bluuebunny/crossref_metadata_2025 huggingface dataset:
Designing Sustainable Hydrophilic Interfaces via Feature Selection from Molecular Descriptors and Time-Domain Nuclear Magnetic Resonance Relaxation Curves
Authors: Okada Masayuki , Amamoto Yoshifumi , Kikuchi Jun
Published: March 2024

Surface modification using hydrophilic polymer coatings is a sustainable approach for preventing membrane clogging due to foulant adhesion to water treatment membranes and reducing membrane-replacement frequency. Typically, both molecular descriptors and time-domain nuclear magnetic resonance (TD-NMR) data, which reveal physicochemical properties and polymer-chain dynamics, respectively, are required to predict the properties and understand the mechanisms of hydrophilic polymer coatings. However, studies on the selection of essential components from high-dimensional data and their application to the prediction of surface properties are scarce. Therefore, we developed a method for selecting features from combined high-dimensional molecular descriptors and TD-NMR data. The molecular descriptors of the monomers present in polyethylene terephthalate films were calculated using RDKit, an open-source chemoinformatics toolkit, and TD-NMR spectroscopy was performed over a wide time range using five-pulse sequences to investigate the mobility of the polymer chains. The model that analyzed the data using the random forest algorithm, after reducing the features using gradient boosting machine-based recursive feature elimination, achieved the highest prediction accuracy. The proposed method enables the extraction of important elements from both descriptors of surface properties and can contribute to the development of new sustainable materials and material-specific informatics methodologies encompassing multiple information modalities.
Long-Term Dynamic Monitoring and Driving Force Analysis of Eco-Environmental Quality in China
Authors: Zhang Weiwei , Liu Zixi , Qin Kun , Dai Shaoqing , Lu Huiyuan , Lu Miao , Ji Jianwan , Yang Zhaohui , Chen Chao , Jia Peng
Published: March 2024

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors.
SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea
Authors: Zhang Mengmeng , Han Guijun , Wu Xiaobo , Li Chaoliang , Shao Qi , Li Wei , Cao Lige , Wang Xuan , Dong Wanqiu , Ji Zenghua
Published: March 2024

We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin.
Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method
Authors: Pampanoni Valerio , Fascetti Fabio , Cenci Luca , Laneve Giovanni , Santella Carla , Boccia Valentina
Published: March 2024

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR.
AIDB-Net: An Attention-Interactive Dual-Branch Convolutional Neural Network for Hyperspectral Pansharpening
Authors: Sun Qian , Sun Yu , Pan Chengsheng
Published: March 2024

Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this paper, we proposed an Attention-Interactive Dual-Branch Convolutional Neural Network (AIDB-Net) for HS pansharpening. Our model purely consists of convolutional layers and simultaneously inherits the strengths of both convolution and self-attention, especially the modeling of short- and long-range dependencies. Specially, we first extract, tokenize, and align the hyperspectral image (HSI) and panchromatic image (PAN) by Overlapping Patch Embedding Blocks. Then, we specialize a novel Spectral-Spatial Interactive Attention which is able to globally interact and fuse the cross-modality features. The resultant token-global similarity scores can guide the refinement and renewal of the textural details and spectral characteristics within HSI features. By deeply combined these two paradigms, our AIDB-Net significantly improve the pansharpening performance. Moreover, with the acceleration by the convolution inductive bias, our interactive attention can be trained without large scale dataset and achieves competitive time cost with its counterparts. Compared with the state-of-the-art methods, our AIDB-Net makes 5.2%, 3.1%, and 2.2% improvement on PSNR metric on three public datasets, respectively. Comprehensive experiments quantitatively and qualitatively demonstrate the effectiveness and superiority of our AIDB-Net.
A Satellite View of the Wetland Transformation Path and Associated Drivers in the Greater Bay Area of China during the Past Four Decades
Authors: Sun Kun , Yu Weiwei
Published: March 2024

