bluuebunny/crossref_metadata_2025 huggingface dataset:
Fabrication of Ni−MOF−74@PA−PEI for Radon Removal under Ambient Conditions
Authors: Liu Xi , Sun Yuan , Wang Chunlai , Lv Li , Liang Yun
Published: April 2023

Radon is one of the 19 carcinogenic substances identified by the World Health Organization, posing a significant threat to human health and the environment. Properly removing radon under ambient conditions remains challenging. Compared with traditional radon−adsorbent materials such as activated carbon and zeolite, metal–organic framework (MOF) materials provide a high specific surface area, rich structure, and designability. However, MOF material powders demonstrate complications regarding practical use, such as easy accumulation, deactivation, and difficult recovery. Ni−MOF−74 was in situ grown on a porous polyacrylic acid (PA) spherical substrate via stepwise negative pressure impregnation. Ni−MOF−74 was structured as one−dimensional rod−shaped crystals (200–300 nm) in large−pore PA microspheres, whose porous structure increased the diffusion of radon gas. The radon adsorption coefficient of a Ni−MOF−74@PA−polyethyleneimine composite material was 0.49 L/g (293 K, relative humidity of 20%, air carrier). In comparison with pristine Ni−MOF−74 powder, our composite material exhibited enhanced adsorption and longer penetration time. The radon adsorption coefficient of the composite material was found to be from one to two orders of magnitude higher than that of zeolite and silica gel. The proposed material can be used for radon adsorption while overcoming the formation problem of MOF powders. Our preparation approach can provide a reference for the composite process of MOFs and polymers.
Techno-Economic Modelling of Solar Photovoltaic (PV) Power Transformers in South Africa
Authors: Thango Bonginkosi A.
Published: April 2023

The increasing demand for electricity in South Africa has led to a rise in the deployment of solar photovoltaic systems. The integration of these systems requires the installation of power transformers to convert the generated DC power into AC power that can be fed into the grid. However, the high cost of these transformers can hinder the widespread adoption of solar PV systems. Consequently, the primary objective of this work is to introduce a novel service-lifetime loss estimation method that acknowledges the unique requirements of transformers facilitating solar photovoltaic applications. In reality, these transformers are required to facilitate a sporadic energy source, which advocates for an appropriate method to assess the cost of their service-lifetime losses and total ownership cost. Thus far, the energy elements of classical coal-power generation are inadequate, and in conformity with operational and financial considerations of solar PVs, a novel method incorporating the outcomes of a transformer facilitating a solar PV plant is proposed. The proposed approach has been benchmarked with existing methods and the distinction between proposed and existing methods is highlighted. The study further proposes various regression models for estimating the purchase price for transformers specifically intended to exclusively serve in solar PV applications, which are not available in the current South African standards. The percentage errors between the conventional method and the proposed solar PV method are relatively high, ranging from 34.66% to 44.03%. This indicates that the two methods produce significantly different results, and the proposed solar PV method may not be an accurate replacement for the conventional method.
Who Was a Bahā’ī in the Upper Echelons of Qājār Iran?
Authors: Momen Moojan
Published: April 2023

This paper addresses two questions: first, that of the nature of multiple religious identities in a traditional society; second, that of who can be identified as Bahā’īs in the upper echelons of Qājār Iran. The paper identifies five criteria by which individuals can be identified as having been Bahā’īs and suggests that, since none of these are usually conclusive by themselves, more than one of the criteria should be fulfilled before we label someone as a Bahā’ī. The various grades of being a Bahā’ī are also examined. The paper lists a number of examples of people from the Qājār royal family and from among the highest echelons of the Qājār administration who fulfill these criteria. It also looks at two individuals who have not been claimed to be Bahā’īs in the usual Iranian and Bahā’ī histories, and yet, if a close study of their lives is made, considerable evidence can be accumulated that they may have been crypto-Bahā’īs. In all, this paper indicates that there may have been many Bahā’īs in the upper strata of Qājār society, that this is a factor that has not previously been sufficiently recognized and needs to be examined for the light that it may shed on other matters.
Mary’s Transparent Beauty in St. Bernard’s Aesthetics
Authors: Pradier Adrián
Published: April 2023

