bluuebunny/crossref_metadata_2025 huggingface dataset:
Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis
Authors: Yu Hongjuan , Chen Qiujie , Sun Yu , Sosnica Krzysztof
Published: May 2021

This study analyzes the geophysical signals in J2 time series from 1976 to 2020 by using singular spectrum analysis (SSA) and the Lomb-Scargle (L-S) periodogram for the first time. The results of SSA indicate that the secular trend is characterized by a superposition of the secular linear decrease with a rate of approximately (−5.80 ± 0.08) × 10−11/yr and an obvious quadratic rate of (2.38 ± 0.02) × 10−13/yr2. Besides, the annual, semi-annual, and 10.6-year signals with determining for the first time its amplitude of 5.01 × 10−11, are also detected by SSA, where their stochastic behavior can be maintained to the greatest extent. The 18.6-year signal cannot be detected by SSA even when the window size of 18.6 years was selected, while L-S periodogram can detect the signal of 18.6 years after removing the 18.6-year tidal theoretical value and the linear trend, proving the existence of the tidal variations of 18.6 years in the residual time series. Nevertheless, the 10.6-year signal can be found only after removing the secular trend. This fact suggests that the advantages of different methods used will lead to different sensitivity to the particular signals hard to be detected. Finally, the reconstructed ΔJ2 time series through the sum of the climate-driven contributions from glacial isostatic adjustment (GIA), Antarctic ice sheets (ANT), atmosphere (ATM), continental glaciers (GLA), Greenland ice sheets (GRE), ocean bottom pressure (OBP), and terrestrial water storage (TWS) by using GRACE gravity field solution and geophysical models agrees very well with that of the observed ΔJ2 from SLR in terms of the amplitude and phase. About 81.5% of observed ΔJ2 can be explained by the reconstructed value. ATM, TWS, and OBP are the most significant contributing sources for seasonal signals in ΔJ2 time series, explaining up to 40.1%, 31.9%, and 26.3% of the variances of observed ΔJ2. These three components contribute to the annual and semi-annual variations of the observed ΔJ2 up to 30.1% and 1.6%, 30.8% and 1.0%, as well as 25.4% and 0.7%, respectively. GRE, ANT, and GLA have ~3 to ~7-year periodic fluctuations and a positive linear trend, excluding GIA.
GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain
Authors: Oliveira Rui Jorge , Caldeira Bento , Teixidó Teresa , Borges José Fernando
Published: May 2021

Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation.
Modeling Gully Erosion Susceptibility to Evaluate Human Impact on a Local Landscape System in Tigray, Ethiopia
Authors: Busch Robert , Hardt Jacob , Nir Nadav , Schütt Brigitta
Published: May 2021

In recent years, modeling gully erosion susceptibility has become an increasingly popular approach for assessing the impact of different land degradation factors. However, different forms of human influence have so far not been identified in order to form an independent model. We investigate the spatial relation between gully erosion and distance to settlements and footpaths, as typical areas of human interaction, with the natural environment in rural African areas. Gullies are common features in the Ethiopian Highlands, where they often hinder agricultural productivity. Within a catchment in the north Ethiopian Highlands, 16 environmental and human-related variables are mapped and categorized. The resulting susceptibility to gully erosion is predicted by applying the Random Forest (RF) machine learning algorithm. Human-related and environmental factors are used to generate independent susceptibility models and form an additional inclusive model. The resulting models are compared and evaluated by applying a change detection technique. All models predict the locations of most gullies, while 28% of gully locations are exclusively predicted using human-related factors.
Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data
Authors: Jabs-Sobocińska Zofia , Affek Andrzej N. , Ewiak Ireneusz , Nita Mihai Daniel
Published: May 2021

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity.
Reverse-Time Migration Imaging of Ground-Penetrating Radar in NDT of Reinforced Concrete Structures
Authors: Shen Ruiqing , Zhao Yonghui , Hu Shufan , Li Bo , Bi Wenda
Published: May 2021

