bluuebunny/crossref_metadata_2025 huggingface dataset:
An Approach Towards Optimization Appraisal of Thermal Conductivity of Magnetic Thermoplastic Elastomeric Nanocomposites Using Response Surface Methodology
Authors: Flaifel Moayad Husein
Published: September 2020

This study investigates the optimization of thermal conductivity of nickel zinc ferrite incorporated thermoplastic natural rubber nanocomposites using response surface methodology (RSM). The experimental runs were based on face-centered central composite design (FCCD) where three levels were designated for both temperature and magnetic filler content. The analysis of variance (ANOVA) results showed that the implemented technique is significant with an F-value of 35.7 and a p-value of <0.0001. Moreover, the statistical inference drawn from the quadratic model suggests a saddle response behavior the thermal conductivity took when both factors were correlated. The factors’ optimal set confined within the practical range led to a thermal conductivity of 1.05 W/m·K, a value which is believed to be associated with an optimal percolated network that served as efficacious thermal pathways in the fabricated nanocomposites. These results are believed to contribute to the potential employability of magnetic polymer nanocomposites (MPNCs) in electronic packaging applications.
Effect of POSS-Modified Montmorillonite on Thermal, Mechanical, and Electrical Properties of Unsaturated Polyester Nanocomposites
Authors: Divakaran Nidhin , B. Kale Manoj , Dhamodharan Duraisami , Mubarak Suhail , Wu Lixin , Wang Jianlei
Published: September 2020

Montmorillonite (MMT) displays excellent cohesion with an unsaturated polyester (UP) matrix to generate a material which exhibits an extensive range of commercial applications. The organic modification of MMT using polyhedral oligomeric silsesquioxanes (POSS) and the effect of POSS-MMT on the thermal, mechanical, and electrical properties of UP are reported here. Transmission electron microscopy (TEM) images were used to characterize the modification of MMT using POSS. Modified MMT (POSS-MMT) was incorporated, at different wt.% (0.5, 1, 3, 5), into UP via in-situ polymerization. The presence of POSS-MMT enhanced the characteristic properties of UP as a consequence of good dispersion in the polymer matrix. Scanning electron microscopy (SEM) images support effective POSS-MMT dispersion leading to tensile strength enhancement of a UP/POSS-MMT nanocomposite (3 wt.% POSS-MMT) by 54.7% as compared to that for unmodified UP. TGA displays a 35 °C improvement of thermal stability (10% mass loss) at 5% POSS-MMT incorporation, while the electrical conductivity is improved by 108 S/m (3 wt.% POSS-MMT) in comparison to that for unmodified UP. The conventional obstacle of UP associated with shrinkage weight loss during curing seems to be moderated with POSS-MMT incorporation (3%) resulting in a 27.8% reduction in shrinkage weight loss. These fabricated nanocomposites expand the versatility of UP as a high-performance material owing to enhancements of properties.
Optimization of the Rubber Formulation for Footwear Applications from the Response Surface Method
Authors: Srewaradachpisal Satta , Dechwayukul Charoenyutr , Chatpun Surapong , Spontak Richard J. , Thongruang Wiriya
Published: September 2020

Impact force remains the primary cause of foot injury and general discomfort with regard to footwear. The footwear industry traditionally relies on modified elastomers (including natural rubber) whose properties can be physically adjusted by varying the constituents in the rubber formulations. This work aims to investigate the effect of filler/plasticizer fractions on shock attenuation of natural rubber soles. The statistical response surface method (RSM) was used to optimize the loading of natural rubber, fillers (carbon black and china clay) and a plasticizer (paraffinic oil). A novel predictive equation addressing the effects of additives on the physical and mechanical properties of the shoe sole was successfully created using the RSM. Our results demonstrate how the concentrations of these components regulate final properties, such as impact force absorption and hardness, in the commercial manufacture of shoe soles. While a higher loading level of plasticizer promotes reductions in hardness and impact force, as well as energy dissipation, in these modified elastomers, these properties were improved by increasing the filler content.
Investigation of the Mechanical Properties of Parts Fabricated with Ultrasonic Micro Injection Molding Process Using Polypropylene Recycled Material
Authors: Gaxiola-Cockburn Rafael , Martínez-Romero Oscar , Elías-Zúñiga Alex , Olvera-Trejo Daniel , Reséndiz-Hernández José , Soria-Hernández Cintya
Published: September 2020

