bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulations of Snowmelt Runoff in a High-Altitude Mountainous Area Based on Big Data and Machine Learning Models: Taking the Xiying River Basin as an Example
Authors: Wang Guoyu , Hao Xiaohua , Yao Xiaojun , Wang Jian , Li Hongyi , Chen Rensheng , Liu Zhangwen
Published: February 2023

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m3/s and 4.554 m3/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m3/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m3/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.
Assessment of Irrigation Demands Based on Soil Moisture Deficits Using a Satellite-Based Hydrological Model
Authors: Sushanth Kallem , Behera Abhijit , Mishra Ashok , Singh Rajendra
Published: February 2023

Soil moisture deficit is an essential element in the estimation of irrigation demands, both spatially and temporarily. The determination of temporal and spatial variations of soil moisture in a river basin is challenging in many aspects; however, distributed hydrological modelling with remote sensing inputs is an effective way to determine soil moisture. In this research, a water demand module was developed for a satellite-based National Hydrological Model—India (NHM-I) to estimate distributed irrigation demands based on soil moisture deficits. The NHM-I is a conceptual distributed model that was explicitly developed to utilize the products from remote sensing satellites. MOD13Q1.5 data were used in this study to classify paddy and irrigated dry crops. Along with the above data, the DEM, Leaf Area Index, FAO soil map, and crop characteristics data were also used as inputs. The NHM-I with water demand module was evaluated in the Damodar river basin, India, from 2009 to 2018. The integrated NHM-I model simulated the irrigation demands effectively with remote sensing data. The temporal analysis reveals that soil moisture deficits in the Kharif season varied annually from 2009 to 2018; however, soil moisture deficits in the Rabi season were almost constant. The 50% Allowable Moisture Depletion (AMD-50) scenario can reduce the irrigation demand of 1966 MCM compared to the Zero Allowable Moisture Depletion (AMD-0) scenario. The highest annual irrigation demand (8923 MCM) under the AMD-50 scenario occurred in the 2015–2016 season, while the lowest (6344 MCM) happened in 2013–2014 season. With a new water demand module and remote sensing inputs, the NHM-I will provide a platform to assess spatial and temporal irrigation demands and soil moisture.
Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification
Authors: Xu Yanbing , Zhang Yanmei , Yue Tingxuan , Yu Chengcheng , Li Huan
Published: February 2023

Due to a lack of labeled samples, deep learning methods generally tend to have poor classification performance in practical applications. Few-shot learning (FSL), as an emerging learning paradigm, has been widely utilized in hyperspectral image (HSI) classification with limited labeled samples. However, the existing FSL methods generally ignore the domain shift problem in cross-domain scenes and rarely explore the associations between samples in the source and target domain. To tackle the above issues, a graph-based domain adaptation FSL (GDAFSL) method is proposed for HSI classification with limited training samples, which utilizes the graph method to guide the domain adaptation learning process in a uniformed framework. First, a novel deep residual hybrid attention network (DRHAN) is designed to extract discriminative embedded features efficiently for few-shot HSI classification. Then, a graph-based domain adaptation network (GDAN), which combines graph construction with domain adversarial strategy, is proposed to fully explore the domain correlation between source and target embedded features. By utilizing the fully explored domain correlations to guide the domain adaptation process, a domain invariant feature metric space is learned for few-shot HSI classification. Comprehensive experimental results conducted on three public HSI datasets demonstrate that GDAFSL is superior to the state-of-the-art with a small sample size.
Higher Sensitivity of NIRv,Rad in Detecting Net Primary Productivity of C4 Than that of C3: Evidence from Ground Measurements of Wheat and Maize
Authors: Chen Siru , Zhao Wenhui , Zhang Renxiang , Sun Xun , Zhou Yangzhen , Liu Leizhen
Published: February 2023

