bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Simulation and Prediction of Land Use/Land Cover in the Transnational Ili-Balkhash Basin
Authors: Kou Jing , Wang Jinjie , Ding Jianli , Ge Xiangyu
Published: June 2023

Exploring the future trends of land use/land cover (LULC) changes is significant for the sustainable development of a region. The simulation and prediction of LULC in a large-scale basin in an arid zone can help the future land management planning and rational allocation of resources in this ecologically fragile region. Using the whole Ili-Balkhash Basin as the study area, the patch-generating land use simulation (PLUS) model and a combination of PLUS and Markov predictions (PLUS–Markov) were used to simulate and predict land use in 2020 based on the assessment of the accuracy of LULC classification in the global dataset. The accuracy of simulations and predictions using the model were measured for LULC data covering different time periods. Model settings with better simulation results were selected for simulating and predicting possible future land use conditions in the basin. The future predictions for 2025 and 2030, which are based on historical land change characteristics, indicate that the overall future spatial pattern of LULC in the basin remains relatively stable in general without the influence of other external factors. Over the time scale of the future five years, the expansion of croplands and barren areas in the basin primarily stems from the loss of grasslands. Approximately 48% of the converted grassland areas are transformed into croplands, while around 40% are converted into barren areas. In the longer time scale of the future decade, the conversion of grasslands to croplands in the basin is also evident. However, the expansion phenomenon of urban and built-up lands at the expense of croplands is more significant, with approximately 774.2 km2 of croplands developing into urban and built-up lands. This work provides an effective new approach for simulating and predicting LULC in data-deficient basins at a large scale in arid regions, thereby establishing a foundation for future research on the impact of human activities on basin hydrology and related studies.
Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data
Authors: Wang Jie , Yao Chunjing , Ma Hongchao , Xu Junhao , Qian Chen
Published: June 2023

The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide the precise height and detailed structure of trees, and can estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. First, the tree crowns are segmented using the profile-rotating algorithm, and tree positions are registered based on dominant tree heights. Second, considering the correlation between crown shape and tree species, we use DBN classifier to identify species using features of crowns, which are extracted to 1D array. Third, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass, and stock volume can be extracted from trees, enabling accurate large-scale forest surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east–west crown width, north–south crown width, diameter at breast height, aboveground biomass, and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based survey is practical and can be widely applied in forest resource monitoring.
Kalman Filter, ANN-MLP, LSTM and ACO Methods Showing Anomalous GPS-TEC Variations Concerning Turkey’s Powerful Earthquake (6 February 2023)
Authors: Akhoondzadeh Mehdi
Published: June 2023

On 6 February 2023, at 1:17:34 UTC, a powerful Mw = 7.8 earthquake shook parts of Turkey and Syria. Investigating the behavior of different earthquake precursors around the time and location of this earthquake can facilitate the creation of an earthquake early warning system in the future. Total electron content (TEC) obtained from the measurements of GPS satellites is one of the ionospheric precursors, which in many cases has shown prominent anomalies before the occurrence of strong earthquakes. In this study, five classical and intelligent anomaly detection algorithms, including median, Kalman filter, artificial neural network (ANN)-multilayer perceptron (MLP), long short-term memory (LSTM), and ant colony optimization (ACO), have been used to detect seismo-anomalies in the time series of TEC changes in a period of about 4 months, from 1 November 2022 to 17 February 2023. All these algorithms show outstanding anomalies in the period of 10 days before the earthquake. The median method shows clear TEC anomalies in 1, 2 and, 3 days before the event. Since the behavior of the time series of a TEC parameter is complex and nonlinear, by implementing the Kalman filter method, pre-seismic anomalies were observed in 1, 2, 3, 5, and 10 days prior to the main shock. ANN as an intelligent-method-based machine learning also emphasizes the abnormal behavior of the TEC parameter in 1, 2, 3, 6, and 10 days before the earthquake. As a deep-learning-based predictor, LSTM indicates that the TEC value in the 10 days prior to the event has crossed the defined permissible limits. As an optimization algorithm, the ACO method shows behavior similar to Kalman filter and MLP algorithms by detecting anomalies 3, 7, and 10 days before the earthquake. In a previous paper, the author showed the findings of implementing a fuzzy inference system (FIS), indicating that the magnitude of the mentioned powerful earthquake could be predicted during about 9 to 1 day prior to the event. The results of this study also confirm the findings of another study. Therefore, considering that different lithosphere–atmosphere–ionosphere (LAI) precursors and different predictors show abnormal behavior in the time period before the occurrence of large earthquakes, the necessity of creating an earthquake early warning system based on intelligent monitoring of different precursors in earthquake-prone areas is emphasized.
Deformation Monitoring and Analysis of Reservoir Dams Based on SBAS-InSAR Technology—Banqiao Reservoir
Authors: Pang Zhiguo , Jin Qingguang , Fan Peng , Jiang Wei , Lv Juan , Zhang Pengjie , Cui Xiangrui , Zhao Chun , Zhang Zhengjia
Published: June 2023

