bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Mesoscale Dynamics over Northwestern Cuba in the Loop Current Evolution in 2010, during the Deepwater Horizon Incident
Authors: Androulidakis Yannis , Kourafalou Vassiliki , Le Hénaff Matthieu , Kang HeeSook , Ntaganou Nektaria
Published: February 2021

The Loop Current (LC) system controls the connectivity between the northern Gulf of Mexico (GoM) region and the Straits of Florida. The evolution of the LC and the shedding sequence of the LC anticyclonic ring (Eddy Franklin) were crucial for the fate of the hydrocarbons released during the Deepwater Horizon (DwH) oil spill in 2010. In a previous study, we identified LC-related anticyclonic eddies in the southern GoM, named “Cuba anticyclones” (“CubANs”). Here, we investigate the relation between these eddies and LC evolution in 2010, focusing on the DwH period. We use high-resolution model results in tandem with observational data to describe the connection between the LC system evolution within the GoM (LC extensions, Eddy Franklin and LC Frontal Eddies—LCFEs) and the mesoscale dynamics within the Straits of Florida where CubANs propagate. Five periods of CubAN eddy activity were identified during the oil spill period, featuring different formation processes under a combination of local and regional conditions. Most of these cases are related to the retracted LC phases, when the major LC anticyclone (Eddy Franklin in 2010) is detached from the main body and CubAN eddy activity is most likely. However, two cases of CubAN eddy presence during elongated LC were detected, which led to the attenuation of the eastward flows of warm waters through the Straits (Florida Current; outflow), allowing the stronger supply of Caribbean waters through the Yucatan Channel into the Gulf (inflow), which contributed to short-term LC northward extensions. Oceanographic (LCFEs) and meteorological (wind-induced upwelling) conditions contributed to the release of CubANs from the main LC body, which, in tandem with other processes, contributed to the LC evolution during the DwH oil spill incident.
Review of T-2307, an Investigational Agent That Causes Collapse of Fungal Mitochondrial Membrane Potential
Authors: Wiederhold Nathan P.
Published: February 2021

Invasive infections caused by Candida that are resistant to clinically available antifungals are of increasing concern. Increasing rates of fluconazole resistance in non-albicans Candida species have been documented in multiple countries on several continents. This situation has been further exacerbated over the last several years by Candida auris, as isolates of this emerging pathogen that are often resistant to multiple antifungals. T-2307 is an aromatic diamidine currently in development for the treatment of invasive fungal infections. This agent has been shown to selectively cause the collapse of the mitochondrial membrane potential in yeasts when compared to mammalian cells. In vitro activity has been demonstrated against Candida species, including C. albicans, C. glabrata, and C. auris strains, which are resistant to azole and echinocandin antifungals. Activity has also been reported against Cryptococcus species, and this has translated into in vivo efficacy in experimental models of invasive candidiasis and cryptococcosis. However, little is known regarding the clinical efficacy and safety of this agent, as published data from studies involving humans are not currently available.
On the Reciprocal Sums of Products of Balancing and Lucas-Balancing Numbers
Authors: Choo Younseok
Published: February 2021

Recently Panda et al. obtained some identities for the reciprocal sums of balancing and Lucas-balancing numbers. In this paper, we derive general identities related to reciprocal sums of products of two balancing numbers, products of two Lucas-balancing numbers and products of balancing and Lucas-balancing numbers. The method of this paper can also be applied to even-indexed and odd-indexed Fibonacci, Lucas, Pell and Pell–Lucas numbers.
Comparative Metabologenomics Analysis of Polar Actinomycetes
Authors: Soldatou Sylvia , Eldjárn Grímur Hjörleifsson , Ramsay Andrew , van der Hooft Justin J. J. , Hughes Alison H. , Rogers Simon , Duncan Katherine R.
Published: February 2021

