bluuebunny/crossref_metadata_2025 huggingface dataset:
Atomistic Simulations of Plasma-Enhanced Atomic Layer Deposition
Authors: Becker Martin , Sierka Marek
Published: August 2019

Plasma-enhanced atomic layer deposition (PEALD) is a widely used, powerful layer-by-layer coating technology. Here, we present an atomistic simulation scheme for PEALD processes, combining the Monte Carlo deposition algorithm and structure relaxation using molecular dynamics. In contrast to previous implementations, our approach employs a real, atomistic model of the precursor. This allows us to account for steric hindrance and overlap restrictions at the surface corresponding to the real precursor deposition step. In addition, our scheme takes various process parameters into account, employing predefined probabilities for precursor products at each Monte Carlo deposition step. The new simulation protocol was applied to investigate PEALD synthesis of SiO2 thin films using the bis-diethylaminosilane precursor. It revealed that increasing the probability for precursor binding to one surface oxygen atom favors amorphous layer growth, a large number of –OH impurities, and the formation of voids. In contrast, a higher probability for precursor binding to two surface oxygen atoms leads to dense SiO2 film growth and a reduction of –OH impurities. Increasing the probability for the formation of doubly bonded precursor sites is therefore the key factor for the formation of dense SiO2 PEALD thin films with reduced amounts of voids and –OH impurities.
A Modified Fletcher–Reeves Conjugate Gradient Method for Monotone Nonlinear Equations with Some Applications
Authors: Abubakar Auwal Bala , Kumam Poom , Mohammad Hassan , Awwal Aliyu Muhammed , Sitthithakerngkiet Kanokwan
Published: August 2019

One of the fastest growing and efficient methods for solving the unconstrained minimization problem is the conjugate gradient method (CG). Recently, considerable efforts have been made to extend the CG method for solving monotone nonlinear equations. In this research article, we present a modification of the Fletcher–Reeves (FR) conjugate gradient projection method for constrained monotone nonlinear equations. The method possesses sufficient descent property and its global convergence was proved using some appropriate assumptions. Two sets of numerical experiments were carried out to show the good performance of the proposed method compared with some existing ones. The first experiment was for solving monotone constrained nonlinear equations using some benchmark test problem while the second experiment was applying the method in signal and image recovery problems arising from compressive sensing.
New Polynomial Bounds for Jordan’s and Kober’s Inequalities Based on the Interpolation and Approximation Method
Authors: Zhang Lina , Ma Xuesi
Published: August 2019

In this paper, new refinements and improvements of Jordan’s and Kober’s inequalities are presented. We give new polynomial bounds for the s i n c ( x ) and cos ( x ) functions based on the interpolation and approximation method. The results show that our bounds are tighter than the previous methods.
A New Gronwall–Bellman Inequality in Frame of Generalized Proportional Fractional Derivative
Authors: Alzabut Jehad , Sudsutad Weerawat , Kayar Zeynep , Baghani Hamid
Published: August 2019

New versions of a Gronwall–Bellman inequality in the frame of the generalized (Riemann–Liouville and Caputo) proportional fractional derivative are provided. Before proceeding to the main results, we define the generalized Riemann–Liouville and Caputo proportional fractional derivatives and integrals and expose some of their features. We prove our main result in light of some efficient comparison analyses. The Gronwall–Bellman inequality in the case of weighted function is also obtained. By the help of the new proposed inequalities, examples of Riemann–Liouville and Caputo proportional fractional initial value problems are presented to emphasize the solution dependence on the initial data and on the right-hand side.
Padina pavonica Extract Promotes In Vitro Differentiation and Functionality of Human Primary Osteoblasts
Authors: Minetti Mariagiulia , Bernardini Giulia , Biazzo Manuele , Gutierrez Gilles , Geminiani Michela , Petrucci Teresa , Santucci Annalisa
Published: August 2019

