bluuebunny/crossref_metadata_2025 huggingface dataset:
S3L: Spectrum Transformer for Self-Supervised Learning in Hyperspectral Image Classification
Authors: Guo Hufeng , Liu Wenyi
Published: March 2024

In the realm of Earth observation and remote sensing data analysis, the advancement of hyperspectral imaging (HSI) classification technology is of paramount importance. Nevertheless, the intricate nature of hyperspectral data, coupled with the scarcity of labeled data, presents significant challenges in this domain. To mitigate these issues, we introduce a self-supervised learning algorithm predicated on a spectral transformer for HSI classification under conditions of limited labeled data, with the objective of enhancing the efficacy of HSI classification. The S3L algorithm operates in two distinct phases: pretraining and fine-tuning. During the pretraining phase, the algorithm learns the spatial representation of HSI from unlabeled data, utilizing a masking mechanism and a spectral transformer, thereby augmenting the sequence dependence of spectral features. Subsequently, in the fine-tuning phase, labeled data is employed to refine the pretrained weights, thereby improving the precision of HSI classification. Within the comprehensive encoder–decoder framework, we propose a novel spectral transformer module specifically engineered to synergize spatial feature extraction with spectral domain analysis. This innovative module adeptly navigates the complex interplay among various spectral bands, capturing both global and sequential spectral dependencies. Uniquely, it incorporates a gated recurrent unit (GRU) layer within the encoder to enhance its ability to process spectral sequences. Our experimental evaluations across several public datasets reveal that our proposed method, distinguished by its spectral transformer, achieves superior classification performance, particularly in scenarios with limited labeled samples, outperforming existing state-of-the-art approaches.
Spatial–Temporal Approach and Dataset for Enhancing Cloud Detection in Sentinel-2 Imagery: A Case Study in China
Authors: Gong Chengjuan , Yin Ranyu , Long Tengfei , Jiao Weili , He Guojin , Wang Guizhou
Published: March 2024

Clouds often cause challenges during the application of optical satellite images. Masking clouds and cloud shadows is a crucial step in the image preprocessing workflow. The absence of a thermal band in products of the Sentinel-2 series complicates cloud detection. Additionally, most existing cloud detection methods provide binary results (cloud or non-cloud), which lack information on thin clouds and cloud shadows. This study attempted to use end-to-end supervised spatial–temporal deep learning (STDL) models to enhance cloud detection in Sentinel-2 imagery for China. To support this workflow, a new dataset for time-series cloud detection featuring high-quality labels for thin clouds and haze was constructed through time-series interpretation. A classification system consisting of six categories was employed to obtain more detailed results and reduce intra-class variance. Considering the balance of accuracy and computational efficiency, we constructed four STDL models based on shared-weight convolution modules and different classification modules (dense, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and transformer). The results indicated that spatial and temporal features were crucial for high-quality cloud detection. The STDL models with simple architectures that were trained on our dataset achieved excellent accuracy performance and detailed detection of clouds and cloud shadows, although only four bands with a resolution of 10 m were used. The STDL models that used the Bi-LSTM and that used the transformer as the classifier showed high and close overall accuracies. While the transformer classifier exhibited slightly lower accuracy than that of Bi-LSTM, it offered greater computational efficiency. Comparative experiments also demonstrated that the usable data labels and cloud detection results obtained with our workflow outperformed the results of the existing s2cloudless, MAJA, and CS+ methods.
Satellite-Derived Indicators of Drought Severity and Water Storage in Estuarine Reservoirs: A Case Study of Qingcaosha Reservoir, China
Authors: Yuan Rui , Xu Ruiyang , Zhang Hezhenjia , Qiu Cheng , Zhu Jianrong
Published: March 2024

Estuarine reservoirs are critical for freshwater supply and security, especially for regions facing water scarcity challenges due to climate change and population growth. Conventional methods for assessing drought severity or monitoring reservoir water level and storage are often limited by data availability, accessibility and quality. We present an approach for monitoring estuarine reservoir water levels, storage and extreme drought via satellite remote sensing and waterline detection. Based on the CoastSat algorithm, Landsat-8 and Sentinel-2 images from 2013 to 2022 were adopted to extract the waterline of Qingcaosha Reservoir, the largest estuarine reservoir in the world and a key source of freshwater for Shanghai, China. This study confirmed the accuracy of the satellite-extracted results through two main methods: (1) calculating the angle of the central shoal slope in the reservoir using the extracted waterline data and measured water levels and (2) inverting the time series of water levels for comparison with measured data. The correlation coefficient of the estimated water level reached ~0.86, and the root mean square error (RMSE) of the estimated shoal slope was ~0.2°, indicating that the approach had high accuracy and reliability. We analyzed the temporal and spatial patterns of waterline changes and identified two dates (21 February 2014 and 15 October 2022) when the reservoir reached the lowest water levels, coinciding with periods of severe saltwater intrusions in the estuary. The extreme drought occurrences in the Qingcaosha Reservoir were firstly documented through the utilization of remote sensing data. The results also indicate a strong resilience of the Qingcaosha Reservoir and demonstrate that the feasibility and utility of using satellite remote sensing and waterline detection for estuarine reservoir storage can provide timely and accurate information for water resource assessment, management and planning.
Broadband Source Localization Using Asynchronous Distributed Hydrophones Based on Frequency Invariability of Acoustic Field in Shallow Water
Authors: Li Hui , Huang Jun , Xu Zhezhen , Yang Kunde , Qin Jixing
Published: March 2024

