bluuebunny/crossref_metadata_2025 huggingface dataset:
Water Balance Standardization Approach for Reconstructing Runoff Using GPS at the Basin Upstream
Authors: Fok Hok Sum , Zhou Linghao , Liu Yongxin , Tenzer Robert , Ma Zhongtian , Zou Fang
Published: June 2020

While in-situ estuarine discharge has been correlated and reconstructed well with localized remotely-sensed data and hydraulic variables since the 1990s, its correlation and reconstruction using averaged GPS-inferred water storage from satellite gravimetry (i.e., GRACE) at the basin upstream based on the water balance standardization (WBS) approach remains unexplored. This study aims to illustrate the WBS approach for reconstructing monthly estuarine discharge (in the form of runoff (R)) at Mekong River Delta, by correlating the averaged GPS-inferred water storage from GRACE of the upstream Mekong Basin with the in-situ R at the Mekong River Delta estuary. The resulting R based on GPS-inferred water storage is comparable to that inferred from GRACE, regardless of in-situ stations within Mekong River Delta being used for the R reconstruction. The resulting R from the WBS approach with GPS water storage converted by GRACE mascon solution attains the lowest normalized root-mean-square error of 0.066, and the highest Pearson correlation coefficient of 0.974 and Nash-Sutcliffe efficiency of 0.950. Regardless of using either GPS-inferred or GRACE-inferred water storage, the WBS approach shows an increase of 1–4% in accuracy when compared to those reconstructed from remotely-sensed water balance variables. An external assessment also exhibits similar accuracies when examining the R estimated at another station location. By comparing the reconstructed and estimated Rs between the entrance and the estuary mouth, a relative error of 1–4% is found, which accounts for the remaining effect of tidal backwater on the estimated R. Additional errors might be caused by the accumulated errors from the proposed approach, the unknown signals in the remotely-sensed water balance variables, and the variable time shift across different years between the Mekong Basin at the upstream and the estuary at the downstream.
Radar Aeroecology
Authors: Kelly Jeffrey F. , Stepanian Phillip M.
Published: June 2020

Aeroecology is an emerging discipline founded by Tom Kunz and colleagues in the early 2000s to address the challenges of studying animal flight in the lower atmosphere [...]
The Use of Common Knowledge in Fuzzy Logic Approach for Vineyard Site Selection
Authors: Chrobak Katarzyna , Chrobak Grzegorz , Kazak Jan K.
Published: June 2020

A multitude of factors considered necessary for an informed choice of the location of the vineyard can be overwhelming for the decision-maker. Is there still a place for knowledge valuable from the perspective of an experienced winegrower in the era of precise measurements? The informative use of so-called common knowledge is possible owing to fuzzy-based techniques, which allow for the representation of intuitive notions in terms of quantitative measures. The work uses tools based on fuzzy logic to cover the scope of common knowledge within the decision-making process. Owing to its flexibility and ability to deal with imprecise input data while maintaining the simple construction, the fuzzy logic solution filled the gap between GIS data and wine grower’s experience. Based on the data from the thematic literature, a set of rules was created to interpret the relationships between popular site selection criteria. The dynamics and manner of interaction between variables were determined using adequate membership functions. Pre-processing using GIS with remote sensing data was considered as a preliminary stage for the analysis. By using the graphical interface, the system operation facilitates the work of a potential user. The obtained results indicated the possibility of an alternative approach to classical analyses by replacing or extending the meaning of some variables using information based on feelings and perceptions. Research constitutes a premise for the further development of expert systems using widely understood domain knowledge.
Landmarks as Cultural Heritage Assets Affecting the Distribution of Settlements in Rural Areas—An Analysis Based on LIDAR DTM, Digital Photographs, and Historical Maps
Authors: Prus Barbara , Wilkosz-Mamcarczyk Magdalena , Salata Tomasz
Published: June 2020

