bluuebunny/crossref_metadata_2025 huggingface dataset:
Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster
Authors: Cheng Deqiang , Cui Yifei , Li Zhenhong , Iqbal Javed
Published: May 2021

A catastrophic tailings dam failure disaster occurred in Brumadinho, Brazil on 25 January 2019, which resulted in over 270 casualties, 24,000 residents evacuated, and a huge economic loss. Environmental concerns were raised for the potential pollution of water due to tailings waste entering the Paraopeba River. In this paper, a detailed analysis has been carried out to investigate the disaster conditions of the Brumadinho dam failure using satellite images with different spatial resolutions. Our in-depth analysis reveals that the hazard chain caused by this failure contained three stages, namely dam failure, mudflow, and the hyperconcentrated flow in the Paraopeba River. The variation characteristics of turbidity of the Rio Paraopeba River after the disaster have also been investigated using high-resolution remote sensing images, followed by a qualitative analysis of the impacts on the downstream reservoir of the Retiro Baixo Plant that was over 300 km away from the dam failure origin. It is believed that, on the one hand, the lack of dam stability management at the maintenance stage was the main cause of this disaster. On the other hand, the abundant antecedent precipitation caused by extreme weather events should be a critical triggering factor. Furthermore, the spatiotemporal pattern mining of global tailings dam failures revealed that the Brumadinho dam disaster belonged to a Consecutive Hot Spot area, suggesting that the regular drainage inspection, risk assessment, monitoring, and early warning of tailings dam in Consecutive Hot Spot areas still need to be strengthened for disaster mitigation.
Identify Informative Bands for Hyperspectral Target Detection Using the Third-Order Statistic
Authors: Geng Xiurui , Wang Lei , Ji Luyan
Published: May 2021

Constrained energy minimization (CEM) has been proposed and widely researched in the field of hyperspectral target detection. Generally, it selects one of the target spectra as the representative and then keeps its output constant while minimizing the average filter output energy of the data. However, it has been proven that as the number of bands (L) increases, CEM will gradually lower the average filter output energy when keeping the representative’s output constant. Unavoidably, due to the inherent spatial and temporal variation of the spectra, this will lead to an unreasonable phenomenon, i.e., if L is particularly large, when adding more bands, CEM will suppress more and more pixels, even including the target pixels. This means that the optimal solution of CEM may not correspond to the target detection result that we desire. To deal with this, in this paper, we introduce the third-order statistic (skewness) of the CEM model, served as an auxiliary index to determine whether a band is beneficial to target detection or not. Theoretically, we prove that the skewness index can always exclude the noisy bands with Gaussian distribution. In addition, experiments on several widely used remote sensing data indicate that the index can also efficiently identify informative bands for a better target detection performance.
Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images: Demonstration with Perseverance Rover Landing Site and Mars Science Targets
Authors: Tao Yu , Conway Susan J. , Muller Jan-Peter , Putri Alfiah R. D. , Thomas Nicolas , Cremonese Gabriele
Published: May 2021

The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4–5 m/pixel spatial resolution. Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted. In this work, we propose a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images, and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over 8 science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface. Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.
Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring
Authors: Lee Soo-Jin , Kim Nari , Lee Yangwon
Published: May 2021

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.
Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm
Authors: Yin Xiaoyan , Hu Zhiqun , Zheng Jiafeng , Li Boyong , Zuo Yuanyuan
Published: May 2021

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.
Incremental Learning in Modelling Process Analysis Technology (PAT)—An Important Tool in the Measuring and Control Circuit on the Way to the Smart Factory
Authors: Choudhary Shivani , Herdt Deborah , Spoor Erik , García Molina José Fernando , Nachtmann Marcel , Rädle Matthias
Published: May 2021

