bluuebunny/crossref_metadata_2025 huggingface dataset:
Nutrition, Microbiota and Role of Gut-Brain Axis in Subjects with Phenylketonuria (PKU): A Review
Authors: Verduci Elvira , Carbone Maria Teresa , Borghi Elisa , Ottaviano Emerenziana , Burlina Alberto , Biasucci Giacomo
Published: October 2020

The composition and functioning of the gut microbiota, the complex population of microorganisms residing in the intestine, is strongly affected by endogenous and exogenous factors, among which diet is key. Important perturbations of the microbiota have been observed to contribute to disease risk, as in the case of neurological disorders, inflammatory bowel disease, obesity, diabetes, cardiovascular disease, among others. Although mechanisms are not fully clarified, nutrients interacting with the microbiota are thought to affect host metabolism, immune response or disrupt the protective functions of the intestinal barrier. Similarly, key intermediaries, whose presence may be strongly influenced by dietary habits, sustain the communication along the gut-brain-axis, influencing brain functions in the same way as the brain influences gut activity. Due to the role of diet in the modulation of the microbiota, its composition is of high interest in inherited errors of metabolism (IEMs) and may reveal an appealing therapeutic target. In IEMs, for example in phenylketonuria (PKU), since part of the therapeutic intervention is based on chronic or life-long tailored dietetic regimens, important variations of the microbial diversity or relative abundance have been observed. A holistic approach, including a healthy composition of the microbiota, is recommended to modulate host metabolism and affected neurological functions.
Maternal High Fat Diet Programs Male Mice Offspring Hyperphagia and Obesity: Mechanism of Increased Appetite Neurons via Altered Neurogenic Factors and Nutrient Sensor AMPK
Authors: Desai Mina , Ferrini Monica G. , Han Guang , Narwani Kavita , Ross Michael G.
Published: October 2020

Maternal high-fat (HF) is associated with offspring hyperphagia and obesity. We hypothesized that maternal HF alters fetal neuroprogenitor cell (NPC) and hypothalamic arcuate nucleus (ARC) development with preferential differentiation of neurons towards orexigenic (NPY/AgRP) versus anorexigenic (POMC) neurons, leading to offspring hyperphagia and obesity. Furthermore, these changes may involve hypothalamic bHLH neuroregulatory factors (Hes1, Mash1, Ngn3) and energy sensor AMPK. Female mice were fed either a control or a high fat (HF) diet prior to mating, and during pregnancy and lactation. HF male newborns were heavier at birth and exhibited decreased protein expression of hypothalamic bHLH factors, pAMPK/AMPK and POMC with increased AgRP. As adults, these changes persisted though with increased ARC pAMPK/AMPK. Importantly, the total NPY neurons were increased, which was consistent with the increased food intake and adult fat mass. Further, NPCs from HF newborn hypothalamic tissue showed similar changes with preferential NPC neuronal differentiation towards NPY. Lastly, the role of AMPK was further confirmed with in vitro treatment of Control NPCs with pharmacologic AMPK modulators. Thus, the altered ARC development of HF offspring results in excess appetite and reduced satiety leading to obesity. The underlying mechanism may involve AMPK/bHLH pathways.
Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan
Authors: Shinozaki Nana , Murakami Kentaro
Published: October 2020

We evaluated the energy and nutrient intake estimates of popular Japanese diet-tracking mobile applications (apps). We identified five diet-tracking apps in the iTunes store during August 2020. A researcher entered the dietary data from a one-day paper-based dietary record (DR) previously obtained from apparently healthy free-living adults (15 males and 15 females; 22–65 years) into each app. The energy and nutrient intakes estimated by the apps were compared with those calculated using the Standard Tables of Food Composition in Japan based on the paper-based DR (reference method). The number of dietary variables available ranged from one (energy in Mogutan) to 17 (FiNC). Compared to the DR-based estimates, the median energy intake was significantly overestimated by MyFitnessPal, Asken, Calomiru, and Mogutan. Moreover, the intakes of many nutrients were overestimated by Asken and Calomiru and underestimated by MyFitnessPal. For energy intake, the Spearman correlation coefficient between the DR and the apps was lowest for Mogutan (0.76) and highest for FiNC (0.96). The median correlation coefficient for nutrient intakes was lower in MyFitnessPal (0.50) than in the other three apps (0.80 in Asken, 0.87 in FiNC, and 0.88 in Calomiru). These results suggest that intake calculations differ among apps. Further evaluation is needed in free-living settings, where users input their own food intake.
Use of an Animal Model to Evaluate Anxiolytic Effects of Dietary Supplementation with Tilia tomentosa Moench Bud Extracts
Authors: Turrini Federica , Vallarino Giulia , Cisani Francesca , Donno Dario , Beccaro Gabriele Loris , Zunin Paola , Boggia Raffaella , Pittaluga Anna , Grilli Massimo
Published: October 2020

