bluuebunny/crossref_metadata_2025 huggingface dataset:
Sheep Dung Composition and Phosphorus and Potassium Release Affected by Grazing Intensity and Pasture Development Stage in an Integrated Crop-Livestock System
Authors: Arnuti Fernando , Denardin Luiz Gustavo de O. , Nunes Pedro Arthur de A. , Alves Lucas A. , Cecagno Diego , de Assis Júlia , Schaidhauer Walker da S. , Anghinoni Ibanor , Chabbi Abad , César de F. Carvalho Paulo
Published: August 2020

Animal grazing in integrated crop-livestock systems (ICLS) results in continuous nutrient release to forage plants and crops in succession. This study aimed to assess sheep dung composition and decomposition rates under distinct grazing intensities and at different development stages of Italian ryegrass pasture (Lolium multiflorum Lam.), and to evaluate dung phosphorus (P) and potassium (K) release dynamics during two annual ICLS cycles (2015 and 2016) in southern Brazil. Treatments consisted of two grazing intensities (moderate and light) and two pasture development stages (vegetative and post-flowering), arranged in a randomized complete block design with split-split-plots and four replicates. Dry matter (DM) decomposition and P and K release rates were determined using litter bags with sheep dung. Grazing intensity did not affect sheep dung composition. Forage consumed at different development stages altered sheep dung composition, decomposition, and P and K release rates. Dung sampled at pasture vegetative stage showed P and K contents 16% and 7% higher, respectively, than dung from the post-flowering stage. Dung collected at pasture post-flowering stage had 26% more cellulose and 34% more hemicellulose compared to dung from the vegetative stage in 2016. P and K release was greater for dung from pasture vegetative stage, reaching 3.7 and 12.9 kg ha−1 of P and K, respectively. Further evaluations are still needed considering the quantification and release of nutrients in each of the different compartments (pasture, urine, and dung residues) that compose the system.
Synthesis of Degraded Limonoid Analogs as New Antibacterial Scaffolds against Staphylococcus aureus
Authors: Ferrera-Suanzes Marta , Prieto Victoria , Medina-Olivera Antonio J. , Botubol-Ares José Manuel , Galán-Sánchez Fátima , Rodríguez-Iglesias Manuel A. , Hernández-Galán Rosario , Durán-Peña María Jesús
Published: August 2020

Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA) have become serious infections in humans and ruminants. S. aureus strains are showing rapid changes to develop resistance in traditional antibiotic-containing systems. In the continuous fierce fight against the emergent multi-drug resistant bacterial strains, straightforward and scalable synthetic procedures to produce new active molecules are in demand. Analysis of molecular properties points to degraded limonoids as promising candidates. In this article, we report a simple synthetic approach to obtain degraded limonoid analogs as scaffolds for new antibacterial molecules. The minimum inhibitory concentrations against S. aureus were evaluated for the stereoisomer mixtures by the broth microdilution method. Analysis of results showed that the acetylated derivatives were the most active of them all.
Active Pulmonary Tuberculosis in Elderly Patients: A 2016–2019 Retrospective Analysis from an Italian Referral Hospital
Authors: Di Gennaro Francesco , Vittozzi Pietro , Gualano Gina , Musso Maria , Mosti Silvia , Mencarini Paola , Pareo Carlo , Di Caro Antonino , Schininà Vincenzo , Girardi Enrico , Palmieri Fabrizio
Published: August 2020

Tuberculosis (TB) in the elderly (>65 years old) has increasingly become a global health problem. It has long been recognized that older people are vulnerable to developing tuberculosis. We retrospectively evaluated data from patients older than 65 years diagnosed with pulmonary TB admitted to the National Institute for Infectious Diseases L. Spallanzani, Rome, Italy, from 1 January 2016 to 31 December 2019. One hundred and six consecutive patients were diagnosed with pulmonary TB and 68% reported at least one comorbidity and 44% at least one of the TB risk-factors. Out of the 26 elderly patients who reported an adverse event, having risk factors for TB (O.R. (Odds Ratios) = 1.45; 95% CI 1.12–3.65) and the presence of cavities on Chest X-rays (O.R. = 1.42; 95% CI 1.08–2.73) resulted in being more likely to be associated with adverse events in elderly patients. Having weight loss (O.R. = 1.31; 95% CI 1.08–1.55) and dyspnea (O.R. = 1.23; 95% CI 1.13–1.41) resulted in being significant predictors of unsuccessful treatment outcome in elderly patients. Older people with TB represent a vulnerable group, with high mortality rate, with a challenging diagnosis. Hospitalizations in tertiary referral hospital with clinical expertise in TB management can be useful to improve the outcome of these fragile patients.
Bioinformatic Analysis of 1000 Amphibian Antimicrobial Peptides Uncovers Multiple Length-Dependent Correlations for Peptide Design and Prediction
Authors: Wang Guangshun
Published: August 2020