Marine algae have gained much importance in the development of nutraceutical products due to their high content of bioactive compounds. In this work, we investigated the activity of Padina pavonica with the aim to demonstrate the pro-osteogenic ability of its extract on human primary osteoblast (HOb). Our data indicated that the acetonic extract of P. pavonica (EPP) is a safe product as it did not show any effect on osteoblast viability. At the same time, EPP showed to possess a beneficial effect on HOb functionality, triggering their differentiation and mineralization abilities. In particular EPP enhanced the expression of the earlier differentiation stage markers: a 5.4-fold increase in collagen type I alpha 1 chain (COL1A1), and a 2.3-fold increase in alkaline phosphatase (ALPL), as well as those involved in the late differentiation stage: a 3.7-fold increase in osteocalcin (BGLAP) expression and a 2.8-fold in osteoprotegerin (TNFRSF11B). These findings were corroborated by the enhancement in ALPL enzymatic activity (1.7-fold increase) and by the reduction of receptor activator of nuclear factor-κB ligand (RANKL) and osteoprotegerin (OPG) ratio (0.6-fold decrease). Moreover, EPP demonstrated the capacity to enhance the bone nodules formation by 3.2-fold in 4 weeks treated HOb. Therefore, EPP showed a significant capability of promoting osteoblast phenotype. Given its positive effect on bone homeostasis, EPP could be used as a useful nutraceutical product that, in addition to a healthy lifestyle and diet, can be able to contrast and prevent bone diseases, especially those connected with ageing, such as osteoporosis (OP).
The Scottish Intercollegiate Guidelines Network (SIGN) 157: Guidelines on Risk Reduction and Management of Delirium
Authors: Soiza Roy L. , Myint Phyo K.
Published: August 2019

The Scottish Intercollegiate Guidelines Network (SIGN) guideline on delirium is a major advance on existing guidelines on this condition. This is particularly important given the evidence it is frequently under-diagnosed and inadequately managed despite being common and frequently associated with significant patient and carer distress and poor outcomes. The guidelines recommend using the 4A’s test to help detect delirium. A bundle of mostly non-pharmacological therapies minimise the risk of developing delirium and can help those who develop the condition. The importance of medical optimisation by an experienced professional in those at risk of delirium is highlighted with new recommendations for people in intensive care and surgical settings. There is guidance on follow-up of people with delirium, which should become routine. This commentary piece focusses on areas with the greatest potential to improve the experience and outcomes of those with delirium, and briefly discusses areas of ongoing uncertainty.
Advances of Metabolomics in Fungal Pathogen–Plant Interactions
Authors: Chen Fangfang , Ma Ruijing , Chen Xiao-Lin
Published: August 2019

Plant disease caused by fungus is one of the major threats to global food security, and understanding fungus–plant interactions is important for plant disease control. Research devoted to revealing the mechanisms of fungal pathogen–plant interactions has been conducted using genomics, transcriptomics, proteomics, and metabolomics. Metabolomics research based on mass spectrometric techniques is an important part of systems biology. In the past decade, the emerging field of metabolomics in plant pathogenic fungi has received wide attention. It not only provides a qualitative and quantitative approach for determining the pathogenesis of pathogenic fungi but also helps to elucidate the defense mechanisms of their host plants. This review focuses on the methods and progress of metabolomics research in fungal pathogen–plant interactions. In addition, the prospects and challenges of metabolomics research in plant pathogenic fungi and their hosts are addressed.
Large Plasmid Complement Resolved: Complete Genome Sequencing of Lactobacillus plantarum MF1298, a Candidate Probiotic Strain Associated with Unfavorable Effect
Authors: McLeod Anette , Fagerlund Annette , Rud Ida , Axelsson Lars
Published: August 2019

