bluuebunny/crossref_metadata_2025 huggingface dataset:
Nanotechnology-Based Medical Devices for the Treatment of Chronic Skin Lesions: From Research to the Clinic
Authors: Ruggeri Marco , Bianchi Eleonora , Rossi Silvia , Vigani Barbara , Bonferoni Maria Cristina , Caramella Carla , Sandri Giuseppina , Ferrari Franca
Published: August 2020

Chronic wounds, such as pressure ulcers, diabetic ulcers, venous ulcers and arterial insufficiency ulcers, are lesions that fail to proceed through the normal healing process within a period of 12 weeks. The treatment of skin chronic wounds still represents a great challenge. Wound medical devices (MDs) range from conventional and advanced dressings, up to skin grafts, but none of these are generally recognized as a gold standard. Based on recent developments, this paper reviews nanotechnology-based medical devices intended as skin substitutes. In particular, nanofibrous scaffolds are promising platforms for wound healing, especially due to their similarity to the extracellular matrix (ECM) and their capability to promote cell adhesion and proliferation, and to restore skin integrity, when grafted into the wound site. Nanotechnology-based scaffolds are emphasized here. The discussion will be focused on the definition of critical quality attributes (chemical and physical characterization, stability, particle size, surface properties, release of nanoparticles from MDs, sterility and apyrogenicity), the preclinical evaluation (biocompatibility testing, alternative in vitro tests for irritation and sensitization, wound healing test and animal wound models), the clinical evaluation and the CE (European Conformity) marking of nanotechnology-based MDs.
pH-Responsive Gamma-Irradiated Poly(Acrylic Acid)-Cellulose-Nanocrystal-Reinforced Hydrogels
Authors: Wan Ishak Wan Hafizi , Yong Jia Oo , Ahmad Ishak
Published: August 2020

A pH-sensitive poly(acrylic acid) composite hydrogel was successfully synthesized via gamma irradiation and reinforced with cellulosic materials of different sizes. Cellulose was extracted from rice husks via alkali and bleaching treatment, and an acid hydrolysis treatment was performed to extract cellulose nanocrystals (CNCs). Morphological observation of cellulose and CNCs using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) revealed diameters of 22–123 μm and 5–16 nm, respectively. The swelling properties of the fabricated poly(acrylic acid)/cellulosic hydrogels were found to respond to changes in pH, and CNC-reinforced hydrogels performed better than cellulose-reinforced hydrogels. The highly crystalline CNC provided a greater storage modulus, hence acting as a better reinforcing material for poly(acrylic acid)-based hydrogels. SEM showed that hydrogels reinforced with the CNC nanofillers contained a homogeneous pore distribution and produced better interfacial interactions than those reinforced with the cellulose microfillers, thus performing better as hydrogels. These findings demonstrate that gamma-irradiated poly(acrylic acid) hydrogels reinforced with CNCs exhibit a better stimuli response toward pH than poly(acrylic acid) hydrogels reinforced with cellulose.
Thermal Degradation of Polystyrene (PS) Nanocomposites Loaded with Sol Gel-Synthesized ZnO Nanorods
Authors: Farha Ashraf H. , Al Naim Abdullah F. , Mansour Shehab A.
Published: August 2020

Thermal degradation of polystyrene/ZnO (PS/ZnO) nanocomposites was investigated in this study. PS/ZnO polymer nanocomposites were prepared by using ZnO nanorods as nanofillers that were prepared via the sol-gel route. The as-prepared ZnO nanoparticles showed nanocrystallites in rod-like shapes with a non-uniform hexagonal cross-section and diameter varying from 40 to 75 nm. PS/ZnO nanocomposites with ZnO nanoparticles content ranging from 0–3 wt% are prepared via the common casting method. Even dispersion for ZnO nanoparticles within as-prepared PS/ZnO nanocomposites was verified through SEM/EDX measurements. Thermal degradation of the samples was checked by using the thermogravimetric (TG) analysis and differential scanning calorimetry (DSC) under non-isothermal conditions and a constant heating rate of 10 °C min. The thermal stability of the nanocomposite is elevated compared to that of pristine PS due to the addition of the ZnO nanoparticles. The homogeneity of the PS/ZnO nanocomposites is verified by systematic increases in thermal degradation with increasing ZnO content. The characterization degradation temperatures at different weight loss percentages of ZnO nanoparticles increase at high ZnO wt%. Static activation energy of decomposing is based on TGA data. Activation energies showed some enhancement after the addition of ZnO nanorods into the PS matrix. Enhancing the thermal stability of PS with ZnO addition within the investigated ZnO concentration range is verified by TG, DSC results.
Development and Characterization of Membranes with PVA Containing Silver Particles: A Study of the Addition and Stability
Authors: Thompson Audie K. , Hackett Cannon , Grady Tony L. , Enyinnia Silver , Moore Quincy C. , Nave Felecia M.
Published: August 2020