This paper introduces a model-independent passive source localization method, employing asynchronous distributed hydrophones in shallow water. Based on the frequency invariability of the acoustic field, assuming the correct source range information, the warped spectra of received signals at distributed hydrophones exhibit identical shapes. Subsequently, a cost function is formulated to mutually align the warped spectra, with its maximum point indicating the source location. The proposed method can locate the source in two-dimensional horizontal space without requiring either angle- or time-synchronization information. Numerical simulations are conducted to demonstrate the performance of the proposed method.
Short-Term Intensity Prediction of Tropical Cyclones Based on Multi-Source Data Fusion with Adaptive Weight Learning
Authors: Tian Wei , Song Ping , Chen Yuanyuan , Xu Haifeng , Jin Cheng , Sian Kenny Thiam Choy Lim Kam
Published: March 2024

Tropical cyclones (TCs) can cause significant economic damage and loss of life in coastal areas. Therefore, TC prediction has become a crucial topic in current research. In recent years, TC track prediction has progressed considerably, and intensity prediction remains a challenge due to the complex mechanism of TC structure. In this study, we propose a model for short-term intensity prediction based on adaptive weight learning (AWL-Net) for the evolution of the TC’s structure as well as intensity changes, exploring the multidimensional fusion of features including TC morphology, structure, and scale. Furthermore, in addition to using satellite imageries, we construct a dataset that can more comprehensively explore the degree of TC cloud organization and structure evolution. Considering the information difference between multi-source data, a multi-branch structure is constructed and adaptive weight learning (AWL) is designed. In addition, according to the three-dimensional dynamic features of TC, 3D Convolutional Gated Recurrent (3D ConvGRU) is used to achieve feature enhancement, and then 3D Convolutional Neural Network (CNN) is used to capture and learn TC temporal and spatial features. Experiments on a sample of northwest Pacific TCs and official agency TC intensity prediction records are used to validate the effectiveness of our proposed model, and the results show that our model is able to focus well on the spatial and temporal features associated with TC intensity changes, with a root mean square error (RMSE) of 10.62 kt for the TC 24 h intensity forecast.
On the Importance of Precise Positioning in Robotised Agriculture
Authors: Nijak Mateusz , Skrzypczyński Piotr , Ćwian Krzysztof , Zawada Michał , Szymczyk Sebastian , Wojciechowski Jacek
Published: March 2024

The precision of agro-technical operations is one of the main hallmarks of a modern approach to agriculture. However, ensuring the precise application of plant protection products or the performance of mechanical field operations entails significant costs for sophisticated positioning systems. This paper explores the integration of precision positioning based on the global navigation satellite system (GNSS) in agriculture, particularly in fieldwork operations, seeking solutions of moderate cost with sufficient precision. This study examines the impact of GNSSs on automation and robotisation in agriculture, with a focus on intelligent agricultural guidance. It also discusses commercial devices that enable the automatic guidance of self-propelled machinery and the benefits that they provide. This paper investigates GNSS-based precision localisation devices under real field conditions. A comparison of commercial and low-cost GNSS solutions, along with the integration of satellite navigation with advanced visual odometry for improved positioning accuracy, is presented. The research demonstrates that affordable solutions based on the common differential GNSS infrastructure can be applied for accurate localisation under real field conditions. It also underscores the potential of GNSS-based automation and robotisation in transforming agriculture into a more efficient and sustainable industry.
Development and Testing of a Rocket-Based Sensor for Atmospheric Sensing Using an Unmanned Aerial System
Authors: Thalman Ryan
Published: March 2024