As a highly productive and biologically diverse ecosystem, wetlands provide unique habitat for a wide array of plant and animal species. Owing to the strong disturbance by human activities and climate change, wetland degradation and fragmentation have become a common phenomenon across the globe. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a typical case. The GBA has experienced explosive growth in the population and economy since the early 1980s, which has resulted in complicated transitions between wetlands and non-wetlands. However, our knowledge about the transformation paths, associated drivers, and ecological influence of the GBA’s wetlands is still very limited. Taking advantage of the land use maps generated from Landsat observations over the period of 1980–2020, here, we quantified the spatiotemporal transformation paths of the GBA’s wetlands and analyzed the associated drivers and ecological influence. We found that the dominant transformation path between wetland and non-wetland was from wetland to built-up land, which accounted for 98.4% of total wetland loss. The primary transformation path among different wetland types was from coastal shallow water and paddy land to reservoir/pond, with the strongest transformation intensity in the 1980s. The driving forces behind the wetland change were found to vary by region. Anthropogenic factors (i.e., population growth and urbanization) dominated in highly developed cities, while climate factors and aquaculture had a greater influence in underdeveloped cities. The findings presented in this study will provide a reference for wetland management and planning in the GBA.
Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery
Authors: Watt Michael S. , Estarija Honey Jane C. , Bartlett Michael , Main Russell , Pasquini Dalila , Yorston Warren , McLay Emily , Zhulanov Maria , Dobbie Kiryn , Wardhaugh Katherine , Hossain Zulfikar , Fraser Stuart , Buddenbaum Henning
Published: March 2024

Myrtle rust is a very damaging disease, caused by the fungus Austropuccinia psidii, which has recently arrived in New Zealand and threatens the iconic tree species pōhutukawa (Metrosideros excelsa). Canopy-level hyperspectral and thermal images were taken repeatedly within a controlled environment, from 49 inoculated (MR treatment) and 26 uninoculated (control treatment) pōhutukawa plants. Measurements were taken prior to inoculation and six times post-inoculation over a 14-day period. Using indices extracted from these data, the objectives were to (i) identify the key thermal and narrow-band hyperspectral indices (NBHIs) associated with the pre-visual and early expression of myrtle rust and (ii) develop a classification model to detect the disease. The number of symptomatic plants increased rapidly from three plants at 3 days after inoculation (DAI) to all 49 MR plants at 8 DAI. NBHIs were most effective for pre-visual and early disease detection from 3 to 6 DAI, while thermal indices were more effective for detection of disease following symptom expression from 7 to 14 DAI. Using results compiled from an independent test dataset, model performance using the best thermal indices and NBHIs was excellent from 3 DAI to 6 DAI (F1 score 0.81–0.85; accuracy 73–80%) and outstanding from 7 to 14 DAI (F1 score 0.92–0.93; accuracy 89–91%).
An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance
Authors: Xu Wenwen , Xiao Jiankang , Xu Dalong , Wang Hao , Cao Jianyin
Published: March 2024

A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases.
Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks
Authors: Bi Xiaodan , Zhao Lian
Published: March 2024

With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme.
UAV Detection Using Reinforcement Learning
Authors: AlKhonaini Arwa , Sheltami Tarek , Mahmoud Ashraf , Imam Muhammad
Published: March 2024

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.
Detecting Forged Audio Files Using “Mixed Paste” Command: A Deep Learning Approach Based on Korean Phonemic Features
Authors: Son Yeongmin , Park Jae Wan
Published: March 2024

The ubiquity of smartphones today enables the widespread utilization of voice recording for diverse purposes. Consequently, the submission of voice recordings as digital evidence in legal proceedings has notably increased, alongside a rise in allegations of recording file forgery. This trend highlights the growing significance of audio file authentication. This study aims to develop a deep learning methodology capable of identifying forged files, particularly those altered using “Mixed Paste” commands, a technique not previously addressed. The proposed deep learning framework is a composite model, integrating a convolutional neural network and a long short-term memory model. It is designed based on the extraction of features from spectrograms and sequences of Korean consonant types. The training of this model utilizes an authentic dataset of forged audio recordings created on an iPhone, modified via “Mixed Paste”, and encoded. This hybrid model demonstrates a high accuracy rate of 97.5%. To validate the model’s efficacy, tests were conducted using various manipulated audio files. The findings reveal that the model’s effectiveness is not contingent on the smartphone model or the audio editing software employed. We anticipate that this research will advance the field of audio forensics through a novel hybrid model approach.
Multimodal Mobile Robotic Dataset for a Typical Mediterranean Greenhouse: The GREENBOT Dataset
Authors: Cañadas-Aránega Fernando , Blanco-Claraco Jose Luis , Moreno Jose Carlos , Rodriguez-Diaz Francisco
Published: March 2024

