bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning
Authors: Tang Jie , Jiang Fugen , Long Yi , Fu Liyong , Sun Hua
Published: January 2022

Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.
Comprehensive Analyses of PPP-B2b Performance in China and Surrounding Areas
Authors: Liu Yan , Yang Cheng , Zhang Mengni
Published: January 2022

BeiDou Global Navigation Satellite System (BDS-3) provides a regional Precise Point Positioning (PPP) service, called PPP-B2b, for users in China and surrounding areas through B2b signal transmitted from its three geostationary earth orbit (GEO) satellites. The information broadcasted by the B2b signal include satellite orbit corrections, satellite clock offset corrections, and differential code bias (DCB) corrections of BDS-3 satellites. In this study, the accuracies of PPP-B2b corrections along with real-time PPP performance are comprehensively evaluated referenced to precise orbit and clock products from GFZ and the precise DCB products from CAS. The result indicates that the accuracy of the BDS-3 broadcast orbit is similar to that of the PPP-B2b real-time orbit. The PPP-B2b clock offset correction improved the satellite clock offset precision of the BDS-3 broadcast ephemeris. The Signal-in-Space Range Error (SISRE) of broadcast ephemeris and PPP-B2b are calculated, which are 0.536 and 1.24 m, respectively. The large SISRE value of PPP-B2b is caused by the satellite-specified systematic bias to IGS final products. The positioning performance evaluation of real-time PPP with B2b service is carried out and compared with the real-time product provided by Wuhan University (WHU) based on the eight IGS MGEX stations in China and surrounding countries. The positioning accuracy of static positioning mode with PPP-B2b service achieved centimeter-level accuracy in the selected station, and that of kinematic positioning mode achieved decimeter-level accuracy. The availability rate of PPP-B2b corrections in the surrounding area of China, however, degrades from 88.76% to 60.91% in the selected stations. The accuracy of the PPP solution using PPP-B2b correction is better than that of using WHU real-time product within China. The positioning performance of stations located at the boundary of the PPP-B2b service area, however, is affected by the number of PPP-B2b available satellites. The positioning accuracy in kinematic positioning mode is worse than that of using WHU real-time precise product.
Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT–2 SAR Images and CNNs
Authors: Saberi Nastaran , Scott Katharine Andrea , Duguay Claude
Published: January 2022

With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as convolutional neural networks (CNNs) is not yet accurate enough to replace manual analysis for operational ice chart mapping. Understanding the uncertainties associated with CNN model predictions can help to quantify errors and, therefore, guide efforts on potential enhancements using more–advanced DL models and/or synergistic approaches. This paper evaluates an approach for estimating the aleatoric uncertainty [a measure used to identify the noise inherent in data] of CNN probabilities to map ice and open water with a custom loss function applied to RADARSAT–2 HH and HV observations. The images were acquired during the 2014 ice season of Lake Erie and Lake Ontario, two of the five Laurentian Great Lakes of North America. Operational image analysis charts from the Canadian Ice Service (CIS), which are based on visual interpretation of SAR imagery, are used to provide training and testing labels for the CNN model and to evaluate the accuracy of the model predictions. Bathymetry, as a variable that has an impact on the ice regime of lakes, was also incorporated during model training in supplementary experiments. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice. Results are evaluated quantitatively (accuracy metrics) and qualitatively (visual comparisons). Ice and open water scores were improved in some sections of the lakes by using aleatoric loss and including bathymetry. In Lake Erie, the ice score was improved by ∼2 on average in the shallow near–shore zone as a result of better mapping of dark ice (low backscatter) in the western basin. As for Lake Ontario, the open water score was improved by ∼6 on average in the deepest profundal off–shore zone.
ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment
Authors: Saini Jagriti , Dutta Maitreyee , Marques Gonçalo
Published: January 2022

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.
A Case Study of Upper Limb Robotic-Assisted Therapy Using the Track-Hold Device
Authors: Righi Marco , Magrini Massimo , Dolciotti Cristina , Moroni Davide
Published: January 2022

