bluuebunny/crossref_metadata_2025 huggingface dataset:
Progresses on the Use of Two-Photon Absorption Laser Induced Fluorescence (TALIF) Diagnostics for Measuring Absolute Atomic Densities in Plasmas and Flames
Authors: Gazeli Kristaq , Lombardi Guillaume , Aubert Xavier , Duluard Corinne Y. , Prasanna Swaminathan , Hassouni Khaled
Published: March 2021

Recent developments in plasma science and technology have opened new areas of research both for fundamental purposes (e.g., description of key physical phenomena involved in laboratory plasmas) and novel applications (material synthesis, microelectronics, thin film deposition, biomedicine, environment, flow control, to name a few). With the increasing availability of advanced optical diagnostics (fast framing imaging, gas flow visualization, emission/absorption spectroscopy, etc.), a better understanding of the physicochemical processes taking place in different electrical discharges has been achieved. In this direction, the implementation of fast (ns) and ultrafast (ps and fs) lasers has been essential for the precise determination of the electron density and temperature, the axial and radial gradients of electric fields, the gas temperature, and the absolute density of ground-state reactive atoms and molecules in non-equilibrium plasmas. For those species, the use of laser-based spectroscopy has led to their in situ quantification with high temporal and spatial resolution, with excellent sensitivity. The present review is dedicated to the advances of two-photon absorption laser induced fluorescence (TALIF) techniques for the measurement of reactive species densities (particularly atoms such as N, H and O) in a wide range of pressures in plasmas and flames. The requirements for the appropriate implementation of TALIF techniques as well as their fundamental principles are presented based on representative published works. The limitations on the density determination imposed by different factors are also discussed. These may refer to the increasing pressure of the probed medium (leading to a significant collisional quenching of excited states), and other issues originating in the high instantaneous power density of the lasers used (such as photodissociation, amplified stimulated emission, and photoionization, resulting to the saturation of the optical transition of interest).
Stability and Performance Analysis of Bioethanol Production with Delay and Growth Inhibition in a Continuous Bioreactor with Recycle
Authors: Alqahtani Rubayyi T. , Bhowmik Samir Kumar , Ajbar Abdelhamid , Boumaza Mourad
Published: March 2021

This paper proposes and analyzes a mathematical model for the production of bioethanol in a continuous bioreactor with recycling. The kinetics correspond to the use of Saccharomyces bayanus for the fermentation of sugars found in wastewater from soft drinks. The proposed model considers product growth latency, which was experimentally found in batch studies of ethanol production. Furthermore, the inhibition effect of ethanol is expressed by a modified version of the classical Andrew’s model for substrate inhibition. The proposed model consists of only three ordinary differential equations containing a minimal number of operating parameters, which include the bioreactor residence time, glucose feed concentration, recycle ratio and the fraction of biomass removed from the reactor by the flow. The positivity and the boundedness of solutions of the model were confirmed under reasonable restrictions of parameters. The stability analysis showed that there is a value of residence time at which an exchange of stability occurs between the trivial washout and non-washout solutions. This critical value depends only on the substrate feed concentration, biomass death rate, recycle ratio and purge fraction. Dynamic simulations of the model were carried out for substrate concentration in the range of 100–250 g/L, commonly used for the production of ethanol. An inverse response due to the inhibition effects of ethanol was observed in the time evolution of substrate and biomass concentrations. Parametric studies showed that ethanol concentration increases with the recycle ratio, with the inverse of residence time and with the inverse of purge fraction. The effect of ethanol latency has, on the other hand, a substantial effect on ethanol concentration. Despite its unstructured nature and the fact that some parameters such as temperature and acidity were not taken into consideration, the proposed model managed to provide useful results on the bioreactor-settler stability and the effect of key parameters on its dynamic behavior, which could pave the way for future optimization studies.
Effect of Aspect Ratio on the Mixing Performance in the Kenics Static Mixer
Authors: Jiang Xingren , Yang Ning , Wang Rijie
Published: March 2021

