bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles
Authors: Wei Mengfan , Wang Hongyan , Zhang Yuan , Li Qiangzi , Du Xin , Shi Guanwei , Ren Yiting
Published: February 2023

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition.
Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV
Authors: Xu Sizhe , Xu Xingang , Blacker Clive , Gaulton Rachel , Zhu Qingzhen , Yang Meng , Yang Guijun , Zhang Jianmin , Yang Yongan , Yang Min , Xue Hanyu , Yang Xiaodong , Chen Liping
Published: February 2023

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.
Determination of Requirements for the Improvement of Occupational Safety in the Cleaning of Vertical Tanks of Petroleum Products
Authors: Ramírez-Peña Magdalena , Cerezo-Narváez Alberto , Pastor-Fernández Andrés , Otero-Mateo Manuel , Ballesteros-Pérez Pablo
Published: February 2023

Since the beginning of the second industrial revolution, the use of tanks for the storage of petroleum products ensured the permanent supply of equipment that depended on fossil fuel derived from petroleum, either for direct consumption or as an element for power generation. For correct operation, periodic cleaning of these confined spaces was required, being a common practice for the direct exposure of operators to explosive atmospheres. Currently, there are many industries that keep this kind of deposit, and cleaning works are considered of high occupational risk. In this context, the question arises as to whether human–machine collaboration thanks to the technologies that compose Industry 5.0 can mitigate these risks while generating a sustainable balance by optimizing costs and protecting the environment. In the present work, the analytic hierarchy process (AHP) method is used to prioritize the requirements that should be compiled to establish safe protocols in tank cleaning works, solving the multi-criteria problem. Results prove that a couple of alternatives improve the working conditions of the people involved in this process: the chemical cleaning and the robotic cleaning, which approximately accounts for two thirds of the decision. These requirements are aligned with the Industry 5.0 paradigm, encouraging the use of robots for high-risk processes, and influencing human behavior. In addition, cost reduction is achieved without compromising on quality of service or delivery schedule, thus enabling a circular economy that promotes occupational safety in company policies.
A Preliminary Cytotoxicity Study of Fagonia arabica against Breast (MCF-7), Oral (KB-3-1), and Lung Cancer (A-549) Cell Lines: A Study Supported by Molecular Marker Analysis Using Dual Staining Dyes
Authors: Walbi Ismail A. , Alshabi Ali Mohamed , Alkahtani Saad Ahmed , Shaikh Ibrahim Ahmed , Abdel-Wahab Basel A. , Khateeb Masood Medleri , Habeeb Mohammed Shafiuddin , Orabi Mohamed A. A. , Shettar Arun K. , Hoskeri Joy H.
Published: February 2023

Aim: The objective of this research is to present a phytochemical profile of Fagonia arabica and to investigate the cytotoxic potential of its extracts against breast, oral, and lung cancer cell lines using MTT assay and dual staining-based mechanistic analysis. Methods: The progressive extraction of F. arabica was carried out using the Soxhlet extraction technique. The total phenolic and flavonoid content was calculated as part of the phytochemical profiling performed using GCMS and LCMS methods. The MTT assay was utilized to assess the cytotoxicity against normal L929 cells, as well as malignant A549, MCF-7, and KB-3-1cell lines. Results: The phenolic compounds and flavonoids were the two main elements of the F. arabica methanolic extract, with 1323 µg GAE/g of dry weight and 523.07 µg QE/g of dry weight, respectively. The presence of the functional phytochemicals was verified by GCMS and LCMS analyses. Toxicity testing on the L929 cell line found that the F. arabica methanol extract was the least harmful, with the highest IC50 (296.11 µg/mL). The MTT assay for cell viability against MCF-7 and KB-3-1 yielded significant results, with IC50 values of 135.02 µg/mL and 195.21 µg/mL, respectively. The aqueous extract exhibited significant cytotoxicity against the A549 cell lines (IC50 116.06 µg/mL). The molecular marker analyses using dual staining revealed that the methanolic extract successfully triggered apoptosis in the different cancer cells tested. Conclusion: The present data suggest that the methanol extract of F. arabica has substantial cytotoxic action against lung, breast, and oral cancer cell lines. Thus, F. Arabica would be a promising source of anticancer medicines, warranting more research to identify the lead molecules with anticancer properties.
Farlie–Gumbel–Morgenstern Bivariate Moment Exponential Distribution and Its Inferences Based on Concomitants of Order Statistics
Authors: Arun Sasikumar Padmini , Chesneau Christophe , Maya Radhakumari , Irshad Muhammed Rasheed
Published: February 2023

