bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Ash Dieback in Europe—An Unrevealed Perspective for Remote Sensing?
Authors: Gašparović Mateo , Pilaš Ivan , Klobučar Damir , Gašparović Iva
Published: February 2023

The ash dieback pandemic, caused by the invasive fungus Hymenoscyphus fraxineus, represents one of Europe’s biggest threats to preserving natural biodiversity. To ensure the suppression of forest damage caused by fungi, timely recognition of the symptoms of ash dieback and further continuous monitoring on an adequate spatial scale are essential. Visual crown damage assessment is currently the most common method used for identifying ash dieback, but it lacks the spatial and temporal coverage required for effective disease suppression. Remote sensing technologies, with the capabilities of fast and repetitive retrieval of information over a large spatial scale, could present efficient supplementary methods for ash damage detection and disease monitoring. In this study, we provided a synthesis of the existing remote sensing methods and applications that considers ash dieback disease, and we described the lifecycle of the disease using the major symptoms that remote sensing technologies can identify. Unfortunately, although effective methods of monitoring biotic damage through remote sensing have been developed, ash dieback has only been addressed in two research studies in the United Kingdom and Germany. These studies were based on single-date hyperspectral and very-high-resolution imagery in combination with machine learning, using previously specified ground-truth information regarding crown damage status. However, no study exists using high-resolution imagery such as Sentinel-2 or radar Sentinel-1, although some preliminary project results show that these coarser sources of information could be applicable for ash dieback detection and monitoring in cases of Fraxinus angustifolia, which forms pure, more homogenous stands in Southern Europe.
Recognition of Abnormal Individuals Based on Lightweight Deep Learning Using Aerial Images in Complex Forest Landscapes: A Case Study of Pine Wood Nematode
Authors: Zhang Zuyi , Wang Biao , Chen Wenwen , Wu Yanlan , Qin Jun , Chen Peng , Sun Hanlu , He Ao
Published: February 2023

Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose significant challenges to automatic identification. Therefore, taking pine nematode disease as an example, this paper proposes D-SCNet, an intelligent monitoring network for abnormal individuals applicable to UAV visible images. In this method, the convolutional block attention model and simplified dense block are introduced to enhance the semantic analysis ability of abnormal individual identification, use multi-level information of abnormal individuals well, enhance feature transfer as well as feature weights between network layers, and selectively focus on abnormal features of individuals while reducing feature redundancy and parameter and improving monitoring accuracy and efficiency. This method uses lightweight deep learning models through weak information sources to achieve rapid monitoring of a large range of abnormal individuals in complex environments. With the advantages of low cost, high efficiency, and simple data sources, it is expected to further enhance the practicality and universality of intelligent monitoring of anomalous individuals by UAV remote sensing.
Editorial for the Special Issue “GNSS, Space Weather and TEC Special Features”
Authors: Kos Serdjo , Fernández José , Prieto Juan F.
Published: February 2023

For high-quality scientific communication in the field of technical and natural sciences, it is of utmost importance to ensure clarity of the text, logical mathematical argumentation, and the possibility of verifying the obtained theoretical results using appropriate experiments [...]
An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data
Authors: Radman Ali , Shah-Hosseini Reza , Homayouni Saeid
Published: February 2023

SAR data provide sufficient information for burned area detection in any weather condition, making it superior to optical data. In this study, we assess the potential of Sentinel-1 SAR images for precise forest-burned area mapping using deep convolutional neural networks (DCNN). Accurate mapping with DCNN techniques requires high quantity and quality training data. However, labeled ground truth might not be available in many cases or requires professional expertise to generate them via visual interpretation of aerial photography or field visits. To overcome this problem, we proposed an unsupervised method that derives DCNN training data from fuzzy c-means (FCM) clusters with the highest and lowest probability of being burned. Furthermore, a saliency-guided (SG) approach was deployed to reduce false detections and SAR image speckles. This method defines salient regions with a high probability of being burned. These regions are not affected by noise and can improve the model performance. The developed approach based on the SG-FCM-DCNN model was investigated to map the burned area of Rossomanno-Grottascura-Bellia, Italy. This method significantly improved the burn detection ability of non-saliency-guided models. Moreover, the proposed model achieved superior accuracy of 87.67% (i.e., more than 2% improvement) compared to other saliency-guided techniques, including SVM and DNN.
Diurnal and Seasonal Variability of the Atmospheric Boundary-Layer Height in Marseille (France) for Mistral and Sea/Land Breeze Conditions
Authors: Riandet Aurélie , Xueref-Remy Irène , Popovici Ioana , Lelandais Ludovic , Armengaud Alexandre , Goloub Philippe
Published: February 2023

