bluuebunny/crossref_metadata_2025 huggingface dataset:
Inter-Annual Climate Variability Impact on Oil Palm Mapping
Authors: Troya Fernando , Bernardino Paulo N. , Somers Ben
Published: June 2022

The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satellite imagery. However, accuracy can be hampered by atmospheric phenomena that can drastically change climatic conditions in tropical regions, affecting the spectral properties of the vegetation. In this sense, we studied the accuracy of palm plantation mapping by using features from different regions of the electromagnetic spectrum and a data fusion approach, and then compared the changes in accuracy over the years 2016, 2017, and 2018 (two of them with reported climatic anomalies). Optical-based maps obtained higher accuracy than thermal- and microwave-based maps, but they were the most affected by inter-annual climate variability (error margin between 5 and 10%), while thermal-based maps were the least affected (error margin between 8 and 9%). Data fusion combinations improved accuracy and reduced dissimilarities between years (e.g., phenology-based map accuracy changed by up to 20.8%, while phenology fused with microwave features changed by up to 6.8%). We conclude that inter-annual climate variability on land-cover mapping should be considered, especially if the outputs will be used as input in future studies.
PolSAR Scene Classification via Low-Rank Constrained Multimodal Tensor Representation
Authors: Ren Bo , Chen Mengqian , Hou Biao , Hong Danfeng , Ma Shibin , Chanussot Jocelyn , Jiao Licheng
Published: June 2022

Polarimetric synthetic aperture radar (PolSAR) data can be acquired at all times and are not impacted by weather conditions. They can efficiently capture geometrical and geographical structures on the ground. However, due to the complexity of the data and the difficulty of data availability, PolSAR image scene classification remains a challenging task. To this end, in this paper, a low-rank constrained multimodal tensor representation method (LR-MTR) is proposed to integrate PolSAR data in multimodal representations. To preserve the multimodal polarimetric information simultaneously, the target decompositions in a scene from multiple spaces (e.g., Freeman, H/A/α, Pauli, etc.) are exploited to provide multiple pseudo-color images. Furthermore, a representation tensor is constructed via the representation matrices and constrained by the low-rank norm to keep the cross-information from multiple spaces. A projection matrix is also calculated by minimizing the differences between the whole cascaded data set and the features in the corresponding space. It also reduces the redundancy of those multiple spaces and solves the out-of-sample problem in the large-scale data set. To support the experiments, two new PolSAR image data sets are built via ALOS-2 full polarization data, covering the areas of Shanghai, China, and Tokyo, Japan. Compared with state-of-the-art (SOTA) dimension reduction algorithms, the proposed method achieves the best quantitative performance and demonstrates superiority in fusing multimodal PolSAR features for image scene classification.
RSSGG_CS: Remote Sensing Image Scene Graph Generation by Fusing Contextual Information and Statistical Knowledge
Authors: Lin Zhiyuan , Zhu Feng , Wang Qun , Kong Yanzi , Wang Jianyu , Huang Liang , Hao Yingming
Published: June 2022

To semantically understand remote sensing images, it is not only necessary to detect the objects in them but also to recognize the semantic relationships between the instances. Scene graph generation aims to represent the image as a semantic structural graph, where objects and relationships between them are described as nodes and edges, respectively. Some existing methods rely only on visual features to sequentially predict the relationships between objects, ignoring contextual information and making it difficult to generate high-quality scene graphs, especially for remote sensing images. Therefore, we propose a novel model for remote sensing image scene graph generation by fusing contextual information and statistical knowledge, namely RSSGG_CS. To integrate contextual information and calculate attention among all objects, the RSSGG_CS model adopts a filter module (FiM) that is based on adjusted transformer architecture. Moreover, to reduce the blindness of the model when searching semantic space, statistical knowledge of relational predicates between objects from the training dataset and the cleaned Wikipedia text is used as supervision when training the model. Experiments show that fusing contextual information and statistical knowledge allows the model to generate more complete scene graphs of remote sensing images and facilitates the semantic understanding of remote sensing images.
Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed
Authors: Honar Pajooh Houshyar , Rashid Mohammad A. , Alam Fakhrul , Demidenko Serge
Published: June 2022