Considerable attention has been given to the species Lactobacillus plantarum regarding its probiotic potential. L. plantarum strains have shown health benefits in several studies, and even nonstrain-specific claims are allowed in certain markets. L. plantarum strain MF1298 was considered a candidate probiotic, demonstrating in vitro probiotic properties and the ability to survive passage through the human intestinal tract. However, the strain showed an unfavorable effect on symptoms in subjects with irritable bowel syndrome in a clinical trial. The properties and the genome of this strain are thus of general interest. Obtaining the complete genome of strain MF1298 proved difficult due to its large plasmid complement. Here, we exploit a combination of sequencing approaches to obtain the complete chromosome and plasmid assemblies of MF1298. The Oxford Nanopore Technologies MinION long-read sequencer was particularly useful in resolving the unusually large number of plasmids in the strain, 14 in total. The complete genome sequence of 3,576,440 basepairs contains 3272 protein-encoding genes, of which 315 are located on plasmids. Few unique regions were found in comparison with other L. plantarum genomes. Notably, however, one of the plasmids contains genes related to vitamin B12 (cobalamin) turnover and genes encoding bacterial reverse transcriptases, features not previously reported for L. plantarum. The extensive plasmid information will be important for future studies with this strain.
Trends and Correlates of Overweight among Pre-School Age Children, Adolescent Girls, and Adult Women in South Asia: An Analysis of Data from Twelve National Surveys in Six Countries over Twenty Years
Authors: Harding Kassandra L. , Aguayo Victor M. , Webb Patrick
Published: August 2019

Overweight has become a global pandemic and is associated with a rise in diet-related non-communicable diseases and associated co-morbidities. Most of the world’s undernourished people live in South Asia, yet the number of overweight and obese individuals in this region is growing. This study explores trends and correlates of overweight among pre-school age children, adolescent girls, and adult women in South Asia. Using pooled data from 12 national surveys in six countries, generalized linear mixed models were run to analyze relationships. Overweight children had significantly higher odds than non-overweight children of having an overweight mother (Adjusted Odds Ratio: 1.34, p < 0.01). Overweight adolescent girls were more likely to come from a wealthier household (Adjusted Prevalence Ratio (APR): 2.46, p < 0.01) in an urban area (1.74, p < 0.01), and have formal education (1.22, p < 0.01), compared to non-overweight girls. Similar relationships were seen among overweight vs. non-overweight adult women. In Bangladesh, India, and Nepal, overweight among girls and women increased over time, while differentials associated with household wealth, urban residence, and formal education attenuated over time. Overweight and obesity are becoming more prevalent across South Asia in a context of persisting undernutrition. Once a condition of the wealthier, more educated and urban, rates of overweight are increasing among poorer, less educated, and rural women. This requires immediate attention to ‘multi-use’ policies and programmes.
Socio-Ecological Influences on Adolescent (Aged 10–17) Alcohol Use and Unhealthy Eating Behaviours: A Systematic Review and Synthesis of Qualitative Studies
Authors: Scott Stephanie , Elamin Wafa , Giles Emma L. , Hillier-Brown Frances , Byrnes Kate , Connor Natalie , Newbury-Birch Dorothy , Ells Louisa
Published: August 2019

Excess body weight and risky alcohol consumption are two of the greatest contributors to global disease. Alcohol use contributes directly and indirectly to weight gain. Health behaviours cluster in adolescence and track to adulthood. This review identified and synthesised qualitative research to provide insight into common underlying factors influencing alcohol use and unhealthy eating behaviours amongst young people aged 10–17. Sixty two studies met inclusion criteria. Twenty eight studies focused on alcohol; 34 focused on eating behaviours. Informed by principles of thematic analysis and meta-ethnography, analysis yielded five themes: (1) use of alcohol and unhealthy food to overcome personal problems; (2) unhealthy eating and alcohol use as fun experiences; (3) food, but not alcohol, choices are based on taste; (4) control and restraint; and (5) demonstrating identity through alcohol and food choices. Young people faced pressure, reinforced by industry, to eat and drink in very specific ways, with clear social consequences if their attitudes or behaviour were deemed unacceptable. No qualitative studies were identified with an explicit and concurrent focus on adolescent eating behaviours and alcohol consumption. Further exploratory work is needed to examine the links between food and alcohol in young people’s emotional, social and cultural lives.
Curcumin Attenuates the Pathogenicity of Entamoeba histolytica by Regulating the Expression of Virulence Factors in an Ex-Vivo Model Infection
Authors: Rangel-Castañeda Itzia Azucena , Carranza-Rosales Pilar , Guzmán-Delgado Nancy Elena , Hernández-Hernández José Manuel , González-Pozos Sirenia , Pérez-Rangel Armando , Castillo-Romero Araceli
Published: August 2019