Accurate quantification of net primary productivity (NPP) is key to estimating vegetation productivity and studying the global carbon cycle. However, C3 and C4 plants vary in their living environments and potential productivity due to their different photosynthetic pathways. This study thus explored the potential of the near-infrared radiance of vegetation (NIRv,Rad) to track hourly and daily changes in C3 and C4 plants and investigate whether their photosynthetic pathways affect the relationship between NPP and NIRv,Rad. Statistics including long-term spectral data, the growth environment, and physiological indicators were collected using an automatic spectral monitoring system. The vegetation index NIRv,Rad was extracted from the collected spectral data and NPP was calculated using the collected net photosynthetic rate and leaf area index. The results showed that NIRv,Rad can effectively respond to NPP changes in C3 and C4 plants on hourly and daily scales. The NPP–NIRv,Rad relationship on the hourly scale was superior, with R2 values for winter wheat and summer maize of 0.81 and 0.70, respectively. Furthermore, when the accumulation of NPP was equal, the NIRv,Rad of summer maize showed larger changes, indicating that it was more sensitive to NPP change for this species than for winter wheat. Overall, the study demonstrated that NIRv,Rad can serve as an effective proxy indicator of NPP. In addition, this study provides a theoretical basis and scientific guidance for the construction of new simple models and realizing efficient agriculture, as well as a new method for rapid and accurate quantification of the NPP of C3 and C4 plants at geospatial scales in future research.
A Comparative Study of Several Popular Models for Near-Land Surface Air Temperature Estimation
Authors: Yang Dewei , Zhong Shaobo , Mei Xin , Ye Xinlan , Niu Fei , Zhong Weiqi
Published: February 2023

Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process regression models by combining MODIS data, DEM data, and meteorological station data to estimate the NLSAT in China’s mainland and compare them with actual NLSAT observations. The results show that there is a significant correlation between the model estimates and the actual temperature observations. Among the tested models, the random forest performed the best, followed by the support vector machine and the Gaussian process regression, then the neural network, the long short-term memory, and the bi-directional long short-term memory models. Overall, for estimates in different seasons, the best results were obtained in winter, followed by spring, autumn, and summer successively. According to different geographic areas, random forest was the best model for Northeast, Northwest, North, Southwest, and Central China, and the support vector machine was the best model for South and East China.
Detection and Attribution of Alpine Inland Lake Changes by Using Random Forest Algorithm
Authors: Guo Wei , Ni Xiangnan , Mu Yi , Liu Tong , Zhang Junzhe
Published: February 2023

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 108 m3·yr−1 (R2 = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 108 m3·yr−1, R2 = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management.
Hyperspectral Image Classification via Information Theoretic Dimension Reduction
Authors: Islam Md Rashedul , Siddiqa Ayasha , Ibn Afjal Masud , Uddin Md Palash , Ulhaq Anwaar
Published: February 2023

Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene; therefore, object classification becomes the most difficult task for such a high-dimensional HSI data cube. Additionally, the HSI’s spectral bands exhibit a high correlation, and a large amount of spectral data creates high dimensionality issues as well. Dimensionality reduction is, therefore, a crucial step in the HSI classification pipeline. In order to identify a pertinent subset of features for effective HSI classification, this study proposes a dimension reduction method that combines feature extraction and feature selection. In particular, we exploited the widely used denoising method minimum noise fraction (MNF) for feature extraction and an information theoretic-based strategy, cross-cumulative residual entropy (CCRE), for feature selection. Using the normalized CCRE, minimum redundancy maximum relevance (mRMR)-driven feature selection criteria were used to enhance the quality of the selected feature. To assess the effectiveness of the extracted features’ subsets, the kernel support vector machine (KSVM) classifier was applied to three publicly available HSIs. The experimental findings manifest a discernible improvement in classification accuracy and the qualities of the selected features. Specifically, the proposed method outperforms the traditional methods investigated, with overall classification accuracies on Indian Pines, Washington DC Mall, and Pavia University HSIs of 97.44%, 99.71%, and 98.35%, respectively.
Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia
Authors: Lukas Paulos , Melesse Assefa M. , Kenea Tadesse Tujuba
Published: February 2023