Anxiety disorders are common and complex psychiatric syndromes affecting a broad spectrum of patients. On top of that, we know that aging produces an increase in anxiety vulnerability and sedative consumption. Moreover, stress disorders frequently show a clear gender susceptibility. Currently, the approved pharmacological strategies have severe side effects such as hallucinations, addiction, suicide, insomnia, and loss of motor coordination. Dietary integration with supplements represents an intriguing strategy for improving the efficacy and the safety of synthetic anxiolytics. Accordingly, a recent article demonstrated that glyceric bud extracts from Tilia tomentosa Moench (TTBEs) exert effects that are consistent with anxiolytic activity. However, the effects of these compounds in vivo are unknown. To examine this question, we conducted behavioral analysis in mice. A total of 21 days of oral supplements (vehicle and TTBEs) were assessed by Light Dark and Hole Board tests in male and female mice (young, 3 months; old, 24 months). Interestingly, the principal component analysis revealed gender and age-specific behavioral modulations. Moreover, the diet integration with the botanicals did not modify the body weight gain and the daily intake of water. Our results support the use of TTBEs as dietary supplements for anxiolytic purposes and unveil age and gender-dependent responses.
Human T-cell Leukemia Virus Type 1 and Strongyloides stercoralis: Partners in Pathogenesis
Authors: Dykie Adam , Wijesinghe Tharaka , Rabson Arnold B. , Madugula Kiran , Farinas Christian , Wilson Sydney , Abraham David , Jain Pooja
Published: October 2020

Infection with human T-cell leukemia/lymphoma virus type 1 (HTLV-1) has been associated with various clinical syndromes including co-infection with Strongyloides stercoralis, which is an intestinal parasitic nematode and the leading cause of strongyloidiasis in humans. Interestingly, HTLV-1 endemic areas coincide with regions citing high prevalence of S. stercoralis infection, making these communities optimal for elucidating the pathogenesis of co-infection and its clinical significance. HTLV-1 co-infection with S. stercoralis has been observed for decades in a number of published patient cases and case series; however, the implications of this co-infection remain elusive. Thus far, data suggest that S. stercoralis increases proviral load in patients co-infected with HTLV-1 compared to HTLV-1 infection alone. Furthermore, co-infection with HTLV-1 has been associated with shifting the immune response from Th2 to Th1, affecting the ability of the immune system to address the helminth infection. Thus, despite this well-known association, further research is required to fully elucidate the impact of each pathogen on disease manifestations in co-infected patients. This review provides an analytical view of studies that have evaluated the variation within HTLV-1 patients in susceptibility to S. stercoralis infection, as well as the effects of strongyloidiasis on HTLV-1 pathogenesis. Further, it provides a compilation of available clinical reports on the epidemiology and pathology of HTLV-1 with parasitic co-infection as well as data from mechanistic studies suggesting possible immunopathogenic mechanisms. Furthermore, specific areas of potential future research have been highlighted to facilitate advancing understanding of the complex interactions between these two pathogens.
Religious Education in Denmark: Towards a More Multifaceted Subject?
Authors: Böwadt Pia Rose
Published: October 2020

