bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Spent Coffee Grounds on the Crystallinity and Viscoelastic Behavior of Polylactic Acid Composites
Authors: de Bomfim Anne Shayene Campos , de Oliveira Daniel Magalhães , Benini Kelly Cristina Coelho de Carvalho , Cioffi Maria Odila Hilário , Voorwald Herman Jacobus Cornelis , Rodrigue Denis
Published: June 2023

This work investigated the addition of spent coffee grounds (SCG) as a valuable resource to produce biocomposites based on polylactic acid (PLA). PLA has a positive biodegradation effect but generates poor proprieties, depending on its molecular structure. The PLA and SCG (0, 10, 20 and 30 wt.%) were mixed via twin-screw extrusion and molded by compression to determine the effect of composition on several properties, including mechanical (impact strength), physical (density and porosity), thermal (crystallinity and transition temperature) and rheological (melt and solid state). The PLA crystallinity was found to increase after processing and filler addition (34–70% in the 1st heating) due to a heterogeneous nucleation effect, leading to composites with lower glass transition temperature (1–3 °C) and higher stiffness (~15%). Moreover, the composites had lower density (1.29, 1.24 and 1.16 g/cm3) and toughness (30.2, 26.8 and 19.2 J/m) as the filler content increased, which is associated with the presence of rigid particles and residual extractives from SCG. In the melt state, polymeric chain mobility was enhanced, and composites with a higher filler content became less viscous. Overall, the composite with 20 wt.% SCG provided the most balanced properties being similar to or better than neat PLA but at a lower cost. This composite could be applied not only to replace conventional PLA products, such as packaging and 3D printing, but also to other applications requiring lower density and higher stiffness.
Different Roles between PEDOT:PSS as Counter Electrode and PEDOT:Carrageenan as Electrolyte in Dye-Sensitized Solar Cell Applications: A Systematic Literature Review
Authors: Nurazizah Euis Siti , Aprilia Annisa , Risdiana Risdiana , Safriani Lusi
Published: June 2023

Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) has been mostly used as a counter electrode to give a high performance of dye-sensitized solar cell (DSSC). Recently, PEDOT doped by carrageenan, namely PEDOT:Carrageenan, was introduced as a new material to be applied on DSSC as an electrolyte. PEDOT:Carrageenan has a similar synthesis process as PEDOT:PSS, owing to their similar ester sulphate (-SO3H) groups in both PSS and carrageenan. This review provides an overview of the different roles between PEDOT:PSS as a counter electrode and PEDOT:Carrageenan as an electrolyte for DSSC applications. The synthesis process and characteristics of PEDOT:PSS and PEDOT:Carrageenan were also described in this review. In conclusion, we found that the primary role of PEDOT:PSS as a counter electrode is to transfer electrons back to cell and accelerate redox reaction with its superior electrical conductivity and high electrocatalytic activity. PEDOT:Carrageenan as an electrolyte has not shown the main role for regenerating the dye sensitized at the oxidized state, probably due to its low ionic conductivity. Therefore, PEDOT:Carrageenan still obtained a low performance of DSSC. Additionally, the future perspective and challenges of using PEDOT:Carrageenan as both electrolyte and counter electrode are described in detail.
Biodegradable Cellulose/Polycaprolactone/Keratin/Calcium Carbonate Mulch Films Prepared in Imidazolium-Based Ionic Liquid
Authors: Stojanović Dušica , Ivanovska Aleksandra , Barać Nemanja , Dimić-Misić Katarina , Kostić Mirjana , Radojević Vesna , Janaćković Djordje , Uskoković Petar , Barceló Ernest , Gane Patrick
Published: June 2023

