bluuebunny/crossref_metadata_2025 huggingface dataset:
Impacts of Phase Noise on the Anti-Jamming Performance of Power Inversion Algorithm
Authors: Zhou Minglei , Wang Qing , He Fangmin , Meng Jin
Published: March 2022

Power inversion (PI) is a known adaptive beamforming algorithm that is widely used in wireless communication systems for anti-jamming purposes. The PI algorithm is typically implemented in a digital domain, which requires the radio-frequency signals to be down-converted into base-band signals, and then sampled by ADCs. In practice, the down-conversion circuit will introduce phase noises into the base-band signals, which may degrade the performance of the algorithm. At present, the impacts of phase noise on the PI algorithm have not been studied, according to the open literature, which is, however, important for practical design. Therefore, in this paper, we present a theoretical analysis on the impacts, provide a new mathematical model of the PI algorithm, and offer a closed-form formula of the interference cancellation ratio (ICR) to quantify the relations between the algorithm performance and the phase noise level, as well as the number of auxiliary antennas. We find that the ICR in decibel decreases logarithmically linearly with the phase noise variance. In addition, the ICR improves with an increasing number of auxiliary antennas, but the increment is upper-bounded. The above findings are verified with both simulated and measured phase noise data.
Multi-Time Scale Trading Simulation of Source Grid Load Storage Based on Continuous Trading Mechanism for China
Authors: Dou Xun , Song Li , Zhang Shengnan , Ding Lulu , Shao Ping , Cao Xiaojun
Published: March 2022

The proportion of new energy in power systems is increasing yearly. How to deal with the adverse impact of new energy output uncertainty on its participation in trading from the mechanism level is an urgent problem in China that must be solved. A source grid load storage (SGLS) continuous trading mechanism and a multi-time scale trading simulation method are proposed which meet the needs of Chinese new energy consumption and satisfies the trading needs of Chinese power market players. Firstly, the connection mechanism of mid-long term, day-ahead, and intra-day SGLS interactive trading is established, and the meaning and ways of continuous development are defined. Secondly, the clearing model of SGLS trading based on the continuous trading mechanism is established to provide mathematical models and strategic methods for various resources to participate in SGLS trading. Then, the multi-time scale trading simulation of SGLS based on the continuous trading mechanism is carried out to obtain the trading strategies of different trading subjects. The example results show that compared with the trading mechanism based on deviation assessment, the one-day trading cost is reduced by 4.20% and the consumption rate of new energy is increased by 6.53%. It can be seen that the mid-long term–day-ahead–day SGLS interactive trading connection mechanism has advantages in reducing trading costs and improving the consumption rate of new energy. It can flexibly deal with the trading scenario of domestic new energy consumption and new energy reverse peak shaving, which has an effect on the adverse impact of trading and operation deviation caused by source load uncertainty on trading.
Research on the Applicability of Vibration Signals for Real-Time Train and Track Condition Monitoring
Authors: Celiński Ireneusz , Burdzik Rafał , Młyńczak Jakub , Kłaczyński Maciej
Published: March 2022

The purpose of this research was to analyze the possibilities for the application of vibration signals in real-time train and track control. Proper experiments must be performed for the validation of the methods. Research on vibration in the context of transport must entail many of the different nonlinear dynamic forces that may occur while driving. Therefore, the paper addresses two research cases. The developed application contains the identification of movement and dynamics and the evaluation of the technical state of the rail track. The statistics and resultant vector methods are presented. The paper presents other useful metrics to describe the dynamical properties of the driving train. The angle of the resultant horizontal and vertical accelerations is defined for the evaluation of the current position of cabin. It is calculated as an inverse tangent function of current longitudinal and transverse, longitudinal and vertical, transverse, and vertical accelerations. Additionally, the resultant vectors of accelerations are calculated.
Microtomographic Analysis of a Palaeolithic Wooden Point from the Ljubljanica River
Authors: Puhar Enej Guček , Korat Lidija , Erič Miran , Jaklič Aleš , Solina Franc
Published: March 2022