Amphibians are widely distributed on different continents, except for the polar regions. They are important sources for the isolation, purification and characterization of natural compounds, including peptides with various functions. Innate immune antimicrobial peptides (AMPs) play a critical role in warding off invading pathogens, such as bacteria, fungi, parasites, and viruses. They may also have other biological functions such as endotoxin neutralization, chemotaxis, anti-inflammation, and wound healing. This article documents a bioinformatic analysis of over 1000 amphibian antimicrobial peptides registered in the Antimicrobial Peptide Database (APD) in the past 18 years. These anuran peptides were discovered in Africa, Asia, Australia, Europe, and America from 1985 to 2019. Genomic and peptidomic studies accelerated the discovery pace and underscored the necessity in establishing criteria for peptide entry into the APD. A total of 99.9% of the anuran antimicrobial peptides are less than 50 amino acids with an average length of 24 and a net charge of +2.5. Interestingly, the various amphibian peptide families (e.g., temporins, brevinins, esculentins) can be connected through multiple length-dependent relationships. With an increase in length, peptide net charge increases, while the hydrophobic content decreases. In addition, glycine, leucine, lysine, and proline all show linear correlations with peptide length. These correlations improve our understanding of amphibian peptides and may be useful for prediction and design of new linear peptides with potential applications in treating infectious diseases, cancer and diabetes.
A Hundred Years of Bacteriophages: Can Phages Replace Antibiotics in Agriculture and Aquaculture?
Authors: Sieiro Carmen , Areal-Hermida Lara , Pichardo-Gallardo Ángeles , Almuiña-González Raquel , de Miguel Trinidad , Sánchez Sandra , Sánchez-Pérez Ángeles , Villa Tomás G.
Published: August 2020

Agriculture, together with aquaculture, supplies most of the foodstuffs required by the world human population to survive. Hence, bacterial diseases affecting either agricultural crops, fish, or shellfish not only cause large economic losses to producers but can even create food shortages, resulting in malnutrition, or even famine, in vulnerable populations. Years of antibiotic use in the prevention and the treatment of these infections have greatly contributed to the emergence and the proliferation of multidrug-resistant bacteria. This review addresses the urgent need for alternative strategies for the use of antibiotics, focusing on the use of bacteriophages (phages) as biocontrol agents. Phages are viruses that specifically infect bacteria; they are highly host-specific and represent an environmentally-friendly alternative to antibiotics to control and kill pathogenic bacteria. The information evaluated here highlights the effectiveness of phages in the control of numerous major pathogens that affect both agriculture and aquaculture, with special emphasis on scientific and technological aspects still requiring further development to establish phagotherapy as a real universal alternative to antibiotic treatment.
BSM-Data Reuse Model Based on In-Vehicular Computing
Authors: Benaissa Khireddine , Bitam Salim , Mellouk Abdelhamid
Published: August 2020

Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy.
Experimental Study of the Effect of Hydrotreated Vegetable Oil and Oxymethylene Ethers on Main Spray and Combustion Characteristics under Engine Combustion Network Spray A Conditions
Authors: Pastor José V. , García-Oliver José M. , Micó Carlos , García-Carrero Alba A. , Gómez Arantzazu
Published: August 2020

