bluuebunny/crossref_metadata_2025 huggingface dataset:
Periportal Edema as an Extrarenal Manifestation of Acute Pyelonephritis
Authors: Chang Yu-Yun , Kuo Kuei-Hong
Published: March 2024

Acute pyelonephritis is a common infection of the upper urinary tract that affects approximately 250,000 adults in the United States. Individuals with acute pyelonephritis require hospitalization and intravenous antimicrobial therapy. Diagnoses of acute pyelonephritis are made on the basis of clinical and laboratory findings. Individuals with complex or severe acute pyelonephritis undergo contrast-enhanced computed tomography (CT) for the diagnosis and assessment of perirenal abnormalities. However, extrarenal manifestations, such as periportal edema and gallbladder wall thickening, may complicate the diagnostic process. We report the case of a 42-year-old woman who presented with fever, dysuria, and flank pain—the hallmarks of urosepsis. CT results confirmed acute pyelonephritis accompanied by periportal edema and elevated levels of hepatic enzymes and C-reactive protein. Despite antibiotic intervention, febrile episodes persisted for 4 days and abated over a fortnight. The patient’s blood and urine cultures yielded negative results, which may be attributed to her prior antimicrobial treatment. Recognition of extrarenal signs in acute pyelonephritis is crucial for obtaining accurate diagnoses and understanding their clinical implications.
Ultrasound Biomicroscopy as a Novel, Potential Modality to Evaluate Anterior Segment Ophthalmic Structures during Spaceflight: An Analysis of Current Technology
Authors: Soares Benjamin , Ong Joshua , Osteicoechea Daniela , Kadipasaoglu Cihan Mehmet , Waisberg Ethan , Sarker Prithul , Zaman Nasif , Tavakkoli Alireza , Vizzeri Gianmarco , Lee Andrew G.
Published: March 2024

Ocular health is currently a major concern for astronauts on current and future long-duration spaceflight missions. Spaceflight-associated neuro-ocular syndrome (SANS) is a collection of ophthalmic and neurologic findings that is one potential physiologic barrier to interplanetary spaceflight. Since its initial report in 2011, our understanding of SANS has advanced considerably, with a primary focus on posterior ocular imaging including fundus photography and optical coherence tomography. However, there may be changes to the anterior segment that have not been identified. Additional concerns to ocular health in space include corneal damage and radiation-induced cataract formation. Given these concerns, precision anterior segment imaging of the eye would be a valuable addition to future long-duration spaceflights. The purpose of this paper is to review ultrasound biomicroscopy (UBM) and its potential as a noninvasive, efficient imaging modality for spaceflight. The analysis of UBM for spaceflight is not well defined in the literature, and such technology may help to provide further insights into the overall anatomical changes in the eye in microgravity.
Nodal Low-Grade B-Cell Lymphoma Co-Expressing CD5 and CD10 but Not CD23, IRTA1, or Cyclin D1: The Diagnostic Challenge of a Splenic Marginal Zone Lymphoma
Authors: Win Khin-Than , Hsieh Yen-Chuan , Wu Hung-Chang , Chuang Shih-Sung
Published: March 2024

The diagnosis of lymphoma is based on histopathological and immunophenotypical features. CD5 and CD10 are traditionally considered a T-cell antigen and a germinal center B-cell antigen, respectively. It is very unusual for a low-grade B-cell lymphoma (BCL) to co-express CD5 and CD10. Although the biologic basis or clinical significance of such co-expression is unclear, this rare event may pose a significant diagnostic challenge. Here, we report a case of a 63-year-old male presenting with bilateral cervical lymphadenopathy and lymphocytosis. Histologically, the nodal tumor was largely diffuse with neoplastic small atypical lymphocytes co-expressing CD5, CD10, and CD20, but not CD23 or cyclin D1. The leukemic cells in the peripheral blood exhibited hairy projections. Taking together the marked splenomegaly, involvement of lymph nodes, bone marrow, and peripheral blood, a final diagnosis of splenic marginal zone lymphoma (SMZL) was reached. The patient was alive with partial response for 10 months after immunochemotherapy. The dual expression of CD5 and CD10 is extremely unusual for low-grade BCL and may lead to an erroneous diagnosis. Integrating the findings into peripheral blood smear tests, flow cytometry, histopathology, imaging, and clinical features is mandatory to exclude other lymphoma types and to reach a correct diagnosis, particularly for a case with nodal presentation.
Iterated Crank–Nicolson Method for Peridynamic Models
Authors: Liu Jinjie , Appiah-Adjei Samuel , Brio Moysey
Published: March 2024