The subject of the beauty of the Virgin Mary was a delicate one in medieval aesthetic thought. Halfway between the sacred and the profane, the theological and the anthropological, the question of Mary’s beauty opened up a strictly material dimension of appreciation that could generate problems related to decorum. However, the progressive humanization of Marian images from the thirteenth century onwards invites us to wonder if there was not, after all, a way to balance or, better, to sublimate the immaterial beauty of Mary, Mother of God, and material beauty of Mary, the young virgin of Nazareth. Taking as our leitmotiv a fictional scene from Umberto Eco’s novel The Name of the Rose, we will analyze St. Bernard’s position on this issue, because he was particularly influential on this matter in his own time and later, since his work brings together not only Marian concerns of deep theological depth, but also aesthetic questions that can contribute to clarifying this question.
Calling Back the Soul: From Apocryphal Buddhist Sutras to Onmyōdō Rituals
Authors: Liang Chenxue
Published: April 2023

Three Japanese Buddhist scriptural manuscripts related to the practice of calling back the soul are kept in Nanatsudera, Hōbodaiin in Toji, and Kōshōji, respectively. They show complex lineage connections that have been discussed little. This paper discusses the relations between the two sutras contained in the three manuscripts, traces their respective origins, and analyzes how Japanese authors transformed the apocryphal sutras into a liturgical text. Both the Nanatsudera and the Hōbodaiin manuscripts consist of the Sutra on Calling Back the Soul, while the Kōshōji manuscript comprises the Duxing Sutra. All of them were classified as Buddhist apocrypha written by Chinese authors. While the two sutras share similar text structures and the use of words, their contents reveal remarkable differences. The Sutra on Calling Back the Soul focuses mainly on how to release the souls of the dead and how to prevent fulian (reconnection with the dead), while the Duxing Sutra focuses on the healing of diseases and the alleviation of disasters through the restitution of the souls of the living. The Sutra on Calling Back the Soul has exerted a long-lasting effect in Japan where it was paraphrased into a liturgical text for the removal of disasters and the prolongation of life.
Many Healings of the Woman with the Flow of Blood
Authors: Lomperis Ekaterina N.
Published: April 2023

With the emergence of the modern quest for the historical Jesus, theologians began increasingly questioning traditional views of Jesus as a healer of human bodies. While a growing suspicion of Jesus’s role as a literal healer of the body is commonly traced to the influence of the Enlightenment, in this essay, I will suggest that the roots of this theological marginalization run deeper, in the sixteenth-century Protestant Reformations, when supernatural did not yet equal superstitious. The essay will examine two representative exegeses of the healing of the woman with the flow of blood in Mark 5:25–34, offered by Martin Luther and John Calvin. My analysis will reveal a shift of hermeneutical emphases from the bleeding woman’s restoration to the dynamics of her faith, and consequently, a new Protestant vision of Jesus’s role in the story, which I will argue occurred due to the new theological importance placed on faith by Protestant reformers.
Heuristic Biases as Mental Shortcuts to Investment Decision-Making: A Mediation Analysis of Risk Perception
Authors: Jain Jinesh , Walia Nidhi , Singla Himanshu , Singh Simarjeet , Sood Kiran , Grima Simon
Published: April 2023

In the last two decades, research on behavioural biases has grown dramatically, fuelled by rising academic interest and zeal for publication. The present study explores the mediating role of risk perception on the relationship between heuristic biases and individual equity investors’ decision-making. The study uses Partial Least Square Structural Equation Modelling (PLS–SEM) to examine the survey data from 432 individual equity investors trading at the National Stock Exchange (NSE) in India. Risk perception is found to play a partial mediating role in the relationship amid overconfidence bias and investment decision-making, availability bias and investment decision-making, gamblers’ fallacy bias and investment decision-making and anchoring bias and investment decision-making, whereas it is found to play the full mediating role in the relationship between representativeness bias and investment decision-making. The result of the present study provides valuable insights into the different behavioural biases of capital market participants and other stakeholders such as equity investors, financial advisors, and policymakers. The present study solely relied on the heuristic biases of individual equity investors. However, in the real world, many other factors may impact the investment decision of individual equity investors. This has been considered a limitation of the study. The present study solely relied on the heuristic biases of individual equity investors. However, in the real world, many other factors may impact the investment decision of individual equity investors. This has been considered a limitation of the study.
UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites
Authors: Shin Young-Ha , Shin Sang-Yeop , Rastiveis Heidar , Cheng Yi-Ting , Zhou Tian , Liu Jidong , Zhao Chunxi , Varinlioğlu Günder , Rauh Nicholas K. , Matei Sorin Adam , Habib Ayman
Published: April 2023

