bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid Assessment of Architectural Traits in Pear Rootstock Breeding Program Using Remote Sensing Techniques
Authors: Raman Mugilan Govindasamy , Marzougui Afef , Teh Soon Li , York Zara B. , Evans Kate M. , Sankaran Sindhuja
Published: March 2023

Over the decades in the US, the introduction of rootstocks with precocity, stress tolerance, and dwarfing has increased significantly to improve the advancement in modern orchard systems for high production of tree fruits. In pear, it is difficult to establish modern high-density orchard systems due to the lack of appropriate vigor-controlling rootstocks. The measurement of traits using unmanned aerial vehicle (UAV) sensing techniques can help in identifying rootstocks suitable for higher-density plantings. The overall goal of this study is to optimize UAV flight parameters (sensor angles and direction) and preprocessing approaches to identify ideal flying parameters for data extraction and achieving maximum accuracy. In this study, five UAV missions were conducted to acquire high-resolution RGB imagery at different sensor inclination angles (90°, 65°, and 45°) and directions (forward and backward) from the pear rootstock breeding plot located at a research orchard belonging to the Washington State University (WSU) Tree Fruit Research and Extension Center in Wenatchee, WA, USA. The study evaluated the tree height and canopy volume extracted from four different integrated datasets and validated the accuracy with the ground reference data (n = 504). The results indicated that the 3D point cloud precisely measured the traits (0.89 < r < 0.92) compared to 2D datasets (0.51 < r < 0.75), especially with 95th percentile height measure. The integration of data acquired at different angles could be used to estimate the tree height and canopy volume. The integration of sensor angles during UAV flight is therefore critical for improving the accuracy of extracting architecture to account for varying tree characteristics and orchard settings and may be useful to further precision orchard management.
Ground Surface Deformation Analysis Integrating InSAR and GPS Data in the Karstic Terrain of Cheria Basin, Algeria
Authors: Hamdi Loubna , Defaflia Nabil , Merghadi Abdelaziz , Fehdi Chamssedine , Yunus Ali P. , Dou Jie , Pham Quoc Bao , Abdo Hazem Ghassan , Almohamad Hussein , Al-Mutiry Motrih
Published: March 2023

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system.
Spatial Correlations of Land Use Carbon Emissions in Shandong Peninsula Urban Agglomeration: A Perspective from City Level Using Remote Sensing Data
Authors: Zhao Lin , Yang Chuan-hao , Zhao Yu-chen , Wang Qian , Zhang Qi-peng
Published: March 2023

The spatial and temporal characteristics of land use carbon emissions are relevant to the sustainable use of land resources. Although spatial and temporal studies have been conducted on land use carbon emissions, the spatial correlation of land use carbon emissions at the city level still requires further research. Here, we estimated the distribution of carbon emissions at the city level in Shandong Peninsula urban agglomeration in spatial and temporal terms based on land use remote sensing data and fossil energy consumption data during 2000–2019. The results showed that the land use change in the 16 cities in the study area was the conversion of cropland to construction land. Carbon emissions from land use had an upward trend for all 16 cities overall during the period of 2000–2019, but the incremental carbon emissions trended downward after 2010. Among them, Jinan and Qingdao had higher carbon emissions than other cities. In addition, we also found that land use carbon emissions at the city level were characterized by stochasticity, while per capita carbon emissions displayed geospatial aggregation. Among them, Yantai displayed a spatial pattern of high–high clustering of carbon emissions, while Jining presented a spatial pattern of low–low clustering in terms of land-average carbon emissions and carbon emissions per capita during 2000–2019. The results of the study are important for guiding the achievement of urban carbon emission reduction and carbon neutrality targets at the city level.
Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat
Authors: Chan Aland H. Y. , Guizar-Coutiño Alejandro , Kalamandeen Michelle , Coomes David A.
Published: March 2023