Blockchain technology, with its decentralization characteristics, immutability, and traceability, is well-suited for facilitating secure storage, sharing, and management of data in decentralized Internet of Things (IoT) applications. Despite the increasing development of blockchain platforms, there is still no comprehensive approach for adopting blockchain technology in IoT systems. This is due to the blockchain’s limited capability to process substantial transaction requests from a massive number of IoT devices. Hyperledger Fabric (HLF) is a popular open-source permissioned blockchain platform hosted by the Linux Foundation. This article reports a comprehensive empirical study that measures HLF’s performance and identifies potential performance bottlenecks to better meet the requirements of blockchain-based IoT applications. The study considers the implementation of HLF on distributed large-scale IoT systems. First, a model for monitoring the performance of the HLF platform is presented. It addresses the overhead challenges while delivering more details on system performance and better scalability. Then, the proposed framework is implemented to evaluate the impact of varying network workloads on the performance of the blockchain platform in a large-scale distributed environment. In particular, the performance of the HLF is evaluated in terms of throughput, latency, network size, scalability, and the number of peers serviceable by the platform. The obtained experimental results indicate that the proposed framework can provide detailed real-time performance evaluation of blockchain systems for large-scale IoT applications.
An Engineering Model of Magnetic Flux Density and Electromagnetic Force Density at the Structural Discontinuity within Transformer Cores
Authors: Li Lingzhi , Du Xuhao , Pan Jie , Jiang Xishan
Published: June 2022

The structural discontinuities in the form of air gaps in transformer cores cause the concentration of electromagnetic force, which is an important source of transformer vibration and noise. In this paper, an engineering model of magnetic flux density and electromagnetic force density on transformer core discontinuities is analytically developed. Based on a reasonable structural simplification and assumptions, magnetic flux density and electromagnetic force density are deduced as explicit functions of the geometric, material, and electrical excitation characteristics of the gap region and the transformer core. The accuracy of the established model is validated by the finite element method (FEM) combined with a magnetic measurement experiment. According to this engineering model, the electromagnetic force density can be reduced by decreasing the gap ratio and increasing the gap thickness to a reasonable level. The outcome of this paper can help to understand the physical mechanism of the electromagnetic force generated by core air gap discontinuities, which is meaningful for noise control and the condition monitoring of transformers.
Biogenic VOCs Emission Profiles Associated with Plant-Pest Interaction for Phenotyping Applications
Authors: Valencia-Ortiz Milton , Marzougui Afef , Zhang Chongyuan , Bali Sapinder , Odubiyi Steven , Sathuvalli Vidyasagar , Bosque-Pérez Nilsa A. , Pumphrey Michael O. , Sankaran Sindhuja
Published: June 2022

Pest attacks on plants can substantially change plants’ volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and associated resistance. Furthermore, the identification of biomarkers—specific biogenic VOCs—associated with the resistance can serve as a non-destructive and rapid tool for phenotyping applications. This research aims to compare the VOCs emission profiles under diverse conditions to identify constitutive (also referred to as green VOCs) and induced (resulting from biotic/abiotic stress) VOCs released in potatoes and wheat. In the first study, wild potato Solanum bulbocastanum (accession# 22; SB22) was inoculated with Meloidogyne chitwoodi race 1 (Mc1), and Mc1 pathotype Roza (SB22 is resistant to Mc1 and susceptible to pathotype Roza), and VOCs emission profiles were collected using gas chromatography-flame ionization detection (GC-FID) at different time points. Similarly, in the second study, the VOCs emission profiles of resistant (‘Hollis’) and susceptible (‘Alturas’) wheat cultivars infested with Hessian fly insects were evaluated using the GC-FID system. In both studies, in addition to variable plant responses (susceptibility to pests), control treatments (non-inoculated or non-infested) were used to compare the VOCs emission profiles resulting from differences in stress conditions. The common VOC peaks (constitutive VOCs) between control and infected/infested samples, and unique VOC peaks (induced VOCs) presented only in infected/infested samples were analyzed. In the potato-nematode study, the highest unique peak was found two days after inoculation (DAI) for SB22 inoculated with Mc1 (resistance response). The most common VOC peaks in SB22 inoculated with both Mc1 and Roza were found at 5 and 10 DAI. In the wheat-insect study, only the Hollis showed unique VOC peaks. Interestingly, both cultivars released the same common VOCs between control and infected samples, with only a difference in VOC average peak intensity at 22.4 min retention time where the average intensity was 4.3 times higher in the infested samples of Hollis than infested samples of Alturas. These studies demonstrate the potential of plant VOCs to serve as a rapid phenotyping tool to assess resistance levels in different crops.
A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm
Authors: Sefati Seyed Salar , Halunga Simona
Published: June 2022

The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user’s needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user’s needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.
Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner
Authors: Loukatos Dimitrios , Lygkoura Kalliopi-Agryri , Maraveas Chrysanthos , Arvanitis Konstantinos G.
Published: June 2022

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people’s negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented.
State Estimation of Gas-Lifted Oil Well Using Nonlinear Filters
Authors: Adukwu Ojonugwa , Odloak Darci , Saad Amir Muhammed , Junior Fuad Kassab
Published: June 2022

