bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time PCR Assay for Rapid and Simultaneous Detection of vanA and vanB Genes in Clinical Strains
Authors: Zerrouki Hanane , Rebiahi Sid-Ahmed , Hadjadj Linda , Rolain Jean-Marc , Diene Seydina M.
Published: November 2021

Here, we develop a robust and sensitive real-time PCR assay which allows the simultaneous detection of vanA and vanB genes using common primers. The system was designed using the Primer3 online software. The specificity of primers and probes was first checked by in silico PCR and by BlastN analysis. The genomic DNA of 255 bacterial isolates, including Enterococcus spp., Gram-negative, and Gram-positive strains, as well as a collection of 50 stool and 50 rectal swab samples, were tested to evaluate the specificity of the new real-time PCR (RT-PCR) system. The results of the designed RT-PCR were 100% specific and 100% positive on tested vancomycin resistant isolates harboring either the vanA or vanB gene. RT-PCR assays were negative for all other bacterial species tested including vancomycin-sensitive Enterococci and Enterococcus strains harboring vanC genes. The limit of detection of vanA and vanB genes by RT-PCR assay was 47 CFU/mL and 32 CFU/mL, respectively. The rapid and accurate detection of vancomycin-resistant Enterococci is the cornerstone for minimizing the risk of nosocomial transmissions and outbreaks. We believe that this assay will strengthen routine diagnostics and surveillance programs.
β-Defensin 2, an Antimicrobial Peptide, as a Novel Biomarker for Ulcerative Interstitial Cystitis; Can β-Defensin 2 Suspect the Dysbiosis of Urine Microbiota?
Authors: Lee Sang Wook , Kim Si Hyun , Lee Kwang Woo , Kim Woong Bin , Choi Hae Woong , Moon Ji Eun , Moon Ahrim , Kim Young Ho
Published: November 2021

As urine is not sterile, inflammatory reactions caused by dysbiosis of the urinary microbiota may induce interstitial cystitis. A study was conducted to determine whether β-defensin 2 (BD-2), a specific antimicrobial peptide in the bladder, could be used as a novel diagnostic marker for ulcerative interstitial cystitis (IC). Urine samples from three female groups were examined: healthy controls (n = 34, Control group), non-Hunner type IC (n = 40, NHIC group), and Hunner type IC (n = 68, HIC group). Urine samples were collected via a transurethral catheter and assayed for BD-2 levels using enzyme linked immunosorbent assay. Under general or regional anesthesia, cystoscopy with diagnostic and therapeutic hydrodistension was performed in NHIC and HIC groups patients. These patients underwent a biopsy of the bladders. Based on the urinary specimens from 142 patients, BD-2 expression was found to be 18-fold higher in patients with Hunner type IC than in patients with non-Hunner type IC. The enhanced secretion of BD-2 exhibited a strong correlation with increased mast cell counts associated with bladder IC pathology. Enhanced urinary secretion of the antimicrobial peptide BD-2 from Hunner type IC patients associated with clinical phenotypes and demonstrated relatively robust levels to be used as a potential biomarker. Moreover, the increased urinary level of BD-2 may suggest a new possibility of biomarkers caused by dysbiosis of the urinary microbiota in ulcerative IC.
Energy, Exergy, Exergoeconomic and Exergoenvironmental Impact Analyses and Optimization of Various Geothermal Power Cycle Configurations
Authors: Shamoushaki Moein , Aliehyaei Mehdi , Rosen Marc A.
Published: November 2021