To meet the demands of the chemical and pharmaceutical process industry for a combination of high measurement accuracy, product selectivity, and low cost of ownership, the existing measurement and evaluation methods have to be further developed. This paper demonstrates the attempt to combine future Raman photometers with promising evaluation methods. As part of the investigations presented here, a new and easy-to-use evaluation method based on a self-learning algorithm is presented. This method can be applied to various measurement methods and is carried out here using an example of a Raman spectrometer system and an alcohol-water mixture as demonstration fluid. The spectra’s chosen bands can be later transformed to low priced and even more robust Raman photometers. The evaluation method gives more precise results than the evaluation through classical methods like one primarily used in the software package Unscrambler. This technique increases the accuracy of detection and proves the concept of Raman process monitoring for determining concentrations. In the example of alcohol/water, the computation time is less, and it can be applied to continuous column monitoring.
An Evaluation of Motion Trackers with Virtual Reality Sensor Technology in Comparison to a Marker-Based Motion Capture System Based on Joint Angles for Ergonomic Risk Assessment
Authors: Vox Jan P. , Weber Anika , Wolf Karen Insa , Izdebski Krzysztof , Schüler Thomas , König Peter , Wallhoff Frank , Friemert Daniel
Published: May 2021

The reproduction and simulation of workplaces, and the analysis of body postures during work processes, are parts of ergonomic risk assessments. A commercial virtual reality (VR) system offers the possibility to model complex work scenarios as virtual mock-ups and to evaluate their ergonomic designs by analyzing motion behavior while performing work processes. In this study a VR tracking sensor system (HTC Vive tracker) combined with an inverse kinematic model (Final IK) was compared with a marker-based optical motion capture system (Qualisys). Marker-based optical motion capture systems are considered the gold standard for motion analysis. Therefore, Qualisys was used as the ground truth in this study. The research question to be answered was how accurately the HTC Vive System combined with Final IK can measure joint angles used for ergonomic evaluation. Twenty-six subjects were observed simultaneously with both tracking systems while performing 20 defined movements. Sixteen joint angles were analyzed. Joint angle deviations between ±6∘ and ±42∘ were identified. These high deviations must be considered in ergonomic risk assessments when using a VR system. The results show that commercial low-budget tracking systems have the potential to map joint angles. Nevertheless, substantial weaknesses and inaccuracies in some body regions must be taken into account. Recommendations are provided to improve tracking accuracy and avoid systematic errors.
Improved Data Association of Hypothesis-Based Trackers Using Fast and Robust Object Initialization
Authors: Dolatabadi Marzieh , Elfring Jos , van de Molengraft René
Published: May 2021

The tracking of Vulnerable Road Users (VRU) is one of the vital tasks of autonomous cars. This includes estimating the positions and velocities of VRUs surrounding a car. To do this, VRU trackers must utilize measurements that are received from sensors. However, even the most accurate VRU trackers are affected by measurement noise, background clutter, and VRUs’ interaction and occlusion. Such uncertainties can cause deviations in sensors’ data association, thereby leading to dangerous situations and potentially even the failure of a tracker. The initialization of a data association depends on various parameters. This paper proposes steps to reveal the trade-offs between stochastic model parameters to improve data association’s accuracy in autonomous cars. The proposed steps can reduce the number of false tracks; besides, it is independent of variations in measurement noise and the number of VRUs. Our initialization can reduce the lag between the first detection and initialization of the VRU trackers. As a proof of concept, the procedure is validated using experiments, simulation data, and the publicly available KITTI dataset. Moreover, we compared our initialization method with the most popular approaches that were found in the literature. The results showed that the tracking precision and accuracy increase to 3.6% with the proposed initialization as compared to the state-of-the-art algorithms in tracking VRU.
Microwave Sensors for In Situ Monitoring of Trace Metals in Polluted Water
Authors: Frau Ilaria , Wylie Stephen , Byrne Patrick , Onnis Patrizia , Cullen Jeff , Mason Alex , Korostynska Olga
Published: May 2021

Thousands of pollutants are threatening our water supply, putting at risk human and environmental health. Between them, trace metals are of significant concern, due to their high toxicity at low concentrations. Abandoned mining areas are globally one of the major sources of toxic metals. Nowadays, no method can guarantee an immediate response for quantifying these pollutants. In this work, a novel technique based on microwave spectroscopy and planar sensors for in situ real-time monitoring of water quality is described. The sensors were developed to directly probe water samples, and in situ trial measurements were performed in freshwater in four polluted mining areas in the UK. Planar microwave sensors were able to detect the water pollution level with an immediate response specifically depicted at three resonant peaks in the GHz range. To the authors’ best knowledge, this is the first time that planar microwave sensors were tested in situ, demonstrating the ability to use this method for classifying more and less polluted water using a multiple-peak approach.
A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City
Authors: Abdeen Mohammad A. R. , Nemer Ibrahim A. , Sheltami Tarek R.
Published: May 2021