This paper presents an innovative dataset designed explicitly for challenging agricultural environments, such as greenhouses, where precise location is crucial, but GNNS accuracy may be compromised by construction elements and the crop. The dataset was collected using a mobile platform equipped with a set of sensors typically used in mobile robots as it was moved through all the corridors of a typical Mediterranean greenhouse featuring tomato crops. This dataset presents a unique opportunity for constructing detailed 3D models of plants in such indoor-like spaces, with potential applications such as robotized spraying. For the first time, to the authors’ knowledge, a dataset suitable to test simultaneous localization and mapping (SLAM) methods is presented in a greenhouse environment, which poses unique challenges. The suitability of the dataset for this purpose is assessed by presenting SLAM results with state-of-the-art algorithms. The dataset is available online.
A Scheduler for Smart Home Appliances Based on a Novel Concept of Tariff Space
Authors: Rebouças Coutinho Luis Rodolfo , Barroso Giovanni Cordeiro , Prata Bruno de Athayde
Published: March 2024

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day from official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time-shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method referred to as tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (hierarchical) available in the language portfolio in which the project has been executed (the Julia language). As a result, while both methodologies yield similar results, the approach presented in this article demonstrates a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.
Semantic Segmentation of Remote Sensing Images Depicting Environmental Hazards in High-Speed Rail Network Based on Large-Model Pre-Classification
Authors: Dong Qi , Chen Xiaomei , Jiang Lili , Wang Lin , Chen Jiachong , Zhao Ying
Published: March 2024

With the rapid development of China’s railways, ensuring the safety of the operating environment of high-speed railways faces daunting challenges. In response to safety hazards posed by light and heavy floating objects during the operation of trains, we propose a dual-branch semantic segmentation network with the fusion of large models (SAMUnet). The encoder part of this network uses a dual-branch structure, in which the backbone branch uses a residual network for feature extraction and the large-model branch leverages the results of feature extraction generated by the segment anything model (SAM). Moreover, a decoding attention module is fused with the results of prediction of the SAM in the decoder part to enhance the performance of the network. We conducted experiments on the Inria Aerial Image Labeling (IAIL), Massachusetts, and high-speed railway hazards datasets to verify the effectiveness and applicability of the proposed SAMUnet network in comparison with commonly used semantic segmentation networks. The results demonstrated its superiority in terms of both the accuracies of segmentation and feature extraction. It was able to precisely extract hazards in the environment of high-speed railways to significantly improve the accuracy of semantic segmentation.
Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach
Authors: Li Jiaojiao , Meng Xiaolin , Hu Liangliang , Bao Yan
Published: March 2024

Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge’s health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents.
Enhancing Query Formulation for Universal Image Segmentation
Authors: Qu Yipeng , Kim Joohee
Published: March 2024

Recent advancements in image segmentation have been notably driven by Vision Transformers. These transformer-based models offer one versatile network structure capable of handling a variety of segmentation tasks. Despite their effectiveness, the pursuit of enhanced capabilities often leads to more intricate architectures and greater computational demands. OneFormer has responded to these challenges by introducing a query-text contrastive learning strategy active during training only. However, this approach has not completely addressed the inefficiency issues in text generation and the contrastive loss computation. To solve these problems, we introduce Efficient Query Optimizer (EQO), an approach that efficiently utilizes multi-modal data to refine query optimization in image segmentation. Our strategy significantly reduces the complexity of parameters and computations by distilling inter-class and inter-task information from an image into a single template sentence. Furthermore, we propose a novel attention-based contrastive loss. It is designed to facilitate a one-to-many matching mechanism in the loss computation, which helps object queries learn more robust representations. Beyond merely reducing complexity, our model demonstrates superior performance compared to OneFormer across all three segmentation tasks using the Swin-T backbone. Our evaluations on the ADE20K dataset reveal that our model outperforms OneFormer in multiple metrics: by 0.2% in mean Intersection over Union (mIoU), 0.6% in Average Precision (AP), and 0.8% in Panoptic Quality (PQ). These results highlight the efficacy of our model in advancing the field of image segmentation.
Analytical Analysis of Factors Affecting the Accuracy of a Dual-Heat Flux Core Body Temperature Sensor
Authors: Żmigrodzki Jakub , Cygan Szymon , Łusakowski Jan , Lamprecht Patryk
Published: March 2024

