bluuebunny/crossref_metadata_2025 huggingface dataset:
Macrophyte-Based Assessment of Upland Rivers: Bioindicators and Biomonitors
Authors: Gecheva Gana , Stankova Silviya , Varbanova Evelina , Kaynarova Lidia , Georgieva Deyana , Stefanova Violeta
Published: March 2023

For the first time, a macrophyte-based assessment of ecological status was related to the accumulated heavy metals and trace elements (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn) in aquatic plants. Three moss and two vascular plant species were applied as biomonitors: Fontinalis antipyretica Hedw., Leptodictyum riparium (Hedw.) Warnst., Platyhypnidium riparioides (Hedw.) Dixon, invasive Elodea canadensis Michx., and Myriophyllum spicatum L. Three streams were assessed as good at a high ecological status which correlated with low contamination based on calculated contamination factors (CFs) and metal pollution index (MPI). Two sites evaluated in moderate ecological status were revealed to be in heavy trace element contamination. The most significant was the accumulation of moss samples from the Chepelarska River under mining impact. Mercury exceeded the environmental quality standard (EQS) for biota in three of the studied upland river sites.
Effect of Potassium Ferrate as a Dewatering Conditioner on Sludge Pyrolysis Characteristics and the Releasing Characteristics of Nitrogen, Sulfur, and Chlorine during Sewage Sludge Pyrolysis
Authors: Zhang Hua , Tao Wenpan , Hou Mingming , Ran Maoqian , Chen Chi , Liu Jingcui , Tu Liang , Huang Lan , Deng Peiyao , Chen Dan , Wu Yan
Published: March 2023

Sludge pyrolysis is a promising method for treating excess sludge as a by-product of municipal sewage plants, allowing for energy self-sufficiency and resource recovery. Before sludge pyrolysis begins, a few conditioning agents are added to the sludge that promote sludge dewatering. Potassium ferrate (K2FeO4) is applied as a conditioning agent with both cracking and flocculation effects, but the effects of K2FeO4 on the release characteristics of nitrogen, sulfur, and chlorine during sludge pyrolysis have not been elucidated. In this study, we analyzed the sludge pyrolysis characteristics and chemical state changes of N, S, and Cl contaminants in the dewatered sludge after K2FeO4 conditioning before and after pyrolysis. Further, the release characteristics of condensable/noncondensable gases during pyrolysis were assessed using thermogravimetric mass spectrometry (TG-MS) and pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) analyses before and after conditioning. We found that potassium pertechnetate reduced the activation energy required for the sludge in the pyrolysis process. Noticeably this process made the sludge more susceptible to thermal decomposition leading to volatile production and also influenced the release of different contaminants generated by the pyrolysis process. Moreover, K2FeO4 promoted the release of C/H/O gases and reduced the release of N/S/Cl pollutant gases from the sludge. Overall, this study provides a theoretical basis for the selection of conditioning agents for the sludge conditioning and dewatering steps during the sludge pyrolysis process.
God of Montaigne, Spinoza, and Derrida—The Marrano (Crypto)Theology of Survival
Authors: Bielik-Robson Agata
Published: March 2023

In this essay I offer an outline of a theology of survival as it emerges from the writings of the three modern Marrano thinkers: Michel de Montaigne, Baruch Spinoza, and Jacques Derrida. I will argue that, in their thought which is deeply concerned with the apology of life, the Marrano choice of living on over the martyrological death becomes affirmed as the right thing to do despite the price of forced conversion—and that this choice, once reflected and accepted, modifies the Jewish doctrine of life (torat hayim), by adding to it a new messianic dimension. In my interpretation, the Marranos will emerge as the agents of the messianic inversion, leading from the tragic predicament of the victims of coercion to the radical hope of the “rejected stones” and capable of once again reinventing and rejuvenating the messianic message of the Abrahamic religions, conceived as God’s superior commandment to choose life. From Montaigne, through Spinoza, to Derrida, life understood primarily as survival becomes an object of a new affirmation: it begins to glow as a secret treasure of Judaism which the Marranos simultaneously left behind and preserved in a new messianic-universal form.
A Bridge to Nowhere: Enabling Autonomy in a Case of Failed ECMO Rescue of Bleomycin-Induced Pulmonary Toxicity
Authors: Hall James , Khilkin Michael , Murphy Sara , Botros George
Published: March 2023