Continuous manufacturing has received increasing interest because of the advantages of intrinsic safety and enhanced mass transfer in the pharmaceutical industry. However, the difficulty for scale-up has limited the application of continuous manufacturing for a long time. Recently, the tubular flow reactor equipped with the Kenics static mixer appears to be a solution for the continuous process scale-up. Although many influence factors on the mixing performance in the Kenics static mixer have been investigated, little research has been carried out on the aspect ratio. In this study, we used the coefficient of variation as the mixing evaluation index to investigate the effect of the aspect ratio (0.2–2) on the Kenics static mixer’s mixing performance. The results indicate that a low aspect ratio helps obtain a shorter mixing time and mixer length. This study suggests that adjusting the aspect ratio of the Kenics static mixer can be a new strategy for the scale-up of a continuous process in the pharmaceutical industry.
Batch and Fixed-Bed Biosorption of Pb (II) Using Free and Alginate-Immobilized Spirulina
Authors: Villen-Guzman Maria , Jiménez Carlos , Rodriguez-Maroto Jose Miguel
Published: March 2021

The valorization of Spirulina as a potential biosorption material to treat contaminated wastewater was evaluated. Batch experiments were conducted to study the influence of pH value and ionic strength on the biosorption capacity of Spirulina. Higher removal capacity was observed at pH 5.2, while higher ionic strength was found to result in lower adsorption capacity, which suggests that ion exchange is a relevant mechanism for Pb (II) adsorption on Spirulina. The immobilization of Spirulina on alginate beads was found not only to increase the adsorption capacity, but also to overcome limitations such as unacceptable pressure drops on column systems. The Langmuir model was the most appropriate model to describe the biosorption equilibrium of lead by free and immobilized Spirulina. The experimental breakthrough curves were evaluated using the Thomas, Bohart-Adams, and dose-response models. The experimental results were most properly described by the dose-response model, which is consistent with previous results. The adsorption capacity of Spirulina was found to increase linearly with the influent lead concentration (in the range 4–20 mg L−1) at 1.6 mL min−1 flow rate. Batch and column experiments were compared to better understand the biosorption process. The promising results obtained indicate the potential use of Spirulina immobilized on alginate beads to treat industrial wastewater polluted with toxic metals.
An Investigation of Heat Transfer Performance in an Agitated Vessel
Authors: Mahir Maha , El Maakoul Anas , Khay Ismail , Saadeddine Said , Bakhouya Mohamed
Published: March 2021

Agitated vessels (or mechanically stirred reactors) are heat exchange devices that are most widely used in many chemical and biochemical process industries, such as anaerobic digestion process. The mixing and heat transfer performances in these vessels are of crucial importance for increasing the energy efficiency in both batch and continuous processes. In this paper, a series of experiments were conducted to investigate heat transfer performance in agitated vessels for various configurations. In fact, this study examines the effects of heat transfer geometry (wall jacket and helical coils), heating power, and stirring speed, on the heating performance of two stirred fluids—water alone and a mixture of water and food waste. The experiments were conducted using a jacketed insulation tank with a helical coil and a propeller agitator. In each experiment, a transient method, based on measuring the temperature dependency on time, and solving the unsteady enthalpy balance, was used to determine the overall heat transfer coefficients between the agitated fluid and the heating surface. Finally, an extensive analysis of the reduced data was conducted based on temperature, heating time, heat transfer rate, heat transfer coefficient, and thermal resistance. The main finding was that the presence of food waste in agitated vessels reduces the heat rate of the agitated fluid with an average of 18.13% and 49.51%, respectively, for the case of JHX and CHX, and creates additional fouling, which further limits the heat transfer.
Mechanical and Market Study for Sand/Recycled-Plastic Cobbles in a Medium-Size Colombian City
Authors: Sanchez-Echeverri Luz Adriana , Tovar-Perilla Nelson Javier , Suarez-Puentes Juana Gisella , Bravo-Cervera Jorge Enrique , Rojas-Parra Daniel Felipe
Published: March 2021