Non-invasive core body temperature (CBT) measurements using temperature and heat-flux have become popular in health, sports, work safety, and general well-being applications. This research aimed to evaluate two commonly used sensor designs: those that combine heat flux and temperature sensors, and those with four temperature sensors. We used analytical methods, particularly uncertainty analysis calculus and Monte Carlo simulations, to analyse measurement accuracy, which depends on the accuracy of the temperature and flux sensors, mechanical construction parameters (such as heat transfer coefficient), ambient air temperature, and CBT values. The results show the relationship between the accuracy of each measurement method variant and various sensor parameters, indicating their suitability for different scenarios. All measurement variants showed unstable behaviour around the point where ambient temperature equals CBT. The ratio of the heat transfer coefficients of the dual-heat flux (DHF) sensor’s channels impacts the CBT estimation uncertainty. An analysis of the individual components of uncertainty in CBT estimates reveals that the accuracy of temperature sensors significantly impacts the overall uncertainty of the CBT measurement. We also calculated the theoretical limits of measurement uncertainty, which varied depending on the method variant and could be as low as 0.05 °C.
EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN
Authors: Arif Aniqa , Wang Yihe , Yin Rui , Zhang Xiang , Helmy Ahmed
Published: March 2024

Analysis of brain signals is essential to the study of mental states and various neurological conditions. The two most prevalent noninvasive signals for measuring brain activities are electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number of channels, provides richer spatial information. Although a few previous studies have explored the use of multimodal deep-learning models to analyze brain activity for both EEG and fNIRS, subject-independent training–testing split analysis remains underexplored. The results of the subject-independent setting directly show the model’s ability on unseen subjects, which is crucial for real-world applications. In this paper, we introduce EF-Net, a new CNN-based multimodal deep-learning model. We evaluate EF-Net on an EEG-fNIRS word generation (WG) dataset on the mental state recognition task, primarily focusing on the subject-independent setting. For completeness, we report results in the subject-dependent and subject-semidependent settings as well. We compare our model with five baseline approaches, including three traditional machine learning methods and two deep learning methods. EF-Net demonstrates superior performance in both accuracy and F1 score, surpassing these baselines. Our model achieves F1 scores of 99.36%, 98.31%, and 65.05% in the subject-dependent, subject-semidependent, and subject-independent settings, respectively, surpassing the best baseline F1 scores by 1.83%, 4.34%, and 2.13% These results highlight EF-Net’s capability to effectively learn and interpret mental states and brain activity across different and unseen subjects.
Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea
Authors: Lee Junghee , Lim Joongbin , Lee Jeongho , Park Juhan , Won Myoungsoo
Published: March 2024

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.
DataMesh+: A Blockchain-Powered Peer-to-Peer Data Exchange Model for Self-Sovereign Data Marketplaces
Authors: Merlec Mpyana Mwamba , In Hoh Peter
Published: March 2024

In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model’s acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance.
Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed
Authors: Hoang Minh Long , Matrella Guido , Ciampolini Paolo
Published: March 2024

This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users’ heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording. A photoplethysmographic sensor is simultaneously checked for ground truth reference. A dataset has been built, by acquiring measures in a real-world set-up: 10 participants were involved, resulting in 120 min of acceleration traces which were utilized to train and evaluate various Artificial Intelligence (AI) algorithms. The experimental analysis utilizes K-fold cross-validation to ensure robust model testing across different subsets of the dataset. Various ML and DL algorithms are compared, each being trained and tested using the collected data. The Random Forest algorithm exhibited the highest accuracy among all compared models. While it requires longer training time compared to some ML models such as Naïve Bayes, Linear Discrimination Analysis, and K-Nearest Neighbour Classification, it keeps substantially faster than Support Vector Machine and Deep Learning models. The Random Forest model demonstrated robust performance metrics, including recall, precision, F1-scores, macro average, weighted average, and overall accuracy well above 90%. The study highlights the better performance of the Random Forest algorithm for the specific use case, achieving superior accuracy and performance metrics in detecting user heartbeats in comparison to other ML and DL models tested. The drawback of longer training times is not too relevant in the long-term monitoring target scenario, so the Random Forest model stands out as a viable solution for real-time ballistocardiographic heartbeat detection, showcasing potential for healthcare and wellness monitoring applications.
Chemical Characterization of Human Body Odor Headspace Components
Authors: Medrano A. Celeste , Cantu Ariela , Aviles-Rosa Edgar O. , Hall Nathaniel J. , Maughan Michele N. , Gadberry Jenna D. , Prada-Tiedemann Paola A.
Published: March 2024