The Track-Hold System (THS) project, developed in a healthcare facility and therefore in a controlled and protected healthcare environment, contributes to the more general and broad context of Robotic-Assisted Therapy (RAT). RAT represents an advanced and innovative rehabilitation method, both motor and cognitive, and uses active, passive, and facilitating robotic devices. RAT devices can be equipped with sensors to detect and track voluntary and involuntary movements. They can work in synergy with multimedia protocols developed ad hoc to achieve the highest possible level of functional re-education. The THS is based on a passive robotic arm capable of recording and facilitating the movements of the upper limbs. An operational interface completes the device for its use in the clinical setting. In the form of a case study, the researchers conducted the experimentation in the former Tabarracci hospital (Viareggio, Italy). The case study develops a motor and cognitive rehabilitation protocol. The chosen subjects suffered from post-stroke outcomes affecting the right upper limb, including strength deficits, tremors, incoordination, and motor apraxia. During the first stage of the enrolment, the researchers worked with seven patients. The researchers completed the pilot with four patients because three of them got a stroke recurrence. The collaboration with four patients permitted the generation of an enlarged case report to collect preliminary data. The preliminary clinical results of the Track-Hold System Project demonstrated good compliance by patients with robotic-assisted rehabilitation; in particular, patients underwent a gradual path of functional recovery of the upper limb using the implemented interface.
Efficient Spatiotemporal Attention Network for Remote Heart Rate Variability Analysis
Authors: Kuang Hailan , Lv Fanbing , Ma Xiaolin , Liu Xinhua
Published: January 2022

Studies have shown that ordinary color cameras can detect the subtle color changes of the skin caused by the heartbeat cycle. Therefore, cameras can be used to remotely monitor the pulse in a non-contact manner. The technology for non-contact physiological measurement in this way is called remote photoplethysmography (rPPG). Heart rate variability (HRV) analysis, as a very important physiological feature, requires us to be able to accurately recover the peak time locations of the rPPG signal. This paper proposes an efficient spatiotemporal attention network (ESA-rPPGNet) to recover high-quality rPPG signal for heart rate variability analysis. First, 3D depth-wise separable convolution and a structure based on mobilenet v3 are used to greatly reduce the time complexity of the network. Next, a lightweight attention block called 3D shuffle attention (3D-SA), which integrates spatial attention and channel attention, is designed to enable the network to effectively capture inter-channel dependencies and pixel-level dependencies. Moreover, ConvGRU is introduced to further improve the network’s ability to learn long-term spatiotemporal feature information. Compared with existing methods, the experimental results show that the method proposed in this paper has better performance and robustness on the remote HRV analysis.
Kinematic Loggers—Development of Rugged Sensors and Recovery Systems for Field Measurements of Stone Rolling Dynamics and Impact Accelerations during Floods
Authors: Biggs Hamish , Starr Andrew , Smith Brendon , de Lima Steve , Sykes Julian , Haddadchi Arman , Smart Graeme , Hicks Murray
Published: January 2022

Discrete particle dynamics is one of the least understood aspects of river bedload transport, but in situ measurement of stone movement during floods poses a significant technical challenge. A promising approach to address this knowledge gap is to use sensors embedded within stones. Sensors must be waterproof and recoverable after being transported downstream and potentially buried by other sediment. To address this challenge rugged sensors (Kinematic Loggers) were developed for deployment inside stones (ranging in size from cobbles to boulders) during floods. The sensors feature a 9-axis inertial measurement unit, 3-axis high-g accelerometer, 128 MB flash memory, and a 433 MHz LoRa radio transmission module for sensor recovery. The sensors are enclosed in rugged waterproof housings for deployment in extreme conditions (i.e., bedload transport during floods). Novel relay units and drone-based recovery systems were also developed for finding the sensors after field deployments. Firmware to control the sensors and relay units was developed, as well as software for configuring the sensors and an android application for communicating with the sensors via the LoRa radio transmission module. This paper covers the technical development of the sensors, mounting them inside stones, and field recovery tests. Although designed for measurement of coarse bedload transport and particle dynamics during floods, the sensors are equally applicable for deployment in other harsh environments, such as to study landslide and rockfall dynamics.
Assertive, Selective, Scalable IoT-Based Warning System
Authors: Filip Ion-Dorinel , Iliescu Cristian-Mihai , Pop Florin
Published: January 2022