The final decision of the owner of the plot who plans to build a house depends on many factors most of which are of legal and financial nature. The authors demonstrate that the decisions regarding specific location within the plot of land are influenced by intangible components as well, namely the intention to have the best view. The view is often related to the occurrence of landmarks with prominent visual impact in the landscape that determine visual connections. The rural landscape is determined by the spatial arrangement including the buildings, the shape of public spaces, ownership divisions, and the land distribution. Being an element of rural cultural heritage, the arrangement of buildings is influenced by a vast number of factors such as geographical, historical, physical, and socio-economic ones. This article focuses on determining the interaction between the settlement locations and zones with an excellent, unique view of characteristic, well-known architectural landmarks. Mapping of viewsheds of many features is a critical element of the landscape planning process and facilitates the protection of cultural heritage assets. The analysis involved LiDAR DTM (Digital Terrain Model created in Light Detection and Ranging technology), digital photographs, and historical maps. In terms of the administrative subdivision, the area comprises 20 localities. The landmark visibility analysis for locations of the buildings covered a 140 km2 area of Carpathian Foothills in southern Poland. The article combines experiences in the field of landscape architecture, spatial planning and the use of Geographic Information System (GIS) technology. The examples show that the modern development layout refers to the historical structure and the development of a new settlement tissue has a cultural background and is influenced by spatial landmarks.
Extending the Global Space-Based Inter-Calibration System (GSICS) to Tie Satellite Radiances to an Absolute Scale
Authors: Hewison Tim J. , Doelling David R. , Lukashin Constantine , Tobin David , O. John Viju , Joro Sauli , Bojkov Bojan
Published: June 2020

The Global Space-based Inter-Calibration System (GSICS) routinely monitors the calibration of various channels of Earth-observing satellite instruments and generates GSICS Corrections, which are functions that can be applied to tie them to reference instruments. For the infrared channels of geostationary imagers GSICS algorithms are based on comparisons of collocated observations with hyperspectral reference instruments; whereas Pseudo Invariant Calibration Targets are currently used to compare the counterpart channels in the reflected solar band to multispectral reference sensors. This paper discusses how GSICS products derived from both approaches can be tied to an absolute scale using specialized satellite reference instruments with SI-traceable calibration on orbit. This would provide resilience against gaps between reference instruments and drifts in their calibration outside their overlap period and allow construction of robust and harmonized data records from multiple satellite sources to build Fundamental Climate Data Records, as well as more uniform environmental retrievals in both space and time, thus improving inter-operability.
Vegetation Phenology Influenced by Rapid Urbanization of The Yangtze Delta Region
Authors: Ding Haiyong , Xu Luming , Elmore Andrew J. , Shi Yuli
Published: June 2020

Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.
An Analysis of Urbanisation Dynamics with the Use of the Fuzzy Set Theory—A Case Study of the City of Olsztyn
Authors: Biłozor Andrzej , Cieślak Iwona , Czyża Szymon
Published: June 2020

Quantitative, qualitative and structural changes in land use that occur in a given location over time are a manifestation and a measure of urban development. Urbanisation is a process of spatial diffusion that spreads from the urban core to peripheral areas. Urban development is linked with human activities in a specific location and in a given period of time. In the context of spatial management, urbanisation is a process where less intensive land-use types are replaced by more intensive forms of land use. The demand for new land for residential development, the search for alternative locations for housing construction and the development of sustainable land management plans require new methods that support decision-making in the process of land conversion in peri-urban areas. The aim of this study was to develop a methodology for identifying and localising the boundaries of urban development with the use of the fuzzy set theory and to analyse the rate of changes in land use based on data for 2005–2010–2017. The proposed method supports the identification and localisation of urbanised areas and an evaluation of the degree of urbanisation in the interval [0,1]. The case study was conducted in the Polish city of Olsztyn.
Satellite-Based Monitoring and Modeling of Ground Movements Caused by Water Rebound
Authors: Malinowska Agnieszka A. , Witkowski Wojciech T. , Guzy Artur , Hejmanowski Ryszard
Published: June 2020