The need to satisfy the increasing demand for building materials and the challenge of reusing plastic to help improve the critical environmental crisis has led to the recycling of plastic waste, which is further exploited and transformed into new and creative materials for the construction industry. This study looked into the use of low-density recycled polyethylene (LDPE) to produce non-conventional plastic sand cobbles. LDPE waste was melted in order to obtain enough fluid consistency which was then mixed with sand in a 25/75 plastic-sand ratio respectively, such a mixture helped producing cobbles of 10 cm × 20 cm × 4 cm. Water absorption, weight, and density measurements were performed on both commercial and non-conventional plastic sand cobbles. Moreover, compression, bending, and wear resistance were also conducted as part of their mechanical characterization. Plastic sand cobbles showed lower water absorption and density values than commercial cobbles. The mechanical properties evaluated showed that plastic sand cobbles have a higher modulus of rupture and wear resistance than commercial cobbles. In addition, plastic sand cobbles meet the Colombian Technical Standard in lightweight traffic for pedestrians and vehicle, officially known as Norma Técnica Colombiana (NTC), with 25.5 MPa, 16.3 MPa, and 12 mm compression resistance, modulus of rupture and footprint length in wear resistance respectively. Finally, a market study was conducted to establish a factory to produce this type of cobbles in Ibague, Colombia. Not only the study showed positive financial indicators, which means that it is feasible running a factory to manufacture plastic sand cobbles in the city of Ibague, but it also concluded that nonconventional plastic sand cobbles could be explored to provide a comprehensive alternative to LDPE waste.
Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
Authors: Lark Tyler J. , Schelly Ian H. , Gibbs Holly K.
Published: March 2021

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.
Errors in Broadband Permittivity Determination Due to Liquid Surface Distortions in Semi-Open Test Cell
Authors: Kalisiak Michał , Wiatr Wojciech
Published: March 2021

We study how surface distortions of liquid samples due to a meniscus and a tilt of a semi-open coaxial test cell affect errors in a broadband permittivity determination. The study is based on the scattering parameters, obtained using the electromagnetic simulations of samples with flat and distorted surfaces in a broad frequency range up to 18 GHz. The parameters are processed with the classic Nicolson–Ross–Weir (NRW) method and our new meniscus removal technique. We analyze the errors for several samples of different properties, such as distilled water and propan-2-ol. The results show that the meniscus removal technique is more robust and provides smaller errors in the permittivity determination compared to the classic NRW method. The effect of the cell tilt, to our best knowledge, has not been considered in the literature yet.
Sampling-Based Path Planning for High-Quality Aerial 3D Reconstruction of Urban Scenes
Authors: Yan Feihu , Xia Enyong , Li Zhaoxin , Zhou Zhong
Published: March 2021

Unmanned aerial vehicles (UAVs) can capture high-quality aerial photos and have been widely used for large-scale urban 3D reconstruction. However, even with the help of commercial flight control software, it is still a challenging task for non-professional users to capture full-coverage aerial photos in complex urban environments, which normally leads to incomplete 3D reconstruction. In this paper, we propose a novel path planning method for the high-quality aerial 3D reconstruction of urban scenes. The proposed approach first captures aerial photos, following an initial path to generate a coarse 3D model as prior knowledge. Then, 3D viewpoints with constrained location and orientation are generated and evaluated, according to the completeness and accuracy of the corresponding visible regions of the prior model. Finally, an optimized path is produced by smoothly connecting the optimal viewpoints. We perform an extensive evaluation of our method on real and simulated data sets, in comparison with a state-of-the-art method. The experimental results indicate that the optimized trajectory generated by our method can lead to a significant boost in the performance of aerial 3D urban reconstruction.
Soil Moisture Estimation Synergy Using GNSS-R and L-Band Microwave Radiometry Data from FSSCat/FMPL-2
Authors: Munoz-Martin Joan Francesc , Llaveria David , Herbert Christoph , Pablos Miriam , Park Hyuk , Camps Adriano
Published: March 2021

