bluuebunny/crossref_metadata_2025 huggingface dataset:
Complications Following Irinotecan-Loaded Microsphere Chemoembolization of Colorectal Metastatic Liver Lesions Associated with Hepatic-Artery Branch Temporary Stasis
Authors: Szemitko Marcin , Golubinska-Szemitko Elzbieta , Sienko Jerzy , Falkowski Aleksander
Published: June 2021

Chemoembolization with irinotecan-loaded microspheres has proven effective in the treatment of unresectable liver metastases in the course of colorectal cancer (CRC). Most researchers recommend slowly administering the embolizate at the level of the lobar arteries, without obtaining visible stasis. However, there are reports of a relationship between postoperative embolizate retention in metastatic lesions and the response to treatment. To retain residual embolizate throughout the entire neoplastic lesion requires a temporary flow stop (stasis) within all supply vessels, which may cause temporary stasis in subsegmental or even segmental vessels. Objective: To assess the risk of complications and post-embolization syndrome severity following chemoembolization of CRC metastatic liver lesions with microspheres loaded with Irinotecan, with regard to hepatic-artery branch level of temporary stasis. Patients and methods: The study included 52 patients (29 female, 23 male) with liver metastases from CRC, who underwent 202 chemoembolization treatments (mean: 3.88 per patient) with microspheres loaded with 100 mg irinotecan. Postembolization syndrome (PES) severity and complication occurrence were assessed with regard to the hepatic-artery branch level of temporary stasis. Adverse events were assessed according to Cancer Therapy Evaluation Program Common Terminology Criteria for Adverse Events. Results: Median survival from the start of chemoembolization was 13 months. From 202 chemoembolization sessions, 15 (7.4%) significant complications were found. The study found a significant relationship between the branch level of temporary stasis and the presence of complications (p < 0.001), with the highest number of complications observed with temporary stasis in segmental vessels. PES was diagnosed after 103 (51%) chemoembolization treatments. A significant association was found between PES severity and the branch level of temporary stasis (p < 0.001). Conclusions: The branch level of temporary stasis affected the severity of post-embolization syndrome. A significant association was found between the branch level of temporary stasis obtained in chemoembolization procedures and the presence of complications. The apparent lack of change in numbers of complications when stasis was applied at tumor supply vessels or subsegmental arteries may indicate the safe use of temporary stasis in some cases where colorectal cancer metastases are treated. Further research is needed to determine the most effective chemoembolization technique.
GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory
Authors: Liu Tingliang , Yan Jing , Wang Yanxin , Xu Yifan , Zhao Yiming
Published: June 2021

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.
The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again
Authors: Safron Adam
Published: June 2021

Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).
Perceived Lighting Uniformity on Pedestrian Roads: From an Architectural Perspective
Authors: Wänström Lindh Ulrika , Jägerbrand Annika K.
Published: June 2021

Lighting uniformity is a key factor in traffic safety, and it could even result in energy savings for light installations. However, highly uniform horizontal road lighting for motorized vehicles may not be optimal for pedestrian roads. Therefore, it is important to evaluate the way in which pedestrians experience road lighting uniformity. Accordingly, we employed a qualitative approach to examine pedestrian road lighting uniformity. Visual analyses were used to exemplify and discuss the perceived uniformity. The case studies were performed on three pedestrian roads with similar light installations. The results show that the experience of road lighting uniformity differs substantially between the three roads. Based on the case studies, there are many aspects that need to be considered beyond the light falling on the horizontal surfaces. This study suggests that the visual experience of road lighting uniformity for pedestrians is difficult to estimate with photometric values because the visual impact of uniformity is highly influenced by the spatial context and landscape.
Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges
Authors: Gholizadeh Nastaran , Musilek Petr
Published: June 2021

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.
Common Perceptions about the Use of Fillers in Silicone Rubber Insulation Housing Composites
Authors: Ghunem Refat Atef , Hadjadj Yazid , Parks Harold
Published: June 2021