Extracorporeal membrane oxygenation (ECMO) can be a life-saving intervention in cases of potentially reversible refractory respiratory failure. One such indication can be bleomycin-induced lung injury. However, in some cases, the injury can be so severe that it becomes irreversible and creates complex medical decisions regarding life support and the continuation of care when no additional therapeutic options are feasible, particularly in cases of patients who were young and fully functional prior to an acute illness. In cases of full pulmonary replacement with mechanical support and the degree of functionality that can be attained utilizing modalities such as ECMO can obscure the true severity of illness and make end-of-life decisions significantly harder for families and caregivers.
Laser Scanning and Ambient Vibration Study of Juma Mosque in Khiva (Uzbekistan) with Subsequent Finite Element Modeling of Its Minaret
Authors: Takhirov Shakhzod , Rakhmonov Bakhodir , Nafasov Ravshanbek , Samandarov Abbos , Sultanova Sevara
Published: March 2023

This paper presents the results of a multiyear program conducted in Khiva, by a research team from the University of California, Berkeley (USA) and Urgench State University, Urgench (Uzbekistan). It was focused on the Juma (Djuma) Mosque in Itchan Kala (Khiva, Uzbekistan). The main objective was to generate a digital twin of the mosque, with an accuracy of a few millimeters, by utilizing a laser scanner. The idea of a digital twin was expanded further, to ensure that the physical properties and structural response of the digital twin were closely correlated to that of the actual object. To achieve this objective, the following was conducted. First, a laser scanning of the historic monument was conducted. The laser scans were collected by a terrestrial laser scanner. Subsequently, a study of the monument’s structural response was conducted in ambient vibration tests that focused on measuring the resonant frequencies of the mosque’s minaret. Based on all of the information collected during both field studies, a sophisticated finite element model of the minaret was developed. The calibration of the model was based on the results of the ambient vibration study. The performance of the model was shown to be close to that of the actual monument. The digital twin and associated numerical model will be used in structural health monitoring, numerical predictions of the structural performance, and in the development of restoration strategies.
Autonomous Detection of Mouse-Ear Hawkweed Using Drones, Multispectral Imagery and Supervised Machine Learning
Authors: Amarasingam Narmilan , Hamilton Mark , Kelly Jane E. , Zheng Lihong , Sandino Juan , Gonzalez Felipe , Dehaan Remy L. , Cherry Hillary
Published: March 2023

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation.
Detection of Artificial Seed-like Objects from UAV Imagery
Authors: Bomantara Yanuar A. , Mustafa Hasib , Bartholomeus Harm , Kooistra Lammert
Published: March 2023

In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.
Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm
Authors: Uribeetxebarria Asier , Castellón Ander , Aizpurua Ana
Published: March 2023

Accurately estimating wheat yield is crucial for informed decision making in precision agriculture (PA) and improving crop management. In recent years, optical satellite-derived vegetation indices (Vis), such as Sentinel-2 (S2), have become widely used, but the availability of images depends on the weather conditions. For its part, Sentinel-1 (S1) backscatter data are less used in agriculture due to its complicated interpretation and processing, but is not impacted by weather. This study investigates the potential benefits of combining S1 and S2 data and evaluates the performance of the categorical boosting (CatBoost) algorithm in crop yield estimation. The study was conducted utilizing dense yield data from a yield monitor, obtained from 39 wheat (Triticum spp. L.) fields. The study analyzed three S2 images corresponding to different crop growth stages (GS) GS30, GS39-49, and GS69-75, and 13 Vis commonly used for wheat yield estimation were calculated for each image. In addition, three S1 images that were temporally close to the S2 images were acquired, and the vertical-vertical (VV) and vertical-horizontal (VH) backscatter were calculated. The performance of the CatBoost algorithm was compared to that of multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) algorithms in crop yield estimation. The results showed that the combination of S1 and S2 data with the CatBoost algorithm produced a yield prediction with a root mean squared error (RMSE) of 0.24 t ha−1, a relative RMSE (rRMSE) 3.46% and an R2 of 0.95. The result indicates a decrease of 30% in RMSE when compared to using S2 alone. However, when this algorithm was used to estimate the yield of a whole plot, leveraging information from the surrounding plots, the mean absolute error (MAE) was 0.31 t ha−1 which means a mean error of 4.38%. Accurate wheat yield estimation with a spatial resolution of 10 m becomes feasible when utilizing satellite data combined with CatBoost.
Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review
Authors: Ali Muhammad Haris , Popescu Ioana , Jonoski Andreja , Solomatine Dimitri P.
Published: March 2023