Ionic liquid 1-butyl-3-methylimidazolium chloride [BMIM][Cl] was used to prepare cellulose (CELL), cellulose/polycaprolactone (CELL/PCL), cellulose/polycaprolactone/keratin (CELL/PCL/KER), and cellulose/polycaprolactone/keratin/ground calcium carbonate (CELL/PCL/KER/GCC) biodegradable mulch films. Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectroscopy, optical microscopy, and Field-Emission Scanning Electron Microscopy (FE-SEM) were used to verify the films’ surface chemistry and morphology. Mulch film made of only cellulose regenerated from ionic liquid solution exhibited the highest tensile strength (75.3 ± 2.1 MPa) and modulus of elasticity of 944.4 ± 2.0 MPa. Among samples containing PCL, CELL/PCL/KER/GCC is characterized by the highest tensile strength (15.8 ± 0.4 MPa) and modulus of elasticity (687.5 ± 16.6 MPa). The film’s breaking strain decreased for all samples containing PCL upon the addition of KER and KER/GCC. The melting temperature of pure PCL is 62.3 °C, whereas that of CELL/PCL film has a slight tendency for melting point depression (61.0 °C), which is a characteristic of partially miscible polymer blends. Furthermore, Differential Scanning Calorimetry (DSC) analysis revealed that the addition of KER or KER/GCC to CELL/PCL films resulted in an increment in melting temperature from 61.0 to 62.6 and 68.9 °C and an improvement in sample crystallinity by 2.2 and 3.0 times, respectively. The light transmittance of all studied samples was greater than 60%. The reported method for mulch film preparation is green and recyclable ([BMIM][Cl] can be recovered), and the inclusion of KER derived by extraction from waste chicken feathers enables conversion to organic biofertilizer. The findings of this study contribute to sustainable agriculture by providing nutrients that enhance the growth rate of plants, and hence food production, while reducing environmental pressure. The addition of GCC furthermore provides a source of Ca2+ for plant micronutrition and a supplementary control of soil pH.
Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving
Authors: Jin Xianjian , Yang Hang , He Xiongkui , Liu Guohua , Yan Zeyuan , Wang Qikang
Published: June 2023

The stable detection and tracking of high-speed vehicles on the road by using LiDAR can input accurate information for the decision-making module and improve the driving safety of smart cars. This paper proposed a novel LiDAR-based robust vehicle detection method including three parts: point cloud clustering, bounding box fitting and point cloud recognition. Firstly, aiming at the problem of clustering quality degradation caused by the uneven distribution of LiDAR point clouds and the difference in clustering radius between point cloud clusters in traditional DBSCAN (TDBSCAN) obstacle clustering algorithms, an improved DBSCAN algorithm based on distance-adaptive clustering radius (ADBSCAN) is designed, and a point cloud KD-Tree data structure is constructed to speed up the traversal of the algorithm; meanwhile, the OPTICS algorithm is introduced to enhance the performance of the proposed algorithm. Then, by adopting different fitting strategies for vehicle contour points in various states, the adaptability of the bounding box fitting algorithm is improved; Moreover, in view of the shortcomings of the poor robustness of the L-shape algorithm, the principal component analysis method (PCA) is introduced to obtain stable bounding box fitting results. Finally, considering the time-consuming and low-accuracy training of traditional machine learning algorithms, advanced PointNet in deep learning technique is built to send the point cloud within the bounding box of a high-confidence vehicle into PointNet to complete vehicle recognition. Experiments based on our autonomous driving perception platform and the KITTI dataset prove that the proposed method can stably complete vehicle target recognition and achieve a good balance between time-consuming and accuracy.
Assessment of the Spatiotemporal Impact of Water Conservation on the Qinghai–Tibet Plateau
Authors: Wen Xin , Shao Huaiyong , Wang Ying , Lv Lingfeng , Xian Wei , Shao Qiufang , Shu Yang , Yin Ziqiang , Liu Shuhan , Qi Jiaguo
Published: June 2023

The Qinghai–Tibet Plateau is a proven essential water conservation region in Asia. However, various factors, such as anthropogenic activities, climate, and vegetation significantly affect its water conservation. Along these lines, a deep understanding of the spatiotemporal patterns of water conservation for this plateau and relevant influencing elements is considered of great importance. This paper calculates the water conservation on the Qinghai–Tibet Plateau based on the InVEST model, and given that the evapotranspiration data are an important parameter of the InVEST model, this study selects the mainstream evapotranspiration data to compare the accuracy of the simulated water yield, and also selects the most accurate remote sensing evapotranspiration data examined in the study to carry out the study of water conservation on the Qinghai–Tibet Plateau. Due to the large area of the Qinghai–Tibet Plateau and the various types of climate and ecological zones, this paper analyzes the spatial and temporal variations of water conservation on the Qinghai–Tibet Plateau in each ecological zone and climate zone division and detects the factors affecting water conservation on the Qinghai–Tibet Plateau by using the geo-detector method. From our analysis, the following outcomes are proven: on the Qinghai–Tibet Plateau, (1) the overall water conservation decreased from southeast to northwest; (2) the water conservation of the studied plateau in 1990, 2000, 2010, and 2020 was 656.56, 590.85, 597.4, and 651.85 mm, respectively; (3) precipitation, evapotranspiration, and NDVI exhibited a positive relationship with water conservation; (4) the precipitation factor had the biggest impact on the spatial distinctions of the water resource governance; (5) the above factors are combined with the slope factor and the interaction of each factor to improve water conservation. Our work provides valuable insights for the further implementation of ecological projects with a view to enhancing water resource management methods.
DCCaps-UNet: A U-Shaped Hyperspectral Semantic Segmentation Model Based on the Depthwise Separable and Conditional Convolution Capsule Network
Authors: Wei Siqi , Liu Yafei , Li Mengshan , Huang Haijun , Zheng Xin , Guan Lixin
Published: June 2023