The focus of this work is the extension of nonlinear state estimation methods to gas-lifted systems. The extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF) were used to estimate the nonlinear states. Brief descriptions of the filters were first presented starting from the linear Kalman filter. Hypothesis tests on the expectation of the residuals were performed to show how close to optimal the estimation methods are and it showed the UKF estimates to be slightly better than EKF while PF performs the worst. The PF has poor accuracy using residual visualisation, hypothesis test and the root mean squared error (RMSE) values of the residuals. The gas-lifted system exhibits casing heading instability where the states show oscillatory behaviour depending on the value of the input but the results here do not change in a known way for each filter as the input is changed from the non-oscillatory region to the oscillatory region. Therefore, for this noise distribution and model assumption, either the EKF or UKF can be used for nonlinear state estimation with UKF better preferred if computational cost is not considered when control solutions are used in gas-lifted system.
Pixel Image Analysis and Its Application with an Alcohol-Based Liquid Scintillator for Particle Therapy
Authors: Choi Ji-Won , Choi Ji-Young , Jang Hanil , Joo Kyung-Kwang , Kim Byoung-Chan
Published: June 2022

We synthesized an alcohol-based liquid scintillator (AbLS), and we implemented an auxiliary monitoring system with short calibration intervals using AbLS for particle therapy. The commercial liquid scintillator used in previous studies did not allow the user to control the chemical ratio and its composition. In our study, the chemical ratio of AbLS was freely controlled by simultaneously mixing water and alcohol. To make an equivalent substance to the human body, 2-ethoxyethanol was used. There was no significant difference between AbLS and water in areal density. As an application of AbLS, the range was measured with AbLS using an electron beam in an image analysis that combined AbLS and a digital phone camera. Given a range–energy relationship for the electron expressed as areal density, the electron beam range (cm) in water can be easily estimated. To date, no literature report for the direct comparison of a pixel image analysis and Monte Carlo (MC) simulation has been published. Furthermore, optical tomography of the inverse problem was performed with AbLS and a mobile phone camera. Analyses of optical tomography images provide deeper insight into Radon transformation. In addition, the human phantom, which is difficult to compose with semiconductor diodes, was easily implemented as an image acquisition and analysis system.
A Ka-Band Integrated Six-Port Chip for Analog Complex Correlator
Authors: He Wangdong , Chen Xi , Gong Jianhao , Hu Anyong , Miao Jungang
Published: June 2022

Six-port technology has been widely used in microwave systems, such as interferometric passive imaging. In this paper, an integrated Ka-band (32–36 GHz) six-port chip based on the 0.15-μm GaAs technology is designed and fabricated to simplify the circuit structure and miniaturize the volume of the imaging system. The designed chip integrates two amplifiers, two phase shifters, and a six-port circuit as part of an analog complex correlator. In this integrated chip, the crosstalk between the two amplifiers cannot be ignored. This paper analyzes the influence of the isolation between two amplifiers on the correlation results to guide the six-port chip design. In addition, considering that the radiometer system receives a broadband noise signal, the phase shifter needs to ensure that the phase shift range of each frequency point is the same under the same control conditions. Therefore, the phase shifter is designed with a high-pass and low-pass structure. The measurement results show that the isolation between the two amplifiers is greater than 20 dB, and the measured phase shift range and phase shift range error of the designed chip are 220° and 10°, respectively, with the control voltage varying from 0 to 1.5 V, which meets the requirements of the system.
CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis
Authors: Wang Fan , Zhao Yingxiang , Shi Gang , Cui Qing , Cao Tengfei , Jiang Xian , Hou Yongjie , Zhuang Rujun , Mei Yunfei
Published: June 2022

Point cloud processing based on deep learning is developing rapidly. However, previous networks failed to simultaneously extract inter-feature interaction and geometric information. In this paper, we propose a novel point cloud analysis module, CGR-block, which mainly uses two units to learn point cloud features: correlated feature extractor and geometric feature fusion. CGR-block provides an efficient method for extracting geometric pattern tokens and deep information interaction of point features on disordered 3D point clouds. In addition, we also introduce a residual mapping branch inside each CGR-block module for the further improvement of the network performance. We construct our classification and segmentation network with CGR-block as the basic module to extract features hierarchically from the original point cloud. The overall accuracy of our network on the ModelNet40 and ScanObjectNN benchmarks achieves 94.1% and 83.5%, respectively, and the instance mIoU on the ShapeNet-Part benchmark also achieves 85.5%, proving the superiority of our method.
A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions
Authors: Toma Rafia Nishat , Piltan Farzin , Im Kichang , Shon Dongkoo , Yoon Tae Hyun , Yoo Dae-Seung , Kim Jong-Myon
Published: June 2022

Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.
Toward the Web of Industrial Things: A Publish-Subscribe Oriented Architecture for Data and Power Management
Authors: Bartoli Claudio , Bonanni Michele , Chiti Francesco , Pierucci Laura , Cidronali Alessandro , Collodi Giovanni , Maddio Stefano
Published: June 2022

The foundation of an energy sustainable Web of Industrial Things (WoIT) is facing several open issues due to the constraints imposed by the involved devices, the technological heterogeneity and the complex interactions and, hence, communications patterns. Towards this goal, in this paper, a general framework inspired by the Publish-Subscribe principle have been proposed, in order to jointly optimize the service requirements and the network availability. In particular, in this paper we focus on a holistic design with the objective to manage power budget distribution, in order to support applications that extend the basic publish-and-subscribe scheme.The involved WoIT nodes functionalities, interfaces and hardware architectures have been designed, with a special focus on control protocols. The introduced integrated solution has been validated in scenarios minimising and possibly balancing the power consumption. The achieved results show an average improvement of 45% for the communications performance with the wireless power management.
Optimization of Joint Decision of Transport Mode and Path in Multi-Mode Freight Transportation Network
Authors: Lu Yang , Wang Shuaiqi
Published: June 2022

This paper mainly studies the joint decision of transportation mode and path in the multi-mode transportation network to provide the optimal plan for freights. This paper constructs a multi-mode transportation network system by setting virtual connections between networks with different transportation modes. The Dijkstra and multi-objective optimization algorithms are used to select the path in the network. After determining the optimal path, the paths’ time, cost, and risk functions are established. The multi-objective function is converted into a single objective function by setting constraint conditions through the analytic hierarchy process. Then, the function is optimized by using the gradient descent method. Finally, the transportation plan for the case of chemical freights is formulated by using the above algorithms. The results show that the proposed algorithm can successfully find the solution for the joint decision of transportation mode and path in the complex network. After a quantitative analysis of the planned effect, the optimization actions of changing the initial transportation time and adjusting the upper limit of resources are proposed. The study findings provide a theoretical basis for improving the efficiency of the comprehensive transportation network.
Synthesis, Structural Characterization, Conformational and Topological Classification of Different Salts in the 2,2-Dimethylpropane-1,3-diamine/HCl/H2O-System
Authors: Heimgert Jaqueline , Morsbach Florian , Kleinschmidt Martin , Reiss Guido J.
Published: June 2022

The reaction of 2,2-dimethylpropane-1,3-diamine (dmpn) with an excess of concentrated aqueous hydrochloric acid yielded colorless crystals of 2,2-dimethylpropane-1,3-diaminium dichloride, dmpnH2Cl2 (1), in addition to small amounts of a monohydrate, dmpnH2Cl2∙H2O (2). The compounds were studied via X-ray crystallography, IR and Raman spectroscopy, NMR spectroscopy and thermal analysis. Single crystal structure determinations on 1 and 2 showed that dmpnH2Cl2 exists in two polymorphic forms, 1a and 1b. The crystal structure of 1b showed to be much more complex than that of 1a. In the crystal structure of 2, four (dmpnH2)2+ cations and eight chloride anions form a cage constructed by N−H∙∙∙Cl hydrogen bonds. In the center of these cages water dimers with a O∙∙∙O distance of 2.776 (8) Å are present. In addition, a conformational analysis of the 2,2-dimethylpropane-1,3-diaminium cation was performed. The results are compared to the experimental findings of 1a, 1b, 2 and other related hydrogen bonded salt structures from the Cambridge crystallographic structure database (CCDC). Last, a topological classification of the solid-state structures of 1a and 2 was performed and the simplified topological networks are discussed.
The Utility of Lesser Trochanter Version to Estimate Femoral Anteversion in Total Hip Arthroplasty: A Three-Dimensional Computed Tomography Study
Authors: Mohamed Thajudeen Mohamed Zaim bin , Mahmood Merican Azhar , Hashim Muhammad Sufian , Nordin Abid
Published: June 2022

