bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Characterization, Intra-Species Diversity and Abundance of Freshwater Plesiomonas shigelloides Isolates
Authors: Ekundayo Temitope , Okoh Anthony
Published: July 2020

Molecular signatures of Plesiomonas shigelloides strain specific to pathogenic and nonpathogenic variants are not well established till present. There is a need for intra-species barcoding of P. shigelloides to aid infection control. This study aims at characterizing and assessing intra-species diversity and abundance of P. shigelloides isolated from three freshwaters in the Eastern Cape Province. The study used a Plesiomonas-specific PCR to characterize the isolates. Intra-species (dis)similarities were assessed using ERIC-PCR and (GTG)5-PCR techniques. The DNA fingerprints produced were electrophoresed, digitized, and documented via computer-assisted pattern analysis. The fingerprints were analyzed using neighbor-joining clustering (NJC) based on Euclidean similarity index. Results revealed 80%, 83.64%, and 80% of the water samples from Tyhume, Kat, and Kubusie rivers, respectively, positive for P. shigelloides isolation. The prevalence of P. shigelloides from sites ranged from 13.5% to 88.9%. NJC delineated 48 isolates to 8 clades (ERIC-fingerprints) and 34 isolates into 7 clades ((GTG)5-fingerprints). The relative abundance of unique strains ranged from 6.3% to 22.9% via the two methods. Both fingerprinting approaches have strain-differentiating potential for P. shigelloides, however ERIC-PCR possessed higher resolution (D = 37.46) advantage over (GTG)5-PCR (D = 29.64). In conclusion, the study achieved intra-species diversity and abundance of P. shigelloides from aquatic milieu and provide further opportunity for intra-species-specific barcoding.
Analysis of Cytoplasmic and Secreted Proteins of Staphylococcus aureus Revealed Adaptive Metabolic Homeostasis in Response to Changes in the Environmental Conditions Representative of the Human Wound Site
Authors: M. Alreshidi Mousa , Dunstan R. Hugh , M. Macdonald Margaret , K. Singh Vineet , K. Roberts Tim
Published: July 2020

The pathogenesis of Staphylococcus aureus is mainly attributed to its capability to adjust to changes in environmental conditions, including those present on human skin or within a wound site. This study investigated the changes in the cytoplasmic and secreted proteins in S. aureus that occurred in response to alterations in the environmental parameters that could be found in the human wound site. In total, sixty differentially regulated cytoplasmic proteins were detected using a label-free quantification approach, and these proteins were classified into ten molecular functions: protein biosynthesis, glycolysis, signal transduction, metabolism, cell cycle, transport, energy generation, cell anchorage, nucleotide biosynthesis and unknown. These changes represented characteristic protein profiles when evaluated by principal component analysis. The bacterium responded to elevated NaCl at pH 6 by decreasing the abundance of the majority of cytoplasmic proteins, while at pH 8 there was an increase in the levels of cytoplasmic proteins in comparison to the untreated cells. The analysis of the secreted proteins showed that there was a high degree of difference in both the intensity and the distribution of many individual protein bands in response to environmental challenges. From these results, it was deduced that specific metabolic homeostasis occurred under each combination of defined environmental conditions.
Alkalinity, and Not the Oxidation State of the Organic Substrate, Is the Key Factor in Domestic Wastewater Treatment by Mixed Cultures of Purple Phototrophic Bacteria
Authors: Nairn Carol , Rodríguez Iván , Segura Yolanda , Molina Raúl , González-Benítez Natalia , Molina Mari Carmen , Simarro Raquel , Melero Juan Antonio , Martínez Fernando , Puyol Daniel
Published: July 2020