In this research, we design the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, a bivariate analogue of the moment exponential distribution, using the Farlie–Gumbel–Morgenstern approach. With the analysis of real-life data, the competitiveness of the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution in comparison with the other Farlie–Gumbel–Morgenstern distributions is discussed. Based on the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, we develop the distribution theory of concomitants of order statistics and derive the best linear unbiased estimator of the parameter associated with the variable of primary interest (study variable). Evaluations are also conducted regarding the efficiency comparison of the best linear unbiased estimator relative to the respective unbiased estimator. Additionally, empirical illustrations of the best linear unbiased estimator with respect to the unbiased estimator are performed.
Understanding the Influencing Factors of Enterprise Transformation and Upgrading Capability: A Case Study of the National Innovation Demonstration Zones, China
Authors: Ma Zongguo , Fan Xueai , Zhang Yanli , Hu Beibei
Published: February 2023

Being at the forefront of China’s regional innovation and development, enterprises in the National Innovation Demonstration Zones must take the lead in completing high-quality transformation and upgrading. In this study, we use data from 1770 manufacturing companies from China’s 21 National Innovation Demonstration Zones. Based on the multi-factor influence model, we quantitatively study a series of factors that affect enterprise transformation and upgrading capability in terms of innovation-driven input, economically beneficial output, regional high-quality development, and the enterprise’s basic environment. The research results show a good trend in enterprise transformation and upgrading capability, and 45% of the enterprises have higher capabilities than the average of these zones. There are significant differences in these capabilities across the different National Innovation Demonstration Zones. Economically beneficial output has a relatively large impact on enterprise transformation and upgrading capability, while innovation-driven input, regional high-quality development, and the enterprise’s basic environment all have a relatively small impact. We suggest that the National Innovation Demonstration Zones and relevant departments should provide policy guarantees for enterprise transformation and upgrading in terms of regional systems, industrial chain layout, and soft environment optimization, so as to fully enable policy benefits from top-level design.
Achieving Personalized Precision Education Using the Catboost Model during the COVID-19 Lockdown Period in Pakistan
Authors: Asad Rimsha , Altaf Saud , Ahmad Shafiq , Shah Noor Mohamed Adamali , Huda Shamsul , Iqbal Sofia
Published: February 2023

With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student’s participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students’ performance.
Road Junction Configurations and the Severity of Traffic Accidents in Japan
Authors: Wada Yoshifumi , Asami Yasushi , Hino Kimihiro , Nishi Hayato , Shiode Shino , Shiode Narushige
Published: February 2023

In many countries, 40–60% of the traffic accidents occur at junctions, making the reduction of junction accidents paramount to achieving UN Sustainable Development Goals. In Japan, the road safety guidelines specify the proximity between junctions and non-perpendicular angles at junctions as the two main risk factors behind junction accidents, yet their impact remains understudied. Using binomial logistic regression models, this study investigates the impact of junction intervals and junction angles on the severity of traffic accidents. The study found that, in general, (1) shorter intervals between adjacent junctions helps reduce the risk of serious accidents, which is the opposite of the current road safety guidelines in Japan, and (2) results from the junction angle analysis were mixed but there was no evidence that the roads should meet at a right angle to reduce traffic accidents. Some types of accidents also returned a non-linear curve, e.g., vehicle-to-vehicle collisions at four-armed junctions involving a driver aged 65 years and over have the highest risk of fatal/serious accidents when adjacent junctions were 32 m apart, and the risk reduces at a shorter or longer interval. These results suggest that the current road safety guidelines require updating to improve road safety around junctions.
Spatial-Temporal Characteristics and Driving Factors of Coupling Coordination between the Digital Economy and Low-Carbon Development in the Yellow River Basin
Authors: Xu Zhenhua , Ci Fuyi
Published: February 2023