The presented research aimed to evaluate the spatio-temporal distribution of ground movements caused by groundwater head changes induced by mining. The research was carried out in the area of one of the copper ore and anhydrite mines in Poland. To determine ground movements, classical surveying results and the persistent scatter Satellite Radar Interferometry (PSInSAR) method were applied. The mining operation triggered significant subsidence, reaching 1.4 m in the years 1944–2015. However, subsidence caused by groundwater pumping was about 0.3 m. After mine closure, an ongoing groundwater rebound was observed. Hence, land uplift occurred, reaching no more than 29 mm/y. The main part of the investigation concerned developing a novel method for uplift prediction. Therefore, an attempt was made to comparatively analyze the dynamics of ground movements correlated with the mine life and hydrogeological condition. These analyses allowed the time factor for the modeling of land uplift to be determined. The investigation also revealed that in the next six years, the uplift will reach up to 12 mm/y. The developed methodology could be applied in any post-mining area where groundwater-rebound-related uplift is observed.
Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes
Authors: Ali Abebe Mohammed , Darvishzadeh Roshanak , Skidmore Andrew , Heurich Marco , Paganini Marc , Heiden Uta , Mücher Sander
Published: June 2020

Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
Enhancing Key Management in LoRaWAN with Permissioned Blockchain
Authors: Ribeiro Victor , Holanda Raimir , Ramos Alex , Rodrigues Joel J. P. C.
Published: June 2020

Low-Power Wide-Area Network (LPWAN) is one of the enabling technologies of the Internet of Things (IoT), and focuses on providing long distance connectivity for a vast amount of smart devices. Currently, LoRa is one of the leading LPWAN solutions available for public use. In LPWANs, especially in LoRa, security is a major concern due to the resource constraints of the devices, the sensitivity level of the transmitted data, the large amount of connected devices, among other reasons. This paper studies the key management mechanism of LoRaWAN environments. A secure architecture for key management based on smart contracts and permissioned blockchain to enhance security and availability in LoRaWAN networks is proposed. To demonstrate the feasibility of the proposed blockchain-based LoRaWAN architecture, a working prototype has been created using open-source tools and commodity hardware. Performance analysis shows that the prototype presents similar execution time and latency values, when compared to a traditional system, especially for small and medium-sized LoRaWAN networks. We also discuss why the proposed solution can be used in environments with a large number of end-devices.
Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City
Authors: Li Dazhou , Lin Chuan , Gao Wei , Meng Zihui , Song Qi
Published: June 2020

As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the “last mile” of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.
How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations
Authors: Brattich Erika , Bracci Alessandro , Zappi Alessandro , Morozzi Pietro , Di Sabatino Silvana , Porcù Federico , Di Nicola Francesca , Tositti Laura
Published: June 2020

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30′ N, 11°21′ E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution.
Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing
Authors: Li Yuxiao , Zhou Ciming , Fan Dian , Liang Sijing , Qian Li
Published: June 2020

This paper proposes a novel iteration Bayesian reweighed (IBR) algorithm to obtain accurate estimates of a measurement parameter that uses only a few noisy measurement data. The method is applied to optimize the frequency fluctuation in an optical carrier-based microwave interferometry (OCMI) system. The algorithm iteratively estimates the frequency of the S-parameter valley point by collecting training samples to rebalance the weights between prior samples, which reduces the impact of noise in the system. Simulation shows that the estimated result of this algorithm is closer to the true value than that of the maximum likelihood estimation (MLE) using the same amount of measured data. Under the influence of system noise, this algorithm optimizes the frequency fluctuation of the S-parameter and reduces the impact of individual measured data. In this study, we applied the algorithm in the strain sensing experiment and compared it with the MLE. When axial strain changes 240 με, the IBR algorithm yields a deviation of 36 με, which is a significant reduction from 138 με (using the MLE method). Moreover, the average error rate decreases from 25% to 3% (with the MLE method), suggesting that the linear fitting degree of the estimated results and accuracy of the system are improved. Moreover, the algorithm has a wide range of applicability, for it can handle different application models in the OCMI system and the systems with frequency fluctuation problems.
Fiber Optic Particle Plasmon Resonance-Based Immunoassay Using a Novel Multi-Microchannel Biochip
Authors: Chiang Chang-Yue , Chen Chien-Hsing , Wang Chien-Tsung
Published: June 2020