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.
Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images
Authors: Salunke Rakesh , Nobahar Masoud , Alzeghoul Omer Emad , Khan Sadik , La Cour Ian , Amini Farshad
Published: April 2023

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.
R-PCR: Recurrent Point Cloud Registration Using High-Order Markov Decision
Authors: Cheng Xiaoya , Yan Shen , Liu Yan , Zhang Maojun , Chen Chen
Published: April 2023

Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).
An Efficient Calibration System of Optical Interferometer for Measuring Middle and Upper Atmospheric Wind
Authors: Zhu Guangyi , Zhu Yajun , Kaufmann Martin , Wang Tiancai , Liu Weijun , Xu Jiyao
Published: April 2023

Detection of the Doppler shift of airglow radiation in the middle and upper atmosphere is one of the most important methods for remote sensing of the atmospheric wind field. Laboratory and routine field calibration of an optical interferometer for wind measurement is very important. We report a novel calibration system that simulates a frequency shift of airglow emission lines introduced by wind in the middle and upper atmosphere for calibrating passive optical interferometers. The generator avoids the shortcomings of traditional motor-driven Doppler-shift generators in terms of stability and security while improving accuracy and simplifying assemblies. A simulated wind speed can be determined simultaneously using the light-beat method. The wind error simulated by the generator mainly comes from the light source, which is about 0.63 m/s. An experimental demonstration was conducted using a calibrated Fabry–Perot interferometer and showed that the root mean square of the measurement uncertainty is 0.91 m/s. The novel calibration system was applied to calibrate an asymmetric spatial heterodyne spectrometer (ASHS)-type interferometer successfully. The results demonstrate the feasibility of the system.
Upscaling of Latent Heat Flux in Heihe River Basin Based on Transfer Learning Model
Authors: Lin Jing , Xu Tongren , Zhang Gangqiang , He Xiangping , Liu Shaomin , Xu Ziwei , Zhao Lifang , Xu Zongbin , Wang Jiancheng
Published: April 2023

Latent heat flux (LE) plays an essential role in the hydrological cycle, surface energy balance, and climate change, but the spatial resolution of site-scale LE extremely limits its application potential over a regional scale. To overcome the limitation, five transfer learning models were constructed based on artificial neural networks (ANNs), random forests (RFs), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM) to upscale LE from site scale to regional scale in Heihe River basin (HRB). The instance-transfer approach that utilizes data samples outside of HRB was used in the transfer learning models. Moreover, the Bayesian-based three-cornered hat (BTCH) method was used to fuse the best three upscaling results from ANN, RF, and XGBoost models to improve the accuracy of the results. The results indicated that the transfer learning models perform best when the transfer ratio (the data samples ratio between external and HRB dataset) was 0.6. Specifically, the coefficient of determination (R2) and root mean squared errors (RMSE) of LE upscaled by ANN model was improved or reduced by 6% or 17% than the model without external data. Furthermore, the BTCH method can effectively improve the performance of single transfer learning model with the highest accuracy (R2 = 0.83, RMSE = 18.84 W/m2). Finally, the LE upscaling model based on transfer learning model demonstrated great potential in HRB, which may be applicable to similar research in other regions.
Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake
Authors: Draz Muhammad Umar , Shah Munawar , Jamjareegulgarn Punyawi , Shahzad Rasim , Hasan Ahmad M. , Ghamry Nivin A.
Published: April 2023

Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (Mw 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.
Effects of Atmospheric Correction on Remote Sensing Statistical Inference in an Aquatic Environment
Authors: Zhu Weining , Xia Wei
Published: April 2023

Atmospheric correction (AC) plays a critical role in the preprocessing of remote sensing images. Although AC is necessary for applications based on remote sensing inversion, it is not always required for those based on remote sensing classification. Recently, remote sensing statistical inference has been proposed for evaluating water quality. However, input data for these models have always been remote sensing reflectance (Rrs), which requires AC. This raises the question of whether AC is necessary for remote sensing statistical inference. We conducted a theoretical analysis and image validations by testing 24 water bodies observed by Landsat-8 and compared their spectral probability distributions (SPDs) calculated from Rrs before and after AC (using the ACOLITE model). Additionally, we tested and found that, if we use remote sensing inference as a tool to quantitatively infer statistical parameters of a specific waterbody, it is better to perform atmospheric correction. However, if the quantitative inference is applied to a large number of water bodies and high inference accuracy is not required, atmospheric correction may not be necessary, and a quick calculation based on the strong correlations between Rrs at the surface and sensor-observed reflectance can be used as a substitute.
Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding
Authors: Ramachandran Rakhee , Bajón Fernández Yadira , Truckell Ian , Constantino Carlos , Casselden Richard , Leinster Paul , Rivas Casado Mónica
Published: April 2023

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies.
An Assessment of Horse-Drawn Vehicle Incidents from U.S. News Media Reports within AgInjuryNews
Authors: Becklinger Nicole
Published: April 2023

Some old-order Anabaptist communities rely on animal-drawn vehicles for transportation and farm work. This research examines reports involving horse-drawn vehicles found in the AgInjuryNews dataset, which provides a publicly accessible collection of agricultural injury reports primarily gathered from news media. The goals of this research are to characterize the reports and to compare results with previous research to assess the utility of using AgInjuryNews to examine horse-drawn vehicle incidents. A total of 38 reports representing 83 victims were identified. Chi-square tests comparing victim and incident traits for fatal and nonfatal injuries were significant for the victim’s role in the incident, vehicle type, presence of a motor vehicle, rear-ending by a motor vehicle, spooked horses, a victim being run over or struck by a vehicle, and a victim being ejected or falling from a vehicle. Additional analysis of incidents involving horse-drawn farm equipment showed that a significantly higher proportion of off-road incidents were fatal compared to on-road incidents. The proportion of fatal injuries in the AgInjuryNews dataset was approximately 10 times higher than observed in a study using Pennsylvania Department of Transportation (DOT) data. Compared to previous research, the AgInjuryNews reports contained a higher proportion of incidents where a motor vehicle rear-ended a horse-drawn vehicle, and fewer cases of horse-drawn vehicles being struck by motor vehicles while crossing or entering a main road and making left turns. Reports of buggy crashes found in AgInjuryNews differed from those found in a Nexis Uni search in that the bulk of the articles from Nexis Uni referred to cases involving criminal charges for impaired driving or hit-and-run crashes. While it is evident that the reports included in the sample are incidents that media sources find compelling rather than comprehensive injury surveillance, it is possible to gain new insights using the AgInjuryNews reports.
Extraction of Organochlorine Pesticides from Porous Membrane Packed Dried Fish Samples: Method Development and Greenness Evaluation
Authors: Sajid Muhammad , Alhooshani Khalid
Published: April 2023