This paper discusses the lessons learned about fillers that are added to silicone rubber insulation housing composites, in order to improve the erosion resistance against the dry-band arcing are presented and common perceptions. Common practices that employ alumina tri-hydrate are reviewed, including the additional influential effect of the water of hydration in the suppression of the dry-band arcing. The effect of the water of hydration is shown to be dependent on various factors, such as the hydrated filler level and the type of the hydrated filler. More recent paradigms in which hydrated fillers have not been employed are also reviewed. Volume and shield actions of fillers are essential aspects that need to be understood in the design of silicone rubber insulation housing composites for new applications such as HVDC. In addition, the thermal degradation mechanisms of silicone rubber and the corresponding suppression effects of the added fillers are summarized.
Variable-Frequency Pulse Width Modulation Circuits for Resonant Wireless Power Transfer
Authors: Tang Li-Chuan , Jeng Shyr-Long , Chang Edward-Yi , Chieng Wei-Hua
Published: June 2021

In this paper, we develop a variable-frequency pulse width modulation (VFPWM) circuit for input control of 6.78-MHz resonant wireless power transfer (WPT) systems. The zero-voltage switching control relies on the adjustments of both duty cycle and switching frequency for the class-E amplifier used in the WPT as the power transmission unit. High-frequency pulse wave modulation integrated circuits exist, but some have insufficiently high frequency or unfavorable resolution for duty cycle tuning. The novelty of this work is the VFPWM circuit design that we put together. A voltage-controlled oscillator (VCO) of radio frequency and capacitor-coupled difference amplifiers are used to simultaneously perform the frequency and duty cycle tuning required in resonant WPT applications. Different circuit topologies of VFPWM are compared analytically and numerically. The most favorable circuit topology, enabling independent control of the frequency and duty cycle, is employed in experiments. The experimental results demonstrate the validity of the novel VFPWM, which is capable of operating at 6.78-MHz and has a duty ratio adjustable from 20% to 45% of the range applicable in the resonant WPT applications.
Impact of Harmonic Currents of Nonlinear Loads on Power Quality of a Low Voltage Network–Review and Case Study
Authors: Michalec Łukasz , Jasiński Michał , Sikorski Tomasz , Leonowicz Zbigniew , Jasiński Łukasz , Suresh Vishnu
Published: June 2021

The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (THDI). The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents’ pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation.
Analytical Optimal Design of a Two-Phase Axial-Gap Transverse Flux Motor
Authors: Ballestín-Bernad Víctor , Artal-Sevil Jesús Sergio , Domínguez-Navarro José Antonio
Published: June 2021

Transverse flux motors (TFMs) are being investigated to be used in vehicle traction applications due to their high torque density. In this paper, a two-phase axial-gap transverse flux motor is designed for an electric scooter, proposing a novel analytical design method. First, the dimensioning equations of the motor are obtained based on the vehicle requirements, and the stationary dq model is calculated. Then, the motor is optimized using a multiobjective genetic algorithm, and finally a 3D-FEM verification is made. Both the motor structure and the design method aim to have a low complexity, in order to favor the sizing and manufacturing processes through a low computation time and simple core shapes. This approach has not yet been explored in axial-gap TFMs.
Does CSR Affect the Profitability and Valuation of Energy Companies? An Example from Poland
Authors: Zieliński Mariusz , Jonek-Kowalska Izabela
Published: June 2021

The article refers to the issues of financial profitability of undertaking CSR activities, which is widely reported in literature. The four largest electricity producing companies in Poland were selected for the analysis. The research period covers the years 2009–2019, when the index of socially responsible companies was operating on the Warsaw Stock Exchange. The main purpose of the article is to compare the profitability ratios and quotations of energy companies in Poland declaring themselves socially responsible with companies of the same sector that have not expressed such a declaration. The results obtained on the basis of descriptive statistics, concerning profitability ratios and stock market quotations, indicate no relationship between their level and stability and the companies’ declarations of compliance with social responsibility. Companies declaring themselves socially responsible were placed in the ranking between the results of companies that did not belong to the indicated index. This may be the result of the specific situation of energy companies in Poland.
Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study
Authors: Babić Darko , Babić Dario , Fiolić Mario , Šarić Željko
Published: June 2021

Advanced Driver Assistance System (ADAS) represents a collection of vehicle-based intelligent safety systems. One in particular, Traffic Sign Recognition System (TSRS), is designed to detect and interpret roadside information in the form of signage. Even though TSRS has been on the market for more than a decade now, the available ones differ in hardware and software solutions they use, as well as in quantity and typology of signs they recognize. The aim of this study is to determine whether differences between detection and readability accuracy of market-ready TSRS exist and to what extent, as well as how different levels of “graphical changes” on the signs affect their accuracy. For this purpose, signs (“speed limit” and “prohibition of overtaking”) were placed on a test field and 17 vehicles from 14 different car brands underwent testing. Overall, the results showed that sign detection and readability by TSRS differ between car brands and that even small changes in the design of signs can drastically affect TSRS accuracy. Even in a controlled environment where no sign has been altered, there has been a 5% margin of misread signs.
Two-Stage Biodiesel Synthesis from Used Cooking Oil with a High Acid Value via an Ultrasound-Assisted Method
Authors: Hsiao Ming-Chien , Lin Wei-Ting , Chiu Wei-Cheng , Hou Shuhn-Shyurng
Published: June 2021