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made available for LULC change modeling and prediction via the QGIS MOLUSCE plugin. Transition potential modeling was computed, and future LULC changes were predicted using the CA-ANN model. An overall accuracy of 86.53% and an overall kappa value of 0.82 were obtained by comparing the actual data of 2022 with the simulated LULC data from the same year. The study findings revealed that between 2022 and 2037, agricultural land (63.09%) and shrubland (5.74%) showed significant increases, and forest (−48.10%) and grassland (−0.31%) decreased. From 2037 to 2052, the built-up area (2.99%) showed a significant increase, and forest and agricultural land (−2.55%) showed a significant decrease. From 2052 to 2067, the projected LULC simulation result showed that agricultural land (3.15%) and built-up area (0.32%) increased, and forest (−1.59%) and shrubland (−0.56%) showed significant decreases. According to the study’s findings, the main drivers of LULC changes are the expansion of built-up areas and agricultural land, which calls for a thorough investigation using additional data and models to give planners and policymakers clear information on LULC changes and their environmental effects.
Effects of Charge Traps on Hysteresis in Organic Field-Effect Transistors and Their Charge Trap Cause Analysis through Causal Inference Techniques
Authors: Kim Somi , Yoo Hochen , Choi Jaeyoung
Published: February 2023

Hysteresis in organic field-effect transistors is attributed to the well-known bias stress effects. This is a phenomenon in which the measured drain-source current varies when sweeping the gate voltage from on to off or from off to on. Hysteresis is caused by various factors, and one of the most common is charge trapping. A charge trap is a defect that occurs in an interface state or part of a semiconductor, and it refers to an electronic state that appears distributed in the semiconductor’s energy band gap. Extensive research has been conducted recently on obtaining a better understanding of charge traps for hysteresis. However, it is still difficult to accurately measure or characterize them, and their effects on the hysteresis of organic transistors remain largely unknown. In this study, we conduct a literature survey on the hysteresis caused by charge traps from various perspectives. We first analyze the driving principle of organic transistors and introduce various types of hysteresis. Subsequently, we analyze charge traps and determine their influence on hysteresis. In particular, we analyze various estimation models for the traps and the dynamics of the hysteresis generated through these traps. Lastly, we conclude this study by explaining the causal inference approach, which is a machine learning technique typically used for current data analysis, and its implementation for the quantitative analysis of the causal relationship between the hysteresis and the traps.
SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research
Authors: Gao Wei , Liu Yunqing , Zeng Yi , Liu Quanyang , Li Qi
Published: February 2023

The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models to have high confidence in output errors. There are potential risks in a system, and input data that contain confrontation samples can be easily used by malicious people to attack the system. For a safe and stable model, attack algorithms need to be studied. The goal of traditional attack algorithms is to destroy models. When defending against attack samples, a system does not consider the generalization ability of the model. Therefore, this paper introduces an attack algorithm which can improve the generalization of models by based on the attributes of Gaussian noise, which is widespread in actual SAR systems. The attack data generated by this method have a strong effect on SAR ship detection models and can greatly reduce the accuracy of ship recognition models. While defending against attacks, filtering attack data can effectively improve the model defence capabilities. Defence training greatly improves the anti-attack capacity, and the generalization capacity of the model is improved accordingly.
Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis
Authors: Nardino Vanni , Guzzi Donatella , Lastri Cinzia , Palombi Lorenzo , Coluccia Giulio , Magli Enrico , Labate Demetrio , Raimondi Valentina
Published: February 2023

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV–Vis) stellar spectroscopy. The instrument—which is pixel-limited in the entire 300 nm–650 nm spectral range—features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system.
Numerical Design of a Thread-Optimized Gripping System for Lap Joint Testing in a Split Hopkinson Apparatus
Authors: Moreira Bernardo S. , Nunes Paulo D. P. , da Silva Carlos M. , Tenreiro António Francisco G. , Lopes António M. , Carbas Ricardo J. C. , Marques Eduardo A. S. , Parente Marco P. L. , da Silva Lucas F. M.
Published: February 2023