In this work, ultrasound-assisted solvent extraction was utilized for extraction of organochlorine pesticides from membrane-protected dried fish samples. The dried fish samples were packed inside a porous membrane bag which was immersed in a solvent and subjected to ultrasonication. After the extraction process, the sample-containing bag was separated from the extract. Since samples were packed inside the membrane, their separation did not require centrifugation or filtration. Moreover, the complex components of the biota matrix may also retain inside the porous membrane bag, alleviating the requirement of extract cleanup before analysis. The parameters that can affect the ultrasound-assisted solvent extraction of membrane-protected dried fish samples were suitably optimized. These parameters include the extraction solvent and its volume, the sample amount, ultrasound intensity and extraction time. Under the optimum extraction conditions, good linearity was achieved for all the tested organochlorine pesticides, with the coefficients of determination (R2) higher than 0.9922 for the linear ranges from 5–1000, 10–1000 and 20–1000 ng/g. The values of intra-day and inter-day relative standard deviations were ≤13.8%. The limit of detection ranged from 1.5 to 6.8 ng/g. The spiked relative recoveries were in the range of 87.3–104.2%. This method demonstrated excellent figures of merit and could find potential applications in routine analytical laboratories. Finally, the greenness of this method was evaluated using the green analytical procedure index and analytical greenness calculator metrics.
A Qualitative Investigation of European Grain Legume Supply Markets through the Lens of Agroecology in Four Companies
Authors: Lybæk Rikke , Hauggaard-Nielsen Henrik
Published: April 2023

Four companies in Europe were chosen as case studies using an information-oriented selection procedure on the basis of their location, processing equipment and size, and through the adoption of a “maximum variation” approach. The purpose was to investigate how legume processors of various sizes and in different locations in the European protein legume market are working to increase their market share within the paradigm of efficiency gains from agricultural specialisation and trade in order to improve both food availability and security. After identifying company typologies and characteristics, the company case studies along the legume value chain were investigated. The analysis revealed that the supply market for grain legumes is expected to grow rapidly, and the various processors are increasing their market shares in terms of product type and geography. Based on the principles of agroecology and levels of food system change, the companies were found to be adopting promising business strategies, but are not disrupting the competitive patterns among existing European legume supply companies and food consumers that dominate the current regime. System change is far from taking place. However, conventional perspectives are being challenged and new ways of operating are being developed, indicating that a significant transition can be initiated that would move legumes away from being a niche sector, demonstrating to legume processors and consumers in the regime that there is an alternative and more sustainable pathway for the future, possibly stimulating larger-scale initiatives.
The Sustainability of Form-Focused Instruction in Classrooms: Chinese Secondary School EFL Teachers’ Beliefs and Practices
Authors: Li Changying , Xu Jinfen
Published: April 2023

Form-focused instruction occupies an important position in China’s secondary schools. However, middle school English teaching has been criticized for the sustainability of form-focused instruction. The new English Curriculum Standards for China’s secondary English teaching suggest that grammar teaching should be integrated into communicative activities, but no precise guidance is provided on how to integrate grammar into communicative activities. This study investigates teachers’ beliefs and practices about form-focused instruction, as well as the factors influencing their beliefs and practices, in the context of China’s secondary schools. Data were collected through 33 classroom observations and interviews with 3 teachers with different levels of experience. The findings reveal that, overall, the teachers preferred form-focused instruction, focus on forms and explicit teaching. The teachers with more teaching experience adopted communicative approaches of form-focused instruction, integrating form and meaning at different times inductively. The teacher with fewer teaching years adopted traditional, isolated, and deductive methods of grammar teaching. The differences between the teachers were found to be influenced by various factors, including teaching and research activities, curriculum standards, examinations, and learning experience. The teachers’ beliefs and practices and the gaps and connections between theoretical domains and classroom practices were discussed and implications were provided.
Wind Power Interval Prediction via an Integrated Variational Empirical Decomposition Deep Learning Model
Authors: Zhao Shuling , Zhao Sishuo
Published: April 2023

As global demand for renewable energy increases, wind energy has become an important source of clean energy. However, due to the instability and unpredictability of wind energy, predicting wind power becomes one of the keys to resolving the instability of wind power. The current point prediction model of wind power output has limitations and randomness in processing information. In order to improve the prediction accuracy and efficiency of wind power, a multi-step interval prediction method (VMD-TCN) is proposed in this article, which uses variational modal decomposition and an improved temporal convolutional network model to predict wind power. Additionally, it introduces attention mechanism, further improving the prediction performance of the model. The method first uses empirical mode decomposition to decompose the wind power generation sequence into six parts and obtains the trend, oscillation and noise components of the output power sequence; then, it optimizes the parameters of the six components, respectively, and uses the interval prediction method combined with the temporal convolutional network to construct a new power prediction model. Experiments show that the proposed method can effectively improve the prediction performance of the power prediction model, and it has strong robustness in interval prediction and high sensitivity to load changes, which can well help power system scheduling and new energy consumption.
The Role of Landscape in Sustainable Tourism Development—A Study of Identification and Evaluation of Landscape Qualities of the Vrbanja Basin in Bosnia and Herzegovina
Authors: Rajčević Vesna , Tomić Tanja Mišlicki , Medar-Tanjga Irena , Trifunović Mlađen , Živak Neda , Petrašević Aleksandra
Published: April 2023