Marseille (France) is a city on the Mediterranean coast characterized by two specific wind patterns: mistral (northwesterly wind blowing above 10 m/s) and sea/land breezes (southwesterly wind during daytime/northeasterly wind during the nighttime, blowing below 6 m/s). For the first time, this study investigates the diurnal and seasonal variability in the atmospheric boundary-layer height (ABLH) in Marseille for both wind patterns. A 532 nm aerosol lidar was installed in the urban center in the summer of 2021. The lidar installed in the winter of 2021–2022 had an additional near-infrared channel at 808 nm. The ABLH was extracted from the lidar datasets using a Haar wavelet method. For well-established mistral conditions, the ABLH reached to about 1000 m and showed a diurnal amplitude of ~650 m in winter and 740 m in summer, with a morning growth rate limited by turbulence. During sea breeze situations, the ABLH maxima were lower in both seasons (300–600 m) due to the sea’s thermal inertia. During land breeze situations, ABLH minima were estimated to be lower than 150 m. In summer, the Haar method was unable to calculate them because of unpronounced aerosol layers. While the near-infrared channel gives better results, the polarization of the green channel allows us to understand the type of aerosols and thus the origin of the air mass; a combination of the two gives complementary information.
A Target-Based Non-Uniformity Self-Correction Method for Infrared Push-Broom Hyperspectral Sensors
Authors: Wu Bing , Liu Chengyu , Xu Rui , He Zhiping , Liu Bin , Chen Wangli , Zhang Qing
Published: February 2023

Non-uniformity in the response of spectral image elements is an inevitable phenomenon in hyperspectral imaging, which mainly manifests itself as the presence of band noise in the acquired hyperspectral data. This problem is prominent in the infrared band owing to the detector material, operating environment, and other factors. Non-uniformity is an important factor that can affect the quality of the hyperspectral data, which has a serious impact on both data analysis and applications and requires corrections via technical means wherever possible. This paper proposes a novel target-based non-uniformity self-correction method for infrared push-broom hyperspectral images. The Mars Mineralogical Spectrometer (MMS) onboard the Tianwen-1 orbiter was used as the research and application object. The model is constructed and applied to the target scene characteristics and detection patterns of Mars remote sensing exploration, which are combined with the causes of noise generation in the infrared spectral image bands. The design of the MMS dual-channel Visible-Near-Infrared (V-NIR) and Near-Mid-Infrared (N-MIR) co-field of view co-target detection and laboratory calibration data for the V-NIR spectral band can achieve non-uniformity corrections (NUCs). Therefore, for the MMS in-orbit Mars exploration mission, the method selected spectral data (920–1055 nm) characterized by a reduced atmospheric influence to iteratively obtain the homogeneous region, which was used to calculate the non-uniformity correction factor for the N-MIR spectral band. This method was compared, validated, and evaluated with other conventional methods using both laboratory and in-orbit hyperspectral data. The results showed that the experimental data corrections were comparable to laboratory calibrations, with a maximum relative deviation of <2.6%. These results prove that our method not only provides an excellent non-uniformity correction, but also ensures spectral fidelity. It can thus be used as a non-uniformity correction process for the MMS and similar hyperspectral imagers.
Camouflaged Object Detection Based on Ternary Cascade Perception
Authors: Jiang Xinhao , Cai Wei , Ding Yao , Wang Xin , Yang Zhiyong , Di Xingyu , Gao Weijie
Published: February 2023

Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)50 that reaches 91.41, an AP75 that improves to 73.04, and significantly higher detection accuracy and confidence.
Global and Local Graph-Based Difference Image Enhancement for Change Detection
Authors: Zheng Xiaolong , Guan Dongdong , Li Bangjie , Chen Zhengsheng , Pan Lefei
Published: February 2023

