bluuebunny/crossref_metadata_2025 huggingface dataset:
Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey
Authors: Erdebilli Babek , Devrim-İçtenbaş Burcu
Published: July 2022

Predicting medical waste (MW) properly is vital for an effective waste management system (WMS), but it is difficult because of inadequate data and various factors that impact MW. This study’s primary objective was to develop an ensemble voting regression algorithm based on machine learning (ML) algorithms such as random forests (RFs), gradient boosting machines (GBMs), and adaptive boosting (AdaBoost) to predict the MW for Istanbul, the largest city in Turkey. This was the first study to use ML algorithms to predict MW, to our knowledge. First, three ML algorithms were developed based on official data. To compare their performances, performance measures such as mean absolute deviation (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were calculated. Among the standalone ML models, RF achieved the best performance. Then, these base models were used to construct the proposed ensemble voting regression (VR) model utilizing weighted averages according to the base models’ performances. The proposed model outperformed three baseline models, with the lowest RMSE (843.70). This study gives an effective tool to practitioners and decision-makers for planning and constructing medical waste management systems by predicting the MW quantity.
A Primal–Dual Fixed-Point Algorithm for TVL1 Wavelet Inpainting Based on Moreau Envelope
Authors: Ren Zemin , Zhang Qifeng , Yuan Yuxing
Published: July 2022

In this paper, we present a novel variational wavelet inpainting based on the total variation (TV) regularization and the l1-norm fitting term. The goal of this model is to recover incomplete wavelet coefficients in the presence of impulsive noise. By incorporating the Moreau envelope, the proposed model for wavelet inpainting can better handle the non-differentiability of the l1-norm fitting term. A modified primal dual fixed-point algorithm is developed based on the proximity operator to solve the proposed variational model. Moreover, we consider the existence of solution for the proposed model and the convergence analysis of the developed iterative scheme in this paper. Numerical experiments show the desirable performance of our method.
A Quantum Planner for Robot Motion
Authors: Chella Antonio , Gaglio Salvatore , Pilato Giovanni , Vella Filippo , Zammuto Salvatore
Published: July 2022

The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot’s behavior. According to the production rules, the planning of the robot activities is processed in a recognize–act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up.
Influence of Method and Frequency of Profit Tax Payments on Company Financial Indicators
Authors: Brusov Peter , Filatova Tatiana
Published: July 2022

In practice, profit tax payments are (1) made more frequently than annually and (2) can be made in advance. To study the influence of these two factors on the financial indicators of a company, we generalized the Brusov–Filatova–Orekhova (BFO) theory for the case of advance profit tax payments with an arbitrary frequency for the first time. Using modified BFO formulae, we showed that all financial indicators of a company, such as company value, the weighted average cost of capital (WACC) and equity cost (ke), depend on the frequency of the profit tax payments. We found that the WACC increased with the payments and the company value decreased with the payments. This meant that more infrequent payments could be beneficial for the company. The tilt angle of the equity cost (ke(L)) also increased with the payments. Depending on the age of the company, the equity cost either decreased with L for all payment frequencies or increased for some frequencies. We compared the obtained results to those that we described recently for profit tax payments at the end of the financial period and found them to be totally different. We found that in spite the fact that the WACC decreased with the payments and the company value increased with the payments, the WACC value in this case turned out to be bigger and the company value turned out to be smaller than in the case of advance profit tax payments of any frequency. This underlined the importance of advance profit tax payments. Regulator recommendations were also developed to encourage the practice of advance profit tax payments due to the understanding of the benefits of this for both parties: the companies and the state. A new effect was discovered: the decrease in equity cost with an increase in the level of leverage (L).
Fractional Advection Diffusion Models for Radionuclide Migration in Multiple Barriers System of Deep Geological Repository
Authors: Yang Shuai , Wei Qing , An Lu
Published: July 2022