The concept of landscape has opened up a vast field of contemporary multidisciplinary research. Character identification, typology, digital presentation, protection and evaluation from the aspect of tourist suitability are some of the main topics. Unfortunately, in Bosnia and Herzegovina, there is a very small number of complex studies that try to study landscapes in the function of tourism development. The main purpose of the research is the identification of landscape characters, the creation of their typology, and the assessment of their suitability for the development of sustainable tourism in the area of the Vrbanja river basin. The methodological procedure consisted of three steps. The first was a desk study, which included a review of data from the available literature and maps of hydrological, geological, pedological, geomorphological, and climate studies of this area. In this way, the geographical basis of the research was created in the form of four thematic maps (relief, hydrographic, pedological, and forest complex maps) using the ArcGIS 10.8 software. The second step was the spatial identification of landscape types in the area of the studied basin. This was done in ArcGIS 10.8 by overlaying and combining the obtained thematic maps of the basin. The result was a map with four general types and 14 subtypes within the Vrbanja basin. The third and final step was the evaluation and assessment of the suitability of each type of landscape for tourism activities. The assessment was made by scoring factors of anthropogenic influence, natural characteristics, and visual impression on a Likert scale of 1–4. The final results of the research show that the high mountain areas with forest and semi-rural type and the hilly areas with forest and rural landscape type have the greatest overall suitability for the development of tourism.
Perceived Health Impacts, Sources of Information and Individual Actions to Address Air Quality in Two Cities in Nigeria
Authors: Chukwu Timothy M. , Morse Stephen , Murphy Richard J.
Published: April 2023

Poor air quality (PAQ) has serious effects on the environment, climate change, and human health. This study investigated the perceived health impacts of PAQ in two cities in Nigeria (Abuja and Enugu), including whether PAQ may have an interaction with COVID-19 infection and intensity. A recent report published in the Lancet has pointed to the complexity of the health care system in Nigeria and a lack of data on disease burden, so the research in this paper took a self-reporting (perceptual) approach to exploring the health impacts of PAQ. The research also sought to explore the main sources of information used by people to inform them about air quality (AQ) and the actions they are likely to take to address PAQ. The results imply that many of the respondents in the two cities perceived their health to be adversely affected by PAQ and that PAQ worsens both the chances of infection and the intensity of COVID-19. Unsurprisingly, older people were found to be more vulnerable to the health impacts of PAQ. Most respondents, especially younger ones, obtained their information on AQ via electronic media (internet, social media) rather than printed media. Respondents considered that the primary action to address PAQ is proper waste management. Paying the government to address PAQ was regarded as the least likely action, although the government was acknowledged as having a key responsibility.
Climate Change in the Biodiversity and Forest Strategies in Greece Using Discourse Analysis and Text Mining: Is an Integration into a Cost-Efficient Natural Resources Policy Feasible?
Authors: Papaspyropoulos Konstantinos G. , Liakou Harikleia , Dimopoulos Panayotis
Published: April 2023