Change detection (CD) is an important research topic in remote sensing, which has been applied in many fields. In the paper, we focus on the post-processing of difference images (DIs), i.e., how to further improve the quality of a DI after the initial DI is obtained. The importance of DIs for CD problems cannot be overstated, however few methods have been investigated so far for re-processing DIs after their acquisition. In order to improve the DI quality, we propose a global and local graph-based DI-enhancement method (GLGDE) specifically for CD problems; this is a plug-and-play method that can be applied to both homogeneous and heterogeneous CD. GLGDE first segments the multi-temporal images and DIs into superpixels with the same boundaries and then constructs two graphs for the DI with superpixels as vertices: one is the global feature graph that characterizes the association between the similarity relationships of connected vertices in the multi-temporal images and their changing states in a DI, the other is the local spatial graph that exploits the change information and contextual information of the DI. Based on these two graphs, a DI-enhancement model is built, which constrains the enhanced DI to be smooth on both graphs. Therefore, the proposed GLGDE can not only smooth the DI but also correct the it. By solving the minimization model, we can obtain an improved DI. The experimental results and comparisons on different CD tasks with six real datasets demonstrate the effectiveness of the proposed method.
DBFNet: A Dual-Branch Fusion Network for Underwater Image Enhancement
Authors: Sun Kaichuan , Tian Yubo
Published: February 2023

Due to the absorption and scattering effects of light propagating through water, underwater images inevitably suffer from severe degradation, such as color casts and losses of detail. Many existing deep learning-based methods have demonstrated superior performance for underwater image enhancement (UIE). However, accurate color correction and detail restoration still present considerable challenges for UIE. In this work, we develop a dual-branch fusion network, dubbed the DBFNet, to eliminate the degradation of underwater images. We first design a triple-color channel separation learning branch (TCSLB), which balances the color distribution of underwater images by learning the independent features of the different channels of the RGB color space. Subsequently, we develop a wavelet domain learning branch (WDLB) and design a discrete wavelet transform-based attention residual dense module to fully employ the wavelet domain information of the image to restore clear details. Finally, a dual attention-based selective fusion module (DASFM) is designed for the adaptive fusion of latent features of the two branches, in which both pleasing colors and diverse details are integrated. Extensive quantitative and qualitative evaluations of synthetic and real-world underwater datasets demonstrate that the proposed DBFNet significantly improves the visual quality and shows superior performance to the compared methods. Furthermore, the ablation experiments demonstrate the effectiveness of each component of the DBFNet.
Former-CR: A Transformer-Based Thick Cloud Removal Method with Optical and SAR Imagery
Authors: Han Shuning , Wang Jianmei , Zhang Shaoming
Published: February 2023

In the field of remote sensing, cloud and cloud shadow will result in optical remote sensing image contamination, particularly high cloud cover, which will result in the complete loss of certain ground object information. The presence of thick cloud severely limits the use of optical images in production and scientific research, so it is critical to conduct further research into removing the thick cloud occlusion in optical images to improve the utilization rate of optical images. The state-of-the-art cloud removal methods proposed are largely based on convolutional neural network (CNN). However, due to CNN’s inability to gather global content information, those cloud removal approaches cannot be improved further. Inspired by the transformer and multisource image fusion cloud removal method, we propose a transformer-based cloud removal method (Former-CR), which directly reconstructs cloudless images from SAR images and cloudy optical images. The transformer-based model can efficiently extract and fuse global and local context information in SAR and optical images, generating high-quality cloudless images with higher global consistency. In order to enhance the global structure, local details, and visual effect of the reconstructed image, we design a new loss function to guide the image reconstruction. A comparison with several SAR-based cloud removal methods through qualitative and quantitative experimental evaluation on the SEN12MS-CR dataset demonstrates that our proposed method is effective and superior.
Surface Subsidence Characteristics and Causes in Beijing (China) before and after COVID-19 by Sentinel-1A TS-InSAR
Authors: Sheng Haiquan , Zhou Lv , Huang Changjun , Ma Shubian , Xian Lingxiao , Chen Yukai , Yang Fei
Published: February 2023

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing’s geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing’s transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent.
An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
Authors: Zhao Yibo , Lei Shaogang , Zhu Guoqing , Shi Yunxi , Wang Cangjiao , Li Yuanyuan , Su Zhaorui , Wang Weizhong
Published: February 2023