Based on the multiple barriers concept of deep geological disposal of high-level waste, fractional advection diffusion equations for radionuclide migration in multiple layers low-permeability porous media are proposed in this work. The presented fractional advection diffusion models in terms of different definitions of fractional derivative are analytically addressed via the Laplace integral transform method. This work provides a theoretical foundation for further simulations of radionuclide migration in the multiple barriers system of the high-level waste repository.
Some Generalized Properties of Poly-Daehee Numbers and Polynomials Based on Apostol–Genocchi Polynomials
Authors: Usman Talha , Khan Nabiullah , Aman Mohd , Al-Omari Shrideh , Nonlaopon Kamsing , Choi Junesang
Published: July 2022

Numerous polynomial variations and their extensions have been explored extensively and found applications in a variety of research fields. The purpose of this research is to establish a unified class of Apostol–Genocchi polynomials based on poly-Daehee polynomials and to explore some of their features and identities. We investigate these polynomials via generating functions and deduce various identities, summation formulae, differential and integral formulas, implicit summation formulae, and several characterized generating functions for new numbers and polynomials. Finally, by using an operational version of Apostol–Genocchi polynomials, we derive some results in terms of new special polynomials. Due to the generic nature of the findings described here, they are used to reduce and generate certain known or novel formulae and identities for relatively simple polynomials and numbers.
First Characterization of Ostreopsis cf. ovata (Dinophyceae) and Detection of Ovatoxins during a Multispecific and Toxic Ostreopsis Bloom on French Atlantic Coast
Authors: Chomérat Nicolas , Antajan Elvire , Auby Isabelle , Bilien Gwenael , Carpentier Liliane , Casamajor Marie-Noëlle de , Ganthy Florian , Hervé Fabienne , Labadie Magali , Méteigner Claire , Paradis Camille , Perrière-Rumèbe Myriam , Sanchez Florence , Séchet Véronique , Amzil Zouher
Published: July 2022

Blooms of the benthic toxic dinoflagellate genus Ostreopsis have been recorded more frequently during the last two decades, particularly in warm temperate areas such as the Mediterranean Sea. The proliferation of Ostreopsis species may cause deleterious effects on ecosystems and can impact human health through skin contact or aerosol inhalation. In the eastern Atlantic Ocean, the toxic O. cf. ovata has not yet been reported to the north of Portugal, and the only species present further north was O. cf. siamensis, for which the toxic risk is considered low. During summer blooms of unidentified Ostreopsis species on the French Basque coast (Atlantic) in 2020 and 2021, people suffered from irritations and respiratory disorders, and the number of analyzed cases reached 674 in 2021. In order to investigate the causes, sampling was carried out during summer 2021 to (i) taxonomically identify Ostreopsis species present using a molecular approach, (ii) isolate strains from the bloom and culture them, and (iii) characterize the presence of known toxins which may be involved. For the first time, this study reports the presence of both O. cf. siamensis and O. cf. ovata, for which the French Basque coast is a new upper distribution limit. Furthermore, the presence of ovatoxins a, b, c, and d in the environmental sample and in a cultivated strain in culture confirmed the toxic nature of the bloom and allowed identifying O. cf. ovata as the producer. The present data identify a new health risk in the area and highlight the extended distribution of some harmful dinoflagellates, presumably in relation to climate change.
Connection of Isolated Stereoclusters by Combining 13C-RCSA, RDC, and J-Based Configurational Analyses and Structural Revision of a Tetraprenyltoluquinol Chromane Meroterpenoid from Sargassum muticum
Authors: Fuentes-Monteverde Juan Carlos C. , Nath Nilamoni , Forero Abel M. , Balboa Elena M. , Navarro-Vázquez Armando , Griesinger Christian , Jiménez Carlos , Rodríguez Jaime
Published: July 2022