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.
Climatic Constraints to Monthly Vegetation Dynamics in Desert Areas Over the Silk Road Economic Belt
Authors: Ma Yu-Jun , Shi Fang-Zhong , Hu Xia , Li Xiao-Yan
Published: March 2021

The sustainability of vulnerable eco-environment over the Silk Road Economic Belt is under threat of climate change, and the identification of vegetation constraints by sub-optimum climatic conditions is critically essential to maintain existing dryland ecosystems. To better understand how the vegetation varies at monthly scale and its effect by climate conditions in different desert areas, this study first investigated the seasonal variation of the normalized difference vegetation index (NDVI). Then, we analyzed the time effects of diverse climatic factors (air temperature, solar radiation, precipitation) on NDVI and estimated the limitation of NDVI by these climatic factors in different desert areas. The result showed that the mean monthly NDVI during 1982–2015 showed a unimodal variation in most desert areas, with high values in late spring and summer over cold arid areas, in early spring or early autumn over hot arid areas, and in summer over polar areas, respectively. Solar radiation and precipitation in cold arid areas presented 1–2 month lag or accumulation effect on NDVI, while precipitation in most hot arid areas showed no remarkable time-lag but 3 month accumulation effect, and all three climate factors in polar areas exhibited 1–3 month accumulation effect. The explanatory power of climatic conditions for vegetation dynamics considering time effects increased by 3.4, 10.8, and 5.9% for the cold arid areas, hot arid areas, and polar areas (i.e., relative increase of 4.1, 25.4, and 8.2%), respectively. The main climatic constraints to vegetation dynamics were the water condition in hot arid areas (>78%) and the temperature condition in polar areas (>67%), while cold arid areas were simultaneously limited by the water and temperature conditions (>76% in total). These results provide a detailed understanding of vegetation variation and ecological projection, which are very important to implement adaption measures for dryland ecosystems over the Silk Road Economic Belt.
Geomorphological and Spatial Characteristics of Underwater Volcanoes in the Easternmost Australian-Antarctic Ridge
Authors: Choi Hakkyum , Kim Seung-Sep , Park Sung-Hyun , Kim Hyoung Jun
Published: March 2021

Underwater volcanoes and their linear distribution on the flanks of mid-ocean ridges are common submarine topographic structures at intermediate- and fast-spreading systems, where sufficient melt supplies are often available. Such magma sources beneath the seafloor located within a few kilometers of the corresponding ridge-axis tend to concentrate toward the axis during the upwelling process and contribute to seafloor formation. As a result, seamounts on the flanks of the ridge axis are formed at a distance from the spreading axis and distributed asymmetrically about the axis. In this study, we examined three linearly aligned seamount chains on the flanks of the KR1 ridge, which is the easternmost and longest Australian-Antarctic Ridge (AAR) segment. The AAR is an intermediate-spreading rate system located between the Southeast Indian Ridge and Macquarie Triple Junction of the Australian-Antarctic-Pacific plates. By inspecting the high-resolution shipboard multi-beam bathymetric data newly acquired in the study area, we detected 20 individual seamounts. The volcanic lineament runs parallel to the spreading direction of the KR1 segment. The geomorphologic parameters of height, basal area, volume, and summit types of the identified seamounts were individually measured. We also investigated the spatial distribution of the seamounts along the KR1 segment, which exhibits large variations in axial morphology with depth along the ridge axis. Based on the geomorphology and spatial distribution, all the KR1 seamounts can be divided into two groups: the subset seamounts of volcanic chains distributed along the KR1 segment characterized by high elevation and large volume, and the small seamounts distributed mostly on the western KR1. The differences in the volumetric magnitude of volcanic eruptions on the seafloor and the distance from the given axis between these two groups indicate the presence of magma sources with different origins.
Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM
Authors: Ge Gengyu , Zhang Yi , Jiang Qin , Wang Wei
Published: March 2021

Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail.
An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations
Authors: Serrano-Mamolar Ana , Arevalillo-Herráez Miguel , Chicote-Huete Guillermo , Boticario Jesus G.
Published: March 2021

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.
Noninvasive Assessment of Neuromechanical Coupling and Mechanical Efficiency of Parasternal Intercostal Muscle during Inspiratory Threshold Loading
Authors: Lozano-García Manuel , Estrada-Petrocelli Luis , Torres Abel , Rafferty Gerrard F. , Moxham John , Jolley Caroline J. , Jané Raimon
Published: March 2021

This study aims to investigate noninvasive indices of neuromechanical coupling (NMC) and mechanical efficiency (MEff) of parasternal intercostal muscles. Gold standard assessment of diaphragm NMC requires using invasive techniques, limiting the utility of this procedure. Noninvasive NMC indices of parasternal intercostal muscles can be calculated using surface mechanomyography (sMMGpara) and electromyography (sEMGpara). However, the use of sMMGpara as an inspiratory muscle mechanical output measure, and the relationships between sMMGpara, sEMGpara, and simultaneous invasive and noninvasive pressure measurements have not previously been evaluated. sEMGpara, sMMGpara, and both invasive and noninvasive measurements of pressures were recorded in twelve healthy subjects during an inspiratory loading protocol. The ratios of sMMGpara to sEMGpara, which provided muscle-specific noninvasive NMC indices of parasternal intercostal muscles, showed nonsignificant changes with increasing load, since the relationships between sMMGpara and sEMGpara were linear (R2 = 0.85 (0.75–0.9)). The ratios of mouth pressure (Pmo) to sEMGpara and sMMGpara were also proposed as noninvasive indices of parasternal intercostal muscle NMC and MEff, respectively. These indices, similar to the analogous indices calculated using invasive transdiaphragmatic and esophageal pressures, showed nonsignificant changes during threshold loading, since the relationships between Pmo and both sEMGpara (R2 = 0.84 (0.77–0.93)) and sMMGpara (R2 = 0.89 (0.85–0.91)) were linear. The proposed noninvasive NMC and MEff indices of parasternal intercostal muscles may be of potential clinical value, particularly for the regular assessment of patients with disordered respiratory mechanics using noninvasive wearable and wireless devices.
Clinical and Research Solutions to Manage Obstructive Sleep Apnea: A Review
Authors: Xia Fen , Sawan Mohamad
Published: March 2021

Obstructive sleep apnea (OSA), a common sleep disorder disease, affects millions of people. Without appropriate treatment, this disease can provoke several health-related risks including stroke and sudden death. A variety of treatments have been introduced to relieve OSA. The main present clinical treatments and undertaken research activities to improve the success rate of OSA were covered in this paper. Additionally, guidelines on choosing a suitable treatment based on scientific evidence and objective comparison were provided. This review paper specifically elaborated the clinically offered managements as well as the research activities to better treat OSA. We analyzed the methodology of each diagnostic and treatment method, the success rate, and the economic burden on the world. This review paper provided an evidence-based comparison of each treatment to guide patients and physicians, but there are some limitations that would affect the comparison result. Future research should consider the consistent follow-up period and a sufficient number of samples. With the development of implantable medical devices, hypoglossal nerve stimulation systems will be designed to be smart and miniature and one of the potential upcoming research topics. The transcutaneous electrical stimulation as a non-invasive potential treatment would be further investigated in a clinical setting. Meanwhile, no treatment can cure OSA due to the complicated etiology. To maximize the treatment success of OSA, a multidisciplinary and integrated management would be considered in the future.
DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson’s Disease
Authors: Rosenblum Sara , Richardson Ariella , Meyer Sonya , Nevo Tal , Sinai Maayan , Hassin-Baer Sharon
Published: March 2021

Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog—a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual–spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.
NORA: An Approach for Transforming Network Management Policies into Automated Planning Problems
Authors: Rodriguez-Vivas Angela , Caicedo Oscar Mauricio , Ordoñez Armando , Nobre Jéferson Campos , Granville Lisandro Zambenedetti
Published: March 2021