In this study, ultrasound was used to accelerate two-stage (esterification–transesterification) catalytic synthesis of biodiesel from used cooking oil, which originally had a high acid value (4.35 mg KOH/g). In the first stage, acid-catalyzed esterification reaction conditions were developed with a 9:1 methanol/oil molar ratio, sulfuric acid dosage at 2 wt %, and a reaction temperature of 60 °C. Under ultrasound irradiation for 40 min, the acid value was effectively decreased from 4.35 to 1.67 mg KOH/g, which was decreased to a sufficient level (<2 mg KOH/g) to avoid the saponification problem for the subsequent transesterification reaction. In the following stage, base-catalyzed transesterification reactions were carried out with a 12:1 methanol/oil molar ratio, a sodium hydroxide dosage of 1 wt %, and a reaction temperature of 65 °C. Under ultrasound-assisted transesterification for 40 min, the conversion rate of biodiesel reached 97.05%, which met the requirement of EN 14214 standard, i.e., 96.5% minimum. In order to evaluate and explore the improvement of the ultrasound-assisted two-stage (esterification–transesterification) process in shortening the reaction time, additional two-stage biodiesel synthesis experiments using the traditional mechanical stirring method under the optimal conditions were further carried out in this study. It was found that, under the same optimal conditions, using the ultrasound-assisted two-stage process, the total reaction time was significantly reduced to only 80 min, which was much shorter than the total time required by the conventional method of 140 min. It is worth noting that compared with the traditional method without ultrasound, the intensification of the ultrasound-assisted two-stage process significantly shortened the total time from 140 min to 80 min, which is a reduction of 42.9%. It was concluded that the ultrasound-assisted two-stage (esterification–transesterification) catalytic process is an effective and time-saving method for synthesizing biodiesel from used cooking oil with a high acid value.
Research on Temperature Monitoring Method of Cable on 10 kV Railway Power Transmission Lines Based on Distributed Temperature Sensor
Authors: Chen Kai , Yue Yi , Tang Yuejin
Published: June 2021

Railway power transmission lines (RPTL) are power lines that provide nontraction power supply for railways, such as communications and signals along the railway. With the advancement of technology, power cables are being used more and more widely. Operational experience has shown that during the operation of power cables, abnormal heat is often caused by fault factors such as poor joint crimping and severe partial discharge caused by insulation defects, leading to cable burns in extreme cases. Distributed temperature sensors (DTS), a kind of spatial continuous temperature sensor using sensing optical fiber, can measure the temperature along the cable and are expected to realize on-line monitoring and positioning of cable heating faults. This paper first builds a finite element model of the cable under various faults to calculate the distribution characteristics of the temperature field of the faulty cable. Then the results are verified through experiments with the external sensing fiber and the artificially manufactured heating points of the cable. The conclusions show that it is feasible to use a distributed sensing fiber to monitor and locate the heating fault of power cable.
The Interconnection and Damping Assignment Passivity-Based Control Synthesis via the Optimal Control Method for Electric Vehicle Subsystems
Authors: Lozynskyy Andriy , Perzyński Tomasz , Kozyra Jacek , Biletskyi Yurii , Kasha Lidiia
Published: June 2021

The interconnection between optimal control theory and the theory of energy-shaping control is described in our paper. For linear and nonlinear systems, the application of the theory of optimal control for the synthesis of parameters of energy-shaping control matrices is demonstrated in detail. The use of a Riccati equation allows us to form an optimality criterion and to synthesize the energy-shaping control system that provides the desired transient processes. The proposed approach was applied to the synthesis of control influences for electric vehicle subsystems, such as a two-mass system and a permanent magnets synchronous motor. The results of computer simulation studies, as well as those conducted on real experimental installations, are given in this paper.
Investigation of Wood Flour Size, Aspect Ratios, and Injection Molding Temperature on Mechanical Properties of Wood Flour/Polyethylene Composites
Authors: Golmakani Mohammad E. , Wiczenbach Tomasz , Malikan Mohammad , Aliakbari Reza , Eremeyev Victor A.
Published: June 2021