Biosynthetic and chemical datasets are the two major pillars for microbial drug discovery in the omics era. Despite the advancement of analysis tools and platforms for multi-strain metabolomics and genomics, linking these information sources remains a considerable bottleneck in strain prioritisation and natural product discovery. In this study, molecular networking of the 100 metabolite extracts derived from applying the OSMAC approach to 25 Polar bacterial strains, showed growth media specificity and potential chemical novelty was suggested. Moreover, the metabolite extracts were screened for antibacterial activity and promising selective bioactivity against drug-persistent pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii was observed. Genome sequencing data were combined with metabolomics experiments in the recently developed computational approach, NPLinker, which was used to link BGC and molecular features to prioritise strains for further investigation based on biosynthetic and chemical information. Herein, we putatively identified the known metabolites ectoine and chrloramphenicol which, through NPLinker, were linked to their associated BGCs. The metabologenomics approach followed in this study can potentially be applied to any large microbial datasets for accelerating the discovery of new (bioactive) specialised metabolites.
Chitin Degradation Machinery and Secondary Metabolite Profiles in the Marine Bacterium Pseudoalteromonas rubra S4059
Authors: Wang Xiyan , Isbrandt Thomas , Strube Mikael Lenz , Paulsen Sara Skøtt , Nielsen Maike Wennekers , Buijs Yannick , Schoof Erwin M. , Larsen Thomas Ostenfeld , Gram Lone , Zhang Sheng-Da
Published: February 2021

Genome mining of pigmented Pseudoalteromonas has revealed a large potential for the production of bioactive compounds and hydrolytic enzymes. The purpose of the present study was to explore this bioactivity potential in a potent antibiotic and enzyme producer, Pseudoalteromonas rubra strain S4059. Proteomic analyses (data are available via ProteomeXchange with identifier PXD023249) indicated that a highly efficient chitin degradation machinery was present in the red-pigmented P. rubra S4059 when grown on chitin. Four GH18 chitinases and two GH20 hexosaminidases were significantly upregulated under these conditions. GH19 chitinases, which are not common in bacteria, are consistently found in pigmented Pseudoalteromonas, and in S4059, GH19 was only detected when the bacterium was grown on chitin. To explore the possible role of GH19 in pigmented Pseudoalteromonas, we developed a protocol for genetic manipulation of S4059 and deleted the GH19 chitinase, and compared phenotypes of the mutant and wild type. However, none of the chitin degrading ability, secondary metabolite profile, or biofilm-forming capacity was affected by GH19 deletion. In conclusion, we developed a genetic manipulation protocol that can be used to unravel the bioactive potential of pigmented pseudoalteromonads. An efficient chitinolytic enzyme cocktail was identified in S4059, suggesting that this strain could be a candidate with industrial potential.
Bio-Guided Isolation of Antimalarial Metabolites from the Coculture of Two Red Sea Sponge-Derived Actinokineospora and Rhodococcus spp.
Authors: Alhadrami Hani A. , Thissera Bathini , Hassan Marwa H. A. , Behery Fathy A. , Ngwa Che Julius , Hassan Hossam M. , Pradel Gabriele , Abdelmohsen Usama Ramadan , Rateb Mostafa E.
Published: February 2021

Coculture is a productive technique to trigger microbes’ biosynthetic capacity by mimicking the natural habitats’ features principally by competition for food and space and interspecies cross-talks. Mixed cultivation of two Red Sea-derived actinobacteria, Actinokineospora spheciospongiae strain EG49 and Rhodococcus sp. UR59, resulted in the induction of several non-traced metabolites in their axenic cultures, which were detected using LC–HRMS metabolomics analysis. Antimalarial guided isolation of the cocultured fermentation led to the isolation of the angucyclines actinosporins E (1), H (2), G (3), tetragulol (5) and the anthraquinone capillasterquinone B (6), which were not reported under axenic conditions. Interestingly, actinosporins were previously induced when the axenic culture of the Actinokineospora spheciospongiae strain EG49 was treated with signalling molecule N-acetyl-d-glucosamine (GluNAc); this finding confirmed the effectiveness of coculture in the discovery of microbial metabolites yet to be discovered in the axenic fermentation with the potential that could be comparable to adding chemical signalling molecules in the fermentation flask. The isolated angucycline and anthraquinone compounds exhibited in vitro antimalarial activity and good biding affinity against lysyl-tRNA synthetase (PfKRS1), highlighting their potential developability as new antimalarial structural motif.
Serological Parameters and Vascular Investigation for a Better Assessment in DVT during Pregnancy—A Systematic Review
Authors: Filip Catalina , Socolov Demetra Gabriela , Albu Elena , Filip Cristiana , Serban Roxana , Popa Radu Florin
Published: February 2021