Most dams in China have been operating for a long time and are products of the economic and technical limitations at the time of construction. Due to decades of aging engineering and ancillary problems, these reservoirs pose great threats to the safety of local people and the development of the surrounding economy. In this study, the surface deformation information for the Banqiao Reservoir is monitored with the small baseline subset–synthetic aperture radar interferometry (SBAS-InSAR) method using 80 Sentinel-1A images acquired from 3 January 2020 to 20 August 2022. Additionally, ground measurements from the BeiDou ground-based deformation monitoring stations were collected to validate the InSAR results. Based on the InSAR results, the spatiotemporal deformation features of the dam were analyzed in detail. The results show that the deformation in most areas, including the dam in the study area, is relatively stable, and the regional deformation velocity of the Banqiao Reservoir dam and other hydraulic engineering facilities varies between −1 mm/y and −4 mm/y. The Ru River area has a relatively obvious subsidence trend, and the maximum subsidence velocity reaches 30 mm/y. The InSAR monitoring results are consistent with the change trend in the BeiDou ground-based deformation measurement results. The monitoring results for the reservoir dam area provide a reference for local sustainable development and geological disaster prevention.
NSKY-CD: A System for Cloud Detection Based on Night Sky Brightness and Sky Temperature
Authors: Massetti Luciano , Materassi Alessandro , Sabatini Francesco
Published: June 2023

Cloud cover is important meteorological information that still requires expensive equipment to be monitored from the ground, especially at night. The use of artificial lights at night causes light pollution, and clouds amplify this by reflecting light downward. In addition, cloud thermal radiation emissions affect sky temperature. In this study, we describe a system (NSKY-CD) that can be used to detect clouds at night since it integrates a sky quality meter that measures night sky brightness (NSB) and an air temperature and an infrared temperature sensor that measure sky temperature. We defined a cloud detection method based on fixed threshold values determined with two different procedures that we called ‘optimal’ and ‘antimode’. We then quantitatively assessed the performance of these methods in detecting the presence or absence of clouds in the urban area of Florence during two full moon cycles. Accuracy for the ‘optimal’ method varied between 87% and 91%, while for the ‘antimode’ method, it varied between 86% and 89%. Our results suggest that the two parameters are complementary since NSB has a better performance on moonless nights, and the difference between air temperature and sky temperature has a better performance on moonlit nights. Our method can also be used to analyze historical series of NSB to estimate cloud presence backwards, thus providing important information for meteorological, environmental and astronomical studies.
Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM
Authors: Li Liangchao , Liu Haijun , Le Huijun , Yuan Jing , Shan Weifeng , Han Ying , Yuan Guoming , Cui Chunjie , Wang Junling
Published: June 2023

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.
SDGs and ESG Criteria in Housing: Defining Local Evaluation Criteria and Indicators for Verifying Project Sustainability Using Florence Metropolitan Area as a Case Study
Authors: Battisti Fabrizio
Published: June 2023