In this paper, we explore the iterated Crank–Nicolson (ICN) algorithm for the one-dimensional peridynamic model. The peridynamic equation of motion is an integro-differential equation that governs structural deformations such as fractures. The ICN method was originally developed for hyperbolic advection equations. In peridynamics, we apply the ICN algorithm for temporal discretization and the midpoint quadrature method for spatial integration. Several numerical tests are carried out to evaluate the performance of the ICN method. In general, the ICN method demonstrates second-order accuracy, consistent with the Störmer–Verlet (SV) method. When the weight is 1/3, the ICN method behaves as a third-order Runge–Kutta method and maintains strong stability-preserving (SSP) properties for linear problems. Regarding energy conservation, the ICN algorithm maintains at least second-order accuracy, making it superior to the SV method, which converges linearly. Furthermore, selecting a weight of 0.25 results in fourth-order superconvergent energy variation for the ICN method. In this case, the ICN method exhibits energy variation similar to that of the fourth-order Runge–Kutta method but operates approximately 20% faster. Higher-order convergence for energy can also be achieved by increasing the number of iterations in the ICN method.
Projected Heat Waves in Ecuador under Climate Change: Insights from HadGEM-RegCM4 Coupled Model
Authors: Portalanza Diego , Ortega Carlos , Garzon Liliam , Bello Melissa , Zuluaga Cristian Felipe , Bresciani Caroline , Durigon Angelica , Ferraz Simone
Published: March 2024

This study examines heat wave projections across Ecuador’s Coastal, Highlands, and Amazon regions for 1975–2004 and 2070–2099 under Representative Concentration Pathways (RCP) scenarios 2.6, 4.5, and 8.5. Employing dynamic downscaling, we identify significant increases in heatwave intensity and maximum air temperatures (Tmax), particularly under RCP 8.5, with the Coastal region facing the most severe impacts. A moderate positive correlation between Tmax and climate indices such as the Pacific Decadal Oscillation (PDO) and the Oceanic Niño Index (ONI) suggests regional climatic influences on heatwave trends. These findings highlight the critical need for integrated climate adaptation strategies in Ecuador, focusing on mitigating risks to health, agriculture, and ecosystems. Proposed measures include urban forestry initiatives and the promotion of cool surfaces, alongside enhancing public awareness and access to cooling resources. This research contributes to the understanding of climate change impacts in Latin America, underscoring the urgency of adopting targeted adaptation and resilience strategies against urban heat island effects in Ecuador’s urban centers.
Digital vs. Hybrid: Comparing Two Versions of a Board Game for Teacher Training
Authors: Pozzi Francesca , Ceregini Andrea , Ivanov Stanislav , Passarelli Marcello , Persico Donatella , Volta Erica
Published: March 2024

This study compares two versions (one digital, one hybrid) of a serious board game for teacher training called the “4Ts game”. Teachers play the game in groups to learn about—and directly engage in—the joint design of collaborative learning activities for their students by choosing the Tasks to be proposed, the Timing of activities, the Technologies to be used, and the Team composition, in an iterative process of decision making. The game comes in three versions: fully tangible, digital, and hybrid. This paper focuses on the interaction design of the digital and hybrid versions. In both cases, teachers pick cards up from four decks, read the prompts provided in the cards, and place them on a board to design a learning activity together. Their decision-making process is scaffolded by the digital or hybrid game versions, which provide feedback and suggestions and guide teachers toward the creation of a coherent design. The user experience is quite different in the two formats. In the hybrid game, teachers physically manipulate tangible cards on a tabletop board, and the board status is replicated on a laptop application that displays automatic feedback and guidance. By contrast, the digital version is played using an Interactive Whiteboard with touch-screen capabilities, thereby allowing teachers to manipulate digital cards on a digital board. The game was used in the context of two training initiatives targeting in-service school teachers (N = 42). Data were collected on acceptance of the model upon which the game is built, acceptance of the game itself, overall user satisfaction, and knowledge gains. Results show that teachers generally liked both versions of the game, especially the opportunity provided for receiving guidance in the design process. Additionally, teachers’ knowledge about learning design and collaborative learning increased significantly between the pre- and post-test for both the digital and the hybrid game groups. However, few significant differences were found between the groups that used the digital and hybrid versions of the game: the digital version was perceived as being slightly easier to use (p < 0.001). Overall, the study suggests that both versions of the game have the potential for teacher training, while the user interface of the hybrid version should be further refined to fully harness the game’s potential.
Analysis of Digital Teacher Education: Key Aspects for Bridging the Digital Divide and Improving the Teaching–Learning Process
Authors: Val Sonia , López-Bueno Helena
Published: March 2024