Pregnancy and the postpartum period represent a condition characterized by a thrombotic predisposition. The majority of pregnant women do not face acute or severe thrombotic events. In general, mild inconveniences such as leg swelling or moderately painful thrombotic events (phlebitis) are encountered. However, when pregnancy is associated with inherited or acquired deficits that affect homeostasis, the risk of acute or even life-threatening events can increase significantly. The major consequence is the loss of the fetus or the venous thromboembolism that endangers the mother’s life. Venous thromboembolism is caused by deep vein thrombosis, therefore timely detection and especially the assessment of the extent of the thrombotic event are crucial. In this paper we have summarized the most important paraclinical investigations. The study emphasizes the importance of selecting the methods of investigation. The right choice allows establishing a correct diagnosis and individualizing the treatment.
Variability of Lipids in Human Milk
Authors: Selvalatchmanan Jayashree , Rukmini A.V. , Ji Shanshan , Triebl Alexander , Gao Liang , Bendt Anne , Wenk Markus , Gooley Joshua , Torta Federico
Published: February 2021

Lipids in breastmilk play a critical role in infant growth and development. However, few studies have investigated sources of variability of both high- and low-abundant milk lipids. The objective of our study was to investigate individual and morning–evening differences in the human milk lipidome. In this study, a modified two-phase method (MTBE: Methanol 7:2) was validated for the extraction of lipids from human breastmilk. This method was then applied to samples from a group of 20 healthy women to measure inter- and intra-individual (morning versus evening) variability of the breastmilk lipidome. We report here the levels of 237 lipid species from 13 sub-classes using reversed-phase liquid chromatography mass spectrometry (RP-LCMS) and direct-infusion mass spectrometry (DI-MS). About 85% of lipid species showed stable inter-individual differences across time points. Half of lipid species showed higher concentrations in the evening compared with the morning, with phosphatidylethanolamines (PEs) and triacylglycerols (TAGs) exhibiting the largest changes. In morning and evening samples, the biological variation was greater for diacylglycerols (DAGs) and TAGs compared with phospholipids and sphingolipids, and the variation in DAGs and TAGs was greater in evening samples compared with morning samples. These results demonstrate that variation in the milk lipidome is strongly influenced by individual differences and time of day.
Alteration of Gut Microbiota in Carbapenem-Resistant Enterobacteriaceae Carriers during Fecal Microbiota Transplantation According to Decolonization Periods
Authors: Lee Jin-Jae , Yong Dongeun , Suk Ki Tae , Kim Dong Joon , Woo Heung-Jeong , Lee Seung Soon , Kim Bong-Soo
Published: February 2021

Fecal microbiota transplantation (FMT) has been suggested as an alternative therapeutic option to decolonize carbapenem-resistant Enterobacteriaceae (CRE). However, the analysis of gut microbiota alteration in CRE carriers during FMT is still limited. Here, gut microbiota changes in CRE carriers were evaluated during FMT according to decolonization periods. The decolonization of 10 CRE carriers was evaluated after FMT, using serial consecutive rectal swab cultures. Alterations of gut microbiota before and after FMT (56 serial samples) were analyzed using high-throughput sequencing. The decolonization rates of CRE carriers were 40%, 50%, and 90% within 1, 3 and 5 months after initial FMT, respectively. Gut microbiota significantly changed after FMT (p = 0.003). Microbiota alteration was different between the early decolonization carriers (EDC) and late decolonization carriers (LDC). Microbiota convergence in carriers to donors was detected in EDC within 4 weeks, and keystone genera within the Bacteroidetes were found in the gut microbiota of EDC before FMT. The relative abundance of Klebsiella was lower in EDC than in LDC, before and after FMT. Our results indicate that FMT is a potential option for CRE decolonization. The gut microbiota of CRE carriers could be used to predict decolonization timing after FMT, and determine repeated FMT necessity.
Inoculation of the Leishmania infantum HSP70-II Null Mutant Induces Long-Term Protection against L. amazonensis Infection in BALB/c Mice
Authors: Soto Manuel , Ramírez Laura , Solana José Carlos , Cook Emma C. L. , Hernández-García Elena , Requena José María , Iborra Salvador
Published: February 2021