Domestic wastewater treatment by purple phototrophic bacteria (PPB) is based on the assimilative uptake of organics and nutrients into the bacterial biomass. Thereby, it strongly depends on the carbon/nutrients ratio of the wastewater. The physiological COD/N/P ratio for PPB growth in domestic wastewater makes the addition of an external organic carbon source necessary in order to allow for an efficient process. However, PPB need a source of alkalinity (as CO2) to grow on reduced organics that serves as an electron acceptor since biohydrogen production (an alternative electron sink) is inhibited by ammonium. A preliminary experiment showed that high nutrients-loading wastewater was limited by CO2 imbalance, leading to poor removal efficiencies. Subsequently, the effect of the oxidation state of the organics added as external organic carbon sources to PPB reactors treating low nutrients-loading domestic wastewater has been analyzed. Three organics were used as additives to PPB development in four consecutive batches: acetate (more oxidized), ethanol and butyrate (more reduced). The PPB population was settled and the general performance under the three situations, in terms of organics, N and P assimilation, and growth kinetics was not significantly different irrespective of the external organic carbon source. The reactors were dominated by PPB, though reduced organics allowed for dominance of Rhodopseudomonas palustris, whereas oxidized organics caused co-dominance of R. palustris and Rhodobacter capsulatus. Thereby, alkalinity (as bicarbonate), and not the oxidation state of the organics, is the key parameter for the efficient treatment of domestic wastewater by PPB.
L-(+)-Lactic Acid from Reed: Comparing Various Resources for the Nutrient Provision of B. coagulans
Authors: Schroedter Linda , Schneider Roland , Remus Lisa , Venus Joachim
Published: July 2020

Biotechnological production of lactic acid (LA) is based on the so-called first generation feedstocks, meaning sugars derived from food and feed crops such as corn, sugarcane and cassava. The aim of this study was to exploit the potential of a second generation resource: Common reed (Phragmites australis) is a powerfully reproducing sweet grass which grows in wetlands and creates vast monocultural populations. This lignocellulose biomass bears the possibility to be refined to value-added products, without competing with agro industrial land. Besides utilizing reed as a renewable and inexpensive substrate, low-cost nutritional supplementation was analyzed for the fermentation of thermophilic Bacillus coagulans. Various nutritional sources such as baker’s and brewer’s yeast, lucerne green juice and tryptone were investigated for the replacement of yeast extract. The structure of the lignocellulosic material was tackled by chemical treatment (1% NaOH) and enzymatic hydrolysis (Cellic® CTec2). B. coagulans DSM ID 14-300 was employed for the homofermentative conversion of the released hexose and pentose sugars to polymerizable L-(+)-LA of over 99.5% optical purity. The addition of autolyzed baker’s yeast led to the best results of fermentation, enabling an LA titer of 28.3 g L−1 and a yield of 91.6%.
The Use of LiDAR-Derived DEM in Flood Applications: A Review
Authors: Muhadi Nur Atirah , Abdullah Ahmad Fikri , Bejo Siti Khairunniza , Mahadi Muhammad Razif , Mijic Ana
Published: July 2020

Flood occurrence is increasing due to escalated urbanization and extreme climate change; hence, various studies on this issue and methods of flood monitoring and mapping are also increasing to reduce the severe impacts of flood disasters. The advancement of current technologies such as light detection and ranging (LiDAR) systems facilitated and improved flood applications. In a LiDAR system, a laser emits light that travels to the ground and reflects off objects like buildings and trees. The reflected light energy returns to the sensor, whereby the time interval is recorded. Since the conventional methods cannot produce high-resolution digital elevation model (DEM) data, which results in low accuracy of flood simulation results, LiDAR data are extensively used as an alternative. This review aims to study the potential and the applications of LiDAR-derived DEM in flood studies. It also provides insight into the operating principles of different LiDAR systems, system components, and advantages and disadvantages of each system. This paper discusses several topics relevant to flood studies from a LiDAR-derived DEM perspective. Furthermore, the challenges and future perspectives regarding DEM LiDAR data for flood mapping and assessment are also reviewed. This study demonstrates that LiDAR-derived data are useful in flood risk management, especially in the future assessment of flood-related problems.
A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data
Authors: Yang Junming , Yao Yunjun , Wei Yongxia , Zhang Yuhu , Jia Kun , Zhang Xiaotong , Shang Ke , Bei Xiangyi , Guo Xiaozheng
Published: July 2020

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.
Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review
Authors: El Mahrad Badr , Newton Alice , Icely John , Kacimi Ilias , Abalansa Samuel , Snoussi Maria
Published: July 2020

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.
Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability
Authors: Uezato Tatsumi , Fauvel Mathieu , Dobigeon Nicolas
Published: July 2020

Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.
Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification
Authors: Yang Ming-Der , Huang Kai-Hsiang , Tsai Hui-Ping
Published: July 2020