The stringent emission regulations have motivated the development of cleaner fuels as diesel surrogates. However, their different physical-chemical properties make the study of their behavior in compression ignition engines essential. In this sense, optical techniques are a very effective tool for determining the spray evolution and combustion characteristics occurring in the combustion chamber. In this work, quantitative parameters describing the evolution of diesel-like sprays such as liquid length, spray penetration, ignition delay, lift-off length and flame penetration as well as the soot formation were tested in a constant high pressure and high temperature installation using schlieren, OH∗ chemiluminescence and diffused back-illumination extinction imaging techniques. Boundary conditions such as rail pressure, chamber density and temperature were defined using guidelines from the Engine Combustion Network (ECN). Two paraffinic fuels (dodecane and a renewable hydrotreated vegetable oil (HVO)) and two oxygenated fuels (methylal identified as OME1 and a blend of oxymethylene ethers, identified as OMEx) were tested and compared to a conventional diesel fuel used as reference. Results showed that paraffinic fuels and OMEx sprays have similar behavior in terms of global combustion metrics. In the case of OME1, a shorter liquid length, but longer ignition delay time and flame lift-off length were observed. However, in terms of soot formation, a big difference between paraffinic and oxygenated fuels could be appreciated. While paraffinic fuels did not show any significant decrease of soot formation when compared to diesel fuel, soot formed by OME1 and OMEx was below the detection threshold in all tested conditions.
A New Vector-Based Signal Processing Method of Four-Sensor Probe for Measuring Local Gas–Liquid Two-Phase Flow Parameters Together with Its Assessment against One Bubbly Flow
Authors: Qu Xiaohang , Guo Qianjian , Zhang Yi , Qi Xiaoni , Liu Lei
Published: August 2020

A multiphase flow measurement technique plays a critical role in the studies of heat and mass transfer characteristics and mechanism of the gas–liquid two-phase, the practical measurement of the gas–liquid flow and the improvement of multiphase theoretical models. The four-sensor electrical probe as an emerging measurement method has been proved to be able to get the local flow parameters of multi-dimensional two-phase flow. However, few studies have been reported using the four-sensor probe to obtain the interface information (e.g., the interface direction and velocity). This paper presents a new signal processing method by which the interface direction and velocity can be obtained, besides void fraction, interfacial area concentration (IAC) and bubble chord length. The key solution is to employ the vector-based calculating method, which possesses the merits of simplicity and efficiency, to gain the interface velocity vector through legitimately assuming a direction of the interface velocity. A miniaturized four-sensor electrical probe was made and a gas–liquid two-phase flow experiment was performed to test the proposed signal process scheme. The two-phase flow was controlled to be in cap-bubble flow regime. To validate the availability and reliability of the proposed method, the local flow parameters obtained by the probe measurement were compared with the results from visual measurement technique in the same flow conditions. The comparison indicates that the above local flow parameters from four-sensor probe measurement are in good agreement with the visual measurement results, with maximum deviations of chord length of 8.7%, thereby proving the correctness of the proposed method.
An Attention-Based Latent Information Extraction Network (ALIEN) for High-Order Feature Interactions
Authors: Huang Ruo , McIntyre Shelby , Song Meina , E Haihong , Ou Zhonghong
Published: August 2020

One of the primary tasks for commercial recommender systems is to predict the probabilities of users clicking items, e.g., advertisements, music and products. This is because such predictions have a decisive impact on profitability. The classic recommendation algorithm, collaborative filtering (CF), still plays a vital role in many industrial recommender systems. However, although straight CF is good at capturing similar users’ preferences for items based on their past interactions, it lacks regarding (1) modeling the influences of users’ sequential patterns from their individual history interaction sequences and (2) the relevance of users’ and items’ attributes. In this work, we developed an attention-based latent information extraction network (ALIEN) for click-through rate prediction, to integrate (1) implicit user similarity in terms of click patterns (analogous to CF), and (2) modeling the low and high-order feature interactions and (3) historical sequence information. The new model is based on the deep learning, which goes beyond the capabilities of econometric approaches, such as matrix factorization (MF) and k-means. In addition, the approach provides explainability to the recommendation by interpreting the contributions of different features and historical interactions. We have conducted experiments on real-world datasets that demonstrate considerable improvements over strong baselines.
3D Dilatometer Time-Series Analysis for a Better Understanding of the Dynamics of a Giant Slow-Moving Landslide
Authors: Blahůt Jan , Balek Jan , Eliaš Michal , Meletlidis Stavros
Published: August 2020

