bluuebunny/crossref_metadata_2025 huggingface dataset:
Grassland Conservation Effectiveness of National Nature Reserves in Northern China
Authors: Zhao Siqing , Zhao Xiang , Zhao Jiacheng , Liu Naijing , Sun Mengmeng , Mu Baohui , Sun Na , Guo Yinkun
Published: April 2022

Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness in grasslands. Based on a multiyear time series of the growing season composite from 2000 to 2020, we proposed an effectiveness score to assess the effectiveness of the NNRs, using the 250 m MOD13Q1 NDVI data with Theil–Sen and Mann–Kendall trend analysis methods. We found the following: 22 of 30 NNRs were deemed effective in protecting the Inner Mongolian grasslands. The NNRs increased pixels with a sustainable trend 19.26% and 20.55% higher than the unprotected areas and the IMAR, respectively. The pixels with a CVNDVI < 0.1 (i.e., NDVI coefficient of variation) in the NNRs increased >35.22% more than those in the unprotected areas and the IMAR. The NDVI changes within the NNRs showed that 63.64% of NNRs had a more significant trend of greening than before the change point, which suggests a general greening in NNRs. We also found that the NNRs achieved heterogeneous effectiveness scores across protection types. Forest ecology protection and wildlife animal protection types are the most efficient, whereas wildlife vegetation protection is the least effective type. This study enriches the understanding of grassland conservation and sheds light on the future direction of the sustainable management of NNRs.
The August 2019 Piton de la Fournaise (La Réunion Island) Eruption: Analysis of the Multi-Source Deformation Pattern Detected through Sentinel-1 DInSAR Measurements
Authors: Valerio Emanuela , De Luca Claudio , Lanari Riccardo , Manzo Mariarosaria , Battaglia Maurizio
Published: April 2022

Piton de la Fournaise is one of the most active worldwide volcanoes, located on the southeastern part of La Réunion Island. In this work, we focus on the eruption that occurred on the southeastern flank of this volcano, inside the Enclos Fouqué caldera, from 11 to 15 August 2019. This distal event was characterized by the opening of two eruptive fissures and accompanied by shallow volcano–tectonic earthquakes. We exploit the ground displacements using Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, which include the ground deformations generated during both the pre- and co-eruptive phases. To investigate the sources responsible for the detected ground displacements, we perform an analytical modeling of the retrieved DInSAR measurements. Our results reveal the presence of five volcanic sources (i.e., one sill-like source and four dikes), whose concomitant action during the pre- and co-eruptive phases generated the complex detected deformation pattern. The retrieved volcanic sources correlate well with the location of the opened fissures, the spatial distribution and the temporal evolution of the recorded seismicity, and other geophysical evidence already known in the literature.
Drone-Borne Ground-Penetrating Radar for Snow Cover Mapping
Authors: Vergnano Andrea , Franco Diego , Godio Alberto
Published: April 2022

Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020–2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air–snow interface and the snow–ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (<200 kg/m3) and this limits the detectability of the air–snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow–air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system.
Remote Sensing Monitoring of the Spatial Pattern of Greening and Browning in Xilin Gol Grassland and Its Response to Climate and Human Activities
Authors: Hui Jiawei , Chen Zanxu , Ye Baoying , Shi Chu , Bai Zhongke
Published: April 2022

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area’s vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection.
PlumeTraP: A New MATLAB-Based Algorithm to Detect and Parametrize Volcanic Plumes from Visible-Wavelength Images
Authors: Simionato Riccardo , Jarvis Paul A. , Rossi Eduardo , Bonadonna Costanza
Published: April 2022