Infection with the enteric protozoan Entamoeba histolytica is still a serious public health problem, especially in developing countries. Amoebic liver abscess (ALA) is the most common extraintestinal manifestation of the amoebiasis, and it can lead to serious and potentially life-threatening complications in some people. ALA can be cured by metronidazole (MTZ); however, because it has poor activity against luminal trophozoites, 40–60% of treated patients get repeated episodes of invasive disease and require repeated treatments that can induce resistance to MTZ, this may emerge as an important public health problem. Anti-virulence strategies that impair the virulence of pathogens are one of the novel approaches to solving the problem. In this study, we found that low doses of curcumin (10 and 50 μM) attenuate the virulence of E. histolytica without affecting trophozoites growth or triggering liver injury. Curcumin (CUR) decreases the expression of genes associated with E. histolytica virulence (gal/galnac lectin, ehcp1, ehcp5, and amoebapore), and is correlated with significantly lower amoebic invasion. In addition, oxidative stress is critically involved in the etiopathology of amoebic liver abscess; our results show no changes in mRNA expression levels of superoxide dismutase (SOD) and catalase (CAT) after E. histolytica infection, with or without CUR. This study provides clear evidence that curcumin could be an anti-virulence agent against E. histolytica, and makes it an attractive potential starting point for effective treatments that reduce downstream amoebic liver abscess.
Impairment of the Developing Human Brain in Iron Deficiency: Correlations to Findings in Experimental Animals and Prospects for Early Intervention Therapy
Authors: Markova Veronika , Holm Charlotte , Pinborg Anja Bisgaard , Thomsen Lars Lykke , Moos Torben
Published: August 2019

Due to the necessity of iron for a variety of cellular functions, the developing mammalian organism is vulnerable to iron deficiency, hence causing structural abnormalities and physiological malfunctioning in organs, which are particularly dependent on adequate iron stores, such as the brain. In early embryonic life, iron is already needed for proper development of the brain with the proliferation, migration, and differentiation of neuro-progenitor cells. This is underpinned by the widespread expression of transferrin receptors in the developing brain, which, in later life, is restricted to cells of the blood–brain and blood–cerebrospinal fluid barriers and neuronal cells, hence ensuring a sustained iron supply to the brain, even in the fully developed brain. In embryonic human life, iron deficiency is thought to result in a lower brain weight, with the impaired formation of myelin. Studies of fully developed infants that have experienced iron deficiency during development reveal the chronic and irreversible impairment of cognitive, memory, and motor skills, indicating widespread effects on the human brain. This review highlights the major findings of recent decades on the effects of gestational and lactational iron deficiency on the developing human brain. The findings are correlated to findings of experimental animals ranging from rodents to domestic pigs and non-human primates. The results point towards significant effects of iron deficiency on the developing brain. Evidence would be stronger with more studies addressing the human brain in real-time and the development of blood biomarkers of cerebral disturbance in iron deficiency. Cerebral iron deficiency is expected to be curable with iron substitution therapy, as the brain, privileged by the cerebral vascular transferrin receptor expression, is expected to facilitate iron extraction from the circulation and enable transport further into the brain.
Gadamer, Levinas, and the Hermeneutic Ontology of Ethics
Authors: King Christopher
Published: August 2019

Much debate has been held over the question of whether Hans-Georg Gadamer’s hermeneutic approach to ethics and the other can do justice to the alterity of the other, as exemplified in Emmanuel Levinas’s approach to ethics as first philosophy. The challenge to Gadamer and to hermeneutics more generally, comes obliquely from Levinas and more directly, from Robert Bernasconi, who argues that Gadamer cannot account for an otherness that ends in incomprehensibility as one finds in encounters between persons of asymmetrical power relations—oppressed and oppressor, privileged and marginalized. Bernasconi’s critique has resulted in a flurry of hermeneutic responses that insist that Gadamer’s hermeneutics can, if understood in the right way, accommodate the other and serve as the foundation for robust ethical treatment of the other. I argue in this paper that participants in this debate have been insufficiently attentive to the ontologies that underlie the accounts of self and other in Gadamer and in Levinas. Because Gadamer and Levinas begin from different ontologies, their accounts of ethics and of the ground of ethics differ.
On Theoretical Incomprehensibility
Authors: Minati Gianfranco
Published: August 2019