The article deals with religious education in Denmark, especially the impact of philosophy of life in the curriculum. It will address the question of whether “Christian Studies” has become more multifaceted, or whether it is really a Christian upbringing/existential upbringing in disguise. The article begins with a presentation of the religious, political, and the pedagogical landscapes in Denmark. Subsequently, it examines the impact of the “Philosophy of Life” elements, which form part of the subject taught under “Christian Studies” (Kristendomskundskab). This is followed by an analysis of the new legal framework for “Christian Studies”, which now highlights areas, such as comparative religion or sociology of religion. It also examines the work of the committee, formed by the former Minister of Education in spring 2019, which had the task of revitalizing the curriculum of “Christian Studies” in the Danish school system.
Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images
Authors: Kalantar Bahareh , Ueda Naonori , Al-Najjar Husam A. H. , Halin Alfian Abdul
Published: October 2020

In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.
A Method for Monitoring and Forecasting the Heading and Flowering Dates of Winter Wheat Combining Satellite-Derived Green-up Dates and Accumulated Temperature
Authors: Huang Xin , Zhu Wenquan , Wang Xiaoying , Zhan Pei , Liu Qiufeng , Li Xueying , Sun Lixin
Published: October 2020

Heading and flowering are two key phenological stages in the growth process of winter wheat. It is of great significance for agricultural management and scientific research to accurately monitor and forecast the heading and flowering dates of winter wheat. However, the monitoring accuracy of existing methods based on remote sensing needs to be improved, and these methods cannot realize forecasting in advance. This study proposed an accumulated temperature method (ATM) for monitoring and forecasting the heading and flowering dates of winter wheat from the perspective of thermal requirements for crop growth. The ATM method consists of three key procedures: (1) extracting the green-up date of winter wheat as the starting point of temperature accumulation with the dynamic threshold method from remotely sensed vegetation index (VI) time-series data, (2) calculating the accumulated temperature and determining the thermal requirements from the green-up date to the heading date or the flowering date based on phenology observation samples, and (3) combining the satellite-derived green-up date, daily temperature data, and thermal requirements to monitor and forecast the heading date and flowering date of winter wheat. When applying the ATM method to winter wheat in the North China Plain during 2017–2019, the root mean square error (RMSE) for the estimated heading date was between 4.76 and 6.13 d and the RMSE for the estimated flowering date was between 5.30 and 6.41 d. By contrast, the RMSE for the heading and flowering dates estimated by the widely used maximum vegetation index method was approximately 10 d. Furthermore, the forecasting accuracy of the ATM method was also high, and the RMSE was approximately 6 d. In summary, the proposed ATM method can be used to accurately monitor and forecast the heading and flowering dates of winter wheat in large spatial scales and it performs better than the existing maximum vegetation index method.
Geomatics and EO Data to Support Wildlife Diseases Assessment at Landscape Level: A Pilot Experience to Map Infectious Keratoconjunctivitis in Chamois and Phenological Trends in Aosta Valley (NW Italy)
Authors: Orusa Tommaso , Orusa Riccardo , Viani Annalisa , Carella Emanuele , Borgogno Mondino Enrico
Published: October 2020

Geomatics and satellite remote sensing offer useful analysis tools for several technical-scientific fields. This work, with reference to a regional case of study, investigates remote sensing potentialities for describing relationships between environment and diseases affecting wildlife at landscape level in the light of climate change effects onto vegetation. Specifically, the infectious keratoconjunctivitis (IKC) of chamois (Rupicapra rupicapra L.) in Aosta Valley (NW Italy) was investigated at the regional level. IKC (Mycoplasma conjunctivae) is a contagious disease for domestic and wild ruminants (Caprinae and Ovinae). Two types of analysis were performed: one aimed at exploring by remotely sensed data phenological metrics (PMs) and evapotranspiration (ET) trends of vegetation in the area; one investigating the correlation between PMs and ET, versus IKC prevalence. The analysis was based on TERRA MODIS image time series ranging from 2000 to 2019. Ground data about IKC were available for a shorter time range: 2009–2019. Consequently, PMs and ET trend investigations were focused on the whole times range (2000–2019); conversely, correlation analysis was achieved with reference to the reduced 2009–2019 period. The whole study was based on freely available data from public archives. MODIS products, namely MOD13Q1 v.6 and MOD16A2, were used to derive PM and ET trends, respectively. Shuttle Radar Topography Mission (SRTM) Digital Terrain Model (DTM) was used to describe local topography; CORINE Land Cover map was adopted to describe land use classes. PMs and ET (as derivable from EO data) proved to significantly changed their values in the last 20 years, with a continuous progressive trend. As far as correlation analysis was concerned, ET and some PMs (specifically, End of Season (EOS) and Length of Season (LOS) proved significantly condition IKC prevalence. According to results, the proposed methodology can be retained as an effective tool for supporting public health and eco-pathological sectors. Specifically, it can be intended for a continuous monitoring of effects that climatic dynamics determine onto wild animals in the Alpine area, included diseases and zoonosis, moving future environmental management and planning towards the One Health perspective.
Developing an Automated Gas Sampling Chamber for Measuring Variations in CO2 Exchange in a Maize Ecosystem at Night
Authors: Li Chaoqun , Han Wenting , Peng Manman , Zhang Mengfei
Published: October 2020