The critical issue facing hyperspectral image (HSI) classification is the imbalance between dimensionality and the number of available training samples. This study attempted to solve the issue by proposing an integrating method using minimum noise fractions (MNF) and Hilbert–Huang transform (HHT) transformations into artificial neural networks (ANNs) for HSI classification tasks. MNF and HHT function as a feature extractor and image decomposer, respectively, to minimize influences of noises and dimensionality and to maximize training sample efficiency. Experimental results using two benchmark datasets, Indian Pine (IP) and Pavia University (PaviaU) hyperspectral images, are presented. With the intention of optimizing the number of essential neurons and training samples in the ANN, 1 to 1000 neurons and four proportions of training sample were tested, and the associated classification accuracies were evaluated. For the IP dataset, the results showed a remarkable classification accuracy of 99.81% with a 30% training sample from the MNF1–14+HHT-transformed image set using 500 neurons. Additionally, a high accuracy of 97.62% using only a 5% training sample was achieved for the MNF1–14+HHT-transformed images. For the PaviaU dataset, the highest classification accuracy was 98.70% with a 30% training sample from the MNF1–14+HHT-transformed image using 800 neurons. In general, the accuracy increased as the neurons increased, and as the training samples increased. However, the accuracy improvement curve became relatively flat when more than 200 neurons were used, which revealed that using more discriminative information from transformed images can reduce the number of neurons needed to adequately describe the data as well as reducing the complexity of the ANN model. Overall, the proposed method opens new avenues in the use of MNF and HHT transformations for HSI classification with outstanding accuracy performance using an ANN.
IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.
Authors: Ketchum David , Jencso Kelsey , Maneta Marco P. , Melton Forrest , Jones Matthew O. , Huntington Justin
Published: July 2020

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California.
Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds
Authors: López-Granados Francisca , Torres-Sánchez Jorge , Jiménez-Brenes Francisco M. , Oneka Oihane , Marín Diana , Loidi Maite , de Castro Ana I. , Santesteban L. G.
Published: July 2020

Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use of UAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: (1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and (2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold.
A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
Authors: Zhang Zhen , He Changxin , Yang Kuo
Published: July 2020

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.
Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization
Authors: Liu Ning , Zhao Ruomei , Qiao Lang , Zhang Yao , Li Minzan , Sun Hong , Xing Zizheng , Wang Xinbing
Published: July 2020

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.
Aspergillus Species Discrimination Using a Gas Sensor Array
Authors: Capuano Rosamaria , Paba Emilia , Mansi Antonella , Marcelloni Anna Maria , Chiominto Alessandra , Proietto Anna Rita , Zampetti Emiliano , Macagnano Antonella , Lvova Larisa , Catini Alexandro , Paolesse Roberto , Tranfo Giovanna , Di Natale Corrado
Published: July 2020

The efficiency of electronic noses in detecting and identifying microorganisms has been proven by several studies. Since volatile compounds change with the growth of colonies, the identification of strains is highly dependent on the growing conditions. In this paper, the effects of growth were investigated with different species of Aspergillus, which is one of the most studied microorganisms because of its implications in environmental and food safety. For this purpose, we used an electronic nose previously utilized for volatilome detection applications and based on eight porphyrins-functionalized quartz microbalances. The volatile organic compounds (VOCs) released by cultured fungi were measured at 3, 5, and 10 days after the incubation. The signals from the sensors showed that the pattern of VOCs evolve with time. In particular, the separation between the three studied strains progressively decreases with time. The three strains could still be identified despite the influence of culture time. Linear Discriminant Analysis (LDA) showed an overall accuracy of 88% and 71% in the training and test sets, respectively. These results indicate that the presence of microorganisms is detectable with respect to background, however, the difference between the strains changes with the incubation time.
Designing a Cyber-Physical System for Ambient Assisted Living: A Use-Case Analysis for Social Robot Navigation in Caregiving Centers
Authors: Calderita Luis V. , Vega Araceli , Barroso-Ramírez Sergio , Bustos Pablo , Núñez Pedro
Published: July 2020