This paper presents a methodological approach to the time-series analysis of movement monitoring data of a large slow-moving landslide. It combines different methods of data manipulation to decrease the subjectivity of a researcher and provides a fully quantitative approach for analyzing large amounts of data. The methodology was applied to 3D dilatometric data acquired from the giant San Andrés Landslide on El Hierro in the Canary Islands in the period from October 2013 to April 2019. The landslide is a creeping volcanic flank collapse showing a decrease of speed of movement during the monitoring period. Despite the fact that clear and unambiguous geological interpretations cannot be made, the analysis is capable of showing correlations of the changes of the movement with increased seismicity and, to some point, with precipitation. We consider this methodology being the first step in automatizing and increasing the objectivity of analysis of slow-moving landslide monitoring data.
Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters
Authors: Haghi Mostafa , Danyali Saeed , Thurow Kerstin , Warnecke Joana M. , Wang Ju , Deserno Thomas M.
Published: August 2020

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations.
Water-Soluble Sugars of Pedigreed Sorghum Mutant Stalks and Their Recovery after Pretreatment
Authors: Xu Youjie , Li Jun , Xin Zhanguo , Bean Scott R. , Tilley Michael , Wang Donghai
Published: August 2020

Chemical composition of biomass, especially carbohydrate content, is a critical indicator of a biomass source’s potential for biofuel applications. This study characterized physico-chemical properties of stalks from 16 representative pedigreed sorghum mutant lines. The objectives of this study were to evaluate the recovery of sucrose and its hydrolysis products, glucose and fructose, during dilute sulfuric acid pretreatment at conditions typically used for lignocellulosic biomass, and to determine the relationship between water-extractive contents and sugar recovery after pretreatment. Dilute acid-pretreated sorghum stalks had enzymatic saccharification of >82.4% glucose yield for all treated samples with more than 82.3% cellulose recovery and 85% hemicellulose removal. A single-step, one-pot process was recommended for sorghum mutant stalks with low water-extractive content (<35%, w/w) to reduce processing cost and minimize wastewater disposal since the majority of sugars will be recovered after dilute acid pretreatment with minimal degradation products. However, for sorghum mutant stalks with high water-extractive content (>35%, w/w), a pre-washing step is beneficial to recover the water-soluble sugars before subjecting to the pretreatment process in order to avoid sugar losses during the pretreatment stage. Thus, different processing technologies should be applied to lignocellulosic biomass with various water-extractive contents and water-soluble sugar concentrations.
Driver Attention Area Extraction Method Based on Deep Network Feature Visualization
Authors: Han Guodong , Zhao Shuanfeng , Wang Pengfei , Li Shijun
Published: August 2020

The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s understanding of the target information in the image. In addition, driving behavior is the driver’s response based on the analysis of road information, which is not available in the current intelligent driving system. In order to solve this problem, our paper proposes a driver attention area extraction method based on deep network feature visualization. In our method, we construct a Driver Behavior Information Network (DBIN) to map the relation between image information and driving behavior. Then we use the Deep Network Feature Visualization method (DNFV) to determine the driver’s attention area. The experimental results show that our method can extract effective road information from a real traffic scene picture and obtain the driver’s attention area. Our method can provide a useful theoretical basis and related technology of visual perception for future intelligent driving systems, driving training and assisted driving systems.
Multiscale Analysis of the Influence of Steel Fiber Reinforcement on the Shear Strength of Post-Tensioned Dry Joints
Authors: Marin-Montin Jorge , Alcalde María , Cifuentes Héctor , Montero-Chacón Francisco
Published: August 2020