A rare and valuable Palaeolithic wooden point, presumably belonging to a hunting weapon, was found in the Ljubljanica River in Slovenia in 2008. In order to prevent complete decay, the waterlogged wooden artefact had to undergo conservation treatment, which usually involves some expected deformations of structure and shape. To investigate these changes, a series of surface-based 3D models of the artefact were created before, during and after the conservation process. Unfortunately, the surface-based 3D models were not sufficient to understand the internal processes inside the wooden artefact (cracks, cavities, fractures). Since some of the surface-based 3D models were taken with a microtomographic scanner, we decided to create a volumetric 3D model from the available 2D tomographic images. In order to have complete control and greater flexibility in creating the volumetric 3D model than is the case with commercial software, we decided to implement our own algorithm. In fact, two algorithms were implemented for the construction of surface-based 3D models and for the construction of volumetric 3D models, using (1) unsegmented 2D images CT and (2) segmented 2D images CT. The results were positive in comparison with commercial software and new information was obtained about the actual state and causes of the deformation of the artefact. Such models could be a valuable aid in the selection of appropriate conservation and restoration methods and techniques in cultural heritage research.
Arbitrary Font Generation by Encoder Learning of Disentangled Features
Authors: Lee Jeong-Sik , Baek Rock-Hyun , Choi Hyun-Chul
Published: March 2022

Making a new font requires graphical designs for all base characters, and this designing process consumes lots of time and human resources. Especially for languages including a large number of combinations of consonants and vowels, it is a heavy burden to design all such combinations independently. Automatic font generation methods have been proposed to reduce this labor-intensive design problem. Most of the methods are GAN-based approaches, and they are limited to generate the trained fonts. In some previous methods, they used two encoders, one for content, the other for style, but their disentanglement of content and style is not sufficiently effective in generating arbitrary fonts. Arbitrary font generation is a challenging task because learning text and font design separately from given font images is very difficult, where the font images have both text content and font style in each image. In this paper, we propose a new automatic font generation method to solve this disentanglement problem. First, we use two stacked inputs, i.e., images with the same text but different font style as content input and images with the same font style but different text as style input. Second, we propose new consistency losses that force any combination of encoded features of the stacked inputs to have the same values. In our experiments, we proved that our method can extract consistent features of text contents and font styles by separating content and style encoders and this works well for generating unseen font design from a small number of reference font images that are human-designed. Comparing to the previous methods, the font designs generated with our method showed better quality both qualitatively and quantitatively than those with the previous methods for Korean, Chinese, and English characters. e.g., 17.84 lower FID in unseen font compared to other methods.
Towards ‘Fourth Paradigm’ Spectral Sensing
Authors: Webler Forrest Simon , Spitschan Manuel , Andersen Marilyne
Published: March 2022

Reconstruction algorithms are at the forefront of accessible and compact data collection. In this paper, we present a novel reconstruction algorithm, SpecRA, that adapts based on the relative rarity of a signal compared to previous observations. We leverage a data-driven approach to learn optimal encoder-array sensitivities for a novel filter-array spectrometer. By taking advantage of the regularities mined from diverse online repositories, we are able to exploit low-dimensional patterns for improved spectral reconstruction from as few as p=2 channels. Furthermore, the performance of SpecRA is largely independent of signal complexity. Our results illustrate the superiority of our method over conventional approaches and provide a framework towards “fourth paradigm” spectral sensing. We hope that this work can help reduce the size, weight and cost constraints of future spectrometers for specific spectral monitoring tasks in applied contexts such as in remote sensing, healthcare, and quality control.
Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning
Authors: Aggarwal Apeksha , Srivastava Akshat , Agarwal Ajay , Chahal Nidhi , Singh Dilbag , Alnuaim Abeer Ali , Alhadlaq Aseel , Lee Heung-No
Published: March 2022

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.
Threats Detection during Human-Computer Interaction in Driver Monitoring Systems
Authors: Kashevnik Alexey , Ponomarev Andrew , Shilov Nikolay , Chechulin Andrey
Published: March 2022