Traditional hyperspectral image semantic segmentation algorithms can not fully utilize the spatial information or realize efficient segmentation with less sample data. In order to solve the above problems, a U-shaped hyperspectral semantic segmentation model (DCCaps-UNet) based on the depthwise separable and conditional convolution capsule network was proposed in this study. The whole network is an encoding–decoding structure. In the encoding part, image features are firstly fully extracted and fused. In the decoding part, images are then reconstructed by upsampling. In the encoding part, a dilated convolutional capsule block is proposed to fully acquire spatial information and deep features and reduce the calculation cost of dynamic routes using a conditional sliding window. A depthwise separable block is constructed to replace the common convolution layer in the traditional capsule network and efficiently reduce network parameters. After principal component analysis (PCA) dimension reduction and patch preprocessing, the proposed model was experimentally tested with Indian Pines and Pavia University public hyperspectral image datasets. The obtained segmentation results of various ground objects were analyzed and compared with those obtained with other semantic segmentation models. The proposed model performed better than other semantic segmentation methods and achieved higher segmentation accuracy with the same samples. Dice coefficients reached 0.9989 and 0.9999. The OA value can reach 99.92% and 100%, respectively, thus, verifying the effectiveness of the proposed model.
Spatiotemporal Evolution of Arid Ecosystems Using Thematic Land Cover Products
Authors: Xu Lili , Chen Tianyu , Li Baolin , Yuan Yecheng , Tsendbazar Nandin-Erdene
Published: June 2023

The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change.
Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models
Authors: Sayyad Sameer , Kumar Satish , Bongale Arunkumar , Kotecha Ketan , Abraham Ajith
Published: June 2023

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.
Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach
Authors: Wang Zhiwen , Sun Shouwang , Li Yiwei , Yue Zixiang , Ding Youliang
Published: June 2023

Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system’s service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm’s performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission.
All-Optical, Air-Coupled Ultrasonic Detection of Low-Pressure Gas Leaks and Observation of Jet Tones in the MHz Range
Authors: Scheuer Kyle G. , DeCorby Ray G.
Published: June 2023

We used an ultrasensitive, broadband optomechanical ultrasound sensor to study the acoustic signals produced by pressurized nitrogen escaping from a variety of small syringes. Harmonically related jet tones extending into the MHz region were observed for a certain range of flow (i.e., Reynolds number), which is in qualitative agreement with historical studies on gas jets emitted from pipes and orifices of much larger dimensions. For higher turbulent flow rates, we observed broadband ultrasonic emission in the ~0–5 MHz range, which was likely limited on the upper end due to attenuation in air. These observations are made possible by the broadband, ultrasensitive response (for air-coupled ultrasound) of our optomechanical devices. Aside from being of theoretical interest, our results could have practical implications for the non-contact monitoring and detection of early-stage leaks in pressured fluid systems.
Optimal Linear Filter Based on Feedback Structure for Sensing Network with Correlated Noises and Data Packet Dropout
Authors: Shang Weichen , Yu Hang , Li Qingyu , Zhang He , Dai Keren
Published: June 2023

This paper is concerned with the estimation of correlated noise and packet dropout for information fusion in distributed sensing networks. By studying the problem of the correlation of correlated noise in sensor network information fusion, a matrix weight fusion method with a feedback structure is proposed to deal with the interrelationship between multi-sensor measurement noise and estimation noise, and the method can achieve optimal estimation in the sense of linear minimum variance. Based on this, a method is proposed using a predictor with a feedback structure to compensate for the current state quantity to deal with packet dropout that occurs during multi-sensor information fusion, which can reduce the covariance of the fusion results. Simulation results show that the algorithm can solve the problem of information fusion noise correlation and packet dropout in sensor networks, and effectively reduce the fusion covariance with feedback.
Investigation of the Temperature Actions of Bridge Cables Based on Long-Term Measurement and the Gradient Boosted Regression Trees Method
Authors: Wang Fen , Dai Gonglian , Liu Yonglu , Ge Hao , Rao Huiming
Published: June 2023