Measurements of the vertical structure of the lower atmosphere are important to the understanding of air quality. Unmanned Aerial Systems (UASs, drones) can provide low cost, repeatable measurements of the temperature, pressure, and relative humidity. A set of inexpensive sensors controlled with an Arduino microprocessor board were tested on a UAS against a meteorology grade sensor. Two modes of operation for sampling were tested: a forward moving sampler and a vertical ascent sampler. A small particle sensor (Sensiron SPS30) was integrated and was capable of retrieving vertical aerosol distributions during an inversion event. The thermocouple-based temperature probe and the relative humidity measurement on the Bosch BME280 sensor correlated well with the meteorological sensor. The temperature and relative humidity sensors were then deployed on a rocket sounding platform. The rocket sounding system performed well up to a height of 400 m. The inexpensive sensors were found to perform adequately for low-cost development and uses in education and research.
A Combined Method of Seismic Monitoring and Transient Electromagnetic Detection for the Evaluation of Hydraulic Fracturing Effect in Coal Burst Prevention
Authors: Bian Jiang , Liu Aixin , Yang Shuo , Lu Qiang , Jia Bo , Li Fuhong , Ma Xingen , Gong Siyuan , Cai Wu
Published: March 2024

In order to mitigate the risk of roof-dominated coal burst in underground coal mining, horizontal long borehole staged hydraulic fracturing technology has been prevailingly employed to facilitate the weakening treatment of the hard roof in advance. Such weakening effect, however, can hardly be evaluated, which leads to a lack of a basis in which to design the schemes and parameters of hydraulic fracturing. In this study, a combined underground–ground integrated microseismic monitoring and transient electromagnetic detection method was utilized to carry out simultaneous evaluations of the seismic responses to each staged fracturing and the apparent resistivity changes before and after all finished fracturing. On this basis, the comparable and applicable fracturing effects on coal burst prevention were evaluated and validated by the distribution of microseismic events and their energy magnitude during the mining process. Results show that the observed mining-induced seismic events are consistent with the evaluation results obtained from the combined seismic-electromagnetic detection method. However, there is a limited reduction effect on resistivity near the fractured section that induces far-field seismic events. Mining-induced seismic events are concentrated primarily within specific areas, while microseismic events in the fractured area exhibit high frequency but low energy overall. This study validates the rationality of combined seismic-electromagnetic detection results and provides valuable insights for optimizing fracturing construction schemes as well as comprehensively evaluating outcomes associated with underground directional long borehole staged hydraulic fracturing.
Underwater Rescue Target Detection Based on Acoustic Images
Authors: Hu Sufeng , Liu Tao
Published: March 2024

In order to effectively respond to floods and water emergencies that result in the drowning of missing persons, timely and effective search and rescue is a very critical step in underwater rescue. Due to the complex underwater environment and low visibility, unmanned underwater vehicles (UUVs) with sonar are more efficient than traditional manual search and rescue methods to conduct active searches using deep learning algorithms. In this paper, we constructed a sound-based rescue target dataset that encompasses both the source and target domains using deep transfer learning techniques. For the underwater acoustic rescue target detection of small targets, which lack image feature accuracy, this paper proposes a two-branch convolution module and improves the YOLOv5s algorithm model to design an acoustic rescue small target detection algorithm model. For an underwater rescue target dataset based on acoustic images with a small sample acoustic dataset, a direct fine-tuning using optical image pre-training lacks cross-domain adaptability due to the different statistical properties of optical and acoustic images. This paper therefore proposes a heterogeneous information hierarchical migration learning method. For the false detection of acoustic rescue targets in a complex underwater background, the network layer is frozen during the hierarchical migration of heterogeneous information to improve the detection accuracy. In addition, in order to be more applicable to the embedded devices carried by underwater UAVs, an underwater acoustic rescue target detection algorithm based on ShuffleNetv2 is proposed to improve the two-branch convolutional module and the backbone network of YOLOv5s algorithm, and to create a lightweight model based on hierarchical migration of heterogeneous information. Through extensive comparative experiments conducted on various acoustic images, we have thoroughly validated the feasibility and effectiveness of our method. Our approach has demonstrated state-of-the-art performance in underwater search and rescue target detection tasks.
Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network
Authors: Tiboni Monica , Remino Carlo
Published: March 2024

Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.
Development of Wafer-Type Plasma Monitoring Sensor with Automated Robot Arm Transfer Capability for Two-Dimensional In Situ Processing Plasma Diagnosis
Authors: Park Haewook , Kim Juhyun , Cho Sungwon , Kim Kyunghyun , Jang Sungho , Choi Younsok , Lee Hohyun
Published: March 2024