This paper presents an approach and a case study for threat detection during human–computer interaction, using the example of driver–vehicle interaction. We analyzed a driver monitoring system and identified two types of users: the driver and the operator. The proposed approach detects possible threats for the driver. We present a method for threat detection during human–system interactions that generalizes potential threats, as well as approaches for their detection. The originality of the method is that we frame the problem of threat detection in a holistic way: we build on the driver–ITS system analysis and generalize existing methods for driver state analysis into a threat detection method covering the identified threats. The developed reference model of the operator–computer interaction interface shows how the driver monitoring process is organized, and what information can be processed automatically, and what information related to the driver behavior has to be processed manually. In addition, the interface reference model includes mechanisms for operator behavior monitoring. We present experiments that included 14 drivers, as a case study. The experiments illustrated how the operator monitors and processes the information from the driver monitoring system. Based on the case study, we clarified that when the driver monitoring system detected the threats in the cabin and notified drivers about them, the number of threats was significantly decreased.
Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research
Authors: Huang Jianwei , Kwan Mei-Po , Cai Jiannan , Song Wanying , Yu Changda , Kan Zihan , Yim Steve Hung-Lam
Published: March 2022

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.
Drone-Mountable Gas Sensing Platform Using Graphene Chemiresistors for Remote In-Field Monitoring
Authors: Park Jaewoo , Jumu Franklyn , Power Justin , Richard Maxime , Elsahli Yomna , Jarkas Mohamad Ali , Ruan Andy , Luican-Mayer Adina , Ménard Jean-Michel
Published: March 2022

We present the design, fabrication, and testing of a drone-mountable gas sensing platform for environmental monitoring applications. An array of graphene-based field-effect transistors in combination with commercial humidity and temperature sensors are used to relay information by wireless communication about the presence of airborne chemicals. We show that the design, based on an ESP32 microcontroller combined with a 32-bit analog-to-digital converter, can be used to achieve an electronic response similar, within a factor of two, to state-of-the-art laboratory monitoring equipment. The sensing platform is then mounted on a drone to conduct field tests, on the ground and in flight. During these tests, we demonstrate a one order of magnitude reduction in environmental noise by reducing contributions from humidity and temperature fluctuations, which are monitored in real-time with a commercial sensor integrated to the sensing platform. The sensing device is controlled by a mobile application and uses LoRaWAN, a low-power, wide-area networking protocol, for real-time data transmission to the cloud, compatible with Internet of Things (IoT) applications.
Amalgamation of Geometry Structure, Meteorological and Thermophysical Parameters for Intelligent Prediction of Temperature Fields in 3D Scenes
Authors: Cao Yuan , Li Ligang , Ni Wei , Liu Bo , Zhou Wenbo , Xiao Qi
Published: March 2022

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.
Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models
Authors: Križaj Janez , Dobrišek Simon , Štruc Vitomir
Published: March 2022

Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results.
Parallel Genetic Algorithms’ Implementation Using a Scalable Concurrent Operation in Python
Authors: Skorpil Vladislav , Oujezsky Vaclav
Published: March 2022

This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master–Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results’ implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.
Exploiting Plasmonic Phenomena in Polymer Optical Fibers to Realize a Force Sensor
Authors: Arcadio Francesco , Zeni Luigi , Cennamo Nunzio
Published: March 2022

In this work, a novel sensing approach to realize a force optical fiber sensor is designed, developed, and experimentally tested. The proposed sensing methodology exploits the effects of deformation due to an applied force on a patch of plastic optical fiber (POF) connected at the input of a surface plasmon resonance (SPR) sensor realized in a D-shaped POF. Therefore, the proposed force sensor system consists of an SPR D-shaped POF sensor, connected to a spectrometer, within input of a POF patch, connected to a light source used for interacting with the applied force. When the applied force on the patch changes, the mode profile of the light in the multimode POF patch and the SPR-POF sensor change too, so the SPR spectra shift. The obtained experimental results demonstrate that the proposed sensor has a resolution of the force sensor equal to about 22 mN and an excellent linear response in the range from 0 N to 0.5 N.
Study on Method for Measuring Coating Emissivity by Applying Active Irradiation Based on Infrared Thermal Imager
Authors: Li Yiwen , Zhang Puyousen , Chen Ge , Li Yao , Hua Weizhuo , Li Yuqin , Jiao Zhaoqiang
Published: March 2022