With the evolution of technology, developed systems have become more complex and faster. Thirty years ago, there were no protocols or databases dedicated to developing and implementing IoT projects. We currently have protocols such as MQTT, AMQP, CoAP, and databases such as InfluxDB. They are built to support a multitude of data from an IoT system and scale very well with the system. This paper presents the design and implementation of an IoT alert system that uses MQTT and InfluxDB to collect and store data. We design a scalable system to display assertive alerts on a Raspberry Pi. Each user can select a subset of alerts in our system using a web interface. We present a bibliographic study of SoTA, the proposed architecture, the challenges posed by such a system, a set of tests for the performance and feasibility of the solution, and a set of conclusions and ideas for further developments.
Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms
Authors: Ali Ashraf , Samara Weam , Alhaddad Doaa , Ware Andrew , Saraereh Omar A.
Published: January 2022

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.
Analytical Modeling for Identification of the Machine Code Architecture of Cyberphysical Devices in Smart Homes
Authors: Kotenko Igor , Izrailov Konstantin , Buinevich Mikhail
Published: January 2022

Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices. The situation becomes more complicated in the case of a Smart Home, since its devices can have different processor architectures (means instruction sets). In the case of cyberphysical devices of the Smart Home, the destruction of machine code due to physical influences is also possible. Therefore, the first step is to correctly identify the processor architecture. In the interests of this, a machine code model is proposed that has a formal notation and takes into account the possibility of code destruction. The article describes the full cycle of research (including experiment) in order to obtain this model. The model is based on byte-frequency machine code signatures. The experiment resulted in obtaining template signatures for the Top-16 processor architectures: Alpha, X32, Amd64, Arm64, Hppa64, I486, I686, Ia64, Mips, Mips64, Ppc, Ppc64, RiscV64, S390, S390x and Sparc64.
A Survey of Blind Modulation Classification Techniques for OFDM Signals
Authors: Kumar Anand , Majhi Sudhan , Gui Guan , Wu Hsiao-Chun , Yuen Chau
Published: January 2022

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.
Tunnel Magnetoresistance Sensor with AC Modulation and Impedance Compensation for Ultra-Weak Magnetic Field Measurement
Authors: Zhao Wenlei , Tao Xinchen , Ye Chaofeng , Tao Yu
Published: January 2022

Tunnel magnetoresistance (TMR) is a kind of magnetic sensor with the advantages of low cost and high sensitivity. For ultra-weak and low-frequency magnetic field measurement, the TMR sensor is affected by the 1/f noise. This paper proposes an AC modulation method with impedance compensation to improve the performance. The DC and AC characteristics of the sensors were measured and are presented here. It was found that both the equivalent resistance and capacitor of the sensors are affected by the external magnetic field. The TMR sensors are connected as a push–pull bridge circuit to measure the magnetic field. To reduce the common-mode noise, two similar bridge circuits form a magnetic gradiometer. Experimental results show that the sensor’s sensitivity in the low-frequency range is obviously improved by the modulation and impedance compensation. The signal-to-noise ratio of the sensor at 1 Hz was increased about 25.3 dB by the AC modulation, impedance compensation, and gradiometer measurement setup. In addition, the sensitivity of the sensor was improved from 165.2 to 222.1 mV/V/mT. Ultra-weak magnetic signals, namely magnetocardiography signals of two human bodies, were measured by the sensor in an unshielded environment. It was seen that the R peak of MCG can be clearly visualized from the recorded signal.
Paper-Based Microfluidics Perform Mixing Effects by Utilizing Planar Constricted–Expanded Structures to Enhance Chaotic Advection
Authors: Weng Chen-Hsun , Hsu Pei-Pei , Huang An-Yu , Lin Jr-Lung
Published: January 2022