Leishmania amazonensis parasites are etiological agents of cutaneous leishmaniasis in the New World. BALB/c mice are highly susceptible to L. amazonensis challenge due to their inability to mount parasite-dependent IFN-γ-mediated responses. Here, we analyzed the capacity of a single administration of the LiΔHSP70-II genetically-modified attenuated L. infantum line in preventing cutaneous leishmaniasis in mice challenged with L. amazonensis virulent parasites. In previous studies, this live attenuated vaccine has demonstrated to induce long-protection against murine leishmaniasis due to Old World Leishmania species. Vaccinated mice showed a reduction in the disease evolution due to L. amazonensis challenge, namely reduction in cutaneous lesions and parasite burdens. In contrast to control animals, after the challenge, protected mice showed anti-Leishmania IgG2a circulating antibodies accompanied to the induction of Leishmania-driven specific IFN-γ systemic response. An analysis performed in the lymph node draining the site of infection revealed an increase of the parasite-specific IFN-ϒ production by CD4+ and CD8+ T cells and a decrease in the secretion of IL-10 against leishmanial antigens. Since the immunity caused by the inoculation of this live vaccine generates protection against different forms of murine leishmaniasis, we postulate LiΔHSP70-II as a candidate for the development of human vaccines.
Thermal Annealing and Phase Transformation of Serpentine-Like Garnierite
Authors: Kumar Arun , Cassetta Michele , Giarola Marco , Zanatta Marco , Le Guen Monique , Soraru Gian Domenico , Mariotto Gino
Published: February 2021

This study is focused on the vibrational and microstructural aspects of the thermally induced transformation of serpentine-like garnierite into quartz, forsterite, and enstatite occurring at about 620 °C. Powder specimens of garnierite were annealed in static air between room temperature and 1000 °C. The kinetic of the transformation was investigated by means of thermogravimetric and differential thermal analysis, and the final product was extensively characterized via micro-Raman spectroscopy and X-ray diffraction. Our study shows that serpentine-like garnierite consists of a mixture of different mineral species. Furthermore, these garnierites and their composition can provide details based on the mineralogy and the crystalline phases resulting from the thermal treatment.
Using Technology-Supported Transfer of Care Systems: Informing Good Practice Recommendations
Authors: James Robert , Mantzourani Efi , Way Cheryl , Gray Alistair , Burnley Melissa , Hodson Karen
Published: February 2021

The Discharge Medicines Review (DMR) referral system, Refer-to-Pharmacy (RTP), PharmOutcomes and Help for Harry are UK transfer of care systems that aim to reduce the risks associated with hospital discharge. These systems use technology to facilitate the transmission of discharge information to community pharmacy, allowing community pharmacists to provide an adherence-support service. Despite the evidence that these systems benefit patient safety, there is a paucity of literature on their use. This study aimed to describe, compare and contrast these systems to highlight areas that could inform good practice recommendations. A rapid literature review was completed, and from the twenty-six sources of literature that were synthesised, three themes were identified for further exploration in semi-structured interviews with key informants: implementation, system attributes and stakeholder engagement. The key informants were purposively sampled for their role in the development and/or strategic implementation of each transfer of care system (n = 4). Audio recordings were transcribed ad verbatim and analysed both deductively and inductively. One interview was undertaken for each of the DMR, RTP and PharmOutcomes systems. Although all systems shared the same aim, differences were identified such as automated feedback for referrals, marketing strategies and practitioner accountability. Good practice recommendations suggested in this study could be applied to the future development of such systems.
Optimal Water Management in Agro-Industrial Districts: An Energy Hub’s Case Study in the Southeast of Spain
Authors: Ramos-Teodoro Jerónimo , Gil Juan D. , Roca Lidia , Rodríguez Francisco , Berenguel Manuel
Published: February 2021