The quality of teacher education plays a key role in equipping students with the skills they need and it is important in avoiding learning inequalities. To bridge the digital divide and ensure that all students have equal access to technology and digital resources as well as advanced teaching–learning processes using digital tools, it is crucial to analyse the current state of teacher education in order to identify the key issues. The sample in this research consisted of 325 master of education students and in-service teachers studying at various universities (in Spain, Cyprus, and México) in the 2022–2023 academic year who filled out a questionnaire partly based on DigComEdu and this was expanded with questions relating to other digital and educational aspects. The findings reveal that there is potential for enhancement in many areas. Despite teachers having technical training associated with their profession, they lack the necessary training to overcome inequalities or digital gaps. Moreover, it is striking that those who are already working as teachers are the most pessimistic about aspects such as the use of digital resources, perhaps because of the difficulties they face in their daily work.
Development and Validation of Scientific Inquiry Literacy Instrument (SILI) Using Rasch Measurement Model
Authors: Darman Dina Rahmi , Suhandi Andi , Kaniawati Ida , Samsudin Achmad , Wibowo Firmanul Catur
Published: March 2024

This research aims to develop an instrument of knowledge and skills called the Scientific Inquiry Literacy Instrument (SILI). Instrument measurement analysis uses the Rasch model by utilizing the Winsteps application. This research uses mixed methods research that combines interviews as qualitative research and tests as quantitative research. The research design used is Sequential Exploratory. The SILI contains 28 knowledge aspect indicators and 37 skills aspect indicators. Based on the Outfit mean square (MNSQ), Z-Standard value (ZSTD), and point measure correlation value (Pt Mean Corr), it was obtained that the three aspects of the SILI meet the criteria for an acceptable item in the Rasch measurement model. Based on item analysis biased towards gender, region, and science major, all SILI items in knowledge and skills meet the probability criteria > 0.05, so all items can be used without harming one group. The findings of the research dimensionality of the SILI for aspects of knowledge and skills are 26.9% and 20.4%. Thus, all aspects of the SILI can measure what should be measured and fulfill all the criteria for items accepted based on the Rasch model for measuring science inquiry literacy.
The Influence of Active and Passive Procrastination on Academic Performance: A Meta-Analysis
Authors: Kooren Niek Sebastiaan , Van Nooijen Christine , Paas Fred
Published: March 2024

The relationship between academic performance and procrastination has been well documented over the last twenty years. The current research aggregates existing research on this topic. Most of the studies either find no result or a small negative result. However, recent studies suggest that procrastination can have a positive influence on academic performance if the procrastination is active instead of passive. To analyse the effect of active procrastination on academic performance, a meta-analysis was conducted. The analysis includes 96 articles with 176 coefficients including a combined average of 55,477 participants related to the correlation between academic performance and procrastination. The analysis uncovered a modest negative correlation between academic performance and procrastination overall. Importantly, the type of procrastination exerted a substantial impact on the strength of this correlation: active procrastination demonstrated a small positive effect size, whereas passive procrastination registered a small negative effect size. Additionally, participant-specific characteristics and indicators further modulated the magnitude of the correlation. The implications of this research extend to underscoring a potential beneficial aspect of procrastination, specifically elucidating how certain types of procrastination can positively influence academic performance.
Intangible Cultural Heritage Course Design in the Digital Age and Its Effects of Interdisciplinary Workshop
Authors: Xia Jia , Yuizono Takaya , Wang Tzu-Yang , Kim Eunyoung
Published: March 2024

