bluuebunny/crossref_metadata_2025 huggingface dataset:
Seasonal and Interhemispheric Effects on the Diurnal Evolution of EIA: Assessed by IGS TEC and IRI-2016 over Peruvian and Indian Sectors
Authors: Wan Xin , Zhong Jiahao , Xiong Chao , Wang Hui , Liu Yiwen , Li Qiaoling , Kuai Jiawei , Cui Jun
Published: December 2021

The global total electron content (TEC) map in 2013, retrieved from the International Global Navigation Satellite Systems (GNSS) Service (IGS), and the International Reference Ionosphere (IRI-2016) model are used to monitor the diurnal evolution of the equatorial ionization anomaly (EIA). The statistics are conducted during geomagnetic quiet periods in the Peruvian and Indian sectors, where the equatorial electrojet (EEJ) data and reliable TEC are available. The EEJ is used as a proxy to determine whether the EIA structure is fully developed. Most of the previous studies focused on the period in which the EIA is well developed, while the period before EIA emergence is usually neglected. To characterize dynamics accounting for the full development of EIA, we defined and statistically analyzed the onset, first emergence, and the peaks of the northern crest and southern crest based on the proposed crest-to-trough difference (CTD) profiles. These time points extracted from IGS TEC show typical annual cycles in the Indian sector which can be summarized as winter hemispheric priority, i.e., the development of EIA in the winter hemisphere is ahead of that in the summer hemisphere. However, these same time points show abnormal semiannual cycles in the Peruvian sector, that is, EIA develops earlier during two equinoxes/solstices in the northern/southern hemisphere. We suggest that the onset of EIA is a consequence of the equilibrium between sunlight ionization and ambipolar diffusion. However, the latter term is not considered in modeling the topside ionosphere in IRI-2016, which results in a poor capacity in IRI to describe the diurnal evolution of EIA. Meridional neutral wind’s modulation on the ambipolar diffusion can explain the annual cycle observed in the Indian sector, while the semiannual variation seen in the Peruvian sector might be due to additional competing effects induced by the F region height changes.
UAV Thermal Images for Water Presence Detection in a Mediterranean Headwater Catchment
Authors: Micieli Massimo , Botter Gianluca , Mendicino Giuseppe , Senatore Alfonso
Published: December 2021

As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or inaccessible areas are investigated. In this study, an innovative method for water presence detection in the river network based on UAV thermal infrared remote sensing (TIR) images supported by RGB images is evaluated using data gathered in a representative catchment located in Southern Italy. Fourteen flights were performed at different times of the day in three periods, namely, October 2019, February 2020, and July 2020, at two different heights leading to ground sample distances (GSD) of 2 cm and 5 cm. A simple methodology that relies on the analysis of raw data without any calibration is proposed. The method is based on the identification of the thermal signature of water and other land surface elements targeted by the TIR sensor using specific control matrices in the image. Regardless of the GSD, the proposed methodology allows active stream identification under weather conditions that favor sufficient drying and heating of the surrounding bare soil and vegetation. In the surveys performed, ideal conditions for unambiguous water detection in the river network were found with air–water thermal differences higher than 5 °C and accumulated reference evapotranspiration before the survey time of at least 2.4 mm. Such conditions were not found during cold season surveys, which provided many false water pixel detections, even though allowing the extraction of useful information. The results achieved led to the definition of tailored strategies for flight scheduling with different levels of complexity, the simplest of them based on choosing early afternoon as the survey time. Overall, the method proved to be effective, at the same time allowing simplified monitoring with only TIR and RGB images, avoiding any photogrammetric processes, and minimizing postprocessing efforts.
Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China
Authors: Qin Yuehan , Zhang Xinle , Zhao Zhifang , Li Ziyang , Yang Changbi , Huang Qunying
Published: December 2021

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.
Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web Services
Authors: Namouchi Slim , Farah Imed
Published: December 2021

Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgrading, monitoring of changes as well as virtual reality. These applications are essentially composed of models of structures, urban elements, ground surface and vegetation. This paper presents a workflow for modeling the structure of buildings by using laser-scanned data (LiDAR) and multi-spectral images in order to develop a 3D web service for a smart city concept. Optical vertical photography is generally utilized to extract building class, while LiDAR data is used as a source of information to create the structure of the 3D building. The building reconstruction process presented in this study can be divided into four main stages: building LiDAR points extraction, piecewise horizontal roof clustering, boundaries extraction and 3D geometric modeling. Finally, an architecture for a 3D smart service based on the CityGML interchange format is proposed.
Analysis of Climate Change Effects on Surface Temperature in Central-Italy Lakes Using Satellite Data Time-Series
Authors: De Santis Davide , Del Frate Fabio , Schiavon Giovanni
Published: December 2021

Evaluation of the impact of climate change on water bodies has been one of the most discussed open issues of recent years. The exploitation of satellite data for the monitoring of water surface temperatures, combined with ground measurements where available, has already been shown in several previous studies, but these studies mainly focused on large lakes around the world. In this work the water surface temperature characterization during the last few decades of two small–medium Italian lakes, Lake Bracciano and Lake Martignano, using satellite data is addressed. The study also takes advantage of the last space-borne platforms, such as Sentinel-3. Long time series of clear sky conditions and atmospherically calibrated (using a simplified Planck’s Law-based algorithm) images were processed in order to derive the lakes surface temperature trends from 1984 to 2019. The results show an overall increase in water surface temperatures which is more evident on the smallest and shallowest of the two test sites. In particular, it was observed that, since the year 2000, the surface temperature of both lakes has risen by about 0.106 °C/year on average, which doubles the rate that can be retrieved by considering the whole period 1984–2019 (0.053 °C/year on average).
Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies
Authors: Pourzeynali Solmaz , Zhu Xinqun , Ghari Zadeh Ali , Rashidi Maria , Samali Bijan
Published: December 2021

Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.
Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons
Authors: Laneve Giovanni , Bruno Milena , Mukherjee Arghya , Messineo Valentina , Giuseppetti Roberto , De Pace Rita , Magurano Fabio , D'Ugo Emilio
Published: December 2021

The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems.
Testing for Wearability and Reliability of TPU Lamination Method in E-Textiles
Authors: Veske Paula , Bossuyt Frederick , Vanfleteren Jan
Published: December 2021

Electronic textiles (e-textiles) and wearable computing have been emerging increasingly during the last decade. Since the market interest and predictions have grown, the research into increasing reliability and durability of wearables and e-textiles is developing rapidly. The washability of different integration methods and resistance to mechanical stress are the main obstacles being tackled. However, the freedom of movement and overall comfort is still often overlooked during the development phase. It is essential to see the e-textile product as a whole and consider several aspects of user experience. This work will focus on developing and improving the thermoplastic polyurethane (TPU) lamination integration method for e-textiles. In the work, a stretchable copper-polyimide based circuit was laminated onto knit fabric using various TPU films and stacks. The study shares measurable characteristics to determine which material assembly and design would ensure the highest durability for the electronics part without losing its original textile softness, flexibility and stretchability.
Two Wire Sensor for Measuring the Velocity of Non-Isothermal Flows
Authors: Socha Katarzyna , Jamróz Paweł
Published: December 2021

Changes in the temperature of the medium significantly affect the static characteristics of hot-wire anemometry measuring wires, which causes errors in the results of flow velocity measurements. High temperatures of the medium make it necessary to additionally heat the sensor to even higher temperatures, which may lead to its damage due to wire burnout. The article proposes a solution to the problem of measuring the flow velocity in conditions of non-stationary temperatures with the use of the method of cross-correlation of signals from two-wire resistance thermometers. The main assumptions of the method and its experimental verification were presented.
Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices
Authors: Wójcikowski Marek
Published: December 2021

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.
Design and Measurement of a Dual FBG High-Precision Shape Sensor for Wing Shape Reconstruction
Authors: Wu Huifeng , Liang Lei , Wang Hui , Dai Shu , Xu Qiwei , Dong Rui
Published: December 2021