Burn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited its development in wet tropical and subtropical regions due to high cloud cover and rapid burn-area revegetation. Here, we describe a 34-year Landsat-based burn-area product for wet, subtropical Hong Kong. We overcame technical obstacles by adopting a new LTS fire burn-area detection pipeline that (1) Automatically uniformized Landsat scenes by weighted histogram matching; (2) Estimated pixel resemblance to burn areas based on a random forest model trained on the number of days between the fire event and the date of burn-area detection; (3) Iteratively merged features created by thresholding burn-area resemblance to generate burn-area polygons with detection dates; and (4) Estimated the burn severity of burn-area pixels using a time-series compatible approach. When validated with government fire records, we found that the LTS fire product carried a low area of omission (11%) compared with existing burn-area products, such as GABAM (49%), MCD64A1 (72%), and FireCCI51 (96%) while effectively controlling commission errors. Temporally, the LTS fire pipeline dated 76.9% of burn-area polygons within two months of the actual fire event. The product represents the first Landsat-based burn-area product in wet tropical and subtropical Asia that covers the entire time series. We believe that burn-area products generated from algorithms like LTS fire will effectively bridge the gap between remote sensing and field-based studies on wet tropical and subtropical fire ecology.
Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer
Authors: D’Amelio Alessandro , Patania Sabrina , Buršić Sathya , Cuculo Vittorio , Boccignone Giuseppe
Published: March 2023

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.
Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers
Authors: Wilson Alphus Dan , Forse Lisa Beth
Published: March 2023

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.
Improving TDOA Radar Performance in Jammed Areas through Neural Network-Based Signal Processing
Authors: Gotthans Jakub , Gotthans Tomas , Novak David
Published: March 2023

This paper presents a method for estimating the position of a target under jammed conditions using the Time Difference of Arrival (TDOA) method. The algorithm utilizes a deep neural network to overcome the challenges posed by the jammed conditions. The simulations and results indicate that the presented method is more accurate and efficient than the traditional TDOA methods.
SENS+: A Co-Existing Fabrication System for a Smart DFA Environment Based on Energy Fusion Information
Authors: Chang Teng-Wen , Huang Hsin-Yi , Hong Cheng-Chun , Datta Sambit , Nakapan Walaiporn
Published: March 2023

In factories, energy conservation is a crucial issue. The co-fabrication space is a modern-day equivalent of a new factory type, and it makes use of Internet of Things (IoT) devices, such as sensors, software, and online connectivity, to keep track of various building features, analyze data, and produce reports on usage patterns and trends that can be used to improve building operations and the environment. The co-fabrication user requires dynamic and flexible space, which is different from the conventional user’s usage. Because the user composition in a co-fabrication space is dynamic and unstable, we cannot use the conventional approach to assess their usage and rentals. Prototyping necessitates a specifically designed energy-saving strategy. The research uses a “seeing–moving–seeing” design thinking framework, which enables designers to more easily convey their ideas to others through direct observation of the outcomes of their intuitive designs and the representation of their works through design media. The three components of human behavior, physical manufacture, and digital interaction are primarily the focus of this work. The computing system that connects the physical machine is created through communication between the designer and the digital interface, giving the designer control over the physical machine. It is an interactive fabrication process formed by behavior. The Sensible Energy System+ is an interactive fabrication process of virtual and real coexistence created by combining the already-existing technology, the prototype fabrication machine, and SENS. This process analyzes each step of the fabrication process and energy, fits it into the computing system mode to control the prototype fabrication machine, and reduces the problem between virtual and physical fabrication and energy consumption.
Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring
Authors: Atif Othmane , Lee Jonguk , Park Daihee , Chung Yongwha
Published: March 2023

The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs’ health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog’s behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog’s behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog’s location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog’s health and welfare.
Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction
Authors: Gu Junhua , Jia Zhihao , Cai Taotao , Song Xiangyu , Mahmood Adnan
Published: March 2023

Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.
Sequential Dual Coating with Thermosensitive Polymers for Advanced Fiber Optic Temperature Sensors
Authors: Salunkhe Tejaswi Tanaji , Kim Il Tae
Published: March 2023

We systematically designed dual polymer Fabry–Perrot interferometer (DPFPI) sensors, which were used to achieve highly sensitive temperature sensors. The designed and fabricated DPFPI has a dual polymer coating layer consisting of thermosensitive poly (methyl methacrylate) (PMMA) and polycarbonate (PC) polymers. Four different DPFPI sensors were developed, in which different coating optical path lengths and the resultant optical properties were generated by the Vernier effect, changing the sequence of the applied polymers and varying the concentration of the coating solutions. The experimental results confirmed that the PC_PMMA_S1 DPFPI sensor delivered a temperature sensitivity of 1238.7 pm °C−1, which was approximately 4.4- and 1.4-fold higher than that of the PMMA and PMMA_PC_S1-coated sensor, respectively. Thus, the results reveal that the coating sequence, the compact thickness of the dual polymer layers, and the resultant optical parameters are accountable for achieving sensors with high sensitivity. In the PC_ PMMA-coated sensor, the PMMA outer layer has comparatively better optical properties than the PC, which might produce synergistic effects that create a large wavelength shift with small temperature deviations. Therefore, it is considered that the extensive results with the PC_PMMA_S1 DPFPI sensor validate the efficacy, repeatability, reliability, quick reaction, feasibility, and precision of the temperature readings.
Joint Masked Face Recognition and Temperature Measurement System Using Convolutional Neural Networks
Authors: Tsai Tsung-Han , Lu Ji-Xiu , Chou Xuan-Yu , Wang Chieng-Yang
Published: March 2023

With the outbreak of COVID-19, epidemic prevention has become a way to prevent the spread of epidemics. Many public places, such as hospitals, schools, and office places, require disinfection and temperature measurement. To implement epidemic prevention systems and reduce the risk of infection, it is a recent trend to measure body temperature through non-contact sensing systems with thermal imaging cameras. Compared to fingerprints and irises, face recognition is accurate and does not require close contact, which significantly reduces the risk of infection. However, masks block most facial features, resulting in the low accuracy of face recognition systems. This work combines masked face recognition with a thermal imaging camera for use as an automated attendance system. It can record body temperature and recognize the person at the same time. Through the designed UI system, we can search the attendance information of each person. We not only provide the design method based on convolutional neural networks (CNNs), but also provide the complete embedded system as a real demonstration and achieve a 94.1% accuracy rate of masked face recognition in the real world. With the face recognition system combined with a thermal imaging camera, the purpose of screening body temperature when checking in at work can be achieved.
Asymmetry Optimization for 10 THz OPC Transmission over the C + L Bands Using Distributed Raman Amplification
Authors: Rosa Paweł , Martella Giuseppe Rizzelli , Ania Castañón Juan Diego , Tan Mingming
Published: March 2023

An optimized design for a broadband Raman optical amplifier in standard single-mode fiber covering the C and L bands is presented, to be used in combination with wideband optical phase conjugation (OPC) nonlinearity compensation. The use of two Raman pumps and fiber Bragg grating reflectors at different wavelengths for the transmitted (C band) and conjugated (L band) WDM channels is proposed to extend bandwidth beyond the limits imposed by single-wavelength pumping, for a total 10 THz. Optimization of pump and reflector wavelength, as well as pump powers, allows us to achieve low asymmetry across the whole transmission band for optimal nonlinearity compensation. System performance is simulated to estimate OSNR, gain flatness and nonlinear Kerr distortion.
Sandwich Face Layer Debonding Detection and Size Estimation by Machine-Learning-Based Evaluation of Electromechanical Impedance Measurements
Authors: Kralovec Christoph , Lehner Bernhard , Kirchmayr Markus , Schagerl Martin
Published: March 2023