Energy, exergy, and exergoeconomic evaluations of various geothermal configurations are reported. The main operational and economic parameters of the cycles are evaluated and compared. Multi-objective optimization of the cycles is conducted using the artificial bee colony algorithm. A sensitivity assessment is carried out on the effect of production well temperature variation on system performance from energy and economic perspectives. The results show that the flash-binary cycle has the highest thermal and exergy efficiencies, at 15.6% and 64.3%, respectively. The highest generated power cost and pay-back period are attributable to the simple organic Rankine cycle (ORC). Raising the well-temperature can increase the exergy destruction rate in all configurations. However, the electricity cost and pay-back period decrease. Based on the results, in all cases, the exergoenvironmental impact improvement factor decreases, and the temperature rises. The exergy destruction ratio and efficiency of all components for each configuration are calculated and compared. It is found that, at the optimum state, the exergy efficiencies of the simple organic Rankine cycle, single flash, double flash, and flash-binary cycles respectively are 14.7%, 14.4%, 12.6%, and 14.1% higher than their relevant base cases, while the pay-back periods are 10.6%, 1.5% 1.4%, and 0.6% lower than the base cases.
Full-Duplex Relay with Delayed CSI Elevates the SDoF of the MIMO X Channel
Authors: Zhang Tong , Chen Gaojie , Wang Shuai , Wang Rui
Published: November 2021

In this article, the sum secure degrees-of-freedom (SDoF) of the multiple-input multiple-output (MIMO) X channel with confidential messages (XCCM) and arbitrary antenna configurations is studied, where there is no channel state information (CSI) at two transmitters and only delayed CSI at a multiple-antenna, full-duplex, and decode-and-forward relay. We aim at establishing the sum-SDoF lower and upper bounds. For the sum-SDoF lower bound, we design three relay-aided transmission schemes, namely, the relay-aided jamming scheme, the relay-aided jamming and one-receiver interference alignment scheme, and the relay-aided jamming and two-receiver interference alignment scheme, each corresponding to one case of antenna configurations. Moreover, the security and decoding of each scheme are analyzed. The sum-SDoF upper bound is proposed by means of the existing SDoF region of two-user MIMO broadcast channel with confidential messages (BCCM) and delayed channel state information at the transmitter (CSIT). As a result, the sum-SDoF lower and upper bounds are derived, and the sum-SDoF is characterized when the relay has sufficiently large antennas. Furthermore, even assuming no CSI at two transmitters, our results show that a multiple-antenna full-duplex relay with delayed CSI can elevate the sum-SDoF of the MIMO XCCM. This is corroborated by the fact that the derived sum-SDoF lower bound can be greater than the sum-SDoF of the MIMO XCCM with output feedback and delayed CSIT.
On the Neutrosophic, Pythagorean and Some Other Novel Fuzzy Sets Theories Used in Decision Making: Invitation to Discuss
Authors: Sevastjanov Pavel , Dymova Ludmila , Kaczmarek Krzysztof
Published: November 2021

In this short paper, a critical analysis of the Neutrosophic, Pythagorean and some other novel fuzzy sets theories foundations is provided, taking into account that they actively used for the solution of the decision-making problems. The shortcomings of these theories are exposed. It is stated that the independence hypothesis, which is a cornerstone of the Neutrosophic sets theory, is not in line with common sense and therefore leads to the paradoxical results in the asymptotic limits of this theory. It is shown that the Pythagorean sets theory possesses questionable foundations, the sense of which cannot be explained reasonably. Moreover, this theory does not completely solve the declared problem. Similarly, important methodological problems of other analyzed theories are revealed. To solve the interior problems of the Atanassov’s intuitionistic fuzzy sets and to improve upon them, this being the reason most of the criticized novel sets theories were developed, an alternative approach based on extension of the intuitionistic fuzzy sets in the framework of the Dempster–Shafer theory is proposed. No propositions concerned with the improvement of the Cubic sets theory and Single-Valued Neutrosophic Offset theory were made, as their applicability was shown to be very dubious. In order to stimulate discussion, many statements are deliberately formulated in a hardline form.
A Rapid Transition from Campus to Emergent Distant Education; Effects on Students’ Study Strategies in Higher Education
Authors: Boström Lena , Collén Charlotta , Damber Ulla , Gidlund Ulrika
Published: November 2021