This study focused on evaluating human body odor volatiles using a chamber approach. Ten participants were asked to sit inside the chamber for 1 h, while using SPME as the extraction technique for vapor sampling. A total of 105 compounds were detected across participants, with nonanal having the highest frequency. PCA statistical analysis depicted tighter clustering in female whole-body odor profiles when compared to males, thus corroborating gender odor differences. Concurrently, various biospecimens (hand, axillary, breath) from the same participants allowed for a comparison between whole-body and individual biospecimen odor signatures. When comparing whole-body sampling and distinctive biospecimens, nonanal and decanal were the only odor volatiles shared. Statistical clustering depicted higher similarity within the odor profiles of individual biospecimens compared to odor profiles of the whole body, indicating distinctiveness of the odor chemical landscape as a function of sampling region. Overall, this study demonstrated that SPME-GC/MS methodology was successful in the extraction, detection, and identification of previously reported human scent volatiles when employing the human chamber for whole-body sampling. Our presented testing paradigm allows for a direct comparison of odor volatiles across the full body and specific body locations that allows odor markers to be furthered exploited for diagnostic and biological detection contexts.
“It’s Like Having an Uncontrolled Situation”: Using Body Maps to Understand the Embodied Experiences of People with Hidradenitis Suppurativa, a Chronic Dermatological Condition
Authors: Ingraham Natalie , Duong Kelly , Hann Lena R.
Published: March 2024

Hidradenitis suppurativa (HS) is a chronic, inflammatory, and often debilitating skin condition that includes painful “flares” in the groin, genital, and underarms. (1) Background: Patients with HS have the highest reported mental health comorbidities among dermatological conditions. Qualitative social science research about HS is limited, so this study aimed to understand the lived experiences of people with HS through body mapping. Body mapping is a participatory research process where participants illustrate a drawing of their body with images, symbols, and words that represent their embodied experience. (2) Methods: This study recruited 30 participants from a previous survey about HS experiences. Participants selected from pre-made body silhouettes based on their body shape, illustrated a body map about their HS experience, then shared their body map during in-depth interviews. Interviews and body maps were analyzed with the same codebook created with inductive and deductive codes. (3) Results: The body map drawings yielded rich visual data and the mapping process helped participants express their HS experiences in unique ways that cannot always be captured with textual data alone. (4) Conclusions: This study adds to the limited social science literature about HS and introduces body mapping as a relevant qualitative method for exploring chronic dermatological conditions.
The Application of Environmental, Social and Governance Standards in Operational Risk Management in SSC in Poland
Authors: Zaporowska Zuzanna , Szczepański Marek
Published: March 2024

Organizations are facing increasing pressure to be transparent about their performance and to accept responsibility for their impacts on both society and the environment. The role of ESG is essential from a reporting standpoint. New regulations are forcing organizations to focus more on cascading ESG risk management in order to ensure that the various ESG objectives are analyzed and monitored at the group and subsidiary levels. This article employed the results of an empirical study conducted on risk management concerning shared service centers in Poland. In addition, a case study was conducted based on their internal reports and financial statements. At this stage, SSCs are focusing solely on financial risks and are ignoring the broader perspective. Thus, the promotion of ESG practices in organizations currently represents the most critical factor. ESG-related activities should be cascaded to company subsidiaries, especially those that employ internally separated processes, operate globally and are responsible for end-to-end processes. Based on an analysis of financial statements, few entities currently even consider operational risks, including ESG-related risks. Companies should re-examine their internal governance approach so as to ensure the effective cascading of ESG objectives to the lower levels of the organizational structure.
Adapting Water and Soil Management to Climate Change
Authors: Kourgialas Nektarios N. , Anastopoulos Ioannis , Stefanakis Alexandros
Published: March 2024

Sustainable agriculture largely depends on the stability of the soil ecosystem and the hydrological cycle dynamics [...]
Color Authenticity for the Sustainable Development of Historical Areas: A Case Study of Shiquan
Authors: Miao Mingrui , Feng Lyuhang , Wu Yao , Zhu Rong , Xu Dawei
Published: March 2024

