bluuebunny/crossref_metadata_2025 huggingface dataset:
Advances in High-Energy-Resolution CdZnTe Linear Array Pixel Detectors with Fast and Low Noise Readout Electronics
Authors: Mele Filippo , Quercia Jacopo , Abbene Leonardo , Benassi Giacomo , Bettelli Manuele , Buttacavoli Antonino , Principato Fabio , Zappettini Andrea , Bertuccio Giuseppe
Published: February 2023

Radiation detectors based on Cadmium Zinc Telluride (CZT) compounds are becoming popular solutions thanks to their high detection efficiency, room temperature operation, and to their reliability in compact detection systems for medical, astrophysical, or industrial applications. However, despite a huge effort to improve the technological process, CZT detectors’ full potential has not been completely exploited when both high spatial and energy resolution are required by the application, especially at low energies (<10 keV), limiting their application in energy-resolved photon counting (ERPC) systems. This gap can also be attributed to the lack of dedicated front-end electronics which can bring out the best in terms of detector spectroscopic performances. In this work, we present the latest results achieved in terms of energy resolution using SIRIO, a fast low-noise charge sensitive amplifier, and a linear-array pixel detector, based on boron oxide encapsulated vertical Bridgman-grown B-VB CZT crystals. The detector features a 0.25-mm pitch, a 1-mm thickness and is operated at a −700-V bias voltage. An equivalent noise charge of 39.2 el. r.m.s. (corresponding to 412 eV FWHM) was measured on the test pulser at 32 ns peaking time, leading to a raw resolution of 1.3% (782 eV FWHM) on the 59 keV line at room temperature (+20 °C) using an uncollimated 241Am, largely improving the current state of the art for CZT-based detection systems at such short peaking times, and achieving an optimum resolution of 0.97% (576 eV FWHM) at 1 µs peaking time. The measured energy resolution at the 122 keV line and with 1 µs peaking time of a 57Co raw uncollimated spectrum is 0.96% (1.17 keV). These activities are in the framework of an Italian collaboration on the development of energy-resolved X-ray scanners for material recycling, medical applications, and non-destructive testing in the food industry.
Adjustable Vibration Exciter Based on Unbalanced Motors
Authors: Osadchyy Volodymyr , Nazarova Olena , Hutsol Taras , Glowacki Szymon , Mudryk Krzysztof , Bryś Andrzej , Rud Anatolii , Tulej Weronika , Sojak Mariusz
Published: February 2023

In European industry, such as metallurgical, mining and processing, construction, food, and chemical, vibration exciters are used, which indicates their wide and, in some cases, unique technological capabilities. The most common are electromagnetic and unbalanced vibration exciters. The advantages of electromagnetic vibration exciters include the ability to control the amplitude of the vibration by changing the electrical power supplied; the disadvantages are high material consumption. However, unbalanced vibration exciters have low energy efficiency, which is associated with difficult start-up conditions and with an overestimated mechanical power of the vibration exciter in relation to the power required by the technology itself, which is due to the need to minimize the effect of the technological load on the operating mode of the vibrating unit. Adjusting the amplitude of the disturbing force of unbalanced vibration exciters, regardless of the vibration frequency, will make it possible to reduce the installed power of the unit by passing the resonant frequency with a minimum disturbing force and compensating for the effect of the process load by means of a closed-loop electric drive. In the course of the study, an analytical description of the interaction of the rotating unbalances located on a common movable platform was obtained. On the basis of these analytical dependencies, a mathematical model was developed that takes into account the dynamic characteristics of a frequency-controlled asynchronous electric drive of a closed-loop control system for the mutual arrangement of rotating unbalances. The simulation results confirmed the possibility of using the specified electric drive to control the oscillation amplitude directly in the process of operation of a four-unbalanced vibration exciter. A physical experiment was carried out to determine the transient processes of changing the angular velocity of an induction motor with an abrupt change in the frequency converter setting. On the basis of this experiment, the previously created mathematical model was refined in terms of describing the dynamic parameters of the electric drive. The proposed structure of the control system, the performance of which has been confirmed by mathematical modeling, makes it possible to implement an adjustable four-unbalanced vibration exciter using single commercially available asynchronous vibrators.
Network Anomaly Intrusion Detection Based on Deep Learning Approach
Authors: Wang Yung-Chung , Houng Yi-Chun , Chen Han-Xuan , Tseng Shu-Ming
Published: February 2023