Enhancing the level of coupling coordination between the digital economy and low-carbon development is not only an inevitable choice for implementing the strategy of ecological protection and high-quality development in the Yellow River Basin, but also a key path to achieve China’s “Double Carbon” goal. The level of coupling coordination between the digital economy and low-carbon development in 78 cities in the Yellow River Basin from 2011 to 2020 is measured by a coupling coordination model, and the spatial-temporal characteristics and driving factors are analysed using the Dagum Gini coefficient, spatial autocorrelation model and geographic detector. This study found the following: (1) Rapid growth of the digital economy, with the slow growth of low-carbon development. The degree of coupling coordination of the two systems steadily improved and moved from a stage of near-disorder to primary coordination. (2) The degree of coupling coordination is spatially characterised by lower reaches > middle reaches > upper reaches, and provincial capitals and some coastal cities have a higher level of coupling coordination. Spatial differences in coupling coordination tend to widen, with inter-regional differences being the main source of overall differences. (3) There was a significant positive spatial correlation in the degree of coupling coordination. Local spatial clustering characteristics were dominated by High-High (H-H) clustering areas in Shandong and Low-Low (L-L) clustering areas in south-eastern Gansu. (4) The degree of coupling coordination was driven by both internal and external factors of the two systems, with internet penetration and the size of the telecommunications industry within the digital economy system as the most important factors driving the coupling coordination, and the interactions between the different drivers were all enhanced.
Mission Efficiency Analysis of For-Profit Microfinance Institutions with Categorical Output Variables
Authors: Sohn So Young , Ju Yonghan
Published: February 2023

The primary objective of microfinance institutions (MFIs) is to provide financial services to low-income clients and underprivileged women. As such, evaluating the efficiency of MFIs should take into account categorical output factors such as outreach and financial intermediation, rather than using the same metrics applied to traditional banks and credit unions. However, under adverse economic situations, one can expect the phenomenon of mission drift of for-profit MFIs such as microfinance banks and credit unions. When a mission drift occurs, MFIs intend to entertain wealthier clients to maximize profits, crowding out the poor ones. This paper empirically examines if such a phenomenon was observed during the global financial crisis period in Latin America and the Caribbean region using categorical Data Envelopment Analysis (DEA) data that have not been considered for the analysis of MFI efficiency. In addition, we conducted two-limit Tobit regression to find significant factors for MFI efficiency. We confirm that for-profit MFIs did not experience mission drift during the adverse economic situation while country, disclosure requirements, institutions’ age, and scale affected the efficiency of the for-profit MFIs. This indicates that for-profit MFIs in Latin America and the Caribbean region performed well in terms of their missions for micro-finance such as outreach, financial intermediation, as well as profit. The financially underprivileged faced a lack of household and business capital under the economic crisis. Based on the results, we conclude that support policies for younger and non-traditional MFIs to help the socially disadvantaged should be actively established for their sustainability in adverse economic situations.
Deep Learning in Forest Tree Species Classification Using Sentinel-2 on Google Earth Engine: A Case Study of Qingyuan County
Authors: He Tao , Zhou Houkui , Xu Caiyao , Hu Junguo , Xue Xingyu , Xu Liuchang , Lou Xiongwei , Zeng Kai , Wang Qun
Published: February 2023

Forest tree species information plays an important role in ecology and forest management, and deep learning has been used widely for remote sensing image classification in recent years. However, forest tree species classification using remote sensing images is still a difficult task. Since there is no benchmark dataset for forest tree species, a forest tree species dataset (FTSD) was built in this paper to fill the gap based on the Sentinel-2 images. The FTSD contained nine kinds of forest tree species in Qingyuan County with 8,815 images, each with a resolution of 64 × 64 pixels. The images were produced by combining forest management inventory data and Sentinel-2 images, which were acquired with less than 20% clouds from 1 April to 31 October, including the years 2017, 2018, 2019, 2020, and 2021. Then, the images were preprocessed and downloaded from Google Earth Engine (GEE). Four different band combinations were compared in the paper. Moreover, a Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) were also calculated using the GEE. Deep learning algorithms including DenseNet, EfficientNet, MobileNet, ResNet, and ShuffleNet were trained and validated in the FTSD. RGB images with red, green, and blue (PC1, PC2, and NDVI) obtained the highest validation accuracy in four band combinations. ResNet obtained the highest validation accuracy in all algorithms after 500 epochs were trained in the FTSD, which reached 84.91%. As a famous and widely used remote sensing classification satellite imagery dataset, NWPU RESISC-45 was also trained and validated in the paper. ResNet achieved a high validation accuracy of 87.90% after training 100 epochs in NWPU RESISC-45. The paper shows in forest tree species classification based on remote sensing images and deep learning that (1) PCA and NDVI can be combined to improve the accuracy of classification; (2) ResNet is more suitable than other deep learning algorithms including DenseNet, EfficientNet, MobileNet, and ShuffleNet in remote sensing classification; and (3) being too shallow or deep in ResNet does not perform better in the FTSD, that is, 50 layers are better than 34 and 101 layers.
Measuring Resident Participation in the Renewal of Older Residential Communities in China under Policy Change
Authors: Wu Jiaqi , Li Wenbo , Xu Wenting , Yuan Lin
Published: February 2023