Objective: Femoral anteversion is an important parameter that can prevent complication following total hip arthroplasty (THA) caused by improper positioning of the implant. However, assessing the femoral anteversion can be challenging in situations with significant defect of the femoral neck. In this study, the lesser trochanter version was nominated as alternative parameter to femoral anteversion. So, the main objective of this study is to determine whether the femoral anteversion correlates with the lesser trochanter version. Design: Retrospective study. Methods: Three-dimensional images of 100 femora were generated and their femoral anteversion and lesser trochanter version was measured. Correlation between the parameters were calculated. Results: The mean lesser trochanter version was 38.54° ± 7.86° (mean ± SD), while the mean femoral anteversion was 11.84° ± 10.06°. The lesser trochanter version was inversely correlated with the femoral anteversion with a correlation coefficient of −0.72. Conclusions: The lesser trochanter should be considered as an additional bony landmark to assess proper implant positioning in THA.
Is Geopolitical Risk Powerful Enough to Affect Carbon Dioxide Emissions? Evidence from China
Authors: Wang Kai-Hua , Kan Jia-Min , Jiang Cui-Feng , Su Chi-Wei
Published: June 2022

Escalating geopolitical factors are closely related to climate warming, but researchers have not fully considered this. Therefore, the purpose of this study is to explore the interaction between geopolitical risk (GPR) and carbon dioxide (CO2) in China. This paper uses the recently developed GPR index and a bootstrap Granger rolling-window estimation. Empirical results spanning different subsamples reveal a two-way causal relationship between GPR and CO2. GPR transforms energy consumption and economic activities through trade disputes, military deployments and energy issues, which have a complex impact on CO2 emissions. Oppositely, CO2 emissions affect GPR through changes in international cooperation and shaping of geopolitical systems. In view of these empirical results, we put forward several policy recommendations. The Chinese government can effectively consider GPR to control CO2 emissions by increasing green investment and signing environmental contracts. Enterprises must focus on research and development (R&D) and investment in new energy innovations. In addition, international organizations can be a useful tool for monitoring decarbonization policies and resolving conflicts between countries.
Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions
Authors: Abdekareem Mohamed , Al-Arifi Nasir , Abdalla Fathy , Mansour Abbas , El-Baz Farouk
Published: June 2022

Remote sensing and GIS approaches have provided valuable information on modeling water resources, particularly in arid regions. The Sahara of North Africa, which is one of the driest regions on Earth, experienced several pluvial conditions in the past that could have stored significant amounts of groundwater. Thus, harvesting the stored water by revealing the groundwater prospective zones (GWPZs) is highly important to water security and the management of water resources which are necessary for sustainable development in such regions. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Tropical Rainfall Measuring Mission (TRMM), and Landsat-8 OLI data have all successfully revealed the geologic, geomorphic, climatic, and hydrologic features of Wadi El-Tarfa east of Egypt’s Nile River. The fusion of eleven predictive GIS maps including lithology, radar intensity, lineament density, altitude, slope, depressions, curvature, topographic wetness index (TWI), drainage density, runoff, and rainfall data, after being ranked and normalized through the GIS-based analytic hierarchy process (AHP) and weighted overlay methods, allowed the GWPZs to be demarcated. The resulting GWPZs map was divided into five classes: very high, high, moderate, low, and very low potentiality, which cover about 10.32, 24.98, 30.47, 24.02, and 10.20% of the entire basin area, respectively. Landsat-8 and its derived NDVI that was acquired on 15 March 2014, after the storm of 8–9 March 2014, along with existing well locations validated the GWPZs map. The overall results showed that an integrated approach of multi-criteria through a GIS-based AHP has the capability of modeling groundwater resources in arid regions. Additionally, probing areas of GWPZs is helpful to planners and decision-makers dealing with the development of arid regions.
Optimizing Conduit Hydropower Potential by Determining Pareto-Optimal Trade-Off Curve
Authors: van Dijk Marco , van Vuuren Stefanus Johannes , Cavazzini Giovanna , Niebuhr Chantel Monica , Santolin Alberto
Published: June 2022

In numerous locations of bulk water supply/distribution systems, energy is dissipated by pressure-reducing devices, whereas it could be recovered by means of turbines or pumps as turbines. These pipe systems, owned and operated by municipalities, water utilities, large water-consuming industries, and mines, could be used as a source of renewable sustainable energy. However, the exploitation of these systems presents several issues related to the complexity of the operational optimization of the hydropower generation facilities and to the potential negative impact on the reliability of the system itself. We have developed a novel procedure to optimize the energy generation in such a conduit system by assessing the interrelationship of storage volumes, demand patterns, operating cycles, and electricity tariff structures. The procedure is a multi-objective genetic algorithm designed to provide a solution to maximize electricity generation and thus revenue and to minimize the risk involved in supplying the demand. A Pareto-optimal trade-off curve is set up, indicating the potential benefit (revenue) versus the reliability index (supply security). The results indicate that a Pareto-optimal trade-off curve was generated from which a solution could be selected which would improve the weekly revenue by up to 7.5%, while still providing a reliable water supply system.
Pro-Circular Consumer Profile: An Approach to Their Identification and Characterization Based on the Components of the Value-Belief-Norm Theory
Authors: Arias Claudia , Quiroga Beltrán Jhon Mario , Martínez Ariza Javier Mauricio , Cadena Lozano Javier Bernardo , Bello Bernal Miguel Angel
Published: June 2022