As one of the most important greenhouse gases, water vapor plays a vital role in various weather and climate processes. In recent years, a near-infrared ratio technique based on satellite images has become a research hotspot in the field of precipitable water vapor (PWV) monitoring. This study proposes a Level 2A PWV data retrieval method based on Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. The radiative transfer model MODTRAN 5 is used to simulate the atmospheric radiative transfer process and obtain lookup tables (LUTs) for PWV retrieval. The spatial distribution of S2-L2A PWV is validated using Global Positioning System (GPS), Terra-MODIS PWV product (MOD05), and Level 2A product provided by ESA (ESA-L2A), while the time series results are evaluated using MOD05. Results show that the PWV retrieved by S2-L2A is both highly correlated and has low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; as the elevation increases, the PWV value decreases. This study also analyzes the error distribution of the PWV data retrieved by S2-L2A, and finds that inversion error increases with AOT value, but decreases with elevation and normalized difference vegetation index (NDVI). Compared with the three water vapor products, the PWV data retrieved by the proposed method has high accuracy and can provide large-scale and high-spatial-resolution PWV data for many research fields, such as agriculture and meteorology.
Delineation of Wetland Areas in South Norway from Sentinel-2 Imagery and LiDAR Using TensorFlow, U-Net, and Google Earth Engine
Authors: Bakkestuen Vegar , Venter Zander , Ganerød Alexandra Jarna , Framstad Erik
Published: February 2023

Wetlands are important habitats for biodiversity and provide ecosystem services such as climate mitigation and carbon storage. The current wetland mapping techniques in Norway are tedious and costly, and remote sensing provides an opportunity for large-scale mapping and ecosystem accounting. We aimed to implement a deep learning approach to mapping wetlands with Sentinel-2 and LiDAR data over southern Norway. Our U-Net model, implemented through Google Earth Engine and TensorFlow, produced a wetland map with a balanced accuracy rate of 90.9% when validated against an independent ground-truth sample. This represents an improvement upon manually digitized land cover maps in Norway, which achieved accuracy rates of 46.8% (1:50,000 map) and 42.4% (1:5000 map). Using our map, we estimated a total wetland coverage area of 12.7% in southern Norway, which is double the previous benchmark estimates (5.6%). We followed an iterative model training and evaluation approach, which revealed that increasing the quantity and coverage of labeled wetlands greatly increases the model performance. We highlight the potential of satellite-based wetland maps for the ecosystem accounting of changes in wetland extents over time—something that is not feasible with traditional mapping methods.
An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake
Authors: Zhang Fei , Duan Pan , Jim Chi , Johnson Verner , Liu Changjiang , Chan Ngai , Tan Mou , Kung Hsiang-Te , Shi Jingchao , Wang Weiwei
Published: February 2023

Ebinur Lake is the largest brackish-water lake in Xinjiang, China. Strong winds constantly have an impact on this shallow water body, causing high variability in turbidity of water. Therefore, it is crucial to continuously monitor suspended particulate matter (SPM) for water quality management. This research aims to develop an advanced spatiotemporal fusion model based on the inversion technique that enables time-continuous and detailed monitoring of SPM over an intermontane lake. The findings shows that: (1) the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) fusion in blue, green, red, and near infrared (NIR) bands was better than the flexible spatiotemporal data fusion (FSDAF) model in extracting SPM information; (2) the inversion model constructed by random forest (RF) outperformed the support vector machine (SVM) and partial least squares (PLS) algorithms; and (3) the SPM concentrations acquired from the fused images of Landsat 8 OLI and ESTARFM matched with the actual data of Ebinur Lake based on the visual perspective and accuracy assessment.
Two-Stage Multi-Channel Fault Detection and Remaining Useful Life Prediction Model of Internal Gear Pumps Based on Robust-ResNet
Authors: Zheng Jianbo , Liao Jian , Zhu Yaqin
Published: February 2023