The evaluation and inspection of steel bars in reinforced concrete structures are critical for prolonging the service life of buildings. In this regard, ground-penetrating radar (GPR) has been a crucial alternative due to its non-invasiveness and convenience. This paper reports the experimental activities on a test-site area inside a camp in Shanghai, China. To assess the concrete structures of the building, GPR was employed for the detection and localization of rebars in columns, beams, and floors. From the GPR B-scan profiles acquired using a high-frequency antenna, the exact quantity of reinforcements was identified according to the hyperbola responses. Considering the difficulty of inferring the exact position and the scale of the rebars, we applied reverse time migration (RTM) to collapse the hyperbolic response and retrieve the target in a migrated image. To verify the effectiveness of the RTM algorithm, we carried out an experiment on a concrete model with three reinforced bars. We also utilized the RTM algorithm to process the B-scan profiles collected in a column that was later excavated. The imaging results validated the capacity of RTM in localizing and shaping rebars. Then, we employed the RTM algorithm for the GPR B-scan data collected from the other column. Based on the imaging profile, the quantity and positions of the rebars were correctly determined. Moreover, the thickness of the protective layer was evaluated according to the migrated result. These results demonstrate that GPR combined with RTM could provide useful foundation data for structural evaluation.
Self-Powered Smart Beehive Monitoring and Control System (SBMaCS)
Authors: Ntawuzumunsi Elias , Kumaran Santhi , Sibomana Louis
Published: May 2021

Beekeeping in Africa has been practiced for many years through successive generations and along inherited patterns. Beekeepers continue to face challenges in accessing consistent and business-driven markets for their bee products. In addition, the honeybee populations are decreasing due to colony collapse disorder (CCD), fire, loss of bees in swarming, honey buggers and other animals, moths, starvation, cold weather, and Varoa mites. The main issues are related to un-controlled temperature, humidity, and traditional management of beekeeping. These challenges result in low production of honey and colony losses. The control of the environmental conditions within and surrounding the beehives are not available to beekeepers due to the lack of monitoring systems. A Smart Beehive System using Internet of Things (IoT) technology would allow beekeepers to keep track of the amount of honey created in their hives and bee colonies even when they are far from their hives, through mobile phones, which would curtail the challenges currently faced by the beekeepers. However, there are challenges in the design of energy-efficient embedded electronic devices for IoT. A promising solution is to provide energy autonomy to the IoT nodes that will harvest residual energy from ambient sources, such as motion, vibrations, light, or heat. This paper proposes a Self-Powered Smart Beehive Monitoring and Control System (SBMaCS) using IoT to support remote follow-up and control, enhancing bee colonies’ security and thus increasing the honey productivity. First, we develop the SBMaCS hardware prototype interconnecting various sensors, such as temperature sensor, humidity sensor, piezoelectric transducer—which will work as a weight sensor—motion sensor, and flame sensor. Second, we introduce energy harvesting models to self-power the SBMaCS by analyzing the (i) energy harvested from adult bees’ vibrations, (ii) energy harvesting through the piezoelectric transducer, and (iii) radio frequency energy harvesting. Third, we develop a mobile phone application that interacts with the SBMaCS hardware to monitor and control the various parameters related to the beehives. Finally, the SBMaCS PCB layout is also designed. SBMaCS will help beekeepers to successfully monitor and control some important smart beekeeping activities wherever they are using their mobile phone application.
Deep Learning Methods for Improving Pollen Monitoring
Authors: Kubera Elżbieta , Kubik-Komar Agnieszka , Piotrowska-Weryszko Krystyna , Skrzypiec Magdalena
Published: May 2021

The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen grains are deposited on adhesive-coated tape, and slides are then prepared, which require further analysis by specialized personnel. Deep learning can be used to recognize pollen taxa based on microscopic images. This paper presents a method for recognizing a taxon based on microscopic images of pollen grains, allowing the pollen monitoring process to be automated. In this research, a deep CNN (convolutional neural network) model was built from scratch. Publicly available deep neural network models, pre-trained on image data (not including microscopic pictures), were also used. The results show that even a simple deep learning model produces quite good results when the classification of pollen grain taxa is performed directly from the images. The best deep learning model achieved 97.88% accuracy in the difficult task of recognizing three types of pollen grains (birch, alder, and hazel) with similar structures. The derived models can be used to build a system to support pollen monitoring experts in their work.
Toward an Estimation of the Optical Feedback Factor C on the Fly for Displacement Sensing
Authors: Bernal Olivier D. , Zabit Usman , Jayat Francis , Bosch Thierry
Published: May 2021