The advances of the Internet of Things, robotics, and Artificial Intelligence, to give just a few examples, allow us to imagine promising results in the development of smart buildings in the near future. In the particular case of elderly care, there are new solutions that integrate systems that monitor variables associated with the health of each user or systems that facilitate physical or cognitive rehabilitation. In all these solutions, it is clear that these new environments, usually called Ambient Assisted Living (AAL), configure a Cyber-Physical System (CPS) that connects information from the physical world to the cyber-world with the primary objective of adding more intelligence to these environments. This article presents a CPS-AAL for caregiving centers, with the main novelty that includes a Socially Assistive Robot (SAR). The CPS-AAL presented in this work uses a digital twin world with the information acquired by all devices. The basis of this digital twin world is the CORTEX cognitive architecture, a set of software agents interacting through a Deep State Representation (DSR) that stored the shared information between them. The proposal is evaluated in a simulated environment with two use cases requiring interaction between the sensors and the SAR in a simulated caregiving center.
Recent Progress in Flexible Wearable Sensors for Vital Sign Monitoring
Authors: Liu Jihong , Liu Meilin , Bai Yu , Zhang Jiahao , Liu Hongwei , Zhu Wenbin
Published: July 2020

With the development of flexible electronic materials, as well as the wide development and application of smartphones, the cloud, and wireless systems, flexible wearable sensor technology has a significant and far-reaching impact on the realization of personalized medical care and the reform of the consumer market in the future. However, due to the high requirements for accuracy, reliability, low power consumption, and less data error, the development of these potential areas is full of challenges. In order to solve these problems, this review mainly searches the literature from 2008 to May 2020, based on the PRISMA process. Based on them, this paper reviews the latest research progress of new flexible materials and different types of sensors for monitoring vital signs (including electrophysiological signals, body temperature, and respiratory frequency) in recent years. These materials and sensors can help realize accurate signal detection based on comfortable and sustainable observation, and may likely be applied to future daily clothing.
Elevation Angle Estimations of Wide-Beam Acoustic Sonar Measurements for Autonomous Underwater Karst Exploration
Authors: Breux Yohan , Lapierre Lionel
Published: July 2020

This paper proposes a solution for merging the measurements from two perpendicular profiling sonars with different beam-widths, in the context of underwater karst (cave) exploration and mapping. This work is a key step towards the development of a full 6D pose SLAM framework adapted to karst aquifer, where potential water turbidity disqualifies vision-based methods, hence relying on acoustic sonar measurements. Those environments have complex geometries which require 3D sensing. Wide-beam sonars are mandatory to cover previously seen surfaces but do not provide 3D measurements as the elevation angles are unknown. The approach proposed in this paper leverages the narrow-beam sonar measurements to estimate local karst surface with Gaussian process regression. The estimated surface is then further exploited to infer scaled-beta distributions of elevation angles from a wide-beam sonar. The pertinence of the method was validated through experiments on simulated environments. As a result, this approach allows one to benefit from the high coverage provided by wide-beam sonars without the drawback of loosing 3D information.
Multi-Beat Averaging Reveals U Waves Are Ubiquitous and Standing Tall at Elevated Heart Rates Following Exercise
Authors: Al-Karadi Marwa S. , Langley Philip
Published: July 2020

The reporting of U wave abnormalities is clinically important, but the measurement of this small electrocardiographic (ECG) feature is extremely difficult, especially in challenging recording conditions, such as stress exercise, due to contaminating noise. Furthermore, it is widely stated that ECG U waves are rarely observable at heart rates greater than 90 bpm. The aims of the study were (i) to assess the ability of multi-beat averaging to reveal the presence of U waves in ECGs contaminated by noise following exercise and (ii) to quantify the effect of exercise on U wave amplitude. The multi-beat averaging algorithm was applied to recover U waves in 20 healthy subjects in pre- and post-exercise recordings. Average beats were generated from 30 beat epochs. The prevalence of U waves and their amplitudes were measured in pre- and post-exercise recordings and changes in amplitude due to exercise were quantified. U waves were present in all subjects in pre-exercise recordings. Following exercise, U waves could not be seen in standard ECG but were observable in all 20 subjects by multi-beat averaging and despite significantly increased mean (±SD) heart rate (63 ± 8 bpm vs. 100 ± 9 bpm, p < 0.0001). Furthermore, U waves were observable in all subjects with heart rates greater than 90 bpm. U waves significantly increased in amplitude following exercise (38 ± 15 μV vs. 80 ± 48 μV, p = 0.0005). Multi-beat averaging is effective at recovering U waves contaminated by noise due to exercise. U waves were measurable in all subjects, dispelling the myth that U waves are rarely seen at elevated heart rates. U waves exhibit increased amplitudes at elevated heart rates following exercise.
Organic Molecules: Is It Possible to Distinguish Aromatics from Aliphatics Collected by Space Missions in High-Speed Impacts?
Authors: Burchell Mark , Harriss Kathryn
Published: July 2020