This article presents a literature review focusing on international research concerning distance education and students’ study strategies during the last 20 years. As distance education in higher education is a steadily growing trend and in particular because the COVID-19 pandemic has escalated the transition from campus education to different forms of distance education, knowledge about students’ study strategies and appropriate teaching strategies has become highly important. With this research review, we aimed to identify patterns and trends in research on distance education focusing students study strategies before and after the pandemic. The research synthesis identified and interpreted similarities and differences in the studies’ designs and findings, which we analyzed using integrative thematic analysis. Students’ study strategies seem to have changed to some extent during the pandemic, with more emphasis on their own responsibility and the need for a developed teaching strategy to align with the changing framework related to the emergency provision of distance education. We concluded that students have to develop metacognitive strategies, because self-regulated learning and a more flexible pedagogy seem to be important in teachers’ transitions and competence in digitalization. Therefore, more research targeting these aspects is needed.
Experimental Application of Bluetooth Low Energy Connectionless in Smart Cities
Authors: García-Ortiz Juan Carlos , Silvestre-Blanes Javier , Sempere-Payá Víctor
Published: November 2021

Communication networks are a key element in the development of Smart Cities. This field is a constantly evolving environment, for which new protocols are constantly appearing. Due to the heterogeneous nature of the technologies, the most appropriate candidate must be selected in order to get the best performance to satisfy the application requirements. One of these protocols is Bluetooth Low Energy (BLE), particularly with the upgrades introduced in version 5.x. Its new features are focused on providing increased range, improving robustness, and expanding beaconing capabilities. Connectionless applications such as information broadcasting in Smart Cities could take advantage of this protocol. Furthermore, the wide availability on common devices (mobile phones, car infotainment, etc.), the deployment of these applications can be carried out easily and at low cost. This paper presents an experimental evaluation of the new robust, long-range radio mode of BLE over a set of Smart Cities scenarios, taking into account different conditions such as wireless interference, distances, dynamicity, etc. The results show a promising performance of the protocol even with these constraints.
Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification
Authors: Lovera Fernando Andres , Cardinale Yudith Coromoto , Homsi Masun Nabhan
Published: November 2021

The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.
Radiation-Tolerant All-Digital PLL/CDR with Varactorless LC DCO in 65 nm CMOS
Authors: Biereigel Stefan , Kulis Szymon , Moreira Paulo , Kölpin Alexander , Leroux Paul , Prinzie Jeffrey
Published: November 2021

This paper presents the first fully integrated radiation-tolerant All-Digital Phase-Locked Loop (PLL) and Clock and Data Recovery (CDR) circuit for wireline communication applications. Several radiation hardening techniques are proposed to achieve state-of-the-art immunity to Single-Event Effects (SEEs) up to 62.5 MeV cm2 mg−1 as well as tolerance to the Total Ionizing Dose (TID) exceeding 1.5 Grad. The LC Digitally Controlled Oscillator (DCO) is implemented without MOS varactors, avoiding the use of a highly SEE sensitive circuit element. The circuit is designed to operate at reference clock frequencies from 40 MHz to 320 MHz or at data rates from 40 Mbps to 320 Mbps and displays a jitter performance of 520 fs with a power dissipation of only 11 mW and an FOM of −235 dB.
A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
Authors: Ge Yuwei , Zhang Tao , Liang Haihua , Jiang Qingfeng , Wang Dan
Published: November 2021

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.
Possibility of Power Electronics-Based Control Analysis of a Self-Excited Induction Generator (SEIG) for Wind Turbine and Electrolyzer Application
Authors: Na Woonki , Muljadi Eduard , Han Seungyun , Tagayi Roland Kobla , Kim Jonghoon
Published: November 2021