The internal gear pump is simple in structure, small in size and light in weight. It is an important basic component that supports the development of hydraulic system with low noise. However, its working environment is harsh and complex, and there are hidden risks related to reliability and exposure of acoustic characteristics over the long term. In order to meet the requirements of reliability and low noise, it is very necessary to make models with strong theoretical value and practical significant to accurately monitor health and predict the remaininglife of the internal gear pump. This paper proposed a multi-channel internal gear pump health status management model based on Robust-ResNet. Robust-ResNet is an optimized ResNet model based on a step factor h in the Eulerian approach to enhance the robustness of the ResNet model. This model was a two-stage deep learning model that classified the current health status of internal gear pumps, and also predicted the remaining useful life (RUL) of internal gear pumps. The model was tested in an internal gear pump dataset collected by the authors. The model was also proven to be useful in the rolling bearing data from Case Western Reserve University (CWRU). The accuracy results of health status classification model were 99.96% and 99.94% in the two datasets. The accuracy of RUL prediction stage in the self-collected dataset was 99.53%. The results demonstrated that the proposed model achieved the best performance compared to other deep learning models and previous studies. The proposed method was also proven to have high inference speed; it could also achieve real-time monitoring of gear health management. This paper provides an extremely effective deep learning model for internal gear pump health management with great application value.
Technical Failures in Helicopters: Non-Powerplant-Related Accidents
Authors: de Voogt Alex , Nero Keyashia
Published: February 2023

Technical failures in helicopters are a main concern for helicopter safety. The prominence of mechanical failures differs for specific helicopter operations. This analysis used 151 General Aviation accident reports from the National Transportation Safety Board online database from an 11-year time period. The information in each report was collated, including the list of findings for each accident. Possible relations between causes and specific flight operations were analyzed by looking at significant differences between expected and actual values within the dataset of categorical data. It is found that the proportion of fatal accidents in this category of accidents is low (16.6%) compared with the percentage of fatal helicopter accidents in general, as well as those of specific helicopter operations. Instructional flights appear significantly more likely to be associated with maintenance-related causes. Causes related to fatigue of aircraft parts are more often associated with ferry and positional flights, as well as helicopters with turbo-shaft engines. Future research is recommended for these specific associations to further mitigate the number of accidents with non-powerplant failures.
Lessons Learned from the Colorado Project to Comprehensively Combat Human Trafficking
Authors: Miller Annie , Laser Julie , Alejano-Steele Annjanette , Napolitano Kara , George Nevita , Connot Natcha , Finger Amanda
Published: February 2023

Countering human trafficking at a statewide level requires a combination of knowledge from lived experience, inter-sector collaborations, and evidence-based tools to measure progress. Since 2010, the nonprofit Laboratory to Combat Human Trafficking (LCHT) has collected and analyzed the data on how partners and organizations across the state work toward ending human trafficking. LCHT uses Community-Based Participatory Research (CBPR) to measure and illuminate promising paths toward ending human trafficking. Through CBPR, many collaborative working documents and activities have been created: Colorado Action Plans, Policy Recommendations, a Partnership Toolkit, and Partnership Convenings. This paper provides a single case study analysis of the Colorado Project, from 2013 through 2023, and offers a glimpse into the goals for the Colorado Project 2028. The ideas, strengths, and challenges presented here can guide other local efforts to support data-informed responses to trafficking. The CBPR methodology sheds light on the changes in Colorado’s anti-trafficking movement and the actions taken on behalf of partnerships (task forces and coalitions) across the state of Colorado. This paper offers a roadmap for collaborative design and decision-making among academic, nonprofit, and public sector partners seeking to conduct research on social movements utilizing a community-engaged process.
Biochar Influences Phytochemical Concentrations of Viola cornuta Flowers
Authors: Regmi Abishkar , Poudyal Shital , Singh Sukhbir , Coldren Cade , Moustaid-Moussa Naima , Simpson Catherine
Published: February 2023