According to recent estimates (Reuters), the cost of implementing the sustainable development goals of Agenda 2030 will be USD 176 trillion. This amount seems unattainable, even when considering the public resources currently available to governments that are part of the United Nations. It is undoubtedly necessary to involve the private financial sector, within the so-called finance for sustainable development. To achieve the SDGs, it may be also necessary to schedule local-level initiatives and actions that consider the environmental, social, and governance (ESG) criteria, which can be used to source forms of private finance for sustainable development and contribute to the implementation of the SDGs. Based on these observations, this article deals with the study of clearly defined parameters: (1) the factors that should be considered when assessing the sustainability of a (local) real-estate project and, in particular, of a housing project, in order for the project to be considered sustainable and participate, albeit pro rata, in achieving one (or more) of the eleven SDG sub-goals; and (2) funding opportunities in the world of sustainable-development finance. The specific goal of the research contribution presented in this paper was to use the Delphi method to define a set of local-level evaluation criteria and indicators for real-estate projects, specifically housing projects, with an ESG matrix and in line with some of the SDG 11 targets, considering the Florence Metropolitan Area as a case study. The application of the Delphi method to the case study made it possible to test the usability of this method for the definition of the criteria and indicators, at the local level, for assessing the level of implementation of the SDGs. Specifically, a set of 48 criteria and 74 indicators were defined for assessing the consistency of housing projects with SDG 11 and ESG criteria.
Remediation of Acid Mine Drainage in the Haizhou Open-Pit Mine through Coal-Gangue-Loaded SRB Experiments
Authors: Dong Yanrong , Gao Ziqing , Di Junzhen , Wang Dong , Yang Zhenhua , Guo Xuying , Li Ying , Zhu Xiaotong , Wang Guixian
Published: June 2023

To address the pollution problem of acid mine drainage (AMD) characterized by high concentrations of Fe2+, Mn2+, and SO42−, a combination of coal gangue (CG) and sulfate-reducing bacteria (SRB) was employed. The effects of coal-gangue dosage, SRB inoculation concentration, and temperature on AMD treatment with coal-gangue-loaded SRB were determined through single-factor experiments and response surface methodology (RSM) experiments. By considering the principles of adsorption isotherms, adsorption kinetics, and reduction kinetics, the removal mechanisms of SO42−, Fe2+, and Mn2+ in AMD using coal gangue-loaded SRB in the the Haizhou open-pit mine was revealed. The results showed that the overall effectiveness of the four types of coal-gangue-loaded SRB in repairing AMD was as follows: 3# CG-loaded SRB > 2# CG-loaded SRB > 1# CG-loaded SRB > 4# CG-loaded SRB, with coal-gangue-loaded SRB in the the Haizhou open-pit mine showing the best performance. According to the RSM test, the optimum conditions for repairing AMD with coal-gangue-loaded SRB in the open-pit mine were a coal-gangue dosage of 52 g, SRB inoculation concentration of 11.7%, and temperature of 33.4 °C. The order of factors affecting the removal of SO42− and Fe2+ from AMD by SRB loaded on coal gangue was SRB inoculation concentration > temperature > coal-gangue dosage. For Mn2+, the order of influence was temperature > SRB inoculation concentration > coal-gangue dosage. In the process of repairing Fe2+ with coal-gangue-loaded SRB in the the Haizhou open-pit mine, the biological activity metabolism of SRB played a leading role, while the adsorption isotherm of Mn2+ followed the Freundlich model. The adsorption kinetics of coal-gangue-loaded SRB in the the Haizhou open-pit mine for Fe2+ and Mn2+ in AMD conformed to Lagergren’s second-order kinetic model, while the reduction kinetics of SO42− conformed to a first-order reaction model.
Vermibiochar: A Novel Approach for Reducing the Environmental Impact of Heavy Metals Contamination in Agricultural Land
Authors: Rehman Sami ur , De Castro Federica , Marini Paolo , Aprile Alessio , Benedetti Michele , Fanizzi Francesco Paolo
Published: June 2023

Environmental pollution has become a pressing concern worldwide due to the accumulation of pollutants from industries and agricultural sectors in soil and water environments. Heavy metals pose severe hazards to the environment, plants, and human health. Consequently, an eco-friendly technique is needed to combat environmental pollutants. Vermibiochar, a product prepared through the combined action of earthworms and biochar, demonstrates great potential in reducing heavy metal concentrations in contaminated soil. Its large surface area and high cation exchange capacity enhance the sorption of contaminants onto the vermibiochar surface, reducing their bioavailability. This review highlights the roles played by earthworms and biochar in heavy metal detoxification and immobilization. It discusses the current methods of remediation, vermibiochar production, its effects on soil properties and plant growth, and biochar’s impact on earthworm growth and reproduction. The studies reviewed suggest that vermibiochar is a novel strategy for addressing heavy metal contamination.
Determinants of Learners’ Self-Directed Learning and Online Learning Attitudes in Online Learning
Authors: Li Jing , Wu Chi-Hui
Published: June 2023

