection Enhanced by Image Augmentation with StyleGAN2-ADA for YOLOv8 and RT-DETR Models
Authors: Park Ganghyun , Lee Yangwon
Published: October 2024

Wildfires pose significant environmental and societal threats, necessitating improved early detection methods. This study investigates the effectiveness of integrating real-time object detection deep learning models (YOLOv8 and RT-DETR) with advanced data augmentation techniques, including StyleGAN2-ADA, for wildfire smoke detection. We evaluated model performance on datasets enhanced with fundamental transformations and synthetic images, focusing on detection accuracy. YOLOv8X demonstrated superior overall performance with AP@0.33 of 0.962 and AP@0.5 of 0.900, while RT-DETR-X excelled in small object detection with a 0.983 detection rate. Data augmentation, particularly StyleGAN2-ADA, significantly enhanced model performance across various metrics. Our approach reduced average detection times to 1.52 min for YOLOv8X and 2.40 min for RT-DETR-X, outperforming previous methods. The models demonstrated robust performance under challenging conditions, like fog and camera noise, providing reassurance of their effectiveness. While false positives remain a challenge, these advancements contribute significantly to early wildfire smoke detection capabilities, potentially mitigating wildfire impacts through faster response times. This research establishes a foundation for more effective wildfire management strategies and underscores the potential of deep learning applications in environmental monitoring.
Fortification of Orange and Apple Juices with Ferulic Acid: Implications for Food Safety and Quality
Authors: Abadias Maribel , Bobo Gloria , Anguera Marina , Ortiz-Solà Jordi , Aguiló-Aguayo Ingrid
Published: October 2024

In recent years, the awareness of healthier lifestyles among consumers has driven to an increased interest in more natural, nutritious, and low-processed foods. Ferulic acid, one of the most abundant phenolic acids in plants, has demonstrated a wide spectrum of antimicrobial activities and a range of biomedical effects, including antioxidant, antiallergic, hepatoprotective, anticarcinogenic, anti-inflammatory, and antithrombotic, among others. The objective of this work was to study the antilisterial effect of ferulic acid (FA, 1500 mg/L) on fresh, ready-to-eat orange (FOJ) and apple (FAJ) juices and evaluate its effect on product quality. The results showed that FA reduced the Listeria monocytogenes population after 9 days of storage at 4 °C, but no effect on the indigenous microbiota was observed. The titratable acidity and color significantly changed. The antioxidant capacity and total phenolic content significantly increased with the addition of FA, being at least two times greater in fortified juices. FAJ and FOJ containing FA were scored lower (6.8 and 5.7 on a 9-point hedonic scale, respectively) than their respective controls. Overall, our results demonstrated that FA treatment could be a useful strategy to maintain the safety of fresh apple and orange juices and increase the antioxidant activity and phenolic content. The potential industrial applications and health benefits of the fortification of fruit juices with FA should be further explored.
The Effect of Fatty Acids Profile in Potato and Corn Chips on Consumer Preferences
Authors: Gaytancıoğlu Okan , Yılmaz Fuat , Geçgel Ümit
Published: October 2024

The global market for potato and corn chips is rapidly expanding due to the modern fast-paced lifestyle. However, the high fat content, especially saturated fats in these deep-fried snacks, poses significant health risks such as hypertension, coronary heart disease, and diabetes. In this study, fatty acid profiles of commercially available corn and potato chips are analyzed and their impacts on consumer preferences in Turkey is examined. The findings reveal notable differences in the nutritional content between potato and corn chips, with potato chips generally having higher fat and protein content. The survey results indicate that consumer preferences are significantly influenced by age, education level, and occupation. The factor analysis identified three main components affecting purchasing decisions: nutritional value and additives, hygiene and brand quality, and price and affordability. Considering these insights, manufacturers should be encouraged to reformulate their products to meet the increasing demand for healthier options, emphasize food safety standards, and balance product quality with affordability to appeal to a broader range of consumers.