The present research proposes a two-step physics- and machine-learning(ML)-based electromechanical impedance (EMI) measurement data evaluation approach for sandwich face layer debonding detection and size estimation in structural health monitoring (SHM) applications. As a case example, a circular aluminum sandwich panel with idealized face layer debonding was used. Both the sensor and debonding were located at the center of the sandwich. Synthetic EMI spectra were generated by a finite-element(FE)-based parameter study, and were used for feature engineering and ML model training and development. Calibration of the real-world EMI measurement data was shown to overcome the FE model simplifications, enabling their evaluation by the found synthetic data-based features and models. The data preprocessing and ML models were validated by unseen real-world EMI measurement data collected in a laboratory environment. The best detection and size estimation performances were found for a One-Class Support Vector Machine and a K-Nearest Neighbor model, respectively, which clearly showed reliable identification of relevant debonding sizes. Furthermore, the approach was shown to be robust against unknown artificial disturbances, and outperformed a previous method for debonding size estimation. The data and code used in this study are provided in their entirety, to enhance comprehensibility, and to encourage future research.
Multiparametric Remote Investigation in the near-IR through Optical Fiber for In Situ Measurements
Authors: Fede Letizia , Lefrere Gregory , Hjeij Maroun , Le Page Ronan , Poffo Luiz , Goujon Jean-Marc , Gratiet Aymeric Le
Published: March 2023

Diffuse reflectance spectroscopy (DRS) has proven to be a powerful, reliable, and non-invasive optical method for characterizing a specimen. Nevertheless, these methods are based on a rudimentary interpretation of the spectral response and can be irrelevant to understanding 3D structures. In this work, we proposed adding optical modalities into a customized handheld probe head in order to increase the number of parameters in DRS acquired from the light/matter interaction. It consists of (1) placing the sample in a reflectance manual rotation stage to collect spectral backscattered angularly resolved light and (2) illuminating it with two sequential linear polarization orientations. We demonstrate that this innovative approach leads to a compact instrument, capable of performing fast polarization-resolved spectroscopic analysis. Due to the significant amount of data available with this technique in a short time, we observe sensitive quantitative discrimination between two types of biological tissue provided by a raw rabbit leg. We believe that this technique can pave the way for rapid meat quality check or biomedical diagnosis of pathological tissues in situ at an early stage.
Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances
Authors: Wang Liu , Chen Guifen , Chen Guangjiao
Published: March 2023

A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module for subsystem filtering and estimation due to its high stability under Gaussian distribution. Second, the signals of each subsystem are fused by invoking the inverse covariance cross-fusion algorithm, and the convex optimization problem with high-dimensional weight coefficients is solved. At the same time, the algorithm reduces the burden of data computation, and data fusion time is saved. Finally, the GM-CPHD filter is added to the conventional ICI structure, and the generalization capability of the parallel inverse covariance intersection Gaussian mixture cardinalized probability hypothesis density (PICI-GM-CPHD) algorithm reduces the nonlinear complexity of the system. An experiment on the stability of Gaussian fusion models is organized and linear and nonlinear signals are compared by simulating the metrics of different algorithms, and the results show that the improved algorithm has a smaller metric OSPA error than other mainstream algorithms. Compared with other algorithms, the improved algorithm improves the signal processing accuracy and reduces the running time. The improved algorithm is practical and advanced in terms of multisensor data processing.
Cluster Analysis of Seismicity in the Eastern Gulf of Corinth Based on a Waveform Template Matching Catalog
Authors: Kapetanidis Vasilis , Michas Georgios , Spingos Ioannis , Kaviris George , Vallianatos Filippos
Published: March 2023