The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops. Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis. Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period. Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were −0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods. The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: ΔCO2 = 0.079 μmol m−2s−1). This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately.
A Portable System for Remote Rehabilitation Following a Total Knee Replacement: A Pilot Randomized Controlled Clinical Study
Authors: Bell Kevin M. , Onyeukwu Chukwudi , Smith Clair N. , Oh Adrianna , Devito Dabbs Annette , Piva Sara R. , Popchak Adam J. , Lynch Andrew D. , Irrgang James J. , McClincy Michael P.
Published: October 2020

Rehabilitation has been shown to improve functional outcomes following total knee replacement (TKR). However, its delivery and associated costs are highly variable. The authors have developed and previously validated the accuracy of a remote (wearable) rehabilitation monitoring platform (interACTION). The present study’s objective was to assess the feasibility of utilizing interACTION for the remote management of rehabilitation after TKR and to determine a preliminary estimate of the effects of the interACTION system on the value of rehabilitation. Specifically, we tested post-operative outpatient rehabilitation supplemented with interACTION (n = 13) by comparing it to a standard post-operative outpatient rehabilitation program (n = 12) using a randomized design. Attrition rates were relatively low and not significantly different between groups, indicating that participants found both interventions acceptable. A small (not statistically significant) decrease in the number of physical therapy visits was observed in the interACTION Group, therefore no significant difference in total cost could be observed. All patients and physical therapists in the interACTION Group indicated that they would use the system again in the future. Therefore, the next steps are to address the concerns identified in this pilot study and to expand the platform to include behavioral change strategies prior to conducting a full-scale randomized controlled trial. Trial registration: ClinicalTrials.gov NCT02646761 “interACTION: A Portable Joint Function Monitoring and Training System for Remote Rehabilitation Following TKA” 6 January 2016.
An Integrated Front-end Circuit Board for Air-Coupled CMUT Burst-Echo Imaging
Authors: Ye Lei , Li Jian , Zhang Hui , Liang Dongmei , Wang Zhuochen
Published: October 2020

To conduct burst-echo imaging with air-coupled capacitive micromachined ultrasonic transducers (CMUTs) using the same elements in transmission and reception, this work proposes a dedicated and integrated front-end circuit board design to build an imaging system. To the best of the authors’ knowledge, this is the first air-coupled CMUT burst-echo imaging using the same elements in transmission and reception. The reported front-end circuit board, controlled by field programmable gate array (FPGA), consisted of four parts: an on-board pulser, a bias-tee, a T/R switch and an amplifier. Working with our 217 kHz 16-element air-coupled CMUT array under 100 V DC bias, the front-end circuit board and imaging system could achieve 22.94 dB signal-to-noise ratio (SNR) in burst-echo imaging in air, which could represent the surface morphology and the three-dimensional form factor of the target. In addition, the burst-echo imaging range of our air-coupled CMUT imaging system, which could work between 52 and 273 mm, was discussed. This work suggests good potential for ultrasound imaging and gesture recognition applications.
Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study
Authors: Abu-Tair Mamun , Djahel Soufiene , Perry Philip , Scotney Bryan , Zia Unsub , Carracedo Jorge Martinez , Sajjad Ali
Published: October 2020