The seaweed Sargassum muticum, collected on the southern coast of Galicia, yielded a tetraprenyltoluquinol chromane meroditerpene compound known as 1b, whose structure is revised. The relative configuration of 1b was determined by J-based configurational methodology combined with an iJ/DP4 statistical analysis and further confirmed by measuring two anisotropic properties: carbon residual chemical shift anisotropies (13C-RCSAs) and one-bond 1H-13C residual dipolar couplings (1DCH-RDCs). The absolute configuration of 1b was deduced by ECD/OR/TD-DFT methods and established as 3R,7S,11R.
Mortality Predictors in Severe SARS-CoV-2 Infection
Authors: Lazar Mihai , Barbu Ecaterina Constanta , Chitu Cristina Emilia , Anghel Ana-Maria-Jennifer , Niculae Cristian-Mihail , Manea Eliza-Daniela , Damalan Anca-Cristina , Bel Adela-Abigaela , Patrascu Raluca-Elena , Hristea Adriana , Ion Daniela Adriana
Published: July 2022

Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.
Simulation Uncertainty for a Virtual Ultrasonic Flow Meter
Authors: Straka Martin , Weissenbrunner Andreas , Koglin Christian , Höhne Christian , Schmelter Sonja
Published: July 2022

Ultrasonic clamp-on meters have become an established technology for non-invasive flow measurements. Under disturbed flow conditions, their measurement values must be adjusted with corresponding fluid mechanical calibration factors. Due to the variety of flow disturbances and installation positions, the experimental determination of these factors often needs to be complemented by computational fluid dynamics (CFD) simulations. From a metrological perspective, substituting experiments with simulation results raises the question of how confidence in a so-called virtual measurement can be ensured. While there are well-established methods to estimate errors in CFD predictions in general, strategies to meet metrological requirements for CFD-based virtual meters have yet to be developed. In this paper, a framework for assessing the overall uncertainty of a virtual flow meter is proposed. In analogy to the evaluation of measurement uncertainty, the approach is based on the utilization of an expanded simulation uncertainty representing the entirety of the computational domain. The study was conducted using the example of an ultrasonic clamp-on meter downstream of a double bend out-of-plane. Nevertheless, the proposed method applies to other flow disturbances and different types of virtual meters. The comparison between laboratory experiments and simulation results with different turbulence modeling approaches demonstrates a clear superiority of hybrid RANS-LES models over the industry standard RANS. With an expanded simulation uncertainty of 1.44 × 10−2, the virtual measurement obtained with a hybrid model allows for a continuous determination of calibration factors applicable to the relevant mounting positions of a real meter at a satisfactory level of confidence.
Comparative Evaluation of an Easy Laboratory Method for the Concentration of Oocysts and Commercial DNA Isolation Kits for the Molecular Detection of Cyclospora cayetanensis in Silt Loam Soil Samples
Authors: Shipley Alicia , Arida Joseph , Almeria Sonia
Published: July 2022

Cyclospora cayetanensis is a protozoan parasite that causes foodborne outbreaks of diarrheal illness (cyclosporiasis) worldwide. Contact with soil may be an important mode of transmission for C. cayetanensis and could play a role in the contamination of foods. However, there is a scarcity of detection methods and studies for C. cayetanensis in soil. Traditional parasitology concentration methods can be useful for the detection of C. cayetanensis, as found for other protozoa parasites of similar size. The present study evaluated a concentration method using flotation in saturated sucrose solution, subsequent DNA template preparation and qPCR following the Bacteriological Analytical Manual (BAM) Chapter 19b method. The proposed flotation method was compared to three commercial DNA isolation kits (Fast DNATM 50 mL SPIN kit for soil (MP Biomedicals, Irvine, CA, USA), Quick-DNATM Fecal/Soil Microbe Midiprep kit (Zymo Research, Irvine, CA, USA) and DNeasy® PowerMax® Soil Kit (Qiagen, Hilden, Germany)) for the isolation and detection of DNA from experimentally seeded C. cayetanensis soil samples (5–10 g with 100 oocysts). Control unseeded samples were all negative in all methods. Significantly lower cycle threshold values (CT) were observed in the 100 oocyst C. cayetanensis samples processed via the flotation method than those processed with each of the commercial DNA isolation kits evaluated (p < 0.05), indicating higher recovery of the target DNA with flotation. All samples seeded with 100 oocysts (n = 5) were positive to the presence of the parasite by the flotation method, and no inhibition was observed in any of the processed samples. Linearity of detection of the flotation method was observed in samples seeded with different levels of oocysts, and the method was able to detect as few as 10 oocysts in 10 g of soil samples (limit of detection 1 oocyst/g). This comparative study showed that the concentration of oocysts in soil samples by flotation in high-density sucrose solutions is an easy, low-cost, and sensitive method that could be implemented for the detection of C. cayetanensis in environmental soil samples. The flotation method would be useful to identify environmental sources of C. cayetanensis contamination, persistence of the parasite in the soil and the role of soil in the transmission of C. cayetanensis.
Characteristics of Fungal Communities and Internal Mildew Occurrence during the Stages of Planting and Storing of Sunflower Seed in China
Authors: Liu Jie , Yang Yang , Xu Zhuopin , Wang Qi , Liu Binmei , Wu Yuejin
Published: July 2022