Currently, few experimental methods exist that enable the mechanical characterization of adhesives under high strain rates. One such method is the Split Hopkinson Bar (SHB) test. The mechanical characterization of adhesives is performed using different specimen configurations, such as Single Lap Joint (SLJ) specimens. A gripping system, attached to the bars through threading, was conceived to enable the testing of SLJs. An optimization study for selecting the best thread was performed, analyzing the thread type, the nominal diameter, and the thread pitch. Afterwards, the gripping system geometry was numerically evaluated. The optimal threaded connection for the specimen consists of a trapezoidal thread with a 14 mm diameter and a 2 mm thread pitch. To validate the gripping system, the load–displacement (P−δ) curve of an SLJ, which was simulated as if it were tested on the SHB apparatus, was compared with an analogous curve from a validated drop-weight test numerical model.
Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach
Authors: Ručevskis Sandris , Rogala Tomasz , Katunin Andrzej
Published: February 2023

Due to the complexity of the fracture mechanisms in composites, monitoring damage using a vibration-based structural response remains a challenging task. This is also complex when considering the physical implementation of a health monitoring system with its numerous uncertainties and constraints, including the presence of measurement noise, changes in boundary and environmental conditions of a tested object, etc. Finally, to balance such a system in terms of efficiency and cost, the sensor network needs to be optimized. The main aim of this study is to develop a cost- and performance-effective data-driven approach to monitor damage in composite structures and validate this approach through tests performed on a physically implemented structural health monitoring (SHM) system. In this study, we combined the mentioned research problems to develop and implement an SHM system to monitor delamination in composite plates using data combined from finite element models and laboratory experiments to ensure robustness to measurement noise with a simultaneous lack of necessity to perform multiple physical experiments. The developed approach allows the implementation of a cost-effective SHM system with validated predictive performance.
LACP-SG: Lightweight Authentication Protocol for Smart Grids
Authors: Tanveer Muhammad , Alasmary Hisham
Published: February 2023

Smart grid (SG) recently acquired considerable attention due to their utilization in sustaining demand response management in power systems. Smart meters (SMs) deployed in SG systems collect and transmit data to the server. Since all communications between SM and the server occur through a public communication channel, the transmitted data are exposed to adversary attacks. Therefore, security and privacy are essential requirements in the SG system for ensuring reliable communication. Additionally, an AuthentiCation (AC) protocol designed for secure communication should be lightweight so it can be applied in a resource-constrained environment. In this article, we devise a lightweight AC protocol for SG named LACP-SG. LACP-SG employs the hash function, “Esch256”, and “authenticated encryption” to accomplish the AC phase. The proposed LACP-SG assures secure data exchange between SM and server by validating the authenticity of SM. For encrypted communication, LACP-SG enables SM and the server to establish a session key (SEK). We use the random oracle model to substantiate the security of the established SEK. Moreover, we ascertain that LACP-SG is guarded against different security vulnerabilities through Scyther-based security validation and informal security analysis. Furthermore, comparing LACP-SG with other related AC protocols demonstrates that LACP-SG is less resource-intensive while rendering better security characteristics.
Efficacy of Individualized Sensory-Based mHealth Interventions to Improve Distress Coping in Healthcare Professionals: A Multi-Arm Parallel-Group Randomized Controlled Trial
Authors: Baumann Hannes , Heuel Luis , Bischoff Laura Louise , Wollesen Bettina
Published: February 2023