Cable-stayed bridges have been commonly used on high-speed railways. The design, construction, and maintenance of cable-stayed bridges necessitate an accurate assessment of the cable temperature field. However, the temperature fields of cables have not been well established. Therefore, this research aims to investigate the distribution of the temperature field, the time variability of temperatures, and the representative value of temperature actions in stayed cables. A cable segment experiment, spanning over one year, is conducted near the bridge site. Based on the monitoring temperatures and meteorological data, the distribution of the temperature field is studied, and the time variability of cable temperatures is investigated. The findings show that the temperature distribution is generally uniform along the cross-section without a significant temperature gradient, while the amplitudes of the annual cycle variation and daily cycle variation in temperatures are significant. To accurately determine the temperature deformation of a cable, it is necessary to consider both the daily temperature fluctuations and the annual cycle of uniform temperatures. Then, using the gradient boosted regression trees method, the relationship between the cable temperature and multiple environmental variables is explored, and representative cable uniform temperatures for design are obtained by the extreme value analysis. The presented data and results provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.
Modelling Polarization Effects in a CdZnTe Sensor at Low Bias
Authors: Pipek Jindřich , Grill Roman , Betušiak Marián , Iniewski Kris
Published: June 2023

Semi-insulating CdTe and CdZnTe crystals fabricated into pixelated sensors and integrated into radiation detection modules have demonstrated a remarkable ability to operate under rapidly changing X-ray irradiation environments. Such challenging conditions are required by all photon-counting-based applications, including medical computed tomography (CT), airport scanners, and non-destructive testing (NDT). Although, maximum flux rates and operating conditions differ in each case. In this paper, we investigated the possibility of using the detector under high-flux X-ray irradiation with a low electric field satisfactory for maintaining good counting operation. We numerically simulated electric field profiles visualized via Pockels effect measurement in a detector affected by high-flux polarization. Solving coupled drift–diffusion and Poisson’s equations, we defined the defect model, consistently depicting polarization. Subsequently, we simulated the charge transport and evaluated the collected charge, including the construction of an X-ray spectrum on a commercial 2-mm-thick pixelated CdZnTe detector with 330 µm pixel pitch used in spectral CT applications. We analyzed the effect of allied electronics on the quality of the spectrum and suggested setup optimization to improve the shape of the spectrum.
FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks
Authors: Bae Seongwoo , Kim Haechan , Lee Seongjoo , Jung Yunho
Published: June 2023

Keyword spotting (KWS) systems are used for human–machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device. The design achieves significant area efficiency by redundantly utilizing bitwise operators in the computation of the binarized neural network (BNN) and ternary neural network (TNN). In a complementary metal-oxide semiconductor (CMOS) 40 nm process environment, the DS-BTNN accelerator demonstrated significant efficiency. Compared with a design approach where BNN and TNN were independently developed and subsequently integrated as two separate modules into the system, our method achieved a 49.3% area reduction while yielding an area of 0.558 mm2. The designed KWS system, which was implemented on a Xilinx UltraScale+ ZCU104 field-programmable gate array (FPGA) board, receives real-time data from the microphone, preprocesses them into a mel spectrogram, and uses this as input to the classifier. Depending on the order, the network operates as a BNN or a TNN for WUW recognition and command classification, respectively. Operating at 170 MHz, our system achieved 97.1% accuracy in BNN-based WUW recognition and 90.5% in TNN-based command classification.
Community Voices on the Experiences of Community-Based Participatory Research in the Environmental Justice Movement
Authors: Boston P. Qasimah , Strouble Bruce , Balogun Aisha , Lugo-Martinez Beto , McClain Mildred , Mitchell Mary Miaisha , Wasserman Kim , Rahn David , Greenberg Molly , Garibay Cecilia
Published: June 2023