The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models—deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM—were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device.
Evaluation of Electrical Impedance Spectra by Long Short-Term Memory to Estimate Nitrate Concentrations in Soil
Authors: Ma Xiaohu , Bifano Luca , Fischerauer Gerhard
Published: February 2023

Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents the characteristics of an impedance sensor used to estimate the nitrate concentration in soil based on the sensitivity of the soil dielectric constant to ion conductivity and on electrical double layer effects at electrodes. The impedance of synthetic sandy soil samples with nitrate nitrogen concentrations ranging from 0 to 15 mg/L was measured at frequencies between 20 Hz and 5 kHz and noticeable conductance and susceptance effects were observed. Long short-term memory (LSTM), a variant of recurrent artificial neural networks (RNN), was investigated with respect to its suitability to extract nitrate concentrations from the measured impedance spectra and additional physical properties of the soils, such as mass density and water content. Both random forest and LSTM were tested as feature selection methods. Then, numerous LSTMs were trained to estimate the nitrate concentrations in the soils. To increase estimation accuracy, hyperparameters were optimized with Bayesian optimization. The resulting optimal regression model showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95. Thus, it could be demonstrated that the system has the potential to monitor nitrate concentrations in soils in real time and in situ.
Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: A Smart Home Use Case
Authors: Feng Xinyao , Ahvar Ehsan , Lee Gyu Myoung
Published: February 2023

This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for home and/or hot water. When the residents leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving at their residences, the cooling or heating devices start working automatically to adjust the home or water temperature according to the residents’ preference (i.e., X degree Celsius). This can help reduce the energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this paper uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residents’ behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i.e., arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber’s dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers.
RGB-D-Based Stair Detection and Estimation Using Deep Learning
Authors: Wang Chen , Pei Zhongcai , Qiu Shuang , Tang Zhiyong
Published: February 2023

Stairs are common vertical traffic structures in buildings, and stair detection tasks are important in environmental perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a stair detection network with red-green-blue (RGB) and depth inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB feature maps and the depth feature maps and fuse the features effectively in different scenes. In addition, we propose several postprocessing algorithms, including a stair line clustering algorithm and a coordinate transformation algorithm, to obtain the stair geometric parameters. Experiments show that our method has better performance than existing the state-of-the-art deep learning method, and the accuracy, recall, and runtime are improved by 5.64%, 7.97%, and 3.81 ms, respectively. The improved indexes show the effectiveness of the multimodal inputs and the selective module. The estimation values of stair geometric parameters have root mean square errors within 15 mm when ascending stairs and 25 mm when descending stairs. Our method also has extremely fast detection speed, which can meet the requirements of most real-time applications.
Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
Authors: Siegl Manuel , Kämpf Manuel , Geier Dominik , Andreeßen Björn , Max Sebastian , Zavrel Michael , Becker Thomas
Published: February 2023

A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%).
Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach
Authors: Zhang Daoquan , Li Deping , Zhou Liang , Wu Jiejie
Published: February 2023

Fine classification of urban nighttime lighting is a key prerequisite step for small-scale nighttime urban research. In order to fill the gap of high-resolution urban nighttime light image classification and recognition research, this paper is based on a small rotary-wing UAV platform, taking the nighttime static monocular tilted light images of communities near Meixi Lake in Changsha City as research data. Using an object-oriented classification method to fully extract the spectral, textural and geometric features of urban nighttime lights, we build four types of classification models based on random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT), respectively, to finely extract five types of nighttime lights: window light, neon light, road reflective light, building reflective light and background. The main conclusions are as follows: (i) The equal division of the image into three regions according to the visual direction can alleviate the variable scale problem of monocular tilted images, and the multiresolution segmentation results combined with Canny edge detection are more suitable for urban nighttime lighting images; (ii) RF has the highest classification accuracy among the four classification algorithms, with an overall classification accuracy of 95.36% and a kappa coefficient of 0.9381 in the far view region, followed by SVM, KNN and DT as the worst; (iii) Among the fine classification results of urban light types, window light and background have the highest classification accuracy, with both UA and PA above 93% in the RF classification model, while road reflective light has the lowest accuracy; (iv) Among the selected classification features, the spectral features have the highest contribution rates, which are above 59% in all three regions, followed by the textural features and the geometric features with the smallest contribution rates. This paper demonstrates the feasibility of nighttime UAV static monocular tilt image data for fine classification of urban light types based on an object-oriented classification approach, provides data and technical support for small-scale urban nighttime research such as community building identification and nighttime human activity perception.
Double-Versus Triple-Potential Well Energy Harvesters: Dynamics and Power Output
Authors: Margielewicz Jerzy , Gąska Damian , Caban Jacek , Litak Grzegorz , Dudziak Agnieszka , Ma Xiaoqing , Zhou Shengxi
Published: February 2023