In this work, we propose our newly developed wafer-type plasma monitoring sensor based on a floating-type double probe method that can be useful for two-dimensional (2D) in situ plasma diagnosis within a semiconductor processing chamber. A key achievement of this work is the first realization of an ultra-thin plasma monitoring sensor with a system thickness of ~1.4 mm, which supports a fully automated robot arm transfer capability for in situ plasma diagnosis. To the best of our knowledge, it is the thinnest accomplishment among all wafer-type plasma monitoring sensors. Our proposed sensor is assembled with two Si wafers and SiO2-based probes; accordingly, it makes it possible to monitor the actual dynamics of processing plasmas under electrostatic chucking (ESC) conditions. Also, it allows for the prevention of chamber contamination issues after continuously exposing the radio frequency (RF) to various processing gases. Using a test-bed chamber, we successfully demonstrated the feasibility and system performance of the proposed sensor, including robot arm transfer capability, vacuum and thermal stress durability, and data integrity and reproducibility. Consequently, compared with the conventional plasma diagnostic tools, we expect that our proposed sensor will be highly beneficial for tool-to-tool matching (TTTM) and/or for studying various plasma-related items by more accurately providing the parameters of processing plasmas, further saving both time and manpower resources required for preventive maintenance (PM) routines as well.
Torsional Vibration Analysis Using Rotational Laser Vibrometers
Authors: Chatterton Steven , Dassi Ludovico , Gheller Edoardo , Ghisi Tommaso , Vania Andrea , Pennacchi Paolo
Published: March 2024

Torsional vibration is a critical phenomenon in rotor dynamics. It consists of an oscillating movement of the shaft and causes failures in multiple oscillating fields of application. This type of vibration is more difficult to measure than lateral vibration. Torsional vibrometers are generally invasive and require a complicated setup, as well as being inconvenient for field measurements. One of the most reliable, non-invasive, and transportable measuring techniques involves the laser torsional vibrometer. For this research, two laser heads with different measurement capabilities were utilized. An experimental test rig was used to perform a relative calibration of the two laser vibrometers. The frequency of the acting force and the rotation speed of the shaft vary in the same range, which is commonly found in rotating machines. Finally, experimental measurements of torsional vibrations using laser vibrometers were compared with numerical results from a 1D finite element model of the same test rig. The main outcome of this paper is the definition of a reliable measuring procedure to exploit two laser vibrometers for detecting torsional mode-shapes and natural frequencies on real machines. The relative calibration of two different measuring heads is described in detail, and the procedure was fundamental to properly correlate measuring signals in two machine sections. A good correspondence between the numerical and experimental results was found.
CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection
Authors: Zhang Lei , Zheng Jiachun , Li Chaopeng , Xu Zhiping , Yang Jiawen , Wei Qiuxin , Wu Xinyi
Published: March 2024

The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve improved target localization. However, existing methods rarely design the transformer itself as a detector, failing to fully leverage the long-range modeling advantages of self-attention. Furthermore, there has been limited research into multi-class SAR target detection. To address these limitations, this study proposes a SAR detector named CCDN-DETR, which builds upon the framework of the detection transformer (DETR). To adapt to the multiscale characteristics of SAR data, cross-scale encoders were introduced to facilitate comprehensive information modeling and fusion across different scales. Simultaneously, we optimized the query selection scheme for the input decoder layers, employing IOU loss to assist in initializing object queries more effectively. Additionally, we introduced constrained contrastive denoising training at the decoder layers to enhance the model’s convergence speed and improve the detection of different categories of SAR targets. In the benchmark evaluation on a joint dataset composed of SSDD, HRSID, and SAR-AIRcraft datasets, CCDN-DETR achieves a mean Average Precision (mAP) of 91.9%. Furthermore, it demonstrates significant competitiveness with 83.7% mAP on the multi-class MSAR dataset compared to CNN-based models.
Demonstration of a Transparent and Adhesive Sealing Top for Microfluidic Lab-Chip Applications
Authors: Agarwal Anurag , Salahuddin Asif , Ahamed Mohammed Jalal
Published: March 2024

A transparent and adhesive film-based enclosing and sealing method is here presented for out-of-cleanroom-based open-form microfluidic devices. The commercially available polyester flexible film known as Microseal ‘B’ is presented in this paper as a cover seal for open-form microfluidic devices. This film is adaptable to high working temperatures and is biocompatible. The quality of the sealing film was investigated by leak tests, fluorescence tests, and contact angle measurements. The investigations revealed its sealing strength, fluorescence detection compatibility, and surface wettability. It was found that the proposed sealing polyester film on the 3D-printed device could sustain a gauge pressure of 2.7 atm at a flow rate of 4 mL/min without any leaks. It also provided fluorescence detection compatibility and an intensity-to-background ratio in the range of 2.3 to 4.5 for particle sizes of 5 μm and 15 μm, respectively, which is comparable with the performances of other sealing materials. The film’s hydrophobicity is comparable to other polymers used in microfluidics. This paper concludes by showcasing some applications of such transparent tops in classical microfluidic devices used for droplet generation and fluid mixing, in order to demonstrate the prospects of this fabrication technique in lab-on-a-chip devices.
LoRaWAN for Vehicular Networking: Field Tests for Vehicle-to-Roadside Communication
Authors: Di Renzone Gabriele , Parrino Stefano , Peruzzi Giacomo , Pozzebon Alessandro , Vangelista Lorenzo
Published: March 2024