Community-based participatory research (CBPR) is increasingly being used by academics to address urban health and inequity. While its foundational literature emphasizes CBPR’s role in eschewing the traditional balance of power between communities and institutions, some scholars and grassroots activists note that it has not consistently delivered community-led, action-oriented strategies. Here we examine gaps between theory and current practice of CBPR. First, we assess its fundamental practices through social movement theory. Second, we examine narratives from grassroots leaders who have utilized CBPR in their environmental justice efforts. Three key facilitating factors are often cited for authentic applications of CBPR: (1) grounding research within community-specific opportunities for change, (2) collaborating with a strong community partner, and (3) facilitating flexibility in research processes. Building on previous scholarship, we investigate the complexity of CBPR implementation that may dampen its potency as a social change strategy and highlight the need for nuanced and critical application of best practices to suit local contexts. This is accomplished by pairing the theoretical framework with the experiences of four community leaders who are members of the Moving Forward Network (MFN). Narratives from their experiences with partnering with academic institutions speak to how theoretical complexities with CBPR occur in practice.
Research on the Yellow River Basin Energy Structure Transformation Path under the “Double Carbon” Goal
Authors: Liang Xiaoxia , Shi Yi , Li Yan
Published: June 2023

The clean utilization of traditional energy and renewable, clean energy utilization are the key points of the energy structure transition in the Yellow River Basin. This paper constructs an evolutionary game model, with the participation of local governments and energy companies, to analyze the dynamic evolution of each game subject. The results from the study highlight three important facts about the energy mix transformation in the Yellow River Basin: (1) the high ratio of traditional clean energy utilization and the low ratio of renewable, clean energy utilization align with the actual energy use in the Yellow River Basin, which can better promote the inclusive development of both types of energy; (2) increasing the capacity to utilize both energy sources can improve the energy system resilience gains of game players, for example, at the immature stage of renewable, clean energy utilization technologies, local government’s willingness to subsidize renewable clean energy utilization is positively related to their energy system resilience gains; and (3) under the premise of ensuring the energy supply, the introduction of penalty parameters can ensure a reasonable share of both types of energy utilization, and an increase in the penalty parameters makes the game participants increase their willingness to implement energy structure transformation policies.
A Systematic Review and Meta-Analysis of the Effectiveness of Non-Face-to-Face Coaching
Authors: Kim Yucheon , Lee Songyi
Published: June 2023

This study examined the effectiveness of non-face-to-face coaching in South Korea in order to present alternatives in the post-COVID-19 environment. The research collected domestic studies on non-face-to-face coaching in South Korea and analysed the studies through a systematic literature review and meta-analysis. Among 1081 papers retrieved from the database, we selected ten papers for meta-analysis. Using the random effect model to measure effect size, the total effect size of non-face-to-face coaching was 0.77. When we divided the effect of non-face-to-face coaching into psychological, cognitive, and physical effects, the cognitive effects were the largest. In addition, examining non-face-to-face coaching by type resulted in a larger effect size of web-based online coaching in comparison to telephone coaching. By contrast, the effect sizes of non-face-to-face coaching by subject had the largest effect size on subjects with the highest level of vulnerability. This study found that non-face-to-face coaching had a large effect, with relatively large cognitive and psychological effects. Future investigations should supplement the present research through follow-up studies on non-face-to-face coaching.
A Study on the Audience Psychological Effects of “Cloud Tourism” Based on Webcast: A New Mechanism for Sustainable Development in the Tourism
Authors: Gong Kedi , Tian Lu , Wu Junyi , Luo Ziming , Xu Quanhong
Published: June 2023

In recent years, ”cloud tourism” has developed rapidly and has gained a wide audience, gradually becoming a new operation form of sustainable development in tourism. However, research on the audience perception, psychological needs, and behavior in this area is still in its infancy. Taking the tourism webcast on the Chinese Weibo platform as an example, this paper constructs a cognitive-emotional model of Chinese tourists’ “cloud tourism” and explores the impact mechanism of “cloud tourism” on audience behavioral willingness by drawing on the “cognitive-emotional” theory combined with text analysis and grounded theory. The findings suggest that “cloud tourism” can satisfy the audience’s cognitive needs to a certain extent, but the experiential nature of tourism is far from sufficient, and it is difficult to realize the essence of tourism. In the future, “cloud tourism” still needs to continuously exert positive effects, becoming a visual presentation of traditional tourism and a novel operation form of sustainable development.
Productivity Analysis and Evaluation of Fault-Fracture Zones Controlled by Complex Fracture Networks in Tight Reservoirs: A Case Study of Xujiahe Formation
Authors: Cai Jiujie , Wang Haibo , Li Fengxia
Published: June 2023