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.
Advanced Solid State Nano-Electrochemical Sensors and System for Agri 4.0 Applications
Authors: Seymour Ian , Narayan Tarun , Creedon Niamh , Kennedy Kathleen , Murphy Aidan , Sayers Riona , Kennedy Emer , O’Connell Ivan , Rohan James F. , O’Riordan Alan
Published: May 2021

Global food production needs to increase in order to meet the demands of an ever growing global population. As resources are finite, the most feasible way to meet this demand is to minimize losses and improve efficiency. Regular monitoring of factors like animal health, soil and water quality for example, can ensure that the resources are being used to their maximum efficiency. Existing monitoring techniques however have limitations, such as portability, turnaround time and requirement for additional reagents. In this work, we explore the use of micro- and nano-scale electrode devices, for the development of an electrochemical sensing platform to digitalize a wide range of applications within the agri-food sector. With this platform, we demonstrate the direct electrochemical detection of pesticides, specifically clothianidin and imidacloprid, with detection limits of 0.22 ng/mL and 2.14 ng/mL respectively, and nitrates with a detection limit of 0.2 µM. In addition, interdigitated electrode structures also enable an in-situ pH control technique to mitigate pH as an interference and modify analyte response. This technique is applied to the analysis of monochloramine, a common water disinfectant. Concerning biosensing, the sensors are modified with bio-molecular probes for the detection of both bovine viral diarrhea virus species and antibodies, over a range of 1 ng/mL to 10 µg/mL. Finally, a portable analogue front end electronic reader is developed to allow portable sensing, with control and readout undertaken using a smart phone application. Finally, the sensor chip platform is integrated with these electronics to provide a fully functional end-to-end smart sensor system compatible with emerging Agri-Food digital decision support tools.
Polyvinyl Chloride Modified Carbon Paste Electrodes for Sensitive Determination of Levofloxacin Drug in Serum, Urine, and Pharmaceutical Formulations
Authors: Abdel-Haleem Fatehy M. , Mahmoud Sonia , Abdel-Ghani Nour Eldin T. , El Nashar Rasha Mohamed , Bechelany Mikhael , Barhoum Ahmed
Published: May 2021

Levofloxacin (LF) is a medically important antibiotic drug that is used to treat a variety of bacterial infections. In this study, three highly sensitive and selective carbon paste electrodes (CPEs) were fabricated for potentiometric determination of the LF drug: (i) CPEs filled with carbon paste (referred to as CPE); (ii) CPE coated (drop-casted) with ion-selective PVC membrane (referred to as C-CPE); (iii) CPE filled with carbon paste modified with a plasticizer (PVC/cyclohexanone) (referenced as P-CPE). The CPE was formulated from graphite (Gr, 44.0%) and reduced graphene oxide (rGO, 3.0%) as the carbon source, tricresyl phosphate (TCP, 47.0%) as the plasticizer; sodium tetrakis[3,5-bis(trifluoromethyl)phenyl] borate (St-TFPMB, 1.0%) as the ion exchanger; and levofloxacinium-tetraphenylborate (LF-TPB, 5.0%) as the lipophilic ion pair. It showed a sub-Nernstian slope of 49.3 mV decade−1 within the LF concentration range 1.0 × 10−2 M to 1.0 × 10−5 M, with a detection limit of 1.0 × 10−5 M. The PVC coated electrode (C-CPE) showed improved sensitivity (in terms of slope, equal to 50.2 mV decade−1) compared to CPEs. After the incorporation of PVC paste on the modified CPE (P-CPE), the sensitivity increased at 53.5 mV decade−1, indicating such improvement. The selectivity coefficient (log KLF2+,Fe+3pot.) against different interfering species (Na+, K+, NH4+, Ca2+, Al3+, Fe3+, Glycine, Glucose, Maltose, Lactose) were significantly improved by one to three orders of magnitudes in the case of C-CPE and P-CPE, compared to CPEs. The modification with the PVC membrane coating significantly improved the response time and solubility of the LF-TPB within the electrode matrix and increased the lifetime. The constructed sensors were successfully applied for LF determination in pharmaceutical preparation (Levoxin® 500 mg), spiked urine, and serum samples with high accuracy and precision.
Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning
Authors: Liang Peng , Shi Wenzhong , Ding Yixing , Liu Zhiqiang , Shang Haolv
Published: May 2021

Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning.
Polyoxazolines with a Vicinally Double-Bioactivated Terminus for Biomacromolecular Affinity Assessment
Authors: Pinzner Florian , Keller Thorsten , Mut Jürgen , Bechold Julian , Seibel Jürgen , Groll Jürgen
Published: May 2021

Interactions between proteins and carbohydrates with larger biomacromolecules, e.g., lectins, are usually examined using self-assembled monolayers on target gold surfaces as a simplified model measuring setup. However, most of those measuring setups are either limited to a single substrate or do not allow for control over ligand distance and spacing. Here, we develop a synthetic strategy, consisting of a cascade of a thioesterification, native chemical ligation (NCL) and thiol-ene reaction, in order to create three-component polymer conjugates with a defined double bioactivation at the chain end. The target architecture is the vicinal attachment of two biomolecule residues to the α telechelic end point of a polymer and a thioether group at the ω chain end for fixating the conjugate to a gold sensor chip surface. As proof-of-principle studies for affinity measurements, we demonstrate the interaction between covalently bound mannose and ConA in surface acoustic wave (SAW) and surface plasmon resonance (SPR) experiments.
Validity of Hip and Ankle Worn Actigraph Accelerometers for Measuring Steps as a Function of Gait Speed during Steady State Walking and Continuous Turning
Authors: Bezuidenhout Lucian , Thurston Charlotte , Hagströmer Maria , Moulaee Conradsson David
Published: May 2021

This study aimed to investigate the accuracy and reliability of hip and ankle worn Actigraph GT3X+ (AG) accelerometers to measure steps as a function of gait speed. Additionally, the effect of the low frequency extension filter (LFEF) on the step accuracy was determined. Thirty healthy individuals walked straight and walked with continuous turns in different gait speeds. Number of steps were recorded with a hip and ankle worn AG, and with a Stepwatch (SW) activity monitor positioned around the right ankle, which was used as a reference for step count. The percentage agreement, interclass correlation coefficients and Bland–Altmann plots were determined between the AG and the reference SW across gait speeds for the two walking conditions. The ankle worn AG with the default filter was the most sensitive for step detection at >0.6 m/s, whilst accurate step detection for gait speeds < 0.6 m/s were only observed when applying the LFEF. The hip worn AG with the default filter showed poor accuracy (12–78%) at gait speeds < 1.0 m/s whereas the accuracy increased to >87% for gait speeds < 1.0 m/s when applying the LFEF. Ankle worn AG was the most sensitive to measure steps at a vast range of gait speeds. Our results suggest that sensor placement and filter settings need to be taken into account to provide accurate estimates of step counts.
An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals
Authors: Vargas-Lopez Olivia , Perez-Ramirez Carlos A. , Valtierra-Rodriguez Martin , Yanez-Borjas Jesus J. , Amezquita-Sanchez Juan P.
Published: May 2021

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.
Towards Automatic Collaboration Analytics for Group Speech Data Using Learning Analytics
Authors: Praharaj Sambit , Scheffel Maren , Schmitz Marcel , Specht Marcus , Drachsler Hendrik
Published: May 2021

Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics.
Cluster Analysis and Model Comparison Using Smart Meter Data
Authors: Shaukat Muhammad Arslan , Shaukat Haafizah Rameeza , Qadir Zakria , Munawar Hafiz Suliman , Kouzani Abbas Z. , Mahmud M. A. Parvez
Published: May 2021

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.
Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care
Authors: Hämäläinen Matti , Mucchi Lorenzo , Caputo Stefano , Biotti Lorenzo , Ciani Lorenzo , Marabissi Dania , Patrizi Gabriele
Published: May 2021

In this paper, we propose an unobtrusive method and architecture for monitoring a person’s presence and collecting his/her health-related parameters simultaneously in a home environment. The system is based on using a single ultra-wideband (UWB) impulse-radar as a sensing device. Using UWB radars, we aim to recognize a person and some preselected movements without camera-type monitoring. Via the experimental work, we have also demonstrated that, by using a UWB signal, it is possible to detect small chest movements remotely to recognize coughing, for example. In addition, based on statistical data analysis, a person’s posture in a room can be recognized in a steady situation. In addition, we implemented a machine learning technique (k-nearest neighbour) to automatically classify a static posture using UWB radar data. Skewness, kurtosis and received power are used in posture classification during the postprocessing. The classification accuracy achieved is more than 99%. In this paper, we also present reliability and fault tolerance analyses for three kinds of UWB radar network architectures to point out the weakest item in the installation. This information is highly important in the system’s implementation.
Review on Medical Implantable Antenna Technology and Imminent Research Challenges
Authors: Soliman Md Mohiuddin , Chowdhury Muhammad E. H. , Khandakar Amith , Islam Mohammad Tariqul , Qiblawey Yazan , Musharavati Farayi , Zal Nezhad Erfan
Published: May 2021