This research focuses on investigating how physical and mechanical properties of polypropylene (PP) recycled material are modified when ultrasonic micro injection molding (UMIM) technology is used to produce material specimens. Experimental characterization by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), Fourier transform infrared (FTIR) spectra, and rheology tests show that the fabricated PP samples were able to withstand up to five times recycled processing before some signs of mechanical and physical properties degradation are observed. Surprisingly, uniaxial extension tests show an increase of 3.07%, 10.97% and 27.33% for Young’s modulus, yield stress and ultimate stress values, respectively, and a slight reduction of 1.29% for the samples elongation at break when compared to the experimental data collected from virgin material samples. The improvement of these mechanical properties in the recycled samples suggests that ultrasonic microinjection produces a mechano-chemical material change.
A Review of Recent Advancements in Electrospun Anode Materials to Improve Rechargeable Lithium Battery Performance
Authors: Lee Byoung-Sun
Published: September 2020

Although lithium-ion batteries have already had a considerable impact on making our lives smarter, healthier, and cleaner by powering smartphones, wearable devices, and electric vehicles, demands for significant improvement in battery performance have grown with the continuous development of electronic devices. Developing novel anode materials offers one of the most promising routes to meet these demands and to resolve issues present in existing graphite anodes, such as a low theoretical capacity and poor rate capabilities. Significant improvements over current commercial batteries have been identified using the electrospinning process, owing to a simple processing technique and a wide variety of electrospinnable materials. It is important to understand previous work on nanofiber anode materials to establish strategies that encourage the implementation of current technological developments into commercial lithium-ion battery production, and to advance the design of novel nanofiber anode materials that will be used in the next-generation of batteries. This review identifies previous research into electrospun nanofiber anode materials based on the type of electrochemical reactions present and provides insights that can be used to improve conventional lithium-ion battery performances and to pioneer novel manufacturing routes that can successfully produce the next generation of batteries.
Development, Characterization and In Vitro Evaluation of Paclitaxel and Anastrozole Co-Loaded Liposome
Authors: Vu Minh Thanh , Nguyen Dinh Tien Dung , Nguyen Ngoc Hoi , Le Van Thu , Dao The Nam , Nguyen Thi Huong , Cong Tien Dung , Pham Truc Le-Buu , Lam Tri Duc , Le Ngoc Thuy Trang
Published: September 2020

Paclitaxel (PTX) and anastrozole (ANA) have been frequently applied in breast cancer treatment. PTX is well-known for its anti-proliferative effect meanwhile ANA has just been discovered to act as an estrogen receptor α (ERα) ligand. The combination therapy of PTX and ANA is expected to improve treating efficiency, as ANA would act as a ligand binding with the ERα gene expressed in breast cancer cells and thereafter PTX would inhibit the division and cause death to those cancer cells. In this study, liposome-based nanocarriers (LP) were developed for co-encapsulation of PTX and ANA to improve the efficacy of the combined drugs in an Estrogen receptor-responsive breast cancer study. PTX-ANA co-loaded LP was prepared using thin lipid film hydration method and was characterized for morphology, size, zeta potential, drug encapsulation and in vitro drug release. In addition, cell proliferation (WST assay) and IN Cell Analyzer were used for in vitro cytotoxicity studies on a human breast cancer cell line (MCF-7). Results showed that the prepared LP and PTX-ANA-LP had spherical vesicles, with a mean particle size of 170.1 ± 13.5 nm and 189.0 ± 22.1 nm, respectively. Controlled and sustained releases were achieved at 72 h for both of the loaded drugs. The in vitro cytotoxicity study found that the combined drugs showed higher toxicity than each single drug separately. These results suggested a new approach to breast cancer treatment, consisting of the combination therapy of PTX and ANA in liposomes based on ER response.
Life Cycle Assessment and Economic Analysis of Biomass Energy Technology in China: A Brief Review
Authors: Chen Shuangyin , Feng He , Zheng Jun , Ye Jianguo , Song Yi , Yang Haiping , Zhou Ming
Published: September 2020