The Corinth Rift, in Central Greece, is one of the most seismically active areas in Europe. In the eastern part of the Gulf of Corinth, which has been the site of numerous large and destructive earthquakes in both historic and modern times, a pronounced earthquake swarm occurred in 2020–2021 at the Perachora peninsula. Herein, we present an in-depth analysis of this sequence, employing a high-resolution relocated earthquake catalog, further enhanced by the application of a multi-channel template matching technique, producing additional detections of over 7600 events between January 2020 and June 2021. Single-station template matching enriches the original catalog thirty-fold, providing origin times and magnitudes for over 24,000 events. We explore the variable levels of spatial and temporal resolution in the catalogs of different completeness magnitudes and also of variable location uncertainties. We characterize the frequency–magnitude distributions using the Gutenberg–Richter scaling relation and discuss possible b-value temporal variations that appear during the swarm and their implications for the stress levels in the area. The evolution of the swarm is further analyzed through spatiotemporal clustering methods, while the temporal properties of multiplet families indicate that short-lived seismic bursts, associated with the swarm, dominate the catalogs. Multiplet families present clustering effects at all time scales, suggesting triggering by aseismic factors, such as fluid diffusion, rather than constant stress loading, in accordance with the spatiotemporal migration patterns of seismicity.
Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions
Authors: Petropoulou Anna Selini , van Marrewijk Bart , de Zwart Feije , Elings Anne , Bijlaard Monique , van Daalen Tim , Jansen Guido , Hemming Silke
Published: March 2023

Recent studies indicate that food demand will increase by 35–56% over the period 2010–2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition “Autonomous Greenhouse Challenge”. This paper describes and analyzes the results of the third edition of this competition. The competition’s goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO2 were decisive in realizing the competition’s goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work.
Application of a Robust Thermoelectric Gas Sensor in Firewood Combustion Exhausts
Authors: Hagen Gunter , Herrmann Julia , Zhang Xin , Kohler Heinz , Hartmann Ingo , Moos Ralf
Published: March 2023

The quality of wood combustion processes can be effectively improved by achieving the automated control of the combustion air feed. For this purpose, continuous flue gas analysis using in situ sensors is essential. Besides the successfully introduced monitoring of the combustion temperature and the residual oxygen concentration, in this study, in addition, a planar gas sensor is suggested that utilizes the thermoelectric principle to measure the exothermic heat generated by the oxidation of unburnt reducing exhaust gas components such as carbon monoxide (CO) and hydrocarbons (CxHy). The robust design made of high-temperature stable materials is tailored to the needs of flue gas analysis and offers numerous optimization options. Sensor signals are compared to flue gas analysis data from FTIR measurements during wood log batch firing. In general, impressive correlations between both data were found. Discrepancies occur during the cold start combustion phase. They can be attributed to changes in the ambient conditions around the sensor housing.
Do the Pelvic and Thorax Movements Differ between the Sexes and Influence Golf Club Velocity in Junior Golfers?
Authors: Gryc Tomas , Zahalka Frantisek , Brožka Matěj , Marenčáková Jitka , Miřátský Petr , Baca Arnold , Stöckl Michael
Published: March 2023

The aim of this study was to determine the differences in golf swing execution in terms of the parameters of the pelvis and thorax movement between the sexes in junior golfers and their relation to the golf club velocity. Elite female and male players (age: 15.4 ± 1.0 and 15.8 ± 1.7 years, respectively) performed 10 golf swings with a driver under laboratory conditions. Pelvis and thorax movement parameters and golf club velocities were measured using a three-dimensional motion capture system. Statistical parametric mapping analysis of pelvis–thorax coupling revealed a significant difference (p < 0.05) between boys and girls during backswing. Analysis of variance showed a significant effect of sex on the parameters of maximal pelvic rotation (F = 6.28, p = 0.02), X-factor (F = 5.41, p = 0.03), and golf club velocity (F = 31.98, p < 0.01). No significant relationship was found between pelvis and thorax movement parameters and golf club velocity in the girls. We found a significant negative relationship between the parameters of maximal thorax rotation and golf club velocity (r = −0.941, p < 0.01) and between X-Factor and golf club velocity (r = −0.847, p < 0.05) in the boys. We suggest that these negative relationships in males were caused by the influence of hormones during their maturation and biological development, where there is decreased flexibility (lower shoulders rotation and X-factor) and growth of muscle strength (higher club head velocity).
Fiber-Reinforced Lightweight Calcium Aluminate Cement-Based Concrete: Effect of Exposure to Elevated Temperatures
Authors: Salli Bideci Özlem , Yılmaz Hakan , Gencel Osman , Bideci Alper , Çomak Bekir , Nodehi Mehrab , Ozbakkaloglu Togay
Published: March 2023