This contribution tentatively outlines the presumed conceptual duality between the issues of incompleteness and incomprehensibility—The first being more formal in nature and able to be declined in various ways until specified in the literature as theoretical incompleteness. This is theoretical and not temporary, which is admissible and the completion prosecutable. As considered in the literature, theoretical incompleteness refers to uncertainty principles in physics, incompleteness in mathematics, oracles for the Turing Machine, logical openness as the multiplicity of models focusing on coherence more than the optimum selections, fuzziness, quasiness, e.g., quasi-crystals, quasi-systems, and quasi-periodicity, which are intended as the space of equivalences that allow for coherent processes of emergence. The issue of incomprehensibility cannot be considered without reference to an agent endowed with cognitive abilities. In this article, we consider incomprehensibility as understood here as not generally scientifically explicable, i.e., with the available knowledge, as such incomprehensibility may be temporary, pending theoretical and technological advances, or deemed to be absolute as coincident with eventual definitive, theoretical non-explicability, and incomprehensibility. We considered the theoretically incomprehensibility mostly in three main ways: as the inexhaustibility of the multiplicity of constructivist reality as given by the theoretically incomprehensible endless loop of incomprehensible–comprehensible, and by existential questions. Moreover, theoretical incomprehensibility is intended as evidence of the logical openness of both the world and of understanding itself. The role of theoretical incomprehensibility is intended as a source of theoretical research issues such as paradoxes and paradigm shifts, where it is a matter of having cognitive strategies and approaches to look for, cohabit, combine, and use comprehensibility and (theoretical) incomprehensibility. The usefulness of imaginary numbers comes to mind. Can we support such research for local, temporary, and theoretical incomprehensibility with suitable approaches such as software tools, for instance, that simulate the logical frameworks of incomprehensibility? Is this a step toward a kind of artificial creativity leading to paradigm shifts? The most significant novelty of the article lies in the focus on the concept of theoretical incomprehensibility and distinguishing it from incomprehensibility and considering different forms of understanding. It is a matter of identifying strategies to act and coexist with the theoretically incomprehensible, to represent and use it, for example when dealing with imaginary numbers and quantum contexts where classical comprehensibility is theoretically impossible. Can we think of forms of non-classical understanding? In this article, these topics are developed in conceptual and philosophical ways.
Optimization of the Curing and Post-Curing Conditions for the Manufacturing of Partially Bio-Based Epoxy Resins with Improved Toughness
Authors: Lascano Diego , Quiles-Carrillo Luis , Torres-Giner Sergio , Boronat Teodomiro , Montanes Nestor
Published: August 2019

This research deals with the influence of different curing and post-curing temperatures on the mechanical and thermomechanical properties as well as the gel time of an epoxy resin prepared by the reaction of diglycidyl ether of bisphenol A (DGEBA) with an amine hardener and a reactive diluent derived from plants at 31 wt %. The highest performance was obtained for the resins cured at moderate-to-high temperatures, that is, 80 ° C and 90 ° C , which additionally showed a significant reduction in the gel time. This effect was ascribed to the formation of a stronger polymer network by an extended cross-linking process of the polymer chains during the resin manufacturing. Furthermore, post-curing at either 125 ° C   or 150 ° C yielded thermosets with higher mechanical strength and, more interestingly, improved toughness, particularly for the samples previously cured at moderate temperatures. In particular, the partially bio-based epoxy resin cured at 80 ° C and post-cured at 150 ° C for 1 h and 30 min, respectively, showed the most balanced performance due to the formation of a more homogeneous cross-linked structure.
Recovering Cobalt and Sulfur in Low Grade Cobalt-Bearing V–Ti Magnetite Tailings Using Flotation Process
Authors: Xiao Junhui , Zhang Yushu
Published: August 2019