This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (<10 km2), meso-scale (10 km2–1000 km2), and macro-scale (>1000 km2). The analysis included a search for the relation between the use of these datasets to different regions and the geographical scale at which they are most widely used. Additionally, co-authorship analysis was performed on the articles to identify the collaboration patterns among researchers. The study further categorized the analysis based on the type of datasets, including rainfall, digital elevation model, land use, soil distribution, leaf area index, snow-covered area, evapotranspiration, soil moisture and temperature. The research concluded by identifying knowledge gaps in the use of each data type at different scales and highlighted the varying performance of datasets across different locations. The findings underscore the importance of selecting the right datasets, which has a significant impact on the accuracy of hydrological models. This study provides valuable insights into the use of remote sensed and/or global datasets in hydrological modelling, and the identified knowledge gaps can inform future research directions.
Major Elements Concentrations in Chang’E-3 Landing Site from Active Particle-Induced X-ray Spectrometer
Authors: Ng Man-Hei , Zhang Xiaoping , Xu Yi , Li Liansheng
Published: March 2023

On 14 December 2013 (UTC), China’s Chang’E-3 (CE-3) succeeded in landing on the Moon’s surface. The CE-3 landing site is in northern Mare Imbrium and several tens of meters away from the rim of a young crater with a few hundred meters in diameter. In-situ measurements of lunar soil around the roving area were conducted from Active Particle-induced X-ray Spectrometer (APXS) onboard Yutu rover. Three relatively young lunar soil samples in the CE-3 landing site were investigated. Previous studies suggested that these samples are a new type of basalt, not discovered yet in previous missions before the CE-3 in-situ measurements. It plays an essential role in promoting the understanding of lunar volcanic history. However, their results are deviated, and thus scrutinizing the data as per our optimized model to derive a more precise result is of necessity. In this paper, we present an optimized model for data analysis based on APXS measurements to derive the major elements concentrations. The optimized model has the advantages of reliability and being independent of calibration by ground standards. The particle size effect is applied in lunar X-ray fluorescence modeling for correction, improving the accuracy in determining the elemental concentrations for the actual measurement. Our results are distinct in the correlation plots by carrying out a comparison with previous lunar regolith samples from Apollo, Luna, and Chang’E-5 missions, indicating that the CE-3 landing site is a new region apart from previous in-situ or laboratory detection prior to the CE-3 measurements. It suggests a kind of young mare basalt with unusual petrological characteristics compared with previous samples and similar geochemical properties of CE-3 landing site and western Procellarum and Imbrium (WPI), with a signature of western Procellarum.
Land Subsidence in Tianjin, China: Before and after the South-to-North Water Diversion
Authors: Yu Xiao , Wang Guoquan , Hu Xie , Liu Yuhao , Bao Yan
Published: March 2023