Implantable antennas are mandatory to transfer data from implants to the external world wirelessly. Smart implants can be used to monitor and diagnose the medical conditions of the patient. The dispersion of the dielectric constant of the tissues and variability of organ structures of the human body absorb most of the antenna radiation. Consequently, implanting an antenna inside the human body is a very challenging task. The design of the antenna is required to fulfill several conditions, such as miniaturization of the antenna dimension, biocompatibility, the satisfaction of the Specific Absorption Rate (SAR), and efficient radiation characteristics. The asymmetric hostile human body environment makes implant antenna technology even more challenging. This paper aims to summarize the recent implantable antenna technologies for medical applications and highlight the major research challenges. Also, it highlights the required technology and the frequency band, and the factors that can affect the radio frequency propagation through human body tissue. It includes a demonstration of a parametric literature investigation of the implantable antennas developed. Furthermore, fabrication and implantation methods of the antenna inside the human body are summarized elaborately. This extensive summary of the medical implantable antenna technology will help in understanding the prospects and challenges of this technology.
In-Vitro Evaluation of the Antioxidant and Anti-Inflammatory Activity of Volatile Compounds and Minerals in Five Different Onion Varieties
Authors: Sami Rokayya , Elhakem Abeer , Alharbi Mona , Almatrafi Manal , Benajiba Nada , Ahmed Mohamed Taha , Fikry Mohammad , Helal Mahmoud
Published: May 2021

Onions contain high antioxidants compounds that fight inflammation against many diseases. The purpose was to investigate some selected bioactive activities of onion varieties (Yellow, Red, Green, Leek, and Baby). Antioxidant assays and anti-inflammatory activities such as NO production with the addition of some bioactive components were determined and analyzed by using a spectrophotometer. Gas chromatography and mass spectrometry (GC–MS) was used for the volatile compounds, while an Atomic absorption spectrometer was used for mineral determinations. Red variety achieved the highest antioxidant activities. The total flavonoids were between (12.56 and 353.53 mg Quercetin/gin dry weight) (dw) and the total phenol was (8.75–25.73 mg/g dw). Leek, Yellow and Green extracts achieved highly anti-inflammatory values (3.71–4.01 μg/mL) followed by Red and Baby extracts, respectively. The highest contents of sodium, potassium, zinc, and calcium were established for Red onions. Furfuraldehyde, 5-Methyl-2-furfuraldehyde, 2-Methyl-2-pentenal, and 1-Propanethiol were the most predominant, followed by a minor abundance of the other compounds such as Dimethyl sulfide, Methyl allyl disulfide, Methyl-trans-propenyl-disulfide, and Methyl propyl disulfide. The results recommend that these varieties could act as sources of essential antioxidants and anti-inflammatories to decrease inflammation and oxidative stresses, especially red onions that recorded high activities.
Andean Blueberry of the Genus Disterigma: A High-Resolution Mass Spectrometric Approach for the Comprehensive Characterization of Phenolic Compounds
Authors: Aita Sara , Capriotti Anna , Cavaliere Chiara , Cerrato Andrea , Giannelli Moneta Benedetta , Montone Carmela , Piovesana Susy , Laganà Aldo
Published: May 2021