In the present research, wood flour reinforced polyethylene polymer composites with a coupling agent were prepared by injection molding. The effects of wood flour size, aspect ratios, and mold injection temperature on the composites’ mechanical properties were investigated. For the preparation of the polymer composites, five different formulations were created. The mechanical properties including tensile strength and the modulus, flexural strength and the modulus, and impact energy were measured. To investigate the changes in the properties resulting from different compositions, mechanical static and impact testing was performed. The obtained results indicate that by reducing the flour size, the tensile strength and modulus, flexural strength, and impact energy were reduced. In contrast, the flexural modulus increased. Furthermore, with the increment of injection molding temperature, the tensile strength and the modulus and the impact energy of the specimens were reduced. On the other hand, the flexural strength and the modulus increased. Thus, an optimized amount of injection molding temperature can provide improvements in the mechanical properties of the composite.
Association between Preoperative Long-Chain Polyunsaturated Fatty Acids and Oxidative Stress Immediately after Total Knee Arthroplasty: A Pilot Study
Authors: Kubo Yusuke , Ikeya Masae , Sugiyama Shuhei , Takachu Rie , Tanaka Maki , Sugiura Takeshi , Kobori Kaori , Kobori Makoto
Published: June 2021

Quadriceps muscle atrophy following total knee arthroplasty (TKA) can be caused by tourniquet-induced ischemia–reperfusion (IR) injury, which is often accompanied by oxidative stress and inflammatory responses. n-3 long-chain polyunsaturated fatty acids (LCPUFAs), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), exert antioxidant and anti-inflammatory effects against IR injury, whereas n-6 LCPUFAs, particularly arachidonic acid (AA), exhibit pro-inflammatory effects and promote IR injury. This study aimed to examine whether preoperative serum EPA + DHA levels and the (EPA + DHA)/AA ratio are associated with oxidative stress immediately after TKA. Fourteen eligible patients with knee osteoarthritis scheduled for unilateral TKA participated in this study. The levels of serum EPA, DHA, and AA were measured immediately before surgery. Derivatives of reactive oxygen metabolites (d-ROMs) were used as biomarkers for oxidative stress. The preoperative serum EPA + DHA levels and the (EPA + DHA)/AA ratio were found to be significantly negatively correlated with the serum d-ROM levels at 96 h after surgery, and the rate of increase in serum d-ROM levels between baseline and 96 h postoperatively. This study suggested the preoperative serum EPA + DHA levels and the (EPA + DHA)/AA ratio can be negatively associated with oxidative stress immediately after TKA.
Breastfeeding Prevalence in Austria according to the WHO IYCF Indicators—The SUKIE-Study
Authors: Bürger Bernadette , Schindler Karin , Tripolt Tanja , Stüger Hans Peter , Wagner Karl-Heinz , Weber Adelheid , Wolf-Spitzer Alexandra
Published: June 2021

Breastfeeding and infant nutrition have an important impact on child health. The last representative data on breastfeeding in Austria was collected in 2006. The SUKIE-Study (Säuglings- und Kinderernährung) is a representative, longitudinal survey (online questionnaire) for participating mothers at four time points (14 days, four, six and 12 months post-partum). Questions on when other foods were first introduced were asked retrospectively. To ensure international comparisons, the World Health Organization’s definitions for breastfeeding, including “Infant and Young Child Feeding” indicators, were used. After eligibility screening, 1214 of 1666 invited mothers were included in the analysis. The initial breastfeeding rate was 97.5% and was reduced to 40.8% after 12 months. The rate of exclusive breastfeeding at one week of age was 55.5% and decreased to 1.9% after six months. Half of the infants received infant formula for the first time within the first three days of life (median). Out of the mothers that did wean breastfeeding in the first 12 months, the median duration was 27 weeks (right-censored data). Compared with 2006, an increase (93.2% to 97.5%) in the initial breastfeeding rate was found. However, other findings show that breastfeeding duration, including exclusive breastfeeding rates, need further improvement.
Household Food Insecurity, Lung Function, and COPD in US Adults
Authors: de Castro Mendes Francisca , Ducharme-Smith Kirstie , Mora-Garcia Gustavo , Alqahtani Saleh A. , Ruiz-Diaz Maria Stephany , Moreira Andre , Villegas Rodrigo , Garcia-Larsen Vanessa
Published: June 2021