Developing technologies for the reduction of biofouling and enhancement of membrane functionality and durability are challenging but critical for the advancement of water purification processes. Silver (Ag) is often used in the process of purification due to its anti-fouling properties; however, the leaching of this metal from a filtration membrane significantly reduces its effectiveness. Our study was designed to integrate the positive characteristics of poly vinyl alcohol (PVA) with the controlled incorporation of nano-scale silver ions across the membrane. This approach was designed with three goals in mind: (1) to improve antifouling activity; (2) to prevent leaching of the metal; and (3) to extend the durability of the functionalized membrane. The fabrication method we used was a modified version of manual coating in combination with sufficient pressure to ensure impregnation and proper blending of PVA with cellulose acetate. We then used the spin coater to enhance the cross-linking reaction, which improved membrane durability. Our results indicate that PVA acts as a reducing agent of Ag+ to Ag0 using X-ray photoelectron spectroscopy analysis and demonstrate that the metal retention was increased by more than 90% using PVA in combination with ultraviolet-photo-irradiated Ag+ reduced to Ag0. The Ag+ ions have sp hybrid orbitals, which accept lone pairs of electrons from a hydroxyl oxygen atom, and the covalent binding of silver to the hydroxyl groups of PVA enhanced retention. In fact, membranes with reduced Ag displayed a more effective attachment of Ag and a more efficient eradication of E. coli growth. Compared to pristine membranes, bovine serum albumin (BSA) flux increased by 8% after the initial addition of Ag and by 17% following ultraviolet irradiation and reduction of Ag, whereas BSA rejection increased by 10% and 11%, respectively. The implementation of this hybrid method for modifying commercial membranes could lead to significant savings due to increased metal retention and membrane effectiveness. These enhancements would ultimately increase the membrane’s longevity and reduce the cost/benefit ratio.
Direction-of-Departure and Direction-of-Arrival Estimation Algorithm Based on Compressive Sensing: Data Fitting
Authors: Paik Ji Woong , Hong Wooyoung , Lee Joon-Ho
Published: August 2020

In this paper, a compressive sensing-based data fitting direction-of-departure/direction-of-arrival (DOD/DOA) estimation algorithm is proposed to apply the superior performance of compressive sensing method to the bistatic MIMO sonar systems. The algorithm proposed in this paper optimizes the output data via convex optimization-based sparse recovery, so that it is possible to estimate the DOD and the DOA for each target accurately. In order to minimize the amount of computation, the cost function with constraint condition is implemented in this paper. Furthermore, the constraint condition parameter of the cost function is analytically derived. Through various simulations, it is shown that the superior DOD and DOA estimation performance of the proposed algorithm and that the analytical derivation of the constraint condition parameter is useful for determination of regularization parameter.
Portable L-Band Radiometer (PoLRa): Design and Characterization
Authors: Houtz Derek , Naderpour Reza , Schwank Mike
Published: August 2020

A low-mass and low-volume dual-polarization L-band radiometer is introduced that has applications for ground-based remote sensing or unmanned aerial vehicle (UAV)-based mapping. With prominent use aboard the ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, L-band radiometry can be used to retrieve environmental parameters, including soil moisture, sea surface salinity, snow liquid water content, snow density, vegetation optical depth, etc. The design and testing of the air-gapped patch array antenna is introduced and is shown to provide a 3-dB full power beamwidth of 37°. We present the radio-frequency (RF) front end design, which uses direct detection architecture and a square-law power detector. Calibration is performed using two internal references, including a matched resistive source (RS) at ambient temperature and an active cold source (ACS). The radio-frequency (RF) front end does not require temperature stabilization, due to characterization of the ACS noise temperature by sky measurements. The ACS characterization procedure is presented. The noise equivalent delta (Δ) temperature (NEΔT) of the radiometer is ~0.14 K at 1 s integration time. The total antenna temperature uncertainty ranges from 0.6 to 1.5 K.
Changes in Snow Cover Dynamics over the Indus Basin: Evidences from 2008 to 2018 MODIS NDSI Trends Analysis
Authors: Ali Sikandar , Cheema Muhammad Jehanzeb Masud , Waqas Muhammad Mohsin , Waseem Muhammad , Awan Usman Khalid , Khaliq Tasneem
Published: August 2020