In this work, the optimal management of the water grid belonging to a pilot agro-industrial district, based on greenhouse cultivation, is analyzed. Different water supply plants are considered in the district, some of them using renewable energies as power sources, i.e., a solar thermal desalination plant and a nanofiltration facility powered up by a photovoltaic field. Moreover, the trade with the water public utility network is also taken into account. As demanding agents, a greenhouse and an office building are contemplated. Due to the different water necessities, demand profiles, and the heterogeneous nature of the different plants considered as supplier agents, the management of the whole plant is not trivial. In this way, an algorithm based on the energy hubs approach, which takes into account economic terms and the optimal use of the available resources in its formulation, is proposed for the pilot district with a cropping area of 616 m2. Simulation results are provided in order to evidence the benefits of the proposed technique in two cases: Case 1 considers the flexible operation of the desalination plant, whereas in Case 2 the working conditions are forced to equal the plant’s maximum capacity (Case 2). A flexible operation results in a weekly improvement of 4.68% in profit, an optimized use of the desalination plant, and a reduction of the consumption of water from the public grid by 58.1%.
Separation of Benzene-Cyclohexane Azeotropes Via Extractive Distillation Using Deep Eutectic Solvents as Entrainers
Authors: Bai Fang , Hua Chao , Li Jing
Published: February 2021

The separation of benzene and cyclohexane azeotrope is one of the most challenging processes in the petrochemical industry. In this paper, deep eutectic solvents (DES) were used as solvents for the separation of benzene and cyclohexane. DES1 (1:2 mix of tetrabutylammonium bromide (TBAB) and levulinic acid (LA)), DES2 (1:2 mix of TBAB and ethylene glycol (EG)) and DES3 (1:2 mix of ChCl (choline chloride) and LA) were used as entrainers, and vapor-liquid equilibrium (VLE) measurements at atmospheric pressure revealed that a DES comprised of a 2:1 ratio of LA and TBAB could break this azeotrope with relative volatility (αij) up to 4.763. Correlation index suggested that the NRTL modelling approach fitted the experimental data very well. Mechanism of extractive distillation gained from FT-IR revealed that with hydrogen bonding and π–π bond interactions between levulinic acid and benzene could be responsible for the ability of this entrainer to break the azeotrope.
Exoproteome Perspective on the Bile Stress Response of Lactobacillus johnsonii
Authors: Bagon Bernadette B. , Valeriano Valerie Diane V. , Oh Ju Kyoung , Pajarillo Edward Alain B. , Lee Ji Yoon , Kang Dae-Kyung
Published: February 2021

Probiotics must not only exert a health-promoting effect but also be capable of adapting to the harsh environment of the gastrointestinal (GI) tract. Probiotics in the GI tract must survive the cell wall-disrupting effect of bile acids. We investigated the exoproteome of Lactobacillus johnsonii PF01 and C1-10 under bile stress. A comparative analysis revealed the similarities between the two L. johnsonii exoproteomes, as well as their different responses to bile. The large number of metabolic proteins in L. johnsonii revealed its metabolic adaptation to meet protein synthesis requirements under bile stress. In addition, cell wall modifications occurred in response to bile. Furthermore, some extracellular proteins of L. johnsonii may have moonlighting function in the presence of bile. Enolase, L-lactate dehydrogenase, glyceraldehyde-3-phosphate dehydrogenase, triosephosphate isomerase, 50s ribosomal protein L7/L12, and cellobiose-specific phosphotransferase system (PTS) sugar transporter were significantly upregulated under bile stress, suggesting a leading role in the collective bile stress response of L. johnsonii from its exoproteome perspective.
Tail Risk and Extreme Events: Connections between Oil and Clean Energy
Authors: Di Febo Elisa , Foglia Matteo , Angelini Eliana
Published: February 2021

Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an asymmetry effect between the response to good versus bad days. The results show how the two markets influence each other more negatively, i.e., extreme negative events significantly impact the other market. Furthermore, we document how the impact of the shock transmitted by oil prices to clean-energy stocks is less than the amount of shock transmitted oppositely. These findings have important implications for investor and renewable energy policies.
A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes
Authors: Liu Yang , Su Hailong , Zeng Cao , Li Xiaoli
Published: February 2021

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).
Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey
Authors: Arshad Saba , Kim Gon-Woo
Published: February 2021

Loop closure detection is of vital importance in the process of simultaneous localization and mapping (SLAM), as it helps to reduce the cumulative error of the robot’s estimated pose and generate a consistent global map. Many variations of this problem have been considered in the past and the existing methods differ in the acquisition approach of query and reference views, the choice of scene representation, and associated matching strategy. Contributions of this survey are many-fold. It provides a thorough study of existing literature on loop closure detection algorithms for visual and Lidar SLAM and discusses their insight along with their limitations. It presents a taxonomy of state-of-the-art deep learning-based loop detection algorithms with detailed comparison metrics. Also, the major challenges of conventional approaches are identified. Based on those challenges, deep learning-based methods were reviewed where the identified challenges are tackled focusing on the methods providing long-term autonomy in various conditions such as changing weather, light, seasons, viewpoint, and occlusion due to the presence of mobile objects. Furthermore, open challenges and future directions were also discussed.
Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic
Authors: Ali Mahmoud N. , Mahmoud Karar , Lehtonen Matti , Darwish Mohamed M. F.
Published: February 2021