There is 0.032% cobalt and 0.56% sulfur in the cobalt-bearing V–Ti tailings in the Panxi Region, with the metal sulfide minerals mainly including FeS2, Fe1−xS, Co3S4, and (Fe,Co)S2, and the gangue minerals mainly including aluminosilicate minerals. The flotation process was used to recover cobalt and sulfur in the cobalt-bearing V–Ti tailings. The results showed that an optimized cobalt–sulfur concentrate with a cobalt grade of 2.08%, sulfur content of 36.12%, sulfur recovery of 85.79%, and cobalt recovery and 84.77% were obtained by flotation process of one roughing, three sweeping, and three cleaning under roughing conditions, which employed pulp pH of 8, grinding fineness of <0.074 mm occupying 80%, flotation concentration of 30%, and dosages of butyl xanthate, copper sulfate, and pine oil of 100 g/t, 30 g/t, and 20 g/t, respectively. Optimized one sweeping, two sweeping, and three sweeping conditions used a pulp pH of 9, and dosages of butyl xanthate, copper sulfate, and pine oil of 50 g/t, 15 g/t, 10 g/t; 25 g/t, 7.5 g/t, 5 g/t; 20 g/t, 5 g/t, 5 g/t, respectively. Optimized one cleaning, two cleaning, and three cleaning condition dosages of sodium silicate of 200 g/t, 100 g/t, 50 g/t, respectively. Study of analysis and characterization of cobalt–sulfur concentrate by X-ray diffraction (XRD), automatic mineral analyzer (MLA), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) showed that the main minerals in cobalt–sulfur concentrate are FeS2, Co3S4 and (Fe,Co)S2, of which FeS2 and (Fe,Co)S2 accounted for 65.64% and Co3S4 for 22.64%. Gangue minerals accounted for 11.72%. The element Co in (Fe,Co)S2 is closely related to pyrite in the form of isomorphism, and the flotability difference between cobalt and pyrite is very small, which makes it difficult to separate cobalt and sulfur. Cobalt–sulfur concentrate can be used as raw material for further separation of cobalt and sulfur in smelting by pyrometallurgical or hydrometallurgical methods.
Digital Twin for Monitoring of Industrial Multi-Effect Evaporation
Authors: Soares Rafael M. , Câmara Maurício M. , Feital Thiago , Pinto José Carlos
Published: August 2019

Digital twins are rigorous mathematical models that can be used to represent the operation of real systems. This connection allows for deeper understanding of the actual states of the analyzed system through estimation of variables that are difficult to measure otherwise. In this context, the present manuscript describes the successful implementation of a digital twin to represent a four-stage multi-effect evaporation train from an industrial sugar-cane processing unit. Particularly, the complex phenomenological effects, including the coupling between thermodynamic and fluid dynamic effects, and the low level of instrumentation in the plant constitute major challenges for adequate process operation. For this reason, dynamic mass and energy balances were developed, implemented and validated with actual industrial data, in order to provide process information for decision-making in real time. For example, the digital twin was able to indicate failure of process sensors and to provide estimates for the affected variables in real time, improving the robustness of the operation and constituting an important tool for process monitoring.
Digitalizing the Paints and Coatings Development Process
Authors: Kern Tomaž , Krhač Eva , Senegačnik Marjan , Urh Benjamin
Published: August 2019

Numerous laboratory tests are used to determine the appropriateness of new formulations in the development process in the paint and coatings industry. New formulations are most often functionally inadequate, unacceptable for environmental or health reasons, or too expensive. Formulators are obliged to repeat laboratory tests until one of the formulations fulfills the minimum requirements. This is cumbersome, slow, and expensive, and can cause ecological problems, wasting materials on tests that do not produce the desired results. The purpose of this research was to find out if there might be a better way forward to increase efficiency and free up formulators to focus on new products. In this experiment, a new paints and coatings development process was redesigned based on the potential benefits of formulation digitalization. Instead of laboratory testing, a digital platform was used that has been developed and stocked with relevant, up-to-date, and complete, usable data. This study found that, by going digital, developers could vastly reduce non-value-added activities in the development process (by as much as 70%) and significantly shorten the entire process throughput time (by up to 48%). Using digital tools to facilitate the development process appears to be a possible way forward for the paint and coatings industry, saving time, materials, and money and protecting the environment.
Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests
Authors: Shimizu Katsuto , Ota Tetsuji , Mizoue Nobuya
Published: August 2019

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.
Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach
Authors: Du Shouji , Du Shihong , Liu Bo , Zhang Xiuyuan
Published: August 2019