There is a growing global trend to encourage citizen participation in solving urban problems, and there is worldwide consensus that the public voice needs to be considered. This study uses an extended Theory of Planned Behavior (TPB) to focus on the factors influencing resident engagement following changes in renewal strategies in older residential communities in China. Empirical analysis was carried out based on structural equation modeling (SEM) of a completed questionnaire with 865 respondents. The results showed that Neighborhood Relations (NR) had a positive impact on Participation Attitude (PA); both PA and Awareness Participation (AP) had a positive effect on Self-evaluation (SE), and SE and AP had a positive impact on Participation Guarantee (PG). A survey was also conducted on residents’ concerns and perceptions of the redevelopment of the old neighborhood, analyzing the current issues of residents’ more significant concerns. It showed that public emergencies can affect residents’ inherent perceptions. This study enriches the China-specific focus of the issue and is a reference for similar studies in the future.
Modelling Distributions of Asian and African Rice Based on MaxEnt
Authors: Lin Yunan , Wang Hao , Chen Yanqing , Tan Jiarui , Hong Jingpeng , Yan Shen , Cao Yongsheng , Fang Wei
Published: February 2023

Rice landraces, including Asian rice (Oryza sativa L.) and African rice (Oryza glaberrima Steud.), provide important genetic resources for rice breeding to address challenges related to food security. Due to climate change and farm destruction, rice landraces require urgent conservation action. Recognition of the geographical distributions of rice landraces will promote further collecting efforts. Here we modelled the potential distributions of eight rice landrace subgroups using 8351 occurrence records combined with environmental predictors with Maximum Entropy (MaxEnt) algorithm. The results showed they were predicted in eight sub-regions, including the Indus, Ganges, Meghna, Mekong, Yangtze, Pearl, Niger, and Senegal river basins. We then further revealed the changes in suitable areas of rice landraces under future climate change. Suitable areas showed an upward trend in most of study areas, while sub-regions of North and Central China and West Coast of West Africa displayed an unsuitable trend indicating rice landraces are more likely to disappear from fields in these areas. The above changes were mainly determined by changing global temperature and precipitation. Those increasingly unsuitable areas should receive high priority in further collections. Overall, these results provide valuable references for further collecting efforts of rice landraces, while shedding light on global biodiversity conservation.
Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia
Authors: Madani Ahmed , Niyazi Burhan
Published: February 2023

Groundwater storage is influenced by many geo-environmental factors. Most of these factors are prepared in the form of categorical data. The present study utilized raster satellite data instead of categorical data and a Random Forest machine learning model to identify groundwater potential zones at the downstream parts of Wadi Yalamlam, western Saudi Arabia. Eighteen groundwater-influenced variables are prepared in continuous raster format from ASTER GDEM, TRMM, and SPOT-5 satellite data. The Random Forest (RF) model is trained using (70%) of the target variable and validated using the rest (30%). The accuracy, sensitivity, and F1-score are all generated to evaluate the model performance. SPOT band 3, band 4, and the rainfall variables are the most important for groundwater potential mapping contributing 11%, 7%, and 8% during the prediction stage. The GDEM elevation variable contributed 6% and the slope variable scored 1%. The main conclusions of the study are: (1) The RF machine learning algorithm successfully identified three groundwater potential zones with an accuracy of 96%. (2) The high, moderate, and low potential groundwater zones covered 11.5%, 59.9%, and 28.6% of the study area respectively. (3) Majority of high and moderate zones lie within the pumping rate range between 10 and 20 m3/day. (4) The approach developed in this study can be applied to any other wadis having the same conditions to help authorities and decision-makers in planning and development projects.
Setting Up Roots: Opportunities for Biocultural Restoration in Recently Inhabited Settings
Authors: Hunt Carter A. , Jones Melanie E. , Bustamante Ernesto , Zambrano Carla , Carrión-Klier Carolina , Jäger Heinke
Published: February 2023