Tephra plumes from explosive volcanic eruptions can be hazardous for the lives and livelihoods of people living in the proximity of volcanoes. Monitoring and forecasting tephra plumes play essential roles in the detection, characterization and hazard assessment of explosive volcanic events. However, advanced monitoring instruments, e.g., thermal cameras, can be expensive and are not always available in monitoring networks. Conversely, visible-wavelength cameras are significantly cheaper and much more widely available. This paper proposes an innovative approach to the detection and parametrization of tephra plumes, utilizing videos recorded in the visible wavelengths. Specifically, we have developed an algorithm with the objectives of: (i) identifying and isolating plume-containing pixels through image processing techniques; (ii) extracting the main geometrical parameters of the eruptive column, such as the height and width, as functions of time; and (iii) determining quantitative information related to the plume motion (e.g., the rise velocity and acceleration) using the physical quantities obtained through the first-order analysis. The resulting MATLAB-based software, named Plume Tracking and Parametrization (PlumeTraP), semi-automatically tracks the plume and is also capable of automatically calculating the associated geometric parameters. Through application of the algorithm to the case study of Vulcanian explosions from Sabancaya volcano (Peru), we verify that the eruptive column boundaries are well recognized, and that the calculated parameters are reliable. The developed software can be of significant use to the wider volcanological community, enabling research into the dynamics of explosive volcanic eruptions, as well as potentially improving the use of visible-wavelength cameras as part of the monitoring networks of active volcanoes. Furthermore, PlumeTraP could potentially find a broader application for the analysis of any other plume-shaped natural or anthropogenic phenomena in visible wavelengths.
Impact of Elevation-Dependent Warming on Runoff Changes in the Headwater Region of Urumqi River Basin
Authors: Zheng Zhouyao , Hong Sheng , Deng Haijun , Li Zhongqin , Jin Shuang , Chen Xingwei , Gao Lu , Chen Ying , Liu Meibing , Luo Pingping
Published: April 2022

Warming in mountainous areas has obvious elevation dependence (warming rate increases with elevation), which deeply impacts runoff change in mountainous areas. This study analysed the influence of elevation-dependent warming on runoff in the headwater region of the Urumqi River Basin (URB) based on meteorological data, remote sensing images, and runoff data. Results indicated a significant warming rate in the URB from 1960 to 2019 (0.362 °C/decade; p < 0.01). The temperature increased with an obvious elevation-dependent warming in the URB, especially during winter. Glaciers sharply retreated in the headwater region of the URB under regional warming, and remote-based results showed that glacier areas decreased by 29.45 km2 (−57.81%) from the 1960s to 2017. The response of glacier mass balance and meltwater runoff to temperature change has a lag of 3 years in the headwater region of the URB. The elevation-dependent warming of temperature changes significantly impacted glacial meltwater runoff in the URB (R2 = 0.49). Rising temperatures altered the glacial meltwater runoff, and the maximum annual runoff of the Urumqi Glacier No. 1 meltwater runoff increased 78.6% in 1990–2017 compared to 1960–1990. During the period of 1960–1996, the total glacial meltwater runoff amounted to 26.9 × 108 m3, accounting for 33.4% of the total runoff during this period, whereas the total glacial meltwater runoff accounted for 51.1% of the total runoff in 1996–2006. Therefore, these results provide a useful reference for exploring runoff changes in mountainous watersheds in the context of elevation-dependent warming.
Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador
Authors: Morante-Carballo Fernando , Bravo-Montero Lady , Carrión-Mero Paúl , Velastegui-Montoya Andrés , Berrezueta Edgar
Published: April 2022

Worldwide, forest fires exert effects on natural ecosystems, contributing to economic/human losses, health effects, and climate change. Spectral indices are an essential tool for monitoring and analyzing forest fires. These indices make it possible to evaluate the affected areas and help mitigate possible future events and reduce damage. The case study addressed in this work corresponds to the Cerro of the Guadual community of La Carolina parish (Ibarra, Ecuador). This work aims to evaluate the degree of severity and the recovery of post-fire vegetation, employing the multitemporal analysis of spectral indices and correlating these with the climatological aspects of the region. The methodological process was based on (i) background information collection, (ii) remote sensing data, (iii) spectral index analysis, (iv) multivariate analysis, and (v) a forest fire action plan proposal. Landsat-8 OLI satellite images were used for multitemporal analysis (2014–2020). Using the dNDVI index, the fire’s severity was classified as unburned and very low severity in regard to the areas that did not regenerate post-fire, which represented 10,484.64 ha. In contrast, the areas classified as high and very high severity represented 5859.06 ha and 2966.98 ha, respectively. In addition, the dNBR was used to map the burned areas. The high enhanced regrowth zones represented an area of 8017.67 ha, whereas the moderate/high-severity to high-severity zones represented 3083.72 ha and 1233.49 ha, respectively. The areas with a high severity level corresponded to native forests, which are challenging to recover after fires. These fire severity models were validated with 31 in situ data from fire-starting points and they presented an accuracy of 99.1% in the high severity category. In addition, through the application of principal component analysis (PCA) with data from four meteorological stations in the region, a bimodal behavior was identified corresponding to the climatology of the area (dry season and rainy season), which is related to the presence of fires (in the dry season). It is essential to note that after the 2014 fire, locally, rainfall decreased and temperatures increased. Finally, the proposed action plan for forest fires made it possible to define a safe and effective evacuation route to reduce the number of victims during future events.
Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision
Authors: Lin Sheng , Zhang Min , Cheng Xi , Wang Liang , Xu Maiping , Wang Hai
Published: April 2022

Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.
Semi-Supervised Adversarial Semantic Segmentation Network Using Transformer and Multiscale Convolution for High-Resolution Remote Sensing Imagery
Authors: Zheng Yalan , Yang Mengyuan , Wang Min , Qian Xiaojun , Yang Rui , Zhang Xin , Dong Wen
Published: April 2022

Semantic segmentation is a crucial approach for remote sensing interpretation. High-precision semantic segmentation results are obtained at the cost of manually collecting massive pixelwise annotations. Remote sensing imagery contains complex and variable ground objects and obtaining abundant manual annotations is expensive and arduous. The semi-supervised learning (SSL) strategy can enhance the generalization capability of a model with a small number of labeled samples. In this study, a novel semi-supervised adversarial semantic segmentation network is developed for remote sensing information extraction. A multiscale input convolution module (MICM) is designed to extract sufficient local features, while a Transformer module (TM) is applied for long-range dependency modeling. These modules are integrated to construct a segmentation network with a double-branch encoder. Additionally, a double-branch discriminator network with different convolution kernel sizes is proposed. The segmentation network and discriminator network are jointly trained under the semi-supervised adversarial learning (SSAL) framework to improve its segmentation accuracy in cases with small amounts of labeled data. Taking building extraction as a case study, experiments on three datasets with different resolutions are conducted to validate the proposed network. Semi-supervised semantic segmentation models, in which DeepLabv2, the pyramid scene parsing network (PSPNet), UNet and TransUNet are taken as backbone networks, are utilized for performance comparisons. The results suggest that the approach effectively improves the accuracy of semantic segmentation. The F1 and mean intersection over union (mIoU) accuracy measures are improved by 0.82–11.83% and 0.74–7.5%, respectively, over those of other methods.
Hyperspectral Image Denoising via Adversarial Learning
Authors: Zhang Junjie , Cai Zhouyin , Chen Fansheng , Zeng Dan
Published: April 2022

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method.
Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources
Authors: Eide Siri S. , Riegler Michael A. , Hammer Hugo L. , Bremnes John Bjørnar
Published: April 2022

Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.
A Systematic Review of the Transthoracic Impedance during Cardiac Defibrillation
Authors: Heyer Yasmine , Baumgartner Daniela , Baumgartner Christian
Published: April 2022

For cardiac defibrillator testing and design purposes, the range and limits of the human TTI is of high interest. Potential influencing factors regarding the electronic configurations, the electrode/tissue interface and patient characteristics were identified and analyzed. A literature survey based on 71 selected articles was used to review and assess human TTI and the influencing factors found. The human TTI extended from 12 to 212 Ω in the literature selected. Excluding outliers and pediatric measurements, the mean TTI recordings ranged from 51 to 112 Ω with an average TTI of 76.7 Ω under normal distribution. The wide range of human impedance can be attributed to 12 different influencing factors, including shock waveforms and protocols, coupling devices, electrode size and pressure, electrode position, patient age, gender, body dimensions, respiration and lung volume, blood hemoglobin saturation and different pathologies. The coupling device, electrode size and electrode pressure have the greatest influence on TTI.
Electropolymerized, Molecularly Imprinted Polymer on a Screen-Printed Electrode—A Simple, Fast, and Disposable Voltammetric Sensor for Trazodone
Authors: Seguro Isabel , Rebelo Patrícia , Pacheco João G. , Delerue-Matos Cristina
Published: April 2022