Wild neotropical blueberries, endemic of Central and South American areas, are promising yet still undisclosed sources of bioactive compounds. Most research studies have addressed wild and cultivated blueberries from Europe and North America, despite the extremely wide variety of wild neotropical species. In the present paper, for the first time, the phenolic composition of Disterigma alaternoides was investigated through ultra-high-performance liquid chromatography coupled to high-resolution mass-spectrometric analysis followed by accurate data analysis and compound validation with a dedicated structure-based workflow. D. alaternoides, which belongs to a closely related genus to that of the common blueberry, grows exclusively in the Andean regions over 2000 above sea level. Thanks to the dedicated analytical platform, 249 phenolic compounds were tentatively identified, including several anthocyanins, flavonoids, phenolic acids, and proanthocyanidins. Thenature and heterogeneity of identified phenolic compounds demonstrate once more the need for a more profound knowledge of such still uncharted matrices.
Static vs. Dynamic Electrostatic Repulsion Reversed Phase Liquid Chromatography: Solutions for Pharmaceutical and Biopharmaceutical Basic Compounds
Authors: Mazzoccanti Giulia , Gasparrini Francesco , Calcaterra Andrea , Villani Claudio , Ciogli Alessia
Published: May 2021

Many efforts have been made to separate basic compounds, which are challenging to resolve in reversed phase liquid chromatography. In this process, they are strongly retained and the peak shape undergoes significant distortion. The principal origin of this has been identified with the non-negligible interaction with residual deprotonated silanols. Consequently, all solutions that efficiently shield silanols are being sought. This review is an upgrade on the use of the electrostatic repulsion reversed phase (ERRP) approach: retention of bases, in protonated form, can be achieved by modulating the charge repulsion caused by the presence of positive charges in the chromatographic system. This study successfully (i) introduced fixed positive charges in the structure of stationary phases, (ii) used cationic and hydrophobic additives in the mobile phase, and (iii) used the ERRP-like approach employed at the preparative level for peptide purification.
Adventure Tourism: Insight from Experienced Visitors of Romanian National and Natural Parks
Authors: Dumitras Diana E. , Mihai Valentin C. , Jitea Ionel M. , Donici Delia , Muresan Iulia C.
Published: May 2021

The rapid growth of adventure tourism is remarked all over the world, being considered as a modern form of tourism. This study attempted to investigate the preferences of experienced visitors of Romanian national and natural parks with the main focus on understanding what motives describe the three elements that define adventure tourism: physical activity, natural environment and cultural immersion, and what is the level of satisfaction regarding the quality of facilities and services. An online survey was filled in by 137 members of adventure tourism groups, being further grouped based on their experiences. Results indicate that experienced participants better appreciated the opportunity to be engaged in physical activity and to explore the natural environment. Cultural experience was perceived as similar by both groups. The level of satisfaction about facilities and services provided by the national and natural parks and adjacent rural communities was similar among groups, except for that about the existing information panels. Future development actions should address both groups to enhance their willingness to revisit the areas and increase the attractiveness of tourism in rural areas situated in the neighborhood of parks.
Thermal Decomposition of [AH][M(HCOO)3] Perovskite-Like Formates
Authors: Gómez-Aguirre Lilián Claudia , Otero-Canabal Jorge , Sánchez-Andújar Manuel , Señarís-Rodríguez María Antonia , Castro-García Socorro , Pato-Doldán Breogán
Published: May 2021

A systematic study of the thermal decomposition of hybrid perovskites of formula [AH][M(HCOO)3] under inert atmosphere was performed by means of thermogravimetry and simultaneous infrared spectroscopy of the evolved gases. The influence of: (i) the metal ion of the [M(HCOO)3]- framework and (ii) the guest [AH]+ cation, in the composition of the final residue was evaluated. In this work, it has been demonstrated that these materials can be used as precursors of metal or metal-oxide compounds—obtained free of carbon—, and that the composition of the final residue is determined by the standard reduction potential of the metal cation of the framework.
Practical Functioning of a Sustainable Urban Complex with a Park—The Case Study of Stavros Niarchos Foundation Cultural Center in Athens
Authors: Makowska Beata
Published: May 2021

Intensive urban development has created a shortage of urban green areas. The need to economically plan and use urban green spaces has fueled the redefinition of public spaces and parks so as to provide the residents with both recreation and relaxation facilities, as well as a forum for contact with culture. This paper discusses the case of the Stavros Niarchos Foundation Cultural Center (SNFCC) in the Kallithea district on the outskirts of Athens, near the Mediterranean Sea. It fills a gap in the research on the aspects of the practical functioning of such facilities. The methodology used in the research included an analysis of the literature, the SNFCC’s reports, and an in situ survey. The cultural center hosts a number of events aimed at promoting Greece’s natural and cultural heritage. The paper includes a detailed analysis of the events organized by the SNFCC in the period 2017–2020 and their immense impact on residents. The aim of the study is to show that the creation of the SNFCC with the park areas has functioned as a factor contributing to the improvement of the quality of urban space and the quality of life of the city’s inhabitants. The paper’s conclusions indicate that the sustainable SNFCC project, which fulfils the urban ecology criteria, has been very well received by the visitors—citizens and tourists alike. A program-centered innovation introduced by the SN Park has added great value to their lives. The project contributes to economic and cultural growth, as well as the protection and promotion of heritage.
Accommodation, Seasonality and Domestic Tourism to National Parks: Implications for Environmental Policy
Authors: Díaz-Pérez Flora Maria , García-González Carlos Gustavo , Fyall Alan
Published: May 2021