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.
A Scalable and Secure Group Key Management Method for Secure V2V Communication
Authors: Aliev Hayotjon , Kim HyungWon , Choi Sunghyun
Published: October 2020

Safety applications based on vehicle-to-everything (V2X) communications can significantly enhance road safety and reduce traffic fatalities. Ensuring the security and privacy of the vehicular network is essential for the widespread adoption of V2X communications for commercial use. V2X safety and service applications require periodic broadcast communications among all the vehicles. However, compared to unicast communication, it is extremely challenging to provide broadcast communication with network security requirements such as confidentiality, in infotainment contents distribution, sensor data sharing, and security credentials management services. To address the providing confidentiality of vehicle-to-vehicle (V2V) broadcasting, we propose a group key management and message encryption method that is secure, lightweight, and scalable. The proposed group key management method can efficiently handle various scenarios like a node joining or leaving the group, with scalable rekeying algorithms. It employs a distributed and scalable architecture that offers several advantages such as the reduction of the key management overhead and the enhancement of the security level by keeping the key sizes with large networks. In addition, the proposed method employs a lightweight matrix-based encryption algorithm that can be easily applicable with the proposed group key management method. Further, we have implemented the proposed method and evaluated the performance using a V2V network simulator with several networks of highly dynamic group members. The simulation results show that the proposed method can reduce computation time for group key generation and message encryption by more than 80% compared to existing methods.
Intelligent UAV Deployment for a Disaster-Resilient Wireless Network
Authors: Hydher Hassaan , Jayakody Dushantha Nalin K. , Hemachandra Kasun T. , Samarasinghe Tharaka
Published: October 2020

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.
Compact Double-Layer FR4-Based Focusing Lens Using High-Efficiency Huygens’ Metasurface Unit Cells
Authors: Islam Kd M. Raziul , Choi Sangjo
Published: October 2020

High transmission efficiency metasurface unit cells have been designed based on surface electric and magnetic impedances derived from Huygens’ principle. However, unit cells for low transmission loss (<1 dB) over a wide transmission phase range require at least three metallic layers, which complicates the unit cell design process. In this paper, we introduce high-efficiency Huygens’ metasurface unit cell topologies in double-layer FR4 printed circuit board (PCB) by implementing surface electric and magnetic current using the top and bottom metallic patterns and via drills. Eleven unit cells were optimized for wide phase coverage (−150° to 150°) with a low average transmission loss of −0.82 dB at 10 GHz. To demonstrate the high-efficiency of the designed unit cells, we designed and fabricated two focusing lenses with dimensions of near 150 × 150 mm (5λ × 5λ) to focus a spherical beam radiated from short focal distances (f = 100 and 60 mm). The fabricated focusing lens showed 12.87 and 13.58 dB focusing gain for f = 100 and 60 mm at 10 GHz, respectively, with a 1 dB fractional gain bandwidth of near 10%. We expect that the proposed focusing lens based on high-efficiency double-layer metasurface unit cells can help realize compact and high-gain focusing lens-integrated antenna systems.
Advanced Modulation Format of Probabilistic Shaping Bit Loading for 450-nm GaN Laser Diode based Visible Light Communication
Authors: Li Guoqiang , Hu Fangchen , Zou Peng , Wang Chaofan , Lin Gong-Ru , Chi Nan
Published: October 2020

Visible light communication is an emerging high-speed optical wireless communication technology that can be a candidate to alleviate pressure on conventional radio frequency-based technology. In this paper, for the first time, the advanced modulation format of probabilistic shaping (PS) bit loading is investigated in a high data rate visible light communication system based on a 450-nm Gallium Nitride laser diode. The characteristic of the system is discussed and PS bit loading discrete multi-tone modulation helps to raise the spectral efficiency and improve the system performance. Higher entropy can be achieved in the same signal-to-noise ratio (SNR) and modulation bandwidth limitation, comparing to bit and power loading. With PS bit loading, an available information rate (AIR) of 10.23 Gbps is successfully achieved at the signal bandwidth of 1.5 GHz in a 1.2 m free space transmission with normalized generalized mutual information above 0.92. And higher AIR can be anticipated with an entropy-loading strategy that fixes the channel characteristic. Experimental results validate that a PS bit loading scheme has the potential to increase the system capacity.
Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation
Authors: Hu Yang , Cook Diane J. , Taylor Matthew E.
Published: October 2020