In recent years, analytical chemistry has been facing new challenges, particularly in developing low-cost, green, and easy-to-reproduce methods. In this work, a simple, reproducible, and low-cost electrochemical (voltammetric) molecularly imprinted polymer (MIP) sensor was designed specifically for the detection of trazodone (TZD). Trazodone (TZD) is an antidepressant drug consumed worldwide since the 1970s. By combining electropolymerization (surface imprinting) with screen-printed electrodes (SPCEs), the sensor is easy to prepare, is environmentally friendly (uses small amounts of reagents), and can be used for in situ analysis through integration with small, portable devices. The MIP was obtained using cyclic voltammetry (CV), using 4-aminobenzoic acid (4-ABA) as the functional monomer in the presence of TZF molecules in 0.1 M HCl. Non-imprinted control was also constructed in the absence of TZD. Both polymers were characterized using CV, and TZD detection was performed with DPV using the oxidation of TZD. The polymerization conditions were studied and optimized. Comparing the TZD signal for MIP/SPCE and NIP/SPCE, an imprinting factor of 71 was estimated, indicating successful imprinting of the TZD molecules within the polymeric matrix. The analytical response was linear in the range of 5–80 µM, and an LOD of 1.6 µM was estimated. Selectivity was evaluated by testing the sensor for molecules with a similar structure to TZD, and the ability of MIP/SPCE to selectively bind to TZD was proven. The sensor was applied to spiked tap water samples and human serum with good recoveries and allowed for a fast analysis (around 30 min).
Remote Sensing System for Motor Nerve Impulse
Authors: Moldovan Carmen Aura , Ion Marian , Dragomir David Catalin , Dinulescu Silviu , Mihailescu Carmen , Franti Eduard , Dascalu Monica , Dobrescu Lidia , Dobrescu Dragos , Gheorghe Mirela-Iuliana , Blystad Lars-Cyril , Ohlckers Per Alfred , Marchetti Luca , Imenes Kristin , Hønsvall Birgitte Kasin , Ramirez-Sarabia Jairo , Lascar Ioan , Neagu Tiberiu Paul , Raita Stefania , Costea Ruxandra , Barbilian Adrian , Gherghiceanu Florentina , Stoica Cristian , Niculae Catalin , Predoi Gabriel , Carbunaru Vlad , Ionescu Octavian , Oproiu Ana Maria
Published: April 2022

In this article, we present our research achievements regarding the development of a remote sensing system for motor pulse acquisition, as a first step towards a complete neuroprosthetic arm. We present the fabrication process of an implantable electrode for nerve impulse acquisition, together with an innovative wirelessly controlled system. In our study, these were combined into an implantable device for attachment to peripheral nerves. Mechanical and biocompatibility tests were performed, as well as in vivo testing on pigs using the developed system. This testing and the experimental results are presented in a comprehensive manner, demonstrating that the system is capable of accomplishing the requirements of its designed application. Most significantly, neural electrical signals were acquired and transmitted out of the body during animal experiments, which were conducted according to ethical regulations in the field.
Ultra-High SNR Demodulation Method for Optical Fiber Sensors Applied in Power Transformer Partial Discharge Detection
Authors: Qiao Jixian , Zhang Weichao , Wang Yanqi , Shao Qibing , Cai Jianlong , Zhao Hong
Published: April 2022

The demodulation method of optical fiber sensors utilized in power transformer partial discharge (PD) detection is insufficient for engineering applications. We design a distributed feedback fiber laser (DFB-FL) PD detection system with an asymmetric 3 × 3 coupler and propose an ultra-high signal-to-noise ratio (SNR) demodulation scheme by eliminating the main factors that affect the traditional method using an asymmetric 3 × 3 coupler. The power transformer PD detection results reveal that the proposed scheme is free from 3 × 3 coupler asymmetry issues, with an average SNR of 38.30 dB, which is much higher than the widely used demodulation method and the piezoelectric transducer sensor. The average SNR of the system is increased by 24.2 dB with the proposed method.
Adaptive Thin Film Temperature Sensor for Bearing’s Rolling Elements Temperature Measurement
Authors: Cui Yunxian , Gao Pengfei , Tang Wuchu , Mo Guowei , Yin Junwei
Published: April 2022