Vehicular wireless networks are one of the most valuable tools for monitoring platforms in the automotive domain. At the same time, Internet of Things (IoT) solutions are playing a crucial role in the same framework, allowing users to connect to vehicles in order to gather data related to their working cycle. Such tasks can be accomplished by resorting to either cellular or non-cellular wireless technologies. While the former can ensure low latency but require high running costs, the latter can be employed in quasi-real-time applications but definitely reduce costs. To this end, this paper proposes the results of two measurement campaigns aimed at assessing the performance of the long-range wide-area network (LoRaWAN) protocol when it is exploited as an enabling technology to provide vehicles with connectivity. Performances are evaluated in terms of packet loss (PL) and received signal strength indicator (RSSI) in wireless links. The two testing scenarios consisted of a transmitter installed on a motorbike running on an elliptical track and a receiver placed in the centre of the track, and a transmitter installed on the roof of a car and a receiver placed next to a straight road. Several speeds were tested, and all the spreading factors (SFs) foreseen by the protocol were examined, showing that the Doppler effect has a marginal influence on the receiving performance of the technology, and that, on the whole, performance is not significantly affected by the speed. Such results prove the feasibility of LoRaWAN links for vehicular network purposes.
Binocular Visual Measurement Method Based on Feature Matching
Authors: Xie Zhongyang , Yang Chengyu
Published: March 2024

To address the issues of low measurement accuracy and unstable results when using binocular cameras to detect objects with sparse surface textures, weak surface textures, occluded surfaces, low-contrast surfaces, and surfaces with intense lighting variations, a three-dimensional measurement method based on an improved feature matching algorithm is proposed. Initially, features are extracted from the left and right images obtained by the binocular camera. The extracted feature points serve as seed points, and a one-dimensional search space is established accurately based on the disparity continuity and epipolar constraints. The optimal search range and seed point quantity are obtained using the particle swarm optimization algorithm. The zero-mean normalized cross-correlation coefficient is employed as a similarity measure function for region growing. Subsequently, the left and right images are matched based on the grayscale information of the feature regions, and seed point matching is performed within each matching region. Finally, the obtained matching pairs are used to calculate the three-dimensional information of the target object using the triangulation formula. The proposed algorithm significantly enhances matching accuracy while reducing algorithm complexity. Experimental results on the Middlebury dataset show an average relative error of 0.75% and an average measurement time of 0.82 s. The error matching rate of the proposed image matching algorithm is 2.02%, and the PSNR is 34 dB. The algorithm improves the measurement accuracy for objects with sparse or weak textures, demonstrating robustness against brightness variations and noise interference.
Changes in Body Composition and Physical Performance after a Six-Week International Tour in Young Chilean Female Tennis Players
Authors: Luna-Villouta Pablo , Contreras Luis Valenzuela , Salazar Cristian Martínez , Valderrama Jorge Flández , Matus-Castillo Carlos , Flores-Rivera Carol , Paredes-Arias Marcelo , Vargas-Vitoria Rodrigo
Published: March 2024

Junior tennis players travel a lot to play tennis tournaments; this causes them to spend a lot of time away from their homes and disrupts their training, which could reduce their performance and increase the risk of injury. The purpose of this study was to analyze the changes in physical performance and body composition after a six-week international tour in young Chilean female tennis players. Thirty young female tennis players (15.4 ± 0.6) participated in this study. Body weight, skinfolds, and perimeters were measured. Body fat percentage (BFP) and skeletal muscle mass (SMM) were calculated. For physical performance, 5 m sprint, the 505 with stationary start test (505 test), the pro-agility test, countermovement jump (CMJ), and medicine ball throw (MBT) were evaluated. The results show a significant increase in BFP and decrease in SMM (p < 0.01; d = −0.18 and 0.19, respectively). In terms of physical performance, 5 m sprint, the 505 test (p < 0.01; d = −0.95 and −0.95, respectively), CMJ, MBT, and HJ significantly decreased post-tour (p < 0.05; d = 0.96, 0.89 and 0.47, respectively). We conclude that, after a six-week international tour, there were changes in body composition and a significant decrease in 5 m sprint, the 505 test, CMJ, and MBT.
Assessing Carbon Emissions from Animal Husbandry in China: Trends, Regional Variations and Mitigation Strategies
Authors: Peng Cheng , Wang Xiaona , Xiong Xin , Wang Yaxing
Published: March 2024

