bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Aquifer System and Groundwater Storage Change Due to South-to-North Water Diversion Project at Huairou Groundwater Reserve Site, Beijing, China, Using Geodetic and Hydrological Data
Authors: Li Mingjia , Sun Jianbao , Xue Lian , Shen Zhengkang , Zhao Bin , Hu Leyin
Published: July 2022

Groundwater overexploitation is a critical issue in the North China Plain (NCP), resulting in groundwater level decline and surface subsidence for the last half-century. This problem, however, has been greatly alleviated by the South-to-North Water Diversion (SNWD) Project since 2015. Monitoring of this process has been steadily improved in recent years using water level and geodetic observations. Here, we characterize the water storage change at the Huairou groundwater reserve site (HGRS) in Beijing due to the SNWD by combining Interferometric Synthetic Aperture Radar (InSAR) data of the Sentinel-1 satellites, continuous Global Positioning System (GPS) data, and well water level data observed during the same time. InSAR observations revealed subsidence up to ~400 mm in the Beijing plain but uplift at ~40 mm in the HGRS during 2015–2019, and more than 70% of the uplift occurred from October 2018 to January 2019. By integrating the most significant uplift deformation during October 2018 to January 2019 with water level observations at the same time, we estimated the storativity of the confined aquifer system at HGRS as 1.68–7.82×10−3, weighing in the correction for effective stress and surface deformation for various situations. Based on the estimated aquifer storativity and the observed water level change in the unconfined and confined aquifer, the recharged water storage for the confined and unconfined aquifers was estimated as 1.20–1.39×107 m3 and ~2.86×108 m3 from 6 October 2018 to 22 January 2019, respectively, which is about 4% and 91% of the surface water recharge through river channels in the same period due to the SNWD Project. Our study demonstrates that integration of geodetic and hydrological data can provide crucial information for the assessment of groundwater circulation and assistance of groundwater management.
Assessment of the Water Vapor Tomography Based on Four Navigation Satellite Systems and Their Various Combinations
Authors: Yang Fei , Wang Jinyang , Wang Hongsen , Gong Xu , Wang Lei , Huang Bin
Published: July 2022

With the gradual improvement of Galileo and the opening of BDS-3 services, water vapor tomography based on multi-GNSS can be effectively carried out to reconstruct three-dimensional water vapor distribution. In this paper, experiments in Hong Kong were conducted to analyze and assess the performances of GPS, BDS, GLONASS, and Galileo and their combinations in water vapor tomography. Numerical results show that the number of available signal rays varies widely in the four satellite systems, and the value can be increased by the combination of satellite systems; the combinations also increase the number of voxels crossed by signal rays, but this value is not directly related to the number of available signal rays; the number and distribution of the voxels with sufficient signal rays, which most closely related to the structure of the tomographic model, show no obvious differences in the four satellite systems and their combinations. Comparative results of slant water vapor (SWV) estimated by GNSS data and water vapor density derived from radiosonde data reveal that the differences in the water vapor tomography of the four satellite systems are small, and their combinations have limited improvement in the tomographic results.
Reactive Control for Collision Evasion with Extended Obstacles
Authors: Kim Jonghoek
Published: July 2022

Evading collisions in three-dimensional underwater environments is critical in exploration of an Autonomous Underwater Vehicle (AUV). In underwater environments, AUV measures an obstacle surface by utilizing a three-dimensional active sonar. This article addresses reactive collision evasion control by considering extended obstacles. Here, an extended obstacle is an arbitrary obstacle that can generate any number of measurements and not a point target generating at most one measurement. Considering 3D environments, our manuscript considers collision evasion with both moving obstacles and static obstacles. The proposed reactive collision evasion controllers are developed by considering hardware limits, such as the maximum speed or acceleration limit of an AUV. We further address how to make an AUV move towards a goal, while avoiding collision with extended obstacles. As far as we know, the proposed collision evasion controllers are novel in handling collision avoidance with an extended obstacle, in the case where an AUV measures 3D-obstacle boundaries by utilizing sonar sensors. The effectiveness of the proposed controllers is demonstrated by MATLAB simulations.
A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar
Authors: Zhang Shuguang , Wang Tong , Liu Cheng , Wang Degen
Published: July 2022

Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations.
Development of the Measurement of Lateral Electron Density (MOLE) Probe Applicable to Low-Pressure Plasma Diagnostics
Authors: Kim Si-jun , Lee Sang-ho , You Ye-bin , Lee Young-seok , Seong In-ho , Cho Chul-hee , Lee Jang-jae , You Shin-jae
Published: July 2022