Calcium aluminate cements (CACs) are a group of rapid-hardening hydraulic binders with a higher aluminum composition and lower ecological footprint compared to their ordinary Portland cement (CEM) counterparts. CACs are commonly known to have higher thermo-durability properties but have previously been observed to experience a major strength loss over time when exposed to thermal and humidity conditions due to the chemical conversion of their natural hydrated products. To address this, in this study, silica fume is added to induce a different hydration phase path suggested by previous studies and utilized in conjunction with fiber-reinforced lightweight pumice to produce lightweight concrete. To closely evaluate the performance of the produced samples with CAC compared to CEM, two different types of cement (CEM and CAC) with different proportions of pumice and crushed stone aggregate at temperatures between 200 and 1000 °C were tested. In this context, sieve analysis, bulk density, flowability, compressive and flexural strength, ultrasonic pulse velocity and weight loss of the different mixes were determined. The results of this study point to the better mechanical properties of CAC samples produced with pumice aggregates (compared to crushed stone) when samples are exposed to high temperatures. As a result, it is found that CACs perform better than CEM samples with lightweight pumice at elevated temperatures, showing the suitability of producing lightweight thermal-resistant CAC-based concretes.
The Financing Efficiency of China’s Industrial Listed Enterprises Based on the Dynamic–Network SBM Model
Authors: Tan Xianhua , Zheng Danting , Zhu Yuanyuan , Na Sanggyun
Published: March 2023

Industry is an important force in China’s economic development; however, with the transformation and upgrading of the industrial structure, a large number of resources have flowed to the tertiary industry, and the funding problem has become one of the main disadvantages restricting China’s industrial enterprises’ sustainable development. This paper aims to point out the problems and improvement directions of financing efficiency of China’s industrial listed enterprises. Based on the two-stage dynamic network SBM (DNSBM) model, this paper evaluates the financing efficiency of 450 of China’s industrial listed enterprises from 2011 to 2017. The results show that: (1) the overall financing efficiency of China’s industrial listed enterprises is low, the funds are not used effectively, and there is great room for improvement; (2) the overall financing efficiency of state-owned enterprises (SOEs) is lower than that of non-state-owned enterprises (NSOEs), the average fund raising efficiency of SOEs is greater than the fund using efficiency, but the opposite is true for NSOEs; (3) the overall financing efficiency of the main-board-listed enterprises is the lowest, and that of the growth enterprise market (GEM) is the highest, the most obvious gap is in the second stage of fund using, but this gap is gradually narrowing; and (4) the overall financing efficiency of China’s industrial enterprises has obvious regional characteristics, the fund raising efficiency value in each region is not much different, while the fund using is significantly different. To improve financing efficiency, enterprises must improve their financing channels, choose the best financing method, maintain a reasonable debt-financing ratio, improve management level and profitability, increase enterprise value, enhance the debt-paying ability, and attract more capital at a low cost. In addition, the government should also provide corresponding financing support policies for different types of enterprises.
Comparative Study of Effective Pretreatments on the Structural Disruption and Hydrodepolymerization of Rice Straw
Authors: Yang Xiaorui , Li Xiaotong , Liang Jinhua , Zhu Jianliang
Published: March 2023