The analysis of seasonality and domestic tourism from the perspective of the accommodation sector has, to date, been unsatisfactorily studied in the domain of visitors to national parks. In light of the scale of accommodation development and its crucial role in tourism, most notably with regard to environmental impact, in-depth knowledge about accommodation market segments and their specific characteristics and patterns of behavior are integral to the development of tourism policy. In the context of domestic tourism, underpinned by an understanding of the theory of planned behavior and push-and-pull motivations, this study examines seasonality and accommodation type used by visitors to national parks in the small islands of the Canaries, Spain with the objective being to design the most appropriate environmental tourism policy. By adopting a Multinomial logistic regression model with 1671 surveys carried out, the study concludes that domestic visitors demonstrate a higher propensity to select environmental-friendly accommodation types during the high season. At the same time, residents who visit the parks in high season assess the lowest value to the preservation level of the parks’ natural resources.
A Comparative Evaluation of Utility Value Based on User Preferences for Urban Streets: The Case of Seoul, Korea
Authors: Seo Minho , Kim Seiyong
Published: May 2021

Currently, there is a lack of objective evaluations clarifying characteristics of urban streets from the users’ perspective, particularly regarding the most effective spatial composition. This study investigated the value of spatial components of urban streets preferred by users through a conjoint analysis based on utility value for six streets representing street types in Seoul and evaluated relative preferences for the main characteristics of urban streets in terms of amenity, placeness, and accessibility. The analysis showed that users consider “amenity” as the most important characteristic of urban streets; “green space composition” was rated highest for utility value. The value exchange relationship of utility related to placeness and amenity within a certain threshold level was also confirmed for each of the three characteristics of urban streets. These results show that prioritizing improvements to amenities and green space promotes urban streets policies and projects, and strengthening placeness-related spatial elements is effective once a certain amenity level is secured. This study contributes to the discussion how to minimize differences in planning and user experience for urban streets by objectifying the relationship between user preferences and characteristics of urban streets through utility value.
Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management
Authors: Kanwal Urooj , Ibrahim Muhammad , Abbas Farhat , Yamin Muhammad , Jabeen Fariha , Shahzadi Anam , Farooque Aitazaz A. , Imtiaz Muhammad , Ditta Allah , Ali Shafaqat
Published: May 2021

Phytoremediation is a cost-effective and environmentally friendly approach that can be used for the remediation of metals in polluted soil. This study used a hedge plant–calico (Alternanthera bettzickiana (Regel) G. Nicholson) to determine the role of citric acid in lead (Pb) phytoremediation by exposing it to different concentrations of Pb (0, 200, 500, and 1000 mg kg−1) as well as in a combination with citric acid concentration (0, 250, 500 µM). The analysis of variance was applied on results for significant effects of the independent variables on the dependent variables using SPSS (ver10). According to the results, maximum Pb concentration was measured in the upper parts of the plant. An increase in dry weight biomass, plant growth parameters, and photosynthetic contents was observed with the increase of Pb application (200 mg kg−1) in soil while a reduced growth was experienced at higher Pb concentration (1000 mg kg−1). The antioxidant enzymatic activities like superoxide dismutase (SOD) and peroxidase (POD) were enhanced under lower Pb concentration (200, 500 mg kg−1), whereas the reduction occurred at greater metal concentration Pb (1000 mg kg−1). There was a usual reduction in electrolyte leakage (EL) at lower Pb concentration (200, 500 mg kg−1), whereas EL increased at maximum Pb concentration (1000 mg kg−1). We concluded that this hedge plant, A. Bettzickiana, has the greater ability to remediate polluted soils aided with citric acid application.