Realizing autonomic management control loops is pivotal for achieving self-driving networks. Some studies have recently evidence the feasibility of using Automated Planning (AP) to carry out these loops. However, in practice, the use of AP is complicated since network administrators, who are non-experts in Artificial Intelligence, need to define network management policies as AP-goals and combine them with the network status and network management tasks to obtain AP-problems. AP planners use these problems to build up autonomic solutions formed by primitive tasks that modify the initial network state to achieve management goals. Although recent approaches have investigated transforming network management policies expressed in specific languages into low-level configuration rules, transforming these policies expressed in natural language into AP-goals and, subsequently, build up AP-based autonomic management loops remains unexplored. This paper introduces a novel approach, called NORA, to automatically generate AP-problems by translating Goal Policies expressed in natural language into AP-goals and combining them with both the network status and the network management tasks. NORA uses Natural Language Processing as the translation technique and templates as the combination technique to avoid network administrators to learn policy languages or AP-notations. We used a dataset containing Goal Policies to evaluate the NORA’s prototype. The results show that NORA achieves high precision and spends a short-time on generating AP-problems, which evinces NORA aids to overcome barriers to using AP in autonomic network management scenarios.
Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation
Authors: Ran Si , Ding Jianli , Liu Bohua , Ge Xiangyu , Ma Guolin
Published: March 2021

As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) structure, U-Net does not require manual intervention, and its high-precision features are widely used in image interpretation. However, as an end-to-end fully convolutional network, U-Net has not explored enough information from the full scale, and there is still room for improvement. In this study, we constructed an effective network module: residual module under a multisensory field (RMMF) to extract multiscale features of target and an attention mechanism to optimize feature information. RMMF uses parallel convolutional layers to learn features of different scales in the network and adds shortcut connections between stacked layers to construct residual blocks, combining low-level detailed information with high-level semantic information. RMMF is universal and extensible. The convolutional layer in the U-Net network is replaced with RMMF to improve the network structure. Additionally, the multiscale convolutional network was tested using RMMF on the Gaofen-2 data set and Potsdam data sets. Experiments show that compared to other technologies, this method has better performance in airborne and spaceborne images.
Lightweight Image Restoration Network for Strong Noise Removal in Nuclear Radiation Scenes
Authors: Sun Xin , Luo Hongwei , Liu Guihua , Chen Chunmei , Xu Feng
Published: March 2021

In order to remove the strong noise with complex shapes and high density in nuclear radiation scenes, a lightweight network composed of a Noise Learning Unit (NLU) and Texture Learning Unit (TLU) was designed. The NLU is bilinearly composed of a Multi-scale Kernel Module (MKM) and a Residual Module (RM), which learn non-local information and high-level features, respectively. Both the MKM and RM have receptive field blocks and attention blocks to enlarge receptive fields and enhance features. The TLU is at the bottom of the NLU and learns textures through an independent loss. The entire network adopts a Mish activation function and asymmetric convolutions to improve the overall performance. Compared with 12 denoising methods on our nuclear radiation dataset, the proposed method has the fewest model parameters, the highest quantitative metrics, and the best perceptual satisfaction, indicating its high denoising efficiency and rich texture retention.
Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision
Authors: Uzair Muhammad , Brinkworth Russell S. A. , Finn Anthony
Published: March 2021