Rice straw (RS) is the most potentially renewable agricultural waste resource widely distributed in nature. Considering the complex recalcitrant structure and components of RS, three pretreatment methods, including high-temperature hydrothermal, medium-temperature microwave, and low-temperature cryocrushing pretreatment were performed. The components and structure of RS residues were examined and analyzed after the pretreatments. Pretreatment with hydrothermal yielded the lowest rice straw recovery (59.0%); after being pretreated at 180 °C for 10 min, the hemicellulose recovery was only 14.1%, and the removal efficiency of lignin was the largest (41.3%), which was 32.2% and 18.8% higher than that achieved from cryocrushing and microwave pretreatment, respectively. Pretreatment with cryocrushing yielded the highest recovery rates of rice straw (92.9%), hemicellulose and cellulose (88.8% and 90.4%, respectively). Results of scanning electron microscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, and the analysis of specific surface area and apertures demonstrated that all three pretreatments could effectively disrupt the structure of RS and reduce its cellulose crystallinity. The three pretreatments were found to enhance the hydrodepolymerization of RS residues. Furthermore, cryocrushing pretreatment yielded the highest cellulose conversion rate (56.8%), and the yields of glucose, xylose, and arabinose were 29.6%, 56.2%, and 17.8%, respectively. Apart from the use of acids and enzymes, hydrodepolymerization of RS was among the few methods that can effectively degrade cellulose, presenting an ideal solution for the degradation of biomass.
Assessing the Impact of Future Sea Level Rise on Blue Carbon Ecosystem Services on Long Island, New York
Authors: Tanner Katherine , Strong Aaron L.
Published: March 2023

Salt marsh ecosystems provide critical climate mitigation ecosystem services through carbon sequestration. Sea level rise (SLR) has variable effects on these ecosystems, both driving marsh migration into upland areas and causing inundation and erosion that reduces marsh extent. How salt marsh carbon sequestration responds to SLR thus represents an important carbon cycle feedback to climate change. Here, we examine the consequences of one meter (1 m) of SLR for salt marsh ecosystem carbon sequestration for Long Island, New York and for the North Fork peninsula in far northeastern Long Island using three different assumptions for salt marsh carbon sequestration rates. For the entirety of Long Island, SLR will reduce future carbon sequestration by 22 million tons of carbon dioxide (CO2) by 2100 under the medium sequestration rate assumption compared to a no-SLR scenario. This represents a net loss of $137.5 billion in carbon sequestration ecosystem service value due to SLR. On the North Fork peninsula, however, SLR increases sequestration by 370,000 tons of CO2 with a medium sequestration rate assumption relative to a no-SLR scenario. However, the magnitude of uncertainty in future carbon sequestration due to different assumptions of carbon sequestration rates is greater than the impact of SLR on carbon sequestration, pointing to the need for the use of field-based measurement of sequestration rates in managing coastal ecosystem response to climate change.
Interest Equilibrium and Path Choice in the Development of Construction Land Decrement: A Theoretical Analysis Based on the Multi-Agent Game Model
Authors: Jiang Zhifa , Li Qiang , Gao Wei , Su Huiyue , Zhang Yuansuo
Published: March 2023

High-quality development is a new-era requirement for modern urban construction. The implementation of construction land decrement has become an inevitable choice to solve the urban land use dilemma and achieve high-quality social and economic development. It is a game process for stakeholders. Based on the multi-agent game model, this research analyzes the game relationship and strategy selection between governments, government and farmers, government and developers, and developers and farmers and examines the balance of interests among multiple subjects. From the perspective of space dimension, index dimension, use dimension and time dimension, a game path is constructed for construction land decrement. The research shows the following: There is a clear relationship between the supervision cost of the higher-level government and the intensity of rewards and punishments for the lower-level government and the implementation of the decrement. The district (county) government’s compensation plan and farmers’ perceptions of the success of the boycott will affect the strategic choices of both parties. Governments at all levels play a direct role in restraining development behavior and supervising rent-seeking behavior after developers intervene in volume reduction. It also proposes optimizing the ratio of game factors such as costs, benefits, rewards, verification and punishment to achieve the game equilibrium of the expression of the interests of village residents, developers and governments at all levels. Based on the path planning and coupling of response, the multi-scheme dynamic path selection for the development of construction land decrement is realized.
Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China
Authors: Zhu Linye , Shi Mingming , Fan Deqin , Tu Kun , Sun Wenbin
Published: March 2023