The basic types of multi-stable energy harvesters are bistable energy harvesting systems (BEH) and tristable energy harvesting systems (TEH). The present investigations focus on the analysis of BEH and TEH systems, where the corresponding depth of the potential well and the width of their characteristics are the same. The efficiency of energy harvesting for TEH and BEH systems assuming similar potential parameters is provided. Providing such parameters allows for reliable formulation of conclusions about the efficiency in both types of systems. These energy harvesting systems are based on permanent magnets and a cantilever beam designed to obtain energy from vibrations. Starting from the bond graphs, we derived the nonlinear equations of motion. Then, we followed the bifurcations along the increasing frequency for both configurations. To identify the character of particular solutions, we estimated their corresponding phase portraits, Poincare sections, and Lyapunov exponents. The selected solutions are associated with their voltage output. The results in this numerical study clearly show that the bistable potential is more efficient for energy harvesting provided the corresponding excitation amplitude is large enough. However, the tristable potential could work better in the limits of low-level and low-frequency excitations.
Validity and Reliability of Inertial Measurement System for Linear Movement Velocity in Flywheel Squat Exercise
Authors: Maroto-Izquierdo Sergio , Nosaka Kazunori , Alarcón-Gómez Jesús , Martín-Rivera Fernando
Published: February 2023

The aim of this study was to examine the validity and reliability of an Inertial Measurement System integrated into a secondary pulley (IMS) for determining linear velocity during flywheel squat exercises. Thirty-one male participants who were highly experienced in a flywheel resistance exercise training performed flywheel squat exercises with three incremental loads, and mean velocity (MV), mean propulsive velocity (MPV) and max velocity (Vmax) of the exercises were simultaneously recorded with a validated linear encoder and the IMS, in two different sessions. Validity was analyzed using ordinary least products regression (OLP), Lin’s concordance correlation coefficient (CCC), and Hedge’s g for the values from the linear encoder and the IMS. Test-retest reliability was determined by coefficient of variation (CV), Intraclass correlation coefficient (ICC), and standard error of measurement (SEM). Results showed a high degree of validity (OLP intercept = −0.09–0.00, OLP slope = 0.95–1.04, CCC = 0.96–0.99, Hedge’s g < 0.192, SEM = 0.04–0.08) and reliability (CV < 0.21%, ICC > 0.88, SEM < 0.08). These results confirm that the IMS provides valid and reliable measures of movement velocity during flywheel squat exercises.
Distributed Impact Wave Detection in Steel I-Beam with a Weak Fiber Bragg Gratings Array
Authors: Wang Yuan , Hoult Neil A. , Woods Joshua E. , Kassenaar Hannah , Bao Xiaoyi
Published: February 2023

In this paper, acoustic, dynamic and static strain variations along a steel I-beam generated by an impact load are reconstructed simultaneously within a single measurement. Based on the chirped pulse φ-OTDR system with the single-shot measurement technique, both a higher strain-sensing resolution and a higher measurable vibration frequency are achieved. In addition, a weak fiber Bragg gratings array (WFBGA) with enhanced Rayleigh reflection is employed as a sensor, providing high signal-to-noise ratio Rayleigh traces, resulting in lower measurement uncertainty. In the experiments, the damping constant and fundamental frequency of the damped harmonic oscillator could then be measured based on the recovered strain variation profile for further structural health analysis. Compared with commercial strain gauges, linear potentiometers, and OFDR systems, the proposed sensing system ensures a distributed, quantitative, and high-frequency sensing ability, with an extensive range of potential applications.
Multi-Lexicon Classification and Valence-Based Sentiment Analysis as Features for Deep Neural Stock Price Prediction
Authors: Velu Shubashini Rathina , Ravi Vinayakumar , Tabianan Kayalvily
Published: February 2023