Internally mildewed sunflower seeds pose a significant risk to human health. To control internal mildew, it is imperative to study its source in the main production area of China, which has been little investigated. Here, high-throughput sequencing was used to characterize the fungal and fungus-seed communities. Alpha diversity and ANOSIM analyses showed mildew did not alter the fungal compositions significantly. STAMP analysis showed that the sunflower seeds were most vulnerable to internal mildew during the field-planting stage. Alternaria was the predominant mildew-causing pathogen of sunflower seeds for consumption, which may originate from seed transmission and colonize at the seed-development stage. Finally, only a few seeds developed internal mildew with a worrisome level of Alternaria contamination in the humid field climate. NMDS analysis showed that climatic factors also played important roles in shaping microbial change during storage, with a relative humidity (RH) of 67% being the critical threshold in normal-temperature warehouses. Internal mildew never occurred below the RH threshold for the microbial community structure, which hardly changed after an average storage duration. The results indicated that a combination of field management to combat Alternaria, pretreatment with 5 KGy γ-irradiation and drying at the time of storage will minimize or prevent internal mildew. This work also provides an empirical framework for studies of mildewing in other shelled seeds.
Identification of Exported Plasmodium falciparum Proteins That Bind to the Erythrocyte Cytoskeleton
Authors: Shakya Bikash , Kilili Geoffrey Kimiti , Wang Ling , Nakayasu Ernesto S. , LaCount Douglas J.
Published: July 2022

Plasmodium proteins are exported to the erythrocyte cytoplasm to create an environment that supports parasite replication. Although hundreds of proteins are predicted to be exported through Plasmodium export element (PEXEL)-dependent and -independent mechanisms, the functions of exported proteins are largely uncharacterized. In this study, we used a biochemical screening approach to identify putative exported P. falciparum proteins that bound to inside-out vesicles prepared from erythrocytes. Out of 69 P. falciparum PEXEL-motif proteins tested, 18 bound to inside-out vesicles (IOVs) in two or more independent assays. Using co-affinity purifications followed by mass spectrometry, pairwise co-purification experiments, and the split-luciferase assay, we identified 31 putative protein–protein interactions between erythrocyte cytoskeletal proteins and predicted exported P. falciparum proteins. We further showed that PF3D7_1401600 binds to the spectrin-binding domain of erythrocyte ankyrin via its MESA erythrocyte cytoskeleton binding (MEC) motif and to the N-terminal domains of ankyrin and 4.1R through a fragment that required an intact Plasmodium helical interspersed sub-telomeric (PHIST) domain. Introduction of PF3D7_1401600 into erythrocyte ghosts increased retention in the microsphiltration assay, consistent with previous data that reported a reduction of rigidity in red blood cells infected with PF3D7_1401600-deficient parasites.
Usage of FTA® Classic Cards for Safe Storage, Shipment, and Detection of Arboviruses
Authors: Krambrich Janina , Bringeland Emelie , Hesson Jenny C. , Hoffman Tove , Lundkvist Åke , Lindahl Johanna F. , Ling Jiaxin
Published: July 2022