FBG shape sensors based on soft substrates are currently one of the research focuses of wing shape reconstruction, where soft substrates and torque are two important factors affecting the performance of shape sensors, but the related analysis is not common. A high-precision soft substrates shape sensor based on dual FBGs is designed. First, the FBG soft substrate shape sensor model is established to optimize the sensor size parameters and get the optimal solution. The two FBG cross-laying method is adopted to effectively reduce the influence of torque, the crossover angle between the FBGs is 2α, and α = 30° is selected as the most sensitive angle to the torquer response. Second, the calibration test platform of this shape sensor is built to obtain the linear relationship among the FBG wavelength drift and curvature, rotation radian loaded vertical force and torque. Finally, by using the test specimen shape reconstruction test, it is verified that this shape sensor can improve the shape reconstruction accuracy, and that its reconstruction error is 6.13%, which greatly improves the fit of shape reconstruction. The research results show that the dual FBG high-precision shape sensor successfully achieves high accuracy and reliability in shape reconstruction.
Deep Learning Approach at the Edge to Detect Iron Ore Type
Authors: Klippel Emerson , Bianchi Andrea Gomes Campos , Delabrida Saul , Silva Mateus Coelho , Garrocho Charles Tim Batista , Moreira Vinicius da Silva , Oliveira Ricardo Augusto Rabelo
Published: December 2021

There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edge artificial intelligence for early detection of landslide risk based on images of iron ore transported on conveyor belts. During this work, we defined the device edge and the deep neural network model. Then, we built a prototype will to collect images that will be used for training the model. This model will be compressed for use in the device edge. This same prototype will be used for field tests of the model under operational conditions. In building the prototype, a real-time clock was used to ensure the synchronization of image records with the plant’s process information, ensuring the correct classification of images by the process specialist. The results obtained in the field tests of the prototype with an accuracy of 91% and a recall of 96% indicate the feasibility of using deep learning at the edge to detect the type of iron ore and prevent its risk of avalanche.
Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices
Authors: Kraft Robin , Reichert Manfred , Pryss Rüdiger
Published: December 2021

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
Low-Temperature Annealing of CdZnTeSe under Bias
Authors: Rejhon Martin , Dedic Vaclav , Grill Roman , Franc Jan , Roy Utpal N. , James Ralph B.
Published: December 2021

We performed a gradual low-temperature annealing up to 360 K on a CdZnTeSe radiation detector equipped with gold and indium electrodes under bias at both polarities. We observed significant changes in the detector’s resistance and space-charge accumulation. This could potentially lead to the control and improvement of the electronic properties of the detector because the changes are accompanied with the reduction in the bulk dark current and surface leakage current. In this article, we present the results of a detailed study of the internal electric field and conductivity changes in CdZnTeSe detector for various annealing steps under bias taking into account different polarities during annealing and subsequent characterization. We observed that low-temperature annealing results in an increase in the barrier height at the contacts that, in general, reduces the dark current and decreases the positive space charge present in the sample compared to the pre-annealed condition.
Numerical Optimization of Refractive Index Sensors Based on Diffraction Gratings with High Aspect Ratio in Terahertz Range
Authors: Kameshkov Oleg , Gerasimov Vasily , Knyazev Boris
Published: December 2021

Terahertz surface plasmon resonance (SPR) sensors have been regarded as a promising technology in biomedicine due to their real-time, label-free, and ultrasensitive monitoring features. Different authors have suggested a lot of SPR sensors, including those based on 2D and 3D metamaterials, subwavelength gratings, graphene, and graphene nanotube, as well as others. However, one of the traditional approaches to realize high sensitivity SPR sensors based on metal diffraction gratings has been studied poorly in the terahertz frequency range. In this article, a linear metal rectangular diffraction grating with high aspect ratio is studied. The influence of the grating structure parameters on the sensor sensitivity is simulated. Effects arising from different ratios of depth and width were discovered and explained. The results show that the sensitivity can be increased to 2.26 THz/RIU when the refractive index range of the gas to measure is between 1 and 1.002 with the resolution 5×10−5 RIU.
Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network
Authors: Kang Junhyuk , Shin Jieun , Shin Jaewon , Lee Daeho , Choi Ahyoung
Published: December 2021