The global COVID-19 pandemic has disrupted traditional learning methods, leading to a surge in online learning. It has been found that the low course completion and performance are associated with online learning. There has been increasing and urgent necessity to identify effective and decisive ways to address these challenges. Self-directed learning and online learning attitudes are key factors that influence learning behavior and outcomes, while the general traditional statistical method often does not perform well in identifying those categories. To fill the gap, this study applies the fuzzy Delphi method and the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method to clarify and analyze the relationship of influence among indicators of self-directed learning and online learning attitudes, develop a cause–effect model, and ultimately identify an effective and decisive strategy for improving online learning. According to the cause–effect relationship among indictors, the computer/smartphone and internet confidence, computer/smartphone usage, and computer/smartphone preference are the three decisive strategical ways for online learning. To improve learners’ attitudes towards online learning, teachers need to develop or improve students’ computer/smartphone and internet confidence, computer/smartphone usage skills, and develop their self-directed learning abilities to inspire and increase their willingness and ability to participate effectively in online courses. Moreover, this study first applies the fuzzy DEMATEL method to assess, analyze and develop a causal model of self-directed learning and online learning attitudes for academics to further explore and confirm the complex interrelationships among the key learning behaviors of online learners.
Research on the Experience of Influencing Elements and the Strategy Model of Children’s Outpatient Medical Services under the Guidance of Design Thinking
Authors: Zhang Xi , Liu Chenyang , Zhang Xuehan
Published: June 2023

Faced with the generally poor experience in pediatric outpatient in China, under the guidance of design thinking, based on the analysis and research of the main elements affecting child outpatients’ medical service experiences, this study proposes a set of strategic models that can improve child outpatients’ medical service experiences. Specifically, this study takes Shanghai Xinhua Hospital as a research case, combined with SPSS data statistics software, and comprehensively uses preliminary field research methods, questionnaire descriptive analysis methods, questionnaire satisfaction analysis methods, and questionnaire principal component analysis (PCA) methods as well as the structured interview method; thus, the main elements affecting child outpatients’ medical service experiences were obtained. Then, according to the main elements, a set of child outpatient medical service strategy models is proposed to improve child outpatients’ medical service experiences. Finally, the effectiveness of the strategy model is tested through satisfaction analysis and simulation case verification. The model is a people-centered, sustainable strategic model. With the support of design thinking, the strategic model takes the experience of children as the core improvement point, which is able to fully protect the rights and demands of child patients. At the same time, this strategy model can also reduce the workload of doctors, improve the operational efficiency of hospitals, promote a more equal distribution of medical resources, and reduce medical service costs. More importantly, it also encourages patients and their families to communicate and express their opinions to medical professionals, which can greatly reduce the tension between doctors and patients and effectively avoid doctor–patient conflicts. This has important implications for the sustainability of healthcare. However, this strategy model is only a guiding strategy for improving outpatient care for children. It does not provide detailed solutions around certain specific issues and specific implementations. At the same time, it is not a complex engineering design system but only provides a reference for improving children’s medical services in terms of strategic logic.
An Active Drying Sensor to Drive Dairy Cow Sprinkling Cooling Systems
Authors: Liberati Paolo
Published: June 2023

The use of sprinkling with ventilation to cool dairy cows is considered an appropriate practice to reduce the negative effects of heat stress. However, due to climate change, water will increasingly become a limited resource, so we need to make water use more and more efficient. For this purpose, an active drying sensor has been developed in order to time the sprinkling cooling system. The sensor reproduces the thermal response of a cow, considering both sensible and latent heat exchange, and is located in the feeding alley, about two meters above the floor. This allows the fabric of the sensor (simulating the fur) to be wetted by the sprinkler, and blown by the fan. The water content of the sensor fabric during the drying time is monitored by measuring its electrical conductivity, allowing the estimation of the time the fur becomes dry. Another two specifically designed instruments are presented, the first to estimate the fur’s water content after spraying, and the second to detect the time the fur became dry. Sensor output, interpreted through a simplified model, gave a predicted drying time with an error ranging between −11.4% and +14.8% (R2 = 0.789). In the commercial barn where the experiments were conducted, the use of the sensor allowed an estimated reduction in water consumption of about 57%, with respect to the fixed timing normally used. As a perspective, the sensor could be used to assess cows’ heat stress level.
Decoupling Relationship between Resource Environment and High-Quality Economic Development in the Yellow River Basin
Authors: Zhao Xiaojing , Li Xuke , Deng Guoqu , Xi Yanling
Published: June 2023