Detrimental effects of chronic stress on healthcare professionals have been well-established, but the implementation and evaluation of effective interventions aimed at improving distress coping remains inadequate. Individualized mHealth interventions incorporating sensor feedback have been proposed as a promising approach. This study aimed to investigate the impact of individualized, sensor-based mHealth interventions focusing on stress and physical activity on distress coping in healthcare professionals. The study utilized a multi-arm, parallel group randomized controlled trial design, comparing five intervention groups (three variations of web-based training and two variations of an app training) that represented varying levels of individualization to a control group. Both self-reported questionnaire data (collected using Limesurvey) as well as electrocardiography and accelerometry-based sensory data (collected using Mesana Sensor) were assessed at baseline and post-intervention (after eight weeks). Of the 995 eligible participants, 170 (26%) completed the post-intervention measurement (Group 1: N = 21; Group 2: N = 23; Group 3: N = 7; Group 4: N = 34; Group 5: N = 16; Control Group: N = 69). MANOVA results indicated small to moderate time-by-group interaction effects for physical activity-related outcomes, including moderate to vigorous physical activity (F(1,5) = 5.8, p = ≤0.001, η2p = 0.057) and inactivity disruption (F(1,5) = 11.2, p = <0.001, η2p = 0.100), in the app-based intervention groups, but not for step counts and inactivity. No changes were observed in stress-related heart rate variability parameters over time. Despite a high dropout rate and a complex study design, the individualized interventions showed initial positive effects on physical activity. However, no significant changes in stress-related outcomes were observed, suggesting that the intervention duration was insufficient to induce physiological adaptations that would result in improved distress coping.
Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner’s Feet
Authors: Kromołowska Klaudia , Kluza Krzysztof , Kańtoch Eliasz , Sulikowski Piotr
Published: February 2023

The objective of the research presented in this paper was to provide a novel open-source strain gauge system that shall enable the measurement of the pressure of a runner’s feet on the ground and the presentation of the results of that measurement to the user. The system based on electronic shoe inserts with 16 built-in pressure sensors laminated in a transparent film was created, consisting of two parts: a mobile application and a wearable device. The developed system provides a number of advantages in comparison with existing solutions, including no need for calibration, an accurate and frequent measurement of pressure distribution, placement of electronics on the outside of a shoe, low cost, and an open-source approach to encourage enhancements and open collaboration.
A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study
Authors: Kalyvas Nektarios , Chamogeorgaki Anastasia , Michail Christos , Skouroliakou Aikaterini , Liaparinos Panagiotis , Valais Ioannis , Fountos George , Kandarakis Ioannis
Published: February 2023

Background: Mammographic digital imaging is based on X-ray sensors with solid image quality characteristics. These primarily include (a) a response curve that yields high contrast and image latitude, (b) a frequency response given by the Modulation Transfer Function (MTF), which enables small detail imaging and (c) the Normalize Noise Power Spectrum (NNPS) that shows the extent of the noise effect on image clarity. Methods: In this work, a methodological approach is introduced and described for creating digital phantom images based on the measured image quality properties of the sensor. For this purpose, a mathematical phantom, simulating breast tissue and lesions of blood, adipose, muscle, Ca and Ca(50%)-P(50%) was created by considering the corresponding X-ray attenuation coefficients. The simulated irradiation conditions of the phantom used four mammographic spectra assuming exponential attenuation. Published data regarding noise and blur of a commercial RadEye HR CMOS imaging sensor were used as input data for the resulting images. Results: It was found that the Ca and Ca(50%)-P(50%) lesions were visible in all exposure conditions. In addition, the W/Rh spectrum at 28 kVp provided more detailed images than the corresponding Mo/Mo spectrum. Conclusions: The presented methodology can act complementarily to image quality measurements, leading to initial optimization of the X-ray exposure parameters per clinical condition.
Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability
Authors: Brezini Salah Eddine , Deville Yannick
Published: February 2023

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method.
Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation
Authors: Rehman Azka , Usman Muhammad , Shahid Abdullah , Latif Siddique , Qadir Junaid
Published: February 2023