This study describes the technological processes and characteristics of biomass direct combustion power generation, biomass gasification power generation, biomass mixed combustion power generation, and biomass biogas power generation in terms of their importance and application in China. Under the perspective of environmental and economic sustainability, the life cycle assessment (LCA) method and dynamic analysis method based on time value are used to simulate and evaluate the environmental loads and economic benefits of different power generation processes. By comparing with coal-fired power generation systems, the environmental and economic benefits of different biomass power generation technologies are illustrated. The results shows that biomass gasification power generation has the best environmental benefits, with a total load of 1.05 × 10−5, followed by biomass biogas power generation (9.21 × 10−5), biomass direct combustion power generation (1.23 × 10−4), and biomass mixed combustion power generation (3.88 × 10−4). Compared with the environmental load of coal-fired power generation, the reduction rate was 97.69%, 79.69%, 72.87%, and 14.56% respectively. According to the analysis of the technical economy evaluation results, when the dynamic pay-back period and IRR (internal rate of return) were used as evaluation indicators, the biomass direct combustion power generation has the best pay-back period (7.71 years) and IRR (19.16%), followed by the biogas power generation, with higher dynamic payback period (12.03 years), and lower IRR (13.49%). For gasification power generation and mixed-combustion power generation, their dynamic payback period is long, and the IRR is low. If net present value (NPV) is selected as the evaluation index, the biogas power generation appears to be the best because its net present value per megawatt is 11.94 million yuan, followed by direct combustion power generation (6.09 million yuan), and the net present value of mixed-combustion power generation and gasification power generation is relatively low. Compared with coal-fired power generation, direct combustion power generation and biogas power generation present significant economic benefits.
Profiling of Inflammatory Proteins in Plasma of HIV-1-Infected Children Receiving Antiretroviral Therapy
Authors: Lemma Mahlet , Petkov Stefan , Bekele Yonas , Petros Beyene , Howe Rawleigh , Chiodi Francesca
Published: September 2020

Treatment of HIV-1-infected patients results in improved clinical and immunological conditions, but severe non-AIDS-related conditions still persist. Novel proteomic platforms have identified inflammatory proteins where abundance is dysregulated in adult treated patients, whereas limited data are available in treated HIV-1 infection of children. Using a proteomic plasma profiling approach comprising 92 inflammation-related molecules, we analyzed specimens from 43 vertically HIV-1-infected children receiving antiretroviral treatment (ART) and matched controls in Ethiopia. The infected children were analyzed as a group and separately, according to age of treatment initiation. Proteins displaying a significantly different abundance between groups were hierarchically clustered and presented in heat maps. Random forest analysis was performed to pin-point proteins discriminating between groups; five proteins (STAMBP, CD5, TFG-α, TRANCE, AXIN1) were the strongest prediction factors for treated HIV-1 infection. TRANCE was previously linked to reduced bone mass levels in HIV-1-infected children. CCL4 chemokine, ligand to HIV-1 co-receptor CCR5, was the most critical protein for successful classification between children who initiated ART at different time points. Our data provide evidence that a dysregulated expression of proteins linked to immunological abnormalities and bone metabolism can be found in HIV-1-infected children with prolonged exposure to ART.
Shani on the Web: Virality and Vitality in Digital Popular Hinduism
Authors: Bhatia Varuni
Published: September 2020

What do god posters circulating online tell us about the practice of popular Hinduism in the age of digital mediatization? The article seeks to address the question by exploring images and god posters dedicated to the planetary deity Shani on Web 2.0. The article tracks Shani’s presence on a range of online platforms—from the religion and culture pages of newspapers to YouTube videos and social media platforms. Using Shani’s presence on the Web as a case study, the article argues that content drawn from popular Hinduism, dealing with astrology, ritual, religious vows and observances, form a significant and substantial aspect of online Hinduism. The article draws attention to the specific affordances of Web 2.0 to radically rethink what engaging with the sacred object in a virtual realm may entail. In doing so, it indicates what the future of Hindu religiosity may look like.
Bank Risk Determinants in Latin America
Authors: Martínez-Malvar Mariña , Baselga-Pascual Laura
Published: September 2020

Systemic Banking crises are a recurrent phenomenon that affects society, and there is a need for a better understanding of the risk factors to support prudential regulation and reduce unnecessary risk intake in the financial system. This paper examines the main bank risk determinants in Latin America. The period analysed covers the timespan from 1999 to 2013, including the systemic banking crisis episodes in Argentina (2001–2003) and Uruguay (2002–2005). We apply a new data-driven comparable methodology to classify and select commercial banks from the sample. We study bank risk proxied by the Z-score. We use the system-GMM estimator as our main empirical analysis method. According to our results, well capitalized, liquid, and traditional commercial banks are less risky. We perform robustness tests by applying OLS, and the results resemble our original model.
Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation
Authors: Cha Yesung , Arami Arash
Published: September 2020

Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions.
Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain
Authors: Chen Wei , Liu Qianjie , Hu Huosheng , Liu Jun , Wang Shaojie , Zhu Qingyuan
Published: September 2020

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg–Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.
In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors
Authors: Kashevnik Alexey , Ponomarev Andrew , Shilov Nikolay , Chechulin Andrey
Published: September 2020

This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use.
A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor
Authors: Zhou Yi , Chen Yufan , Ma Yongbao , Liu Hongqing
Published: September 2020

The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm.
Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck
Authors: Ma Rujin , Zhang Zhen , Dong Yiqing , Pan Yue
Published: September 2020

Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.
Assessment of the Speed Management Impact on Road Traffic Safety on the Sections of Motorways and Expressways Using Simulation Methods
Authors: Oskarbski Jacek , Kamiński Tomasz , Kyamakya Kyandoghere , Chedjou Jean Chamberlain , Żarski Karol , Pędzierska Małgorzata
Published: September 2020

Methods used to evaluate the impact of Intelligent Transport System (ITS) services on road safety are usually based on expert assessments or statistical studies. However, commonly used methods are challenging to apply in the planning process of ITS services. This paper presents the methodology of research using surrogate safety measures calculated and calibrated with the use of simulation techniques and a driving simulator. This approach supports the choice of the type of ITS services that are beneficial for traffic efficiency and road safety. This paper presents results of research on the influence of selected scenarios of variable speed limits on the efficiency and safety of traffic on the sections of motorways and expressways in various traffic conditions. The driving simulator was used to estimate the efficiency of lane-keeping by the driver. The simulation traffic models were calibrated using driving simulator data and roadside sensor data. The traffic models made it possible to determine surrogate safety measures (number of conflicts and their severity) in selected scenarios of using ITS services. The presented studies confirmed the positive impact of Variable Speed Limits (VSLs) on the level of road safety and traffic efficiency. This paper also presents recommendations and plans for further research in this area.
An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning
Authors: Zhu Taiyu , Li Kezhi , Kuang Lei , Herrero Pau , Georgiou Pantelis
Published: September 2020

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70–180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.
OBINTER: A Holistic Approach to Catalyse the Self-Management of Chronic Obesity
Authors: Álvarez Roberto , Torres Jordi , Artola Garazi , Epelde Gorka , Arranz Sara , Marrugat Gerard
Published: September 2020

Obesity is a preventable chronic condition that, in 2016, affected more than 1.9 billion people globally. Several factors have been identified that have a positive impact on long-term weight loss programs such as personalized recommendations, adherence strategies, weight and diet follow-up or physical activity tracking. Recently, various applications have been developed which help patients to self-manage their condition. These apps implement either one or some of these identified factors; however, there is not a single application that combines all of them following a holistic approach. In this context, we developed the OBINTER platform, which assists patients during the weight loss process by targeting user engagement during the longer term. The solution includes a mobile application which allows users to fill out dietetic questionnaires, receive dietetic and nutraceutical plans, track the evolution of their weight and adherence to the diet, as well as track their physical activity via a wearable device. Furthermore, an adherence strategy has been developed as a tool to foster the app usage during the whole weight loss process. In this paper, we present how the OBINTER approach gathers all of these features as well as the positive results of a usability testing study performed to assess the performance and usability of the OBINTER platform.
Reliability and Repeatability Analysis of Indices to Measure Gait Deterioration in MS Patients during Prolonged Walking
Authors: Taborri Juri , Studer Valeria , Grossi Paola , Brambilla Laura , Patanè Fabrizio , Ferrò Maria Teresa , Mantegazza Renato , Rossi Stefano
Published: September 2020