As the importance of measuring electron density has become more significant in the material fabrication industry, various related plasma monitoring tools have been introduced. In this paper, the development of a microwave probe, called the measurement of lateral electron density (MOLE) probe, is reported. The basic properties of the MOLE probe are analyzed via three-dimensional electromagnetic wave simulation, with simulation results showing that the probe estimates electron density by measuring the surface wave resonance frequency from the reflection microwave frequency spectrum (S11). Furthermore, an experimental demonstration on a chamber wall measuring lateral electron density is conducted by comparing the developed probe with the cutoff probe, a precise electron density measurement tool. Based on both simulation and experiment results, the MOLE probe is shown to be a useful instrument to monitor lateral electron density.
Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal
Authors: An Xiang , Liu Yanzhong , Zhao Yixin , Lu Sichao , Stylios George K. , Liu Qiang
Published: July 2022

Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal.
Bed-Exit Behavior Recognition for Real-Time Images within Limited Range
Authors: Lin Cheng-Jian , Wei Ta-Sen , Liu Peng-Ta , Chen Bing-Hong , Shih Chi-Huang
Published: July 2022

In the context of behavior recognition, the emerging bed-exit monitoring system demands a rapid deployment in the ward to support mobility and personalization. Mobility means the system can be installed and removed as required without construction; personalization indicates human body tracking is limited to the bed region so that only the target is monitored. To satisfy the above-mentioned requirements, the behavior recognition system aims to: (1) operate in a small-size device, typically an embedded system; (2) process a series of images with narrow fields of view (NFV) to detect bed-related behaviors. In general, wide-range images are preferred to obtain a good recognition performance for diverse behaviors, while NFV images are used with abrupt activities and therefore fit single-purpose applications. This paper develops an NFV-based behavior recognition system with low complexity to realize a bed-exit monitoring application on embedded systems. To achieve effectiveness and low complexity, a queueing-based behavior classification is proposed to keep memories of object tracking information and a specific behavior can be identified from continuous object movement. The experimental results show that the developed system can recognize three bed behaviors, namely off bed, on bed and return, for NFV images with accuracy rates of 95~100%.
Efficient Multi-Scale Stereo-Matching Network Using Adaptive Cost Volume Filtering
Authors: Jeon Suyeon , Heo Yong Seok
Published: July 2022

While recent deep learning-based stereo-matching networks have shown outstanding advances, there are still some unsolved challenges. First, most state-of-the-art stereo models employ 3D convolutions for 4D cost volume aggregation, which limit the deployment of networks for resource-limited mobile environments owing to heavy consumption of computation and memory. Although there are some efficient networks, most of them still require a heavy computational cost to incorporate them to mobile computing devices in real-time. Second, most stereo networks indirectly supervise cost volumes through disparity regression loss by using the softargmax function. This causes problems in ambiguous regions, such as the boundaries of objects, because there are many possibilities for unreasonable cost distributions which result in overfitting problem. A few works deal with this problem by generating artificial cost distribution using only the ground truth disparity value that is insufficient to fully regularize the cost volume. To address these problems, we first propose an efficient multi-scale sequential feature fusion network (MSFFNet). Specifically, we connect multi-scale SFF modules in parallel with a cross-scale fusion function to generate a set of cost volumes with different scales. These cost volumes are then effectively combined using the proposed interlaced concatenation method. Second, we propose an adaptive cost-volume-filtering (ACVF) loss function that directly supervises our estimated cost volume. The proposed ACVF loss directly adds constraints to the cost volume using the probability distribution generated from the ground truth disparity map and that estimated from the teacher network which achieves higher accuracy. Results of several experiments using representative datasets for stereo matching show that our proposed method is more efficient than previous methods. Our network architecture consumes fewer parameters and generates reasonable disparity maps with faster speed compared with the existing state-of-the art stereo models. Concretely, our network achieves 1.01 EPE with runtime of 42 ms, 2.92 M parameters, and 97.96 G FLOPs on the Scene Flow test set. Compared with PSMNet, our method is 89% faster and 7% more accurate with 45% fewer parameters.
Reliability-Based Large-Vocabulary Audio-Visual Speech Recognition
Authors: Yu Wentao , Zeiler Steffen , Kolossa Dorothea
Published: July 2022