Brain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in terms of texture, morphology, and shape. This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor regions using a multi-scale attention network with novel selective deep supervision (SDS) mechanisms for training. The method utilizes a 3D input composed of five consecutive slices, in addition to a 2D slice, to maintain sequential information. The proposed multi-scale architecture includes two encoding units to extract meaningful global and local features from the 3D and 2D inputs, respectively. These coarse features are then passed through attention units to filter out redundant information by assigning lower weights. The refined features are fed into a decoder block, which upscales the features at various levels while learning patterns relevant to all tumor regions. The SDS block is introduced to immediately upscale features from intermediate layers of the decoder, with the aim of producing segmentations of the whole, enhanced, and core tumor regions. The proposed framework was evaluated on the BraTS2020 dataset and showed improved performance in brain tumor region segmentation, particularly in the segmentation of the core and enhancing tumor regions, demonstrating the effectiveness of the proposed approach. Our code is publicly available.
Smart City Achievement through Implementation of Digital Health Services in Handling COVID-19 Indonesia
Authors: Rohmah Amandita ‘Ainur , Rachmawati Rini , Mei Estuning Tyas Wulan
Published: February 2023

The digital transformation of health services is one of the impacts of the COVID-19 pandemic. Various overseas studies have shown a positive response to digital health services, especially in handling the COVID-19 pandemic. This study aims to compare the implementation of digital health services between regions in Indonesia and analyze strategies in the health sector that are appropriate in the era of digital transformation in handling COVID-19 in Indonesia. The focus of this research is on the implementation of digital transformation of health services as one of the derivatives of the Smart City implementation, especially the dimension of Smart Living. The research was conducted using qualitative methods with systematic literature reviews and SWOT analysis techniques. The typology map of the implementation of digital health services in Indonesia shows that digital health services dominate in the Western and Central parts of Indonesia, which varies widely from the Central and Eastern parts of Indonesia, which tend to have limited forms of digital transformation. Various factors certainly influence the difference in implementing digital health services in each region. Therefore, multiple strategies are needed, such as infrastructure improvement, stakeholder collaboration, and increasing the factors that influence the implementation of digital health services.
Children’s Online Safety: Predictive Factors of Cyberbullying and Online Grooming Involvement
Authors: Tintori Antonio , Ciancimino Giulia , Bombelli Ilaria , De Rocchi Daniele , Cerbara Loredana
Published: February 2023

The increase in the use of the Internet, strongly boosted by the spread of COVID-19, has amplified the risk of involvement in cyberbullying and online grooming among minors. To date, most research on these phenomena has focused on middle and high school students, with fewer studies on younger children. The present study aims to fill this knowledge gap by measuring the spread of cyberbullying and online grooming in a sample of 410 primary school students in the city of Rome and by identifying the main individual and environmental predictors associated with the involvement of children in these phenomena using factor analysis. Results indicate that both cyberbullying and online grooming are widespread among respondents, showing common traits within the four latent dimensions identified. Screen time is among the main predictors of children’s involvement, together with parental supervision, phubbing behaviours, prosocial tendencies and family socio-economic background. These findings highlight the need for further studies on representative samples of this age group, as well as for a greater cooperative effort among schools, parents and caregivers to keep children safe in the virtual world.
How He Got His Scars: Exploring Madness and Mental Health in Filmic Representations of the Joker
Authors: Preston Jeff , Rath-Paillé Lindsay
Published: February 2023

In May of 1939, DC Comics introduced their popular Batman series, but it was a year later when the iconic villain, the Joker, entered the story. What began as a lighthearted pulp comic has since evolved, with Batman’s enemies growing darker and more sinister. In the film, the Joker is now less “clown prince” than violent madman, determined to wreak havoc and spread his warped view of society. Through a thematic discourse analysis, this article explores how Batman films featuring the Joker routinely naturalize and reinforce sanist beliefs about mental illness and are deployed as narrative prostheses to rationalize his heinous crimes. Blending work from both disability studies and mad studies, we explore the cultural construction of madness as animated by filmic representations of the Joker and consider how these narratives inform perceptions of mental illness and subsequently rationalize the disciplining of mad people.
Examining How Equalities Nonprofit Organizations Approach Policy Influencing to Achieve Substantive Representation in Sub-State Government Policymaking
Authors: Sanders Amy
Published: February 2023