The goal of the work is to enhance existing financial market forecasting frameworks by including an additional factor–in this example, a collection of carefully chosen tweets—into a long-short repetitive neural channel. In order to produce attributes for such a forecast, this research used a unique attitude analysis approach that combined psychological labelling and a valence rating that represented the strength of the sentiment. Both lexicons produced extra properties such 2-level polarization, 3-level polarization, gross reactivity, as well as total valence. The emotional polarity explicitly marked into the database contrasted well with outcomes of the innovative lexicon approach. Plotting the outcomes of each of these concepts against actual market rates of the equities examined has been the concluding step in this analysis. Root Mean Square Error (RMSE), preciseness, as well as Mean Absolute Percentage Error (MAPE) were used to evaluate the results. Across most instances of market forecasting, attaching an additional factor has been proven to reduce the RMSE and increase the precision of forecasts over lengthy sequences.
Chemical Influence of Scutellaria baicalensis—Coptis chinensis Pair on the Extraction Efficiencies of Flavonoids and Alkaloids at Different Extraction Times and Temperatures
Authors: Kim Han-Young , Kim Jung-Hoon
Published: February 2023

The Scutellaria baicalensis—Coptis chinensis pair is an herbal combination used for the treatment of various heat-related diseases. During the extraction process, two herbs can mutually influence the extraction efficiency of the chemical constituents contained in each herb. The concentrations of five flavonoids from S. baicalensis and seven alkaloids from C. chinensis were compared in paired or single hot-water extracts at different temperatures (80, 90, and 100 °C) and extraction times (60, 90, and 120 min). Temperature- and time-dependent increases in marker compound concentrations were observed in both paired and single extracts, with the exception of baicalin, berberine, and coptisine in the paired extracts at 100 °C. However, the extractions of the compounds in the paired and single extracts were affected differently by the extraction conditions. Furthermore, the concentrations of most marker compounds in single extracts were 1.09–44.13 times those in paired extracts. The contents of baicalin, wogonoside, coptisine, and berberine, known to be easily aggregated by the flavonoid–alkaloid complex, were changed by 0.024–0.764-fold in the paired extract. The effect of extraction temperature and time on the formation of the flavonoid–alkaloid complex was not significant. The extraction efficiency of the flavonoids and alkaloids can be affected by the pair of S. baicalensis—C. chinensis, which is a primary factor in the chemical modification of two herb-containing herbal extracts.
Potential of Advanced Oxidation as Pretreatment for Microplastics Biodegradation
Authors: Bule Možar Kristina , Miloloža Martina , Martinjak Viktorija , Cvetnić Matija , Kušić Hrvoje , Bolanča Tomislav , Kučić Grgić Dajana , Ukić Šime
Published: February 2023

In the last two decades, microplastics (MP) have been identified as an emerging environmental pollutant. Due to their small size, MP particles may easily enter the food chain, where they can have adverse effects on organisms and the environment in general. The common methods for the removal of pollutants from the environment are not fully effective in the elimination of MP; thus, it is necessary to find a more suitable treatment method(s). Among the various approaches tested, biodegradation is by far the most environmentally friendly and economically acceptable remediation approach. However, it has serious drawbacks, generally related to the rather low removal rate and often insufficient efficiency. Therefore, it would be beneficial to use some of the less economical but more efficient methods as pretreatment prior to biodegradation. Such pretreatment would primarily serve to increase the roughness and hydrophilicity of the surface of MP, making it more susceptible to bioassimilation. This review focuses on advanced oxidation processes (AOPs) as treatment methods that can enhance the biodegradation of MP particles. It considers MP particles of the six most commonly used plastic polymers, namely: polyethylene, polypropylene, polystyrene, polyvinyl chloride, polyethylene terephthalate and polyurethane. The review highlights organisms with a high potential for biodegradation of selected MP particles and presents the potential benefits that AOP pretreatment can provide for MP biodegradation.
A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies
Authors: Ahmadzadeh Sahar , Ajmal Tahmina , Ramanathan Ramakrishnan , Duan Yanqing
Published: February 2023