Audio-visual speech recognition (AVSR) can significantly improve performance over audio-only recognition for small or medium vocabularies. However, current AVSR, whether hybrid or end-to-end (E2E), still does not appear to make optimal use of this secondary information stream as the performance is still clearly diminished in noisy conditions for large-vocabulary systems. We, therefore, propose a new fusion architecture—the decision fusion net (DFN). A broad range of time-variant reliability measures are used as an auxiliary input to improve performance. The DFN is used in both hybrid and E2E models. Our experiments on two large-vocabulary datasets, the Lip Reading Sentences 2 and 3 (LRS2 and LRS3) corpora, show highly significant improvements in performance over previous AVSR systems for large-vocabulary datasets. The hybrid model with the proposed DFN integration component even outperforms oracle dynamic stream-weighting, which is considered to be the theoretical upper bound for conventional dynamic stream-weighting approaches. Compared to the hybrid audio-only model, the proposed DFN achieves a relative word-error-rate reduction of 51% on average, while the E2E-DFN model, with its more competitive audio-only baseline system, achieves a relative word error rate reduction of 43%, both showing the efficacy of our proposed fusion architecture.
Quartz-Enhanced Photothermal Spectroscopy-Based Methane Detection in an Anti-Resonant Hollow-Core Fiber
Authors: Bojęś Piotr , Pokryszka Piotr , Jaworski Piotr , Yu Fei , Wu Dakun , Krzempek Karol
Published: July 2022

In this paper, the combination of using an anti-resonant hollow-core fiber (ARHCF), working as a gas absorption cell, and an inexpensive, commercially available watch quartz tuning fork (QTF), acting as a detector in the quartz-enhanced photothermal spectroscopy (QEPTS) sensor configuration is demonstrated. The proof-of-concept experiment involved the detection of methane (CH4) at 1651 nm (6057 cm−1). The advantage of the high QTF Q-factor combined with a specially designed low-noise amplifier and additional wavelength modulation spectroscopy with the second harmonic (2f-WMS) method of signal analysis, resulted in achieving a normalized noise-equivalent absorption (NNEA) at the level of 1.34 × 10−10 and 2.04 × 10−11 W cm−1 Hz−1/2 for 1 and 100 s of integration time, respectively. Results obtained in that relatively non-complex sensor setup show great potential for further development of cost-optimized and miniaturized gas detectors, taking advantage of the combination of ARHCF-based absorption cells and QTF-aided spectroscopic signal retrieval methods.
A Secrecy Transmission Protocol with Energy Harvesting for Federated Learning
Authors: Xie Ping , Li Fan , You Ilsun , Xing Ling , Wu Honghai , Ma Huahong
Published: July 2022

In federated learning (FL), model parameters of deep learning are communicated between clients and the central server. To better train deep learning models, the spectrum resource and transmission security need to be guaranteed. Toward this end, we propose a secrecy transmission protocol based on energy harvesting and jammer selection for FL, in which the secondary transmitters can harvest energy from the primary source. Specifically, a secondary transmitter STi is first selected, which can offer the best transmission performance for the secondary users to access the primary frequency spectrum. Then, another secondary transmitter STn, which has the best channel for eavesdropping, is also chosen as a friendly jammer to provide secrecy service. Furthermore, we use outage probability (OP) and intercept probability (IP) as metrics to evaluate performance. Meanwhile, we also derive closed-form expressions of OP and IP of primary users and OP of secondary users for the proposed protocol, respectively. We also conduct a theoretical analysis of the optimal secondary transmission selection (OSTS) protocol. Finally, the performance of the proposed protocol is validated through numerical experiments. The results show that the secrecy performance of the proposed protocol is better than the OSTS and OCJS, respectively.
Multidimensional Latent Semantic Networks for Text Humor Recognition
Authors: Xiong Siqi , Wang Rongbo , Huang Xiaoxi , Chen Zhiqun
Published: July 2022

Humor is a special human expression style, an important “lubricant” for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; “discourse understanding” is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.
Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images
Authors: Brkić Ivan , Miler Mario , Ševrović Marko , Medak Damir
Published: July 2022

The United Nations (UN) stated that all new roads and 75% of travel time on roads must be 3+ star standard by 2030. The number of stars is determined by the International Road Assessment Program (iRAP) star rating module. It is based on 64 attributes for each road. In this paper, a framework for highly accurate and fully automatic determination of two attributes is proposed: roadside severity-object and roadside severity-distance. The framework integrates mobile Lidar point clouds with deep learning-based object detection on road cross-section images. The You Only Look Once (YOLO) network was used for object detection. Lidar data were collected by vehicle-mounted mobile Lidar for all Croatian highways. Point clouds were collected in .las format and cropped to 10 m-long segments align vehicle path. To determine both attributes, it was necessary to detect the road with high accuracy, then roadside severity-distance was determined with respect to the edge of the detected road. Each segment is finally classified into one of 13 roadside severity object classes and one of four roadside severity-distance classes. The overall accuracy of the roadside severity-object classification is 85.1%, while for the distance attribute it is 85.6%. The best average precision is achieved for safety barrier concrete class (0.98), while the worst AP is achieved for rockface class (0.72).
A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering
Authors: Liu Qi , Zhou Bin , Zhang Jianyong , Cheng Ruixue , Zhao Xuhao , Zhao Rong , Dai Minglu , Wang Bubin , Wang Yihong
Published: July 2022