To achieve rapid and precise non-contact measurements of coating emissivity at room temperature, a measurement method based on infrared thermal imager was proposed. By applying two irradiations with different energies to the target and reference surfaces, the influences of atmospheric transmittance, radiation of the target itself, environmental radiation, and atmospheric path radiation were eliminated, thereby enabling accurate emissivity measurement. Experiments were designed for validation with a mid-wave infrared thermal imager and a surface blackbody as the radiation source. Several combinations of irradiation energy were set to investigate the effects of average energy and energy difference between the two irradiations on the measured results. The normal emissivity of the coated sample plate in the mid-wave band was measured to generate the image of coating surface emissivity. Then, the emissivity measurement results of the proposed method were compared with those of the energy method and the point emissivity measuring instrument under the same conditions, and the comparison indicated that the proposed method can effectively measure the emissivity of coating. Some factors causing measurement errors were analyzed. Finally, an experiment was designed to compare the measurement speed between the proposed method and the currently used methods, and the experimental results were analyzed.
Local Trust in Internet of Things Based on Contract Theory
Authors: Fragkos Georgios , Minwalla Cyrus , Plusquellic Jim , Tsiropoulou Eirini Eleni
Published: March 2022

Autonomous trust mechanisms enable Internet of Things (IoT) devices to function cooperatively in a wide range of ecosystems, from vehicle-to-vehicle communications to mesh sensor networks. A common property desired in such networks is a mechanism to construct a secure, authenticated channel between any two participating nodes to share sensitive information, nominally a challenging proposition for a large, heterogeneous network where node participation is constantly in flux. This work explores a contract-theoretic framework that exploits the principles of network economics to crowd-source trust between two arbitrary nodes based on the efforts of their neighbors. Each node in the network possesses a trust score, which is updated based on useful effort contributed to the authentication step. The scheme functions autonomously on locally adjacent nodes and is proven to converge onto an optimal solution based on the available nodes and their trust scores. Core building blocks include the use of Stochastic Learning Automata to select the participating nodes based on network and social metrics, and the formulation of a Bayesian trust belief distribution from the past behavior of the selected nodes. An effort-reward model incentivizes selected nodes to accurately report their trust scores and contribute their effort to the authentication process. Detailed numerical results obtained via simulation highlight the proposed framework’s efficacy and performance. The performance achieved near-optimal results despite incomplete information regarding the IoT nodes’ trust scores and the presence of malicious or misbehaving nodes. Comparison metrics demonstrate that the proposed approach maximized the overall social welfare and achieved better performance compared to the state of the art in the domain.
Phase Optimization for Multipoint Haptic Feedback Based on Ultrasound Array
Authors: Long Zhili , Ye Shuyuan , Peng Zhao , Yuan Yuyang , Li Zhuohua
Published: March 2022

Ultrasound-based haptic feedback is a potential technology for human–computer interaction (HCI) with the advantages of a low cost, low power consumption and a controlled force. In this paper, phase optimization for multipoint haptic feedback based on an ultrasound array was investigated, and the corresponding experimental verification is provided. A mathematical model of acoustic pressure was established for the ultrasound array, and then a phase-optimization model for an ultrasound transducer was constructed. We propose a pseudo-inverse (PINV) algorithm to accurately determine the phase contribution of each transducer in the ultrasound array. By controlling the phase difference of the ultrasound array, the multipoint focusing forces were formed, leading to various shapes such as geometries and letters, which can be visualized. Because the unconstrained PINV solution results in unequal amplitudes for each transducer, a weighted amplitude iterative optimization was deployed to further optimize the phase solution, by which the uniform amplitude distributions of each transducer were obtained. For the purpose of experimental verification, a platform of ultrasound haptic feedback consisting of a Field Programmable Gate Array (FPGA), an electrical circuit and an ultrasound transducer array was prototyped. The haptic performances of a single point, multiple points and dynamic trajectory were verified by controlling the ultrasound force exerted on the liquid surface. The experimental results demonstrate that the proposed phase-optimization model and theoretical results are effective and feasible, and the acoustic pressure distribution is consistent with the simulation results.
Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years
Authors: Glisic Branko
Published: March 2022