Thermal infrared imaging provides an effective sensing modality for detecting small moving objects at long range. Typical challenges that limit the efficiency and robustness of the detection performance include sensor noise, minimal target contrast and cluttered backgrounds. These issues become more challenging when the targets are of small physical size and present minimal thermal signatures. In this paper, we experimentally show that a four-stage biologically inspired vision (BIV) model of the flying insect visual system have an excellent ability to overcome these challenges simultaneously. The early two stages of the model suppress spatio-temporal clutter and enhance spatial target contrast while compressing the signal in a computationally manageable bandwidth. The later two stages provide target motion enhancement and sub-pixel motion detection capabilities. To show the superiority of the BIV target detector over existing traditional detection methods, we perform extensive experiments and performance comparisons using high bit-depth, real-world infrared image sequences of small size and minimal thermal signature targets at long ranges. Our results show that the BIV target detector significantly outperformed 10 conventional spatial-only and spatiotemporal methods for infrared small target detection. The BIV target detector resulted in over 25 dB improvement in the median signal-to-clutter-ratio over the raw input and achieved 43% better detection rate than the best performing existing method.
Ranking-Based Salient Object Detection and Depth Prediction for Shallow Depth-of-Field
Authors: Xian Ke , Peng Juewen , Zhang Chao , Lu Hao , Cao Zhiguo
Published: March 2021

Shallow depth-of-field (DoF), focusing on the region of interest by blurring out the rest of the image, is challenging in computer vision and computational photography. It can be achieved either by adjusting the parameters (e.g., aperture and focal length) of a single-lens reflex camera or computational techniques. In this paper, we investigate the latter one, i.e., explore a computational method to render shallow DoF. The previous methods either rely on portrait segmentation or stereo sensing, which can only be applied to portrait photos and require stereo inputs. To address these issues, we study the problem of rendering shallow DoF from an arbitrary image. In particular, we propose a method that consists of a salient object detection (SOD) module, a monocular depth prediction (MDP) module, and a DoF rendering module. The SOD module determines the focal plane, while the MDP module controls the blur degree. Specifically, we introduce a label-guided ranking loss for both salient object detection and depth prediction. For salient object detection, the label-guided ranking loss comprises two terms: (i) heterogeneous ranking loss that encourages the sampled salient pixels to be different from background pixels; (ii) homogeneous ranking loss penalizes the inconsistency of salient pixels or background pixels. For depth prediction, the label-guided ranking loss mainly relies on multilevel structural information, i.e., from low-level edge maps to high-level object instance masks. In addition, we introduce a SOD and depth-aware blur rendering method to generate shallow DoF images. Comprehensive experiments demonstrate the effectiveness of our proposed method.
Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models
Authors: Xie Hailun , Zhang Li , Lim Chee Peng , Yu Yonghong , Liu Han
Published: March 2021

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.
Determination of the Toxic and Nutrient Element Content of Almonds, Walnuts, Hazelnuts and Pistachios by ICP-AES
Authors: Kalogiouri Natasa , Manousi Natalia , Zachariadis George
Published: March 2021

The trace element content of thirty-two nuts including almonds, walnuts, hazelnuts and pistachios available in a Greek market was determined by inductively coupled plasma atomic emission spectrometry (ICP-AES). Wet acid digestion using nitric acid (65%) took place in Teflon autoclaves. The limits of detection (LODs) and limits of quantification (LOQs) ranged between 0.01 (Mg)–2.52 (Cu) μg g−1 and 0.02 (Mg)–8.40 (Cu) μg g−1, respectively. Good method linearity (r2 > 0.9990) was observed for each element at the selected emission lines. The metals were quantified and one-way analysis of variance (ANOVA) was used to examine whether or not there were any statistically significant differences among the metal concentrations inside the different nut species.
Bayesian Bandwidths in Semiparametric Modelling for Nonnegative Orthant Data with Diagnostics
Authors: Kokonendji Célestin C. , Somé Sobom M.
Published: March 2021