Accurate ranging and real-time temperature monitoring are essential for metrology and safety in electrical conduit applications. This paper proposes an acoustic time-of-flight (TOF) estimation method based on the digital lock-in filtering (DLF) technique for conduit ranging and thermometry. The method establishes the relationship between the frequency and the time domain by applying a linear frequency modulated Chirp signal as the sound source and using the DLF technique to extract the first harmonic of the characteristic frequencies of the transmitted and received signals. Acoustic TOF estimation in the conduit is then achieved by calculating the mathematical expectation of the time difference between each characteristic frequency in the time-frequency relationship of the two signals. The experimental results with enhanced noise interference on different conduit lengths and various temperature conditions, proved that the proposed DLF method can establish a robust linear time-frequency relationship according to the characteristics of the Chirp signal, and the measurement accuracy of TOF has also been confirmed. Compared to the conventional method, the DLF method provides the lowest absolute error and standard deviation for both distance and temperature measurements with an enhanced robustness.
BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images
Authors: Atteia Ghada , Alhussan Amel , Samee Nagwan
Published: July 2022

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.
Multi-Perspective Representation to Part-Based Graph for Group Activity Recognition
Authors: Wu Lifang , Lang Xianglong , Xiang Ye , Wang Qi , Tian Meng
Published: July 2022

Group activity recognition that infers the activity of a group of people is a challenging task and has received a great deal of interest in recent years. Different from individual action recognition, group activity recognition needs to model not only the visual cues of individuals but also the relationships between them. The existing approaches inferred relations based on the holistic features of the individual. However, parts of the human body, such as the head, hands, legs, and their relationships, are the critical cues in most group activities. In this paper, we establish the part-based graphs from different viewpoints. The intra-actor part graph is designed to model the spatial relations of different parts for an individual, and the inter-actor part graph is proposed to explore part-level relations among actors, in which visual relation and location relation are both considered. Furthermore, a two-branch framework is utilized to capture the static spatial and dynamic temporal representations simultaneously. On the Volleyball Dataset, our approach obtains a classification accuracy of 94.8%, achieving very competitive performance in comparison with the state of the art. As for the Collective Activity Dataset, our approach improves the accuracy by 0.3% compared with the state-of-the-art results.
Dual-Band Branch-Line Coupler Based on Crossed Lines for Arbitrary Power-Split Ratios
Authors: Chang Hyungjun , Lim Taejun , Dimitrov Kristian Chavdarov , Lee Yongshik
Published: July 2022

Dual-band branch-line couplers with arbitrary power-split ratios are presented. The use of crossed lines at the center of the dual-band coupler enables it to independently provide different power-split ratios to the two bands. Additionally, open stubs are utilized to enhance the stopband responses. The complete design procedure with example design curves is provided. For experimental verification, three dual-band couplers with power-split ratio combinations of +3 dB (S21:S31=2:1) and −3 dB (S21:S31=1:2), −3 dB and +3 dB, and 0 dB (S21:S31=1:1) and +13 dB (S21:S31=20:1) at 1 GHz and 2.5 GHz were designed and fabricated. The measured results are in excellent agreement with the ideal and full-wave simulated results. The measured difference of −13.3 dB between the power-split ratios of the two bands is the largest reported for a dual-band branch-line coupler.
Application of Chromatographic Technology to Determine Aromatic Substances in Tobacco during Natural Fermentation: A Review
Authors: Li Xuefeng , Bin Jun , Yan Xiufang , Ding Mengjiao , Yang Min
Published: July 2022