Strain is one of the most frequently monitored parameters in civil structural health monitoring (SHM) applications, and strain-based approaches were among the first to be explored and applied in SHM. There are multiple reasons why strain plays such an important role in SHM: strain is directly related to stress and deflection, which reflect structural performance, safety, and serviceability. Strain field anomalies are frequently indicators of unusual structural behaviors (e.g., damage or deterioration). Hence, the earliest concepts of strain sensing were explored in the mid-XIX century, the first effective strain sensor appeared in 1919, and the first onsite applications followed in the 1920′s. Today, one hundred years after the first developments, two generations of strain sensors, based on electrical and fiber-optic principles, firmly reached market maturity and established themselves as reliable tools applied in strain-based SHM. Along with sensor developments, the application methods evolved: the first generation of discrete sensors featured a short gauge length and provided a basis for local material monitoring; the second generation greatly extended the applicability and effectiveness of strain-based SHM by providing long gauge and one-dimensional (1D) distributed sensing, thus enabling global structural and integrity monitoring. Current research focuses on a third generation of strain sensors for two-dimensional (2D) distributed and quasi-distributed sensing, based on new advanced technologies. On the occasion of strain sensing centenary, and as an homage to all researchers, practitioners, and educators who contributed to strain-based SHM, this paper presents an overview of the first one hundred years of strain sensing technological progress, with the objective to identify relevant transformative milestones and indicate possible future research directions.
The Evaluation of Amino Acid Profiles in Gluten-Free Mini Sponge Cakes Fortified with Broccoli By-Product
Authors: Drabińska Natalia
Published: March 2022

Many gluten-free products are deficient in amino acids, especially in essential amino acids (EAA). Therefore, the incorporation of additives rich in free amino acids (FAA) into gluten-free products can be a promising strategy to alleviate certain symptoms of celiac disease associated with EAA deficiencies. This study aimed to evaluate the effect of the incorporation of broccoli leaf powder (BLP) into gluten-free mini sponge cakes (GFS) on the profile of FFA. BLP replaced an equivalent amount (2.5%, 5%, 7.5%; w/w) of corn and potato starches in GFS formulation, resulting in B1-B3 formulations. The first step was the selection of the most efficient method for extraction of FAA. Extraction based on 50% methanol (method 1) was compared to extraction by 25% of acetonitrile in 0.1 M hydrochloric acid (method 2). In total, 26 and 14 FAA were found in BLP after extraction using methods 1 and 2, respectively. Moreover, considering the total content of FAA, method 1 was more efficient, reaching a 14-fold higher concentration of FFA in BLP compared to method 2. The incorporation of BLP resulted in a significant increase in FAA, irrespective of the applied extraction method. The total concentrations of NEAA and EAA increased significantly in B3 compared to control GFS. In summary, this study showed that 50% methanol was more efficient for the extraction of FFA from plant and bakery matrices. Moreover, BLP was found as a good source of FFA, including EAA, and the obtained experimental GFS could be considered a promising product for individuals on a gluten-free diet.
The Intensification of Parenting in Germany: The Role of Socioeconomic Background and Family Form
Authors: Walper Sabine , Kreyenfeld Michaela
Published: March 2022