In this paper, a method based on the inherent event-based sampling capability of laser optical feedback interferometry (OFI) is proposed to assess the optical feedback factor C when the laser operates in the moderate and strong feedback regimes. Most of the phase unwrapping open-loop OFI algorithms rely on the estimation of C to retrieve the displacement with nanometric precision. Here, the proposed method operates in open-loop configuration and relies only on OFI’s fringe detection, thereby improving its robustness and ease of use. The proposed method is able to estimate C with a precision of <5%. The obtained performances are compared to three different approaches previously published and the impacts of phase noise and sampling frequency are reported. We also show that this method can assess C on the fly even when C is varying due to speckle. To the best of the authors’ knowledge, these are the first reported results of time-varying C estimation. In addition, through C estimation over time, it could pave the way not only to higher performance phase unwrapping algorithms but also to a better control of the optical feedback level via the use of an adaptive lens and thus to better displacement retrieval performances.
Rotation Active Sensors Based on Ultrafast Fibre Lasers
Authors: Kudelin Igor , Sugavanam Srikanth , Chernysheva Maria
Published: May 2021

Gyroscopes merit an undeniable role in inertial navigation systems, geodesy and seismology. By employing the optical Sagnac effect, ring laser gyroscopes provide exceptionally accurate measurements of even ultraslow angular velocity with a resolution up to 10−11 rad/s. With the recent advancement of ultrafast fibre lasers and, particularly, enabling effective bidirectional generation, their applications have been expanded to the areas of dual-comb spectroscopy and gyroscopy. Exceptional compactness, maintenance-free operation and rather low cost make ultrafast fibre lasers attractive for sensing applications. Remarkably, laser gyroscope operation in the ultrashort pulse generation regime presents a promising approach for eliminating sensing limitations caused by the synchronisation of counter-propagating channels, the most critical of which is frequency lock-in. In this work, we overview the fundamentals of gyroscopic sensing and ultrafast fibre lasers to bridge the gap between tools development and their real-world applications. This article provides a historical outline, highlights the most recent advancements and discusses perspectives for the expanding field of ultrafast fibre laser gyroscopes. We acknowledge the bottlenecks and deficiencies of the presented ultrafast laser gyroscope concepts due to intrinsic physical effects or currently available measurement methodology. Finally, the current work outlines solutions for further ultrafast laser technology development to translate to future commercial gyroscopes.
Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning
Authors: Ashraf Imran , Din Sadia , Hur Soojung , Kim Gunzung , Park Yongwan
Published: May 2021

Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade. The wide proliferation of smartphones and the availability of fast-speed internet have initiated several location-based services. Concerning the importance of precise location information, many sensors are embedded into modern smartphones. Besides Wi-Fi positioning, a rich variety of technologies have been introduced or adopted for indoor positioning such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. However, special emphasis is put on infrastructureless approaches like Wi-Fi and magnetic field-based positioning, as they do not require additional infrastructure. Magnetic field positioning is an attractive solution for indoors; yet lack of public benchmarks and selection of suitable benchmarks are among the big challenges. While several benchmarks have been introduced over time, the selection criteria of a benchmark are not properly defined, which leads to positioning results that lack generalization. This study aims at analyzing various public benchmarks for magnetic field positioning and highlights their pros and cons for evaluation positioning algorithms. The concept of DUST (device, user, space, time) and DOWTS (dynamicity, orientation, walk, trajectory, and sensor fusion) is introduced which divides the characteristics of the magnetic field dataset into basic and advanced groups and discusses the publicly available datasets accordingly.
The Influence of Plasticizers on the Response Characteristics of the Surfactant Sensor for Cationic Surfactant Determination in Disinfectants and Antiseptics
Authors: Sakač Nikola , Madunić-Čačić Dubravka , Karnaš Maja , Đurin Bojan , Kovač Ivan , Jozanović Marija
Published: May 2021