Flavor is an important index to evaluate the sensory quality of tobacco. The process of fermentation is a key step in the production of aromatic substances in tobacco leaves and an important factor in improving their quality. Worldwide, reams of research show that chromatographic technology plays an irreplaceable role in the tobacco aromatic chemistry. Nevertheless, the degradation mechanism of latent aromatic compounds and the formation mechanism of characteristic aromatic substances have not been fully and systematically elucidated. In this study, the latest progress of basic methods, techniques, and research results of the separation, analysis, and identification of aromatic substances in fermented tobacco leaves were reviewed, and the next research and application directions were prospected. It is expected to provide theoretical reference for the study of molecular mechanism of tobacco flavor, reveal the degradation mechanism of potential aroma compounds, and help improve the quality of tobacco.
Release of Selected Non-Intentionally Added Substances (NIAS) from PET Food Contact Materials: A New Online SPE-UHPLC-MS/MS Multiresidue Method
Authors: Aigotti Riccardo , Giannone Nicola , Asteggiano Alberto , Mecarelli Enrica , Dal Bello Federica , Medana Claudio
Published: July 2022

Food contact materials (FCMs) are an underestimated source of food chemical contaminants and a potentially relevant route of human exposure to chemicals that are harmful to the endocrine system. Foods and water are the main sources of exposure due to contact with the packaging materials, often of polymeric nature. European Regulation 10/2011 requires migration tests on FCMs and foodstuffs to evaluate the presence of listed substances (authorized monomers and additives) and non-intentionally added substances (NIAS) not listed in the regulation and not subjected to restrictions. The tests are required to ensure the compliance of packaging materials for the contained foods. NIAS are a heterogeneous group of substances classified with a potential estrogenic or androgenic activity. Subsequently, the evaluation of the presence of these molecules in foods and water is significant. Here we present an online SPE/UHPLC-tandem MS method to quantify trace levels of NIAS in food simulants (A: aqueous 3% acetic acid; B: aqueous 20% ethanol) contained in PET preformed bottles. The use of online SPE reduces systemic errors thanks to the automation of the technique. For the developed analytical method, we evaluate the limit of detection (LOD), the limit of quantitation (LOQ), selectivity, RSD% and BIAS% for LLOQ for a total of twelve NIAS, including monomers, antioxidants, UV-filters and additives. LOD ranged between 0.002 µg/L for bisphenol S and 13.6 µg/L for 2,6-di-tert-butyl-4-methylphenol (BHT). LOQs are comprised between 0.01 µg/L for bisphenol S and 42.2 µg/L for BHT. The online-SPE/UHPLC-tandem MS method is applied to the food simulants contained in several types of PET packaging materials to evaluate the migration of the selected NIAS. The results show the presence (µg/L) of NIAS in the tested samples, underlining the need for a new regulation for these potentially toxic molecules.
Policing Sex Trafficking in the ‘Virtual Red-Light District’: A Research Note
Authors: L’Hoiry Xavier , Moretti Alessandro , Antonopoulos Georgios A.
Published: July 2022

Working alongside a police force in the United Kingdom, the study briefly presented in this note, sought to create an analytical tool (the Sexual Trafficking Identification Matrix—STIM) with which policing actors could distinguish between Adult Services Website (ASW) profiles posted by independent sex workers and profiles created by traffickers. Distinguishing between these will potentially allow law enforcement agencies to prioritise their investigatory efforts more efficiently.
Effects of Coarse Woody Debris on Soil Temperature and Water Content in Two Reconstructed Soils in Reclaimed Boreal Forest
Authors: Dhar Amalesh , Forsch Katryna B. C. , Naeth M. Anne
Published: July 2022

Coarse woody debris (CWD) and cover soils are used to expedite ecological processes in reclaimed boreal forests after oil sands mining. Soil water content and soil temperature are considered key factors for revegetation during mine reclamation as they impact soil surface and atmosphere interactions and plant growth. However, the effects of CWD and cover soils on soil water content and temperature are not well studied. This study assessed the impact of CWD size (large, small) and type (spruce: Picea mariana, aspen: Populus tremuloides) on soil water content and temperature in two soils constructed with forest floor-mineral mix (FMM) and peat-mineral mix (PMM)) at oil sands reclamation sites. Annual and summer precipitation showed year-to-year variability; mean air temperature did not. Soil cover type had a greater impact on moderating soil water content than CWD, with PMM having a stronger influence on water content and temperature than FMM. Adding CWD increased soil water content more in FMM than PMM, and the effect on soil temperature was mostly observed during the summer months. In PMM, spruce small CWD was associated with greater water content, whereas there was no distinct differentiation between CWD size and type in FFM. This study suggests application of CWD in FMM would be more beneficial than in PMM for reclamation.
Stability Analysis and Design of Variable Step-Size P&O Algorithm Based on Fuzzy Robust Tracking of MPPT for Standalone/Grid Connected Power System
Authors: Sibtain Daud , Gulzar Muhammad Majid , Shahid Kamal , Javed Imran , Murawwat Sadia , Hussain Muhammad Majid
Published: July 2022