A prime site of astrobiological interest within the Solar System is the interior ocean of Enceladus. This ocean has already been shown to contain organic molecules, and is thought to have the conditions necessary for more complex organic biomolecules to emerge and potentially even life itself. This sub-surface ocean has been accessed by Cassini, an unmanned spacecraft that interacted with the water plumes ejected naturally from Enceladus. The encounter speed with these plumes and their contents, was between 5 and 15 km s−1. Encounters at such speeds allow analysis of vapourised material from submicron-sized particles within the plume, but sampling micron-sized particles remains an open question. The latter particles can impact metal targets exposed on the exterior of future spacecraft, producing impact craters lined with impactor residue, which can then be analysed. Although there is considerable literature on how mineral grains behave in such high-speed impacts, and also on the relationship between the crater residue and the original grain composition, far less is known regarding the behaviour of organic particles. Here we consider a deceptively simple yet fundamental scientific question: for impacts at speeds of around 5−6 kms−1 would the impactor residue alone be sufficient to enable us to recognise the signature conferred by organic particles? Furthermore, would it be possible to identify the organic molecules involved, or at least distinguish between aromatic and aliphatic chemical structures? For polystyrene (aromatic-rich) and polymethylmethacrylate (solely aliphatic) latex particles impinging at around 5 km s−1 onto metal targets, we find that sufficient residue is retained at the impact site to permit identification of a carbon-rich projectile, but not of the particular molecules involved, nor is it currently possible to discriminate between aromatic-rich and solely aliphatic particles. This suggests that an alternative analytical method to simple impacts on metal targets is required to enable successful collection of organic samples in a fly-by Enceladus mission, or, alternatively, a lower encounter speed is required.
Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds
Authors: Imbach Frank , Candau Robin , Chailan Romain , Perrey Stephane
Published: July 2020

This study assessed the Stryd running power meter validity at sub-maximal speeds (8 to 19 km/h). Six recreational runners performed an incremental indoor running test. Power output (PO), ground contact time (GCT) and leg spring stiffness (LSS) were compared to reference measures recorded by portable metabolic analyser, force platforms and motion capture system. A Bayesian framework was conducted for systems validity and comparisons. We observed strong and positive linear relationships between Stryd PO and oxygen consumption ( R 2 = 0.82 , B F 10 > 100 ), and between Stryd PO and external mechanical power ( R 2 = 0.88 , B F 10 > 100 ). Stryd power meter underestimated PO ( B F 10 > 100 ) whereas GCT and LSS values did not show any significant differences with the reference measures ( B F 10 = 0.008 , B F 10 = 0.007 , respectively). We conclude that the Stryd power meter provides valid measures of GCT and LSS but underestimates the absolute values of PO.
The Multi-State Maritime Transportation System Risk Assessment and Safety Analysis
Authors: Wang Siqi , Yin Jingbo , Khan Rafi Ullah
Published: July 2020

Maritime transportation has a pivotal role in the foreign trade and hence, the world’s economic growth. It augments the realization of “Maritime Silk Road” strategy. However, the catastrophic nature of the maritime accidents has posed a serious threat to life, property, and environment. Maritime transportation safety is a complex system and is prone to human, equipment, and environment-based risks. In the existing literature, the risk assessment studies aimed at the analysis of maritime traffic safety usually consider the state of system as two ultimate states—one is the normal state and the other is the complete failure state. In contrast to the conventional approaches, this study incorporates a multistate criterion for system state giving consideration to the near or partial failures also. A Markov Chain-based methodology was adopted to determine the variations in state system and define the instant at which a low probability incident transforms into a high-risk intolerable event. The analysis imparts critical time nodes that could be utilized to reduce the risk and evade accidents. This study holds practical vitality for the concerned departments to circumvent the potential dangers and devise systematic preemptive procedures before the accident takes place. The results of this study could be employed to augment safety and sustainability of maritime traffic and decrease the associated pollution.
Sustainability and Energy Efficiency: BIM 6D. Study of the BIM Methodology Applied to Hospital Buildings. Value of Interior Lighting and Daylight in Energy Simulation
Authors: Montiel-Santiago Francisco Javier , Hermoso-Orzáez Manuel Jesús , Terrados-Cepeda Julio
Published: July 2020