The intensification of land use and the accelerated integration of three industries (agricultural production, agricultural product processing industry, and agricultural product market service industry) in China have resulted in the continuous expansion of animal husbandry and its industrial chain. This phenomenon has led to a rise in greenhouse gas (GHG) emissions from livestock farming, intensifying climate change and placing strain on worldwide environmental conservation efforts. Life cycle assessment (LCA) was utilized to evaluate carbon emissions from China’s animal husbandry sector from 2012 to 2021. Additionally, the logarithmic mean divisia index (LMDI) decomposition method was employed to examine and elucidate the influential impacts of five factors on carbon emissions. These factors included the efficiency of animal husbandry production, the structure of the agricultural industry, per capita agricultural production income, urbanization, and the total population. The results reveal the following: (1) From 2012 to 2021, China’s animal husbandry sector witnessed a marginal increase in cumulative carbon emissions from 287.74 million tons to 294.73 million tons, with an annual growth rate of 0.42%. (2) Emission contributions were categorized as follows: the production stage (149.61 million tons), the transportation stage (145.07 million tons), and the processing stage (0.05 million tons). (3) The primary factor contributing to the rise in carbon emissions from animal husbandry from 2012 to 2021 was the per capita agricultural production income factor (A3), alongside a notable impact from the total population factor (A5).
Early Warning Systems for World Energy Crises
Authors: Yokuş Turgut
Published: March 2024

Different severe energy crisis episodes have occurred in the world in the last five decades. Energy crises lead to the deterioration of international relations, economic crises, changes in monetary systems, and social problems in countries. This paper aims to show the essential determinants of energy crises by developing a binary logit model that estimates the predictive ability of thirteen indicators in a sample that covers the period from January 1973 to December 2022. The empirical results show that the energy crises are mainly due to energy supply–demand imbalances (petroleum stocks, fossil energy production–consumption imbalances, and changes in energy imports by countries), energy investments (oil and natural gas drilling activities), economic and financial disruptions (inflation, dollar indices, and indices of global real economic activity) and geopolitical risks. Additionally, the model is capable of accurately predicting world energy crisis events with a 99% probability.
Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends
Authors: Wang Jun , Li Mao , Skitmore Martin , Chen Jianli
Published: March 2024

The construction industry is infamous for its high insolvent failure rate because construction projects require complex processes, heavy investment, and long durations. However, there is a lack of a comprehensive framework and a requirement for such a framework in predicting the financial distress of construction firms. This paper reviews relevant literature to summarize the existing knowledge, identify current problems, and point out future research directions needed in this area using a scientometric analysis approach. Based on a total of 93 journal articles relating to predicting construction company failure extracted from multiple databases, this study conducts a holistic review in terms of chronological trends, journal sources, active researchers, frequent keywords, and most cited documents. Qualitative analysis is also provided to explore the data collection and processing procedures, model selection and development process, and detailed performance evaluation metrics. Four research gaps and future directions for predicting construction company failure are presented: selecting a broader data sample, incorporating more heterogeneous variables, balancing model predictability and interpretability, and quantifying the causality and intercorrelation of variables. This study provides a big picture of existing research on predicting construction company insolvent failure and presents outcomes that can help researchers to comprehend relevant literature, directing research policy-makers and editorial boards to adopt the promising themes for further research and development.
Design of Non-Structural Practices for Sustainable Water Quality Improvement in an Urban River: A Case Study of South Korea
Authors: Kang Taesung , Yu Nayeong , Shin Minhwan , Na Kyoungsoo , Lim Kyoung Jae , Kim Jonggun
Published: March 2024

Urban rivers exhibit characteristics of low flow and significant water quality fluctuations, making them susceptible to pollution from various sources such as untreated sewage, non-point pollution within the urban area, and unknown inflows. To address water quality management in urban rivers, precise investigations into background water quality, pollution levels, and the characteristics of pollution sources are essential. Following the identification of pollution sources, sustainable river management strategies, incorporating both structural and non-structural measures, are crucial. This study aims to develop continuous and long-term river management strategies, considering the characteristics of urban river basins, through citizen participation governance and non-structural approaches. Citizen networks were formed for each target urban river, and activities for water quality improvement were proposed and implemented. This study provides phased approaches to citizen participation governance, and activities include citizen-led water quality monitoring, the purification and monitoring of riverbank pollution sources, and water-related education. It emphasizes the importance of local residents’ interest in urban river water quality improvement and underscores the need for sustained activities through local citizen networks. Additionally, active participation and investments from the local government, government agencies, and various experts are deemed essential.
Unveiling the Coupling Coordination and Interaction Mechanism between the Local Heat Island Effect and Urban Resilience in China
Authors: Wang Huihui , Yang Yunsong , Liu Suru , Xue Hanyu , Xu Tingting , He Wanlin , Gao Xiaoyong , Jiang Ruifeng
Published: March 2024