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.
CNN-Based Volume Flow Rate Prediction of Oil–Gas–Water Three-Phase Intermittent Flow from Multiple Sensors
Authors: Li Jinku , Hu Delin , Chen Wei , Li Yi , Zhang Maomao , Peng Lihui
Published: February 2021

In this paper, we propose a deep-learning-based method using a convolutional neural network (CNN) to predict the volume flow rates of individual phases in the oil–gas–water three-phase intermittent flow simultaneously by analyzing the measurement data from multiple sensors, including a temperature sensor, a pressure sensor, a Venturi tube and a microwave sensor. To build datasets, a series of experiments for the oil–gas–water three-phase intermittent flow in a horizontal pipe, in which gas volume fraction and water-in-liquid ratio ranges are 23.77–94.45% and 14.95–86.97%, respectively, and gas flow superficial velocity and liquid flow superficial velocity ranges are 0.66–5.23 and 0.27–2.14 m/s, respectively, have been carried out on a test loop pipeline. The preliminary results indicate that the model can provide relative prediction errors on the testing-1 dataset for the volume flow rates of oil-phase, gas-phase and water-phase within ±10% with 94.49%, 92.56% and 95.71% confidence levels, respectively. Additionally, the prediction results on the testing-2 dataset also demonstrate the generalization ability of the model. The consuming time of a prediction with one sample is 0.43 s on an Intel Xeon CPU E5-2678 v3, and 0.01 s on an NVIDIA GeForce GTX 1080 Ti GPU. Hence, the proposed CNN-based prediction model, which can fulfill the real-time application requirements in the petroleum industry, reveals the potential of using deep learning to obtain accurate results in the multiphase flow measurement field.
Modeling of Laser-Induced Plasmon Effects in GNS-DLC-Based Material for Application in X-ray Source Array Sensors
Authors: Yakunin Alexander N. , Zarkov Sergey V. , Avetisyan Yuri A. , Akchurin Garif G. , Aban’shin Nikolay P. , Tuchin Valery V.
Published: February 2021

An important direction in the development of X-ray computed tomography sensors in systems with increased scanning speed and spatial resolution is the creation of an array of miniature current sources. In this paper, we describe a new material based on gold nanostars (GNS) embedded in nanoscale diamond-like carbon (DLC) films (thickness of 20 nm) for constructing a pixel current source with photoinduced electron emission. The effect of localized surface plasmon resonance in GNS on optical properties in the wavelength range from UV to near IR, peculiarities of localization of field and thermal sources, generation of high-energy hot electrons, and mechanisms of their transportation in vacuum are investigated. The advantages of the proposed material and the prospects for using X-ray computed tomography in the matrix source are evaluated.
Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
Authors: Mondal Sabyasachi , Tsourdos Antonios
Published: February 2021

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.
Active Game-Based Solutions for the Treatment of Childhood Obesity
Authors: González-González Carina S. , Gómez del Río Nazaret , Toledo-Delgado Pedro A. , García-Peñalvo Francisco José
Published: February 2021

Obesity is one of the biggest health problems globally that, together with sedentarism, requires solutions that increase the enthusiasm towards physical activity. Therefore, this paper describes two solutions based on active games using the Kinect sensor and biometric sensors, designed for the outpatient treatment of childhood obesity. The solutions were applied in an intervention program based on active video games and motor games, developed with children in treatment for childhood obesity. An ad hoc questionnaire was used to assess the level of satisfaction, fun, learning, and behavior changes in the children of the experimental group that developed the intervention. The results showed a high index of satisfaction with the intervention program, as well as with the games developed. It is concluded that active video games and group games are highly motivating and can promote behavior change towards healthier life habits in children.
A Multi-Level Feature Fusion Network for Remote Sensing Image Segmentation
Authors: Dong Sijun , Chen Zhengchao
Published: February 2021