This research aims to design a modified P&O algorithm for the efficient tracking of maximum power point (MPPT) for standalone and grid-connected systems. The proposed research work modifies the P&O algorithm for the dc-dc converter where the fixed step size P&O algorithm is translated into variable step size with the help of ant colony optimization (ACO) to generate optimal parameters for the PID controller to generate a variable step size in the P&O algorithm. This variable step size is dependent upon the error that is the difference between the generated power and desired power. By doing this it improves the efficiency of the P&O algorithm and its limitations are overcome. Furthermore, the PV is extended to connect with a grid where the inverter is controlled by a fuzzy logic controller (FLC) so that the combined structure of variable P&O and fuzzy helps to achieve MPP efficiently. The robustness of the proposed work is compared with other state-of-the-art controllers to justify the effectiveness of the proposed work. Finally, a stability test of the system is carried out to verify the overall stability of the power system.
Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network
Authors: Liu Enci , Li Jie , Zheng Anni , Liu Haoran , Jiang Tao
Published: July 2022

As the mobile Internet improves by leaps and bounds, the model of traditional offline used car trading has gradually lost the ability to live up to the needs of consumers, and online used car trading platforms have emerged as the times require. Second-hand car price assessment is the premise of second-hand car trading, and a reasonable price can reflect the objective, fair, and true nature of the second-hand car market. In order to standardize the evaluation standards of used car prices and improve the accuracy of used car price forecasts, the linear correlation between vehicle parameters, vehicle conditions, and transaction factors and used car price was comprehensively investigated, grey relational analysis was applied to filter the feature variables of factors affecting used car price, the traditional BP neural network was also optimized by combining the particle swarm optimization algorithm, and a used car price prediction method based on PSO-GRA-BPNN was proposed. The results show that only the correlation coefficient of new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation. The correlation between new car price, displacement, mileage, gearbox type, fuel consumption, and registration time on used car prices is greater than 0.7, and the impact of other indicators on used car prices is negligible. Compared with the traditional BPNN model and the multiple linear regression, random forest, and support vector machine regression models proposed by other researchers, the MAPE of the PSO-GRA-BPNN model proposed in this paper is 3.936%, which is 30.041% smaller than the error of the other three models. The MAE of the PSO-GRA-BPNN model is 0.475, which is a maximum reduction of 0.622 compared to the other three models. R can reach up to 0.998, and R2 can reach 0.984. Although the longest training time is 94.153 s, the overall prediction effect is significantly better than other used car price prediction models, providing a new idea and method for used car evaluation.
Environmental Regulation, Urban-Rural Income Gap and Agricultural Green Total Factor Productivity
Authors: Ma Guoqun , Lv Danyang , Luo Yuxi , Jiang Tuanbiao
Published: July 2022

Environmental regulation is the basis for achieving green agricultural development, and urban-rural integration is the key to optimizing the allocation of agricultural elements and achieving sustainable agricultural development. This paper aims to investigate the spatial spillover effect of environmental regulation on China’s agricultural green total factor productivity (AGTFP) and examine the mediating effect of the urban-rural income gap. Both the Super-SBM-DEA model and the Global Malmquist–Luenberger productivity index are used to account for the AGTFP of China’s 30 provinces, and the spatial Durbin model and the mediating effect model are used to analyze the impact of environmental regulation. We found that firstly, during the sample period, China’s AGTFP has increased with an average annual growth rate of 3.27%, which is mainly promoted by agricultural green technology progress (AGTC). Secondly, both the direct and spatial effects of environmental regulation on AGTFP show a significant “U”-shaped feature and have regional heterogeneity based on differences in economic development levels and factor endowments. Thirdly, there is an “inverted U”-shaped relationship between environmental regulation and the urban-rural income gap, and the urban-rural income gap negatively affects AGTFP. Based on the empirical results, we propose that the Chinese government should pay attention to green technology innovation, break the market segmentation, promote urban-rural integration, and then promote the AGTFP.
High-Temperature Rheological Properties of Crumb Rubber Composite Modified Asphalt
Authors: Gong Fangyuan , Lin Weijie , Chen Zhenkan , Shen Tao , Hu Chichun
Published: July 2022