This study aimed to design and fabricate planar constricted–expanded structures that are integrated into paper-based channels in order to enhance their chaotic advection and improve their mixing performance. Chromatography papers were used to print paper-based microfluidics using a solid-wax printer. Three different constricted–expanded structures—i.e., zigzag, crossed, and curved channels—were designed in order to evaluate their mixing performance in comparison with that of straight channels. A numerical simulation was performed in order to investigate the mixing mechanism, and to examine the ways in which the planar constricted–expanded structures affected the flow patterns. The experimental and numerical results indicated that the proposed structures can successfully mix confluents. The experimental results revealed that the mixing indices (σ) rose from the initial 20.1% (unmixed) to 34.5%, 84.3%, 87.3%, and 92.4% for the straight, zigzag, curved, and cross-shaped channels, respectively. In addition, the numerical calculations showed a reasonable agreement with the experimental results, with a variation in the range of 1.0–11.0%. In future, we hope that the proposed passive paper-based mixers will be a crucial component in the application of paper-based microfluidic devices.
A Graph Neural Network Based Decentralized Learning Scheme
Authors: Gao Huiguo , Lee Mengyuan , Yu Guanding , Zhou Zhaolin
Published: January 2022

As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices. It is a challenging problem since link loss, partial device participation, and non-independent and identically distributed (non-iid) data distribution would all deteriorate the performance of decentralized learning algorithms. Existing work may restrict to linear models or show poor performance over non-iid data. Therefore, in this paper, we propose a decentralized learning scheme based on distributed parallel stochastic gradient descent (DPSGD) and graph neural network (GNN) to deal with the above challenges. Specifically, each user device participating in the learning task utilizes local training data to compute local stochastic gradients and updates its own local model. Then, each device utilizes the GNN model and exchanges the model parameters with its neighbors to reach the average of resultant global models. The iteration repeats until the algorithm converges. Extensive simulation results over both iid and non-iid data validate the algorithm’s convergence to near optimal results and robustness to both link loss and partial device participation.
Biomarker Metabolites Discriminate between Physiological States of Field, Cave and White-nose Syndrome Diseased Bats
Authors: Doty Anna C. , Wilson A. Dan , Forse Lisa B. , Risch Thomas S.
Published: January 2022

Analysis of volatile organic compound (VOC) emissions using electronic-nose (e-nose) devices has shown promise for early detection of white-nose syndrome (WNS) in bats. Tricolored bats, Perimyotis subflavus, from three separate sampling groups defined by environmental conditions, levels of physical activity, and WNS-disease status were captured temporarily for collection of VOC emissions to determine relationships between these combinations of factors and physiological states, Pseudogymnoascus destructans (Pd)-infection status, and metabolic conditions. Physiologically active (non-torpid) healthy individuals were captured outside of caves in Arkansas and Louisiana. In addition, healthy and WNS-diseased torpid bats were sampled within caves in Arkansas. Whole-body VOC emissions from bats were collected using portable air-collection and sampling-chamber devices in tandem. Electronic aroma-detection data using three-dimensional Principal Component Analysis provided strong evidence that the three groups of bats had significantly different e-nose aroma signatures, indicative of different VOC profiles. This was confirmed by differences in peak numbers, peak areas, and tentative chemical identities indicated by chromatograms from dual-column GC-analyses. The numbers and quantities of VOCs present in whole-body emissions from physiologically active healthy field bats were significantly greater than those of torpid healthy and diseased cave bats. Specific VOCs were identified as chemical biomarkers of healthy and diseased states, environmental conditions (outside and inside of caves), and levels of physiological activity. These results suggest that GC/E-nose dual-technologies based on VOC-detection and analyses of physiological states, provide noninvasive alternative means for early assessments of Pd-infection, WNS-disease status, and other physiological states.
Highly Sensitive Trace Gas Detection Based on In-Plane Single-Quartz-Enhanced Dual Spectroscopy
Authors: Liang Tiantian , Qiao Shunda , Lang Ziting , Ma Yufei
Published: January 2022

For this invited manuscript, an in-plane single-quartz-enhanced dual spectroscopy (IP-SQEDS)-based trace gas sensor was demonstrated for the first time. A single quartz tuning fork (QTF) was employed to combine in-plane quartz-enhanced photoacoustic spectroscopy (IP-QEPAS) with light-induced thermoelastic spectroscopy (LITES) techniques. Water vapor (H2O) was chosen as the target gas. Compared to traditional QEPAS, IP-SQEDS not only allowed for simple structures, but also obtained nearly three times signal amplitude enhancement.
Simulation Tool and Online Demonstrator for CDMA-Based Ultrasonic Indoor Localization Systems
Authors: Pérez-Rubio María Carmen , Hernández Álvaro , Gualda-Gómez David , Murano Santiago , Vicente-Ranera Jorge , Ciudad-Fernández Francisco , Villadangos José Manuel , Nieto Rubén
Published: January 2022