The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.
Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peak Winter and Early Melt Season
Authors: Mahmoodzada Abdul Basir , Varade Divyesh , Shimada Sawahiko
Published: August 2020

The Pamir ranges of the Hindu Kush regions in Afghanistan play a substantial role in regulating the water resources for the Middle Eastern countries. Particularly, the snowmelt runoff in the Khanabad watershed is one of the critical drivers for the Amu River, since it is a primary source of available water in several Middle Eastern countries in the off monsoon season. The purpose of this study is to devise strategies based on active microwave remote sensing for the monitoring of snow depth during the winter and the melt season. For the estimation of snow depth, we utilized a multi-temporal C-band (5.405 GHz) Sentinel-1 dual polarimetric synthetic aperture radar (SAR) with a differential interferometric SAR (DInSAR)-based framework. In the proposed approach, the estimated snowpack displacements in the vertical transmit-vertical receive (VV) and vertical transmit-horizonal receive (VH) channels were improved by incorporating modeled information of snow permittivity, and the scale was enhanced by utilizing snow depth information from the available ground stations. Two seasonal datasets were considered for the experiments corresponding to peak winter season (February 2019) and early melt season (March 2019). The results were validated with the available nearest field measurements. A good correlation determined by the coefficient of determination of 0.82 and 0.57, with root mean square errors of 2.33 and 1.44 m, for the peak winter and the early melt season, respectively, was observed between the snow depth estimates and the field measurements. Further, the snow depth estimates from the proposed approach were observed to be significantly better than the DInSAR displacements based on the correlation with respect to the field measurements.
Deep Hash Remote Sensing Image Retrieval with Hard Probability Sampling
Authors: Shan Xue , Liu Pingping , Gou Guixia , Zhou Qiuzhan , Wang Zhen
Published: August 2020

As satellite observation technology improves, the number of remote sensing images significantly and rapidly increases. Therefore, a growing number of studies are focusing on remote sensing image retrieval. However, having a large number of remote sensing images considerably slows the retrieval time and takes up a great deal of memory space. The hash method is being increasingly used for rapid image retrieval because of its remarkably fast performance. At the same time, selecting samples that contain more information and greater stability to train the network has gradually become the key to improving retrieval performance. Given the above considerations, we propose a deep hash remote sensing image retrieval method, called the hard probability sampling hash retrieval method (HPSH), which combines hash code learning with hard probability sampling in a deep network. Specifically, we used a probability sampling method to select training samples, and we designed one novel hash loss function to better train the network parameters and reduce the hashing accuracy loss due to quantization. Our experimental results demonstrate that HPSH could yield an excellent representation compared with other state-of-the-art hash approaches. For the university of California, merced (UCMD) dataset, HPSH+S resulted in a mean average precision (mAP) of up to 90.9% on 16 hash bits, 92.2% on 24 hash bits, and 92.8% on 32 hash bits. For the aerial image dataset (AID), HPSH+S achieved a mAP of up to 89.8% on 16 hash bits, 93.6% on 24 hash bits, and 95.5% on 32 hash bits. For the UCMD dataset, with the use of data augmentation, our proposed approach achieved a mAP of up to 99.6% on 32 hash bits and 99.7% on 64 hash bits.
Spaceborne SAR Data for Regional Urban Mapping Using a Robust Building Extractor
Authors: Li Juanjuan , Zhang Hong , Wang Chao , Wu Fan , Li Lu
Published: August 2020

With the rapid development of urbanization, timely and accurate information on the spatial distribution of urban areas is essential for urban planning, environmental protection and sustainable urban development. To date, the main problem of urban mapping using synthetic aperture radar (SAR) data are that nonbuilding objects with high backscattering cause high false alarms, while small-scale buildings with low backscattering result in omission errors. In this paper, a robust building-area extraction extractor is proposed to solve the above problems. The specific work includes (1) building a multiscale and multicategory building area dataset to learn enough building features in various areas; (2) designing a multiscale extraction network based on the residual convolutional block (ResNet50) and a pyramid-based pooling module to extract more discriminative features of building areas and introducing the focal loss item as the object function of the network to further extract the small-scale building areas and (3) eliminating the false alarms using the Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) index. GF-3 SAR data with a 10-m resolution of four regions in China are used to validate our method, and the regional building-area mapping results with overall accuracy above 85% and kappa coefficient not less than 0.73 are obtained. Compared with the current popular networks and the global human settlement layer (GHSL) product, our method shows better extraction results and higher accuracy in multiscale building areas. The experiments using Sentinel-1 and ALOS-2/PALSAR-2 data show that the proposed method has good robustness with different SAR data sources.
An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model
Authors: Moreira Bruna Salles , Perkusich Angelo , Luiz Saulo O. D.
Published: August 2020

Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB’s toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.
Wearable Tendon Kinetics
Authors: Harper Sara E. , Roembke Rebecca A. , Zunker John D. , Thelen Darryl G. , Adamczyk Peter G.
Published: August 2020

This study introduces a noninvasive wearable system for investigating tendon loading patterns during outdoor locomotion on variable terrain. The system leverages shear wave tensiometry, which is a new approach for assessing tendon load by tracking wave speed within the tissue. Our wearable tensiometry system uses a battery-operated piezoelectric actuator to induce micron-scale shear waves in a tendon. A data logger monitors wave propagation by recording from two miniature accelerometers mounted on the skin above the tendon. Wave speed is determined from the wave travel time between accelerometers. The wearable system was used to record Achilles tendon wave speed at 100 Hz during 1-km outdoor walking trials in nine young adults. Inertial measurement units (IMUs) simultaneously monitored participant position, walking speed, and ground incline. An analysis of 5108 walking strides revealed the coupled biomechanical effects of terrain slope and walking speed on tendon loading. Uphill slopes increased the tendon wave speed during push-off, whereas downhill slopes increased tendon wave speeds during early stance braking. Walking speed significantly modulated peak tendon wave speed on uphill slopes but had less influence on downhill slopes. Walking speed consistently induced greater early stance wave speeds for all slopes. These observations demonstrate that wearable shear wave tensiometry holds promise for evaluating tendon tissue kinetics in natural environments and uncontrolled movements. There are numerous practical applications of wearable tensiometry spanning orthopedics, athletics, rehabilitation, and ergonomics.
The Security of IP-Based Video Surveillance Systems
Authors: Kalbo Naor , Mirsky Yisroel , Shabtai Asaf , Elovici Yuval
Published: August 2020

Over the last decade, video surveillance systems have become a part of the Internet of Things (IoT). These IP-based surveillance systems now protect industrial facilities, railways, gas stations, and even one’s own home. Unfortunately, like other IoT systems, there are inherent security risks which can lead to significant violations of a user’s privacy. In this review, we explore the attack surface of modern surveillance systems and enumerate the various ways they can be compromised with real examples. We also identify the threat agents, their attack goals, attack vectors, and the resulting consequences of successful attacks. Finally, we present current countermeasures and best practices and discuss the threat horizon. The purpose of this review is to provide researchers and engineers with a better understanding of a modern surveillance systems’ security, to harden existing systems and develop improved security solutions.
Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods
Authors: Obanawa Hiroyuki , Yoshitoshi Rena , Watanabe Nariyasu , Sakanoue Seiichi
Published: August 2020

Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50–100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.
Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture
Authors: Othman Kamal M. , Rad Ahmad B.
Published: August 2020

In this paper, we present a navigation strategy exclusively designed for social robots with limited sensors for applications in homes. The overall system integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities. The component of the system includes several modules, such as doorway detection and room localization via convolutional neural network (CNN), avoiding obstacles via reinforcement learning, passing the doorway via Canny edge’s detection, building an abstract map called a Directional Semantic Topological Map (DST-Map) within the knowledge system, and other predefined layers within the subsumption architecture. The individual modules and the overall system are evaluated in a virtual environment using Webots simulator.
Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task
Authors: Leon-Medina Jersson X. , Anaya Maribel , Pozo Francesc , Tibaduiza Diego
Published: August 2020

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.
Multidirectional Cylindrical Piezoelectric Force Sensor: Design and Experimental Validation
Authors: Lee Ye Rim , Neubauer Justin , Kim Kwang Jin , Cha Youngsu
Published: August 2020