High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications and it also has military and civil applications. The scene analysis needs to be automated as much as possible with high-resolution remote sensing images. This plays a significant role in environmental disaster monitoring, forestry industry, agricultural farming, urban planning, and road analysis. This study proposes a multi-level feature fusion network (MFNet) that can integrate the multi-level features in the backbone to obtain different types of image information. Finally, the experiments in this study demonstrate that the proposed network can achieve good segmentation results in the Vaihingen and Potsdam datasets. By aiming to achieve a large difference in the scale of the target objects in remote sensing images and achieving a poor recognition result for small objects, a multi-level feature fusion solution is proposed in this study. This investigation improves the recognition results of the remote sensing image segmentation to a certain extent.
Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions
Authors: Grigs Oskars , Bolmanis Emils , Galvanauskas Vytautas
Published: February 2021

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O2/CO2, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut+ strain) to significant (for MutS strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut+ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O2 uptake-based (10%), CO2 production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.
Real-Time Detection of Fouling-Layer with a Non-Intrusive Continuous Sensor (NICS) during Thermal Processing in Food Manufacturing
Authors: Rivera Fernando José Cantarero , Mishra Dharmendra K , Ozadali Ferhan , Benyathiar Patnarin
Published: February 2021

The fouling of indirect shell and coil heat exchanger by heavy whipping cream (HWC) and non-fat dry milk (NFDM) was studied at aseptic Ultra-High Temperature (UHT) processing conditions (140 °C) using a novel non-intrusive sensor. The sensor emitted a heat pulse intermittently throughout the duration of the process causing an incremental increase in temperature at the tube external surface. The temperature response of the sensor varied due to the radial growth of the fouling layer formed by certain components of the products. Each heating pulse and the temperature response was studied to estimate the thermal conductivity of the fouling layer using inverse problems and parameter estimation. The changes in thermal conductivity were used as an indication of the fouling layer development during food processing at UHT temperatures. The estimated parameters from experimental results showed a decreasing trend in the thermal conductivity of HWC and NFDM from 0.35 to 0.10 and 0.63 to 0.37, respectively. An image analysis tool was developed and used to measure the fouling layer thickness at the end of each trial. The measured thickness was found to be 0.58 ± 0.15 for HWC and 0.56 ± 0.07 mm for NFDM. The fouling layer resistance for HWC and NFDM was 5.95 × 10−3 ± 1.53 × 10−3 and 1.53 × 10−3 ± 2.0 × 10−4 (m2K)/W, respectively.
A Comparative Analysis of Aerosol Optical Coefficients and Their Associated Errors Retrieved from Pure-Rotational and Vibro-Rotational Raman Lidar Signals
Authors: Zenteno-Hernández José Alex , Comerón Adolfo , Rodríguez-Gómez Alejandro , Muñoz-Porcar Constantino , D’Amico Giuseppe , Sicard Michaël
Published: February 2021

This paper aims to quantify the improvement obtained with a purely rotational Raman (PRR) channel over a vibro-rotational Raman (VRR) channel, used in an aerosol lidar with elastic and Raman channels, in terms of signal-to-noise ratio (SNR), effective vertical resolution, and absolute and relative uncertainties associated to the retrieved aerosol optical (extinction and backscatter) coefficients. Measurements were made with the European Aerosol Research Lidar Network/Universitat Politècnica de Catalunya (EARLINET/UPC) multi-wavelength lidar system enabling a PRR channel at 353.9 nm, together with an already existing VRR (386.7 nm) and an elastic (354.7 nm) channels. Inversions were performed with the EARLINET Single Calculus Chain (SCC). When using PRR instead of VRR, the measurements show a gain in SNR of a factor 2.8 and about 7.6 for 3-h nighttime and daytime measurements, respectively. For 3-h nighttime (daytime) measurements the effective vertical resolution is reduced by 17% (20%), the absolute uncertainty (associated to the extinction) is divided by 2 (10) and the relative uncertainty is divided by 3 (7). During daytime, VRR extinction coefficient is retrieved in a limited height range (<2.2 km) preventing the SCC from finding a suitable calibration range in the search height range. So the advantage of using PRR instead of VRR is particularly evidenced in daytime conditions. For nighttime measurements, decreasing the time resolution from 3 to 1 h has nearly no effect on the relative performances of PRR vs. VRR.
Issues of Data Acquisition and Interpretation of Paraseismic Measuring Signals Triggered by the Detonation of Explosive Charges
Authors: Pyra Józef , Kłaczyński Maciej
Published: February 2021