Climate change and urbanization have led to the increasing prominence of urban heat islands (UHIs) today, posing a huge challenge to cities. Urban resilience (UR) refers to the ability of a city or region to adapt to changes and risks. However, the influence between the heat island effect and regional urban resilience is not well understood. In this study, we proposed a methodological framework for unveiling the coupling coordination and interaction mechanism between UHIs and UR. This study first explored UHIs in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and described the creation of a multidimensional index system that evaluates urban resilience across social, economic, ecological, and engineering dimensions. Furthermore, this study unveiled the coupling coordination effect of UHIs and UR through the coupling coordination degree model, and the influence mechanism between the drivers of UHIs and the change in UR was detected using a geographic probe. The results showed that the UHI region forms a ring-shaped belt around the entrance to the Pearl River Delta. The UHIs of the GBA show a significant trend of expansion and escalation over time. The UR of the GBA shows a spatial distribution pattern of high resilience among regional central cities and low resilience among peripheral cities, with significantly uneven development in sub-resilience dimensions. The UHIs and UR of the GBA showed a certain coupling and coordination effect, improving from barely synergistic to a primary coordination state. Among the drivers of UHIs, population density, precipitation, average nighttime light brightness, and ground-average CO2 emissions have strong explanatory power for the spatial variation in UR. The interaction between two factors has a stronger influence on UR than individual factors. The purpose of this paper is to initially reveal the influence mechanism between UHIs and UR and to provide a theoretical basis for further exploring the path of sustainable urban development.
Green Technology Innovation and Enterprise Performance: An Analysis Based on Causal Machine Learning Models
Authors: Huang Xuanai , Wang Yaozhong , Chen Ying , Hu Zunguo
Published: March 2024

As increasingly stringent environmental regulations are put into effect, Environmental, Social, and Governance (ESG) concepts are being seamlessly integrated into the core of corporate innovation strategies. Due to the quasi-public product perspective of green innovation, the performance of enterprises as a result of green innovation activities exhibits significant heterogeneity. This heterogeneity exists not only between corporate value and financial performance but also among individual enterprises. This paper is based on a sample of 1510 listed Chinese companies examined from 2013 to 2020 and uses machine learning algorithms and quasi-natural experiments to precisely estimate the causal relationship and mechanisms between green innovation and corporate performance. The findings elucidate several critical aspects of green innovation within the corporate sphere: Firstly, rather than attracting green incentives from financial markets, green innovation activities inadvertently stifle the enhancement of corporate value. Secondly, these activities markedly bolster corporate financial performance, primarily by diminishing operational costs, which in turn elevates the return on assets (ROA). Lastly, of all corporate characteristics examined, enterprise size and equity concentration stand out as key determinants influencing the variability in outcomes of green innovation performance. The above findings provide information on the significant implications of enhancing green technology innovation systems and green incentive mechanisms.
Analysis and Simulation of Land Use Changes and Their Impact on Carbon Stocks in the Haihe River Basin by Combining LSTM with the InVEST Model
Authors: Lin Yanzhen , Chen Lei , Ma Ying , Yang Tingting
Published: March 2024

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data in the Haihe River Basin from 2000 to 2020 was studied via Mann–Kendall (MK) trend analysis, the transfer matrix, and land use dynamic attitude. Through integrating the models of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and the Long Short-Term Memory (LSTM), the results of the spatial distribution of land use and carbon stock were obtained and compared with Cellular Automation (CA-Markov), and then applied to predict the spatial distribution in 2025. The results show the following: (1) The land use and land cover (LULC) changes in the Haihe River Basin primarily involve an exchange between cultivated land, forest, and grassland, as well as the conversion of cultivated land to built-up land. This transformation contributes to the overall decrease in carbon storage in the basin, which declined by approximately 1.20% from 2000 to 2020. (2) The LULC prediction accuracy of LSTM is nearly 2.00% higher than that of CA-Markov, reaching 95.01%. (3) In 2025, the area of grassland in Haihe River Basin will increase the most, while the area of cultivated land will decrease the most. The spatial distribution of carbon stocks is higher in the northwest and lower in the southeast, and the changing areas are scattered throughout the study area. However, due to the substantial growth of grassland and forest, the carbon stocks in the Haihe River Basin in 2025 will increase by about 10 times compared with 2020. The research results can provide a theoretical basis and reference for watershed land use planning, ecological restoration, and management.
Earthquake Resilient near Zero Energy Buildings: Attributes and Perspectives
Authors: Anastasiadis Anthimos , Mosoarca Marius
Published: March 2024