A common design concept of the piezoelectric force sensor, which is to assemble a bump structure from a flat or fine columnar piezoelectric structure or to use a specific type of electrode, is quite limited. In this paper, we propose a new design of cylindrical piezoelectric sensors that can detect multidirectional forces. The proposed sensor consists of four row and four column sensors. The design of the sensor was investigated by the finite element method. The response of the sensor to various force directions was observed, and it was demonstrated that the direction of the force applied to the sensor could be derived from the signals of one row sensor and three column sensors. As a result, this sensor proved to be able to detect forces in the area of 225° about the central axis of the sensor. In addition, a cylindrical sensor was fabricated to verify the proposed sensor and a series of experiments were performed. The simulation and experimental results were compared, and the actual sensor response tended to be similar to the simulation.
A Novel Lightweight Authentication Scheme for RFID-Based Healthcare Systems
Authors: Zhu Feng , Li Peng , Xu He , Wang Ruchuan
Published: August 2020

The Internet of Things (IoT) has been integrated into legacy healthcare systems for the purpose of improving healthcare processes. As one of the key technologies of IoT, radio frequency identification (RFID) technology has been applied to offer services like patient monitoring, drug administration, and medical asset tracking. However, people have concerns about the security and privacy of RFID-based healthcare systems, which require a proper solution. To solve the problem, recently in 2019, Fan et al. proposed a lightweight RFID authentication scheme in the IEEE Network. They claimed that their scheme can resist various attacks in RFID systems with low implementation cost, and thus is suitable for RFID-based healthcare systems. In this article, our contributions mainly consist of two parts. First, we analyze the security of Fan et al.’s scheme and find out its security vulnerabilities. Second, we propose a novel lightweight authentication scheme to overcome these security weaknesses. The security analysis shows that our scheme can satisfy the necessary security requirements. Besides, the performance evaluation demonstrates that our scheme is of low cost. Thus, our scheme is well-suited for practical RFID-based healthcare systems.
Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition
Authors: Perez-Gomez Vianney , Rios-Figueroa Homero V. , Rechy-Ramirez Ericka Janet , Mezura-Montes Efrén , Marin-Hernandez Antonio
Published: August 2020

An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fear, anger, and disgust. Inspired by the Facial Action Coding System (FACS) and the Moving Picture Experts Group 4th standard (MPEG-4), an initial set of 89 features was proposed. These features are normalized distances and angles in 2D and 3D computed from 22 facial landmarks. To select a minimum set of features with the maximum classification accuracy, two selection methods and four classifiers were tested. The first selection method, principal component analysis (PCA), obtained 39 features. The second selection method, a genetic algorithm (GA), obtained 47 features. The experiments ran on the Bosphorus and UIVBFED data sets with 86.62% and 93.92% median accuracy, respectively. Our main finding is that the reduced feature set obtained by the GA is the smallest in comparison with other methods of comparable accuracy. This has implications in reducing the time of recognition.
Dry Epidermal Electrodes Can Provide Long-Term High Fidelity Electromyography for Limited Dynamic Lower Limb Movements
Authors: Li Jinfeng , Wang Pulin , Huang Helen J.
Published: August 2020

Due to the limitations of standard wet Silver/Silver Chloride (Ag/AgCl) hydrogel electrodes and the growing demand for long-term high fidelity surface electromyography (EMG) recording, dry epidermal electrodes are of great interest. Evaluating the usability and signal fidelity of dry epidermal electrodes could help determine the extent of potential applications using EMG electrodes. We collected EMG signals over eight days from the right rectus femoris of seven subjects using single-use dry epidermal electrodes and traditional Ag/AgCl electrodes while covered and uncovered during dynamic movements (leg extension, sit-to-stand, and treadmill walking at 0.75 m/s and 1.30 m/s). We quantified signal fidelity using signal-to-noise ratio (SNR); signal-to-motion ratio (SMR); and a metric we previously developed, the Signal Quality Index, which considers that better EMG signal quality requires both good signal-to-noise ratio and good signal-to-motion ratio. Wear patterns over the eight days degraded EMG signal quality. Uncovered epidermal electrodes that remained intact and maintained good adhesion to the skin had signal-to-noise ratios, signal-to-motion ratios, and Signal Quality Index values that were above the acceptable thresholds for limited dynamic lower limb movements (leg extension and sit-to-stand). This indicated that dry epidermal electrodes could provide good signal quality across all subjects for five days for these movements. For walking, the signal-to-noise ratios of the uncovered epidermal electrodes were still above the acceptable threshold, but signal-to-motion ratios and the Signal Quality Index values were far below the acceptable thresholds. The signal quality of the epidermal electrodes that showed no visible wear was stable over five days. As expected, covering the epidermal electrodes improved signal quality, but only for limited dynamic lower limb movements. Overall, single-use dry epidermal electrodes were able to maintain high signal quality for long-term EMG recording during limited dynamic lower limb movements, but further improvement is needed to reduce motion artifacts for whole body dynamic movements such as walking.
National Baselines for Integrated Implementation of an Environmental Sustainable Development Goal Assessed in a New Integrated SDG Index
Authors: Horan David
Published: August 2020