A novel multi-microchannel biochip fiber-optic particle plasmon resonance (FOPPR) sensor system for the simultaneous detection of multiple samples. The system integrates a novel photoelectric system, a lock-in module, and an all-in-one platform incorporating optical design and mechanical design together to improve system stability and the sensitivity of the FOPPR sensor. The multi-microchannel FOPPR biochip has been developed by constructing a multi-microchannel flow-cell composed of plastic material to monitor and analyze five samples simultaneously. The sensor system requires only 30 μL of sample for detection in each microchannel. Moreover, the total size of the multi-microchannel FOPPR sensor chip is merely 40 mm × 30 mm × 4 mm; thus, it is very compact and cost-effective. The analysis was based on calibration curves obtained from real-time sensor response data after injection of sucrose solution, streptavidin and anti-dinitrophenyl (anti-DNP) antibody of known concentrations over the chips. The results show that the multi-microchannel FOPPR sensor system not only has good reproducibility (coefficient of variation (CV) < 10%), but also excellent refractive index resolution (6.23 ± 0.10 × 10−6 refractive index unit (RIU)). The detection limits are 2.92 ± 0.28 × 10−8 g/mL (0.53 ± 0.01 nM) and 7.48 ± 0.40 × 10−8 g/mL (0.34 ± 0.002 nM) for streptavidin and anti-DNP antibody, respectively.
Influence of the Pulsed Voltage Connection on the Electromagnetic Distortion in Full-Size HVDC Cable PEA Measurements
Authors: Mier Escurra Guillermo , Rodrigo Mor Armando , Vaessen Peter
Published: June 2020

Nowadays, with the widespread use of High Voltage Direct Current (HVDC) cables in power systems, the measurements of space charges in full-size cables are becoming more relevant. One of the most common methods used for space charge measurements is the Pulsed Electro-Acoustic (PEA) method. This paper analyzes two factors that influence the electromagnetic interference on the piezoelectric signal. These factors are the connection of the injected pulsed voltage at the PEA test cell and the grounding of the PEA test cell. The influence was analyzed by means of experimental tests to compare different configurations and the electromagnetic distortion created in each one of them. It was observed that the physical location of the pulsed voltage at the electrode has a very important impact on the magnitude of the electromagnetic distortion. Moreover, it is shown that the physical connection of the grounding and the existence of a parasitic capacitance at the PEA test cell are also an important source of distortion.
Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
Authors: Chatterjee Ayan , Gerdes Martin W. , Martinez Santiago G.
Published: June 2020

“Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, the novel coronavirus, is responsible for the ongoing worldwide pandemic. “World Health Organization (WHO)” assigned an “International Classification of Diseases (ICD)” code—“COVID-19”-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with “Middle East Respiratory Syndrome (MERS-CoV)”, “Severe Acute Respiratory Syndrome (SARS-CoV)”, and now with this new virus, namely “SARS-CoV-2”, or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a “spread factor (f)”, and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in “ourworldindata.org (Oxford University database)”, and newly simulated datasets, following the analysis of different univariate “Long Short Term Memory (LSTM)” models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months—January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.
A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors
Authors: Debache Isaac , Jeantet Lorène , Chevallier Damien , Bergouignan Audrey , Sueur Cédric
Published: June 2020