Increasing epidemiological evidence suggests that optimal diet quality helps to improve preservation of lung function and to reduce chronic obstructive pulmonary disease (COPD) risk, but no study has investigated the association of food insecurity (FI) and lung health in the general population. Using data from a representative sample of US adults who participated in the National Health and Nutrition Examination Survey (NHANES) 2007–2012 cycles, we investigated the association between FI with lung function and spirometrically defined COPD in 12,469 individuals aged ≥ 18 years of age. FI (high vs. low) was defined using the US Department of Agriculture’s Food Security Scale). Population-weighted adjusted regression models were used to investigate associations between FI, and forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), their ratio, and spirometrically defined restriction (FVC below the lower limit of normal) and airflow obstruction (COPD). The prevalence of household FI was 13.2%. High household FI was associated with lower FVC (adjusted β-coefficient −70.9 mL, 95% CI −116.6, −25.3), and with higher odds (OR) of spirometric restriction (1.02, 95% CI 1.00, 1.03). Stratified analyses showed similar effect sizes within specific ethnic groups. High FI was associated with worse lung health in a nationally representative sample of adults in the US.
Clinical Significance of Micronutrient Supplementation in Critically Ill COVID-19 Patients with Severe ARDS
Authors: Notz Quirin , Herrmann Johannes , Schlesinger Tobias , Helmer Philipp , Sudowe Stephan , Sun Qian , Hackler Julian , Roeder Daniel , Lotz Christopher , Meybohm Patrick , Kranke Peter , Schomburg Lutz , Stoppe Christian
Published: June 2021

The interplay between inflammation and oxidative stress is a vicious circle, potentially resulting in organ damage. Essential micronutrients such as selenium (Se) and zinc (Zn) support anti-oxidative defense systems and are commonly depleted in severe disease. This single-center retrospective study investigated micronutrient levels under Se and Zn supplementation in critically ill patients with COVID-19 induced acute respiratory distress syndrome (ARDS) and explored potential relationships with immunological and clinical parameters. According to intensive care unit (ICU) standard operating procedures, patients received 1.0 mg of intravenous Se daily on top of artificial nutrition, which contained various amounts of Se and Zn. Micronutrients, inflammatory cytokines, lymphocyte subsets and clinical data were extracted from the patient data management system on admission and after 10 to 14 days of treatment. Forty-six patients were screened for eligibility and 22 patients were included in the study. Twenty-one patients (95%) suffered from severe ARDS and 14 patients (64%) survived to ICU discharge. On admission, the majority of patients had low Se status biomarkers and Zn levels, along with elevated inflammatory parameters. Se supplementation significantly elevated Se (p = 0.027) and selenoprotein P levels (SELENOP; p = 0.016) to normal range. Accordingly, glutathione peroxidase 3 (GPx3) activity increased over time (p = 0.021). Se biomarkers, most notably SELENOP, were inversely correlated with CRP (rs = −0.495), PCT (rs = −0.413), IL-6 (rs = −0.429), IL-1β (rs = −0.440) and IL-10 (rs = −0.461). Positive associations were found for CD8+ T cells (rs = 0.636), NK cells (rs = 0.772), total IgG (rs = 0.493) and PaO2/FiO2 ratios (rs = 0.504). In addition, survivors tended to have higher Se levels after 10 to 14 days compared to non-survivors (p = 0.075). Sufficient Se and Zn levels may potentially be of clinical significance for an adequate immune response in critically ill patients with severe COVID-19 ARDS.
The Role of Plant-Based Protein Functional Food in Preventing Acute Respiratory Disease: A Case Study
Authors: Tarasov Andrei V. , Rakhmanov Rofail S. , Bogomolova Elena S. , Perminova Ludmila A. , Malakhova Zhanna L.
Published: June 2021