One of the least used aspects of BIM (Building Information Modeling) is the ability to obtain the energy model of the building using the BIM methodology known as BIM 6D. This digital information model allows simulating the real energy behavior of the building and the improvement in the building’s lighting systems, both natural and artificial, in particular daylighting. In this way, the BIM 6D simulation allows us to make design and operation decisions for the building, not only for new buildings that must be, in accordance with current legislation, NZEB (Nearly Zero-Energy Building) but also for the rehabilitation of existing buildings. Particularly in buildings for sanitary use, BIM 6D allows an exhaustive analysis of the energy impact of said rehabilitation, guiding it towards an improvement in energy and light efficiency, which in turn provides greater quality and comfort in the use of the sustainable building. This subject of study is especially important in public buildings for hospital use. Buildings where energy efficiency and comfort, oriented towards optimal and efficient lighting, are two fundamental criteria highly appreciated by patients and citizens in general. Once the energy model of the building has been obtained, it is possible to study and identify possible alternatives to improve energy efficiency and improve lighting, as well as to analyze the possibilities of incorporating other more efficient forms of renewable energy, such as the use of daylight. In this work we can see how applying a set of simulated improvement actions in BIM 6D achieves an energy saving of 50% in general and up to 13% only by acting on lighting systems, allowing the decarbonization of buildings with high energy consumption, such as hospitals, and in turn, will lead to an improvement in the energy certification of these buildings; thus achieving a better and higher quality of habitability, using more efficient forms of lighting and transforming buildings into more sustainable spaces.
Learning from Incidents: A Supply Chain Management Perspective in Military Environments
Authors: Cantelmi Raffaele , Di Gravio Giulio , Patriarca Riccardo
Published: July 2020

Supply chain management (SCM) represents a crucial role in the military sector to ensure operation sustainability. Starting from the NATO handbook for military organizational learning, this paper aims at investigating the link between technical inconveniences and sustainable supply chain operations. Taking advantage of the learning from incidents (LFI) models traditionally used in the risk and safety management area, this paper proposes an information management system to support organizational learning from technical inconveniences in a military supply chain. The approach is discussed with reference to the Italian context, in line with international and national standards for technical inconvenience reporting. The results of the paper show the benefits of adopting a systematic LFI system for technical inconveniences, providing related exemplar business intelligence dashboards. Further implications for the generalization of the proposed information management system are presented to foster a healthy and effective reporting environment in military scenarios.
STEM Teaching for the Internet of Things Maker Course: A Teaching Model Based on the Iterative Loop
Authors: Chen Rongjun , Zheng Yani , Xu Xiansheng , Zhao Huimin , Ren Jinchang , Tan Hong-Zhou
Published: July 2020

As the key technology for 5G applications in the future, the Internet of Things (IoT) is developing rapidly, and the demand for the cultivation of engineering talents in the IoT is also expanding. The rise of maker education has brought new teaching inspiration for cultivating innovative technical talents in the IoT. In the IoT maker course, teaching problems include the lack of adequate teaching models, emphasis on products but less emphasis on theory, and letting students imitate practice. Focusing on these problems, this paper proposes a new Science, Technology, Engineering, and Mathematics (STEM) teaching model called Propose, Guide, Design, Comment, Implement, Display and Evaluate (PGDCIDE) for the IoT maker course. The PGDCIDE teaching model is based on STEM teaching and Kolodner’s design-based scientific inquiry learning cycle model, and realizes the combination of “theory, practice, and innovation.” Finally, this paper designs the IoT maker course to practice the PGDCIDE model. The practical results indicate that students significantly improved their emotional level, knowledge level, and innovation level after studying the course. Therefore, the PGDCIDE teaching model proposed in this paper can improve the effectiveness of the IoT maker course teaching and is conducive to the cultivation of students’ sustainable ability in engineering education. It has reference significance for the application of maker courses in engineering education practice.
Construction and Demolition Waste (CDW) Recycling—As Both Binder and Aggregates—In Alkali-Activated Materials: A Novel Re-Use Concept
Authors: Robayo-Salazar Rafael A. , Valencia-Saavedra William , Mejía de Gutiérrez Ruby
Published: July 2020