This work presents the CODEUS platform, which includes a simulation tool together with an online experimental demonstrator to offer analysis and testing flexibility for researchers and developers in Ultrasonic Indoor Positioning Systems (UIPSs). The simulation platform allows most common encoding techniques and sequences to be tested in a configurable UIPS. It models the signal modulation and processing, the ultrasonic transducers’ response, the beacon distribution, the channel propagation effects, the synchronism, and the application of different positioning algorithms. CODEUS provides results and performance analysis for different metrics and at different stages of the signal processing. The UIPS simulation tool is specified by means of the MATLAB© App-Designer environment, which enables the definition of a user-friendly interface. It has also been linked to an online demonstrator that can be managed remotely by means of a website, thus avoiding any hardware requirement or equipment on behalf of researchers. This demonstrator allows the selected transmission schemes, modulation or encoding techniques to be validated in a real UIPS, therefore enabling a fast and easy way of carrying out experimental tests in a laboratory environment, while avoiding the time-consuming tasks related to electronic design and prototyping in the UIPS field. Both simulator and online demonstrator are freely available for researchers and students through the corresponding website.
Analysis of Land Use/Cover Change and Driving Forces in the Selenga River Basin
Authors: Ren Yang , Li Zehong , Li Jingnan , Ding Yan , Miao Xinran
Published: January 2022

The Selenga River basin is an important section of the Sino-Mongolian Economic Corridor. It is an important connecting piece of the Eurasian Continental Bridge and an important part of Northeast Asia. Against the background of the evolution of the geopolitical pattern since the disintegration of the Soviet Union and global warming, based on the land cover data in the Selenga River basin from 1992, 2000, 2009, and 2015, this paper describes the dynamic changes in land use in the basin. Through a logistic model, the driving factors of land cover change were revealed, and the CA-Markov model was used to predict the land cover pattern of 2027. The results showed that (1) from 1992 to 2015, the agricultural population in the Selenga River basin continued to decrease, which led to a reduction in agricultural sown area. The intensification of climate warming and drying had a significant impact on the spatial distribution of crops. Grassland expansion mostly occurred in areas with relatively abundant rainfall, low temperature, and low human activity. (2) The simulation results showed that, according to the current development trend, the construction land area of the Selenga River basin will be slightly expanded in 2027, the area of arable land and grassland will be slightly reduced, and the areas of forest, water/wetland, and bare land will remain stable. In the future, human activities in the basin will increase in the process of the construction of the China-Mongolia-Russia economic corridor. Coupled with global warming, the land/cover of the basin will be affected by both man-made and natural disturbances, and attention should be paid to the possible risk of vegetation degradation.
Textile Slotted Waveguide Antennas for Body-Centric Applications
Authors: Mikulić Davorin , Šopp Evita , Bonefačić Davor , Šipuš Zvonimir
Published: January 2022

One of the major challenges in the development of wearable antennas is to design an antenna that can at the same time satisfy technical requirements, be aesthetically acceptable, and be suitable for wearable applications. In this paper, a novel wearable antenna is proposed—textile realization of a slotted waveguide antenna. The antenna is realized using conductive fabric to manufacture the walls of a rectangular waveguide in which the slots were cut out. All connections and cuts are sewn with conductive thread taking over advantages of the traditional process of manufacturing textile objects. The developed slotted waveguide array prototype, containing three slots and designed for operation in the 5.8-GHz ISM band, is experimentally characterized and compared to an equivalent metallic antenna. The achieved operating bandwidth is larger than 300 MHz in both cases. The measured gain of a textile slotted waveguide array is around 9 dBi with a radiation efficiency larger than 50% in the whole operating bandwidth, i.e., the textile array showed a 2 dB lower gain in comparison to the metallic counterpart. The gain is stable in the whole bandwidth and the radiation patterns do not differ. The results demonstrated that such textile antennas are suitable for body-centric communication and sensor systems and can be integrated into clothing, e.g., into a smart safety vest or into a uniform. Further analysis of various realizations of slotted waveguide antennas is presented showing that different versions of the proposed antenna can be used in all three off-body, on-body, and in-body communication scenarios.
A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems
Authors: Mohanna Ammar , Gianoglio Christian , Rizik Ali , Valle Maurizio
Published: January 2022