Currently, historical areas in China are facing issues of color authenticity and excessive commercialization. Colors, as an important element of historical areas, reflect their history and culture. However, there are some flaws in the existing research methods, so a more scientific and systematic method is needed to obtain accurate and objective color visualization results to promote the sustainable development of historical areas. In order to explore the methods and applications of visualizing the environmental color patterns of historical areas, Shiquan Street in Suzhou, China, was taken as the research object; ArcGIS 10.5 was used to obtain the street-view photo samples; Matlab was used to summarize the architectural colors of the historical areas; and K-means clustering was used to generate the central color card of the historical areas, thus completing the color visualization of the historical areas, followed by further searching for the color patterns and central colors, so as to explore the strategies for sustainable development of historical areas. The various quantified color models of historical areas can explore the regional uniqueness and color locality of historical areas, which avoids problems such as homogenization of block colors, unclear styles and features, and an excessive proportion of commercial colors caused by poor color guidance and control in the design related to historical areas, providing approaches to the research on the harmonious unity of environmental colors and features related to historical areas. A color-based research method has been proposed to protect and maintain the original appearance of historical areas. The application of this method provides a new perspective and possibility for the protection of historical areas, allowing these areas to achieve historical sustainability while maintaining their historical characteristics and values.
Quantifying Resource Nexus: Virtual Water Flows, Water Stress Indices, and Unsustainable Import Fraction in South Korea’s Grain Trade Landscape
Authors: Odey Golden , Adelodun Bashir , Adeyi Qudus , Hammed Akinsoji Adisa , Abiodun Salau Rahmon , Choi Kyung Sook
Published: March 2024

This study delves into the intricate dynamics of South Korea’s grain production and trade, specifically scrutinizing water resource management and sustainability trends from 1991 to 2022. The primary objective was to assess the temporal evolution of South Korea’s agricultural virtual water trade in maize, soybeans and wheat. Employing the virtual water flows assessment method, water stress index, and an unsustainable import fraction (UIF) analysis, this research aimed to uncover key patterns and dynamics within the agricultural grain trade landscape. Utilizing comprehensive datasets encompassing grain trade, as well as water and land footprint data, this study assessed the intricate interplay between agricultural production, trade, and resource utilization. Results showed a reduction in local production for soybeans and a slight increase in domestic wheat production over the study period. In addition, the study revealed an overall increase in the virtual water imports associated with grain crops, with maize exhibiting the steepest upward trend in comparison with the other grains (wheat and soybeans). Furthermore, the study demonstrated that the import of maize contributed the highest amount of water and land savings, implying that this grain crop had the most significant impact on conserving local water and land resources. Environmental impact assessments, including water stress indices and the unsustainable import fractions, contributed to a comprehensive understanding of grain trade sustainability with concrete result values and insights, highlighting the intricate interplay of international grain trade and local water resource conservation in South Korea. By scrutinizing the virtual land and water dimensions of grain trade, this research offers valuable insights for policymakers and researchers striving to navigate the nexus of agriculture, trade, and resource management. The findings hold significance in the context of ensuring food security, optimizing resource allocation, and fostering sustainable agricultural practices in a dynamically evolving global landscape.
Radon and Its Short-Lived Products in Indoor Air: Present Status and Perspectives
Authors: Vaupotič Janja
Published: March 2024

Initially, basic equations are given to express the activity concentrations and concentrations of potential α-energies of radon (222Rn) and thoron (220Tn) and their short-lived products in indoor air. The appearance of short-lived products as a radioactive aerosol is shown, and the fraction of the unattached products is particularly exposed, a key datum in radon dosimetry. This fundamental part is followed by giving the sources of radon and thoron indoors, and thus, their products, and displaying the dependence of their levels on the ground characteristics, building material and practice, and living–working habits of residents. Substantial hourly, daily, and seasonal changes in their activity concentrations are reviewed, as influenced by meteorological parameters (air temperature, pressure, humidity, and wind speed) and human activity (either by ventilation, air conditioning and air filtration, or by generating aerosol particles). The role of the aerosol particle concentration and their size distribution in the dynamics of radon products in indoor air has been elucidated, focusing on the fraction of unattached products. Intensifying combined monitoring of radon short-lived products and background aerosol would improve radon dosimetry approaches in field and laboratory experiments. A profound knowledge of the influence of meteorological parameters and human activities on the dynamics of the behaviour of radon and thoron accompanied by their products in the air is a prerequisite to managing sustainable indoor air quality and human health.