The South-to-North Water Diversion (SNWD) is a multi-decadal infrastructure project in China aimed at alleviating severe water shortages in north China. It has imposed broad social, economic, environmental, and ecological impacts since 2015, particularly in the Beijing-Tianjin metropolitan area. Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) (2014–2021), Global Positioning System (GPS) (2010–2021), and hydraulic-head data are used to assess the impacts on ongoing land subsidence in Tianjin in this study. Additionally, the Principal Component Analysis (PCA) is employed to highlight primary factors controlling the recent land subsidence. Our results show that the reduced groundwater pumping has slowed down the overall subsidence since 2019 due to SNWD. As of 2021, the subsiding area (>5 mm/year) has reduced to about 5400 km2, approximately 85% of the subsiding area before SNWD; the areas of rapid subsidence (>30 mm/year) and extremely rapid subsidence (>50 mm/year) have reduced to 1300 km2 and 280 km2, respectively, approximately 70% and 60% of the areas before SNWD. Recent subsidence (2016–2021) was primarily contributed by the inelastic compaction of clays in deep aquifers of Aquifers III and IV ranging from approximately 200 to 450 m below the land surface. The ongoing rapid subsidence (>30 mm/year) in Tianjin is limited to border areas adjacent to large industrial cities (e.g., Langfang, Tanshan, Cangzhou) in Hebei Province. Ongoing subsidence will cease when hydraulic heads in the deep Aquifers (IV and V) recover to the new pre-consolidation head, approximately 45 m below the land surface, and subsidence will not be reinitiated as long as the hydraulic heads remain above the new pre-consolidation head. This study reveals the importance of coordinating groundwater and surface water uses at local, regional, and national scales for land subsidence mitigation.
Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany
Authors: Dhillon Maninder Singh , Dahms Thorsten , Kübert-Flock Carina , Liepa Adomas , Rummler Thomas , Arnault Joel , Steffan-Dewenter Ingolf , Ullmann Tobias
Published: March 2023

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.
A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning
Authors: Pásztor Marcell , Czanik Csenge , Bondár István
Published: March 2023

Since various phenomena produce infrasound, including both man-made and natural sources, the ever-growing dataflow demands automatic processes via machine learning for signal classification. In this study, we demonstrate a single array approach at the Piszkés-tető (PSZI) infrasound array. Our dataset contains nearly 14,000 manually categorized infrasound detections, processed with the progressive multi channel correlation (PMCC) algorithm from three different sources, such as quarry blasts, storms and signals from a power plant. The dataset was split into a training, a validation and a test subset. Time and frequency domain features as well as PMCC-related features were extracted. Three additional PMCC-related features were constructed in a way to measure the similarity between detections and to be used in single array monitoring. Two different classifiers, support vector machine and random forest, were used for training. Training was performed with three-fold cross validation with grid search. The classifiers were tuned on the training and validation set using the f1 metric (harmonic mean of positive predictive value and true positive rate). Training, validation and testing were performed with and without our three new features that measure similarity between the detections in order to assess their importance in single array monitoring. The selected classifiers reached f1 scores between 0.88 and 0.93. Our results show a promising step toward automatic infrasound event classification.
Study of Ionospheric Bending Angle and Scintillation Profiles Derived by GNSS Radio-Occultation with MetOp-A Satellite
Authors: Prol Fabricio S. , Hoque M. Mainul , Hernández-Pajares Manuel , Yuan Liangliang , Olivares-Pulido Germán , von Engeln Axel , Marquardt Christian , Notarpietro Riccardo
Published: March 2023

In this work, a dedicated campaign by MetOp-A satellite is conducted to monitor the ionosphere based on radio-occultation (RO) measurements provided by the onboard GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding (GRAS). The main goal is to analyze the capabilities of the collected data to represent the bending angle and scintillation profiles of the ionosphere. We compare the MetOp-A products with those generated by other RO missions and explore the spatial/temporal distributions sensed by the MetOp-A campaign. Validation of dual frequency bending angles at the RO tangent points, S4 index, and Rate of the Total electron content Index (ROTI) is performed against independent products from Fengyun-3D and FORMOSAT-7/COSMIC-2 satellites. Our main findings constitute the following: (1) bending angle profiles from MetOp-A agree well with Fengyun-3D measurements; (2) bending angle distributions show a typical S-shape variation along the altitudes; (3) signatures of the sporadic E-layer and equatorial ionization anomaly crests are observed by the bending angles; (4) sharp transitions are observed in the bending angle profiles above ~200 km due to the transition of the daytime/nighttime in addition to the transition of the bottom-side/top-side; and (5) sporadic E-layer signatures are observed in the S4 index distributions by MetOp-A and FORMOSAT-7/COSMIC-2, with expected differences in magnitudes between the GPS (Global Positioning System) L1 and L2 frequencies.
Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms
Authors: Ullah Niamat , Ahmed Zahoor , Kim Jong-Myon
Published: March 2023