In this work we follow a multiscale methodology to characterize the structural performance of post-tensioned steel fiber-reinforced concrete dry joints. At the material level, we use an experimentally validated lattice-particle model whose input parameters are the properties of the different phases themselves (i.e., mortar, aggregates, fibers) and mixing information. This model is used to obtain the mechanical properties used in the structural-level simulations of the joints in terms of constitutive laws. The structural analyses are performed using the concrete damage plasticity model, which allows us to quantify the effect of fiber addition on the shear strength of the dry joints and their ductility. Our simulations agree well with other macroscopic models in the case of plain concrete and show, once again, that the American Association of State Highway Transportation Officials (AASHTO) code overestimates the nominal shear capacity of multiple-keyed joints. Regarding the fiber addition, we observe that it promotes an important increase in the shear capacity, but the prestress level is still more relevant in this sense. Based on our simulations, we propose an updated shear capacity estimate accounting for the fiber volume fraction. Finally, a clear increase in the ductility of the joint is observed when the fiber volume content is increased.
Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification
Authors: Barua Shaibal , Ahmed Mobyen Uddin , Begum Shahina
Published: August 2020

One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.
Age-Related Changes in Attentional Refocusing during Simulated Driving
Authors: Huizeling Eleanor , Wang Hongfang , Holland Carol , Kessler Klaus
Published: August 2020

We recently reported that refocusing attention between temporal and spatial tasks becomes more difficult with increasing age, which could impair daily activities such as driving (Callaghan et al., 2017). Here, we investigated the extent to which difficulties in refocusing attention extend to naturalistic settings such as simulated driving. A total of 118 participants in five age groups (18–30; 40–49; 50–59; 60–69; 70–91 years) were compared during continuous simulated driving, where they repeatedly switched from braking due to traffic ahead (a spatially focal yet temporally complex task) to reading a motorway road sign (a spatially more distributed task). Sequential-Task (switching) performance was compared to Single-Task performance (road sign only) to calculate age-related switch-costs. Electroencephalography was recorded in 34 participants (17 in the 18–30 and 17 in the 60+ years groups) to explore age-related changes in the neural oscillatory signatures of refocusing attention while driving. We indeed observed age-related impairments in attentional refocusing, evidenced by increased switch-costs in response times and by deficient modulation of theta and alpha frequencies. Our findings highlight virtual reality (VR) and Neuro-VR as important methodologies for future psychological and gerontological research.
The Experience of Post-Stroke Pain and The Impact on Quality of Life: An Integrative Review
Authors: Payton Hannah , Soundy Andrew
Published: August 2020

Background: Many people experience post-stroke pain (PSP). It is a long-term consequence of stroke that commonly goes unrecognised and untreated. As a result, an integrative review is needed to identify the primary factors that affect PSP and determine the impact on quality of life (QOL). Methods: An integrative review using a quantitatively led data synthesis, supported by qualitative evidence, was conducted. Results: Fourteen studies were identified and 2415 (968 females, 1447 males) people were included. Five primary themes were identified as effecting the experience of PSP; anxiety, depression, fatigue, cognitive function and physical function. Anxiety, depression and fatigue increase PSP. Pain, depression, fatigue and reduced physical function lower QOL. Conclusions: It is essential that clinicians recognise PSP in order to optimize QOL and function post-stroke. Further research is needed to employ a strategy to identify and objectively quantify PSP and its impact on QOL.
Progress of MOF-Derived Functional Materials Toward Industrialization in Solar Cells and Metal-Air Batteries
Authors: Hilal Mohamed Elhousseini , Aboulouard Abdelkhalk , Akbar Abdul Rehman , Younus Hussein A. , Horzum Nesrin , Verpoort Francis
Published: August 2020

The cutting-edge photovoltaic cells are an indispensable part of the ongoing progress of earth-friendly plans for daily life energy consumption. However, the continuous electrical demand that extends to the nighttime requires a prior deployment of efficient real-time storage systems. In this regard, metal-air batteries have presented themselves as the most suitable candidates for solar energy storage, combining extra lightweight with higher power outputs and promises of longer life cycles. Scientific research over non-precious functional catalysts has always been the milestone and still contributing significantly to exploring new advanced materials and moderating the cost of both complementary technologies. Metal-organic frameworks (MOFs)-derived functional materials have found their way to the application as storage and conversion materials, owing to their structural variety, porous advantages, as well as the tunability and high reactivity. In this review, we provide a detailed overview of the latest progress of MOF-based materials operating in metal-air batteries and photovoltaic cells.
Potential Advantages of Peroxoborates and Their Ester Adducts Over Hydrogen Peroxide as Therapeutic Agents in Oral Healthcare Products: Chemical/Biochemical Reactivity Considerations In Vitro, Ex Vivo And In Vivo
Authors: Grootveld Martin , Lynch Edward , Page Georgina , Chan Wyman , Percival Benita , Anagnostaki Eugenia , Mylona Valina , Bordin-Aykroyd Sonia , Grootveld Kerry L.
Published: August 2020