Surfactant liquid-membrane type sensors are usually made of a PVC, ionophore and a plasticizer. Plasticizers soften the PVC. Due to their lipophilicity, they influence the ion exchange across the membrane, ionophore solubility, membrane resistance and, consequently, the analytical signal. We used the DMI-TPB as an ionophore, six different plasticizers [2-nitrophenyl-octyl-ether (P1), bis(2-ethylhexyl) phthalate (P2), bis(2-ethylhexyl) sebacate (P3), 2-nitrophenyl phenyl ether (P4), dibutyl phthalate (P5) and dibutyl sebacate (P6)] and a PVC to produce ionic surfactant sensors. Sensor formulation with P1 showed the best potentiometric response to four usually used cationic surfactant, with the lowest LOD, 7 × 10−7 M; and potentiometric titration curves with well-defined and sharp inflexion points. The sensor with P6 showed the lowest analytical performances. Surfactant sensor with P1 was selected for quantification of cationic surfactant in model solutions and commercial samples of disinfectants and antiseptics. It showed high accuracy and precision in all determinations, with recovery from 98.2 to 99.6, and good agreement with the results obtained with surfactant sensor used as a referent one, and a standard two-phase titration method. RDS values were lower than 0.5% for all determinations.
Security Measures with Enhanced Behavior Processing and Footprint Algorithm against Sybil and Bogus Attacks in Vehicular Ad Hoc Network
Authors: Stępień Krzysztof , Poniszewska-Marańda Aneta
Published: May 2021

Vehicular ad hoc networks (VANETs) are created according to the principles of ad hoc mobile networks (MANETs), i.e., spontaneous creation of a wireless network for vehicle-to-vehicle (V2V) communication. Each vehicle in this network is treated as a node that is part of the mobile network. VANET turns all cooperating vehicles into a wireless router or node. This makes it possible to connect all cars within range to a stationary unit and create a wide network with a huge range. VANET is widely used for better traffic management, vehicle-to-vehicle communication, and road information provision. The VANET network is exposed to identity and information attacks, concealing or delaying data transmission, or information theft. Therefore, there are multiple types of attack, such as Sybil or bogus, that might harm the whole network infrastructure. The consequences of the mentioned two attacks could lead not only to the given infrastructure but could cause hammering people’s lives. In this paper, we analyze the ongoing methods for preserving Sybil and bogus attacks in a VANET network together with the authors’ methods: the Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm and the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F) algorithm. The first algorithm, the Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm was improved into the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F), presented in the paper. The proposed methods were tested with multiple scenarios using different variations of bogus and Sybil attack and various attacker–victim node number ratios. During analysis, it was observed that detection of all attackers in the network was reduced by approximately 30% in comparison to previous work and that of other cited authors.
Developing Multisensory Approach to the Optical Spectral Analysis
Authors: Bogomolov Andrey
Published: May 2021

This article presents an overview of research aimed at developing a scientific approach to creating multisensor optical systems for chemical analysis. The review is mainly based on the author’s works accomplished over the recent 10 years at Samara State Technical University with broad international cooperation. It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practical examples in different application fields: food and pharmaceutical production, medical diagnostics, and ecological monitoring. The conclusion summarizes trends and prospects of the multisensory approach to optical spectral analysis.
Platinum-Based Interdigitated Micro-Electrode Arrays for Reagent-Free Detection of Copper
Authors: Daly Robert , Narayan Tarun , Shao Han , O’Riordan Alan , Lovera Pierre
Published: May 2021

Water is a precious resource that is under threat from a number of pressures, including, for example, release of toxic compounds, that can have damaging effect on ecology and human health. The current methods of water quality monitoring are based on sample collection and analysis at dedicated laboratories. Recently, electrochemical-based methods have attracted a lot of attention for environmental sensing owing to their versatility, sensitivity and their ease of integration with cost effective, smart and portable readout systems. In the present work, we report on the fabrication and characterization of platinum-based interdigitated microband electrodes arrays, and their application for trace detection of copper. Using square wave voltammetry after acidification with mineral acids, a limit of detection of 0.8 μg/L was achieved. Copper detection was also undertaken on river water samples and compared with standard analytical techniques. The possibility of controlling the pH at the surface of the sensors—thereby avoiding the necessity to add mineral acids—was investigated. By applying potentials to drive the water splitting reaction at one comb of the sensor’s electrode (the protonator), it was possible to lower the pH in the vicinity of the sensing electrode. Detection of standard copper solutions down to 5 μg/L (ppb) using this technique is reported. This reagent free method of detection opens the way for autonomous, in situ monitoring of pollutants in water bodies.
Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications
Authors: Tonelli Alessandro , Mangia Veronica , Candiani Alessandro , Pasquali Francesco , Mangiaracina Tiziana Jessica , Grazioli Alessandro , Sozzi Michele , Gorni Davide , Bussolati Simona , Cucinotta Annamaria , Basini Giuseppina , Selleri Stefano
Published: May 2021

Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.
An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems
Authors: Zheng Hongxing , Yuan Jinpeng
Published: May 2021

Mission planning is the guidance for a UAV team to perform missions, which plays the most critical role in military and civil applications. For complex tasks, it requires heterogeneous cooperative multi-UAVs to satisfy several mission requirements. Meanwhile, airborne sensor allocation and path planning are the critical components of heterogeneous multi-UAVs system mission planning problems, which affect the mission profit to a large extent. This paper establishes the mathematical model for the integrated sensor allocation and path planning problem to maximize the total task profit and minimize travel costs, simultaneously. We present an integrated mission planning framework based on a two-level adaptive variable neighborhood search algorithm to address the coupled problem. The first-level is devoted to planning a reasonable airborne sensor allocation plan, and the second-level aims to optimize the path of the heterogeneous multi-UAVs system. To improve the mission planning framework’s efficiency, an adaptive mechanism is presented to guide the search direction intelligently during the iterative process. Simulation results show that the effectiveness of the proposed framework. Compared to the conventional methods, the better performance of planning results is achieved.
IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case
Authors: Massoud Rana , Berta Riccardo , Poslad Stefan , De Gloria Alessandro , Bellotti Francesco
Published: May 2021

Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users’ privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused.
An Intelligent Self-Service Vending System for Smart Retail
Authors: Xia Kun , Fan Hongliang , Huang Jianguang , Wang Hanyu , Ren Junxue , Jian Qin , Wei Dafang
Published: May 2021

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.
A Capacitive 3-Axis MEMS Accelerometer for Medipost: A Portable System Dedicated to Monitoring Imbalance Disorders
Authors: Szermer Michał , Zając Piotr , Amrozik Piotr , Maj Cezary , Jankowski Mariusz , Jabłoński Grzegorz , Kiełbik Rafał , Nazdrowicz Jacek , Napieralska Małgorzata , Sakowicz Bartosz
Published: May 2021

The constant development and miniaturization of MEMS sensors invariably provides new possibilities for their use in health-related and medical applications. The application of MEMS devices in posturographic systems allows faster diagnosis and significantly facilitates the work of medical staff. MEMS accelerometers constitute a vital part of such systems, particularly those intended for monitoring patients with imbalance disorders. The correct design of such sensors is crucial for gathering data about patient movement and ensuring the good overall performance of the entire system. This paper presents the design and measurements of a three-axis accelerometer dedicated for use in a device which tracks patient movement. Its main focus is the characterization of the sensor, comparing different designs and evaluating the impact of the packaging and readout circuit integration on sensor operation. Extensive testing and measurements confirm that the designed accelerometer works correctly and allows identifying the best design in terms of sensitivity/stability. Moreover, the response of the proposed sensor as a function of the applied acceleration demonstrates very good linearity only if the readout circuit is integrated in the same package as the MEMS sensor.
Sensitivity and Accuracy of Dielectric Measurements of Liquids Significantly Improved by Coupled Capacitive-Dependent Quartz Crystals
Authors: Matko Vojko , Milanovič Miro
Published: May 2021

A method to measure complex permittivity of liquids by using a capacitive-dependent quartz crystal and two quartz oscillators for temperature compensation in the frequency range of 4–10 MHz is described. Complex permittivity can be detected with high precision and sensitivity through a small change of capacitance and conductance, because a change in reactance in series with the quartz crystal impacts its resonant oscillation frequency. The temperature compensation in the range below 0.1 ppm is achieved by using two quartz oscillators that are made of elements of the same quality and have a temperature–frequency pair of quartz crystals. With the help of a reference oscillator, measurements of frequency are more accurate, because the frequency difference is in the kHz region, which also enables further processing of the signal by a microcontroller. With a proper calibration, the accuracy of this highly sensitive quartz crystal method is ±0.05%, which is an order of magnitude lower than that for a capacitance method without quartz crystals. The improved accuracy is of significant importance in the field of power engineering to monitor coolants and lubricants, oils, liquid fuels and other liquids, the dielectric properties of which are crucial for proper operation of devices.
Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse
Authors: Magalhães Sandro Augusto , Castro Luís , Moreira Germano , dos Santos Filipe Neves , Cunha Mário , Dias Jorge , Moreira António Paulo
Published: May 2021