Here we propose a new machine learning algorithm for classification of human activities by means of accelerometer and gyroscope signals. Based on a novel hierarchical system of logistic regression classifiers and a relatively small set of features extracted from the filtered signals, the proposed algorithm outperformed previous work on the DaLiAc (Daily Life Activity) and mHealth datasets. The algorithm also represents a significant improvement in terms of computational costs and requires no feature selection and hyper-parameter tuning. The algorithm still showed a robust performance with only two (ankle and wrist) out of the four devices (chest, wrist, hip and ankle) placed on the body (96.8% vs. 97.3% mean accuracy for the DaLiAc dataset). The present work shows that low-complexity models can compete with heavy, inefficient models in classification of advanced activities when designed with a careful upstream inspection of the data.
An Object Model and Interaction Method for a Simulated Experience of Pottery on a Potter’s Wheel
Authors: Matsumaru Takafumi , Morikawa Ami
Published: June 2020

This paper introduces an object model and an interaction method for a simulated experience of pottery on a potter’s wheel. Firstly, we propose a layered cylinder model for a 3D object of the pottery on a potter’s wheel. Secondly, we set three kinds of deformation functions to form the object model from an initial state to a bowl shape: shaping the external surface, forming the inner shape (deepening the opening and widening the opening), and reducing the total height. Next, as for the interaction method between a user and the model, we prepare a simple but similar method for hand-finger operations on pottery on a potter’s wheel, in which the index finger movement takes care of the external surface and the total height, and the thumb movement makes the inner shape. Those are implemented in the three-dimensional aerial image interface (3DAII) developed in our laboratory to build a simulated experience system. We confirm the operation of the proposed object model (layered cylinder model) and the functions of the prepared interaction method (a simple but similar method to actual hand-finger operations) through a preliminary evaluation of participants. The participants were asked to make three kinds of bowl shapes (cylindrical, dome-shaped, and flat-type) and then they answered the survey (maneuverability, visibility, and satisfaction). All participants could make something like three kinds of bowl shapes in less than 30 min from their first touch.
Shifting the Specificity of E. coli Biosensor from Inorganic Arsenic to Phenylarsine Oxide through Genetic Engineering
Authors: Kim Hyojin , Jeon Yangwon , Lee Woonwoo , Jang Geupil , Yoon Youngdae
Published: June 2020

It has recently been discovered that organic and inorganic arsenics could be detrimental to human health. Although organic arsenic is less toxic than inorganic arsenic, it could form inorganic arsenic through chemical and biological processes in environmental systems. In this regard, the availability of tools for detecting organic arsenic species would be beneficial. Because As-sensing biosensors employing arsenic responsive genetic systems are regulated by ArsR which detects arsenics, the target selectivity of biosensors could be obtained by modulating the selectivity of ArsR. In this study, we demonstrated a shift in the specificity of E. coli cell-based biosensors from the detection of inorganic arsenic to that of organic arsenic, specifically phenylarsine oxide (PAO), through the genetic engineering of ArsR. By modulating the number and location of cysteines forming coordinate covalent bonds with arsenic species, an E. coli cell-based biosensor that was specific to PAO was obtained. Despite its restriction to PAO at the moment, it offers invaluable evidence of the potential to generate new biosensors for sensing organic arsenic species through the genetic engineering of ArsR.
Nanostructured Metal Oxide-Based Acetone Gas Sensors: A Review
Authors: Amiri Vahid , Roshan Hossein , Mirzaei Ali , Neri Giovanni , Ayesh Ahmad I.
Published: June 2020

Acetone is a well-known volatile organic compound that is widely used in different industrial and domestic areas. However, it can have dangerous effects on human life and health. Thus, the realization of sensitive and selective sensors for recognition of acetone is highly important. Among different gas sensors, resistive gas sensors based on nanostructured metal oxide with high surface area, have been widely reported for successful detection of acetone gas, owing to their high sensitivity, fast dynamics, high stability, and low price. Herein, we discuss different aspects of metal oxide-based acetone gas sensors in pristine, composite, doped, and noble metal functionalized forms. Gas sensing mechanisms are also discussed. This review is an informative document for those who are working in the field of gas sensors.
Turning Characteristics of the More-Affected Side in Parkinson’s Disease Patients with Freezing of Gait
Authors: Park Hwayoung , Youm Changhong , Lee Myeounggon , Noh Byungjoo , Cheon Sang-Myung
Published: June 2020