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, big data-based systems, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and big data-based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research.
Do Biomass Technology Innovations Improve Subjective Well-Being? Traditional versus Improved Cookstoves in Uganda
Authors: Namugenyi Irene , Navrud Ståle , Scholderer Joachim , Tione Sarah Ephrida
Published: February 2023

This study investigates the dark side of biomass technology innovations on households’ subjective well-being (SWB) in Uganda. The dark side of biomass technology innovations concerns households moving away from the intangible cultural cooking heritages that they have preserved for a long time. These intangible cultural cooking heritages are important to understand as they contribute to policy decisions on sustainable society transformation (sustainability transitions) in the clean cooking energy sector. This study adds to the scarce literature on innovation for well-being and innovation for transformative change, addressing grand societal challenges while considering the well-being of technology users. Principal component analysis was used to generate the subjective well-being variable from the captured traditional household cooking considerations, perceptions, and practices. Linear regression was used to analyze the effect of improved cookstoves (ICSs) and other factors on the subjective well-being of households in Uganda. The results show that using ICSs moves Ugandan households away from traditional ways of cooking, which reduces the well-being they attach to the intangible cultural heritage of traditional cooking. Thus, innovators, entrepreneurs and promoters of clean cooking technologies should consider the well-being of users along with the benefits of bioenergy innovation to accelerate society transformation (sustainability transitions) in Uganda.
The Role of Long-Term Institutional Ownership in Sustainability Report Assurance: Global Evidence
Authors: Alomran Abdulaziz A. , Alsahali Kholod F.
Published: February 2023

Focusing on the role of the institutional investment horizon as a monitoring mechanism that enhances companies’ sustainability reporting reliability, this study investigates the association between long-term ownership and companies’ decisions to assure their sustainability report. Further, the study examines the moderating effect of the quality of governance on this association. Consistent with the critical mass theory, the study argues that long-term ownership should reach a certain threshold to have an influence on companies’ assurance decisions. The study’s results support the argument and find that long-term ownership is positively and significantly associated with companies’ assurance decisions, and the association is positive and significant only for a high level of long-term ownership in comparison to low- and medium-level long-term ownership. Moreover, the study finds that the association between long-term ownership and assurance is negatively moderated by the quality of governance at both the company and country levels.
Comparative Evaluation of the Immersed-Solid Method for Simulating the Flow Field around Hydrofoil
Authors: Hu Zilong , Zhu Di , Xiao Ruofu , Tao Ran
Published: February 2023

The wall boundary is important in computational-fluid-dynamics simulations. If extremely small leakage, changing leakage or a moving body exists in the simulation case, the difficulty in meshing and solving near-wall flow increases. The immersed-solid method, which inserts a rigid, solid body into the entire fluid domain, was a choice to solve the wall-boundary-solution problems mentioned above, without considering mesh deformation. The purpose of this paper is to verify the effectiveness of the immersed-solid method in the simulation of extremely small leakage, changing leakage or a moving body, and to provide a theoretical basis for the use of the submerged-solid method in engineering. In this study, the NACA0015 hydrofoil was used to check the hydrodynamic characteristics in using the immersed-solid method. The comparative study was conducted at the incidence angle of 8 degrees and a Reynolds number of 5.0 × 105, by using the immersed-solid and traditional no-slip-wall boundary. The results show that the flow striking and separation with pressure rise and drop can be correctly captured using an immersed-solid setup with boundary tracking. However, the accuracy of pressure and velocity field using the immersed-solid method was insufficient. The turbulence-kinetic energy was much higher around the immersed-solid foil body. Generally, the immersed-solid method can qualitatively predict the correct hydrodynamic characteristics. Its convergence ability is better, and it can save approximately 20% of CPU time, even if the grid density is 4.39 times of the traditional no-slip wall. Therefore, the immersed-solid method can be a good choice for engineering-flow cases with complex wall problems.
Service Life Prediction of Flexible Concrete Mattresses (FCMs) in the Yangtze River
Authors: Wang Pingyi , Tian Ye , Wang Meili , Guo Genting
Published: February 2023