Gait deterioration caused by prolonged walking represents one of the main consequences of multiple sclerosis (MS). This study aims at proposing quantitative indices to measure the gait deterioration effects. The experimental protocol consisted in a 6-min walking test and it involved nine patients with MS and twenty-six healthy subjects. Pathology severity was assessed through the Expanded Disability Status Scale. Seven inertial units were used to gather lower limb kinematics. Gait variability and asymmetry were assessed by coefficient of variation (CoV) and symmetry index (SI), respectively. The evolution of ROM (range of motion), CoV, and SI was computed analyzing data divided into six 60-s subgroups. Maximum difference among subgroups and the difference between the first minute and the remaining five were computed. The indices were analyzed for intra- and inter-day reliability and repeatability. Correlation with clinical scores was also evaluated. Good to excellent reliability was found for all indices. The computed standard deviations allowed us to affirm the good repeatability of the indices. The outcomes suggested walking-related fatigue leads to an always more variable kinematics in MS, in terms of changes in ROM, increase of variability and asymmetry. The hip asymmetry strongly correlated with the clinical disability.
Position Estimation in Corridors Along the Coupled Mode of Radiating Cables
Authors: Blaszkiewicz Olga , Sadowski Jaroslaw , Stefanski Jacek
Published: September 2020

Radiating cables are mostly used to provide radio communication in tunnels or corridors, but they can also be used to estimate the position of a mobile terminal along the cable. In this paper, a measuring receiver’s position was estimated by measuring the difference in the direct signal’s reception time, which was generated by a transmitter connected to one end of the radiating cable, and the delayed signal retransmitted from another end. During tests, a relatively narrowband (23 MHz) signal was used in the unlicensed band (2.4 GHz) and 50 m long coupled mode radiating cable. The cable was installed along a corridor in the office building. Measurement results used different equipment configurations (i.e., return signal only amplified or amplified and frequency-shifted), which presented possible sources of errors.
Multi-Tone Frequency Estimation Based on the All-Phase Discrete Fourier Transform and Chinese Remainder Theorem
Authors: Huang Xiangdong , Cao Lu , Lu Wei
Published: September 2020

The closed-form robust Chinese Remainder Theorem (CRT) is a powerful approach to achieve single-frequency estimation from noisy undersampled waveforms. However, the difficulty of CRT-based methods’ extension into the multi-tone case lies in the fact it is complicated to explore the mapping relationship between an individual tone and its corresponding remainders. This work deals with this intractable issue by means of decomposing the desired multi-tone estimator into several single-tone estimators. Firstly, high-accuracy harmonic remainders are calculated by applying all-phase Discrete Fourier Transform (apDFT) and spectrum correction operations on the undersampled waveforms. Secondly, the aforementioned mapping relationship is built up by a novel frequency classifier which fully captures the amplitude and phase features of remainders. Finally, the frequencies are estimated one by one through directly applying the closed-form robust CRT into these remainder groups. Due to all the components (including closed-form CRT, the apDFT, the spectrum corrector and the remainder classifier) only involving slight computation complexity, the proposed scheme is of high efficiency and consumes low hardware cost. Moreover, numeral results also show that the proposed method possesses high accuracy.
Development of the User Requirements for the Canadian WildFireSat Satellite Mission
Authors: Johnston Joshua M. , Jackson Natasha , McFayden Colin , Ngo Phong Linh , Lawrence Brian , Davignon Didier , Wooster Martin J. , van Mierlo Helena , Thompson Dan K. , Cantin Alan S. , Johnston Daniel , Johnston Lynn M. , Sloane Meghan , Ramos Rebecca , Lynham Tim J.
Published: September 2020

In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design. In this study we emphasize the first objective by documenting the results of wildfire management end-user engagement activities which were used to identify the key Fire Management Functionalities (FMFs) required for an Earth Observation wildfire monitoring system. These FMFs are then used to define the User Requirements for the Canadian Wildland Fire Monitoring System (CWFMS) which are refined here for the WildFireSat mission. The User Requirements are divided into Observational, Measurement, and Precision requirements and form the foundation for the design of the WildFireSat mission (currently in Phase-A, summer 2020).
Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability
Authors: Siddiquee Masudur R. , Atri Roozbeh , Marquez J. Sebastian , Hasan S. M. Shafiul , Ramon Rodrigo , Bai Ou
Published: September 2020

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.
Trust in AI Agent: A Systematic Review of Facial Anthropomorphic Trustworthiness for Social Robot Design
Authors: Song Yao , Luximon Yan
Published: September 2020