With the continuous improvement of train speeds, it is necessary to find the possible problems of bearings in time, otherwise they will cause serious consequences. Aiming at the characteristics of rapid temperature change of bearings, a thin film thermocouple temperature sensor was developed to measure the real-time temperature of the bearing’s rolling elements during train operation. Using dc pulse magnetron sputtering technology, Al2O3 film, NiCr film, NiSi film, and SiO2 film were successively deposited on an aluminum alloy substrate. We studied their microstructure, static characteristics, dynamic characteristics, and repeatability. Finally, we installed an adaptive film temperature sensor on the bearing testing machine to measure the temperature of the rolling elements. The results show that the developed temperature sensor has good linearity in the range of 30~180 ℃. The Seebeck coefficient is 40.69 μV/℃, the nonlinear fitting error is less than 0.29%, the maximum repeatability error is less than 4.55%, and the dynamic response time is 1.42 μs. The temperature of the measured rolling elements is 6~10 ℃ higher than that of the outer ring, which can reflect the actual temperature of the bearing operation.
Downlink Performance Modeling and Evaluation of Batteryless Low Power BLE Node
Authors: Sultania Ashish Kumar , Delgado Carmen , Blondia Chris , Famaey Jeroen
Published: April 2022

Deploying low maintenance and long-life systems is an important requirement of emerging commercial Internet of Things (IoT) solutions. Such systems can be envisioned in which the connected devices are powered by energy harvested from ambient sources and stored in long-lifetime capacitors rather than short-lived and polluting batteries. However, due to the unpredictable nature of ambient energy harvesting, such batteryless IoT devices might not always have enough energy to initiate communication. The Bluetooth Low Energy (BLE) specification defines support for Low Power Nodes (LPNs) using the friendship feature, where the LPN is associated with a neighbouring friend node (FN). The LPN can receive downlink (DL) data and remain connected to the network via the FN that buffers the LPN’s incoming packets while allowing the LPN to save energy by sleeping or turning itself off. This novel BLE feature makes the LPN highly suitable to support the connection of batteryless ambiently-powered IoT devices. While the LPN can decide when to transmit uplink (UL) packets and does not depend on the FN to receive downlink (DL) data, the LPN needs to poll its FN to receive the buffered packets. However, the DL packet latency increases with this process due to the buffering time at the FN. Therefore, in this work, we present an analytical model to characterize the performance as a function of DL data latency and packet delivery ratio (PDR) of a batteryless LPN powered by different harvesting powers and capacitor sizes. This would help to optimally choose the correct configuration of the batteryless LPN for its network deployment. We also compare the analytical model and simulation results, showing consistency with an average error of 2.23% for DL data latency and 0.09% for the PDR.
Application of Blockchain Technology in Production Scheduling and Management of Human Resources Competencies
Authors: Balon Barbara , Kalinowski Krzysztof , Paprocka Iwona
Published: April 2022