Many scholars have studied the failure mechanism of waterway regulating structures, but there is still a need to improve research on their reliability and service life in the industry. Currently, flexible concrete mattresses (FCMs) are the regulating structure widely used on the Yangtze River Golden Waterway, so it is important to study their service life. The stability function has been built based on the mechanism of stability against sliding and a time-varying reliability model. Experiments have been conducted to analyze the critical failure load of FCMs, determine the influence parameters, and derive a service life calculation formula. Finally, the middle reaches of the Yangtze River were taken as an example for analysis.
Relationship between Learning Strategies and Motivation of University Students
Authors: Pérez-Navío Eufrasio , Gavín-Chocano Óscar , Checa-Domene Lara , Prieto Marina García-Valdecasas
Published: February 2023

(1) Background: To achieve a quality education, within the university context, has become a fundamental issue in the educational agenda and objectives of Sustainable Development, mainly by corroborating that the knowledge acquired and the level of competence determine academic performance and are related to motivational factors. The objective was to analyze the relationship of the learning strategy through its different dimensions (learning support strategies, intrinsic motivation, and study habits) according to the motivational profile of Education students. (2) Methods: In this study, 436 students from the Faculties of Education of Granada and Jaén (Spain) participated, 318 women (73.6%) and 118 men (26.4%), with a mean age of 19.40 years (±3.67). The ACRA scale on Learning Strategies and the Academic Motivation Scale were used. (3) Results: The results showed that the determination coefficients Learning support strategies [(Q2 = 0.216); (R² = 0.559)], Intrinsic motivation [(Q2 = 0.225); (R² = 0.364)], and Study habits [(Q2 = 0.149); (R² = 0.315)], in the estimation of the measurement model, indicated a great adjustment of the model. (4) Conclusions: The present investigation is not conclusive; however, it presents evidence that could indicate a better performance in the students when implementing motivational strategies.
The Effect of Corporate Greenwashing on Employees’ Environmental Performance: Person–Organization Values Fit Perspective
Authors: Miao Guosen , Chen Guoping , Wang Fan , Das Anupam Kumar
Published: February 2023

In the present study, we construct a model of greenwashing on employees’ environmental performance based on person–organization fit theory. Path analysis and hierarchical regression methods were used to examine randomly selected data collected from 269 employees in eight Chinese gas service and chemical production companies. The results of the analysis reveal that employees’ perceived person–organization values fit mediates the relationship between organizational greenwashing and employees’ environmental performance; employees’ environmental beliefs not only positively moderate the relationship between corporate greenwashing behavior and employees’ perceived person–organization values fit, but also positively moderate the indirect effect of employees’ perceived person–organization values fit between organizational greenwashing and employees’ environmental performance. We provide some theoretical contributions to organizational greenwashing, and practical implications are also offered.
Expansion of Geological CO2 Storage Capacity in a Closed Aquifer by Simultaneous Brine Production with CO2 Injection
Authors: Jung Seungpil
Published: February 2023

Structural trapping is the primary mechanism for intensive CO2 sequestration in saline aquifers. This is the foundation for increasing global CO2 storage; gradual switch to preferable trapping mechanisms, such as residual saturation, dissolution, and mineral trapping, will require a long-time scale. The major constraints limiting the storage capacity of structural trapping are formation pressure and structure size. Over-pressure owing to CO2 injection causes a disruption of seal integrity indicating a failure in geological sequestration. The other constraint on storage capacity is a spill point determining geological storage volume. Overflowing CO2, after filling the storage volume, migrates upward along the aquifer geometry with buoyancy. This study proposes a methodology to maximize CO2 storage capacity of a geological site with a substructure created by an interbedded calcareous layer below spill point. This study provides various conceptual schemes, i.e., no brine production, simultaneous brine production and pre-injection brine production, for geological CO2 storage. By the comparative analysis, location of brine producer, production rate, and distance between injector and producer are optimized. Therefore, the proposed scheme can enhance CO2 storage capacity by 68% beyond the pressure and migration limits by steering CO2 plume and managing formation pressure.
Examining the Antecedents of Blockchain Usage Intention: An Integrated Research Framework
Authors: Ruangkanjanases Athapol , Qhal Eissa Mohammed Ali , Alfawaz Khaled Mofawiz , Hariguna Taqwa
Published: February 2023