Climate change poses major threats to biodiversity and ecosystems. National policies on environmental issues address the problems created by these threats and set targets for their mitigation. In Greece, the National Biodiversity Strategy and the National Forest Strategy analyze, among others, the issue of climate change and how it is related to biodiversity conservation and forest management. As biodiversity and forests are interrelated, common strategies may be similar, overlapping, or opposite. In the present research, the issue of climate change is investigated in the two national strategies for finding out if an integration of policies is feasible. Such an integration may result in a cost efficient and smaller set of solutions that should be determined and may have important results in the funding of natural resources policy. Discourse analysis and content analysis with the text mining approach were used to compare the two policy texts. The results are both methodological and applied. Methodologically, text mining is confirmed in the present research to enhance the objectivity of discourse analysis, and it is recommended to complement it. In terms of the way the two policies deal with climate change, it is found that a future integration of a Biodiversity and Forest Strategy in Greece for climate change issues is relevant and may be applied.
Singularity Properties of Spacelike Circular Surfaces
Authors: Almoneef Areej A. , Abdel-Baky Rashad A.
Published: April 2023

The aim of the paper is on spacelike circular surfaces and singularities in Minkowski 3-space E13. A spacelike circular surface with a stationary radius can be swept out by movable a Lorentzian circle following a non-null curve, which acts as the spine curve. In the study, we have represented spacelike circular surface and have furnished its geometric properties such as singularities and striction curves contrasting with those of ruled surfaces. Subsequently, a new type of spacelike circular surface was distinguished and named as the spacelike roller coaster surface. Meanwhile, we support the outcomes of the work by some examples.
An AI-Based Newly Developed Analytical Formulation for Discharging Behavior of Supercapacitors with the Integration of a Review of Supercapacitor Challenges and Advancement Using Quantum Dots
Authors: Satpathy Sambit , Misra Neeraj , Goyal Vishal , Das Sanchali , Sharma Vishnu , Ali Shabir
Published: April 2023

A supercapacitor is a type of electrical component that has larger capacitance, due to asymmetric behavior with better power density, and lower ESR (effective series resistance) than conventional energy-storage components. Supercapacitors can be used with battery technology to create an effective energy storage system due to their qualities and precise characterization. Studies have shown that the use of quantum dots as electrodes in supercapacitors can significantly increase their effectiveness. In this research article, we have used a Drude model based on free electrons (asymmetric nature) to describe the supercapacitor’s discharging characteristics. Commercially available Nippon DLA and Green-cap supercapacitors were used to verify the Drude model by discharging them through a constant current source using a simple current mirror circuit. The parameters of both the fractional-order models and our suggested method were estimated using the least-squares regression fitting approach. An intriguing finding from the Drude model is the current-dependent behavior of the leakage-parallel resistance in the constant current discharge process. Instead of using the traditional exponential rule, supercapacitors discharge according to a power law. This work reflects the strong symmetry of different aspects of designing a hybrid supercapacitor with high efficiency and reliability.
Weak Gravitational Lensing around Bardeen Black Hole with a String Cloud in the Presence of Plasma
Authors: Atamurotov Farruh , Alibekov Husan , Abdujabbarov Ahmadjon , Mustafa Ghulam , Aripov Mersaid
Published: April 2023

The effect of spacetime curvature on optical properties may provide an opportunity to suggest new tests for gravity theories. In this paper, we investigated gravitational weak lensing around a Bardeen black hole with the string clouds parameter. First, we examined the horizon structure in the presence of string clouds around the gravitational compact object defined by Bardeen spacetime. The effect of gravitational weak lensing in a plasma medium is also discussed. According to the findings, the influence of the string cloud parameter on the circular orbits of a light ray around the black hole is greater than that in the Schwarzschild case, while the influence of the charge is reversed. The deflection angle of light rays in weak lensing is also used to study how much the image is magnified.
A Convolutional Recurrent Neural-Network-Based Machine Learning for Scene Text Recognition Application
Authors: Liu Yiyi , Wang Yuxin , Shi Hongjian
Published: April 2023