Edible flowers are a rich source of phytochemicals with potential health benefits. Yet, changes in production practices can influence the phytochemical composition of edible flowers. Practices such as the addition of biochar have been used to affect growing media properties as well as to conserve peat resources. However, there is little known about how biochar affects the phytochemical composition of edible flowers. To determine if biochar affects phytochemicals in Viola cornuta, four cultivars were subjected to different rates of biochar, with and without fertilizer. At the rate of 10% biochar and without fertilizer application, flower polyphenol and flavonoid concentrations were decreased by 10–20% in two cultivars. However, at 25% biochar, flower polyphenol concentrations varied widely. When fertilizer was added, no effects of biochar were seen. Phytochemical characterization of unfertilized plants further revealed that while increased rates of biochar reduced concentrations of certain antioxidant compounds, these compounds were increased when fertilizer was added. Overall, fertilization can counteract some of the negative effects of biochar on Viola cultivars, resulting in higher nutritional quality and an increase in bioactive compounds produced, providing an ability to replace the peat moss with biochar.
Evolution of Interdependencies between Education and the Labor Market in the View of Sustainable Development and Investment in the Educational System
Authors: Tudor Sorin , Cilan Teodor Florin , Năstase Luiza Loredana , Ecobici Mihaela Loredana , Opran Elena Rodica , Cojocaru Andrei Valentin
Published: February 2023

Education represents the basic pillar of preparing individuals for integration into the labor market, but also is a crucial component of ensuring sustainable development. The purpose of this research was to identify the type of influences existing between education and the labor market in EU member countries in the context of different levels of investment in the educational system. Cluster analysis and the ordinary least squared method were used to identify the type of influences between the indicators characterizing the level of education and the labor market between 2000 and 2021. The empirical results showed that there was a significant negative correlation of the educational dropout rate with the level of employee compensation, number of hours worked by each employee, and their labor productivity, in the countries with the poorest level of educational investment. In the countries with significant investments in education, getting a graduate diploma and participating in vocational training programs led to a better compensation of employees and a higher employee productivity while the financial aid given by the state for pupils and students reduced the number of worked hours, brought down unemployment amongst people with primary and secondary education and, last but not least, increased the employment rate for higher education graduates. An average level of educational investment led to negative influences between early-stages and employees’ payment level and real labor productivity, while becoming involved in educational activities and participating in vocational training programs increased their rates of remuneration and real productivity. A significant impact of higher education graduates on both increasing unemployment rates and falling employment rates was noticed as has been identified in other studies.
Income Inequality, Household Debt, and Consumption Growth in the United States
Authors: Piao Ying’ai , Li Meiru , Sun Hongyuan , Yang Ying
Published: February 2023

In this paper, the SV-TVP-VAR model is used to study the relationships between income inequality, household debt, and consumption growth in the US. This is of great significance for studying whether household debt can become a “substitute” for income and whether it is possible to achieve sustainable growth in consumption under the background of worsening income inequality. According to the research results, the main conclusions are as follows: Firstly, the widening of income inequality would increase consumption in the short term but restrain consumption in the medium and long term, as the relationship between them would turn from positive to negative. Secondly, household debt could improve consumption in the short term but reduce consumption in the medium and long term, with long-term effects being greater than medium-term effects, which means that the long-term negative impact of US household debt on household consumption would be persistent. Lastly, widening income inequality has led to rising household debt over different maturities.
Greenshell Mussel Products: A Comprehensive Review of Sustainability, Traditional Use, and Efficacy
Authors: Miller Matthew R. , Abshirini Maryam , Wolber Frances M. , Tuterangiwhiu Te Rerekohu , Kruger Marlena C.
Published: February 2023

GreenshellTM mussels (GSMs), Perna canaliculus, are Aotearoa/New Zealand’s most important aquaculture species and is sold as a variety of food products worldwide. GSMs are a traditional and culturally valuable food of the Māori people. Following the development of a series of nutraceutical products (dried powders and extracted oils) by the GSM aquaculture industry in the 1960s, there has been an increased scientific interest in the clinical health benefits of GSM products. Omega-3 polyunsaturated fatty acids in GSMs have exhibited significant anti-inflammatory activity, and the clinical evidence has led to GSM powders and oils being extensively promoted as treatments for rheumatoid arthritis and osteoarthritis. This review defines the nutritional composition of GSMs and describes the sustainability of GSMs and their traditional uses. The review also details the health benefits of GSMs in clinical applications and identifies potential mechanisms and molecular pathways initiated by the various bioactive components of GSMs.
Logistics Service Supply Chain Vertical Integration Decisions under Service Efficiency Competition
Authors: Zhang Xiaomeng , Zhao Qilan , Zhang Jianjun , Yue Xiongping
Published: February 2023