This study demonstrates a new intangible cultural heritage (ICH) design course and its effectiveness of interdisciplinary teams with digital technology beyond conventional single-disciplinary teams. A design guideline for ICH was built on the basis of Bronfenbrenner’s Ecological Systems Theory (EST). Then, an evaluation standard involving five criteria (culture fit, creativity, aesthetics, experientiality, diversity) was established. Thirty students participated in the course and were divided into four teams of two types as follows: two interdisciplinary teams consisting of students from different backgrounds and two single-disciplinary teams consisting of students from the visual communication department. Experts assessed the design works and design process of the four teams according to the evaluation criteria as follows: (1) there are differences in the application of the ICH design guideline between the interdisciplinary approach with digital technology and the conventional teaching method, and (2) the ICH design course positively affects the five criteria. The interdisciplinary approach with digital technology in ICH design is more likely to stimulate students’ creativity. These findings emphasize the importance of new ICH design courses and provide insights for future design educators.
A Comprehensive Investigation of Lane-Changing Risk Recognition Framework of Multi-Vehicle Type Considering Key Features Based on Vehicles’ Trajectory Data
Authors: Zheng Liyuan , Liu Weiming
Published: March 2024

To comprehensively investigate the key features of lane-changing (LC) risk for different vehicle types during left and right LC, and to improve the accuracy of LC risk recognition, this paper proposes a key feature selection and risk recognition model based on vehicle trajectory data. Based on a HighD high-precision vehicle trajectory dataset, the trajectory data of LC vehicles and surrounding vehicles of each vehicle type are extracted. SDI (stop distance index) and CI (crash index) are selected as surrogate indicators to calculate the risk exposure level (REL) and risk severity level (RSL). The K-means algorithm is used to cluster the REL and RSL to obtain the LC risk level, which is divided into three levels. The combination of basic features and interaction features of LC vehicles and surrounding vehicles with LC risk levels is constructed as the LC risk feature dataset. Based on the LightGBM (light gradient boosting machine) algorithm, the importance of features is sorted. Finally, a CNN-BiLSTM-Attention model is established to recognize the LC risk of each vehicle type during left and right LC. The results indicate that significant differences exist among different vehicle types and LC directions. Compared with CNNs (convolutional neural networks), LSTM (long short-term memory), and BiLSTM (bi-directional long short-term memory), CNN-BiLSTM-Attention performs best in recognizing the risk of LC in all cases. Moreover, the key feature groups that have the optimal result of recognizing the risk of LC in different cases are obtained.
Combining wav2vec 2.0 Fine-Tuning and ConLearnNet for Speech Emotion Recognition
Authors: Sun Chenjing , Zhou Yi , Huang Xin , Yang Jichen , Hou Xianhua
Published: March 2024

Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve the ability of speech emotion recognition, we propose a novel approach in two folds. Firstly, a feed-forward network with skip connections (SCFFN) is introduced to fine-tune wav2vec 2.0 and extract emotion embeddings. Subsequently, ConLearnNet is employed for emotion classification. ConLearnNet comprises three steps: feature learning, contrastive learning, and classification. Feature learning transforms the input, while contrastive learning encourages similar representations for samples from the same category and discriminative representations for different categories. Experimental results on the IEMOCAP and the EMO-DB datasets demonstrate the superiority of our proposed method compared to state-of-the-art systems. We achieve a WA and UAR of 72.86% and 72.85% on IEMOCAP, and 97.20% and 96.41% on the EMO-DB, respectively.
Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning
Authors: Chen Haisong , Yang Linlin , Wang Aili
Published: March 2024

Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning (ECFML) algorithm. The lightweight MobileViT network is used as the meta-learner of the Model Agnostic Meta-Learning (MAML) algorithm. By learning common knowledge on the local data of multiple clients, and then fine-tuning the model, the number of unnecessary iterations is reduced, and communication efficiency is improved while reducing the number of parameters. The gradient information model is encrypted using the differential privacy of the Laplace mechanism, and the optimal privacy budget is determined through experiments. Experiments on three public datasets (AEEEM, NASA, and Relink) verified the effectiveness of ECFML in terms of parameter quantity, convergence, and model performance of cross-project software defect prediction.
Machine-Learning-Based Traffic Classification in Software-Defined Networks
Authors: Serag Rehab H. , Abdalzaher Mohamed S. , Elsayed Hussein Abd El Atty , Sobh M. , Krichen Moez , Salim Mahmoud M.
Published: March 2024

Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.
Noise-Canceling Channel Estimation Schemes Based on the CIR Length Estimation for IEEE 802.11p/OFDM Systems
Authors: Ko Kyunbyoung , Wang Hanho
Published: March 2024