Exploring the influence of vegetation carbon stocks over land-cover change and the relationship between land-cover change and net primary production (NPP) is of great practical importance for achieving China’s carbon neutrality target. Therefore, this paper analyses the spatio-temporal distribution of land-cover change and NPP change in Inner Mongolia from 2000 to 2020 and explores the vegetation carbon stock change caused by land-cover change, the relationship between land cover and NPP, and the relationship between land-cover change and NPP change. The results show that the main land-cover change in Inner Mongolia during 2000–2020 was the conversion from other land-cover types to grassland, with the conversion of bare land to grassland from 2000 to 2010 covering an area of 20,634.87 km2. During the period of 2000–2020, the high values of NPP were mainly located in northeastern Inner Mongolia, and the low values were mainly distributed in southwestern Inner Mongolia. The total carbon storage changes in vegetation affected by land-cover change during 2000–2010 and 2010–2020 were 10,736,986.11 t and 3,894,272.41 t. The mean values of NPP for different land covers were, in descending order, forest > cultivated land > wetland > grassland > artificial surfaces > shrubland > water bodies > bare land. Between 2000 and 2010, the change in land-cover type to water bodies was the main cause of the decrease in NPP, and the increase in NPP was mainly due to the conversion of other land-cover types to cultivated land, forest, and grassland. The implementation of appropriate conservation and management measures (e.g., the construction of forest and grass ecological protection), planting, and the protection of high-vegetation carbon storage plants and other means can improve the level of vegetation carbon sequestration and protect the ecological environment of Inner Mongolia.
New Policy Research on Education Development and Global Citizenship in a Sustainable Environment
Authors: Fang Hanlin , Zhang Fengrong , Xiao Qianwen , Lin Ciyun
Published: March 2023

At present, multinational and regional educational agencies and researchers have used academic development and global citizenship training to increase globalization. However, owing to the existence of emerging policies, these innovations have raised issues in the educational sector. Therefore, in this paper, EP-SE has been proposed for enhancing the sustainable environment in education and global citizenship. EP-SE seeks to elucidate the educational developments and reveal the part played by non-discursive components in ensuring global citizenship cooperation. The emerging policies for sustainable development emphasize the importance of moral ideals, personal accountability, and engaged global citizenship. Capitalist debate impartially privileges a real economy based on the individual and enhanced income. EP-SE has been developed within the current commodity conditions, critical democracy, and threats to educational development and can serve as a model for sustainable global citizenship development. The experimental results suggest that the proposed model achieves the highest performance compared with the other existing methods.
Corporate Social Responsibility of Financial Cooperatives: A Multi-Level Analysis
Authors: Allen Marie , Tessier Sophie , Laurin Claude
Published: March 2023

Cooperatives, which have a dual mission that includes both business and social goals, are of particular interest for the study of corporate social responsibility (CSR). The aim of this study was to examine how cooperative directors influence the CSR strategies of their organization. We used a multi-level conceptual framework, consisting of micro, meso, and macro levels, to analyze qualitative data (20 interviews, observation of two board meetings and analysis of over 25 public documents) collected through a case study design that focused on the directors of three financial cooperatives operating under a large group of Canadian financial service cooperatives. Our study contributes first by building on prior studies that link CSR goals to the cooperatives’ dual mission and commitment to improving their community. We enrich prior findings by showing how directors play a crucial role in the enactment of the social dimension of CSR, but that conversely, cooperatives are vehicles for directors who want to contribute to the improvement of their community, thus creating a virtuous circle. Secondly, comparing bureaus operating in urban and rural areas allows us to show how the specificities of the community in which a cooperative evolves influence the approach of the directors towards the environmental dimension of CSR.