The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.
Acknowledgment to Reviewers of Sensors in 2021
Authors:   Sensors Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Metal Organic Framework-Based Dispersive Solid-Phase Microextraction of Carbaryl from Food and Water Prior to Detection by Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry
Authors: Habila Mohamed A. , Alhenaki Bushra , El-Marghany Adel , Sheikh Mohamed , Ghfar Ayman A. , ALOthman Zeid A. , Soylak Mustafa
Published: January 2022

In this work, metal organic frameworks (A100 Al-based MOFs) were used in dispersive solid-phase microextraction (DSPME) for the isolation and preconcentration of the carbaryl from vegetable, fruit and water samples. The A100 Al-based MOFs showed excellent behavior for the adsorption of carbaryl from a water–ethanol solution; additionally, carbaryl was easily desorbed with ethyl acetate for detection by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-TMS). The analytical process of DSPME together with UPLC-TMS provides the accurate monitoring of trace carbaryl residues. The results show that the optimal recovery% of carbaryl was obtained at a sample apparent pH of 5, with the application of 1 mL of ethyl acetate to elute the carbaryl from the A100 Al-based MOFs. The limit of detection (LOD) and the limit of quantification (LOQ) were 0.01 mg.L−1 and 0.03 mg.L−1, respectively. The RSD% was 0.8–1.9, and the preconcentration factor was 45. DSPME and UPLC-TMS were successfully used for the isolation and detection of carbaryl in food and water samples.
Acknowledgment to Reviewers of Signals in 2021
Authors:   Signals Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
An Analysis of Comparative Perspectives on Economic Empowerment among Employment-Seeking Survivors of Intimate Partner Violence (IPV) and Service Providers
Authors: Tarshis Sarah , Scott-Marshall Heather , Alaggia Ramona
Published: January 2022

Purpose: The purpose of this study is to compare perspectives on economic empowerment in the context of employment seeking among intimate partner violence (IPV) survivors and service providers specializing in IPV-related trauma. This study addressed the following question is: How do employment-seeking IPV survivors and service providers conceptualize and understand empowerment? Insights into how each group conceives of economic empowerment and its attainment following IPV experiences can help to inform an effective service curriculum that can be used to facilitate optimal employment outcomes among survivors. Methods: A constructivist grounded theory method was used to develop a theoretical framework for conceptualizing how economic empowerment is understood by employment-seeking survivors of IPV, and IPV service providers. Twenty-six participants were recruited (survivors, n = 16; service providers, n = 10) in a large northeastern U.S. city. Interview questions focused on how IPV survivors and service providers identify and conceptualize economic empowerment, and how support services respond to survivors’ needs around empowerment, particularly through help with employment seeking. Results: Data were coded and analyzed following data analysis stages: (a) initial coding; (b) constant comparison; and, (c) focused coding. Three main themes emerged from the narrative data: (1) structural characteristics shape individual experiences and perspectives of empowerment; (2) peer support as an integral component to empowerment; and (3) employment attainment as economic empowerment. Though perspectives on economic empowerment were often aligned, some key differences emerged. Whereas providers tended toward a more restricted, micro-level view of empowerment as primarily an attribute of the individual, survivors were inclined toward a structuralist perspective that considers how individuals’ experiences of empowerment are shaped by broader, institutional-level factors. Conclusions: Findings from this study build on prior research on the experiences of IPV survivors. The focus on experiences of empowerment in the context of employment-seeking can inform work on building more effective support services for survivors that avoid reductionist approaches that could be perceived by survivors as “victim-blaming” by incorporating a sensitivity to empowerment as derived from structural factors that shape individual experience.
On Regret: A Sociological Intersectional Approach
Authors: d’Avelar Maria Madalena
Published: January 2022