Peroxides present in oral healthcare products generally exert favourable protective activities against the development and progression of tooth decay, plaque, gingivitis, and halitosis, etc. However, despite the high level of research focus on hydrogen and carbamide peroxides as therapeutically active (and tooth-whitening) agents, to date the use of alternative chemical forms of peroxides such as peroxoborates for these purposes has received only scant attention. Intriguingly, peroxoborate and its esters with polyols, such as glycerol, have a very diverse chemistry/biochemistry in aqueous solution, for which there is an increasing amount of evidence that it remains distinctive from that of hydrogen peroxide; such properties include self-associative and hydrolytic equilibria, and their abilities to participate in electrophile- or nucleophile-scavenging, metal ion-complexing, redox and free radical reactions, for example. Therefore, the purpose of this detailed commentary is to evaluate both differences and similarities between the molecular/biomolecular reactivities of peroxoborate species and hydrogen peroxide in vitro, ex-vivo and in vivo. It encompasses brief sectional accounts regarding the molecular heterogeneity of peroxoborates, the release of bioactive agents therefrom, and their oxidative attack on oral cavity biomolecules (the nucleophilic or electrophilic character of these oxidations are discussed). Further areas explored are the abilities of borates and peroxoborates to enhance the solubility of iron ions in aqueous solution, their involvements in free radical biochemistry (particularly the complexation of oxygen radical-promoting transition metal ions by, and antioxidant properties of, peroxoborate-polyol ester adducts), and the specific inhibition of protease enzymes. Further aspects focus on the tooth-whitening, oral malodor neutralizing, and potential mutagenic and genotoxic properties of peroxoborates, along with possible mechanisms for these processes. The abilities of peroxoborates, and peroxides in general, to modulate the activities of inflammatory mediators and vitamins, antioxidant or otherwise, are also explored.
Quantum Contextuality and Indeterminacy
Authors: Jaeger Gregg
Published: August 2020

The circumstances of measurement have more direct significance in quantum than in classical physics, where they can be neglected for well-performed measurements. In quantum mechanics, the dispositions of the measuring apparatus-plus-environment of the system measured for a property are a non-trivial part of its formalization as the quantum observable. A straightforward formalization of context, via equivalence classes of measurements corresponding to sets of sharp target observables, was recently given for sharp quantum observables. Here, we show that quantum contextuality, the dependence of measurement outcomes on circumstances external to the measured quantum system, can be manifested not only as the strict exclusivity of different measurements of sharp observables or valuations but via quantitative differences in the property statistics across simultaneous measurements of generalized quantum observables, by formalizing quantum context via coexistent generalized observables rather than only its subset of compatible sharp observables. Here, the question of whether such quantum contextuality follows from basic quantum principles is then addressed, and it is shown that the Principle of Indeterminacy is sufficient for at least one form of non-trivial contextuality. Contextuality is thus seen to be a natural feature of quantum mechanics rather than something arising only from the consideration of impossible measurements, abstract philosophical issues, hidden-variables theories, or other alternative, classical models of quantum behavior.
A Simple Model to Relate the Elastic Ratio Gamma of a Critically Self-Organized Spring-Block Model with the Age of a Lithospheric Downgoing Plate in a Subduction Zone
Authors: Perez-Oregon Jennifer , Muñoz-Diosdado Alejandro , Rudolf-Navarro Adolfo Helmut , Angulo-Brown Fernando
Published: August 2020