Today, enterprises are multitasking, with branches set up all over the world. Virtual enterprises are created to make better use of existing resources, improve the quality of manufactured products and agilely respond to customer requirements. In order to fully meet the requirements of enterprises, a decentralized structure of data registration and transmission and authentication of network users is needed. The information collected via the Internet of Things and flowing based on the properties of the Blockchain (BC) network facilitates enterprise resource planning and enables the integration of internal processes, especially when planning, changing the current or introducing new production. The aim of this paper is to present the concept of using a common data register in BC technology, which enables a number of applications related to the automation of the process of selecting human resources for production tasks. The paper presents an analysis of the problem related to the integration of production scheduling and human resource management with blockchain technology. Also presented is a literature analysis on scheduling, blockchain technology and data storage in the blockchain network. The analysis presents how the blockchain network works and how exactly it fits into production engineering with its advantages and disadvantages. An employee evaluation method based on the resource work history and determination of its current value within individual competencies is presented. The integration of production scheduling and human resource management with the use of BC technology is simulated. The most important advantage is faster and more effective planning thanks to the elimination of all intermediary channels in the flow of production transactions. Production tasks are balanced with production capacity in entities belonging to the virtual enterprise in parallel. For future research, different online planning algorithms will be developed and compared to achieve consortium members’ consensus on production and human resources planning.
A Further Theoretical Study of Capacitive Pressure Sensors Based on Thin Film Elastic Deflection and Parallel Plate Capacitor: Refined Closed-Form Solution and Numerical Calibration
Authors: Guo Ying , Li Bo , Zhang Qi , He Xiao-Ting , Sun Jun-Yi
Published: April 2022

The capacitive pressure sensor based on thin film elastic deflection and a parallel plate capacitor uses a non-conductive elastic annular thin film centrally connected to a conductive, rigid, flat, concentric-circular thin plate as a pressure sensing unit. On application of pressure, the non-conductive thin film deflects elastically, which in turn moves the conductive thin plate (as a movable upper electrode plate of the parallel plate capacitor) towards the lower electrode plate, resulting in a change in the capacitance of the capacitor. Therefore, the applied pressure can be determined by measuring the capacitance change, based on the closed-form solution for the elastic behavior of the annular thin film under pressure. Such capacitive pressure sensors are more suitable for large-sized sensors such as those used for building-facade wind pressure measurements, etc. In this paper, a further theoretical study of such capacitive pressure sensors is presented. The newly presented, more refined closed-form solution can greatly reduce the output pressure error under the same input capacitance, in comparison with the previously presented closed-form solution. A numerical example of how to use the resulting closed-form solution to numerically calibrate input–output characteristics is given for the first time. The variation trend of pressure operation ranges and input–output characteristics with important parametric variations, which can be used for guiding the design of such capacitive pressure sensors, is investigated.
Cartography and Landscape of Agrarian Colonisation Villages in Spain during the 20th Century: Graphic Recording and Reconstruction of the Productive Territory, the Case of El Chaparral (Granada)
Authors: Rodríguez-Aguilera Ana Isabel , Domingo-Santos Juan
Published: April 2022

The Spanish domestic colonisation of the 20th century was a major territorial management and planning project which followed a policy of recovering agricultural land and making it habitable, emulating different international innovative collective models for rural exploitation. In Spain, this led to the creation of approximately three hundred colonisation villages between 1939 and 1977 which, along with their landscapes, represented a comprehensive vision of land intervention, transformed following minimal energy criteria and making use of pre-existing elements and infrastructures. The proposals of this ambitious project already incorporated ideas which later formed the basis of the modern theories of ecological and landscape urbanism and geodesign. This paper presents cartographic research based on the reconstruction of the landscape before and after this operation, following an overlay-mapping methodology applied to the colonised area of El Chaparral (Granada), which provides an extraordinary overview of the territorial transformation process carried out at different scales. The work concludes that the success of the Spanish domestic colonisation model for comprehensive agrarian planning lies in its multiscale and multidisciplinary perspective, which was developed in continuity with and in a way that was respectful of the characteristics and elements of the landscape. For these main reasons, its approaches are valid for future agroecological developments, and the colonisation villages are a valuable model of rural habitability to be updated.
An Empirical Investigation of Reasons Influencing Student Acceptance and Rejection of Mobile Learning Apps Usage
Authors: Al-Bashayreh Mahmood , Almajali Dmaithan , Altamimi Ahmad , Masa’deh Ra’ed , Al-Okaily Manaf
Published: April 2022