This study investigated the turning characteristics of the more-affected limbs in Parkinson’s disease (PD) patients in comparison with that of a control group, and in PD patients with freezing of gait (FOG; freezers) in comparison with those without FOG (non-freezers) for 360° and 540° turning tasks at the maximum speed. A total of 12 freezers, 12 non-freezers, and 12 controls participated in this study. The PD patients showed significantly longer total durations, shorter inner and outer step lengths, and greater anterior–posterior (AP) root mean square (RMS) center of mass (COM) distances compared to those for the controls. The freezers showed significantly greater AP and medial-lateral (ML) RMS COM distances compared to those of non-freezers. The turning task toward the inner step of the more-affected side (IMA) in PD patients showed significantly greater step width, total steps, and AP and ML RMS COM distances than that toward the outer step of the more-affected side (OMA). The corresponding results for freezers revealed significantly higher total steps and shorter inner step length during the 540° turn toward the IMA than that toward the OMA. Therefore, PD patients and freezers exhibited greater turning difficulty in performing challenging turning tasks such as turning with an increased angle and speed and toward the more-affected side.
Photometric Sensing of Active Chlorine, Total Chlorine, and pH on a Microfluidic Chip for Online Swimming Pool Monitoring
Authors: Elmas Sait , Pospisilova Aneta , Sekulska Aneta Anna , Vasilev Vasil , Nann Thomas , Thornton Stephen , Priest Craig
Published: June 2020

A microfluidic sensor was studied for the photometric detection of active chlorine, total chlorine, and pH in swimming pool samples. The sensor consisted of a four-layer borosilicate glass chip, containing a microchannel network and a 2.2 mm path length, 1.7 mL optical cell. The chip was optimised to measure the bleaching of methyl orange and spectral changes in phenol red for quantitative chlorine (active and total) and pH measurements that were suited to swimming pool monitoring. Reagent consumption (60 mL per measurement) was minimised to allow for maintenance-free operation over a nominal summer season (3 months) with minimal waste. The chip was tested using samples from 12 domestic, public, and commercial swimming pools (indoor and outdoor), with results that compare favourably with commercial products (test strips and the N,N’-diethyl-p-phenylenediamine (DPD) method), precision pH electrodes, and iodometric titration.
Bent DNA Bows as Sensing Amplifiers for Detecting DNA-Interacting Salts and Molecules
Authors: Freeland Jack , Zhang Lihua , Wang Shih-Ting , Ruiz Mason , Wang Yong
Published: June 2020

Due to the central role of DNA, its interactions with inorganic salts and small organic molecules are important. For example, such interactions play important roles in various fundamental cellular processes in living systems and are involved in many DNA-damage related diseases. Strategies to improve the sensitivity of existing techniques for studying DNA interactions with other molecules would be appreciated in situations where the interactions are too weak. Here we report our development and demonstration of bent DNA bows for amplifying, sensing, and detecting the interactions of 14 inorganic salts and small organic molecules with DNA. With the bent DNA bows, these interactions were easily visualized and quantified in gel electrophoresis, which were difficult to measure without bending. In addition, the strength of the interactions of DNA with the various salts/molecules were quantified using the modified Hill equation. This work highlights the amplification effects of the bending elastic energy stored in the DNA bows and the potential use of the DNA bows for quantitatively measuring DNA interactions with small molecules as simple economic methods; it may also pave the way for exploiting the bent DNA bows for other applications such as screening DNA-interacting molecules and drugs.
Advances in Smart Environment Monitoring Systems Using IoT and Sensors
Authors: Ullo Silvia Liberata , Sinha G. R.
Published: June 2020

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.
Lane Position Detection Based on Long Short-Term Memory (LSTM)
Authors: Yang Wei , Zhang Xiang , Lei Qian , Shen Dengye , Xiao Ping , Huang Yu
Published: June 2020

Accurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3 (S × 2S), has high accuracy. However, like the traditional LL detection algorithms, they do not use spatial information and have low detection accuracy under occlusion, deformation, worn, poor lighting, and other non-ideal environmental conditions. After studying the spatial information between LLs and learning the distribution law of LLs, an LL prediction model based on long short-term memory (LSTM) and recursive neural network (RcNN) was established; the method can predict the future LL position by using historical LL position information. Moreover, by combining the LL information predicted with YOLO v3 (S × 2S) detection results using Dempster Shafer (D-S) evidence theory, the LL detection accuracy can be improved effectively, and the uncertainty of this system be reduced correspondingly. The results show that the accuracy of LL detection can be significantly improved in rainy, snowy weather, and obstacle scenes.
Measurement of Water Velocity in Gas–Water Two-Phase Flow with the Combination of Electromagnetic Flowmeter and Conductance Sensor
Authors: Yang Qiu-Yi , Jin Ning-De , Zhai Lu-Sheng , Ren Ying-Yu , Yu Chuang , Wei Ji-Dong
Published: June 2020

A method to measure the superficial velocity of the water phase in gas–water flow using an electromagnetic flowmeter (EMF) and rotating electric field conductance sensors (REFCSs) is introduced in this paper. An electromagnetic flowmeter instrument factor model is built and the correlation between electromagnetic flowmeter output and gas holdup in different flow patterns are explored through vertical upward gas–water flow dynamic experiments in a pipe with an inner diameter (ID) of 20 mm. Water superficial velocity is predicted based on pattern identification among bubble, churn, and slug flows. The experimental results show that water superficial velocity can be predicted fairly accurately for bubble, churn, and slug flows with a water cut higher than 60% (absolute average percentage deviation and absolute average deviation are 4.1057% and 0.0281 m/s, respectively). The output of the electromagnetic flowmeter is unstable and invalid in slug flows with a water cut below 60% due to the non-conducting gas slug is almost filling the pipe. Therefore, the electromagnetic flowmeter is not preferred to be used in such conditions.
Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum
Authors: Piotr Konieczka Paweł , Aliaño-González María José , Ferreiro-González Marta , Barbero Gerardo F. , Palma Miguel
Published: June 2020

Aroma is one of the main characteristics of coffee specimens. Different mixtures of Arabica and Robusta coffees are usually found in the market to offer specific aroma or flavor profiles to consumers. However, the mixed samples or their proportions are not always identified in the product labels. Since the price of Arabica is much higher than that of Robusta, this lack of information is not only an economical issue but a possible fraud to consumers, besides the potential allergic reaction that these mixtures may trigger in some individuals. In this paper, two sample preparation techniques were compared before the analysis of the total volatile organic compounds (VOCs) found in Robusta, Arabica, and in the mixture from both coffee types. The comparison of the signals obtained from the analyses showed that the VOCs concentration levels obtained from the headspace (HS) analyses were clearly higher than those obtained from the pre-concentration step where an adsorbent, an active charcoal strip (ACS + HS), was used. In the second part of this study, the possibility of using the headspace gas-chromatography ion mobility spectrometry (HS-GC-IMS) for the discrimination between Arabica, Robusta, and mixed coffee samples (n = 30) was evaluated. The ion mobility sum spectrum (IMSS) obtained from the analysis of the HS was used in combination with pattern recognition techniques, namely linear discrimination analysis (LDA), as an electronic nose. The identification of individual compounds was not carried out since chromatographic information was not used. This novel approach allowed the correct discrimination (100%) of all of the samples. A characteristic fingerprint for each type of coffee for a fast and easy identification was also developed. In addition, the developed method is ecofriendly, so it is a good alternative to traditional approaches.
Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
Authors: Wang Xin , Jiang Zhe , Shen Xiao-Hong
Published: June 2020