As a kind of environmentally friendly material, crumb rubber modified asphalt is widely used in highway engineering to accelerate the consumption of end-life tires. The objective of this study is to investigate the high-temperature rheological properties of crumb rubber composite modified (CRCM) asphalt. In this study, the same content of crumb rubber and different content of composite additives were added to prepare the CRCM asphalt, including CRCM-SBS, CRCM- Sasobit/BRF, and CRCM-RARX. The viscosity, phase angle, rutting factor, critical temperature, storage modulus, non-recoverable creep compliance, percent recovery, stress sensitivity, and the functional groups of all testing specimens were obtained by the rotational viscometer, dynamic shear rheometer, multiple stress creep recovery, and Fourier transform infrared spectroscopy tests to evaluate the high-temperature rheological properties of asphalt. In general, the results were favorable for enhancing the high-temperature performance and reducing the stress sensitivity of asphalt. It showed that the incorporation of crumb rubber and additives increased the viscosity of asphalt under different testing conditions. Additionally, the addition of crumb rubber and additives in the base asphalt could be used to increase elastic components and improve the permanent deformation resistance, and the performances are closely related to the types of additives. Thus, considering the high-temperature performance, the asphalt modified by crumb rubber and RARX additive was recommended to apply to asphalt pavement.
Can Citizen Science in the Humanities and Social Sciences Deliver on the Sustainability Goals?
Authors: Henke Justus
Published: July 2022

Both the sustainability discourse and the debate on citizen science are strongly focused on the natural and technical sciences. Yet, numerous participatory research activities can be identified in the social sciences and humanities that address sustainability issues of various kinds. These have hardly been studied so far, and their contribution to addressing sustainability challenges is poorly known. The study investigates which sustainability topics are taken up by citizen science in the humanities and social sciences, which factors influence the choice of topics, and their implications. For this purpose, the concept of Citizen Social Science (CSS) is taken up and sustainability is operationalized via the Sustainable Development Goals (SDG) and its specific targets. Based on a collection of CSS activities in Germany, the addressed sustainability topics are identified accordingly. It is then analyzed how these patterns depend on the main characteristics of CSS projects. The findings indicate a focus on three SDGs related to education, sustainable cities, and partnerships for the Goals, while at the same time the project consortia are very heterogeneous. CSS shows particular strengths here through its multi-stakeholder approach. Going forward, the linkage of Citizen Science to the SDGs needs to be further formalized so that its transformative effects can be incorporated into SDG monitoring and the scientific institutions need additional incentives to participate in CSS.
Comparative Analysis of the Global Warming Potential (GWP) of Structural Stone, Concrete and Steel Construction Materials
Authors: Kerr Jonathan , Rayburg Scott , Neave Melissa , Rodwell John
Published: July 2022

The manufacturing and construction industries have always been large contributors to global CO2 emissions, largely as a consequence of material choices. Two of the most commonly used building materials are concrete and steel, but both of these industries have been identified as large sources of atmospheric CO2. Therefore, reducing the use of these materials and finding alternatives to them that meet the engineering requirements of a design, while also minimizing emissions, is becoming increasingly important. Stone in its natural form is a zero-carbon emission material and has strong physical properties that make it a viable substitute for concrete and steel, across a range of applications. Yet research into the potential use of stone by the construction industry remains rare. The aim of this research is to investigate whether the use of stone as a building product is a feasible alternative in terms of carbon emissions. This study compares data from 11 Environmental Product Declarations (EPDs) that provide Life Cycle Analysis (LCA) assessments of their considered product (i.e., types of dimensional stone, concrete, or steel). However, this research also highlights some shortcomings in the EPDs that point to a need for greater legitimate engagement with this tool, and for more consistency between the data being presented in EPDs. Global Warming Potential (GWP) data are compared between products to determine the difference in carbon emissions. The results indicate that GWP values for dimensional structural stone (135 kg.CO2/m3) are 45–75% lower than the concrete products considered in this investigation (246–514 kg.CO2/m3), and over 99% lower than certain steel products (22,294–29,202 kg.CO2/m3). This research indicates that stone is demonstrably better in terms of its GWP, and that a more extensive use of structural stone represents a key opportunity for the construction industry to reduce its CO2 emissions.
Effect of Pyrolysis Temperature on Removal Efficiency and Mechanisms of Hg(II), Cd(II), and Pb (II) by Maize Straw Biochar
Authors: Hu Xuebo , Zhang Ruigang , Xia Bing , Ying Rongrong , Hu Zhewei , Tao Xu , Yu Hao , Xiao Fabao , Chu Qiaoying , Chen Hongfeng , Qian Jiazhong
Published: July 2022