Infections caused by arthropod-borne RNA viruses are overrepresented among emerging infectious diseases. Effective methods for collecting, storing, and transporting clinical or biological specimens are needed worldwide for disease surveillance. However, many tropical regions where these diseases are endemic lack analytical facilities and possibility of continuous cold chains, which presents challenges from both a biosafety and material preservation perspective. Whatman® FTA® Classic Cards may serve as an effective and safe option for transporting hazardous samples at room temperature, particularly for RNA viruses classified as biosafety level (BSL) 2 and 3 pathogens, from sampling sites to laboratories. In this study, we investigated the biosafety and perseverance of representative alpha- and flaviviruses stored on FTA® cards. To evaluate the virus inactivation capacity of FTA® cards, we used Sindbis virus (SINV), chikungunya virus (CHIKV), and Japanese encephalitis virus (JEV). We inoculated susceptible cells with dilution series of eluates from viral samples stored on the FTA® cards and observed for cytopathic effect to evaluate the ability of the cards to inactivate viruses. All tested viruses were inactivated after storage on FTA® cards. In addition, we quantified viral RNA of JEV, SINV, and tick-borne encephalitis virus (TBEV) stored on FTA® cards at 4 °C, 25 °C, and 37 °C for 30 days using two reverse transcriptase quantitative PCR assays. Viral RNA of SINV stored on FTA® cards was not reduced at either 4 °C or 25 °C over a 30-day period, but degraded rapidly at 37 °C. For JEV and TBEV, degradation was observed at all temperatures, with the most rapid degradation occurring at 37 °C. Therefore, the use of FTA® cards provides a safe and effective workflow for the collection, storage, and analysis of BSL 2- and 3-virus RNA samples, but there is a risk of false negative results if the cards are stored at higher temperatures for long periods of time. Conscious usage of the cards can be useful in disease surveillance and research, especially in tropical areas where transportation and cold chains are problematic.
Measuring Electrical Responses during Acute Exposure of Roots and Rhizoids of Plants to Compounds Using a Flow-Through System
Authors: Cooper Robin Lewis , Thomas Matthew A. , Vascassenno Rachael M. , Brock Kaitlyn E. , McLetchie David Nicholas
Published: July 2022

Monitoring electrical signals in plants allows the examination of their acute and chronic physiological changes and responses to stimuli. Understanding how plant roots/rhizoids respond to chemical cues in their environment will provide insight into how these structures acquire resources. Chronic exposure to L-glutamate alters root growth and is known to alter Ca2+ flux inside roots. The ionic flux can be detected by electrical changes. A rapid and relatively easy approach is presented to screen the electrical sensitivity of roots/rhizoids to compounds such as amino acids and known agonists/antagonists to receptors and ion channels. The approach uses a background-flow system of basal salt or water; then, the administered compounds are added to the roots/rhizoids while monitoring their electrical responses. As a proof of concept, the response to flow-through of glutamate (1 mM) was targeted at the root/rhizoids of three plants (Arabidopsis thaliana, Pisum sativum and Marchantia inflexa). Both Arabidopsis thaliana and Pisum sativum produced rapid depolarization upon exposure to glutamate, while M. inflexa did not show an electrical response. In some experiments, simultaneous recordings with impedance measures for acute changes and glass electrodes for chronic electrical potential changes were used. The effect of potassium chloride (300 mM) as a depolarizing stimulus produced responses in both P. sativum and M. inflexa. The protocol presented can be used to screen various compounds in a relatively rapid manner for responsiveness by the roots/rhizoids of plants.
RNA Viruses, Pregnancy and Vaccination: Emerging Lessons from COVID-19 and Ebola Virus Disease
Authors: Dhanya Chandrasekharan Rajalekshmi , Shailaja Aswathy , Mary Aarcha Shanmugha , Kandiyil Sumodan Padikkala , Savithri Ambili , Lathakumari Vishnu Sasidharan , Veettil Jayakrishnan Therthala , Vandanamthadathil Jiji Joseph , Madhavan Maya
Published: July 2022