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.
Performance Metric Analysis for a Jamming Detection Mechanism under Collaborative and Cooperative Schemes in Industrial Wireless Sensor Networks
Authors: Cortés-Leal Alejandro , Del-Valle-Soto Carolina , Cardenas Cesar , Valdivia Leonardo J. , Del Puerto-Flores Jose Alberto
Published: December 2021

The emergence of Industry 4.0 technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), has prompted a reconsideration of methodologies for network security as well as reducing operation and maintenance costs, especially at the physical layer, where the energy consumption plays an important role. This article demonstrates through simulations and experiments that, while the cooperative scheme is more efficient when a WSN is at normal operating conditions, the collaborative scheme offers more enhanced protection against the aggressiveness of jamming in the performance metrics, thus making it safer, reducing operation and maintenance costs and laying the foundations for jamming mitigation. This document additionally offers an algorithm to detect jamming in real time. Firstly, it examines the characteristics and damages caused by the type of aggressor. Secondly, it reflects on the natural immunity of the WSN (which depends on its node density and a cooperative or collaborative configuration). Finally, it considers the performance metrics, especially those that impact energy consumption during transmission.
Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis
Authors: Ahmad Zahoor , Nguyen Tuan-Khai , Ahmad Sajjad , Nguyen Cong Dai , Kim Jong-Myon
Published: December 2021

This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first step. Statistical features in time, frequency, and wavelet domains were extracted from the fault-specific frequency band. In the second step, all of the extracted features were combined into a single feature vector called a multi-domain feature pool (MDFP). The multi-domain feature pool results in a larger dimension; furthermore, not all of the features are best for representing the centrifugal pump condition and can affect the condition classification accuracy of the classifier. To obtain discriminant features with low dimensions, this paper introduces a novel informative ratio principal component analysis in the third step. The technique first assesses the feature informativeness towards the fault by calculating the informative ratio between the feature within the class scatteredness and between-class distance. To obtain a discriminant set of features with reduced dimensions, principal component analysis was applied to the features with a high informative ratio. The combination of informative ratio-based feature assessment and principal component analysis forms the novel informative ratio principal component analysis. The new set of discriminant features obtained from the novel technique are then provided to the K-nearest neighbor (K-NN) condition classifier for multistage centrifugal pump condition classification. The proposed method outperformed existing state-of-the-art methods in terms of fault classification accuracy.
Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study
Authors: Budig Mario , Keiner Michael , Stoohs Riccardo , Hoffmeister Meike , Höltke Volker
Published: December 2021

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports.
Analysis of Heart-Sound Characteristics during Motion Based on a Graphic Representation
Authors: She Chen-Jun , Cheng Xie-Feng , Wang Kai
Published: December 2021

In this paper, the graphic representation method is used to study the multiple characteristics of heart sounds from a resting state to a state of motion based on single- and four-channel heart-sound signals. Based on the concept of integration, we explore the representation method of heart sound and blood pressure during motion. To develop a single- and four-channel heart-sound collector, we propose new concepts such as a sound-direction vector of heart sound, a motion–response curve of heart sound, the difference value, and a state-change-trend diagram. Based on the acoustic principle, the reasons for the differences between multiple-channel heart-sound signals are analyzed. Through a comparative analysis of four-channel motion and resting-heart sounds, from a resting state to a state of motion, the maximum and minimum similarity distances in the corresponding state-change-trend graphs were found to be 0.0038 and 0.0006, respectively. In addition, we provide several characteristic parameters that are both sensitive (such as heart sound amplitude, blood pressure, systolic duration, and diastolic duration) and insensitive (such as sound-direction vector, state-change-trend diagram, and difference value) to motion, thus providing a new technique for the diverse analysis of heart sounds in motion.
Enantiomeric Separation and Molecular Modelling of Bioactive 4-Aryl-3,4-dihydropyrimidin-2(1H)-one Ester Derivatives on Teicoplanin-Based Chiral Stationary Phase
Authors: Bolognino Isabella , Carrieri Antonio , Purgatorio Rosa , Catto Marco , Caliandro Rocco , Carrozzini Benedetta , Belviso Benny Danilo , Majellaro Maria , Sotelo Eddy , Cellamare Saverio , Altomare Cosimo Damiano
Published: December 2021