This article demonstrates the possibility of producing alkali-activated materials (AAM) from a mixture of mechanically processed concrete, ceramic, masonry, and mortar wastes, as a sustainable alternative for recycling construction and demolition wastes (CDWs) under real conditions. The addition of 10% Portland cement allowed the materials to cure at room temperature (25 °C). CDW binder achieved a compressive strength of up to 43.9 MPa and it was classified as a general use and low heat of hydration cement according to ASTM C1157. The concrete produced with this cement and the crushed aggregates also from CDW reported a compressive strength of 33.9 MPa at 28 days of curing and it was possible to produce a high-class structural block with 26.1 MPa according to ASTM C90. These results are considered one option in making full use of CDWs as binder and aggregates, using alkaline activation technology thereby meeting the zero-waste objective within the concept of the circular economy.
Influence of Community Design and Sociodemographic Characteristics on Teleworking
Authors: Bhuiyan Mohammad Abu Afrahim , Rifaat Shakil Mohammad , Tay Richard , De Barros Alex
Published: July 2020

The traffic on the roads in many countries has been increasing in recent decades, and the increases in congestion and carbon emission are contributing significantly to climate change. To minimize these adverse effects, the use of more sustainable travel modes, such as public transit, walking, bicycling, carpool and ridesharing, has been widely promoted. Apart from these travel modes, alternatives, such as teleworking, which reduces commute trips, should also be promoted. The objective of this study is to identify different neighborhood design and social characteristics that are associated with teleworking. In this case study, a multiple regression model is applied to 2011 census data and road infrastructure data of 185 communities from the city of Calgary in Canada. In addition, a random intercept model is estimated to account for unobserved heterogeneity. We find that different street patterns, geographical size, land use, mass rapid transit, and road types have a significant effect on teleworking or working-at-home and should be considered when designing new communities. We also find several significant sociodemographic characteristics, including family size, marital status, children, housing type and language. Policy implications based on this research are then provided.
Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation
Authors: Angalaeswari S. , Sanjeevikumar P. , Jamuna K. , Leonowicz Zbigniew
Published: July 2020

This paper proposes the hybrid sequential quadratic programming (SQP) technique based on active set method for identifying the optimal placement and rating of distribution generation (DG) incorporated in radial distribution systems (RDS) for minimizing the real power loss satisfying power balance equations and voltage limits. SQP runs quadratic programming sequentially as a sub-program to obtain the best solution by using an active set method. In this paper, the best optimal solution is selected with less computation time by combining the benefits of both classical and meta-heuristic methods. SQP is a classical method that is more sensitive to initial value selection and the evolutionary methods give approximate solution. Hence, the initial values for the SQP technique were obtained from the meta–heuristic method of Parameter Improved Particle Swarm Optimization (PIPSO) algorithm. The proposed hybrid PIPSO–SQP method was implemented in IEEE 33-bus RDS, IEEE 69-bus RDS, and IEEE 118-bus RDS under different loading conditions. The results show that the proposed method has efficient reduction in real power loss minimization through the enhancement of the bus voltage profile.
Expansive Learning Process of Exercise Organizers: The Case of Major Fire Incident Exercises in Underground Mines
Authors: Karlsson Sofia , Saveman Britt-Inger , Hultin Magnus , Eklund Annika , Gyllencreutz Lina
Published: July 2020

A major fire incident in a Swedish underground mine made the personnel from the mining company and the rescue service realize their limited preparedness. It was the beginning of a collaboration project that included the development of a new exercise model for a more effective joint rescue operation practice. The aim of this study was to explore the collaborative learning process of exercise organizers from the rescue service, mining companies, the emergency medical service, a training company, and academia. The analysis was performed through the application of the theory cycle of expansive learning to the material consisting of documents from 16 collaboration meetings and 11 full-scale exercises. The learning process started by the participants questioning the present practice of the rescue operation and analyzing it by creating a flow chart. An essential part of the process was to model new tools in order to increase the potential for collaboration. The tools were examined and tested during collaboration meetings and implemented during full-scale exercises. The exercise organizers reflected that the process led to organizational development and a better understanding of the other organizations’ perspectives. Consequently, a tentative model for developing the learning process of exercise organizers was developed.