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform.
Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks
Authors: Guo Zhihui , Chen Hongbin , Li Shichao
Published: March 2023

Emergency event monitoring is a hot topic in wireless sensor networks (WSNs). Benefiting from the progress of Micro-Electro-Mechanical System (MEMS) technology, it is possible to process emergency events locally by using the computing capacities of redundant nodes in large-scale WSNs. However, it is challenging to design a resource scheduling and computation offloading strategy for a large number of nodes in an event-driven dynamic environment. In this paper, focusing on cooperative computing with a large number of nodes, we propose a set of solutions, including dynamic clustering, inter-cluster task assignment and intra-cluster one-to-multiple cooperative computing. Firstly, an equal-size K-means clustering algorithm is proposed, which activates the nodes around event location and then divides active nodes into several clusters. Then, through inter-cluster task assignment, every computation task of events is alternately assigned to the cluster heads. Next, in order to make each cluster efficiently complete the computation tasks within the deadline, a Deep Deterministic Policy Gradient (DDPG)-based intra-cluster one-to-multiple cooperative computing algorithm is proposed to obtain a computation offloading strategy. Simulation studies show that the performance of the proposed algorithm is close to that of the exhaustive algorithm and better than other classical algorithms and the Deep Q Network (DQN) algorithm.
A System of Emotion Recognition and Judgment and Its Application in Adaptive Interactive Game
Authors: Lin Wenqian , Li Chao , Zhang Yunjian
Published: March 2023

A system of emotion recognition and judgment (SERJ) based on a set of optimal signal features is established, and an emotion adaptive interactive game (EAIG) is designed. The change in a player’s emotion can be detected with the SERJ during the process of playing the game. A total of 10 subjects were selected to test the EAIG and SERJ. The results show that the SERJ and designed EAIG are effective. The game adapted itself by judging the corresponding special events triggered by a player’s emotion and, as a result, enhanced the player’s game experience. It was found that, in the process of playing the game, a player’s perception of the change in emotion was different, and the test experience of a player had an effect on the test results. A SERJ that is based on a set of optimal signal features is better than a SERJ that is based on the conventional machine learning-based method.
Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems
Authors: Alharbi Hatem A. , Aldossary Mohammad , Almutairi Jaber , Elgendy Ibrahim A.
Published: March 2023

Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be addressed. Therefore, in this paper, to bypass the limited bandwidth and address the potential security threats challenge, a new compression, security, and energy-aware task offloading framework is proposed for the ECC system environment. Specifically, we first introduce an efficient layer of compression to smartly reduce the transmission data over the channel. In addition, to address the security issue, a new layer of security based on an Advanced Encryption Standard (AES) cryptographic technique is presented to protect offloaded and sensitive data from different vulnerabilities. Subsequently, task offloading, data compression, and security are jointly formulated as a mixed integer problem whose objective is to reduce the overall energy of the system under latency constraints. Finally, simulation results reveal that our model is scalable and can cause a significant reduction in energy consumption (i.e., 19%, 18%, 21%, 14.5%, 13.1% and 12%) with respect to other benchmarks (i.e., local, edge, cloud and further benchmark models).
Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis
Authors: Han Shuai , Wang Bowen , Liao Sizhuo , Gao Fei , Chen Mo
Published: March 2023

A transformer’s acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on the transformer end pad falling defect to realize defect identification. Firstly, a quality–spring–damping model is established to analyze the vibration modes and development patterns of the defect. Secondly, short-time Fourier transform is applied to the voiceprint signals, and the time–frequency spectrum is compressed and perceived using Mel filter banks. Thirdly, the time-series spectrum entropy feature extraction algorithm is introduced into the stability calculation, and the algorithm is verified by comparing it with simulated experimental samples. Finally, stability calculations are performed on the voiceprint signal data collected from 162 transformers operating in the field, and the stability distribution is statistically analyzed. The time-series spectrum entropy stability warning threshold is given, and the application value of the threshold is demonstrated by comparing it with actual fault cases.
Identification of Homogeneous Subgroups from Resting-State fMRI Data
Authors: Yang Hanlu , Vu Trung , Long Qunfang , Calhoun Vince , Adali Tülay
Published: March 2023