The enantiomeric separation of 15 racemic 4-aryl-3,4-dihydropyrimidin-2(1H)-one (DHP) alkoxycarbonyl esters, some of which proved to be highly active as A2B adenosine receptor antagonists, was carried out by HPLC on ChirobioticTM TAG, a chiral stationary phase (CSP) bearing teicoplanin aglycone (TAG) as the chiral selector. The racemic compounds were separated under polar organic (PO) conditions. Preliminarily, the same selectands were investigated on three different Pirkle-type CSPs in normal-phase (NP) conditions. A baseline separation was successfully obtained on TAG-based CSPs for the majority of compounds, some of which achieved high enantioselectivity ratios (α > 2) in contrast with the smaller α values (1–1.5) and the lack of baseline resolution observed with the Pirkle-type CSPs. In particular, the racemic tetrazole-fused DHP ester derivatives, namely compounds 8 and 9, were separated on TAG-based HPLC columns with noteworthy α values (8.8 and 6.0, respectively), demonstrating the potential of the method for preparative purposes. A competition experiment, carried out with a racemic analyte (6) by adding N-acetyl-d-alanine (NADA) to the mobile phase, suggested that H-bonding interactions involved in the recognition of the natural dipeptide ligand d-Ala-d-Ala into the TAG cleft should be critical for enantioselective recognition of 4-aryl DHPs by TAG. The X-ray crystal structure of TAG was elucidated at a 0.77 Å resolution, whereas the calculation of molecular descriptors of size, polar, and H-bond interactions, were complemented with molecular docking and molecular dynamics calculations, shedding light on repulsive (steric effects) and attractive (H-bond—polar and apolar) interactions between 4-aryl DHP selectands and TAG chiral selectors.
Framing Studies Evolution in the Social Media Era. Digital Advancement and Reorientation of the Research Agenda
Authors: López-Rabadán Pablo
Published: December 2021

Framing studies remain a powerful line of research in political communication. However, in recent years, coinciding with the emergence of social media, theoretical and operational advances have been detected, as well as a significant reorientation of its research agenda. The interaction between media and platforms such as Twitter or Facebook has built a clearly hybrid communicative environment and profoundly transformed the organization of public debate. This is the case, especially, with processes such as the setting of the public agenda or the construction of interpretive frames. Based on a systematic review of the international reference literature (2011–2021), this article analyses the influence of social media on the evolution of framing studies. Moreover, specifically, the beginning of a new stage of digital development is contextualized, and a triple research impact is explored. The main contributions of the text are that it (1) identifies advances in the theoretical and empirical organization of these studies; (2) explores its reorientation of content towards a greater balance between the analysis of media and political frames; and (3) reviews the recent experimental development of effects studies. Finally, the main challenges for future research in this field are detailed.
Meaningful Aging: A Relational Conceptualization, Intervention, and Its Impacts
Authors: Lou Vivian W. Q.
Published: December 2021

Having a meaningful life is one of the most important goals among older adults. This paper provided an overview of a programme of research and practice on meaningful aging among older Chinese adults. It firstly describes the process of developing and validating a relational conceptualization of a meaningful life (i.e., spiritual well-being) among older Chinese adults from its conceptual roots, development, and validation process since 2009 through an academic–community collaboration. In brief, a meaningful life was attributed to five relationships centered on older adults: the relationship with self, relationship with family, relationship with friends, relationship with people other than family and friends, and relationship with the environment. Secondly, the paper explains a validated assessment tool (e.g., the Spirituality Scale for Chinese Elders, (SSCE)) that was developed accordingly. Evidence-based stratified interventions derived from the conceptualization and operationalization were then introduced including a professionally led group intervention protocol, a volunteer-partner intervention protocol, and a self-help-oriented intervention, which shared eight-session core contents. Good practices in applying various interventions among older adults with diversified backgrounds (e.g., health status, age, and gender) and various service settings (e.g., community, long-term care facilities, and home visits) were then synthesized. Thirdly, feedback from stakeholders is illustrated, and good practices are discussed. In conclusion, a culturally sensitive and meaningful aging framework is timely and impactful for the globally aging world.
Intrinsic Workforce Diversity and Construction Worker Productivity in Pakistan: Impact of Employee Age and Industry Experience
Authors: Maqsoom Ahsen , Mubbasit Hasnain , Alqurashi Muwaffaq , Shaheen Iram , Alaloul Wesam Salah , Musarat Muhammad Ali , Salman Alaa , Aslam Bilal , Zerouali Bilel , Hussein Enas E.
Published: December 2021