Biocultural approaches to restoration, which recognize the unique ways of understanding of socioecological challenges by Indigenous and local communities, have gained traction in recent decades. Yet, less attention has focused on biocultural opportunities where there is no Indigenous population or traditional knowledge to draw upon. This ethnographic study inductively assesses data gathered from interviews with farm owners on Isabela Island in the Galápagos Islands, where human presence is a function of recent migration. These interviews, corroborated with archival information and participant observation, center on farmer attitudes regarding restoration of Scalesia cordata, a highly endangered plant species, endemic to Isabela. The resulting analysis identified four themes of overlap with the biocultural restoration literature: cultural keystone species, sense of place, informational pathways, and recognition of socio-ecological feedback loops. Findings indicate that Scalesia remains a valued cultural keystone species providing tangible and intangible benefits to local residents, and its survival serves as a metaphor for farmers’ own wellbeing. Thus, even locations where place-based knowledge by a native population is not evident, critical biocultural elements exist that can be integrated into restoration efforts. Farmers also exhibited clear connections between restoration and tourism in Galápagos, paving the way for the application of biocultural theory to the analysis of tourism-supported restoration efforts elsewhere.
A Heterogeneity Study on the Effect of Digital Education Technology on the Sustainability of Cognitive Ability for Middle School Students
Authors: Yan Desheng , Li Guangming
Published: February 2023

Digitalization gradually transforms digital education technology from being a teaching means to focusing on the student’s abilities. This study analyzes the data from the China Education Baseline Survey of the Renmin University of China using Coarsened Exact Matching (CEM) and quantile regression methods. The Ordinary Least Squares (OLS) regression is used to test the net effect of digital education technology on students’ academic and cognitive abilities. The OLS result shows that digital education technology has a significantly positive impact on the cognitive ability of middle school students. However, schools focusing on using digital education technology as a means of school management will lower students’ cognitive abilities. Second, the CEM method result shows a significant difference in the cognitive ability scores between students in classrooms with and without digital education technology. This indicates that digital education technology can inspire students’ internal drive, motivate them to learn, and enhance their cognitive abilities. Last, the quantile regression result shows that the use of digital education technology has heterogeneity in the development of the cognitive ability of middle school students. When the cognitive ability quantile point increases, the influence becomes more obvious, changing from not significant to significant, then to very significant. This provides some inspiration for understanding the application of digital education technology. It can be concluded that this is an effective method to study the sustainable development of cognitive abilities through heterogeneity. The method ensures individuals’ holistic and comprehensive development, thus promoting groups’ development and contributing intellectual support to them to adapt to future social requirements and lead future social development. Digital education technology endows students with the cognitive abilities for lifelong learning to solve various problems in future social life, reserve high-quality talent resources for the future, and build a learning society with extensive significance. This paper analyzes the sustainable development of students’ cognitive abilities using emerging digital education technologies. It not only deepens the understanding of sustainable development of education and humans but also provides intellectual support for society’s sustainable development.
Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India
Authors: Satpathi Anurag , Setiya Parul , Das Bappa , Nain Ajeet Singh , Jha Prakash Kumar , Singh Surendra , Singh Shikha
Published: February 2023

Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur (Rcal2 = 1, Rval2= 1 and RMSEcal = 0.002, RMSEval = 0.003) and Surguja (Rcal2 = 1, Rval2= 0.99 and RMSEcal = 0.004, RMSEval = 0.214) districts as compared to the other models, whereas for Bastar, ELNET (Rcal2 = 90, Rval2= 0.48) and LASSO (Rcal2 = 93, Rval2= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R2 = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.
Something Still Remains: Factors Affecting Tsunami Risk Perception on the Coasts Hit by the Reggio Calabria-Messina 1908 Event (Italy)
Authors: Cerase Andrea , Cugliari Lorenzo
Published: February 2023