As an emerging artificial intelligence system, social robot could socially communicate and interact with human beings. Although this area is attracting more and more attention, limited research has tried to systematically summarize potential features that could improve facial anthropomorphic trustworthiness for social robot. Based on the literature from human facial perception, product, and robot face evaluation, this paper systematically reviews, evaluates, and summarizes static facial features, dynamic features, their combinations, and related emotional expressions, shedding light on further exploration of facial anthropomorphic trustworthiness for social robot design.
3D Deep Learning on Medical Images: A Review
Authors: Singh Satya P. , Wang Lipo , Gupta Sukrit , Goli Haveesh , Padmanabhan Parasuraman , Gulyás Balázs
Published: September 2020

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.
Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard
Authors: Domínguez-Niño Jesús María , Oliver-Manera Jordi , Arbat Gerard , Girona Joan , Casadesús Jaume
Published: September 2020

Among the diverse techniques for monitoring soil moisture, capacitance-type soil moisture sensors are popular because of their low cost, low maintenance requirements, and acceptable performance. However, although in laboratory conditions the accuracy of these sensors is good, when installed in the field they tend to show large sensor-to-sensor differences, especially under drip irrigation. It makes difficult to decide in which positions the sensors are installed and the interpretation of the recorded data. The aim of this paper is to study the variability involved in the measurement of soil moisture by capacitance sensors in a drip-irrigated orchard and, using this information, find ways to optimize their usage to manage irrigation. For this purpose, the study examines the uncertainties in the measurement process plus the natural variability in the actual soil water dynamics. Measurements were collected by 57 sensors, located at 10 combinations of depth and position relative to the dripper. Our results showed large sensor-to-sensor differences, even when installed at equivalent depth and coordinates relative to the drippers. In contrast, differences among virtual sensors simulated using a HYDRUS-3D model at those soil locations were one order of magnitude smaller. Our results highlight, as a possible cause for the sensor-to-sensor differences in the measurements by capacitance sensors, the natural variability in size, shape, and centering of the wet area below the drippers, combined with the sharply defined variation in water content at the soil scale perceived by the sensors.
Investigating the Potential Impact of Louisiana Coastal Restoration on the Trace Metal Geochemistry of Constructed Marshlands
Authors: Akintomide Omolola A. , Adebayo Segun A. , Trahan Alexandra S. , Chamberlain Elizabeth , Johannesson Karen H.
Published: September 2020

Coastal restoration through diversion of suspended sediments from the Lower Mississippi River (LMR) into hydrologically isolated marshlands of Mid-Barataria Bay and Mid-Breton Sounds in southern Louisiana has the potential to mobilize lead (Pb), and other trace elements. We investigate the potential impact(s) of the diversion on marsh porewater through analysis of modern riverbank and suspended sediments, compared to sediments from pre-industrial deltaic deposits of LMR. Sequential extraction methods were used to evaluate Pb, cobalt (Co), copper (Cu), nickel (Ni), and zinc (Zn) in the sediments. Our results show that metal contents are higher (e.g., 8- to 10-fold for Pb) in the modern sediments relative to pre-industrial deposits. Also, the reducible fraction, presumably iron/manganese (Fe/Mn) oxides/oxyhydroxides, is the chief reservoir of environmentally available metals. The substantially higher trace metal contents of the modern relative to pre-industrial sediments suggest that the modern sediments contain a sizeable amount of anthropogenic contributions. Furthermore, the concentration of the trace metals in the reducible fraction suggests bioavailability to marsh organisms upon reductive dissolution within the planned, constructed coastal marshes. Still, additional sediment samples from the marshlands during the diversion implementation phase will be necessary to support the preliminary findings in this contribution as it affects coastal marshes and vital local fisheries.
Assessment of Resident Happiness under Uncertainty of Economic Policies: Empirical Evidences from China
Authors: Ouyang Zhigang , Liu Fengyu , Zhai Ge , Bilan Svitlana
Published: September 2020