Smart-Home in a Box (SHiB) is a ubiquitous system that intends to improve older adults’ life quality. SHiB requires self-installation before use. Our previous study found that it is not easy for seniors to install SHiB correctly. SHiB CBLE is a computer-based learning environment that is designed to help individuals install a SHiB kit. This article presents an experiment examining how smart home sensor installation was affected by knowledge gained from two methods, SHiB CBLE, and a written document. Results show that participants who were trained by the CBLE took significantly (p<0.05) less time in the installation session than those in the control group. The accuracy rate of SHiB kit installation is 78% for the group trained by the CBLE and 77% for the control group. Participants trained by the CBLE showed significantly (p<0.01) higher confidence in the actual installation than those in the control group. These results suggest that having a training before the actual installation will help installers avoid unnecessary work, shorten the installation time, and increase installers’ confidence.
A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson’s Disease
Authors: Zhang Han , Li Chuantao , Liu Wei , Wang Jingying , Zhou Junhong , Wang Shouyan
Published: October 2020

The quantitative characterization of movement disorders and their related neurophysiological signals is important for the management of Parkinson’s disease (PD). The aim of this study is to develop a novel wearable system enabling the simultaneous measurement of both motion and other neurophysiological signals in PD patients. We designed a wearable system that consists of five motion sensors and three electrophysiology sensors to measure the motion signals of the body, electroencephalogram, electrocardiogram, and electromyography, respectively. The data captured by the sensors are transferred wirelessly in real time, and the outcomes are analyzed and uploaded to the cloud-based server automatically. We completed pilot studies to (1) test its validity by comparing outcomes to the commercialized systems, and (2) evaluate the deep brain stimulation (DBS) treatment effects in seven PD patients. Our results showed: (1) the motion and neurophysiological signals measured by this wearable system were strongly correlated with those measured by the commercialized systems (r > 0.94, p < 0.001); and (2) by completing the clinical supination and pronation frequency test, the frequency of motion as measured by this system increased when DBS was turned on. The results demonstrated that this multi-sensor wearable system can be utilized to quantitatively characterize and monitor motion and neurophysiological PD.
Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation
Authors: Ibarguren Aitor , Eimontaite Iveta , Outón José Luis , Fletcher Sarah
Published: October 2020

The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.
On Vulnerability of Selected IoT Systems to Radio Jamming—A Proposal of Deployment Practices
Authors: Staniec Kamil , Kowal Michał
Published: October 2020

Weightless and SigFox are both narrowband communication systems designed for the Internet of Things, along with some other counterparts such as LoRa (Long Range) and narrowband Internet of Things (NB-IoT). As systems dedicated specifically for long-range operations, they possess considerable processing gain for energetic link budget improvement and a remarkable immunity to interference. The paper describes outcomes of a measurement campaign during which the Weightless and SigFox performance was tested against variable interference, generated in an anechoic chamber. Results allow the quantitative appraisal of the system behavior under these harsh conditions with respect to different operational modes of the two investigated IoT systems. The outcomes are then investigated with respect to an intentional radio jammer attempting to block a base station (BS) operation by directly radiating an interfering signal towards it. An Interference Margin is proposed for a quantitative expression of a system’s resilience to jamming. This margin, calculated for all available configuration settings, allows the clear assessment of which combination of a system’s operational parameters does and which does not provide immunity to this type of radio attack.
Privacy-Preserving Distributed Analytics in Fog-Enabled IoT Systems
Authors: Zhao Liang
Published: October 2020