Pathogenic viruses with an RNA genome represent a challenge for global human health since they have the tremendous potential to develop into devastating pandemics/epidemics. The management of the recent COVID-19 pandemic was possible to a certain extent only because of the strong foundations laid by the research on previous viral outbreaks, especially Ebola Virus Disease (EVD). A clear understanding of the mechanisms of the host immune response generated upon viral infections is a prime requisite for the development of new therapeutic strategies. Hence, we present here a comparative study of alterations in immune response upon SARS-CoV-2 and Ebola virus infections that illustrate many common features. Vaccination and pregnancy are two important aspects that need to be studied from an immunological perspective. So, we summarize the outcomes and immune responses in vaccinated and pregnant individuals in the context of COVID-19 and EVD. Considering the significance of immunomodulatory approaches in combating both these diseases, we have also presented the state of the art of such therapeutics and prophylactics. Currently, several vaccines against these viruses have been approved or are under clinical trials in various parts of the world. Therefore, we also recapitulate the latest developments in these which would inspire researchers to look for possibilities of developing vaccines against many other RNA viruses. We hope that the similar aspects in COVID-19 and EVD open up new avenues for the development of pan-viral therapies.
Effect of the Gas Diffusion Layer Design on the Water Management and Cell Performance of a PEM Fuel Cell
Authors: Martín-Alcántara Antonio , González-Morán Laura , Pino Javier , Guerra José , Iranzo Alfredo
Published: July 2022

The influence of the different properties of the gas diffusion layer (GDL) on the operation of a liquid-cooled, proton-exchange polymer electrolyte fuel cell (PEMFC) has been studied in this work. Three-dimensional numerical simulations (CFD) have been conducted to compare several commercial GDLs with different properties, analyzing their influence on the cell performance. Specifically, four GDLs (AvCarb P-75, SIGRACET 34BC, SIGRACET 34BA and Toray TGP-H-090) have been studied, two of them including a microporous layer (MPL). The effect of the MPL has been inspected by contrast of the results obtained with the same GDL, with or without MPL. Potentiostatic boundary conditions have been applied, varying the electric potential between 1.05 and 0.35 V to obtain a representative i−V curve with enough resolution. Detailed postprocessing tasks were carried out to gain a deeper understanding on the phenomena occurring within the cell for each GDL. It can be concluded from this work that a high electrical conductivity and a high permeability lead to a better fuel cell performance. On the other hand, although the presence of MPL provides lower permeability leading to a worse overall performance, it has been shown that the lack of it may result in membrane dehydration and cell degradation issues.
The Globalization of Christian Democracy: Religious Entanglements in the Making of Modern Politics
Authors: Forlenza Rosario , Thomassen Bjørn
Published: July 2022

This article throws light on a crucial, yet overlooked, aspect of global entanglements that significantly came to shape modern politics: the global spread of Catholic ideas that, from the late nineteenth century and through the twentieth century, became translated into various political platforms and, eventually, into Christian Democratic parties. The article will cover three broad periods where such global entanglements took shape: the mid-nineteenth century up until World War I, the interwar period, and the aftermath of World War II. We primarily address developments across the Atlantic in Europe and Latin America, while briefly touching upon Asian developments. The article aims to show the role of non-secular ideologies in political globalization processes and the co-existence of centric and multi-polar tendencies in such processes.
Computational Imaging in Dual-Band Infrared Hybrid Optical System with Wide Temperature Range
Authors: Mao Shan , Nie Huaile , Lai Tao , Xie Na
Published: July 2022

The special dispersion and temperature characteristics of diffractive optical element (DOE) make them widely used in optical systems that require both athermalization and achromatic aberrations designs. The multi-layer DOE (MLDOE) can improve the diffraction efficiency of the overall broad waveband, but its diffraction efficiency decreases with changes in ambient temperature. When the ambient temperature changes, the micro-structure heights of MLDOE and the refractive index of the substrate materials change, ultimately affecting its diffraction efficiency, and, further, the optical transform function (OTF). In this paper, the influence of ambient temperature on the diffraction efficiency of MLDOE in a dual-infrared waveband is proposed and discussed, the diffraction efficiency of MLDOE caused by ambient temperature is derived, and a computational imaging method that combines optical design and image restoration is proposed. Finally, a dual-infrared waveband infrared optical system with athermalization and achromatic aberrations corrected based on computational imaging method is designed. Results show that this method can effectively reduce the diffraction efficiency of MLDOE by ambient temperature and improve the imaging quality of hybrid optical systems.
Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device
Authors: Coluzzi Davide , Baselli Giuseppe , Bianchi Anna Maria , Guerrero-Mora Guillermina , Kortelainen Juha M. , Tenhunen Mirja L. , Mendez Martin O.
Published: July 2022

Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.
Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications
Authors: Augello Andrea , Gaglio Salvatore , Lo Re Giuseppe , Peri Daniele
Published: July 2022

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.
Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree
Authors: Zou Zheng , Nie Ming-Xing , Liu Xing-Sheng , Liu Shi-Jian
Published: July 2022

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.
Design of the Automated Calibration Process for an Experimental Laser Inspection Stand
Authors: Klarák Jaromír , Andok Robert , Hricko Jaroslav , Klačková Ivana , Tsai Hung-Yin
Published: July 2022

This paper deals with the concept of the automated calibration design for inspection systems using laser sensors. The conceptual solution is based on using a laser sensor and its ability to scan 3D surfaces of inspected objects in order to create a representative point cloud. Problems of scanning are briefly discussed. The automated calibration procedure for solving problems of errors due to non-precise adjustment of the mechanical arrangement, possible tolerances in assembly, and their following elimination is proposed. The main goal is to develop a system able to measure and quantify the quality of produced objects in the environment of Industry 4.0. Laboratory measurements on the experimental stand, including the principal software solution for automated calibration of laser sensors suitable for gear wheel inspection systems are presented. There is described design of compensation eccentricity by Fourier transform and sinusoidal fitting to identify and suppress the first harmonic component in the data with high precision measuring.
Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning
Authors: Garefalakis Thodoris , Katrakazas Christos , Yannis George
Published: July 2022

Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project “i-DREAMS”, which aims at defining, developing, testing and validating a ‘Safety Tolerance Zone’ (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.
Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications
Authors: Płaza Mirosław , Trusz Sławomir , Kęczkowska Justyna , Boksa Ewa , Sadowski Sebastian , Koruba Zbigniew
Published: July 2022

Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).
GAN-FDSR: GAN-Based Fault Detection and System Reconfiguration Method
Authors: Shen Zihan , Zhao Xiubin , Pang Chunlei , Zhang Liang
Published: July 2022

Fault detection and exclusion are essential to ensure the integrity and reliability of the tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integrated navigation system. A fault detection and system reconfiguration scheme based on generative adversarial networks (GAN-FDSR) for tightly coupled systems is proposed in this paper. The chaotic characteristics of pseudo-range data are analyzed, and the raw data are reconstructed in phase space to improve the learning ability of the models for non-linearity. The trained model is used to calculate generation and discrimination scores to construct fault detection functions and detection thresholds while retaining the generated data for subsequent system reconfiguration. The influence of satellites on positioning accuracy of the system under different environments is discussed, and the system reconfiguration scheme is dynamically selected by calculating the relative differential precision of positioning (RDPOP) of the faulty satellites. Simulation experiments are conducted using the field test data to assess fault detection performance and positioning accuracy. The results show that the proposed method greatly improves the detection sensitivity of the system for small-amplitude faults and gradual faults, and effectively reduces the positioning error during faults.
Mechanisms of NO2 Detection in Hybrid Structures Containing Reduced Graphene Oxide: A Review
Authors: Drewniak Sabina , Drewniak Łukasz , Pustelny Tadeusz
Published: July 2022

The sensitive detection of harmful gases, in particular nitrogen dioxide, is very important for our health and environment protection. Therefore, many papers on sensor materials used for NO2 detection have been published in recent years. Materials based on graphene and reduced graphene oxide deserve special attention, as they exhibit excellent sensor properties compared to the other materials. In this paper, we present the most recent advances in rGO hybrid materials developed for NO2 detection. We discuss their properties and, in particular, the mechanism of their interaction with NO2. We also present current problems occuring in this field.
A Novel Hybrid Algorithm for the Forward Kinematics Problem of 6 DOF Based on Neural Networks
Authors: Zhu Huizhi , Xu Wenxia , Yu Baocheng , Ding Feng , Cheng Lei , Huang Jian
Published: July 2022