The Kaliningrad region is known for its specific climate, which can negatively affect the adaptive potential of the body. This manifests in an increased incidence of respiratory diseases and skin conditions. To prevent high morbidity, a plant protein product was included in the diet of first-year university students. This study aimed to assess the effectiveness of this food intervention in preventing the most common diseases among Kaliningrad students. Two groups of university students took part in the food trial. In the control group, catabolic processes prevailed in nutrient metabolism. Disadaptation manifested itself in the metabolism of proteins, vitamins, minerals, hematopoiesis and humoral immunity. Inflammation was indicated by α1- and α2-globulins, a weak immune response, and IgM and IgG. High oxidative stress and low antioxidative ability of blood serum were observed. The plant-based protein product (FP) helped preserve testosterone level and prevent an increase in catabolic reactions. Moreover, it had a positive effect on both red blood cell hematopoiesis (a smaller increase in the average volume of erythrocytes, the same average concentration and content of hemoglobin, an increased relative red cell distribution width (RDW) and white blood cell hematopoiesis (a beneficial effect for the immune system: lymphocytes, the relative content of neutrophils, monocytes, basophils and eosinophils). The stimulation of humoral immunity was evidenced by beta- and gamma-globulins, an active immune response, the level of IgM and IgG, antioxidant protection, reduction of peroxides and an increase in antioxidant activity of blood serum. The 34-week observation showed a 1.7-fold decrease in the incidence of respiratory illnesses and a 5.7-fold decrease in skin and subcutaneous tissue diseases. Acute respiratory infections were reduced 1.8-fold. There were no cases of community-acquired pneumonia in the treatment group, compared with 55.1‰ in the control group. The incidence of respiratory diseases was 3.3–10.6 times lower in the treatment group than in the control group in weeks 6–19. The findings testify to the prophylactic effect of functional food during social adaptation and acclimatization of students.
Could Vitamin D3 Deficiency Influence Malocclusion Development?
Authors: Leszczyszyn Anna , Hnitecka Sylwia , Dominiak Marzena
Published: June 2021

The abnormal growth of the craniofacial bone leads to skeletal and dental defects, which result in the presence of malocclusions. Not all causes of malocclusion have been explained. In the development of skeletal abnormalities, attention is paid to general deficiencies, including of vitamin D3 (VD3), which causes rickets. Its chronic deficiency may contribute to skeletal malocclusion. The aim of the study was to assess the impact of VD3 deficiency on the development of malocclusions. The examination consisted of a medical interview, oral examination, an alginate impression and radiological imaging, orthodontic assessment, and taking a venous blood sample for VD3 level testing. In about 42.1% of patients, the presence of a skeletal defect was found, and in 46.5% of patients, dentoalveolar malocclusion. The most common defect was transverse constriction of the maxilla with a narrow upper arch (30.7%). The concentration of vitamin 25 (OH) D in the study group was on average 23.6 ± 10.5 (ng/mL). VD3 deficiency was found in 86 subjects (75.4%). Our research showed that VD3 deficiency could be one of an important factor influencing maxillary development. Patients had a greater risk of a narrowed upper arch (OR = 4.94), crowding (OR = 4.94) and crossbite (OR = 6.16). Thus, there was a link between the deficiency of this hormone and the underdevelopment of the maxilla.
Lawsone Derivatives as Efficient Photopolymerizable Initiators for Free-Radical, Cationic Photopolymerizations, and Thiol—Ene Reactions
Authors: Elian Christine , Brezová Vlasta , Sautrot-Ba Pauline , Breza Martin , Versace Davy-Louis
Published: June 2021

Two new photopolymerizable vinyl (2-(allyloxy) 1,4-naphthoquinone, HNQA) and epoxy (2-(oxiran-2yl methoxy) 1,4-naphthoquinone, HNQE) photoinitiators derived from lawsone were designed in this paper. These new photoinitiators can be used as one-component photoinitiating systems for the free-radical photopolymerization of acrylate bio-based monomer without the addition of any co-initiators. As highlighted by the electron paramagnetic resonance (EPR) spin-trapping results, the formation of carbon-centered radicals from an intermolecular H abstraction reaction was evidenced and can act as initiating species. Interestingly, the introduction of iodonium salt (Iod) used as a co-initiator has led to (1) the cationic photopolymerization of epoxy monomer with high final conversions and (2) an increase of the rates of free-radical polymerization of the acrylate bio-based monomer; we also demonstrated the concomitant thiol–ene reaction and cationic photopolymerizations of a limonene 1,2 epoxide/thiol blend mixture with the HNQA/Iod photoinitiating system.
Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions
Authors: Wei Chih-Chiang , Huang Tzu-Heng
Published: June 2021