Mediterranean coasts are prone to tsunamis due to high seismicity in some well-known areas near plate margins. However, tsunamis have a low frequency of occurrence despite having highly destructive potential. The low frequency of occurrence and historicity of the most destructive events lead to minimizing or neglecting this risk. Past research identified socio-demographic and spatial factors that may affect tsunami risk perception. This research is based on CATI survey (Computer Assisted Telephone Interview) to a sample of 5842 respondents designed to investigate whether and how risk perception and risk knowledge were affected by a major event such as the 1908 Reggio Calabria Messina tsunami, by making a comparison between areas hit by that event and unaffected areas, also providing some explanatory hypotheses. Despite differences between Calabria and Sicily, data show higher levels of tsunami risk perception in the area affected by the 1908 event, along with a major role of interpersonal sources, playing a relevant role in information gathering and understanding. Research also suggests the need to better integrate different sources of knowledge to improve people’s understanding so as to effectively cope with tsunami risk.
Sustainable-Oriented Development for Urban Interface of Historic Centers
Authors: Shehata Ahmed Mohamed
Published: February 2023

The Middle East and North Africa (MENA) cities’ historic centers have a unique urban fabric regarding land use, physical characteristics, and environmental performance. Several cities within this region are subject to significant development projects based on demolition and replacement. These projects aim to improve the quality of life and enhance the city’s socioeconomic and sustainability. This paper investigates the physical characteristics of the urban interface between the historical centers and the rest of the cities’ urban expansions to ensure a smooth transition between the historic urban fabric and the rest of the city’s urban fabric. The research objective was fulfilled by developing a framework to classify urban fabric types based on their physical characteristics. Jeddah city was selected as a case study. Six growth phases of the city were identified. Based on this classification, urban fabric samples representing these phases were selected. These urban samples’ physical characteristics were analyzed. Results identified in urban fabric characteristics between the historic center and the other identified urban fabrics within the city, especially the demolished deteriorated surrounding urban areas. Urban features for under-development urban areas were concluded. Design guidelines were suggested for historical centers to achieve homogeneous integrated, sustainable, livable urban areas.
Regulations on Non-Financial Disclosure in Corporate Reporting: A Thematic Review
Authors: Mustafa Khan Nurul Jannah , Mohd Ali Hasani
Published: February 2023

There is a growing call globally for corporations to improve transparency in corporate reporting, along with the surge of enhancing disclosure of non-financial information. Companies are seen as agents for contributing to a better future, and hence could assist in achieving the sustainable development goals (SDGs) 2030, via transparent non-financial disclosure. This review paper is premised on the fact that laws on non-financial disclosure may be useful in enhancing the transparency of companies’ conducts. Hence, this systematic review aims to synthesize the literature from 2014 to 2021 on the patterns and trends relating to regulations on non-financial disclosure in corporate reporting by companies. A keyword search followed by filters provided by the Web of Science Core Collection and SCOPUS databases resulted in a total of 369 documents being found. A total of 62 articles were reviewed after manual filtering and exclusion. A thematic review of these 62 articles identified 20 initial codes, which were then grouped into eight clusters: Directive 2014/95/EU, disclosure approaches, fiduciary duties of directors, stakeholder engagement, the effectiveness of disclosure regulations, the impacts of rules, the role of different actors and corporate accountability. The paper finds that the patterns and trends in the review set the path for future research on laws of non-financial disclosure, as they serve as a guideline for researchers for future studies.
Recent Advances in Metal-Organic Framework (MOF) Asymmetric Membranes/Composites for Biomedical Applications
Authors: Valizadeh Harzand Farrokhfar , Mousavi Nejad Seyyed Navid , Babapoor Aziz , Mousavi Seyyed Mojtaba , Hashemi Seyyed Alireza , Gholami Ahmad , Chiang Wei-Hung , Buonomenna Maria Giovanna , Lai Chin Wei
Published: February 2023