The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15%, an mAP of 51.46% and an inference time of 16.44ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 ms.
Docking-Based Virtual Screening and Molecular Dynamics Simulations of Quercetin Analogs as Enoyl-Acyl Carrier Protein Reductase (InhA) Inhibitors of Mycobacterium tuberculosis
Authors: Pitaloka Dian Ayu Eka , Ramadhan Dwi Syah Fitra , Arfan  , Chaidir Lidya , Fakih Taufik Muhammad
Published: May 2021

The emergence of multidrug-resistant Mycobacterium tuberculosis (MTB) has become a major problem in treating tuberculosis (TB) and shows the need to develop new and efficient drugs for better TB control. This study aimed to use in silico techniques to discover potential inhibitors to the Enoyl-[acyl-carrier-protein] reductase (InhA), which controls mycobacterial cell wall construction. Initially, 391 quercetin analogs present in the KNApSAck_3D database were selected, filters were sequentially applied by docking-based virtual screening. After recategorizing the variables (bond energy prediction and molecular interaction, including hydrogen bond and hydrophobic bond), compounds C00013874, C00006532, and C00013887 were selected as hit ligands. These compounds showed great hydrophobic contributions, and for each hit ligand, 100 ns of molecular dynamic simulations were performed, and the binding free energy was calculated. C00013874 demonstrated the greatest capacity for the InhA enzyme inhibition with ΔGbind = −148.651 kcal/mol compare to NAD (native ligand) presented a ΔGbind = −87.570 kcal/mol. These data are preliminary studies and might be a suitable candidate for further experimental analysis.
Determination of Dehumidification Capacity of Water Wall with Controlled Water Temperature: Experimental Verification under Laboratory Conditions
Authors: Cakyova Katarina , Vranay Frantisek , Vertal Marian , Vranayova Zuzana
Published: May 2021

Water elements with flowing water on the surface are common in buildings as a form of indoor decoration, and they are most often perceived as passive humidifiers. However, by controlling water temperature, they can be also used for air dehumidification. The dehumidification capacity of indoor water elements was investigated experimentally under laboratory conditions. For the experimental verification of dehumidification capacity, a water wall prototype with an effective area of falling water film of 1 m2 and a measuring system were designed and developed. A total of 15 measurements were carried out with air temperatures ranging from 22.1 °C to 32.5 °C and relative humidity from 58.9% to 85.6%. The observed dehumidification capacity varied in the range of 21.99–315.36 g/h for the tested measurements. The results show that the condensation rate is a dynamic process, and the dehumidification capacity of a water wall strongly depends on indoor air parameters (air humidity and temperature). To determine the dehumidification capacity of a water wall for any boundary conditions, the equations were determined based on measured data, and two methods were used: the linear dependence between humidity ratio and condensation rate, and nonlinear surface fitting based on the dependence between the condensation rate, air temperature, and relative humidity.
Development of Platform Independent Mobile Learning Tool in Saudi Universities
Authors: Alghazzawi Daniyal M. , Hasan Syed Hamid , Aldabbagh Ghadah , Alhaddad Mohammed , Malibari Areej , Asghar Muhammad Zubair , Aljuaid Hanan
Published: May 2021

The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.
Empirical Study on the Relationship between Effective Following Behavior and Derived Creative Work Behavior: A Moderating Role of Perceived Organizational Support and Sustainable Leadership
Authors: Wang Xiaoyan , An Liren , Yasir Nosheena , Mahmood Nasir , Gu Ying
Published: May 2021

The leader of an organization and its members together constitute a binary structure of the main body of the organization and achievement of their goals. The existing literature mainly focuses on the characteristics and following behavior of members. Although the Leader-Member Exchange (LMX) theory uniquely explains the relationship between sustainable leaders and followers, it cannot as easily explain the creative work behavior of followers. This paper proposes that effective following behavior can stimulate the intrinsic motivation of followers and make this consistent with their work motivation, thereby producing creative work behavior and leading to the more effective achievement of organizational goals. Moreover, it demonstrates the internal relationships and mechanisms of action between effective following behavior and creative work behavior. Taking the two dimensions of effective following behavior as independent variables and the three dimensions of creative work behavior as dependent variables, a group of hypotheses were presented. The moderating role of the perceived organizational support and sustainable leadership in the relationship between effective following behavior and creative work behavior was demonstrated, and two groups of hypotheses were proposed. Data from 409 surveys, factor analysis, and multiple regression analysis were used to test the hypotheses. The empirical results show that in the organization the radical and incremental creative work behavior of the followers correlates positively with their active, participatory following behavior and the non-blind following behavior. The followers’ non-creative routine work behavior correlates negatively with their active participatory following behavior and has no significant correlation with the non-blind following behavior. In conclusion, sustainable leadership and perceived organizational support can therefore strengthen the relationship between actively participating in following behavior and creative work behavior and weaken the relationship between non-blind following behavior and creative work behavior.
Is the Greening Instrument a Valid Precedent for the New Green Architecture of the CAP? The Case of Spain
Authors: Díaz-Poblete Carmelo , García-Cortijo María Carmen , Castillo-Valero Juan Sebastián
Published: May 2021