Drawing on the international discourse on the intensification of parenting and new data from Germany, this paper aims to contribute to a better understanding of the unique challenges that parents face in the 21st century. We used data from the survey “Parenthood Today”, which was conducted in 2019 to examine parents’ views on parenting in Germany. The data comprised standardized interviews with 1652 mothers and fathers. We focused on three dimensions of parental pressures: namely, time pressure, financial pressure, and pressure that emanates from the educational system. Time pressure referred to the pressure currently felt, whereas financial pressure and pressure from the educational system referred to changes across time. In each of these domains, more than 60% of the parents experienced high (time) or increasing (education and financial) pressure. Binary logistic regressions showed that while parental education was a strong predictor of experiencing an increase in financial pressure, parental education did not matter for other realms of parenting. However, employment and family form were strongly related to parental time pressure. Full-time employed lone mothers, but also non-resident fathers, reported experiencing heavy pressure when trying to balance their roles as a worker and as a carer. Our results draw attention to the importance of better integrating the needs of post-separation families, including of non-resident fathers, in the debate on the “intensification of parenting”.
Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach
Authors: Saha Arnab , Sen Gupta Bhaskar , Patidar Sandhya , Martínez-Villegas Nadia
Published: March 2022

Toxic heavy metals in soil negatively impact soil’s physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliability, and rapidity at a high temporal and spatial resolution, hyperspectral remote sensing within the Vis-NIR region is an indispensable and widely used approach in today’s world for monitoring broad regions and controlling soil arsenic (As) pollution in agricultural land. This study investigates the effectiveness of hyperspectral reflectance approaches in different regions for assessing soil As pollutants, as well as a basic review of space-borne earth observation hyperspectral sensors. Multivariate and various regression models were developed to avoid collinearity and improve prediction capabilities using spectral bands with the perfect correlation coefficients to access the soil As contamination in previous studies. This review highlights some of the most significant factors to consider when developing a remote sensing approach for soil As contamination in the future, as well as the potential limits of employing spectroscopy data.
Evaluation of the Molecular Conformation of Surface Alkyl Chains of Alkylsilane-Derived Hybrid Films Using Sum-Frequency Generation Spectroscopy
Authors: Mizuno Rika , Urata Chihiro , Watanabe Ken , Hozumi Atsushi , Miyamae Takayuki
Published: March 2022

Alkylsilane-derived hybrid films exhibiting excellent dynamic dewetting behaviors toward various liquids are promising, since they are smooth, highly transparent, and a low environmental burden. However, the detailed mechanism of their unique dynamic dewetting behaviors and its relation to the surface segregation of alkylsilanes during the film formation have not yet been clearly identified. In this study, we prepared various hybrid films by varying the mixing ratios of tetraethoxysilane (TEOS) and n-dodecyltriethoxysilane (C12TES) and investigated the changes in the s-CH2/s-CH3 peak strength ratios of the resulting hybrid films under dry and wet conditions by sum-frequency generation (SFG) spectroscopy. When the static/dynamic water contact angles significantly changed, it was clearly observed that the s-CH2/s-CH3 ratio of each hybrid film under dry and wet condition also changed markedly. With increasing TEOS concentration, the static contact angles became smaller, while the contact angle hysteresis tended to increase because of the increase in gauche defects at the air interface and hydrogen bonds. This finding suggests that the concentration and conformation of the alkyl chains derived from surface-segregated C12TES molecules play an important role in determining the final dewetting behaviors of the hybrid films to water.
A Framework for Developing Environmental Justice Indicators
Authors: Browne Geoffrey R. , Gunn Lucy Dubrelle , Davern Melanie
Published: March 2022

Environmental justice (EJ) is a broad discipline that attempts to understand and redress unfair exposure to unhealthy environmental conditions. EJ is often made measurable with indicators, however the capabilities and the limitations of EJ indicators can be difficult for policy makers to understand. Using an exploratory review of EJ literature, this paper performs a research translation role by clarifying the key terms used to describe EJ indicators and by providing conceptual frameworks for developing locally valid EJ indicators for government and community. Issues such as the position of EJ in the context of the social determinants of health, indicator development, and definitions are explored. The exploratory literature review highlighted the potential and limitations of EJ indicators for measuring the extent and impact of EJ issues on human health and the environment. We found that EJ indicators are invariably (1) composed of two or more measures, and (2) developed iteratively, in consultation with those affected. To aid governments and communities, we present both a top-down and a bottom-up framework for developing EJ indicators, with the bottom-up framework guiding the combination of measures from four different core elements; environment, demography, epidemiology and procedures. A list of evidence-based example measures, from the literature, for developing EJ indicators is also provided. It is anticipated that the frameworks and list of EJ example measures will provide guidance for efficiently developing locally relevant EJ indicators.
Significant Factors Causing Delay in the Cambodian Construction Industry
Authors: Tahmasebinia Faham , Song Vicheth
Published: March 2022