The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for time-sensitive applications in which traditional Cloud-based solution is unable to meet due to bandwidth and high latency limitations. In this paper, we develop a distributed analytics framework for fog-enabled IoT systems aiming to avoid raw data movement and reduce latency. The distributed framework leverages the computational capacities of all the participants such as edge devices and fog nodes and allows them to obtain the global optimal solution locally. To further enhance the privacy of data holders in the system, a privacy-preserving protocol is proposed using cryptographic schemes. Security analysis was conducted and it verified that exact private information about any edge device’s raw data would not be inferred by an honest-but-curious neighbor in the proposed secure protocol. In addition, the accuracy of solution is unaffected in the secure protocol comparing to the proposed distributed algorithm without encryption. We further conducted experiments on three case studies: seismic imaging, diabetes progression prediction, and Enron email classification. On seismic imaging problem, the proposed algorithm can be up to one order of magnitude faster than the benchmarks in reaching the optimal solution. The evaluation results validate the effectiveness of the proposed methodology and demonstrate its potential to be a promising solution for data analytics in fog-enabled IoT systems.
treNch: Ultra-Low Power Wireless Communication Protocol for IoT and Energy Harvesting
Authors: Moreno-Cruz Fernando , Toral-López Víctor , Escobar-Molero Antonio , Ruíz Víctor U. , Rivadeneyra Almudena , Morales Diego P.
Published: October 2020

Although the number of Internet of Things devices increases every year, efforts to decrease hardware energy demands and to improve efficiencies of the energy-harvesting stages have reached an ultra-low power level. However, no current standard of wireless communication protocol (WCP) can fully address those scenarios. Our focus in this paper is to introduce treNch, a novel WCP implementing the cross-layer principle to use the power input for adapting its operation in a dynamic manner that goes from pure best-effort to nearly real time. Together with the energy-management algorithm, it operates with asynchronous transmissions, synchronous and optional receptions, short frame sizes and a light architecture that gives control to the nodes. These features make treNch an optimal option for wireless sensor networks with ultra-low power demands and severe energy fluctuations. We demonstrate through a comparison with different modes of Bluetooth Low Energy (BLE) a decrease of the power consumption in 1 to 2 orders of magnitude for different scenarios at equal quality of service. Moreover, we propose some security optimizations, such as shorter over-the-air counters, to reduce the packet overhead without decreasing the security level. Finally, we discuss other features aside of the energy needs, such as latency, reliability or topology, brought again against BLE.
Investigation of Ga2O3-Based Deep Ultraviolet Photodetectors Using Plasma-Enhanced Atomic Layer Deposition System
Authors: Chu Shao-Yu , Shen Meng-Xian , Yeh Tsung-Han , Chen Chia-Hsun , Lee Ching-Ting , Lee Hsin-Ying
Published: October 2020

In this work, Ga2O3 films were deposited on sapphire substrates using a plasma-enhanced atomic layer deposition system with trimethylgallium precursor and oxygen (O2) plasma. To improve the quality of Ga2O3 films, they were annealed in an O2 ambient furnace system for 15 min at 700, 800, and 900 °C, respectively. The performance improvement was verified from the measurement results of X-ray diffraction, X-ray photoelectron spectroscopy, and photoluminescence spectroscopy. The optical bandgap energy of the Ga2O3 films decreased with an increase of annealing temperatures. Metal-semiconductor-metal ultraviolet C photodetectors (MSM UVC-PDs) with various Ga2O3 active layers were fabricated and studied in this work. The cut-off wavelength of the MSM UVC-PDs with the Ga2O3 active layers annealed at 800 °C was 250 nm. Compared with the performance of the MSM UVC-PDs with the as-grown Ga2O3 active layers, the MSM UVC-PDs with the 800 °C-annealed Ga2O3 active layers under a bias voltage of 5 V exhibited better performances including photoresponsivity of 22.19 A/W, UV/visible rejection ratio of 5.98 × 104, and detectivity of 8.74 × 1012 cmHz1/2W−1.
Research on Developing an Outdoor Location System Based on the Ultra-Wideband Technology
Authors: Rykała Łukasz , Typiak Andrzej , Typiak Rafał
Published: October 2020