Circular economies have focused on managing organizations and changes in production and consumption models that lead to the better use of resources, generating the least waste. These changes toward new circular models will only be possible if consumers become involved through their behavior. In this sense, the first step is to understand who pro-circular consumers are and what characterizes them. Thus, this exploratory study aims to profile pro-circular consumers based on their behaviors and to characterize them based on the components of the value-belief-norm (VBN) theory. Using survey data of a representative sample of 417 participants in the city of Medellín (Colombia), as well as cluster and multiple correspondence analyses, this study identified some pro-circular consumer profiles, mainly characterized by factors like moral norms and perceived consumer effectiveness. Our results suggest that even when consumers without habitual behaviors toward circularity exist, those who engage in them do so because they consider it the right thing to do and because they believe that their pro-circular action is effective for solving environmental problems. On the contrary, green consumption values, beliefs about awareness and responsibility toward the environment, and sociodemographic factors do not seem to be associated with and characterize this type of consumer.
Innovation Research in Tourism and Hospitality Field: A Bibliometric and Visualization Analysis
Authors: Işık Cem , Aydın Ekrem , Dogru Tarik , Rehman Abdul , Sirakaya-Turk Ercan , Karagöz Deniz
Published: June 2022

New studies have constantly been emerging in the field of tourism. However, it is not clear to what extent which study contributes to the literature and tourism application areas. There are a few bibliometric studies that illustrate the intellectual structure of the tourism innovation field. Therefore, the purpose of this study is to determine the performance and intellectual structure of research studies conducted on innovation in the field of tourism. For this purpose, a bibliometric analysis of 387 research studies, which were published over the period 1975–2021 and cited in SCI-EXPANDED, SSCI, A&HCI indexes of WoS, is performed. Co-citation analysis indicates that innovation research studies in tourism can be categorized into three groups: (1) conceptualization and types of innovation in tourism, (2) review research on innovation in tourism, and (3) methodological studies. According to the co-word analysis, sharing economy, open innovation, sustainability, and technology are the most popular topics of recent times. A steady increase is observed in the number of citations to articles in the field of tourism innovation after 2008. The research study has crucial impacts in terms of guiding the researchers who would study in this field and providing the intellectual appearance of the field.
Dynamic Relationships between Seafood Exports, Exchange Rate and Industrial Upgrading
Authors: Eegunjobi Ruth , Ngepah Nicholas
Published: June 2022

Few studies have examined the relationship between seafood export performance and exchange rate fluctuations. This paper investigates the short and long-term relationship between variables and the effect of currency depreciation on investment in industrial upgrading in the Namibian seafood industry. Employing the Johansen cointegration technique and a vector error correction model (VECM) on quarterly data from 2008 to 2020, we find that investment in industrial upgrading has a higher impact on exports than exchange rate fluctuations. Therefore, investment in industrial upgrading plays a significant role in mitigating the negative impact of exchange rate volatility. Key policy implications include the need to take advantage of currency depreciation to mitigate challenges to investment in industrial upgrading by increased budgetary allocations.
Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area
Authors: Zhang Huijuan , Liu Wenkai , Zhang Qiuxia , Huang Xiaodong
Published: June 2022

Soil nitrogen is very important for crop growth and development. However, the factors affecting the three-dimensional spatial distribution of soil total nitrogen (TN), particularly in coal mining subsidence areas, are unclear. In this study, Markov geostatistics was used to analyse the three-dimensional spatial distribution characteristics and influential factors of TN by examining 180 soil samples from the Zhaogu mine in China. The results showed that the TN content was significantly different at different soil depths (0–20, 20–40, 40–60 cm) and decreased with increasing soil depth. The variation coefficient of the TN content decreased gradually from top to bottom, ranging from 18.18 to 25.62%. In addition, the TN content was greatly affected by mining subsidence, rainfall, irrigation, fertilization and management mode. The factors that influenced the TN content also varied across different slope positions. The TN content of the upslope was the highest, and the TN content of the middle slope was the lowest. These results can provide research ideas and technical countermeasures for ecological environment improvement and sustainable land development in coal mining subsidence areas.
The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business
Authors: Raposo António , Han Heesup
Published: June 2022