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.
Evaluation of the Properties of Eddy Current Sensors Based on Their Equivalent Parameters
Authors: Dziczkowski Leszek , Tytko Grzegorz
Published: March 2023

This paper presents a practical way of using the method of evaluating the metrological properties of eddy current sensors. The idea of the proposed approach consists of employing a mathematical model of an ideal filamentary coil to determine equivalent parameters of the sensor and sensitivity coefficients of tested physical quantities. These parameters were determined on the basis of the measured value of the real sensor’s impedance. The measurements were carried out with an air-core sensor and an I-core sensor while they were positioned at different distances from the surface of tested copper and bronze plates. An analysis of the influence of the coil’s position in relation to the I core on the equivalent parameters was also carried out, and the interpretation of the results obtained for various sensor configurations was presented in a graphical form. When equivalent parameters and sensitivity coefficients of examined physical quantities are known, it is possible to compare even very different sensors with the employment of one measure. The proposed approach makes it possible to make a significant simplification of the mechanisms of calibration of conductometers and defectoscopes, computer simulation of eddy current tests, creating the scale of a measuring device, and designing sensors.
A Feasibility Study on Monitoring Earthquake-Caused Furniture Vibrations Using Radiofrequency Identification Sensor Tags
Authors: Song Zequn , Rahmadya Budi , Sun Ran , Takeda Shigeki
Published: March 2023

This paper presents a feasibility study on monitoring earthquake-caused furniture vibrations using radiofrequency identification (RFID) sensor tags. Finding unstable objects by exploiting the vibrations caused by weaker earthquakes is effective as one of the potential countermeasures for large-scale earthquakes in earthquake-prone areas. For this purpose, a previously proposed ultrahigh-frequency (UHF)-band RFID-based batteryless vibration/physical shock sensing system enabled long-term monitoring. This RFID sensor system introduced standby and active modes for long-term monitoring. This system enabled lower-cost wireless vibration measurements without affecting the vibration of furniture because the RFID-based sensor tags provide lightweight, low-cost, and battery-free operations. This RFID sensor system observed earthquake-cased furniture vibrations in a room on the fourth floor of a building eight stories high at Ibaraki University, Hitachi, Ibaraki, Japan. The observation results revealed that the RFID sensor tags identified the vibrations of furniture caused by earthquakes. The RFID sensor system also observed the vibration duration times of the objects in a room and specified the most unstable reference object. Hence, the proposed vibration sensing system helped achieve safe living in indoor environments.
Highly Sensitive Capacitive MEMS for Photoacoustic Gas Trace Detection
Authors: Seoudi Tarek , Charensol Julien , Trzpil Wioletta , Pages Fanny , Ayache Diba , Rousseau Roman , Vicet Aurore , Bahriz Michael
Published: March 2023

An enhanced MEMS capacitive sensor is developed for photoacoustic gas detection. This work attempts to address the lack of the literature regarding integrated and compact silicon-based photoacoustic gas sensors. The proposed mechanical resonator combines the advantages of silicon technology used in MEMS microphones and the high-quality factor, characteristic of quartz tuning fork (QTF). The suggested design focuses on a functional partitioning of the structure to simultaneously enhance the collection of the photoacoustic energy, overcome viscous damping, and provide high nominal capacitance. The sensor is modeled and fabricated using silicon-on-insulator (SOI) wafers. First, an electrical characterization is performed to evaluate the resonator frequency response and nominal capacitance. Then, under photoacoustic excitation and without using an acoustic cavity, the viability and the linearity of the sensor are demonstrated by performing measurements on calibrated concentrations of methane in dry nitrogen. In the first harmonic detection, the limit of detection (LOD) is 104 ppmv (for 1 s integration time), leading to a normalized noise equivalent absorption coefficient (NNEA) of 8.6 ⋅ 10−8 Wcm−1 Hz−1/2, which is better than that of bare Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS), a state-of-the-art reference to compact and selective gas sensors.
NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform
Authors: Mitsopoulos Konstantinos , Fiska Vasiliki , Tagaras Konstantinos , Papias Athanasios , Antoniou Panagiotis , Nizamis Konstantinos , Kasimis Konstantinos , Sarra Paschalina-Danai , Mylopoulou Diamanto , Savvidis Theodore , Praftsiotis Apostolos , Arvanitidis Athanasios , Lyssas George , Chasapis Konstantinos , Moraitopoulos Alexandros , Astaras Alexander , Bamidis Panagiotis D. , Athanasiou Alkinoos
Published: March 2023

Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. Results: Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. Conclusions: Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.
LC-MS/MS and GC-MS Analysis for the Identification of Bioactive Metabolites Responsible for the Antioxidant and Antibacterial Activities of Lygodium microphyllum (Cav.) R. Br.
Authors: Teoh Wuen Yew , Yong Yoong Soon , Razali Faizan Naeem , Stephenie Sarah , Dawood Shah Muhammad , Tan Jen Kit , Gnanaraj Charles , Mohd Esa Norhaizan
Published: March 2023

Natural products serve as a valuable source of antioxidants with potential health benefits for various conditions. Lygodium microphyllum (Cav.) R. Br., also known as Old World climbing fern, is an invasive climbing fern native to Southeast Asia, Africa, South America, Australia, and Melanesia. It has been reported to possess interesting pharmacological properties including hepatoprotective and anti-inflammatory mechanisms. This study analyzed the potential bioactive metabolites that contribute to the antioxidant and antimicrobial effects of L. microphyllum (LM) by profiling the crude extract using high-resolution LC-MS/MS and GC-MS systems. Several classes of compounds such as phenolics, flavonoids, terpenoids, steroids, macrolides, vitamins, lipids, and other hydrocarbons were found in the crude extract of LM through non-targeted analysis. A total of 74 compounds were detected in LC-MS/MS, whereas a total of nine compounds were identified in GC-MS. Out of the 74 compounds detected in LC-MS/MS, 34 compounds, primarily quercetin, kaempferol, trifolin, pyroglutamic acid, arachidonic acid, and rutin were reported with antioxidant, antimicrobial, anti-inflammatory, and hepatoprotective activities. The presence of phenolic and flavonoid compounds with reported bioactivities in the crude extract of LM evidence its pharmacological properties.
Design and Implementation of a Real-Time Crowd Monitoring System Based on Public Wi-Fi Infrastructure: A Case Study on the Sri Chiang Mai Smart City
Authors: Wiangwiset Thalerngsak , Surawanitkun Chayada , Wongsinlatam Wullapa , Remsungnen Tawun , Siritaratiwat Apirat , Srichan Chavis , Thepparat Prachya , Bunsuk Weerasak , Kaewchan Aekkaphan , Namvong Ariya
Published: March 2023

The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing.
European Union Smart Mobility–Aspects Connected with Bike Road System’s Extension and Dissemination
Authors: Wolniak Radosław
Published: March 2023

The analysis of scientific research described in peer-reviewed journals demonstrates the significance of bicycle road networks in relation to smart mobility. This research was conducted for European Union cities that meet the criteria of having a developed bicycle route network and are ranked among the top 100 European bicycle-friendliest cities in 2021. The study also analyzed whether each city was on national or international lists of smart cities or had smart city initiatives. The study indicates that a comprehensive and well-developed network of bicycle paths is a crucial element in the development of smart mobility solutions within a modern smart city. Among EU countries, cities in northern and central Europe, particularly in Germany, Sweden, Finland, Denmark, and the Netherlands, have the best-developed bicycle networks. The research on the correlation between the average temperature level in a country and the average values for smart city bike lanes showed a statistically significant negative correlation between the two variables. The lower a country’s average annual temperature, the more that a bicycle infrastructure is present in its smart cities, as measured by the length of bicycle paths and the saturation of the city with bicycle paths per square kilometer and per 1000 residents. After removing outliers (Finland, Ireland, and Luxembourg), a significant relationship was observed between the wealth of a country and the length and density of bicycle paths in smart cities. The linear correlation coefficient between the length of bicycle roads and GDP per capita was found to be −0.73, which is a high coefficient value.