Worker productivity is critical within construction projects as it is the measure of the rate at which work is performed and, more importantly, helps to know how to motivate them to perform at high levels. This research aimed to examine the impact of employee age and industry experience on the intrinsic workforce diversity factors influencing construction worker productivity. Sieving through the previous research and models and theories of analysis, the intrinsic workforce diversity was modeled into the following set of factors, i.e., income, motivation, psychosocial factors, and technical skills. The data were collected by means of a questionnaire survey and examined for the employees having different ages and experiences using the Mann–Whitney U test through SPSS. The results show that employees of varied ages do not concur over motivation-, psychosocial, and technical skills-related workforce diversity factors, whereas employees of varied industrial experiences are in disagreement over some income and motivation related workforce diversity factors. In order to overcome intrinsic workforce diversity, firm support is direly needed for old and mature employees in terms of financial incentives leading to motivation, less supervised scheduling, opportunities for firm advancement, and reporting back every time work is completed. Furthermore, support is required for young employees who are more susceptible due to psychosocial stresses like unevenly distributed work, communication gaps, and technical skills like knowledge of technological equipment and advancement in construction technology which has reduced the skills of workers.
Participatory Action Research for Conservation and Development: Experiences from the Amazon
Authors: Perz Stephen G. , Arteaga Marliz , Baudoin Farah Andrea , Brown I. Foster , Mendoza Elsa Renee Huaman , de Paula Yara Araújo Pereira , Perales Yabar Leonor Mercedes , Pimentel Alan dos Santos , Ribeiro Sabina C. , Rioja-Ballivián Guillermo , Rosero Peña Martha Cecilia , Sanjinez L. L. Cecilia , Selaya G. N. Galia
Published: December 2021

Research that features participation and action orientation, such as participatory action research (PAR), is especially valuable in contexts where there is rapid change, high social inequality, and great uncertainty about the future, which drives stakeholder demands for information to support their goals. The Amazon offers such a context, for it is a region where diverse stakeholders engage in contestation over environmental governance to address issues such as climate change to achieve conservation and sustainable development. Stakeholder mobilization has changed the terms by which research is conducted, from the definition of priority topics to the application of findings. Due to stakeholder mobilization, more and more research in the Amazon is now necessarily participatory, for stakeholders routinely issue demands about how the research will be conducted and for what purpose. In this paper, we provide an overview of several experiences of implementing methods such as PAR by different teams or networks, focusing on the complementary contributions of outside researchers and local stakeholders. The heart of the paper reports on three broad types of experiences focusing on conservation and development in the Amazon: (1) participatory data collection for co-production of knowledge for environmental governance, (2) inclusive environmental monitoring systems, and (3) innovative models of knowledge exchange to facilitate collective action. Within each type, we report multiple experiences with distinct approaches to participation and action in research. These experiences constitute models that can be replicated in other places for broader impact to support conservation and development.
The Importance of Stakeholders in Managing a Safe City
Authors: Wereda Wioletta , Moch Natalia , Wachulak Anna
Published: December 2021

Contemporary cities are complex systems in which there are many interactions and dependencies in relation to the environment. Currently, the development of cities and their safety are among the most important international socio-economic processes. The movement of people to larger agglomerations from smaller towns creates a variety of relationships between actors and often leads to very complicated lives in urban space. Features of contemporary cities include urbanization, personal development opportunities, labor markets, and infrastructure, as well as technological and cybernetic networks that optimize all the processes taking place in agglomerations. It should be emphasized that the main goal of public management in urban space is to create various solutions in the field of safety and thus to improve the quality of. In this respect, the role and influence of stakeholders on the processes of smart and safe city development are important. At each stage of activity, the City Council, local communities, economic entities, scientific institutions, and municipal enterprises are important for a city’s safety. This article emphasizes the importance of stakeholders in the process of managing a safe city. The article presents a literature review, as well as research results based on the example of Polish cities, presenting the importance of stakeholders in managing safe cities.
Psychological Restorative Potential of a Pilot on-Campus Ecological Wetland in Malaysia
Authors: Lee Lai Kuan , Zakaria Nor Azazi , Foo Keng Yuen
Published: December 2021