This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response (CIR). This approach aims to surpass the performance of conventional designs that rely on constructed data pilots (CDPs). In the first step, we not only eliminate noise components but also estimate the channel frequency responses (CFRs) of virtual subcarriers for long preamble parts. Moving on to the second step, we incorporate a modified CDP method without a frequency-domain reliability test and interpolation, taking into account the CFRs of virtual subcarriers obtained at the previous OFDM symbol time. The final step can be implemented as the operation of the inverse fast Fourier transform (IFFT)/nulling/FFT to reduce noise components from the CFRs obtained in the second step and generate CFRs for virtual subcarriers to be used in the next symbol time. The results of our simulations validate the effectiveness of our proposed channel estimation schemes.
Classification Method of 3D Pattern Film Images Using MLP Based on the Optimal Widths of Histogram
Authors: Lee Jaeeun , Choi Hongseok , Kim Jongnam
Published: March 2024

3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and there is little research in this area, making it a necessary field of study. Additionally, 3D pattern film has blurred contours, making it difficult to detect the outlines and challenging to classify. Recently, many machine learning methods have been published for analyzing product quality. However, when there is a small amount of data and most images are similar, using deep learning can easily lead to overfitting. To overcome these limitations, this study proposes a method that uses an MLP (Multilayer Perceptron) model to classify 3D pattern films into genuine and defective products. This approach entails inputting the widths derived from specific points’ heights in the image histogram of the 3D pattern film into the MLP, and then classifying the product as ‘good’ or ‘bad’ using optimal hyper-parameters found through the random search method. Although the contours of the 3D pattern film are blurred, this study can detect the characteristics of ‘good’ and ‘bad’ by using the image histogram. Moreover, the proposed method has the advantage of reducing the likelihood of overfitting and achieving high accuracy, as it reflects the characteristics of a limited number of similar images and builds a simple model. In the experiment, the accuracy of the proposed method was 98.809%, demonstrating superior performance compared to other models.
Limitations of Upper Protective Layers as Pressure Relief Measures for Extra-Thick Coal Seam Mining: Insights from a Case Study
Authors: Chai Yanjiang , Dou Linming , He Jiang , Ma Xiaotao , Lu Fangzhou , He Hu
Published: March 2024

Upper protective layer (UPL) mining is extensively utilised as a pressure relief strategy to prevent outbursts and coal bursts. However, when the excavation height of the protected layer is substantial, the depressurisation efficacy of the protective layer may be diminished. This paper takes the Haishiwan coal mine in China as a case study and explores the stress evolution and influencing factors in the mining of extra-thick coal seam beneath the protective layer through theoretical analysis, numerical simulation, and field observation. The results indicate that increasing the excavation height of the coal seam will lead to the upward development of the collapse zone in the overburden of the goaf, with the “masonry beam” structure formed at a higher position by key strata blocks. The overburden above the masonry beam will be supported by the coal rock masse on both sides of the structure, leading to increased stress on the coal seam near the goaf and eliminating the depressurisation effect of the protective layer. Numerical simulation shows that factors such as faults, protective layers, interlayer spacing, and the height of coal seam excavation significantly affect the stress distribution in the protected layer. With the increase in interlayer spacing and the thickness of coal seam extraction, the stress reduction phenomenon of the UPL gradually decreases, especially with an abnormal stress concentration of the gob-side coal seam. Observations of Surface subsidence and the distribution of mining-induced seismic events corroborate the conclusions of theoretical analysis and numerical simulations. The results offer valuable guidance for the mining of extra-thick coal seams and the selection of the UPL.
Research on Adaptive Droop Control Strategy for a Solar-Storage DC Microgrid
Authors: Xu Zhanpeng , Chen Fuxin , Chen Kewei , Lu Qinfen
Published: March 2024

When the solar-storage DC microgrid operates in islanded mode, the battery needs to stabilize the bus voltage and keep the state of charge (SOC) balanced in order to extend the service life of the battery and the islanded operation time. When there are multiple energy storage units in the DC microgrid, it is necessary to solve the problem of unbalanced circulation and the state of charge between batteries using a reasonable droop control method. In this paper, firstly, the DC–DC charging and discharging circuit of the battery is designed, and the unbalanced SOC of the battery caused by the different impedances of the line is analyzed. Secondly, an adaptive droop control method is proposed to solve the problems of SOC imbalance and current circulation between the batteries. Thirdly, based on MATLAB/SIMULINK R2021b simulation software, the proposed control method is modeled and simulated. Compared with the traditional droop control, the effectiveness of the proposed method is validated.
Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning
Authors: Zhao Jianpeng , Wang Qi , Rong Wei , Zeng Jingbo , Ren Yawen , Chen Hui
Published: March 2024

Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.
An Assessment of CO2 Capture Technologies towards Global Carbon Net Neutrality
Authors: Karayil Amith , Elseragy Ahmed , Aliyu Aliyu M.
Published: March 2024

Carbon dioxide, the leading contributor to anthropogenic climate change, is released mainly via fossil fuel combustion, mostly for energy generation. Carbon capture technologies are employed for reducing the emissions from existing huge point sources, along with capturing them from direct air, to reduce the existing concentration. This paper provides a quantitative analysis of the various subtypes of carbon capture technologies with the aim of providing an assessment of each from technological, social, geo-political, economic, and environmental perspectives. Since the emissions intensity and quantity, along with the social–political–economic conditions, vary in different geographic regions, prioritising and finding the right type of technology is critical for achieving ambitious net-zero targets. Four main types of carbon capture technology were analysed (adsorption, absorption, membrane, and cryogenic) under four scenarios depending on the jurisdiction. The Technique for Order of Preference by Similarity to Ideal Solution (also known as the TOPSIS method) was used to establish a quantitative ranking of each, where weightages were allocated according to the emissions status and economics of each depending on the jurisdiction. Furthermore, forecasting the trends for technology types vis à vis carbon neutral targets between 2040 and 2050 was carried out by applying regression analysis on existing data and the emissions footprint of major contributing countries. The study found the membrane score to be the highest in the TOPSIS analysis in three of the four scenarios analysed. However, absorption remains the most popular for post-combustion capture despite having the highest energy penalty per ton of CO2 capture. Overall, capture rates are well short of projections for carbon neutrality; the methodology put forward for prioritising and aligning appropriate technologies and the region-by-region analysis will help highlight to technocrats, governments, and policymakers the state of the art and how to best utilise them to mitigate carbon emissions—critical in achieving the net-zero goals set at various international agreements on climate change.
Characterization of Swedish Forestry Contractors’ Practices Regarding Occupational Safety and Health Management
Authors: Kronholm Thomas , Olsson Rikard , Thyrel Mikael , Häggström Carola
Published: March 2024

Forestry has many dangers, and much effort has been put into supporting accident prevention by developing legislation and certification systems. In Sweden, forestry contractors have an important role in developing and maintaining a good working environment for the employed forestry workers. The objectives of this study were to describe forestry contractors’ current practices regarding occupational safety and health (OSH) management and identify variations in relation to the contractors’ characteristics and finances, as well as to highlight areas that need improvement. In 2023, data were collected through a survey distributed to 1200 limited liability companies registered to perform logging or silvicultural services, with a response rate of 22%. Multi- and univariate analyses were performed to compare contractor groups, and the results show that larger companies had more well-developed and formalized routines and guidelines for their operations. Geographical differences were also identified; for example, employees in the north were more likely to have access to staff facilities when working in the forest. However, no clear connection between OSH management practices and the contractors’ finances could be demonstrated. The study concludes that there is a need for improvements in OSH work within the industry because many companies do not meet all of the formal requirements that exist today.
Estimating the Aboveground Biomass of Robinia pseudoacacia Based on UAV LiDAR Data
Authors: Cheng Jiaqi , Zhang Xuexia , Zhang Jianjun , Zhang Yanni , Hu Yawei , Zhao Jiongchang , Li Yang
Published: March 2024

Robinia pseudoacacia is widely planted in the Loess Plateau as a major soil and water conservation tree species because of its dense canopy, complex structure, and strong soil and water conservation ability. The precise measurement of small-scale locust forest biomass is crucial to monitoring and evaluating the carbon sequestration functions of soil and water conservation vegetation. This study focuses on an artificial locust forest planted in the early 1990s in Caijiachuan Basin, Jixian County, Shanxi Province. A drone equipped with LiDAR was used to obtain point cloud data and generate a canopy height model. A watershed segmentation algorithm was used to identify tree vertices and extract individual trees. A relationship model between tree height, diameter at breast height, and biomass, combined with sample survey data, was established to explore the spatial distribution of biomass in the artificial locust forest at the level of the entire basin. The results show the following: (1) the structural parameters of locust extracted using UAV point cloud data have a good degree of fit and accuracy, and the recall rate is 72.7%; (2) the average error rate of the extracted maximum tree height value of locust is 7%, that of the minimum tree height value is 14%, and that of the average tree height value is 18%; (3) the average error rate of the extracted maximum diameter at breast height of locust is 15%, that of the minimum diameter at breast height is 37%, and that of the average diameter at breast height is 36%; and (4) the average error rate of the biomass estimation of locust calculated using point cloud data is 16.0%.
Applying an Optimum Bucking Method to Comparing the Volume and Value Recovery of Cut-to-Length and Tree-Length Merchandizing Systems in Piedmont and the Coastal Plain
Authors: Diniz Carlos , Smidt Mathew , Sessions John
Published: March 2024