Economic development highly depends on resources and the environment, especially in the Yellow River Basin (YRB), a crucial ecological protection and high-quality development pilot zone in China. Thus, evaluating high-quality economic development (HQED) and its decoupling relationship with the resource environment is conducive to planning future regional development. First, this paper used the two-stage entropy method to measure the high-quality economic development of cities in the YRB from 2010 to 2019. Then, the comprehensive decoupling analysis of the resource environment and high-quality economic development was carried out based on the Tapio decoupling model. The results showed that: (1) The high-quality economic development level improved slowly in the YRB. Nevertheless, the resource and environmental pressure gradually decreased. (2) Compared with the resource decoupling index, the environmental decoupling index had a greater impact on the comprehensive decoupling index in the YRB. The strong decoupling displayed a spatial diffusion trend from point to surface. (3) Despite the decoupling types having a positive transfer trend in the YRB, the decoupling states of cities fluctuated considerably, and the phenomenon of recoupling occurred frequently. According to the above findings, this study put forward some policy recommendations for achieving strong decoupling in the YRB.
Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries
Authors: Assous Hamzeh F. , AL-Najjar Hazem , Al-Rousan Nadia , AL-Najjar Dania
Published: June 2023

Crop yield prediction is one of the most challenging tasks in agriculture. It is considered to play an important role and be an essential step in decision-making processes. The goal of crop prediction is to establish food availability for the coming years, using different input variables associated with the crop yield domain. This paper aims to predict the yield of five of the Gulf countries’ crops: wheat, dates, watermelon, potatoes, and maize (corn). Five independent variables were used to develop a prediction model, namely year, rainfall, pesticide, temperature changes, and nitrogen (N) fertilizer; all these variables are calculated by year. Moreover, this research relied on one of the most widely used machine learning models in the field of crop yield prediction, which is the neural network model. The neural network model is used because it can predict complex relationships between independent and dependent variables. To evaluate the performance of the prediction models, different statistical evaluation metrics are adopted, including mean square error (MSE), root-mean-square error (RMSE), mean bias error (MBE), Pearson’s correlation coefficient, and the determination coefficient. The results showed that all Gulf countries are affected mainly by four independent variables: year, temperature changes, pesticides, and nitrogen (N) per year. Moreover, the average of the best crop yield prediction results for the Gulf countries showed that the RMSE and R2 are 0.114 and 0.93, respectively. This provides initial evidence regarding the capability of the neural network model in crop yield prediction.
Spatial Correlation Network Structure of Carbon Emission Efficiency of Railway Transportation in China and Its Influencing Factors
Authors: Zhang Ningxin , Zhang Yu , Chen Hanli
Published: June 2023

Railway carbon emissions reduction is of great significance. In this study, carbon emission efficiency in railway transportation in China’s 31 provinces is measured for 2006–2019 based on an unexpected output slack-based measure (SBM) model. A gravity matrix of the spatial correlation network for carbon emission efficiency is constructed using the modified gravity model, the spatial network structure is explored using social network analysis, and the factors influencing the spatial network are analyzed using the quadratic assignment procedure (QAP) model. Based on the results, several conclusions can be drawn: (1) the carbon emissions efficiency of railway transportation in China increased periodically during the study period, but there are still great differences between regions. (2) The carbon emission efficiency in railway transportation shows significant characteristics of spatial correlation networks. (3) The inter-provincial associations gradually increased, while there are still large regional differences in the spatial correlation network. (4) Differences in spatial adjacency, economic development and scientific and technological advancement have significant positive impacts on the spatial correlation network. This research will help policy makers formulate relevant policies to promote the regional coordinated development of low-carbon railway transportation.
Applying Transfer Learning Approaches for Intrusion Detection in Software-Defined Networking
Authors: Chuang Hsiu-Min , Ye Li-Jyun
Published: June 2023

In traditional network management, the configuration of routing policies and associated settings on individual routers and switches was performed manually, incurring a considerable cost. By centralizing network management, software-defined networking (SDN) technology has reduced hardware construction costs and increased flexibility. However, this centralized architecture renders information security vulnerable to network attacks, making intrusion detection in the SDN environment crucial. Machine-learning approaches have been widely used for intrusion detection recently. However, critical issues such as unknown attacks, insufficient data, and class imbalance may significantly affect the performance of typical machine learning. We addressed these problems and proposed a transfer-learning method based on the SDN environment. The following experimental results showed that our method outperforms typical machine learning methods. (1) our model achieved a F1-score of 0.71 for anomaly detection for unknown attacks; (2) for small samples, our model achieved a F1-score of 0.98 for anomaly detection and a F1-score of 0.51 for attack types identification; (3) for class imbalance, our model achieved an F1-score of 1.00 for anomaly detection and 0.91 for attack type identification. In addition, our model required 15,230 seconds (4 h 13 m 50 s) for training, ranking second among the six models when considering both performance and efficiency. In future studies, we plan to combine sampling techniques with few-shot learning to improve the performance of minority classes in class imbalance scenarios.
A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought
Authors: López-Carr David , Pricope Narcisa G. , Mwenda Kevin M. , Daldegan Gabriel Antunes , Zvoleff Alex
Published: June 2023

Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach.
Ammonia Air Stripping from Different Livestock Effluents Prior to and after Anaerobic Digestion
Authors: Scotto di Perta Ester , Grieco Raffaele , Papirio Stefano , Esposito Giovanni , Cervelli Elena , Bovo Marco , Pindozzi Stefania
Published: June 2023

Livestock digestate provides nutrients and organic matter to the soil while increasing agricultural sustainability. Nevertheless, nitrogen (N) losses due to the nutrient surplus in regions characterized by intensive animal farming activities still represent an unsolved issue. For this purpose, digestate needs proper treatment and management to avoid N losses in the environment. In the livestock farming context, anaerobic digestion (AD) can be accompanied by an ammonia stripping (AS) process for N recovery. This paper aims to investigate the feasibility AS prior to and after AD of the manure, focusing on two different livestock farms, representative of dairy cattle and pig breeding in southern Italy. AS was performed at a lab scale by injecting microbubbles of air, which allowed the pH to increase, and thus the removal of ammonia. The results show that treating a dairy raw slurry with high intermediate alkalinity (IA) (6707 mg CaCO3 L−1) with AS may not be convenient in terms of total ammonia nitrogen (TAN) reduction. As a matter of fact, the loss of buffering capacity during the stripping process resulted in a pH never exceeding the value of 9, which could not promote free ammonia volatilization, whereas integrating AD with AS allowed us to obtain a 34% higher TAN reduction under the same stripping conditions at a temperature (T) of 38 °C and a gas-to-liquid ratio (G/L) of 1:1. Therefore, the AS removal efficiency strongly depends on the characteristics (mainly IA) of the treated matrix. High IA values suggest a possible high concentration of volatile fatty acids, which hinders pH increases and, thus, enables ammonia stripping. Despite the initial matrix origin, a low IA compared to the total alkalinity (TA) (<20% of TA) ensures a greater ammonia removal efficiency, which could be similar between digestate and raw manure in the same operative process conditions. Nonetheless, the amount of ammonia stripped is related to the initial TAN concentration of the specific matrix.
A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder
Authors: Liu Kun , Feng Guoqi , Jiang Xingyu , Zhao Wenpeng , Tian Zhiqiang , Zhao Rizheng , Bi Kaihang
Published: June 2023

The driving fatigue state of shield machine drivers directly affects the safe operation and tunneling efficiency of shield machines during metro construction. To cope with the problem that it is challenging to simulate the working conditions and operation process of shield machine drivers using driving simulation platforms and that the existing fatigue feature fusion methods usually show low recognition accuracy, shield machine drivers at Shenyang metro line 4 in China were taken as the research subjects, and a multi-modal physiological feature fusion method based on an L2-regularized stacked auto-encoder was designed. First, the ErgoLAB cloud platform was used to extract the combined energy feature (E), the reaction time, the HRV (heart rate variability) time-domain SDNN (standard deviation of normal-to-normal intervals) index, the HRV frequency-domain LF/HF (energy ratio of low frequency to high frequency) index and the pupil diameter index from EEG (electroencephalogram) signals, skin signals, pulse signals and eye movement data, respectively. Second, the physiological signal characteristics were extracted based on the WPT (wavelet packet transform) method and time–frequency analysis. Then, a method for driving fatigue feature fusion based on an auto-encoder was designed aiming at the characteristics of the L2-regularization method to solve the over-fitting problem of small sample data sets in the process of model training. The optimal hyper-parameters of the model were verified with the experimental method of the control variable, which reduces the loss of multi-modal feature data in compression fusion and the information loss rate of the fused index. The results show that the method proposed outperforms its competitors in recognition accuracy and can effectively reduce the loss rate of deep features in existing decision-making-level fusion.
Mechanical and Sensory Properties of Pulse Gels in the Development of New Plant Based Food
Authors: Moussaoui Djemaa , González Mónica , Rizo Arantxa , Chaya Carolina , Tarrega Amparo
Published: June 2023