Food security is more than a basic requirement for survival; it is a human right that has implications for global safety, economic strength, security, and sustainability [...]
Correction: Long, S. SARS-CoV-2 Subgenomic RNAs: Characterization, Utility, and Perspectives. Viruses 2020, 13, 1923
Authors: Long Samuel
Published: June 2022

The author is no longer affiliated with the original institution “AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA”, so the author wishes to change the affiliation to “Independent Researcher, Clarksburg, MD 20871, USA” [...]
Synthesized Magnolol Derivatives Improve Anti-Micropterus salmoides Rhabdovirus (MSRV) Activity In Vivo
Authors: Jin Yingjie , Yang Fei , Zhang Gengrong , Yu Qing , Wang Gaoxue , Ling Fei , Liu Tianqiang
Published: June 2022

Micropterus salmoides rhabdovirus (MSRV) is a primary viral pathogen in largemouth bass aquaculture, which leads to tremendous economic losses yearly. Currently, there are no approved drugs for the treatment and control of this virus. Our previous studies screened the herb Magnolia officinalis from many traditional Chinese medicines, and we isolated and identified magnolol as its main active compound against multiple rhabdoviruses, including MSRV. On the basis of the structure–activity relationship and pharmacophore model of magnolol, two new magnolol derivatives, namely, hydrogenated magnolol and 2,2′-dimethoxy-magnolol, were designed and synthesized. Their anti-MSRV activities were systematically investigated both in vitro and in vivo. By comparing the half-maximal inhibitory concentration (IC50), it was found that hydrogenated magnolol possessed a higher anti-MSRV activity than magnolol and 2,2′-dimethoxy-magnolol, with an IC50 of 13.37 μM. Furthermore, hydrogenated magnolol exhibited a protective effect on the grass carp ovary (GCO) cell line by reducing the cytopathic effect induced by MSRV. Further studies revealed that hydrogenated magnolol did not directly impact virions or interfere with MSRV adsorption. It worked within the 6–8 h of the phase of virus replication. In vivo treatment of MSRV infection with magnolol and hydrogenated magnolol showed that they significantly improved the survival rate by 44.6% and 62.7%, respectively, compared to MSRV-infected groups. The viral load measured by the expression of viral glycoprotein in the organs including the liver, spleen, and kidney also significantly decreased when fish were intraperitoneally injected at a dose of 20 mg/kg. Altogether, the structural optimization of magnolol via hydrogenation of the propylene groups increased its anti-MSRV activity both in vitro and in vivo. These results may provide a valuable reference for anti-MSRV drug discovery and development in aquaculture.
Access to Vaccination among Disadvantaged, Isolated and Difficult-to-Reach Communities in the WHO European Region: A Systematic Review
Authors: Ekezie Winifred , Awwad Samy , Krauchenberg Arja , Karara Nora , Dembiński Łukasz , Grossman Zachi , del Torso Stefano , Dornbusch Hans Juergen , Neves Ana , Copley Sian , Mazur Artur , Hadjipanayis Adamos , Grechukha Yevgenii , Nohynek Hanna , Damnjanović Kaja , Lazić Milica , Papaevangelou Vana , Lapii Fedir , Stein-Zamir Chen , Rath Barbara , for the ImmuHubs Consortium
Published: June 2022

Vaccination has a significant impact on morbidity and mortality. High vaccination coverage rates are required to achieve herd protection against vaccine-preventable diseases. However, limited vaccine access and hesitancy among specific communities represent significant obstacles to this goal. This review provides an overview of critical factors associated with vaccination among disadvantaged groups in World Health Organisation European countries. Initial searches yielded 18,109 publications from four databases, and 104 studies from 19 out of 53 countries reporting 22 vaccine-preventable diseases were included. Nine groups representing the populations of interest were identified, and most of the studies focused on asylum seekers, refugees, migrants and deprived communities. Recall of previous vaccinations received was poor, and serology was conducted in some cases to confirm protection for those who received prior vaccinations. Vaccination coverage was lower among study populations compared to the general population or national average. Factors that influenced uptake, which presented differently at different population levels, included health service accessibility, language and vaccine literacy, including risk perception, disease severity and vaccination benefits. Strategies that could be implemented in vaccination policy and programs were also identified. Overall, interventions specific to target communities are vital to improving uptake. More innovative strategies need to be deployed to improve vaccination coverage among disadvantaged groups.
Preparation of Micron-Scale Activated Carbon-Immobilized Bacteria for the Adsorption–Biodegradation of Diesel Oil
Authors: Zhang Wei , Kong Xiangke , Han Zhantao , Wang Ping , Ma Lisha , Wang Yanyan , Chen Hongkun
Published: June 2022