Blockchain is considered one of the key technologies that can accelerate the Industrial Revolution 4.0. The intention to use Blockchain can still be improved in several ways, and how users perceive Blockchain is likely to be influenced by how well they understand the underlying theory. This study examines several important factors, namely government regulation, social influence, perceived security, and Blockchain functional benefits, to measure trust and satisfaction with relationship quality, which may influence the intention to use Blockchain. A sample of 460 people participated in the online questionnaire survey, which was then evaluated with SmartPLS 3. The findings reveal that the social influence and Blockchain functional benefits have a substantial impact on relationship quality, which further results in a positive impact on Blockchain usage intention as well. This study can serve as a reference for companies that need to consider the factors discussed in this study when implementing Blockchain technology to achieve marketing goals and generate sustainable Blockchain usage intentions.
Incorporating Traditional Knowledge into Science-Based Sociotechnical Measures in Upper Watershed Management: Theoretical Framework, Existing Practices and the Way Forward
Authors: Nugroho Hunggul Yudono Setio Hadi , Sallata Markus Kudeng , Allo Merryana Kiding , Wahyuningrum Nining , Supangat Agung Budi , Setiawan Ogi , Njurumana Gerson Ndawa , Isnan Wahyudi , Auliyani Diah , Ansari Fajri , Hanindityasari Luthfi , Najib Nardy Noerman
Published: February 2023

In Indonesia, 2145 watersheds currently need to be restored, where around 21 million people spread over ± 23,000 villages live below the poverty line with a high dependence on forests. This condition requires an integrated approach in watershed management, which is aimed at technically restoring environmental conditions and ensuring the welfare of the people in it. One of the strategic approaches that can be taken is to revive local wisdom and traditional knowledge (TK), which has been eroded and neglected, and integrate them with technical approaches based on modern science and knowledge. Based on the author’s research and literature studies, this paper discusses the theoretical framework and implementation practices in integrating traditional knowledge into a science-based sociotechnical system to manage upstream watersheds sustainably. Based on the empirical evidence, efforts to create good biophysical and socio-economic watershed conditions can only be achieved through the active participation of farmers in adopting and integrating scientific technology into their traditional knowledge. This integration is realized in designing and implementing watershed management technology by considering the principles of suitability, applicability, feasibility, and acceptability. In the long term, it is necessary to document TK, patent it, and transfer it to the next generation to ensure that indigenous peoples’ and local communities’ social, cultural, and economic interests are protected.
Feature Recognition of Regional Architecture Forms Based on Machine Learning: A Case Study of Architecture Heritage in Hubei Province, China
Authors: Zou Han , Ge Jing , Liu Ruichao , He Lin
Published: February 2023

Architecture form has been one of the hot areas in the field of architectural design, which reflects regional architectural features to some extent. However, most of the existing methods for architecture form belong to the field of qualitative analysis. Accordingly, quantitative methods are urgently required to extract regional architectural style, identify architecture form, and to and further provide the quantitative evaluation. Based on machine learning technology, this paper proposes a novel method to quantify the feature, form, and evaluation of regional architectures. First, we construct a training dataset—the Chinese Ancient Architecture Image Dataset (CAAID), in which each image is labeled by some experts as having at least one of three typical features such as “High Pedestal”, “Deep Eave” and “Elegant Gable”. Second, the CAAID is used to train our neural network model to identify three kinds of architectural features. In order to reveal the traditional forms of regional architecture in Hubei, we built the Hubei Architectural Heritage Image Dataset (HAHID) as our object dataset, in which we collected architectural images from four different regions including southeast, northeast, southwest, and northwest Hubei. Our object dataset is then fed into our neural network model to predict the typical features for those four regions in Hubei. The obtained quantitative results show that the feature identification of the architectural form is consistent with that of regional architectures in Hubei. Moreover, we can observe from the quantitative results that four geographic regions in Hubei show variation; for instance, the feature of the ‘elegant gable’ in southeastern Hubei is more evident, while the “Deep Eave” in the northwest is more evident. In addition, some new building images are selected to feed into our neural network model and the output quantitative results can effectively identify the corresponding feature style of regional architectures in Hubei. Therefore, our proposed method based on machine learning can be used not only as a quantitative tool to extract features of regional architectures, but also as an effective approach to evaluate architecture forms in the urban renewal process.
Material Flow Analysis in WEEE Management for Circular Economy: A Content Review on Applications, Limitations, and Future Outlook
Authors: Azizi Dhiya Durani Sofian , Hanafiah Marlia M. , Woon Kok Sin
Published: February 2023