The shift towards healthier and sustainable food consumption requires a greater variety of plant-based products. This study aimed to determine how the mechanical and sensory properties of three different pulse gels (chickpea, lentil, or red lentil flour) vary with the ingredients (flour, oil, lemon, and salt content). All pulse flours were able to form self-standing gels. Mechanical properties varied with the pulse type and with the formulation differently depending on the pulse. For all pulses, the hardness and stiffness increased with the flour content and decreased with salt. They decreased with lemon in chickpea gels and increased with oil content in the red lentil gel. The Flash Profile technique provided distinctive sensory characteristics of each pulse gel. The red lentil gels were homogeneous, creamy, and compact. The chickpea gels were also compact, but harder. Lentil flour resulted in rough and sandy gels. For the three pulses, including oil and lemon reduced pulse flavors and increased sour taste. The results of this study give insights into the suitability of pulse gels to be the basis of new solid plant-based products whose sensory properties can be modulated by varying the concentration of natural ingredients.
Computational Methods for Estimating the Evidence and Bayes Factor in SEIR Stochastic Infectious Diseases Models Featuring Asymmetrical Dynamics of Transmission
Authors: Alharthi Muteb Faraj
Published: June 2023

Stochastic epidemic models may offer a vitally essential public health tool for comprehending and regulating disease progression. The best illustration of their importance and usefulness is perhaps the substantial influence that these models have had on the global COVID-19 epidemic. Nonetheless, these models are of limited practical use unless they provide an adequate fit to real-life epidemic outbreaks. In this work, we consider the problem of model selection for epidemic models given temporal observation of a disease outbreak through time. The epidemic models are stochastic individual-based transmission models of the Susceptible–Exposed–Infective–Removed (SEIR) type. The main focus is on the use of model evidence (or marginal likelihood), and hence the Bayes factor is a gold-standard measure of merit for comparing the fits of models to data. Even though the Bayes factor has been discussed in the epidemic modeling literature, little focus has been given to the fundamental issues surrounding its utility and computation. Based on various asymmetrical infection mechanism assumptions, we derive analytical expressions for Bayes factors which offer helpful suggestions for model selection problems. We also explore theoretical aspects that highlight the need for caution when utilizing the Bayes factor as a model selection technique, such as when the within-model prior distributions become more asymmetrical (diffuse or informative). Three computational methods for estimating the marginal likelihood and hence Bayes factor are discussed, which are the arithmetic mean estimator, the harmonic mean estimator, and the power posterior estimator. The theory and methods are illustrated using artificial data.
Spectral Characterization of Graphs with Respect to the Anti-Reciprocal Eigenvalue Property
Authors: Guan Hao , Khan Aysha , Akhter Sadia , Hameed Saira
Published: June 2023

Let G=(V,E) be a simple connected graph with vertex set V and edge set E, respectively. The term “anti-reciprocal eigenvalue property“ refers to a non-singular graph G for which, −1λ∈σ(G), whenever λ∈σ(G), ∀λ∈σ(G). Here, σ(G) is the multiset of all eigenvalues of A(G). Moreover, if multiplicities of eigenvalues and their negative reciprocals are equal, then that graph is said to have strong anti-reciprocal eigenvalue properties, and the graph is referred to as a strong anti-reciprocal graph (or (−SR) graph). In this article, a new family of graphs Fn(k,j) is introduced and the energy of F5(k,k2)k≥2 is calculated. Furthermore, with the help of F5(k,k2), some families of (−SR) graphs are constructed.
Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks
Authors: Zhang Keqin , Yang Jianjie , Lin Zhijian
Published: June 2023

Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.
Grapevine Endophyte Endornavirus and Two New Endornaviruses Found Associated with Grapevines (Vitis vinifera L.) in Idaho, USA
Authors: Dahan Jennifer , Orellana Gardenia E. , Lee Jungmin , Karasev Alexander V.
Published: June 2023