The construction sector is one of the essential sectors in the world; it aids in developing all the countries in the world because it is the foundation of other industries. However, delays in construction projects are a common issue that has been found in all global construction projects. This research aims to understand the issues that lead to construction project delay, ranking the ten most significant causes of construction delay and providing instructive recommendations to overcome the discussed issue in the construction sector. The first ten substantial delay factors are poor planning and scheduling, late delivery of material, change of scope by owners during the construction phase, poor site management and supervision, insufficient finances of contractors or sub-contractors, lack of experience of the contractor’s team, delay due to design error, low productivity of labor, delay in process payment by the owner, and delay in the decision making of the owners and consultants. Subsequently, it was discovered that the contractors, consultants, and owners must cooperate and collaborate in all aspects to improve the construction industry.
The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China
Authors: Wang Linyan , Hu Haiqing , Wang Xianzhu
Published: March 2022

With the growth of urban agglomerations, the spatial diffusion of housing investment is clear; however, little research has been carried out to address its network characteristics and underlying mechanisms of influence. Using data on 35 large and medium-sized cities, this paper applies niche theory to housing investment, constructing a housing investment niche index that includes resources, the housing market, the social economy, and policy. The purpose is to study the characteristics of the network structure and its mechanisms of influence based on an improved gravity model and a temporal exponential random graph model (TERGM). Supported by the analysis of the network structure, we find that the node degree within the network is low, the network density exhibits an inverted “V” shape, and the network level suggests the existence of a “rich cities club”. According to the local network clustering analysis, the cities are divided into three clusters: the Yangtze River Delta region, the Beijing–Tianjin–Hebei region, and the central and eastern regions. Furthermore, analysis of the endogeneity in the structure reveals that there is a hierarchy of cities with high economic development levels, which makes it difficult to establish an investment network with strong relationships. The effects of the attributes are consistent with the predictions of location theory and new economic geography theory. The external network effects conform to the law of the general gravity model. Our research provides insights into the ways in which the misplaced competition in housing investment between cities in a region and the flow of production factors can be reasonably guided.
Early Childhood STEM Education for Sustainable Development
Authors: Campbell Coral , Speldewinde Christopher
Published: March 2022

Early childhood education is crucial for the development of young children’s understanding of the natural world. Children have a role in sustaining a viable environmental and social future. This research interrogated key ideas concerning STEM education for sustainable development, drawing on seminal research and a range of government policy documents to formulate a futures-oriented approach to supporting children to build understandings in early childhood sustainability. Through the use of ethnography, a research methodology that uses both participation and observation of research participants, it became apparent that young children’s play-based learning enabled agentic responses in aligning with early understanding of STEM and sustainability. Using accepted descriptors of international Sustainable Development Goals within an early childhood research study, the research highlights how the development of interactive, learner-centred STEM teaching not only enables investigative, action-adapted learning, but also fosters independent learners who are responsive to their natural environment. The implication of this research is that further development of children’s environmental agency is suggested by the authors. The introduction of a whole-of-kindergarten approach that focuses on the systemic development of quality STEM education is posited as an avenue for educators to build young children’s understandings of sustainable development.
Assessment of the Thermo-Hydraulic Efficiency of an Indoor-Designed Jet Impingement Solar Thermal Collector Roughened with Single Discrete Arc-Shaped Ribs
Authors: Kumar Raj , Cuce Erdem , Kumar Sushil , Thapa Sashank , Gupta Paras , Goel Bhaskar , Saleel C. Ahamed , Shaik Saboor
Published: March 2022