Optical character recognition (OCR) is the process of acquiring text and layout information through analysis and recognition of text data image files. It is also a process to identify the geometric location and orientation of the texts and their symmetrical behavior. It usually consists of two steps: text detection and text recognition. Scene text recognition is a subfield of OCR that focuses on processing text in natural scenes, such as streets, billboards, license plates, etc. Unlike traditional document category photographs, it is a challenging task to use computer technology to locate and read text information in natural scenes. Imaging sequence recognition is a longstanding subject of research in the field of computer vision. Great progress has been made in this field; however, most models struggled to recognize text in images of complex scenes with high accuracy. This paper proposes a new pattern of text recognition based on the convolutional recurrent neural network (CRNN) as a solution to address this issue. It combines real-time scene text detection with differentiable binarization (DBNet) for text detection and segmentation, text direction classifier, and the Retinex algorithm for image enhancement. To evaluate the effectiveness of the proposed method, we performed experimental analysis of the proposed algorithm, and carried out simulation on complex scene image data based on existing literature data and also on several real datasets designed for a variety of nonstationary environments. Experimental results demonstrated that our proposed model performed better than the baseline methods on three benchmark datasets and achieved on-par performance with other approaches on existing datasets. This model can solve the problem that CRNN cannot identify text in complex and multi-oriented text scenes. Furthermore, it outperforms the original CRNN model with higher accuracy across a wider variety of application scenarios.
Virus-Host Dynamics in Archaeal Groundwater Biofilms and the Associated Bacterial Community Composition
Authors: Turzynski Victoria , Griesdorn Lea , Moraru Cristina , Soares André R. , Simon Sophie A. , Stach Tom L. , Rahlff Janina , Esser Sarah P. , Probst Alexander J.
Published: April 2023

Spatial and temporal distribution of lytic viruses in deep groundwater remains unexplored so far. Here, we tackle this gap of knowledge by studying viral infections of Altivir_1_MSI in biofilms dominated by the uncultivated host Candidatus Altiarchaeum hamiconexum sampled from deep anoxic groundwater over a period of four years. Using virus-targeted direct-geneFISH (virusFISH) whose detection efficiency for individual viral particles was 15%, we show a significant and steady increase of virus infections from 2019 to 2022. Based on fluorescence micrographs of individual biofilm flocks, we determined different stages of viral infections in biofilms for single sampling events, demonstrating the progression of infection of biofilms in deep groundwater. Biofilms associated with many host cells undergoing lysis showed a substantial accumulation of filamentous microbes around infected cells probably feeding off host cell debris. Using 16S rRNA gene sequencing across ten individual biofilm flocks from one sampling event, we determined that the associated bacterial community remains relatively constant and was dominated by sulfate-reducing members affiliated with Desulfobacterota. Given the stability of the virus-host interaction in these deep groundwater samples, we postulate that the uncultivated virus-host system described herein represents a suitable model system for studying deep biosphere virus-host interactions in future research endeavors.
The Lytic Activity of Bacteriophage ZCSE9 against Salmonella enterica and Its Synergistic Effects with Kanamycin
Authors: Abdelsattar Abdallah S. , Eita Mohamed Atef , Hammouda Zainab K. , Gouda Shrouk Mohamed , Hakim Toka A. , Yakoup Aghapy Yermans , Safwat Anan , El-Shibiny Ayman
Published: April 2023

Salmonella, the causative agent of several diseases in humans and animals, including salmonellosis, septicemia, typhoid fever, and fowl typhoid, poses a serious threat to global public health and food safety. Globally, reports of therapeutic failures are increasing because of the increase in bacterial antibiotic resistance. Thus, this work highlights the combined phage–antibiotic therapy as a promising approach to combating bacterial resistance. In this manner, the phage ZCSE9 was isolated, and the morphology, host infectivity, killing curve, combination with kanamycin, and genome analysis of this phage were all examined. Morphologically, phage ZCSE9 is a siphovirus with a relatively broad host range. In addition, the phage can tolerate high temperatures until 80 °C with one log reduction and a basic environment (pH 11) without a significant decline. Furthermore, the phage prevents bacterial growth in the planktonic state, according to the results of the time-killing curve. Moreover, using the phage at MOI 0.1 with kanamycin against five different Salmonella serotypes reduces the required antibiotics to inhibit the growth of the bacteria. Comparative genomics and phylogenetic analysis suggested that phage ZCSE9, along with its close relatives Salmonella phages vB_SenS_AG11 and wksl3, belongs to the genus Jerseyvirus. In conclusion, phage ZCSE9 and kanamycin form a robust heterologous antibacterial combination that enhances the effectiveness of a phage-only approach for combating Salmonella.