Regret is more than just an individual cognitive and emotional phenomenon, and it can, and should, be seen as social and cultural as well. Because of this, regret can tell us a lot, both about someone’s biography, and about the society and culture that shape it. In this brief reflection, the aim is to look at regret as a phenomenon worthy of sociological focus. We focus on three main ways in which regret can be understood as a sociological object: regret as a part of someone’s biography, regret as something that is culturally shaped, and regret as a part of collective memory. We also explore the potentialities of using an intersectional framework to analyze regret in its different forms.
Still Mother after All These Years: Infants Still Prefer Mothers over Fathers (If They Have the Choice)
Authors: Zimmermann Peter , Mühling Laura Elise , Lichtenstein Lucie , Iwanski Alexandra
Published: January 2022

Fathering and mothering have changed in many ways within the last decades. Earlier studies showed a clear hierarchy in infant attachment figures with a preference for mothers. This study examined whether infants in the 21st century still prefer mothers over fathers in their expression of attachment behaviors, whether differences in parental involvement still exist, and whether this will result in differences in attachment security to mother and father. A total of 50 German families with infants between 10 and 19 months were observed in an experimental setting and during home visits. Parents reported on their involvement. The results revealed a clear hierarchy with regard to the duration of attachment behaviors directed towards mothers, followed by fathers and strangers. Mothers reported to be more involved in child care on weekdays compared to fathers. Involvement was not associated with attachment variables. Attachment security to mother and father was positively related and did not differ significantly. Infants in the 21st century in a Western country still prefer mothers over fathers in their expression of attachment behaviors. Mothers were more involved in child care than fathers. However, these differences did not result in differences in attachment security to mother and father.
In Pursuit of a Comprehensive Understanding of Expertise Development: A Comparison between Paths to World-Class Performance in Complex Technical vs. Endurance Demanding Sports
Authors: Aalberg Martine , Roaas Truls , Aune Morten , Bjerke Øyvind , Aune Tore
Published: January 2022

A comprehensive understanding of skill acquisition is important for different performance domains, and has practical implications for both sport sciences and public health. The study compared important constraints for expertise development in a physically demanding sport (cross-country skiing) versus a technically demanding sport (freeskiing). Eighteen world-class athletes reported the importance of different constraints for their developmental history subdivided into two age spans: (1) 7–15 years and (2) 16 years until present. The total amount of training did not differ between the groups, but from the age of 16, the cross-country skiers spend approximately 98% of their training specific to their main sport, compared to 75% for freeskiers. No differences were found between the distribution of organized versus non-organized training in main sport, but freeskiers reported a higher amount of unorganized training in other sports after the age of 16. No differences were found in perceived importance of facilities, enjoyment of performing their sport, or the need for early specialization of training. After the age of 16, the cross-country skiers reported a higher need for coach involvement compared to freeskiers. The two sports mainly share common paths to expertise but differ in the need for specific training and coach involvement.
Acknowledgment to Reviewers of Stats in 2021
Authors:   Stats Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Motivation and Eco-Attitudes among Night Runners during the COVID-19 Pandemic
Authors: Rozmiarek Mateusz , León-Guereño Patxi , Tapia-Serrano Miguel Ángel , Thuany Mabliny , Gomes Thayse Natacha , Płoszaj Katarzyna , Firek Wiesław , Malchrowicz-Mośko Ewa
Published: January 2022

Running surrounded by nature at night, often with limited contact with other people, is one of the safest physical activities during the COVID-19 pandemic. The aim of this article is to determine what motivates night recreational runners and to analyse the differences between men and women according to age, and also to see whether the fact of having had COVID-19 or not matters when it comes to athletes’ motivation behind participating in runs. Runners were also asked about participating in cities or out-of-town events in terms of verifying the cleanliness of the air before running, using masks or running in green areas. 233 individuals in total participated in this descriptive, quantitative, cross-sectional study. The questionnaire uses the division of motives used in the Marathon Motivation Scale (MOMS) and a number of additional questions on environmental factors. Overall, the main motivation that drives night-time runners is health orientation, weight concern, personal goal achievement, psychological coping, life meaning and self-esteem. The research also showed higher scores for health orientation, recognition, psychological coping, life meaning and self-esteem among runners who had had COVID-19 than runners who had not had the virus. In addition, respondents clearly indicated that night running makes it easier to fall asleep and improves the quality of sleep. Most runners rated their mental health as being good, did not run in the morning despite better air levels, did not choose running events outside the urban area and did not check the air quality index before running. Therefore, it will be important for coaches, event organisers and other professionals to consider athletes’ age, gender, whether they have had COVID-19 and runners’ approaches to eco-attitudes when trying to understand their reasons for participating in different sports or leisure events, especially those that focus on a priority in shaping environmental attitudes.
Towards Road Sustainability—Part I: Principles and Holistic Assessment Method for Pavement Maintenance Policies
Authors: de Bortoli Anne , Féraille Adélaïde , Leurent Fabien
Published: January 2022