Taiwan is located at the edge of the northwestern Pacific Ocean and within a typhoon zone. After typhoons are generated, strong winds and heavy rains come to Taiwan and cause major natural disasters. This study employed fully convolutional networks (FCNs) to establish a forecast model for predicting the hourly rainfall data during the arrival of a typhoon. An FCN is an advanced technology that can be used to perform the deep learning of image recognition through semantic segmentation. FCNs deepen the neural net layers and perform upsampling on the feature map of the final convolution layer. This process enables FCN models to restore the size of the output results to that of the raw input image. In this manner, the classification of each raw pixel becomes feasible. The study data were radar echo images and ground station rainfall information for typhoon periods during 2013–2019 in southern Taiwan. Two model cases were designed. The ground rainfall image-based FCN (GRI_FCN) involved the use of the ground rain images to directly forecast the ground rainfall. The GRI combined with rain retrieval image-based modular convolutional neural network (GRI-RRI_MCNN) involved the use of radar echo images to determine the ground rainfall before the prediction of future ground rainfall. Moreover, the RMMLP, a conventional multilayer perceptron neural network, was used to a benchmark model. Forecast horizons varying from 1 to 6 h were evaluated. The results revealed that the GRI-RRI_MCNN model enabled a complete understanding of the future rainfall variation in southern Taiwan during typhoons and effectively improved the accuracy of rainfall forecasting during typhoons.
A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information
Authors: Bai Kemeng , Wang Jianzhong , Wang Hongfeng
Published: June 2021

Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.
A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications
Authors: Estrela Priscila Morais Argôlo Bonfim , Albuquerque Robson de Oliveira , Amaral Dino Macedo , Giozza William Ferreira , Júnior Rafael Timóteo de Sousa
Published: June 2021

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.
Sensitive pH Monitoring Using a Polyaniline-Functionalized Fiber Optic—Surface Plasmon Resonance Detector
Authors: Antohe Iulia , Jinga Luiza-Izabela , Antohe Vlad-Andrei , Socol Gabriel
Published: June 2021

In this work, we report results on the fabrication and characterization of a surface plasmon resonance (SPR) pH sensor using platinum (Pt) and polyaniline (PANI) layers successively coated over an unclad core of an optical fiber (FO). The plasmonic thin Pt layer was deposited using a magnetron sputtering technique, while the pH-sensitive PANI layer was synthesized using an electroless polymerization method. Moreover, the formation of PANI film was confirmed by X-ray photoelectron spectroscopy (XPS) technique and its surface morphology was investigated using scanning electron microscopy (SEM). It was found that the PANI/Pt-coated FO-SPR pH sensor exhibits a fast and linear response in either acid or alkali solutions (pH operational range: 1 to 14). The proposed FO-SPR sensor could be used for biomedical applications, environmental monitoring or any remote, real-time on-site measurements.
Paraconsistent Annotated Logic Algorithms Applied in Management and Control of Communication Network Routes
Authors: Da Silva Filho João Inácio , Abe Jair Minoro , Marreiro Alessandro de Lima , Martinez Angel Antonio Gonzalez , Torres Cláudio Rodrigo , Rocco Alexandre , Côrtes Hyghor Miranda , Mario Mauricio Conceição , Pacheco Marcos Tadeu Tavares , Garcia Dorotéa Vilanova , Blos Maurício Fontoura
Published: June 2021

This paper presents a computational method based on non-classical logic dedicated to routing management and information stream control in communication networks. Paraconsistent logic (PL) was used to create an algorithmic structure whose main property is to accept contradiction. Moreover, a computational structure, the denominated paraconsistent data analyzer (PDAPAL2v), was constructed to perform routing management in communication networks. Direct comparisons of PDAPAL2v with a classical logic system that simulates routing conditions were made in the laboratory. In the conventional system, the paraconsistent algorithms were considered as binary logic gates, and in the tests, the same adjustment limits of PDAPAL2v were applied. Using a database with controlled insertion of noise, we obtained an efficacy of 97% in the detection of deteriorated packets with PDAPAL2v and 72% with the conventional simulation system. Functional tests were carried out, showing that PDAPAL2v is able to assess the conditions and degradation of links and perform the analysis and correlation of various inputs and variables, even if the signals have contradictory values. From practical tests in the laboratory, the proposed method represents a new way of managing and controlling communication network routes with good performance.
An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector Embedding
Authors: Hoque Mohammad Shamsul , Jamil Norziana , Amin Nowshad , Lam Kwok-Yan
Published: June 2021

Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.