The climate crisis, the need for a circular economy, and the large financial losses after earthquakes have promoted the concept of the sustainable and resilient design of societies, and more specifically, of lifelines and building environments. Focused on building facilities, it is imperative to prescribe, within the aforementioned framework, the components that characterize earthquake resilient near zero energy buildings (ERnZEBs). Through a conceptual analysis, the goal is to discuss the attributes and perspectives of ERnZEBs within the framework of the view of a designer engaged in practice. This fact introduces an additional factor recognizing that not all projects have the same technical and financial values; the difference in budget, the type of owner, and the investment (private or public, company or private person) play important roles in creating an ERnZE building. In this direction, this paper reviews the basic principles of ERnZEBs, providing a combination of pragmatic considerations while also exploiting the state of the art and practice of current engineering knowledge.
Sustainable Approach to Metal Coin Canceling Methods, Using 3D Modeling and Finite Element Method Analysis
Authors: Gavrilă Cornel Cătălin , Lateş Mihai Tiberiu , Grebenişan Gavril
Published: March 2024

Over time, many minted coins were withdrawn from circulation, being replaced with new ones. The returned, obsolete metal coins were melted in order to ensure sustainable reuse of the alloy for other purposes. Between the withdrawal and melting, some of the metal coins were canceled by the destruction of their original shape and dimensions using adequate tools. The first part of this paper is focused on presenting some insights into the canceling method used on old Romanian nickel coins; also, some examples are presented. The introduction also includes a literature review in the field of coin manufacturing, covering subjects such as metal behavior under striking load and aspects of 3D modeling and FEM analysis as well as explaining some striking errors. The main purpose of this paper is to study the particularities of canceling methods applied to coins, which is conducted on relatively valuable collection metal pieces. In the second part of the paper, an adequate 3D model is computed for the canceling dies and the coin. Then, the assembled models are introduced, corresponding to each canceling case, consisting of the obverse and reverse canceling dies with coins inside them. For each model, the finite element analysis is realized and is achieved for different initial conditions. The final part of the paper presents the analysis results as well as the discussion and conclusions.
Sustainable Geopolymer Structural Insulation Panels Obtained with the Addition of Power Plant Ash and Furnace Slag with Potential Uses in the Fabrication of Specialized Structures
Authors: Ciobanu Romeo C. , Schreiner Cristina , Caramitu Alina R. , Ion Ioana
Published: March 2024

The paper describes the process of obtaining geopolymer composites using raw materials from critical waste, i.e., mixed power plant ash and furnace slag powder. Using such geopolymer composites, structural insulation panels were made in the laboratory, which were subjected to tests specific to construction applications. At the same time, some special properties, such as sound insulation and electromagnetic shielding properties for special applications, were tested. The results obtained from the functional tests led to the conclusion that the panels made of geopolymer composites provided both sound and electromagnetic attenuation values clearly superior to those obtained from autoclaved cellular concrete, brick, or concrete structures, which encourages us to suggest such material concepts for complex shielding purposes. The sustainability of the technology for producing such geopolymer composites was fully demonstrated from the economic, environmental, and social perspectives.
Applications of the Symmetrical Structures of Cassini Ovals
Authors: Grabusts Peter , Uzhga-Rebrov Oleg
Published: March 2024

One of the geometric figures that has symmetry properties is the Cassini oval. The Cassini oval is a curve defined as the locus of points in the plane such that the product of the distances to two fixed points is constant. Cassini ovals are named after the astronomer Giovanni Domenico Cassini, who studied them in 1680. Today, the geometric properties of Cassini ovals are used in many fields: analytical geometry, nuclear physics, radiolocation, and industrial applications. The bistatic radar uses Cassini ovals to detect various targets in radiolocation. Until now, there have been no studies on the clustering capabilities of Cassini ovals. As a novelty, it is hypothesized that clustering possibilities could be used for Cassini ovals. In this article, a study of the capabilities of Cassini ovals in radiolocation was carried out, and their suitability for clustering purposes was shown.
A 3C Authentication: A Cross-Domain, Certificateless, and Consortium-Blockchain-Based Authentication Method for Vehicle-to-Grid Networks in a Smart Grid
Authors: Miao Qianhao , Ren Tianyu , Dong Jiahan , Chen Yanjiao , Xu Wenyuan
Published: March 2024

As an important component of the smart grid, vehicle-to-grid (V2G) networks can deliver diverse auxiliary services and enhance the overall resilience of electrical power systems. However, V2G networks face two main challenges due to a large number of devices that connect to it. First, V2G networks suffer from serious security threats, such as doubtful authenticity and privacy leakage. Second, the efficiency will decrease significantly due to the massive requirements of authentication. To tackle these problems, this paper proposes a cross-domain authentication scheme for V2G networks based on consortium blockchain and certificateless signature technology. Featuring decentralized, open, and transparent transactions that cannot be tampered with, this scheme achieves good performance on both security and efficiency, which proves to be suitable for V2G scenarios in the smart grid.