Ultra-wideband (UWB) technology is one of the most promising wireless communication technologies. Examples of UWB applications include, among others, radiocommunication devices and location systems, due to their operating range, ability to work in outdoor environments, and resistance to multipath effects. This article focuses on the use of UWB technology in constructing a guide localization system for an unmanned ground vehicle (UGV), which is one of the stages of implementing a “follow me” system. This article describes the complete process of UWB signal processing from its acquisition, methods of filtering, and obtained results, to determining the location of the guide. This article examines the possibility of using modified versions of localization algorithms for determining the guide’s location, including trilateration, methods of nonlinear programming, and a geometric algorithm proposed by us. The innovation of this study consists in the implementation of an algorithm that changes the selection of equations (mathematical model) for determining location based on the number of available measurements from UWB sensors.
Intelligent Force-Measurement System Use in Shock Tunnel
Authors: Wang Yunpeng , Jiang Zonglin
Published: October 2020

The inertial vibration of the force measurement system (FMS) has a large influence on the force measuring result of aircraft, especially on some tests carried out in high-enthalpy impulse facilities, such as in a shock tunnel. When force tests are conducted in a shock tunnel, the low-frequency vibrations of the FMS and its motion cannot be addressed through digital filtering because of the inertial forces, which are caused by the impact flow during the starting process of the shock tunnel. Therefore, this paper focuses on the dynamic characteristics of the performance of the FMS. A new method—i.e., deep-learning-based single-vector dynamic self-calibration (DL-based SV-DSC) of an impulse FMS, is proposed to increase the accuracy of aerodynamic force measurements in a shock tunnel. A deep-learning technique is used to train the dynamic model of the FMS in this study. Convolutional neural networks with a simple structure are applied to describe the dynamic modeling so that the low-frequency vibration signals are eliminated from the test results of the shock tunnel. By validation of the force test results measured in a shock tunnel, the current trained model can realize intelligent processing of the balance signals of the FMS. Based on this new method of dynamic calibration, the reliability and accuracy of force data processing are well verified.
Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications
Authors: Hyun Eugin , Jin Young-Seok , Park Jae-Hyun , Yang Jong-Ryul
Published: October 2020

In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion.
Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce
Authors: Addagarla Ssvr Kumar , Amalanathan Anthoniraj
Published: October 2020

The recommender system is the most profound research area for e-commerce product recommendations. Currently, many e-commerce platforms use a text-based product search, which has limitations to fetch the most similar products. An image-based similarity search for recommendations had considerable gains in popularity for many areas, especially for the e-commerce platforms giving a better visual search experience by the users. In our research work, we proposed a machine-learning-based approach for a similar image-based recommender system. We applied a dimensionality reduction technique using Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) for transforming the extracted features into lower-dimensional space. Further, we applied the K-Means++ clustering approach for the possible cluster identification for a similar group of products. Later, we computed the Manhattan distance measure for the input image to the target clusters set for fetching the top-N similar products with low distance measure. We compared our approach with five different unsupervised clustering algorithms, namely Minibatch, K-Mediod, Agglomerative, Brich, and the Gaussian Mixture Model (GMM), and used the 40,000 fashion product image dataset from the Kaggle web platform for the product recommendation process. We computed various cluster performance metrics on K-means++ and achieved a Silhouette Coefficient (SC) of 0.1414, a Calinski-Harabasz (CH) index score of 669.4, and a Davies–Bouldin (DB) index score of 1.8538. Finally, our proposed PCA-SVD transformed K-mean++ approach showed superior performance compared to the other five clustering approaches for similar image product recommendations.
Cognitive Network Science Reconstructs How Experts, News Outlets and Social Media Perceived the COVID-19 Pandemic
Authors: Stella Massimo
Published: October 2020

This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news “COVID-19 is a pandemic”. In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions, while others channelled trust in contagion containment through semantic associations with science. In Italy, the first country to adopt a nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, and where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. Since both trust and fear strongly influence people’s risk-averse behaviour and mental/physical wellbeing, identifying evidence for these emotions is key under a global health crisis. Cognitive network science opens the way to unveiling the emotional framings of massively read news in automatic ways, with relevance for better understanding how information was framed and perceived by large audiences.