Most indicator-based assessments of progress on the Sustainable Development Goals (SDGs) focus on identifying priorities for implementation. However, once priorities are established, policymakers are called to implement them in an integrated way which requires progress not just on a Goal’s targets (siloed approach) but also progress in interrelated policy areas. To assess baselines for integrated implementation, this article introduces a new family of SDG index based on a Goal’s targets and first-order interrelations with other goals that divides targets linked to the prioritized or focal SDG into pressure, impact, and response components. Focusing on an application to SDG14, the conservation and sustainable use of marine resources, an important priority for many small island developing states, the article develops an integrated SDG14 (I-SDG14) index based on an international study of SDG14 interlinkages with indicators selected from SDSN’s global indicator set for all island states with sufficient data available for the year 2018. While all island states assessed face challenges on SDG14, top-performers in terms of I-SDG14 (United Kingdom, New Zealand, Japan, Ireland and Iceland) tend to face greater challenges on pressures, primarily reflecting their performance on targets related to SDGs 2, 12, 13 and 15, whereas bottom-performers (Timor-Leste, Vanuatu, Haiti, Jamaica and Comoros) tend to face greater challenges on responses, i.e., country capacities to influence SDG14, owing to their status on targets related to SDGs 4, 9, 16 and 17. In particular, country scoreboards, “traffic-light” visual representation of performance, and radar-diagrams are used to investigate country-level strengths and challenges for integrated implementation. The proposed index offers a useful starting point to frame discussions with different stakeholders around integrated approaches to implementation and can be flexibly applied to other SDGs and contexts. The article concludes with several suggestions for future research aimed at improving integrated assessments for the SDGs.
A Lightweight Authentication and Authorization Framework for Blockchain-Enabled IoT Network in Health-Informatics
Authors: Tahir Muhammad , Sardaraz Muhammad , Muhammad Shakoor , Saud Khan Muhammad
Published: August 2020

Blockchain and IoT are being deployed at a large scale in various fields including healthcare for applications such as secure storage, transactions, and process automation. IoT devices are resource-constrained, have no capability of security and self-protection, and can easily be hacked or compromised. Furthermore, Blockchain is an emerging technology with immutability features which provide secure management, authentication, and guaranteed access control to IoT devices. IoT is a cloud-based internet service in which processing and collection of user’s data are accomplished remotely. Smart healthcare also requires the facility to provide the diagnosis of patients located remotely. The smart health framework faces critical issues such as data security, costs, memory, scalability, trust, and transparency between different platforms. Therefore, it is important to handle data integrity and privacy as the user’s authenticity is in question due to an open internet environment. Several techniques are available that primarily focus on resolving security issues i.e., forgery, timing, denial of service and stolen smartcard attacks, etc. Blockchain technology follows the rules of absolute privacy to identify the users associated with transactions. The motivation behind the use of Blockchain in health informatics is the removal of the centralized third party, immutability, improved data sharing, enhanced security, and reduced overhead costs in distributed applications. Healthcare informatics has some specific requirements associated with the security and privacy along with the additional legal requirements. This paper presents a novel authentication and authorization framework for Blockchain-enabled IoT networks using a probabilistic model. The proposed framework makes use of random numbers in the authentication process which is further connected through joint conditional probability. Hence, it establishes a secure connection among IoT devices for further data acquisition. The proposed model is validated and evaluated through extensive simulations using the AVISPA tool and the Cooja simulator, respectively. Experimental results analyses show that the proposed framework provides robust mutual authenticity, enhanced access control, and lowers both the communication and computational overhead cost as compared to others.
Does Adoption of Soil and Water Conservation Practice Enhance Productivity and Reduce Risk Exposure? Empirical Evidence from Semi-Arid Tropics (SAT), India
Authors: Kumar Suresh , Singh Dharam Raj , Singh Alka , Singh Naveen Prakash , Jha Girish Kumar
Published: August 2020