This article is concerned with equalities nonprofit organizations’ activities to achieve substantive representation in policy-making through a sub-state government. It draws on three strands of the interest representation literature from equalities theory, nonprofit sector studies, and social movements theory. The analytical framework synthesizes these to provide a new approach for examining equalities nonprofit organizations’ policy influencing. Drawing on equalities theorists’ accounts of mainstreaming, and understandings of campaigns from social movement literature, it explores nonprofit organizations’ positioning in relation to government in order to advance equality. This analysis engages with questions raised by nonprofit scholars about nonprofit organizations’ independence from government and their capacity to retain a critical voice. An overarching institutionalist lens enables an examination of the formal and informal facets that shape policy influencing approaches. The research question is: How have equalities organizations engaged with the institution of a nonprofit-government partnership to promote substantive representation in policy? This research uses semi-structured elite interviews to explore key policy actors’ accounts. The case study is the statutory Welsh nonprofit sector–government partnership. Findings suggest the equalities nonprofit organizations involved in this partnership deploy a sophisticated array of action repertoires as part of an interrelated web of nuanced, multi-positioned influencing activities. This agility enables the sector to maintain some capacity to be critical of the state whilst sustaining informal relations with state policy actors.
“Look at Me, but Better”: The Experience of Young NEET Migrant Women between Vulnerability and Stifled Ambitions
Authors: Zanfrini Laura , Giuliani Cristina
Published: February 2023

The experience and the condition of the vulnerability of young immigrant women with NEET status are not acknowledged in both research and social policy. Within the extreme variety gathered under the term NEET, this present article aims at exploring the experience of a group of young non-EU migrant women aged between 18 and 31 living in Italy, who at the time of their participation in this study, were not engaged in education, employment, or training. Nineteen semi-structured interviews have been collected involving young women who migrated from Bangladesh, Egypt, India, Morocco, Pakistan, and Tunisia, in most cases through family reunification. Thematic analysis carried out on interview transcripts highlights the complexity and richness of the stories and experiences narrated by participants, composing a heterogeneous group marked by different levels of vulnerability and resilience capabilities, language skills, and involvement in the goal of finding a job. The perception of vulnerability that emerges from the women interviewed refers mainly to the relational dimension of life, which appears to be characterized by loneliness, a sense of isolation, and feelings of extraneousness (not belonging) with respect to an external context. Implications for policies aimed at this specific group of foreign women are discussed.
Assessing Area under the Curve as an Alternative to Latent Growth Curve Modeling for Repeated Measures Zero-Inflated Poisson Data: A Simulation Study
Authors: Rodriguez Daniel
Published: February 2023

Researchers interested in the assessment of substance use trajectories, and predictors of change, have several data analysis options. These include, among others, generalized estimating equations and latent growth curve modeling. One difficulty in the assessment of substance use, however, is the nature of the variables studied. Although counting instances of use (e.g., the number of cigarettes smoked per day) would seem to be the best option, such data present difficulties in that the distribution of these variables is not likely normal. Count variables often follow a Poisson distribution, and when dealing with substance use in the general population, there is a preponderance of zeros (representing not using). As such, substance use counts may approximate a zero-inflated Poisson distribution. Unfortunately, analyses with zero-inflated Poisson random variables are not easily accommodated in many types of software and may be beyond access to most researchers. As such, an easier method would benefit researchers interested in assessing substance use change. The purpose of this study is to assess the area under the curve as an option when dealing with repeated measures data and contrast it to one popular method of longitudinal data analysis, latent growth curve modeling. Using a Monte Carlo simulation study with varying sample sizes, we found that the area under the curve performed well with different sample sizes and compared favorably to the performance of latent growth curve modeling, particularly when dealing with smaller sample sizes. The area under the curve may be a simpler alternative for researchers, especially when dealing with smaller sample sizes.
Megalithic Stone Heritage Trail Mapping Using GIS as Tourism Product for Cultural Sustainability in Tambunan
Authors: Sieng Kong Teck , Eboy Oliver Valentine , Pugh-Kitingan Jacqueline , Bee Baszley Bee Basrah , Bagul Awangku Hassanal Bahar Pengiran , Baco Zainuddin
Published: February 2023