The development of tight gas reservoirs presents a significant challenge for sustainable development, as it requires specialized techniques that can have adverse environmental and social impacts. To address these challenges, efficient development technologies, such as multistage hydraulic fracturing, have been adopted to enable access to previously inaccessible natural gas resources, increase energy efficiency and security, and minimizing environmental impacts. This paper proposes a novel evaluation method to analyze the post fracturing productivity controlled by complex fault fracture zones in tight reservoirs. In this article, a systematic method to evaluate stimulated reservoir volume (SRV) and fault-fracture zone complexity after stimulation was established, along with the analysis and prediction of productivity through coupled fall-off and well-test analyses. Focusing on the Xujiahe formation in the Tongnanba anticline of northeastern Sichuan Basin, a 3D geological model was developed to analyze planar heterogeneity. The fall-off analytical model, coupled with rock mechanical parameters and fracturing parameters such as injection rates, fracturing fluid viscosity, and the number of clusters within a single stage, was established to investigate the fracture geometric parameters and complexities of each stage. The trilinear flow model was used to solve the well-test analysis model of multi-stage fractured horizontal wells in tight sandstone gas reservoirs, and well-test curves of the heterogeneous tight sandstone gas fracture network model were obtained. The results show that hydraulic fractures connect the natural fractures in fault-fracture zones. An analysis of the relationship between the fracture geometric outcomes of each segment and the net pressure reveals that as the net pressure in the fracture increases, the area ratio of natural fractures to main fractures increases notably, whereas the half length of the main fracture exhibits a decreasing trend. The overall area of natural fractures following stimulation is 7.64 times greater than that of the main fractures and is mainly a result of the extensive development of natural fractures in the target interval. As the opening ratio of natural fractures increases, the length of the main fractures decreases accordingly. Therefore, increasing net pressure within fractures will significantly enhance the complexity of fracturing fractures in shale gas reservoirs. Furthermore, the initial production of Well X1–10, which is largely controlled by fault-fracture zones, and the cumulative gas production after one year, are estimated. The systematic evaluation method in this study proposed a new way to accurately measure fracturing in tight reservoirs, which is a critical and helpful component of sustainable development in the natural gas industry.
Drilling Path Planning of Rock-Drilling Jumbo Using a Vehicle-Mounted 3D Scanner
Authors: Li Yongfeng , Peng Pingan , Li Huan , Xie Jinghua , Liu Liangbin , Xiao Jing
Published: June 2023

Achieving intelligent rock excavation is an important development direction in underground engineering construction. Currently, some rock-drilling jumbos are able to perform autonomous operations under ideal contour surfaces. However, irregular contour surfaces resulting from factors such as rock characteristics, drilling deviation, and blasting effects present a significant challenge for automated drilling under non-ideal surfaces, which constrains the intelligentization of rock excavation. To address this issue, this paper proposes a method for extracting contour surfaces and planning drilling paths based on a vehicle-mounted 3D scanner. This method effectively extracts contour surfaces and optimizes drilling paths, thereby improving work efficiency and safety. Specifically, the proposed method includes: (i) the real-time scanning of cross-sectional contours using a vehicle-mounted 3D scanner to construct an accurate three-dimensional point-cloud model and obtain contour over-digging information; the acquired data are compared with theoretical drilling maps in the vehicle’s coordinate system to re-plan the blasting-hole point set; (ii) the development of a volume-based dynamic search algorithm based on the irregularities of contour surfaces to detect potential collisions between holes; and (iii) the conversion of the drilling sequence planning based on the new blasting hole point set into a traveling salesman problem (TSP), and optimization using a Hybrid Greedy Genetic Algorithm (HGGA) to achieve path traversal of all drilling positions. The effectiveness of the proposed method was verified using rock excavation in a certain mine as an example. The results show that the overall recognition rate of the contour over-digging reached over 80%, the number of arm collisions was significantly reduced, and the distance traveled by the drilling rig was reduced by 35% using the improved genetic algorithm-based rock-drilling rig path planning.
A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials
Authors: Saad Ahmed Hassan , Nahazanan Haslinda , Yusuf Badronnisa , Toha Siti Fauziah , Alnuaim Ahmed , El-Mouchi Ahmed , Elseknidy Mohamed , Mohammed Angham Ali
Published: June 2023