This study examines the perceptions and engagement tendencies of 788 university students, as well as their relationship with psychological distress, with respect to an on-campus ecological wetland. The students’ awareness, understanding, perceived importance, satisfaction level, and engagement tendency towards the ecological wetland were evaluated using a structured questionnaire. The psychological symptoms were assessed using the Rosenberg Self-Esteem and depression, anxiety, and stress scales, and the predictors of psychological distress were determined. The majority of the students were actively engaged (62.3%), aware (88.3%), and satisfied (51.0%) with the ecological wetland. Gender, age, educational attainment, engagement, perceived importance, and satisfaction level towards the ecological wetland were the predictors of psychological distress. The results outlined the environmental and mental restorative values of the ecological wetland in mediating psychological distress among the university students. These findings shed light on the necessity of preserving the sustainability and integrity of the on-campus ecological wetland. Longitudinal investigations to explore the restorative values of built environments and psychological wellbeing among high-risk populations are warranted.
Does Perceived Advertising Value Alleviate Advertising Avoidance in Mobile Social Media? Exploring Its Moderated Mediation Effects
Authors: Wei Xiaolong , Ko Ilsang , Pearce Alison
Published: December 2021

It is known that perceived intrusiveness and privacy concerns, mediated by irritation, indirectly affect advertising avoidance. This research attempts to verify the importance of perceived advertising value by investigating its moderated mediation effect on the links between those endogenous variables. The research model was empirically verified with data derived from 374 valid off-line responses. Analysis found that both perceived intrusiveness and privacy concerns increased irritation in using mobile social media. Irritation caused by perceived intrusiveness and privacy concerns had positive mediating effects on advertising avoidance. Ubiquity increased perceived intrusiveness and privacy concerns, whereas personalization reduced perceived intrusiveness. Customization increased perceived intrusiveness, whereas informativeness significantly reduced it. Social interaction increased privacy concerns, whereas social integration decreased them. The moderated mediation effect of perceived advertising value among women was negative. In the low-exposure group, a negative moderated mediation effect of perceived advertising value on the relationship between irritation and advertising avoidance was also found.
Enabling Progress in Developing Economies: A Novel Hybrid Decision-Making Model for Green Technology Planning
Authors: Ikram Muhammad , Sroufe Robert , Awan Usama , Abid Nabila
Published: December 2021

Green technologies play an essential role in designing and developing sustainable and environmental strategies at a country level. With a growing emphasis on green technology strategies in strategic decision making, there is an opportunity for developing and implementing strategies to accelerate green technologies in developing economies. Therefore, this study develops an integrated strategic framework based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) for effective green technology planning. The SWOT approach scans internal and external factors and sub-factors essential for green technology planning in Pakistan. Subsequently, the Grey Analytical Hierarchical Process (GAHP) method is employed to compute weights of twenty-one subfactors. Finally, Grey Technique for Order Performance by Similarity to Ideal Solution (GTOPSIS) is used to prioritize eighteen strategies developed for sustainable green technology planning. Results reveal that high productivity potential in the agriculture sector, less foreign investment due to terrorism and security issues, decreasing the cost of green technologies, and the opportunity to build a positive image are key sub-factors for strategic green technology planning. In contrast, improving the security situation in the country to attract foreign direct investment, fewer taxes and duties on import of green technology and lenient rules for local business in transition, and plans and programs to promote green technology in the agriculture sector are observed as optimal strategies. This study is the first to propose a systematic, integrated framework for sustainable green technology planning in Pakistan. Our study fills a gap in the strategic planning and implementation process and provides pathways for policymakers in addressing impediments when implementing green technology planning.