The Common Agricultural Policy 2014–2020, set up in 2015 and extended until 31 December 2022, introduced a payment instrument called Greening. The aim of Greening was to encourage agricultural practices that are beneficial for the environment while also contributing to economic and territorial dynamism. The purpose of this article is to study the effect that Greening has had in Spain. We consider five difference-in-difference models, one for each of the variables proposed: ecological focus areas, permanent grasslands over utilised agricultural area, CO2 emissions stemming from agricultural land use changes and the presence of woody crops, agricultural income, and affiliates of the special agricultural regime. The data used come from the Spanish Ministry of Agriculture, Fisheries, and Food, the Ministry for Inclusion, Social Security, and Migrations, and the Spanish Emissions Inventory System. For the diff-in diff estimations, we use Spain’s regions as control and treatment units from 2011 to 2018, the aim being to provide observations ex ante Greening (2011–2014) and ex post (2015–2018). The results show that Greening had a limited impact, questioning its efficiency for meeting its goal, and that it is not a sound precedent for building the new green architecture of the CAP.
Employee Service Quality at Uzbekistani Halal Restaurants Amid the COVID-19 Pandemic
Authors: Shokhsanam Zoirova , Ahn Young-joo
Published: May 2021

In this study, we examined the employee service dimensions which are associated with satisfaction and customers’ intention to revisit among Uzbekistani customers who visit halal restaurants in Korea. We also investigated the situational factor of the pandemic outbreak and the moderating role of the restaurant locations. A total of 264 respondents participated. The results revealed that behavior quality and appearance were important employee service quality dimensions for halal restaurants. However, interaction quality and expertise quality were not associated with satisfaction. The results may indicate that consumers did not prefer human interactions at the restaurants because of the risk of Coronavirus disease (COVID-19). The results affirmed that customers who positively evaluated their restaurant experience showed a higher intention to visit again. Finally, the findings showed a moderating effect of the location (the capital city vs. others). The customers visiting restaurants in the capital city were likely to be satisfied with employees wearing masks and wearing clean uniforms. These findings could enrich the literature on the multidimensional aspects of service encounters at Uzbekistani halal restaurants considering the tangible and intangible service qualities of employees during COVID-19.
Functionalized Carbon Nanotubes (CNTs) for Water and Wastewater Treatment: Preparation to Application
Authors: Aslam Mian Muhammad-Ahson , Kuo Hsion-Wen , Den Walter , Usman Muhammad , Sultan Muhammad , Ashraf Hadeed
Published: May 2021

As the world human population and industrialization keep growing, the water availability issue has forced scientists, engineers, and legislators of water supply industries to better manage water resources. Pollutant removals from wastewaters are crucial to ensure qualities of available water resources (including natural water bodies or reclaimed waters). Diverse techniques have been developed to deal with water quality concerns. Carbon based nanomaterials, especially carbon nanotubes (CNTs) with their high specific surface area and associated adsorption sites, have drawn a special focus in environmental applications, especially water and wastewater treatment. This critical review summarizes recent developments and adsorption behaviors of CNTs used to remove organics or heavy metal ions from contaminated waters via adsorption and inactivation of biological species associated with CNTs. Foci include CNTs synthesis, purification, and surface modifications or functionalization, followed by their characterization methods and the effect of water chemistry on adsorption capacities and removal mechanisms. Functionalized CNTs have been proven to be promising nanomaterials for the decontamination of waters due to their high adsorption capacity. However, most of the functional CNT applications are limited to lab-scale experiments only. Feasibility of their large-scale/industrial applications with cost-effective ways of synthesis and assessments of their toxicity with better simulating adsorption mechanisms still need to be studied.