According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.
A Novel Numerical Method for Geothermal Reservoirs Embedded with Fracture Networks and Parameter Optimization for Power Generation
Authors: Yan Xufeng , Xue Kangsheng , Liu Xiaobo , Chi Xiaolou
Published: June 2023

Geothermal recovery involves a coupled thermo-hydro-mechanical (THM) process in fractured rocks. A fluid transient equilibrium equation, considering thermal conduction, convection, and heat exchange, is established. The evolution of the reservoir permeability and the variance in the fracture aperture due to a change in the stress field are derived simultaneously. THM coupling is accomplished through iterative hydromechanical and thermo-hydro processes. To overcome the difficulty of geometric discretization, a three-dimensional THM coupler model embedded with discrete fracture networks, using a zero-thickness surface and line elements to simulate fractures and injection/production wells, is established to evaluate the geothermal production. The reliability of the method is verified by a case study. Then, this method is applied to evaluate the influence of the geometric topological characteristics of fracture networks and the fracture aperture on the reservoir temperature evolution and heat extraction effectiveness. The results show that the power generation efficiency and geothermal depletion rate are significantly affected by the injection–production pressure. Injection wells and production wells with pressures higher than the initial fluid pressure in the fractures can be used to significantly increase power generation, but the consumption of geothermal energy and loss of efficiency are significant and rapid. To achieve better benefits for the geothermal recovery system, an optimization algorithm based on simultaneous perturbation stochastic approximation (SPSA) is proposed; it takes the power generation efficiency as the objective function, and the corresponding program is developed using MATLAB to optimize the position and pressure values for each production well. The results show that the heat transfer for the entire EGS reservoir becomes more uniform after optimization, and the heat transfer efficiency is greatly improved.
IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia
Authors: Ali Javed , Madni Syed Hamid Hussain , Jahangeer Mohd Shamim Ilyas , Danish Muhammad Abdullah Ahmed
Published: June 2023

The realm of the Internet of Things (IoT), while continually transforming as a novel paradigm in the nexus of technology and education, still contends with numerous obstacles that hinder its incorporation into higher education institutions’ (HEIs) e-learning platforms. Despite substantial strides in IoT utilization from industrialized nations—the United States, the United Kingdom, Japan, and China serving as prime exemplars—the scope of its implementation in developing countries, notably Saudi Arabia, Malaysia, Pakistan, and Bangladesh, lags behind. A significant gap exists in research centered on the trajectory of IoT integration within e-learning systems of economically disadvantaged nations. Specifically, this study centers on Saudi Arabia to illuminate the main factors catalyzing or encumbering IoT uptake within its HEIs’ e-learning sector. As a preliminary step, this research has embarked on an exhaustive dissection of prior studies to unearth critical variables implicated in the IoT adoption process. Subsequently, we employed an inferential methodology, amassing data from 384 respondents in Saudi Arabian HEIs. Our examination divulges that usability, accessibility, technical support, and individual proficiencies considerably contribute to the rate of IoT incorporation. Furthermore, our data infer that financial obstacles, self-efficacy, interactive capability, online surveillance, automated attendance tracking, training programs, network and data safeguarding measures, and relevant tools significantly influence IoT adoption. Contrarily, factors such as accessibility, internet quality, infrastructure preparedness, usability, privacy concerns, and faculty support appeared to have a negligible impact on the adoption rates within HEIs. This research culminates in offering concrete recommendations to bolster IoT integration within Saudi Arabian HEIs, presenting valuable insights for government entities, policy architects, and HEIs to address the hurdles associated with IoT implementation in the higher education sector.
Computer Network Redundancy Reduction Using Video Compression
Authors: Habib Shabana , Albattah Waleed , Alsharekh Mohammed F. , Islam Muhammad , Shees Mohammad Munawar , Sherazi Hammad I.
Published: June 2023