The paper tackles the issues of data acquisition during the measuring of vibrations caused by the detonation of explosive charges in various types of works (blasting in mines, demolition works, tunneling). Depending on the placement of an explosive charge (a charge detonated on the surface or a charge placed in a hole), it triggers side effects in the form of mechanical vibrations, which are propagated in the environment and may pose a hazard to buildings. In the case of propagation in the air, there is an acoustic wave and an airblast wave. For the assessment analysis on the impact of vibrations on buildings, a ground-propagated signal is used, while what is propagated by air is a disturbance. Selected examples in the paper demonstrate how an acoustic wave and an airblast wave interferes with the signal recorded by geophones. Afterwards, the paper presents the results of the tests conducted at a training area, during which various masses of explosive charges placed in different ways were detonated. The examples demonstrate that this interference may lead to the misinterpretation of recorded measurements. This paper is the first of two papers that will present the results of research into this matter and the suggested resolutions in order to eliminate this interference.
A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery
Authors: Solano-Rojas Braulio , Villalón-Fonseca Ricardo
Published: February 2021

Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86¯ mean accuracy, 0.86¯ mean sensitivity (micro-average), 0.86¯ mean specificity (micro-average), and 0.91¯ area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.
A Fully Open-Source Approach to Intelligent Edge Computing: AGILE’s Lesson
Authors: Vecchio Massimo , Azzoni Paolo , Menychtas Andreas , Maglogiannis Ilias , Felfernig Alexander
Published: February 2021

In this paper, we describe the main outcomes of AGILE (acronym for “Adaptive Gateways for dIverse muLtiple Environments”), an EU-funded project that recently delivered a modular hardware and software framework conceived to address the fragmented market of embedded, multi-service, adaptive gateways for the Internet of Things (IoT). Its main goal is to provide a low-cost solution capable of supporting proof-of-concept implementations and rapid prototyping methodologies for both consumer and industrial IoT markets. AGILE allows developers to implement and deliver a complete (software and hardware) IoT solution for managing non-IP IoT devices through a multi-service gateway. Moreover, it simplifies the access of startups to the IoT market, not only providing an efficient and cost-effective solution for industries but also allowing end-users to customize and extend it according to their specific requirements. This flexibility is the result of the joint experience of established organizations in the project consortium already promoting the principles of openness, both at the software and hardware levels. We illustrate how the AGILE framework can provide a cost-effective yet solid and highly customizable, technological foundation supporting the configuration, deployment, and assessment of two distinct showcases, namely a quantified self application for individual consumers, and an air pollution monitoring station for industrial settings.
Simultaneous Measurements of Dose and Microdosimetric Spectra in a Clinical Proton Beam Using a scCVD Diamond Membrane Microdosimeter
Authors: Loto Oluwasayo , Zahradnik Izabella , Leite Amelia Maia , De Marzi Ludovic , Tromson Dominique , Pomorski Michal
Published: February 2021

A single crystal chemical vapor deposition (scCVD) diamond membrane-based microdosimetric system was used to perform simultaneous measurements of dose profile and microdosimetric spectra with the Y1 proton passive scattering beamline of the Center of Proton Therapy, Institute Curie in Orsay, France. To qualify the performance of the set-up in clinical conditions of hadrontherapy, the dose, dose rate and energy loss pulse-height spectra in a diamond microdosimeter were recorded at multiple points along depth of a water-equivalent plastic phantom. The dose-mean lineal energy (y¯D) values were computed from experimental data and compared to silicon on insulator (SOI) microdosimeter literature results. In addition, the measured dose profile, pulse height spectra and y¯D values were benchmarked with a numerical simulation using TOPAS and Geant4 toolkits. These first clinical tests of a novel system confirm that diamond is a promising candidate for a tissue equivalent, radiation hard, high spatial resolution microdosimeter in beam quality assurance of proton therapy.
EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot
Authors: Feleke Aberham Genetu , Bi Luzheng , Fei Weijie
Published: February 2021

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.