This paper assessed the impact of soil and water conservation practices on farm productivity and risk exposure using data from 1204 plots in the semiarid tropics of India. A probit model was used to assess the determinants of adoption of soil bunds. We employed a moment-based approach for estimating crop revenue, its variability and downside risk exposure, i.e., crop failure. Furthermore, we also used a doubly robust method for assessing the impact of soil bunds on crop revenue, its variability and downside risk. Matching and propensity-based methods were also used to check robustness. The results show that training, access to credit and extension services are key determinants of adoption of soil bunds. Furthermore, the results also suggest that soil bunds not only improve the crop revenue but also reduce its variability. Most interestingly, we show that soil bunds also reduce the chances of downside risk, i.e., crop failure. Therefore, in view of increasing climate change and variability in the semiarid tropics, it can be suggested that soil bunds could be an important adaptation strategy for improving productivity and reducing risk exposure. This paper supports the investment in soil and water conservation technologies for sustaining the livelihood of resource-poor farmers of ecologically fragile regions such as the semiarid tropics.
Sustainable Supply Chain Management—A Literature Review on Emerging Economies
Authors: Sánchez-Flores Rebeca B. , Cruz-Sotelo Samantha E. , Ojeda-Benitez Sara , Ramírez-Barreto Ma. Elizabeth
Published: August 2020

In recent years, the interest in sustainable supply chain management has increased significantly in both business and academic areas. This is reflected in the growing number of articles, conferences, special publications and websites devoted to the subject. Nonetheless, sustainable development in emerging economies just started gaining importance. The objective of this article is to review, from a global perspective, the existing literature regarding sustainable supply chain management (SSCM) in emerging economies. For this purpose, a systematic literature review was performed, analyzing 56 articles from the year 2010 to April 2020 from a descriptive and content approach. Subsequently, the results are presented, showing the increasing interest in SSCM, however lagging behind in emerging economies’ research versus developed ones. Findings indicate the context in developing countries plays an important role when conducting empirical or case study investigations. Additionally, the integration of the three dimensions of sustainability and how they impact supply chain performance becomes crucial to research from an emerging economy perspective. Consequently, limitations of this work are presented, and opportunities are identified for future lines of research, in particular in key supply chain functions. Finally, the conclusion confirms the need for further research from different supply chain viewpoints, such as collaboration, sustainable practices innovation, sourcing and supplier development from emerging countries’ standpoint and background.
Dry Port Terminal Location Selection by Applying the Hybrid Grey MCDM Model
Authors: Tadić Snežana , Krstić Mladen , Roso Violeta , Brnjac Nikolina
Published: August 2020

Globalization and decentralization of production generate the intensive growth of goods and transport flows, mostly performed by the maritime transport. Ports, as the main nodes in the global logistics networks, are becoming congested, space for their expansion limited, and traffic in their hinterland congested. As a solution to these and many other hinterland-transport-related problems stands out the development of dry port (DP) terminals. Selection of their location is one of the most important strategic decisions on which depends their competitiveness in the market and the functionality of the logistics network. Accordingly, the evaluation and selection of locations for the development of the DP in accordance with the requirements of various stakeholders is performed in this paper, as a prerequisite for the establishment of an ecological, economic, and socially sustainable logistics network in the observed area. To solve this problem, a new hybrid model of multi-criteria decision-making (MCDM) that combines Delphi, AHP (Analytical Hierarchy Process), and CODAS (Combinative Distance-based Assessment) methods in a grey environment is developed. The main contributions of this paper are the defined model, the problem-solving approach based on finding a compromise solution, simultaneous consideration of the environmental, economic, and social sustainability of the DP concept and its implementation in the regional international markets. The applicability of the approach and the defined MCDM model is demonstrated by solving a real-life case study of ranking the potential DP locations in the Western Balkans region. Based on the obtained results, it is concluded that in the current market conditions, it would be most realistic to open three DP terminals, in Zagreb, Ljubljana, and Belgrade.
Cognitive Learning Analytics Using Assessment Data and Concept Map: A Framework-Based Approach for Sustainability of Programming Courses
Authors: Omer Uzma , Farooq Muhammad Shoaib , Abid Adnan
Published: August 2020

Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels of specific concepts. Furthermore, the existing work reflects deficiencies in examining the effect of learners’ cognitive performance on subsequent stages of the course. This gap needs to be addressed by introducing more granular and methodical approaches of cognitive analysis for sustaining the programming courses effectively in computer science and associated disciplines. In this article, a framework-based approach is proposed for cognitive learning analytics on the concepts taught in initial level programming courses. The framework serves as a platform that provides structure to the concept data using the technique of concept mapping and examines learners’ cognitive propagation on related concepts using assessment data. Learners’ performance prediction has been examined on relatively higher-level programming concepts through the metrics established from the cognitive maps of learners, acquired by deploying the related layers of framework. Overall maximum prediction accuracy range obtained was 64.81% to 90.86%, which was better than the prediction accuracies presented in most of the related studies.
The Relationship between E-Commerce and Firm Performance: The Mediating Role of Internet Sales Channels
Authors: Šaković Jovanović Jelena , Vujadinović Radoje , Mitreva Elizabeta , Fragassa Cristiano , Vujović Aleksandar
Published: August 2020

This paper postulates that the effect of e-commerce on firm performance is not direct and needs to be examined using mediating factors. The Ordinary Least-Squares (OLS) model was employed with the data of the Flash Eurobarometer 439 Survey entitled The Use of Online Marketplaces and Search Engines by small and medium enterprises. The obtained findings provide support for the mediating hypothesis. To be more precise, while the relationship between e-commerce and firm performance is negative, it is positively mediated by certain types of internet sales channels. In particular, the benefits of e-commerce in terms of higher sales are more pronounced when firms use commercial websites and online marketplaces. On the other hand, the interaction between e-commerce and search engines has an insignificant effect on firm performance. This study advances research on e-commerce by emphasizing the importance of mediating effect.
The Development of a CO2 Emission Coefficient for Medium- and Heavy-Duty Vehicles with Different Road Slope Conditions Using Multiple Linear Regression, and Considering the Health Effects
Authors: Bamrungwong Natthakrit , Vongmanee Varin , Rattanawong Wanchai
Published: August 2020

The current studies on carbon dioxide (CO2) emissions and the impacts on public health focus mainly on evaluating CO2 emissions from two types of emission sources. The first is a fixed source type from industrial plants, which can be controlled or easily evaluated. The second is a mobile source type from the transport sector, especially from medium- and heavy-duty vehicles (MHDVs), which produce high emissions. However, the common methods of evaluation of the average value of CO2 emissions per kilometer of a vehicle use a general Intergovernmental Panel on Climate Change (IPCC) model that does not consider the topography or road conditions. This affects the accuracy of CO2 emission assessments and, in turn, affects the accuracy of any analysis needed to establish health policies and the management of public health within the affected area. In this paper, therefore, we present the development of emission coefficient calculations with varying topography conditions for MHDVs with consideration of the health effects on the populace. The study area was the Asian highway network in Thailand that passes through all regions and is geographically diverse. Data were collected from the Department of Highway’s records, global positioning system (GPS) and electronic control unit (ECU) with data consisting of road data, slope, distance, traffic level and vehicle weight, as well as fuel consumption along the transportation route. To analyze and map out the correlation of the CO2 emission coefficients for each road slope, we determined the coefficient of the CO2 emissions using multiple linear regression analysis and validated this using Huber–White robust standard errors for heteroscedasticity. This method was more cost-efficient and time-efficient compared to the conventional approaches. We also created CO2 emission maps with risk points for health effects, and we propose policy designs and plans to manage the traffic level in each of these areas prone to higher levels of such emissions.
Controlling the Perturbations of Solar Radiation Pressure on the Lorentz Spacecraft
Authors: Mostafa A. , El-Saftawy M. I. , Abouelmagd Elbaz I. , López Miguel A.
Published: August 2020

The aim of the present paper is to analyze the viability of using Lorentz Force (LF) acting on a charged spacecraft to neutralize the effects of Solar Radiation Pressure (SRP) on the longitude of the ascending node and the argument of perigee of the spacecraft’s orbit. In this setting, the Gauss planetary equations for LF and SRP are presented and averaged over the true anomaly. The averaged variations for the longitude of the ascending node (h) and the argument of perigee (g) are invariant under the symmetry (i,g)⟶(−i,−g) due to Lorentz Force. The sum of change rates due to both perturbing forces of LF and SRP is assigned by zero to estimate the charge amount to balance the variation for the argument of perigee and longitude of ascending. Numerical investigations have been developed to show the evolution of the charge quantity for different orbital parameters at both Low Earth and Geosynchronous Orbits.