This study illustrates the impact of single discrete arc-shaped ribs (SDASR)-type artificial roughness on the performance of a jet impingement solar thermal collector (JISTC). The impact of parametric variations of SDASR on the Nusselt number (Nusdr), friction factor (fsdr), and thermohydraulic performance (ηsdr) is examined. The spacer length (Ssdr) of the SDASR was changed from 0 mm to 300 mm in stages of 100 mm during the experiment. The fixed parameters of the SDASR were a relative discrete distance Dd/Lv of 0.67, relative discrete width gw/Hr of 0.87, relative rib height Hr/H of 0.047, relative rib pitch Pr/H of 1.7, angle of an arc αsdr of 60°, jet diameter ratio Dj/Dhy of 0.065, streamwise pitch ratio X/Dhy of 1.72, and spanwise pitch ratio  Y/Dhy of 0.82. The Reynolds number Re was altered from 3000 to 19,000. The Nusdr and fsdr  of a JISTC with a roughened absorber plate was found to be enhanced by 5.25 and 5.98 times as compared to an STC without artificial roughness. The optimal findings of Nusdr, fsdr, and ηsdr were achieved at Ssdr = 0 mm. The maximum value of the ηsdr obtained at Ssdr = 0 mm was 2.9.
Subregion-Scale Geothermal Delineation Based on Image Analysis Using Reflection Seismology and Well Data with an Outlook for Land Use
Authors: Buday-Bódi Erika , Irfan Ali , McIntosh Richard William , Fehér Zsolt Zoltán , Csajbók József , Juhász Csaba , Radócz László , Szilágyi Arnold , Buday Tamás
Published: March 2022

The role of geothermal energy is smaller in the global energy mix than what its potential would indicate, but it can be improved by incorporating geothermal energy potential assessments into spatial planning. For adequate decision support and sustainable utilisation, subregion-scale assessments should be applied due to the high variability in geothermal characteristics. Different GIS tools were used for the interpretation and integration of the different spatial data into one model showing areas with their geothermal characteristics on maps. Considering the present study site with a size of 83 km × 103 km located in NE Hungary, 39 2D reflection seismic sections and high-resolution geological data of 137 thermal wells were interpreted in OpendTect and then in ArcGIS to define spatial differences in geothermal potential. It was found that nine geothermal subregions (GSRs) can be distinguished in the present study site based on the applied GIS algorithms. Each GSR was characterised and land-use structure was studied based on Corine Land Cover 2018. The exploitation of water with at least 30 °C is possible in all GSRs, while the maximum achievable temperature and reservoir geometry vary; a subregion-scale delineation framework is required for regional planning.
COVID-19 and Supply Chain Management: A Review with Bibliometric
Authors: Sombultawee Kedwadee , Lenuwat Pattama , Aleenajitpong Natdanai , Boon-itt Sakun
Published: March 2022

The supply chain and its management are the hidden engines that drive the economy. The COVID-19 pandemic has precipitated an unprecedented sustained challenge to supply chain management around the world, which has highlighted the importance of research on the effects of COVID-19 on supply chain management. Most of the scholarly articles were mainly focused on content analysis to discuss aspects of the research related to COVID-19 and supply chain management. The literature reviews with the bibliometric method involving supply chain management in the COVID-19 context are still scarce. In this paper, we undertook a bibliometric analysis of the research on COVID-19 and supply chain management, finding a total of 257 papers published in 2020 and 2021 to determine the thematic direction of the related flourish of research. The analysis used a combination of bibliographic and network analysis to capture the direction the research has been taking. The study identified the most highly productive authors, prominent journals, and geographic centers of publications about this phenomenon. It also examined networks related to co-authorship, countries of collaboration, co-citations and highly cited authors, and co-wording. The examination identified four thematic clusters, comprised of the impact of the COVID-19 pandemic on supply chains, the improvement of supply chain resilience for viability, technology and innovation for supply chain sustainability, and supply chain risk management in response to COVID-19. These findings support the need for further investigation into supply chain management, the impact of the COVID-19 pandemic on supply chains, and, more broadly, supply chain resilience.