Sustainable learning and education (SLE) is a relatively new ideology based on sustainability principles and developed in response to the United Nations’ recently proclaimed Sustainable Development Goals (SDGs). As a result, technologies should be adopted to equip educational institutions with the tools necessary to attain SLE. Recently, the coronavirus (COVID-19) pandemic has affected educational systems globally, leading them to embrace more innovative technological methods to meet academic demands while maintaining SLE principles. Mobile learning apps (MLA) refers to using the unique capabilities of mobile apps to engage and collaborate towards establishing robust online learning. However, the effectiveness of MLA depends on learners’ acceptance. Therefore, the purpose of this study is to investigate the factors that could affect MLA acceptance. In order to analyze the collected data from 415 Jordanian students among schools and universities, structural equation modeling (SEM) was used. The empirical findings confirm that perceived usefulness and perceived ease of use are significantly influenced by self-efficacy and perceived compatibility. Furthermore, perceived usefulness is significantly influenced by perceived convenience and perceived ease of use. Additionally, perceived enjoyment significantly influences the behavioral intention to use MLA. On the other hand, perceived compatibility has no significant influence on perceived enjoyment. Finally, perceived ease of use, perceived usefulness, and perceived compatibility have no significant effect on behavioral intention to use MLA. This study addresses a critical research gap in the distance learning acceptance literature by proposing an exhaustive model in the post-COVID-19 era that can help to improve students’ performance and outcomes in Jordanian schools and universities.
Green Synthesis of Nanoparticles by Mushrooms: A Crucial Dimension for Sustainable Soil Management
Authors: Elsakhawy Tamer , Omara Alaa El-Dein , Abowaly Mohamed , El-Ramady Hassan , Badgar Khandsuren , Llanaj Xhensila , Törős Gréta , Hajdú Peter , Prokisch József
Published: April 2022

Soil is the main component in the agroecosystem besides water, microbial communities, and cultivated plants. Several problems face soil, including soil pollution, erosion, salinization, and degradation on a global level. Many approaches have been applied to overcome these issues, such as phyto-, bio-, and nanoremediation through different soil management tools. Mushrooms can play a vital role in the soil through bio-nanoremediation, especially under the biological synthesis of nanoparticles, which could be used in the bioremediation process. This review focuses on the green synthesis of nanoparticles using mushrooms and the potential of bio-nanoremediation for polluted soils. The distinguished roles of mushrooms of soil improvement are considered a crucial dimension for sustainable soil management, which may include controlling soil erosion, improving soil aggregates, increasing soil organic matter content, enhancing the bioavailability of soil nutrients, and resorting to damaged and/or polluted soils. The field of bio-nanoremediation using mushrooms still requires further investigation, particularly regarding the sustainable management of soils.
A Reputational-Risk-Based Match Selection Framework for Collaborative Networks in the Logistics Sector
Authors: Anes Vitor , Abreu António , Dias Ana , Calado João
Published: April 2022

Collaborative networks in the logistics sector have proven to be a solution that both meets environmental footprint reduction goals and addresses the impact of rising fuel prices on logistics companies, especially for small- and medium-sized enterprises. Despite these benefits, these collaborative networks have not received the desired amount of participation due to reputational risk. This paper develops a framework for assessing and managing reputational risk to encourage logistics companies’ participation in collaborative networks. To this end, customer satisfaction factors were correlated with logistics operations, and this correlation was then modeled using the Bowtie method, fault trees, event trees, reliability theory, and the Monte Carlo model. The results show that it is possible to implement a structured model that can be easily put into practice. Using an illustrative case study, it is also possible to prioritize three companies according to their reputational risk as assessed by the proposed model. The developed model can promote the sustainability of collaborative networks in the logistics industry by assessing and consistently reducing reputational risk, thus supporting the strengthening of the relationship between suppliers, logistics service providers, and end customers.
Nexus of Innovation Network, Digital Innovation and Frugal Innovation towards Innovation Performance: Investigation of Energy Firms
Authors: Nassani Abdelmohsen A. , Sinisi Crenguta , Paunescu Loredana , Yousaf Zahid , Haffar Mohamed , Kabbani Ahmad
Published: April 2022