Pyrolysis temperature significantly affects the properties of biochar, which in turn can affect the removal of heavy metal ions and the underlying mechanism. In this work, biochars from the pyrolysis of maize straw at 300, 400, and 500 °C (BC300, BC400, and BC500, respectively) and wheat straw at 400 °C (WBC400) were investigated. The influence of production temperature on the adsorption of Hg2+, Cd2+, and Pb2+ by maize straw biochar was investigated by the characterization of the biochars and by adsorption tests. The adsorption capacities of maize and wheat straw biochar were compared in an adsorption experiment. Biochar BC400 showed the best physical and chemical properties and had the largest number of surface functional groups. The pseudo-second-order kinetic model was more suitable for describing the adsorption behavior of metal ions to biochar. The Langmuir model better fit the experimental data. Biochar BC400 had a higher adsorption speed and a stronger adsorption capacity than WBC400. The sorption of Pb2+ and Hg2+ to maize straw biochar followed the mechanisms of surface precipitation of carbonates and phosphates and complexation with oxygenated functional groups and delocalized π electrons. The adsorption mechanism for Cd2+ was similar to those of Hg2+ and Pb2+, but precipitation mainly occurred through the formation of phosphate. In the multi-heavy-metal system, the adsorption of Cd2+ by BC400 was inhibited by Pb2+ and Hg2+. In summary, BC400 biochar was most suitable for the adsorption effect of heavy metals in aqueous solution.
Mapping Urban Green and Its Ecosystem Services at Microscale—A Methodological Approach for Climate Adaptation and Biodiversity
Authors: Boehnke Denise , Krehl Alice , Mörmann Kai , Volk Rebekka , Lützkendorf Thomas , Naber Elias , Becker Ronja , Norra Stefan
Published: July 2022

The current awareness of the high importance of urban green leads to a stronger need for tools to comprehensively represent urban green and its benefits. A common scientific approach is the development of urban ecosystem services (UES) based on remote sensing methods at the city or district level. Urban planning, however, requires fine-grained data that match local management practices. Hence, this study linked local biotope and tree mapping methods to the concept of ecosystem services. The methodology was tested in an inner-city district in SW Germany, comparing publicly accessible areas and non-accessible courtyards. The results provide area-specific [m2] information on the green inventory at the microscale, whereas derived stock and UES indicators form the basis for comparative analyses regarding climate adaptation and biodiversity. In the case study, there are ten times more micro-scale green spaces in private courtyards than in the public space, as well as twice as many trees. The approach transfers a scientific concept into municipal planning practice, enables the quantitative assessment of urban green at the microscale and illustrates the importance for green stock data in private areas to enhance decision support in urban development. Different aspects concerning data collection and data availability are critically discussed.
Effects of Farmyard Manure and Different Phosphorus Inorganic Fertilizer Application Rates on Wheat Cultivation in Phosphorus-Deficient Soil
Authors: Dawar Khadim , Khalil Zeeshan , Mian Ishaq Ahmad , Khan Bushra , Ali Shamsher , Rahi Ashfaq Ahmad , Tahir Muhammad Saeed , Ahmed Niaz , Fahad Shah , Danish Subhan , Syed Asad , Bahkali Ali H. , Datta Rahul
Published: July 2022

Less phosphorus (P) availability in calcareous alkaline soils is one of the major problems in achieving an optimum crop yield. Its deficiency in plants adversely affects growth and yield attributes. To overcome this issue, growers incorporate inorganic P fertilizers. However, the need for time in the sustainable management of soil fertility in terms of P. Farmyard manure (FYM) application is one of the most popular organic amendments in this regard. Thus, the current study was conducted to explore the best application rate of FYM in combination with inorganic P fertilizer single super phosphate (SSP). There were six treatments i.e., control (0F), 100%SSP (100P), 25% FYM and 75% SSP (25F+75P), 50% FYM and 50% SSP (50F+50P), 75% FYM and 25% SSP (75F+25P), and 100% FYM (100F+0P), applied in three replications. The design of the experiment was a randomized complete block design. For assessment of treatment response, two wheat cultivars (V1 = Pirsabak and V2 = Atta Habib) were used. Results showed that the application of 50F+50P significantly improved the plant height (20.69 and 32.01%), spike/m2 (35.19 and 30.10%), grain (41.10 and 38.16%), and leaf P (49.82 and 71.32%) compared to control in V1 and V2, respectively. A significant improvement in the grain and the biological yield of wheat V2 also validated the efficacious functioning of 50F+50P over control. In conclusion, 50F+50P has the potential to enhance wheat growth and nutrient concentration over control. More investigations are required for a more precise and balanced synchronization of FYM and SSP for the achievement of maximum wheat yield.