Orthogonal Chirp Division Multiplexing (OCDM) is a modulation scheme which outperforms the conventional Orthogonal Frequency Division Multiplexing (OFDM) under frequency selective channels by using chirp subcarriers. However, low complexity equalization algorithms for OCDM based systems under doubly selective channels have not been investigated yet. Moreover, in OCDM, the usage of different phase matrices in modulation will lead to extra storage overhead. In this paper, we investigate an OCDM based modulation scheme termed uniform phase-Orthogonal Chirp Division Multiplexing (UP-OCDM) for high-speed communication over doubly selective channels. With uniform phase matrices equipped, UP-OCDM can reduce the storage requirement of modulation. We also prove that like OCDM, the transform matrix of UP-OCDM is circulant. Based on the circulant transform matrix, we show that the channel matrices in UP-OCDM system over doubly selective channels have special structures that (1) the equivalent frequency-domain channel matrix can be approximated as a band matrix, and (2) the transform domain channel matrix in the framework of the basis expansion model (BEM) is a sum of the product of diagonal and circulant matrices. Based on these special channel structures, two low-complexity equalization algorithms are proposed for UP-OCDM in this paper. The equalization algorithms are based on block LDL H factorization and iterative matrix inversion, respectively. Numerical simulations are finally proposed to show the performance of UP-OCDM and the validity of the proposed low complexity equalization algorithms. It is shown that when the channel is doubly selective, UP-OCDM and OCDM have similar BER performance, and both of them outperform OFDM. Moreover, the proposed low complexity equalizers for UP-OCDM both show better BER performance than their OFDM counterparts.
A Multimodal, Adjustable Sensitivity, Digital 3-Axis Skin Sensor Module
Authors: Holgado Alexis Carlos , Tomo Tito Pradhono , Somlor Sophon , Sugano Shigeki
Published: June 2020

This paper presents major improvements to a multimodal, adjustable sensitivity skin sensor module. It employs a geomagnetic 3-axis Hall effect sensor to measure changes in the position of a magnetic field generated by an electromagnet. The electromagnet is mounted on a flexible material, and different current values can be supplied to it, enabling adjustments to the sensitivity of the sensor during operation. Capacitive sensing has been added in this iteration of the module, with two sensing modalities: “pre-touch” detection with proximity sensing and normal force capacitive sensing. The sensor has been designed to be interconnected with other sensor modules to be able to cover large surfaces of a robot with normal and shear force sensing and object proximity detection. Furthermore, this paper introduces important size reductions of the previous sensor design, calibration results, and further analysis of other sensor characteristics.
Effect of Substrate Etching on Terahertz Metamaterial Resonances and Its Liquid Sensing Applications
Authors: Park Sae June , Cunningham John
Published: June 2020

We investigate the effect of substrate etching on terahertz frequency range metamaterials using finite-element method simulations. A blue shift was found in the metamaterial resonance with increasing substrate etch depth, caused by a decrease in the effective refractive index. The relative contribution of the substrate’s refractive index to the effective refractive index was obtained as a function of the etch depth, finding that the decay length of the electric field magnitude below the LC gap is larger for the etched metamaterials due to their lower effective refractive index. We suggest designs for a terahertz metamaterial liquid sensor utilizing substrate etching which shows a significant enhancement in sensitivity compared to unetched sensors using ethanol as an example analyte. The sensitivity of the liquid sensor was enhanced by up to ~6.7-fold, from 76.4 to 514.5 GHz/RIU, for an ethanol liquid layer with a thickness of 60 μm by the incorporation of a substrate etch depth of 30 µm. Since the region of space close to the metamaterial is the most sensitive, however, we find that for small liquid thicknesses, larger etch depths can act to decrease sensitivity, and provide quantitative estimates of this effect.
BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones
Authors: Ganesan Rajesh , Raajini X. Mercilin , Nayyar Anand , Sanjeevikumar Padmanaban , Hossain Eklas , Ertas Ahmet H.
Published: June 2020

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.