Heritage trails are an effective way to preserve cultural sustainability. Nowadays, heritage trails have been widely developed as tourism products either domestically or abroad. Megalithic stones are an ancient tradition of high value that has interesting functions and backgrounds. Moderni-zation with infrastutural development, and changes in religion in society have caused the mega-lithic stone tradition to be less practiced and increasingly declining. One of the effective ways to preserve megalithic stones from further decline is by building heritage trails as a tourism product. Many megalithic stones are located in sloping areas. In this study, least-cost path analysis (LCPA) in GIS applications was used to identify cost-effective and sloping routes to produce heritage trails. Study data were obtained from fieldwork in Tambunan. The benefits of this study provide ex-posure to the outside world about the megalithic stone tradition, and determine the appropriate routes to develop heritage trails in the District, and assist in the economy of the local population.
Improving Traffic Safety through Traffic Accident Risk Assessment
Authors: Hu Zhenghua , Zhou Jibiao , Zhang Enyou
Published: February 2023

The continuous development of sensors and the Internet of Things has produced a large amount of traffic data with location information. The improvement of traffic safety benefits from the availability of traffic accident data. Managers can patrol and control relevant areas in advance with limited police resources, according to the short-term traffic accident predictions. As a result, the possibility of accidents can be reduced, and the level of traffic safety can be improved. The traditional approach to accident prediction relies too much on the subjective experience of traffic managers. Inspired by the deep learning technology in the field of computer vision, this study first divides the road network into regular grids and takes the number of traffic accidents in each grid as the pixel value of an image. Then, a traffic accident prediction approach based on a bi-directional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is proposed. This method mines the regular information hidden in the accident data and introduces densely connected convolutions to further extract the deep spatial-temporal features contained in the traffic accident sequence. Thus, the issues of gradient disappearance and model over-fitting caused by the traditional model in model training can be avoided. Finally, the simulation experiment is carried out on the historical traffic accident data of Yinzhou District, Ningbo City. Results show that BCDU-Net has better accuracy and precision than other models in three data sets: motor vehicle accidents, non-motor vehicle accidents, and single-vehicle accidents. Therefore, the BCDU-Net is more suitable for traffic accident prediction and has good application prospects for improving road safety.
Potential of Repurposing Recycled Concrete for Road Paving: Flexural Strength (FS) Modeling by a Novel Systematic and Evolved RF-FA Model
Authors: Gu Shuwei , Shen Hao , Pang Chuming , Li Zhiping , Liu Long , Liu Huan , Wang Shuai , Song Yaxin , Huang Jiandong
Published: February 2023

Concrete can be recycled after certain processing technologies for use in pavement engineering but the flexural strength (FS) is difficult to predict accurately in the design process. This study proposes a novel systematic and evolved approach to estimate the FS of recycled concrete. The proposed methods are conducted based on the random forest (RF) model as well as the firefly algorithm (FA), where the latter is employed to tune the hyperparameters of the RF model. For this purpose, data sets were collected from previously published literature for the training and verification of the model, and the accuracy of the model was verified by the fitting effect of the predicted and actual values. The results showed that the proposed hybrid machine learning model has a good fitting effect on the predicted and actual values; the calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the proposed model to determine the FS of the recycling concrete. In addition, the study analyzed the sensitivity of the FS of recycled concrete to input variables, and the results showed that effective water-cement ratio (WC), water absorption of recycling concrete (WAR), and water absorption of natural aggregate (WAN) show more obvious influences on FS, so these factors should be paid more attention in future pavement design using the recycling of concrete.