This paper investigated the micron-scale activated carbon (MAC) immobilized diesel-oil-degrading bacteria (bio-MAC) used as remediation materials for the removal of diesel-oil-contaminated water. The high-efficiency indigenous diesel-oil-degrading bacteria were firstly screened and enriched, then the MAC was used as a diesel oil sorbent and biocarrier for the immobilization of degrading bacteria to prepare the bio-MAC material. The removal performance of the bio-MAC was evaluated via a comparison with the freely degrading bacteria and MAC. The SEM results demonstrated that the diesel-oil-degrading bacteria were effectively immobilized and grew well on the surfaces of MAC particles. The concentration of MAC significantly influenced the growth and activity (DHA and LPS) of immobilized bacteria, and the MAC addition of 3.0 g/L was proven to be an optimum amount for the preparation of bio-MAC. The high-throughput sequencing analysis further indicated that the bacteria immobilized on MAC showed higher abundance levels and diversities index values compared to freely suspended bacteria, such as Pseudomonas, Rhodococcus, Bacillus and Microbacterium. The FTIR spectroscopy results showed that the bio-MAC could effectively degrade the aliphatic hydrocarbons, alkenes and aromatic compounds of diesel oil to carboxylic acids, esters, alcohols and other metabolites. When the concentration of diesel oil was 1 g/L, the removal efficiency for the diesel oil of bio-MAC reached 86.35% after 15 days, while only 23.82% and 70.97% of the diesel oil was removed using the same amount of free bacteria and MAC, respectively. The prepared bio-MAC showed a synergic effect of adsorption and biodegradation and efficiently removed diesel oil from wastewater.
Psychological Interventions for Higher Education Students in Europe: A Systematic Literature Review
Authors: Franzoi Isabella Giulia , Sauta Maria Domenica , Barbagli Francesca , Avalle Cristiana , Granieri Antonella
Published: June 2022

During tertiary education, students embark on a journey of role exploration and decision-making about their future, to define who they want to be in their adult lives. Psychological services for students may be needed to help students gain better awareness of their mental health. The purpose of this systematic literature review is to analyze psychological interventions for university students in Europe. The review was conducted per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were identified using the Scopus, Web of Science, PubMed, ProQuest Psychology Journals, PsycINFO, and PsycARTICLES databases. The results were classified into seven categories: counseling and group counseling (eight papers); psychodynamic interventions (six papers); cognitive–behavioral interventions (fifteen papers); other psychological interventions (eighteen papers); mindfulness (nine papers); online interventions (seventeen papers); and app and mobile interventions (ten papers). Student psychological services are becoming one of the most important sources of support for students, and the number of approaches offered is expanding. Our results suggest the need to further investigate and promote the provision of psychological care for university students, explore long-term treatment options, and invest in professionally trained staff.
The Relationship between Food Literacy and Healthy Lifestyle Behaviours
Authors: APAYDIN DEMİRCİ Zeynep , ÇELİK Berrin
Published: June 2022

Bu çalışmada gıda okuryazarlığı ile sağlıklı yaşam biçimi davranışları arasındaki ilişkinin araştırılması amaçlanmıştır. Bu araştırma kesitsel bir alan araştırması olup çalışmanın örneklemi Bilecik Şeyh Edebali Üniversitesi Sağlık Bilimleri Fakültesinde öğrenim gören 210 öğrenciden oluşmaktadır. Verilerin toplanmasında demografik bilgi formu, Algılanan Gıda Okuryazarlığı Ölçeği ve Sağlıklı Yaşam Biçimi Davranışları Ölçeği II kullanılmıştır. Bulgular sonucunda öğrencilerin sağlıklı yaşam biçim davranışları ( x̄ ± ss= 133±11.2) ve gıda okuryazarlığının ( x̄ ± ss= 92±6.9) orta düzeyde olduğu bulunmuştur. Öğrencilerin cinsiyet, yaş, öğretim kademesi, bölüm ve BKİ hem gıda okuryazarlığı ile hem de sağlıklı yaşam biçimi davranışları ile önemli ölçüde ilişkili olmadığı bulunmuştur. Ayrıca öğrencilerin gıda okuryazarlığı ile sağlıklı yaşam biçimi davranışlarının zayıf düzeyde pozitif yönde ilişkili (r =.322, p <.01) olduğu sonucuna ulaşılmıştır. Araştırma sonucunda gıda okuryazarlığı sağlıklı yaşam biçimi davranışlarını (R²= .09, p<0.001) etkilediği ve regresyon modeline göre gıda okuryazarlığı sağlıklı yaşam biçimi davranışının % 9,9’unu açıkladığı bulunmuştur.