In 1980, Ruff and Kanamori (RK) published an article on seismicity and the subduction zones where they reported that the largest characteristic earthquake (Mw) of a subduction zone is correlated with two geophysical quantities: the rate of convergence between the oceanic and continental plates (V) and the age of the corresponding subducting oceanic lithosphere (T). This proposal was synthetized by using an empirical graph (RK-diagram) that includes the variables Mw, V and T. We have recently published an article that reports that there are some common characteristics between real seismicity, sandpaper experiments and a critically self-organized spring-block model. In that paper, among several results we qualitatively recovered a RK-diagram type constructed with equivalent synthetic quantities corresponding to Mw, V and T. In the present paper, we improve that synthetic RK-diagram by means of a simple model relating the elastic ratio γ of a critically self-organized spring-block model with the age of a lithospheric downgoing plate. In addition, we extend the RK-diagram by including some large subduction earthquakes occurred after 1980. Similar behavior to the former RK-diagram is observed and its SOC synthetic counterpart is obtained.
Index Coded Automatic Repeat Request (ARQ)
Authors: Choi Sang Won
Published: August 2020

In this paper, an index-coded Automatic Repeat Request (ARQ) is studied in the perspectives of transmission efficiency and memory overhead. Motivated by reducing significant computational complexity from huge matrix inverse computation of random linear network coding, a near-to-optimal broadcasting scheme, called index-coded Automatic Repeat Request (ARQ) is proposed. The main idea is to consider the principal packet error pattern across all receivers. With the help of coded side information formed by successfully decoded packets associated with the dominant packet error pattern, it is shown that two contradictory performance metrics such as transmission efficiency and transmit (receive) cache memory size for index coding (decoding) can be enhanced with a reasonable trade-off. Specifically, the transmission efficiency of the proposed scheme is proved to be asymptotically optimal, and memory overhead is shown to be asymptotically close to the conventional ARQ scheme. Numerical results also validate the proposed scheme in the sense of memory overhead and transmission efficiency in comparison with the conventional ARQ scheme and the optimal scheme using random linear network coding.
Deep Bi-LSTM Networks for Sequential Recommendation
Authors: Zhao Chuanchuan , You Jinguo , Wen Xinxian , Li Xiaowu
Published: August 2020

Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20).
“Making” Waves: How Young Learners Connect to Their Natural World through Third Space
Authors: Burke Anne , Crocker Abigail
Published: August 2020

In a world that grows increasingly aware of ecological problems such as global warming, rising sea levels, and pollution, we need to reconsider how we connect ourselves to the natural world around us. In this paper, we view makerspaces as ideal locations to shape children’s emotional, sociocultural, and educational consciousnesses about the environment and our multi-layered roles undertaken to live in, and conserve, it. We apply third space, makerspace, and relational value theories in the analysis of a research project conducted with children at an early childhood centre. This project invited children to discuss ocean conservation prompted by the picturebook Flotsam (2006) and create three-dimensional exhibits that express how they visualize ocean conservation. Our research shows that children develop strong emotional connections to tangible representations of conservation when they are given the time to invest in making them, and that these emotional connections are driving forces for relational values that create conservation-oriented mindsets. It also shows how important context is for shaping the ways children learn, and that providing opportunities to examine conservation through makerspaces as a third space encourages children to create empathetic and personal relationships with the natural world.
RETRACTED: Achievement of Gradual Conductance Characteristics Based on Interfacial Phase-Change Memory for Artificial Synapse Applications
Authors: Kang Shinyoung , Lee Juyoung , Kang Myounggon , Song Yunheub
Published: August 2020

In this paper, gradual and symmetrical long-term potentiation (LTP) and long-term depression (LTD) were achieved by applying the optimal electrical pulse condition of the interfacial phase-change memory (iPCM) based on a superlattice (SL) structure fabricated by stacking GeTe/Sb2Te3 alternately to implement an artificial synapse in neuromorphic computing. Furthermore, conventional phase-change random access memory (PCRAM) based on a Ge–Sb–Te (GST) alloy with an identical bottom electrode contact size was fabricated to compare the electrical characteristics. The results showed a reduction in the reset energy consumption of the GeTe/Sb2Te3 (GT/ST) iPCM by more than 69% of the GST alloy for each bottom electrode contact size. Additionally, the GT/ST iPCM achieved gradual conductance tuning and 90.6% symmetry between LTP and LTD with a relatively unsophisticated pulse scheme. Based on the above results, GT/ST iPCM is anticipated to be exploitable as a synaptic device used for brain-inspired computing and to be utilized for next-generation non-volatile memory.
Effective Direct Power Control for a Sensor-Less Doubly Fed Induction Generator with a Losses Minimization Criterion
Authors: A. Mossa Mahmoud , Echeikh Hamdi , Diab Ahmed A. Zaki , Quynh Nguyen Vu
Published: August 2020