Due to the strong correlation between symmetric frames, video signals have a high degree of temporal redundancy. Motion estimation techniques are computationally expensive and time-consuming processes used in symmetric video compression to reduce temporal redundancy. The block-matching technique is, on the other hand, the most popular and efficient of the different motion estimation and compensation techniques. Motion compensation based on the block-matching technique generally uses the minimization of either the mean square error (MSE) or mean absolute difference (MAD) in order to find the appropriate motion vector. This paper proposes to remove the highly temporally redundant information contained in each block of the video signal using the removing temporal redundancy (RTR) technique in order to improve the data rate and efficiency of the video signal. A comparison between the PSNR values of this technique and those of the JPEG video compression standard is made. As a result of its moderate memory and computation requirements, the algorithm was found to be suitable for mobile networks and embedded devices. Based on a detailed set of testing scenarios and the obtained results, it is evident that the RTR compression technique allowed a compression ratio of 22.71 and 95% loss in bit rate reduction while maintaining sufficient intact signal quality with minimized information loss.
Genomic Characterization of a Halovirus Representing a Novel Siphoviral Cluster
Authors: Diao Kaixin , Li Guohui , Sun Xueqin , Yi Hao , Zhang Shiying , Xiao Wei
Published: June 2023

Salt mines are a special type of hypersaline environment. Current research mainly focuses on prokaryotes, and the understanding of viruses in salt mines remains limited. Understanding viruses in hypersaline environments is of great significance for revealing the formation and maintenance of microbial communities, energy flow and element cycling, and host ecological functions. A phage infecting Halomonas titanicae was isolated from Yipinglang Salt Mine in China, designated Halomonas titanicae phage vB_HtiS_YPHTV-1 (YPHTV-1). Transmission electron microscopy revealed that YPHTV-1 had an icosahedral head with a diameter of 49.12 ± 0.15 nm (n = 5) and a long noncontractile tail with a length of 141.7 ± 0.58 nm (n = 5), indicating that it was a siphovirus. The one-step growth curve showed that the burst size of YPHTV-1 was 69 plaque forming units (PFUs) cell−1. The genome of YPHTV-1 was 37,980 bp with a GC content of 36.2%. The phylogenetic analysis of the six conserved proteins indicated that YPHTV-1 formed a cluster with Bacillus phages and was separated from phages infecting Halomonas. The average nucleotide identity (ANI), phylogenetic, and network analyses indicated that the phage YPHTV-1 represented a new genus under Caudoviricetes. In total, 57 open reading frames (ORFs) were predicted in the YPHTV-1 genome, 30 of which could be annotated in the database. Notably, several auxiliary metabolic genes were encoded by YPHTV-1, such as ImmA/IrrE family metalloendopeptidase, mannose-binding lectin (MBL) folding metallohydrolase, M15 family of metal peptidases, MazG-like family protein, O antigen ligase, and acyltransferase. These genes potentially enabled the host bacterium to resist ionizing radiation, ultraviolet light (UV), mitomycin C, β-lactam antibiotic, high osmotic pressure, and nutritional deficiencies. These findings highlight the role of haloviruses in the life cycle of halobacteria.
Decentralized Constructed Wetlands for Wastewater Treatment in Rural and Remote Areas of Semi-arid Regions
Authors: Hendy Ibrahim , Zelenakova Martina , Pietrucha-Urbanik Katarzyna , Salama Yasser , Abu-hashim Mohamed
Published: June 2023

Vertical-flow constructed wetlands (VFCWs) are an innovative and sustainable nature-based technology for wastewater treatment in rural areas. This work aimed to evaluate the treatment performance of VFCWs using real wastewater, which can provide more accurate and reliable results compared with field-based experiments, and to investigate the use of Paulownia trees in VFCWs for wastewater treatment. To compare the efficiency of the plants based on the treatment performance of the VFCWs, three units were prepared and composed of Paulownia, the commonly used Phragmites Australis, and an unplanted unit used as a control during the experimental program. The results show significant reductions in both the chemical oxygen demand (COD) and biochemical oxygen demand (BOD5) levels for both planted units, with removal ratios for COD and BOD5 of 60% to 98%, respectively. Both Paulownia and Phragmites Australis significantly reduced the levels of COD and BOD5 in the effluent, with removal percentages ranging from 57.1% to 98% for COD and 49.1% to 98% for BOD5. The control unit, without plantings, showed a lower but still significant removal percentage for both COD (from 55.1% to 96.1%) and BOD5 (from 48.3% to 97.8%). Thus, the results reveal that the efficiency of constructed wetlands can be significantly enhanced by the presence of suitable plant species, such as Paulownia and Phragmites Australis, and constructed wetlands can be a viable and cost-effective option for the treatment of wastewater in various settings, with the added benefit of using the relevant biodiversity.