In the logistics sector, price competition is no longer the only form of horizontal competition between logistics service integrators; instead, it frequently takes the form of service efficiency competition among chains. Facing fierce market competition, vertical resource integration gradually becomes the trend in logistics industry integration. Using the inverse derivation method and comparative analysis, this study examines the relationship between the overall profit of its chain and that of the rival chain under service efficiency competition with or without the integration strategy. Furthermore, it builds two parallel competition logistics service supply chain models based on the inter-chain Nash competition and Stackelberg game of the chain members. The study results demonstrate that when the cost per unit of service efficiency is fixed, the greater the intensity of competition between chains, the more managers should tend to choose an integration strategy to maximize their profits. More interestingly, we find that the optimal integration decision of the supply chain is independent of the competitive intensity when the cost required to improve the unit service efficiency is extremely high.
Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan
Authors: Al-Quraan Ayman , Al-Mhairat Bashar , Malkawi Ahmad M. A. , Radaideh Ashraf , Al-Masri Hussein M. K.
Published: February 2023

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (ED) and the total wind energy (ET) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m2 to nearly 1250 kWh/m2. In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.
Blockchain-Enabled Sustainable Supply Chain under Information Sharing and Recovery Quality Efforts
Authors: Salikhov Jasur , Hayrutdinov Saidjahon , Muminov Timur K.
Published: February 2023

The objective of this study is to establish an efficient contractual coordination model for advancing the supply chain (SC) towards sustainability with blockchain technology. The problem of SC unsold product remanufacturing is investigated under the influence of efforts on recovery quality and information sharing within blockchain technology. Firstly, a functional model describes how the recovery quality affected by the added effort and demand of the remanufactured products is affected by the price. Secondly, the downstream SC faced market uncertainty with consumer sensitivity analyzed, and the SC information-sharing systems were reconsidered in order to improve consumer satisfaction. Then, under the conditions of information sharing and recovery quality efforts, the decentralized SC decision-making mode is discussed. The above demonstrates that the SC unsold products remanufacturing under the decentralized decision-making mode cannot be coordinated. To prove the efficiency of unsold products remanufacturing we investigated the centralized SC mode as a benchmark, which is known as the most efficient system. Finally, in order to effectively coordinate the whole chain, a mechanism of “cost and revenue sharing” is proposed, and the constraints of supplier’s choice of remanufacturing are given. The results show that the “cost and revenue sharing” mechanism can effectively coordinate the SC and the expected profits of downstream and upstream will be in win–win condition. Moreover, the “cost and revenue sharing” mechanism in a sustainable SC unsold product remanufacturing essentially builds an incentive among members to improve the efficiency of existing resource usage and the environmental implications.
Geographical Detection Analysis and Spatiotemporal Disparity Characteristics of the Coupling Coordination Development between Urbanization and the Eco-Environment
Authors: Chen Xiangman , Liu Xuezhou
Published: February 2023

The study of the coupling coordinated relationship between urbanization and the eco-environment is important to promote regional high-quality development. This paper measured the coupling coordination degree between urbanization and the eco-environment from 2008 to 2020, using the coupling coordination model, depicted its spatiotemporal characteristics with ArcGIS, and explored its driving factors and impacts of the interaction between pairwise factors on it with geo-detection methods. The results show that the coupling coordination level becomes increasingly improved, but there exist widening spatial disparities, and in 2020, its spatial distribution presents characteristics of “decreasing from north to south” and “increasing from west to east”. According to their evolutionary characteristics of coupling coordination categories, cities can be classified into four types: “transition from basic misalignment to primary coordination”, “transition from primary coordination to intermediate coordination”, “transition from primary coordination to senior coordination”, “no hierarchical transition”. Currently, driving factors of industrial structure, opening to the outside world and technological innovation significantly affect the coupling coordination degree, and the interaction between pairwise factors is enhanced. Policies should be implemented in categories to improve the coupling coordination level. Regional cooperation, exchanges, and interconnection of resources and factors should be strengthened to jointly build a coordinated regional development pattern.
Route Optimization of Mobile Medical Unit with Reinforcement Learning
Authors: Maheshwari Shruti , Jain Pramod Kumar , Kotecha Ketan
Published: February 2023