The closed kinematic structure of Gough–Stewart platforms causes the kinematic control problem, particularly forward kinematics. In the traditional hybrid algorithm (backpropagation neural network and Newton–Raphson), it is difficult for the neural network part to train different datasets, causing training errors. Moreover, the Newton–Raphson method is unable to operate on a singular Jacobian matrix. In this study, in order to solve the forward kinematics problem of Gough–Stewart platforms, a new hybrid algorithm is proposed based on the combination of an artificial bee colony (ABC)–optimized BP neural network (ABC–BPNN) and a numerical algorithm. ABC greatly improves the prediction ability of neural networks and can provide a superb initial value to numerical algorithms. In the design of numerical algorithms, a modification of Newton’s method (QMn-M) is introduced to solve the problem that the traditional algorithm model cannot be solved when it is trapped in singular matrix. Results show that the maximal improvement in ABC–BPNN error optimization was 46.3%, while the RMSE index decreased by 42.1%. Experiments showed the feasibility of QMn-M in solving singular matrix data, while the percentage improvement in performance for the average number of iterations and required time was 14.4% and 13.9%, respectively.
Vision-Based Module for Herding with a Sheepdog Robot
Authors: Riego del Castillo Virginia , Sánchez-González Lidia , Campazas-Vega Adrián , Strisciuglio Nicola
Published: July 2022

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.
Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production
Authors: Ibrahim Nehad M. , Alharbi Ali A. , Alzahrani Turki A. , Abdulkarim Abdullah M. , Alessa Ibrahim A. , Hameed Abdullah M. , Albabtain Abdullaziz S. , Alqahtani Deemah A. , Alsawwaf Mohammad K. , Almuqhim Abdullah A.
Published: July 2022

In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments.
Microsphere Coupled Off-Core Fiber Sensor for Ultrasound Sensing
Authors: Tatel Gerard , Bao Xiaoyi
Published: July 2022

A compact fiber ultrasound-sensing device comprising a commercially available Barium Titanate (BaTiO3) glass microsphere coupled to an open cavity off-core Fabry–Perot interferometer (FPI) fiber sensor is proposed and demonstrated. The open cavity is fabricated through splicing two segments of a single mode fiber (SMF-28) at lateral offsets. The lateral offset is matched to the radius of the microsphere to maximize their coupling and allow for an increased sensing response. Furthermore, the microsphere can be moved along the open-air cavity to allow for tuning of the reflection spectrum. The multiple passes of the FPI enabled by the high refractive index microsphere results in a 40 dB enhancement of finesse and achieves broadband ultrasound sensing from 0.1–45.6 MHz driven via a piezoelectric transducer (PZT) centered at 3.7 MHz. The goal is to achieve frequency detection in the MHz range using a repeatable, cost effective, and easy to fabricate FPI sensor design.
Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors
Authors: Fukuda Gen , Kubo Nobuaki
Published: July 2022

This article proposes a novel initial bias estimation method using a trajectory generator (TG). The accuracy of attitude and position estimation in navigation after using the inertial navigation system/Doppler velocity log (INS/DVL) and INS/DVL/gyrocompass (IDG) for 1 h were evaluated, and the results were compared to those obtained using the conventional Kalman filter (KF) estimation method. The probability of a horizontal position error < 1852 m (1 nautical mile) with a bias interval > 400 s was 100% and 9% for the TG and KF, respectively. In addition, the IDG average horizontal position errors over 1 h were 493 m and 507 m for the TG and KF, respectively. Moreover, the amount of variation was 2 m and 27 m for the TG and the KF, respectively. Thus, the proposed method is effective for initial bias estimation of INS/DVL and IDG using micro-electro-mechanical system sensors on a constantly moving vessel.