Metal-organic frameworks (MOFs) are a new class of porous crystalline materials composed of metal and organic material. MOFs have fascinating properties, such as fine tunability, large specific surface area, and high porosity. MOFs are widely used for environmental protection, biosensors, regenerative medicine, medical engineering, cell therapy, catalysts, and drug delivery. Recent studies have reported various significant properties of MOFs for biomedical applications, such as drug detection and delivery. In contrast, MOFs have limitations such as low stability and low specificity in binding to the target. MOF-based membranes improve the stability and specificity of conventional MOFs by increasing the surface area and developing the possibility of MOF-ligand binding, while conjugated membranes dramatically increase the area of active functional groups. This special property makes them attractive for drug and biosensor fabrication, as both the spreading and solubility components of the porosity can be changed. Asymmetric membranes are a structure with high potential in the biomedical field, due to the different characteristics on its two surfaces, the possibility of adjusting various properties such as the size of porosity, transfer rate and selectivity, and surface properties such as hydrophilicity and hydrophobicity. MOF assisted asymmetric membranes can provide a platform with different properties and characteristics in the biomedical field. The latest version of MOF materials/membranes has several potential applications, especially in medical engineering, cell therapy, drug delivery, and regenerative medicine, which will be discussed in this review, along with their advantages, disadvantages, and challenges.
Solitary Waves and Their Interactions in the Cylindrical Korteweg–De Vries Equation
Authors: Hu Wencheng , Ren Jingli , Stepanyants Yury
Published: February 2023

We consider approximate, exact, and numerical solutions to the cylindrical Korteweg–de Vries equation. We show that there are different types of solitary waves and obtain the dependence of their parameters on distance. Then, we study the interaction of solitary waves of different types.
Review of the State of the Art for Radial Rotating Heat Pipe Technology Potentially Applicable to Gas Turbine Cooling
Authors: Wang Zhao , Turan Ali , Craft Timothy
Published: February 2023

Improvements in the efficiency of gas turbine engines over the decades have led to increasing turbine inlet temperatures. This, in turn, has resulted in the need to cool the turbine blades themselves to avoid damage to them. While air-cooling and film-cooling methods have been adopted as the primary methods of gas turbine blade cooling, the heat pipe cooling method shows greater potential in terms of temperature uniformity, maximum allowable gas temperature, reliability, and durability. This paper reviews the state-of-the-art research activities on the radial rotating heat pipes (RRHP) potentially applicable to gas turbine cooling. The emergence of the heat-pipe-cooled turbine blade concept, designs, and variants will be described at the beginning. Then the paper will review the literature addressing the heat transfer performance of RRHPs, and the effects on them of rotational forces, working fluid properties, and geometry, as well as operational limits they may be subject to. Additionally, the effects of secondary flow and numerical simulation of RRHPs will be reviewed and discussed. It can be concluded that fundamental studies are still needed for the understanding of the RRHP, as well as the improvement of numerical models.
Heavy Metal Contamination in the Aquatic Ecosystem: Toxicity and Its Remediation Using Eco-Friendly Approaches
Authors: Singh Veer , Singh Nidhi , Rai Sachchida Nand , Kumar Ashish , Singh Anurag Kumar , Singh Mohan P. , Sahoo Ansuman , Shekhar Shashank , Vamanu Emanuel , Mishra Vishal
Published: February 2023

Urbanization and industrialization are responsible for environmental contamination in the air, water, and soil. These activities also generate large amounts of heavy metal ions in the environment, and these contaminants cause various types of health issues in humans and other animals. Hexavalent chromium, lead, and cadmium are toxic heavy metal ions that come into the environment through several industrial processes, such as tanning, electroplating, coal mining, agricultural activities, the steel industry, and chrome plating. Several physical and chemical methods are generally used for the heavy metal decontamination of wastewater. These methods have some disadvantages, including the generation of secondary toxic sludge and high operational costs. Hence, there is a need to develop a cost-effective and eco-friendly method for the removal of heavy metal ions from polluted areas. Biological methods are generally considered eco-friendly and cost-effective. This review focuses on heavy metal contamination, its toxicity, and eco-friendly approaches for the removal of heavy metals from contaminated sites.
Hofmann vs. Paracelsus: Do Psychedelics Defy the Basics of Toxicology?—A Systematic Review of the Main Ergolamines, Simple Tryptamines, and Phenylethylamines
Authors: Henríquez-Hernández Luis , Rojas-Hernández Jaime , Quintana-Hernández Domingo , Borkel Lucas
Published: February 2023