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.
Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images
Authors: Zheng Zhifeng , Cao Jiannong , Lv Zhiyong , Benediktsson Jón Atli
Published: August 2019

In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial–spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.
Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery
Authors: Li Siqi , Quackenbush Lindi J. , Im Jungho
Published: August 2019

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies.
Rapid Detection of HLA-B*57:01-Expressing Cells Using a Label-Free Interdigitated Electrode Biosensor Platform for Prevention of Abacavir Hypersensitivity in HIV Treatment
Authors: Chan Jianxiong , Soraya Gita V. , Craig Lauren , Uddin Shah M. , Todaro Marian , Huynh Duc H. , Abeyrathne Chathurika D. , Kostenko Lyudmila , McCluskey James , Skafidas Efstratios , Kwan Patrick
Published: August 2019

Pre-treatment screening of individuals for human leukocyte antigens (HLA) HLA-B*57:01 is recommended for the prevention of life-threatening hypersensitivity reactions to abacavir, a drug widely prescribed for HIV treatment. However, the implementation of screening in clinical practice is hindered by the slow turnaround time and high cost of conventional HLA genotyping methods. We have developed a biosensor platform using interdigitated electrode (IDE) functionalized with a monoclonal antibody to detect cells expressing HLA-B*57:01. This platform was evaluated using cell lines and peripheral blood mononuclear cells expressing different HLA-B alleles. The functionalized IDE sensor was able to specifically capture HLA-B*57:01 cells, resulting in a significant change in the impedance magnitude in 20 min. This IDE platform has the potential to be further developed to enable point-of-care HLA-B*57:01 screening.
Sensorless Control of the Permanent Magnet Synchronous Motor
Authors: Urbanski Konrad , Janiszewski Dariusz
Published: August 2019

This paper describes the study and experimental verification of sensorless control of permanent magnet synchronous motors with a high precision drive using two novel estimation methods. All the studies of the modified Luenberger observer, reference model, and unscented Kalman filter are presented with algorithm details. The main part determines trials with a full range of reference speeds with a special near-zero speed area taken into account. In order to compare the estimation performances of the observers, both are designed for the same motor and control system and run in the same environment. The experimental results indicate that the presented methods are capable of tracking the actual values of speed and motor position with small deviation, sufficient for precise control.
putEMG—A Surface Electromyography Hand Gesture Recognition Dataset
Authors: Kaczmarek Piotr , Mańkowski Tomasz , Tomczyński Jakub
Published: August 2019

In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject’s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin’s and Du’s feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.
In-Mold Sensors for Injection Molding: On the Way to Industry 4.0
Authors: Ageyeva Tatyana , Horváth Szabolcs , Kovács József Gábor
Published: August 2019

The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the plastic part is formed. Manufacturing process data in the mold cavity can be obtained with the help of sensors. Although many sensors are available nowadays, those appropriate for in-mold measurements have certain peculiarities. This study presents a comprehensive overview of in-mold process monitoring tools and methods for injection molding process control. It aims to survey the recent development of standard sensors used in the industry for the measurement of in-mold process parameters, as well as research attempts to develop unique solutions for solving certain research and industrial problems of injection molding process monitoring. This review covers the established process monitoring techniques—direct temperature and pressure measurement with standard sensors and with the newly developed sensors, as well as techniques for the measurement of indirect process parameters, such as viscosity, warpage or shrinkage.
Research on Filtering Algorithm of MEMS Gyroscope Based on Information Fusion
Authors: Guo Hui , Hong Huajie
Published: August 2019

As an important inertial sensor, the gyroscope is mainly used to measure angular velocity in inertial space. However, due to the influence of semiconductor thermal noise and electromagnetic interference, the output of the gyroscope has a certain random noise and drift, which affects the accuracy of the detected angular velocity signal, thus interfering with the accuracy of the stability of the whole system. In order to reduce the noise and compensate for the drift of the MEMS (Micro Electromechanical System) gyroscope during usage, this paper proposes a Kalman filtering method based on information fusion, which uses the MEMS gyroscope and line accelerometer signals to implement the filtering function under the Kalman algorithm. The experimental results show that compared with the commonly used filtering methods, this method allows significant reduction of the noise of the gyroscope signal and accurate estimation of the drift of the gyroscope signal, and thus improves the control performance of the system and the stability accuracy.
Automatic Fault Detection and Isolation Method for Roller Bearing Using Hybrid-GA and Sequential Fuzzy Inference
Authors: Kobayashi Yusuke , Song Liuyang , Tomita Masaru , Chen Peng
Published: August 2019