Multivariate nonnegative orthant data are real vectors bounded to the left by the null vector, and they can be continuous, discrete or mixed. We first review the recent relative variability indexes for multivariate nonnegative continuous and count distributions. As a prelude, the classification of two comparable distributions having the same mean vector is done through under-, equi- and over-variability with respect to the reference distribution. Multivariate associated kernel estimators are then reviewed with new proposals that can accommodate any nonnegative orthant dataset. We focus on bandwidth matrix selections by adaptive and local Bayesian methods for semicontinuous and counting supports, respectively. We finally introduce a flexible semiparametric approach for estimating all these distributions on nonnegative supports. The corresponding estimator is directed by a given parametric part, and a nonparametric part which is a weight function to be estimated through multivariate associated kernels. A diagnostic model is also discussed to make an appropriate choice between the parametric, semiparametric and nonparametric approaches. The retention of pure nonparametric means the inconvenience of parametric part used in the modelization. Multivariate real data examples in semicontinuous setup as reliability are gradually considered to illustrate the proposed approach. Concluding remarks are made for extension to other multiple functions.
An Analysis of Sustainability Integration in Business School Curricula: Evidence from Korea
Authors: Jun Hannah , Moon Seoyoung
Published: March 2021

Given the growing importance of corporate sustainability in the academic literature and in practice, this study investigates to what extent business schools in South Korea have been reflecting sustainability-linked themes in the curriculum. Based on a review of 20,507 course syllabi from ten sample universities between 2013 and 2019, our findings show an increase in the absolute number and proportion of sustainability-linked courses in Korean business schools, increasing from 12.9% of total courses in 2013 to 14.7% in 2019. The most prominent sustainability keywords were “ethics” and “corporate social responsibility,” with most courses reflecting sustainability keywords by allocating a few weeks to sustainability issues (sustainability-inclusive) rather than sustainability serving as the major theme of the course (sustainability-focused). In terms of degree program, sustainability-linked courses accounted for nearly 15% of total courses at the undergraduate and Master of Business Administration (MBA) levels, respectively, and just 7% of graduate (Master’s/Ph.D.) programs in Business Administration. While our findings suggest overall progress in incorporating sustainability themes in business schools, course offerings are fragmented and generally focus on a narrow concept of ethics rather than constituting a comprehensive curriculum that weaves sustainability throughout functional majors.
How Does Nutrition Feature in Climate-Smart Agricultural Policy in Southern Africa? A Systematic Policy Review
Authors: Beattie Shaun , Sallu Susannah M.
Published: March 2021

The ability to produce and supply more food that is both nutritious and environmentally sustainable is a momentous challenge facing Africa. Where climate change is expected to negatively impact the agricultural resource of many parts of Southern Africa specifically. Climate-Smart Agriculture (CSA) has emerged as an approach considered capable of transforming and realigning agricultural systems to support food and nutritional security, and development under a changing climate. For sustainable food and nutrition security to be achieved, an effective policy environment is required that supports the widespread adoption of CSA application. In light of this context, this study aims to better understand nutrition’s current position within CSA-related policy at the national level by systematically reviewing all agriculture-related policy documents across Malawi, Tanzania, and Zambia, published between 2010 and 2019. The main findings show that efforts to address nutrition are being made within all countries and a sizeable number of policies, with crop-diversification and intensification presented as popular practices promoted as part of CSA. Nonetheless, the widespread adoption of these efforts remains weak and policies lack detail and instruction for the delivery of nutritional security. Cross-ministerial collaboration is recognised as essential for an improved policy environment, but few provide plans to strengthen such linkages or to include nutritional strategies. Clearer actions and policy outlines that promote nutrition as part of CSA are necessary if more effective action is to be achieved.
The Role of Port Authority in Port Governance and Port Community System Implementation
Authors: Tijan Edvard , Jović Marija , Panjako Ana , Žgaljić Dražen
Published: March 2021

This paper researches the role of the port authorities in port governance, and the role of the port authorities in Port Community System implementation. The authors provide the theoretical frameworks of seaports, port authorities, port governance, and Port Community Systems. The literature review was carried out using the Web of Science database and additional relevant sources. The authors concluded that although different port governance models exist (regarding the type of port authority), there is no evidence as to which governance model is universally preferable, as it is specific to each seaport. In addition, the research has shown that port authorities play a very important role in the implementation of a Port Community System, increasing the sustainability of seaport operations. Its implementation enables the port authorities to evolve into real digital hubs and neutral data managers, which ultimately leads to the optimization of seaport processes and more efficient use of transport infrastructure.