Psychedelics are experiencing a strong renaissance and will soon be incorporated into clinical practice. However, there is uncertainty about how much harm they can cause at what doses. This review aimed to collect information on the health-hazardous doses of psychedelic substances, to be aware of the risks to which patients may be subjected. We focused on ergolamines, simple tryptamines, and phenylethylamines. We reviewed articles published in major medical and scientific databases. Studies reporting toxic or lethal doses in humans and animals were included. We followed PRISMA criteria for revisions. We identified 3032 manuscripts for inclusion. Of these, 33 were ultimately useful and gave relevant information about effects associated with high psychedelics doses. Despite having different molecular structures and different mechanisms of action, psychedelics are effective at very low doses, are not addictive, and are harmful at extremely high doses. For LSD and psilocybin, no dose has been established above which the lives of users are endangered. In contrast, MDMA appears to be the most dangerous substance, although reports are biased by recreational missuses. It seems that it is not only the dose that makes the poison. In the case of psychedelics, the set and setting make the poison.
A Current Perspective on the Potential of Nanomedicine for Anti-Tuberculosis Therapy
Authors: Borah Slater Khushboo , Kim Daniel , Chand Pooja , Xu Ye , Shaikh Hanif , Undale Vaishali
Published: February 2023

Tuberculosis (TB) is one of the ten infectious diseases that cause the highest amount of human mortality and morbidity. This infection, which is caused by a single pathogen, Mycobacterium tuberculosis, kills over a million people every year. There is an emerging problem of antimicrobial resistance in TB that needs urgent treatment and management. Tuberculosis treatment is complicated by its complex drug regimen, its lengthy duration and the serious side-effects caused by the drugs required. There are a number of critical issues around drug delivery and subsequent intracellular bacterial clearance. Drugs have a short lifespan in systemic circulation, which limits their activity. Nanomedicine in TB is an emerging research area which offers the potential of effective drug delivery using nanoparticles and a reduction in drug doses and side-effects to improve patient compliance with the treatment and enhance their recovery. Here, we provide a minireview of anti-TB treatment, research progress on nanomedicine and the prospects for future applications in developing innovative therapies.
Rotavirus Infection and Genotyping in Yantai, Shandong Province, 2017–2019
Authors: Sun Zhenlu , Zhang Guifang , Li Chunyan , Niu Peihua , Li Xia , Gao Qiao , Guo Kai , Zhang Ruiqing , Wang Ji , Ma Xuejun
Published: February 2023

Purpose: Rotavirus (RV) ranked first among infectious diarrhea-causing pathogens in Yantai from 2017 to 2019. This study investigated the seroserotypes of RV in Yantai, Shandong, from 2017 to 2019 to identify the dominant serotypes and explore the epidemic pattern, aiming to effectively reduce the infection rate, better guide vaccination, and help in epidemiological prevention and control. Methods: A total of 2227 human diarrhea samples were collected from 2017 to 2019 in Yantai. The VP7 (G serotype) and VP4 (P serotype) genes of 467 RV-positive samples were amplified using two-round nested reverse transcription–polymerase chain reaction for G/P genotyping. Results: The genotyping results of RV in Yantai from 2017 to 2019 revealed that G9 was the dominant serotype for all G serotypes, P[8] was the dominant serotype for all P serotypes, and G9P[8] was the dominant serotype for all G/P combinations. G9 serotype accounted for 60.84%, 95.65%, and 83.76% of the total RV samples collected in 2017, 2018, and 2019, respectively. P[8] accounted for 75.52%, 94.69%, and 88.89% of the RV-positive samples collected in 2017, 2018, and 2019, respectively. G9P[8] accounted for 60.84%, 94.69%, and 83.76% of the total RV samples collected in 2017, 2018, and 2019, respectively. Of the total 467 samples from 2017 to 2019, G2P[4] accounted for 3.64% (17/467), G3P[8] for 1.28% (6/467), and G1P[8] for 0.86% (4/467). Conclusion: This study revealed the epidemiological characteristics of RV infection and the development pattern of dominant serotypes in Yantai in recent years, guiding the selection of RV vaccines. The prioritization of vaccines containing G9 serotype for infants in Yantai in recent years is recommended.