Though accelerometers for condition diagnosis of a bearing is preferably placed at the nearest position of the bearing as possible, in some plant equipment, the accelerometer is difficult to set near the diagnosed bearing, and in many cases, sensors have to be placed at a location far from the diagnosed bearing to measure signals for diagnosing bearing faults. Since, in these cases, the measured signals contain stronger noise than the signal measured near the diagnosed bearing, bearing faults are more difficultly to be detected. In order to overcome the above difficulty, this paper proposes a new fault auto-detection method by which the signals measured by an accelerometer located at a far point from the diagnosed bearing can be used to simply and accurately detect the bearing faults automatically. Firstly, the hybrid GA (the combination of genetic algorithm and tabu search) is used to automatically search and determine the optimum cutoff frequency of the high-pass filter to extract the fault signal of the abnormal bearing. Secondly, the bearing faults are precisely diagnosed by possibility theory and fuzzy inference. Finally, in order to demonstrate the effectiveness of these proposed methods, these methods were applied to bearing diagnostics using vibration signals measured at the far point of the diagnostic bearing, and the efficiency of these methods was verified by the results of automatic bearing fault diagnosis.
Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant
Authors: Borhani Alireza , Pätzold Matthias , Yang Kun
Published: August 2019

While aging is a serious global concern, in-home healthcare monitoring solutions are limited to context-aware systems and wearable sensors, which may easily be forgotten or ignored for privacy and comfort reasons. An emerging non-wearable fall detection approach is based on processing radio waves reflected off the body, who has no active interaction with the system. This paper reports on an indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of fall incidents and some daily life activities on the temporal and spectral properties of the indoor channel under both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The time-frequency characteristic of the channel has been thoroughly investigated by spectrogram analysis. Studying the instantaneous Doppler characteristics shows that the Doppler spread ignores small variations of the channel (especially under OLOS conditions), but highlights coarse ones caused by falls. The channel properties studied in this paper can be considered to be new useful metrics for the design of reliable fall detection algorithms. We share all measured data files with the community through Code Ocean. The data can be used for validating a new class of channel models aiming at the design of smart activity recognition systems via a software-based approach.
Selective Detection of Nitrogen-Containing Compound Gases
Authors: Yoo Ran , Lee Hyun-Sook , Kim Wonkyung , Park Yunji , Koo Aran , Jin Sang-Hyun , Pham Thang Viet , Kim Myung Jong , Maeng Sunglyul , Lee Wooyoung
Published: August 2019

N-containing gaseous compounds, such as trimethylamine (TMA), triethylamine (TEA), ammonia (NH3), nitrogen monoxide (NO), and nitrogen dioxide (NO2) exude irritating odors and are harmful to the human respiratory system at high concentrations. In this study, we investigated the sensing responses of five sensor materials—Al-doped ZnO (AZO) nanoparticles (NPs), Pt-loaded AZO NPs, a Pt-loaded WO3 (Pt-WO3) thin film, an Au-loaded WO3 (Au-WO3) thin film, and N-doped graphene—to the five aforementioned gases at a concentration of 10 parts per million (ppm). The ZnO- and WO3-based materials exhibited n-type semiconducting behavior, and their responses to tertiary amines were significantly higher than those of nitric oxides. The N-doped graphene exhibited p-type semiconducting behavior and responded only to nitric oxides. The Au- and Pt-WO3 thin films exhibited extremely high responses of approximately 100,000 for 10 ppm of triethylamine (TEA) and approximately −2700 for 10 ppm of NO2, respectively. These sensing responses are superior to those of previously reported sensors based on semiconducting metal oxides. On the basis of the sensing response results, we drew radar plots, which indicated that selective pattern recognition could be achieved by using the five sensing materials together. Thus, we demonstrated the possibility to distinguish each type of gas by applying the patterns to recognition techniques.