The present research aims to find how firms achieve innovation performance through innovation networks. This study also explores the mediating role of digital innovation and the moderating role of frugal innovation. Quantitative research design is used for data collection and analysis. To analyze the study’s hypotheses we select the energy firms which aim to attain innovation performance. An indirect effect with Soble test was used to check mediation analysis. The results proved that the innovation performance of energy firms is predicted by innovation networks. Our findings proved that digital innovation acts as a mediator between innovation networks and innovation performance links. Our results also show that frugal innovation strengthens the interplay between innovation networks and innovation performance links. This study highlights how energy firms can stabilize innovation performance through the combined influence of innovation networks, digital innovation and frugal innovation. The managers of energy firms should prefer innovation networks to update their knowledge about the upcoming/latest procedures to achieve innovation performance. Moreover, the role of digital innovation in the current digital world is also very important, and the present study used it as mediator.
An Energy Performance Contract Optimization Approach to Meet the Competing Stakeholder Expectations under Uncertainty: A Canadian Case Study
Authors: Prabatha Tharindu , Hewage Kasun , Sadiq Rehan
Published: April 2022

Energy performance contracts (EPC) can address economic sustainability challenges associated with residential energy retrofitting projects, including funding limitations, poor quality of project delivery, and landlord-tenant dilemma. Literature has overlooked the impact of weighted average cost of capital (WACC) and funding sources in EPC planning. However, the WACC, stakeholder priorities, and uncertainties can alter the project outcomes. This study proposes a Monte-Carlo simulation based non-linear multi-objective optimization approach to address the aforementioned challenges. A case study conducted in British Columbia indicated that the maximum overall project profitability can vary between $18,035 and $20,626 with decision priorities. The overall project profitability can vary over 9% due to uncertainties. The project profits can change over $3000 due to changes in the WACC. These observations confirmed the criticality of accounting for WACC, stakeholder priorities, and uncertainties in EPC planning. The risk of compensating for the performance compromises and profits increases simultaneously for the energy services company with the increasing contract periods, while it is inverse for the owners. Therefore, the contract period must be decided considering the profit expectations and risk tolerance of the stakeholders. Extended contract periods allow lower capital contributions from the building owners, potentially solving the principal-agent disputes in rental buildings.
Cadmium-Tolerant Plant Growth-Promoting Bacteria Curtobacterium oceanosedimentum Improves Growth Attributes and Strengthens Antioxidant System in Chili (Capsicum frutescens)
Authors: Patel Mitesh , Patel Kartik , Al-Keridis Lamya Ahmed , Alshammari Nawaf , Badraoui Riadh , Elasbali Abdelbaset Mohamed , Al-Soud Waleed Abu , Hassan Md Imtaiyaz , Yadav Dharmendra Kumar , Adnan Mohd
Published: April 2022

The remediation of potentially toxic element-polluted soils can be accomplished through the use of microbial and plant-assisted bioremediation. A total of 32 bacteria were isolated from soil samples contaminated with potentially toxic elements. The isolated bacterial strain DG-20 showed high tolerance to cadmium (up to 18 mM) and also showed bioaccumulative Cd removal properties, as demonstrated by atomic absorption spectroscopy studies. By sequencing the 16S rRNA gene, this strain was identified as Curtobacterium oceanosedimentum. Under stress and normal conditions, isolate DG-20 also produced a wide range of plant growth promoting traits, including ammonia production (51–73 µg/mL) and IAA production (116–183 µg/mL), alongside siderophore production and phosphate solubilization. Additionally, pot experiments were conducted to determine whether the strain could promote Chili growth when Cd salts are present. Over the control, bacterial colonization increased root and shoot lengths significantly up to 58% and 60%, respectively. Following inoculation with the Cd-tolerant strain, the plants also increased in both fresh and dry weight. In both the control and inoculated plants, Cd was accumulated more in roots than in shoots, indicating that Chili was phytostabilizing Cd levels. Besides improving the plant attributes, Cd-tolerant bacteria were also found to increase the amount of total chlorophyll, proline, total phenol, and ascorbic acid in the soil when added to the soil. These results suggest that the inoculant provides protection to plants from negative effects. The results of the present study predict that the combined properties of the tested strain in terms of Cd tolerance and plant growth promotion can be exploited for the purpose of the bioremediation of Cd, and for the improvement of Chili cultivation in soils contaminated with Cd.