The main aim of sustainable development is to increase the quality of life and resident happiness as the latter is one of the most important indicators for the assessment of quality of life. However, due to the uncertainty of economic policies, it will not only have a direct impact on resident well-being but may also indirectly affect resident well-being through specific channels. The economic policies are aimed at the achievement of sustainable development, therefore it is very important to investigate the influences of the uncertainty of economic policies on resident happiness. This allows to assess the sustainability of policies in terms of their inputs to the quality of life. The direct impact of economic policy uncertainty on resident well-being and the mediating effects of household asset allocation and the expectation on the above influencing relationship were analyzed based on the uncertainty index of China’s economic policies and households database of China Family Panel Studies (CFPS) during the period 2010–2018, so as to clarify the complete path of economic policy uncertainty affecting resident happiness. Results show that the uncertainty of economic policy significantly reduces resident happiness. In the context of the uncertainty of economic policies, families can relieve such negative impacts as an increasing proportion of financial assets in their total assets. However, decreases in asset shares for household consumer goods and future expectation obviously lower resident happiness. The research conclusions provide important input in research on the influences of the uncertainty of economic policy on the quality of life and offer new evidences for the development of “happiness economics”.
Towards a Neomedieval Urban Future: Neoliberal or Sustainable?
Authors: Terlouw Kees
Published: September 2020

The role of cities in the transformation of society is discussed. The growing importance of cities and their global networks undermine the nation state. This is a reversal of the development of the modern state which, over several centuries, increased its control over its territory and cities. Such changes have generated renewed interest in the Middle Ages. The relations between Medieval cities and territorial states were part of complex and shifting political arrangements, involving urban networks and overlapping claims to authority over territories. The general characteristics of prospective neomedieval political systems are discussed in more detail and applied to the regulatory challenges faced by neoliberalism and the transformation to a circular economy. The shift in the focus of neoliberal policy from the competitiveness of cities to that of metropolitan regions, with diverging urban and provincial interests hampers neomedievalist coordination. The cooperation between urban and provincial interests can however be realised in the transformation from a linear to a more circular economy, where metropolitan regions are well suited to accommodate the diverging aspects and forms of territorial regulation in a neomedievalist manner.
Analysis of Urban Morphological Effect on the Microclimate of the Urban Residential Area of Kampung Baru in Kuala Lumpur Using a Geospatial Approach
Authors: Zaki Sheikh Ahmad , Azid Nor Suhada , Shahidan Mohd Fairuz , Hassan Mohamad Zaki , Md Daud Mohd Yusof , Abu Bakar Nor Azlina , Mat Ali Mohamed Sukri , Yakub Fitri
Published: September 2020

As a Malay Agricultural Settlement established in 1900, Kampung Baru which is located in Kuala Lumpur, has become a subject of prolonged national interests in terms of economic, social, environment, and political issues along with the pressure of modern and future development. This study investigated the urban morphological impact of Kampung Baru on the intensity of urban heat island (UHI) by developing a smart geodatabase for urban climatic mapping. The database provided baseline data which was crucial to unveil the spatiotemporal characteristics of UHI in Kampung Baru. Determination of the urban heat island intensity (UHII) in Kampung Baru was carried out through two approaches, mobile and fixed measurements. In a period of six days, the mobile measurement was conducted within the target area at night using a motorcycle equipped with the temperature and relative humidity data logger while the fixed measurement was conducted using the similar equipment installed at a school building in the area. Building height data were also collected while building footprints were digitized using a topographical map and the satellite image was used as the base map. To estimate the UHII, the reference data for rural temperature was obtained from the Malaysian Meteorological Department (MMD). All of the data were analyzed using ArcGIS to portray the temperature pattern in the study area. The analysis revealed the presence of UHI effect in Kampung Baru at the average building height of six to ten metres. The results of the fixed measurement showed an island-like local maximum in the study area with the average and maximum UHII values of 4.4 °C and 6.0 °C, respectively. The results from the mobile measurement also showed that the highest temperature was recorded in Kampung Baru rather than in the surrounding areas of different land-use types throughout the observation days. The spatial temperature distribution in the study area also showed that the most affected part was the south-west of Kampung Baru which is surrounded by tall buildings. The findings of this study could be utilized in the planning of new development in the city of Kuala Lumpur.
Method for a Multi-Vehicle, Simulation-Based Life Cycle Assessment and Application to Berlin’s Motorized Individual Transport
Authors: Syré Anne Magdalene , Heining Florian , Göhlich Dietmar
Published: September 2020

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system-wide transition has not been addressed sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric-, diesel- and gasoline-powered vehicle models. We use a transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently, we analyze the product systems’ vehicle production, use phase and end-of-life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of complete car-based transportation in urban agglomerations and is able to analyze different transport sectors.