The present paper is concerned with introducing an effective direct power control (DPC) approach for a sensor-less doubly fed induction generator (DFIG). The derivation of the proposed DPC approach is performed in a systematic manner in which the design of the rotor current controllers is well analyzed, which clarifies the real base of the control system as when and why it works properly. The operation of the proposed DPC approach is based on the stator voltage-oriented control principle in which the stator voltage is aligned with the quadrature axis of the rotating reference frame. To obtain maximum generation efficiency, the reactive power reference value is derived based on a loss minimization criterion (LMC) that is described and analyzed in detail. To enhance the robustness of the control system, an effective rotor position estimator is proposed that is robust against the system uncertainties, such as the parameters’ variation. To validate the effectiveness of the proposed sensor-less DPC approach, the DFIG dynamic performance is tested for a wide range of operating speeds. The obtained results confirm and emphasize the feasibility of the proposed control approach and its LMC methodology in improving the generation efficiency and in obtaining high dynamic performance from the DFIG.
Using Augmented Reality and Internet of Things for Control and Monitoring of Mechatronic Devices
Authors: Stark Erich , Kučera Erik , Haffner Oto , Drahoš Peter , Leskovský Roman
Published: August 2020

At present, computer networks are no longer used to connect just personal computers. Smaller devices can connect to them even at the level of individual sensors and actuators. This trend is due to the development of modern microcontrollers and singleboard computers which can be easily connected to the global Internet. The result is a new paradigm—the Internet of Things (IoT) as an integral part of the Industry 4.0; without it, the vision of the fourth industrial revolution would not be possible. In the field of digital factories it is a natural successor of the machine-to-machine (M2M) communication. Presently, mechatronic systems in IoT networks are controlled and monitored via industrial HMI (human-machine interface) panels, console, web or mobile applications. Using these conventional control and monitoring methods of mechatronic systems within IoT networks, this method may be fully satisfactory for smaller rooms. Since the list of devices fits on one screen, we can monitor the status and control these devices almost immediately. However, in the case of several rooms or buildings, which is the case of digital factories, ordinary ways of interacting with mechatronic systems become cumbersome. In such case, there is the possibility to apply advanced digital technologies such as extended (computer-generated) reality. Using these technologies, digital (computer-generated) objects can be inserted into the real world. The aim of this article is to describe design and implementation of a new method for control and monitoring of mechatronic systems connected to the IoT network using a selected segment of extended reality to create an innovative form of HMI.
Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor
Authors: Alanis Alma Y. , Rios Jorge D. , Gomez-Avila Javier , Zuniga Pavel , Jurado Francisco
Published: August 2020

This work introduces a neural-feedback control scheme for discrete-time quantized nonlinear systems with time delay. Traditionally, a feedback controller is designed under ideal assumptions that are unrealistic for real-work problems. Among these assumptions, they consider a perfect communication channel for controller inputs and outputs; such a perfect channel does not consider delays, or noise introduced by the sensors and actuators even if such undesired phenomena are well-known sources of bad performance in the systems. Moreover, traditional controllers are also designed based on an ideal plant model without considering uncertainties, disturbances, sensors, actuators, and other unmodeled dynamics, which for real-life applications are effects that are constantly present and should be considered. Furthermore, control system design implemented with digital processors implies sampling and holding processes that can affect the performance; considering and compensating quantization effects of measured signals is a problem that has attracted the attention of control system researchers. In this paper, a neural controller is proposed to overcome the problems mentioned above. This controller is designed based on a neural model using an inverse optimal approach. The neural model is obtained from available measurements of the state variables and system outputs; therefore, uncertainties, disturbances, and unmodeled dynamics can be implicitly considered from the available measurements. This paper shows the performance and effectiveness of the proposed controller presenting real-time results obtained on a linear induction motor prototype. Also, this work includes stability proof for the whole scheme using the Lyapunov approach.