In this paper, we propose a solution for optimizing the routes of Mobile Medical Units (MMUs) in the domain of vehicle routing and scheduling. The generic objective is to optimize the distance traveled by the MMUs as well as optimizing the associated cost. These MMUs are located at a central depot. The idea is to provide improved healthcare to the rural people of India. The solution is obtained in two stages: preparing a mathematical model with the most suitable parameters, and then in the second phase, implementing an algorithm to obtain an optimized solution. The solution is focused on multiple parameters, including the number of vans, number of specialists, total distance, total travel time, and others. The solution is further supported by Reinforcement Learning, explaining the best possible optimized route and total distance traveled.
China’s Hydropower Resources and Development
Authors: Xiao Ling , Wang Jing , Wang Binglin , Jiang He
Published: February 2023

Hydroenergy is developed due to its low-cost and near-zero pollution emission properties; therefore, the efficient management of hydroenergy is an important goal of sustainable development for any nation, especially for China, since it owns the most abundant water resources around the world. Developing hydroenergy is not only an effective response to the energy crisis but also a positive way to cope with climate change in China. Nevertheless, research on hydroenergy in China is still not comprehensive. This study reviews hydroenergy development in China by combining its geographical characteristics and hydroenergy reserves. The general condition of hydropower development including large- and medium-scale hydropower stations and small hydropower development is presented. This article illuminates the potential problems and existing challenges in China’s hydropower development and relevant exploitation suggestions are provided for hydropower development in the future.
Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study
Authors: Santana Thiago Pires , Horta Nicole , Revez Catarina , Dias Rui Manuel Teixeira Santos , Zebende Gilney Figueira
Published: February 2023

The energy sector has been the main economic hub in everyone’s lives and in world geopolitics. Consequently, oil, gas, electricity and energy from renewable sources (wind and solar) are traded on the stock market, and all interconnected around the world. On the other hand, a global health crisis, such as COVID-19, can produce a great economic catastrophe. In this scenario, a robust statistical analysis will be performed here with respect to the concept of interdependence and contagion effect. For this project, we chose to study the relationship between the main source of energy (crude oil, WTI and Brent) and two (Gold and Silver) precious metals (which are a safe haven for investment). Therefore, with the novelty of the application of ρDCCA and ΔρDCCA coefficients before and during the COVID-19 crisis (announced by the World Health Organization), the interdependence and the contagion effect were calculated. We verified that COVID-19 had no influence on contagion effect between crude oil in its indexes, WTI and Brent, since they have already shown to be highly interdependent, both before and after the World Health Organization COVID-19 decree. Likewise, COVID-19 had a significant influence on the crude oil and precious metal sectors, which was evident as we identified an increase in its interdependence, with a clearly positive contagion. These results show that COVID-19 imposed a restructuring in the relationship between energy (crude oil) and precious metals. More details will be presented throughout this article.
Smartphone Addiction Assessment Using Pythagorean Fuzzy CRITIC-TOPSIS
Authors: Ertemel Adnan Veysel , Menekse Akin , Camgoz Akdag Hatice
Published: February 2023

Addiction to smartphones, particularly among adolescents, has reached alarming proportions, rivaling or perhaps exceeding internet addiction as the most widespread kind of dependence in modern culture. Evaluating the degree of problematic smartphone use habits by experts and identifying the vulnerable ones to steer to the right treatment program has become a critical issue. Since such a task may involve an abundance of criteria and candidates, as well as the inherent subjectivity of multiple decision experts participating in the process, the assessment of smartphone addiction can be framed as an uncertain multi-criteria decision-making (MCDM) problem. As an extension of intuitionistic fuzzy sets, Pythagorean fuzzy sets can be used to efficiently manage ambiguity and uncertainty during decision-making. This study provides an integrated fuzzy MCDM methodology based on Pythagorean fuzzy sets for evaluating the smartphone addiction level of adolescents. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to determine the importance levels of criteria in an objective manner, and smartphone addiction levels of the selected candidates are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A sensitivity analysis is conducted to examine the variations in candidate rankings caused by changes to the criteria and weights of the decision experts. Moreover, in the context of comparative analysis, the Evaluation based on Distance from Average Solution (EDAS) approach is used to validate the acquired findings.