Dynamic prices and markets create value for contractors who can readily evaluate the gross and net income differences for alternative merchandizing systems. The majority of the southern U.S.A. relies on tree-length merchandizing, with occasional identification and merchandizing of logs for a specific market or specific tree dimensions or qualities. Cut-to-length (CTL) merchandizing has generated more value when compared to tree-length (TL) marketing, but these comparisons have been limited to specific stands and markets (specifications and prices). The study objective was to demonstrate a process for evaluating cut-to-length and tree-length merchandizing systems in their production of gross value by applying a dynamic programming stem-level optimum bucking approach that maximizes the stem value given specific market conditions. TL merchandizing resulted in a better volume recovery for both regions, but the value recovery was better for CTL merchandizing. Observing the value recovery by diameter class, DSH classes of up to 100 mm had a similar value in both merchandizing systems, but CTL merchandizing yielded a greater or similar value per cubic meter across the range for larger tree sizes. Access to tree data and merchandizing tools needs to be addressed so wood suppliers and landowners may benefit from stem optimization and sensor technology being embedded into modern harvesters and processors.
Residents’ Willingness to Pay for Forest Ecosystem Services Based on Forest Ownership Classification in South Korea
Authors: Son Young-Gyun , Lee Yohan , Jo Jang-Hwan
Published: March 2024

South Korea is one of the countries with a significant proportion of its national territory covered by forests. However, it remains unclear what management strategies for providing forest ecosystem services are preferred by the residents. This study explores South Korean residents’ preferences for managing local forest ecosystem services, with a focus on how these preferences vary by forest ownership. Using the choice experiment method, this research identified residents’ willingness to pay for seven key local forest ecosystem services, along with a tax measure. The findings indicate a strong preference for biodiversity as the primary ecosystem service in both national/public and private forests, with residents willing to pay an average of KRW 28,370 (USD 21.80) per household per year and KRW 31,670 (USD 24.34) per household per year, respectively, for its enhancement. Preferences varied depending on forest ownership, with noticeable differences in perceptions of services like water supply, non-timber forest product supply, carbon storage, and recreation. Based on forest ownership, these variations in perceptions highlight the importance of managing ecosystem services in line with national/public forests and private forests, which significantly influences residents’ preferences. The study emphasizes the necessity of formulating ecosystem service management policies that account for the region’s unique natural resource characteristics, aiming to maximize ecological benefits for the local population.
Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model
Authors: Sun Jiaqian , Mao Fangjie , Du Huaqiang , Li Xuejian , Xu Cenheng , Zheng Zhaodong , Teng Xianfeng , Ye Fengfeng , Yang Ningxin , Huang Zihao
Published: March 2024

Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.
Effect of High Altitude on Serum Biochemical Parameters, Immunoglobulins, and Rumen Metabolism of Sanhe Heifers
Authors: Zhang Xinyu , Cao Zhijun , Yang Hongjian , Wang Yajing , Wang Wei , Li Shengli
Published: March 2024

Rumen metabolism is closely related to feed utilization and the environmental adaptability of cows. However, information on the influence of altitude on ruminal metabolism is limited. Our study aimed to investigate differences in rumen metabolism and blood biochemical indicators among Sanhe heifers residing at various altitudes. A total of 20 serum and ruminal fluid samples were collected from Sanhe heifers in China, including those from Hulunbeier City (approximately 700 m altitude; 119°57′ E, 47°17′ N; named LA) and Lhasa City (approximately 3650 m altitude; 91°06′ E, 29°36′ N; named HA). Compared with LA heifers, HA heifers had higher levels of serum cortisol, glucose, and blood urea nitrogen (p < 0.05) and lower Ca2+ concentrations (p < 0.05). Using liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomic technology, we identified a significant difference in 312 metabolites between the LA and HA groups. Metabolic pathway analysis, based on significantly different rumen metabolites, identified 20 enriched metabolic pathways within hierarchy III, which are encompassed within 6 broader metabolic pathways in hierarchy I. This study constitutes the first elucidation of the altitudinal adaptation mechanism of ruminants from the perspective of rumen metabolism, thereby offering a novel angle for investigating high-altitude adaptation in both humans and animals.
Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure
Authors: Kalyani Yogeswaranathan , Vorster Liam , Whetton Rebecca , Collier Rem
Published: March 2024