Five virus genomes, ranging between 12.0 and 12.3 kb in length and identified as endornaviruses, were discovered through a high-throughput sequencing (HTS) analysis of the total RNA samples extracted from two wine grape cultivars collected in the State of Idaho. One was found in a declining Chardonnay vine and was determined to be a local isolate of grapevine endophyte endornavirus (GEEV), and four others represented two novel endornaviruses named grapevine endornavirus 1 (GEV1) and grapevine endornavirus 2 (GEV2). All three virus genomes span a large, single open reading frame encoding polyproteins with easily identifiable helicase (HEL) and RNA-dependent RNA polymerase (RdRP) domains, while the GEV2 polyprotein also contains a glycosyltransferase domain. The GEV1 genome found in an asymptomatic Cabernet franc vine was related to, but distinct from, GEEV: the 5′-proximal, 4.7 kb segment of the GEV1 genome had a 72% identical nucleotide sequence to that of GEEV, while the rest of the genome displayed no significant similarity to the GEEV nucleotide sequence. Nevertheless, the amino acid sequence of the RdRP domain of GEV1 exhibited the closest affinity to the RdRP of GEEV. GEV2 was found in declining Chardonnay and asymptomatic Cabernet franc vines as three genetic variants exhibiting a 91.9–99.8% nucleotide sequence identity among each other; its RdRP had the closest affinity to the Shahe endorna-like virus 1 found in termites. In phylogenetic analyses, the RdRP and HEL domains of the GEV1 and GEV2 polyproteins were placed in two separate clades inside the large lineage of alphaendornaviruses, showing an affinity to GEEV and Phaseolus vulgaris endornavirus 1, respectively.
A Multimodal Approach towards Genomic Identification of Protein Inhibitors of Uracil-DNA Glycosylase
Authors: Muselmani Wael , Kashif-Khan Naail , Bagnéris Claire , Santangelo Rosalia , Williams Mark A. , Savva Renos
Published: June 2023

DNA-mimicking proteins encoded by viruses can modulate processes such as innate cellular immunity. An example is Ung-family uracil-DNA glycosylase inhibition, which prevents Ung-mediated degradation via the stoichiometric protein blockade of the Ung DNA-binding cleft. This is significant where uracil-DNA is a key determinant in the replication and distribution of virus genomes. Unrelated protein folds support a common physicochemical spatial strategy for Ung inhibition, characterised by pronounced sequence plasticity within the diverse fold families. That, and the fact that relatively few template sequences are biochemically verified to encode Ung inhibitor proteins, presents a barrier to the straightforward identification of Ung inhibitors in genomic sequences. In this study, distant homologs of known Ung inhibitors were characterised via structural biology and structure prediction methods. A recombinant cellular survival assay and in vitro biochemical assay were used to screen distant variants and mutants to further explore tolerated sequence plasticity in motifs supporting Ung inhibition. The resulting validated sequence repertoire defines an expanded set of heuristic sequence and biophysical signatures shared by known Ung inhibitor proteins. A computational search of genome database sequences and the results of recombinant tests of selected output sequences obtained are presented here.
CD40 Signaling in Mice Elicits a Broad Antiviral Response Early during Acute Infection with RNA Viruses
Authors: Rogers Kai J. , Richards Paige T. , Zacharias Zeb R. , Stunz Laura L. , Vijay Rahul , Butler Noah S. , Legge Kevin L. , Bishop Gail A. , Maury Wendy
Published: June 2023

Macrophages are critical in the pathogenesis of a diverse group of viral pathogens, both as targets of infection and for eliciting primary defense mechanisms. Our prior in vitro work identified that CD40 signaling in murine peritoneal macrophages protects against several RNA viruses by eliciting IL-12, which stimulates the production of interferon gamma (IFN-γ). Here, we examine the role of CD40 signaling in vivo. We show that CD40 signaling is a critical, but currently poorly appreciated, component of the innate immune response using two distinct infectious agents: mouse-adapted influenza A virus (IAV, PR8) and recombinant VSV encoding the Ebola virus glycoprotein (rVSV-EBOV GP). We find that stimulation of CD40 signaling decreases early IAV titers, whereas loss of CD40 elevated early titers and compromised lung function by day 3 of infection. Protection conferred by CD40 signaling against IAV is dependent on IFN-γ production, consistent with our in vitro studies. Using rVSV-EBOV GP that serves as a low-biocontainment model of filovirus infection, we demonstrate that macrophages are a CD40-expressing population critical for protection within the peritoneum and T-cells are the key source of CD40L (CD154). These experiments reveal the in vivo mechanisms by which CD40 signaling in macrophages regulates the early host responses to RNA virus infection and highlight how CD40 agonists currently under investigation for clinical use may function as a novel class of broad antiviral treatments.