Recently, the material flow analysis (MFA) of waste from electrical and electronic equipment (WEEE) has attracted much interest from researchers worldwide because of its function as a decision-making tool for moving towards a circular economy and sustainability in WEEE management. However, review studies on the development of MFA in WEEE management studies are still lacking. To fill this gap, 115 MFA studies published in various databases between 2010 and 2022 were analysed to provide a comprehensive overview of current research progress and recommendations for future studies. Three major elements from previous studies were reviewed using content analysis: (i) the trend of publishing in OECD and non-OECD countries over time; (ii) the scope and boundaries of the aspect studied, and the use of MFA in forecasting the generation, flow, and stock of WEEE; and (iii) providing a future outlook for MFA studies based on its limitations. Further analysis has revealed a considerable increase in the number of studies on MFA of WEEE in major OECD nations; however, the number of studies on concentrated areas of WEEE, particularly in non-OECD countries, remains low. Another limitation of MFA research is the scope and boundaries, with very few studies addressing the aspects of uncertainty analysis, circular economy, and life cycle analysis. Due to the increasing number of newly developed methodologies and the absence of thorough evaluation on this study topic, few studies properly utilised the static or dynamic MFA model. Thus, based on the research limitations and gaps discussed, recommendations for future studies are proposed.
The Impact of Urban Expressways on the Street Space of Traditional Tibetan Villages in Kham, Taking Daofu County as an Example
Authors: Xie Ningling , Cheng Bin
Published: February 2023

Villages are integral to population gathering in the Tibetan ethnic area of Kham, and their spatial formation is influenced by various factors such as topography, river and canyon direction, etc. This study focuses on the impact of urban expressways on traditional villages there. In this study, we take 18 well-preserved traditional villages in the Kham area as examples, construct an axial model and a visual field model from the perspective of space syntax, simulate the basic data required for the algorithm, and further calculate the categorized spatial measures and star model of the streets. One-way ANOVA and Tukey’s multiple comparisons test are adopted to statistically analyze the above spatial measures. The results show that villages intersecting urban expressways have high categorized spatial measures. In addition, it is found that the sample adjacent to the expressway with the lowest mean NACH value (1.396) has the highest mean NAIN value (1.703). The sample with the highest mean NAIN (0.930) intersecting the urban expressway has the lowest mean NACH (0.743). The above approach presents certain reference values for the urbanization of traditional villages in India and Nepal, where topographic and climatic conditions are similar.
Oat–Field Pea Intercropping for Sustainable Oat Production: Effect on Yield, Nutritive Value and Environmental Impact
Authors: Tamiru Metekia , Alkhtib Ashraf , Belachew Basha , Demeke Solomon , Worku Zemene , Wamatu Jane , Burton Emily
Published: February 2023

The aim of the study is to evaluate the effect of Oat–field pea intercropping on the yield, nutritive value, and environmental impact of oat grown under a reduced level of nitrogen fertilisation. The trial was laid out in a randomized complete block design with the following treatments: oat-0 (oat (Avena sativa L., SRCP X 80 Ab 2291 variety) without N fertilization (urea)), oat-23 (oat fertilised with 23 kg N/ha), oat-46 (oat fertilised with 46 kg N/ha), O1P1 (oat intercropped with field pea (Pisum sativum L., local variety) a ratio of 1:1), O1P2 (oat intercropped with field pea a ratio of 1:2), and O2P1 (oat intercropped with field pea at a ratio of 2:1). All of the experimental plots received standard husbandry practices except for nitrogen fertilisation. Soil pH, organic matter, total nitrogen, available phosphorus, and organic carbon were determined before and after planting. The effect of nitrogen fertilization and intercropping of oat with field pea on carbon footprint, acidification footprint, eutrophication footprint, and human toxicity footprint was calculated for each plot. Oat-0 significantly reduced the total nitrogen content of the soil, while there was no significant effect of the other treatments. O2P1 significantly out-yielded all control groups; however, it was not significantly different from fertilisation treatments. Intercropping with field pea did not significantly increase the cost of production of dry matter, crude protein, or dry matter digestibility compared to control groups. Intercropping with field pea significantly reduced the carbon footprint, acidification, eutrophication, and human toxicity footprint compared to the control groups. Therefore, oat–field pea intercrops are recommended for the production of high-quality forage at low N input with reduced environmental impact.