Assessing the holistic sustainability of public policies remains a challenge rarely taken up due to a lack of adequate assessing methods. Frequently, only environmental and/or financial aspects are addressed, rather than the three pillars, including macro- and micro-economic as well as social performance. This paper presents an assessment method to fully compare the performance of pavement resurfacing policies for all its stakeholders and considering pavement–vehicle interactions. First, an analytical and then systemic approach to road maintenance highlights all its stakeholders, and a complete set of sustainability indicators is proposed to quantify the various impacts of maintenance programs: tax revenues, road operator’s and users’ savings, domestic production and employment, net present value, users’ time savings and noise reduction health benefits, as well as protection of natural resources, biodiversity and human health. Second, specific physical models of road condition (International Roughness Index) and its role in pavement–vehicle interaction in terms of vehicle consumption and wear as well as traffic noise are introduced. Then, equations to calculate these indicators are presented based on a comparison of existing assessment methods. The final transdisciplinary method pulls from road engineering, industrial ecology, acoustics and economics. It especially combines environmental and economic life cycle assessments and economic input–output analysis, as well as financial and socioeconomic appraisals. Finally, this article takes up the interdisciplinary challenge of building a fully holistic assessment method to help decision makers properly address sustainability, and its general algorithm can be adapted to assess a variety of transportation policies.
The Butterfly Framework for the Assessment of Transitions towards a Circular and Climate Neutral Society
Authors: Bos Harriëtte , de Haas Wim , Jongschaap Raymond
Published: January 2022

The Butterfly framework of Wageningen University & Research (WUR) for assessing transitions towards a circular and climate-neutral society is presented. The Butterfly framework is built after analysis of existing frameworks that could only partly comply with the needs of the full set of stakeholders interlinked and operating in domains like society and well-being; food, feed, and biobased production; natural resources and living environment. It shows that for adequate action perspectives on and in these domains, the socio-ecological, socio-technical, and socio-institutional subsystems should be fully integrated, and stakeholders should be equally consulted and appreciated. In order to advance and integrate action perspectives of different stakeholders in the light of the transition to circularity with high-level ambitions like climate neutrality, stakeholders (groups) need to understand their position and links in a full systems perspective, which the Butterfly framework provides.
The Transformation of Healthcare Buildings: The Challenges of the University of Pavia for Urban Regeneration
Authors: Greco Alessandro , Besana Daniela , Giacometti Valentina , Mericco Mauro , Lombardi Silvia , Borlini Andrea , Turino Roberto , Ruggeri Cristiana
Published: January 2022

The theme of urban regeneration is currently one of the main actions of transformation policies. It presents an opportunity for cities’ design, in particular historical centers, through joint actions aimed at creating synergies and connections between social, political, economic, and environmental aspects. It also becomes an opportunity to enhance the huge and complex Cultural Heritage, full of architectural values, memory, and history. At the same time, it appears increasingly inadequate and obsolete compared to the new needs of a changing society, of regulatory aspects, and of a general compatible approach to intervention to the more general policies of sustainable development, as required by the 2030 Agenda for Sustainable Development. A total of two case studies of the reuse and enhancement of hospital buildings of the University of Pavia are presented, which has a large building heritage spread throughout the city. In recent years, owing to the opportunities offered by funding, it has started transformation policies on some abandoned buildings through projects based on an interdisciplinary approach and a participatory design strategy. The article testifies to a potential method of intervention that can be exported to other contexts, after a previous test of the compatibility on the existing and on the urban context.