In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, system, or process. Although it is relatively new in the agricultural domain, it has gained increasing attention recently. Recent reviews of DTs show that this technology has the potential to revolutionise agriculture management and activities. It can also provide numerous benefits to all agricultural stakeholders, including farmers, agronomists, researchers, and others, in terms of making decisions on various agricultural processes. In smart crop farming, DTs help simulate various farming tasks like irrigation, fertilisation, nutrient management, and pest control, as well as access real-time data and guide farmers through ‘what-if’ scenarios. By utilising the latest technologies, such as cloud–fog–edge computing, multi-agent systems, and the semantic web, farmers can access real-time data and analytics. This enables them to make accurate decisions about optimising their processes and improving efficiency. This paper presents a proposed architectural framework for DTs, exploring various potential application scenarios that integrate this architecture. It also analyses the benefits and challenges of implementing this technology in agricultural environments. Additionally, we investigate how cloud–fog–edge computing contributes to developing decentralised, real-time systems essential for effective management and monitoring in agriculture.
Triggering Pyro-Convection in a High-Resolution Coupled Fire–Atmosphere Simulation
Authors: Couto Flavio Tiago , Filippi Jean-Baptiste , Baggio Roberta , Campos Cátia , Salgado Rui
Published: March 2024

This study aimed to assess fire–atmosphere interactions using the fully coupled Meso-NH–ForeFire system. We focused on the Pedrógão Grande wildfire (28,914 ha), which occurred in June 2017 and was one of the deadliest and most damaging fires in Portugal’s history. Two simulations (control and fully coupled fire–atmosphere) were performed for three two-way nested domains configured with horizontal resolutions of 2 km, 0.4 km, and 0.08 km, respectively, in the atmospheric model Meso-NH. Fire propagation was modeled within the innermost domain with ForeFire, which solves the fire front with a 20 m resolution, producing the heat and vapor fluxes which are then injected into the atmospheric model. A simplified homogeneous fuel distribution was used in this case study. The fully coupled experiment helped us to characterize the smoke plume structure and identify two different regimes: (1) a wind-driven regime, with the smoke plume transported horizontally southward and in the lower troposphere, and (2) a plume-dominated regime, in which the simulated smoke plume extended vertically up to upper levels, favoring the formation of a pyro-cloud. The simulations were compared, and the results suggest that the change in the fire regime was caused by an outflow that affected the main fire front. Furthermore, the fully coupled simulation allowed us to explore the change in meteorology caused by an extreme fire, namely through the development of a pyro-cloud that also induced outflows that reached the surface. We show that the Meso-NH–ForeFire system may strongly contribute to an improved understanding of extreme wildfires events and associated weather phenomena.
Quasi-P-Wave Reverse Time Migration in TTI Media with a Generalized Fractional Convolution Stencil
Authors: Qin Shanyuan , Yang Jidong , Qin Ning , Huang Jianping , Tian Kun
Published: March 2024

In seismic modeling and reverse time migration (RTM), incorporating anisotropy is crucial for accurate wavefield modeling and high-quality images. Due to the trade-off between computational cost and simulation accuracy, the pure quasi-P-wave equation has good accuracy to describe wave propagation in tilted transverse isotropic (TTI) media. However, it involves a fractional pseudo-differential operator that depends on the anisotropy parameters, making it unsuitable for resolution using conventional solvers for fractional operators. To address this issue, we propose a novel pure quasi-P-wave equation with a generalized fractional convolution operator in TTI media. First, we decompose the conventional pure quasi-P-wave equation into an elliptical anisotropy equation and a fractional pseudo-differential correction term. Then, we use a generalized fractional convolution stencil to approximate the spatial-domain pseudo-differential term through the solution of an inverse problem. The proposed approximation method is accurate, and the wavefield modeling method based on it also accurately describes quasi-P-wave propagation in TTI media. Moreover, it only increases the computational cost for calculating mixed partial derivatives compared to those in vertical transverse isotropic (VTI) media. Finally, the proposed wavefield modeling method is utilized in RTM to correct the anisotropic effects in seismic imaging. Numerical RTM experiments demonstrate the flexibility and viability of the proposed method.