A self-excited induction generator (SEIG) is very simple and robust, has a reduced unit size, is easy to implement and simple to control, and requires very little maintenance compared to other types of generators. In variable operating conditions, the SEIG requires a power electronics interface to transform from the variable frequency voltage output of the generator to a battery voltage output or the related applications. In our study, we tied the SEIG to the power electronics system comprising a diode rectifier and DC/DC converter, and then a final DC load for fuel cell applications was connected. An example of such an application is an electrolyzer where an equivalent circuit is modeled for use in this study. To accomplish the proposed system, we utilized PSCAD and MATLAB for its simulation, control, and analysis. A new system configuration considering three different wind speeds and breaker conditions is modeled and analyzed. The results show that the suggested strategies in this study would contribute to designing and analyzing a more practical power electronics interface system for a wind turbine generator with a DC load.
Radio Access Evaluation of Commercial 5G Service
Authors: Martínez José Antonio , Moreno José Ignacio , Rivera Diego , Berrocal Julio
Published: November 2021

Wireless communication networks are enhancing faster than anyone could imagine. As everybody knows, 5G is the future and the study of it is very valuable nowadays. In this context, this paper provides a characterization of the deployment of a 5G access network by an operator in Spain, identifying its capacity and the actual use to which it is being subjected today. For this, sizing methods and tools will be used to qualify the capacity of the cells currently displayed, determining a better performance than we might initially think. This paper proposes a theoretical model which identifies relevant parameters for cell dimensioning, and determining that an expansion of cell’s capacity will be necessary at a 70% of load. Subsequently, this model is evaluated, analyzing real data via a vendor, showing a high performance, but discovering that some methods used in the current deployment, such as DSS, are, perhaps, not as expected. In addition, when comparing the 5G yield 4G, the power and potential future of the former is apparent.
RT-Sniper: A Low-Overhead Defense Mechanism Pinpointing Cache Side-Channel Attacks
Authors: Song Minkyu , Lee Junyeon , Suh Taeweon , Koo Gunjae
Published: November 2021

Since cache side-channel attacks have been serious security threats to multi-tenant systems, there have been several studies to protect systems against the attacks. However, the prior studies have limitations in determining only the existence of the attack and/or occupying too many computing resources in runtime. We propose a low-overhead pinpointing solution, called RT-Sniper, to overcome such limitations. RT-Sniper employs a two-level filtering mechanism to minimize performance overhead. It first monitors hardware events per core and isolates a suspected core to run a malicious process. Then among the processes running on the selected core, RT-Sniper pinpoints a malicious process through a per-process monitoring approach. With the core-level filtering, RT-Sniper has an advantage in overhead compared to the previous works. We evaluate RT-Sniper against Flush+Reload and Prime+Probe attacks running SPEC2017, LMBench, and PARSEC benchmarks on multi-core systems. Our evaluation demonstrates that the performance overhead by RT-Sniper is negligible (0.3% for single-threaded applications and 2.05% for multi-threaded applications). Compared to the previous defense solutions against cache side-channel attacks, RT-Sniper exhibits better detection performance with lower performance overhead.
Optimal Design of a Flyback Microinverter Operating under Discontinuous-Boundary Conduction Mode (DBCM)
Authors: Christidis Georgios , Nanakos Anastasios , Tatakis Emmanuel
Published: November 2021

The flyback converter has been widely used in Photovoltaic microinverters, operating either in Discontinuous, Boundary, or Continuous Conduction Mode (DCM, BCM, CCM). The recently proposed hybrid DBCM operation inherits the merits of both DCM and BCM. In this work, the necessary analytical equations describing the converter operation for any given condition under DBCM are derived, and are needed due to the hybrid nature of the modulation strategy during each sinusoidal wave. Based on this analysis, a design optimization sequence used to maximize the weighted efficiency of the inverter under DBCM is then applied. The design procedure is based on a power loss analysis for each converter component and focuses on the appropriate selection of the converter parameters. To achieve this, accurate, fully parameterized loss models of the converter components are implemented. The power loss analysis is then validated by applying the optimization methodology to build an experimental prototype operating in DBCM.
Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
Authors: Sadeghi Mohammad , Mollahasani Shahram , Erol-Kantarci Melike
Published: November 2021

Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model.
Optimal Slow Steaming Speed for Container Ships under the EU Emission Trading System
Authors: Goicoechea Nestor , Abadie Luis María
Published: November 2021

Slow steaming is an operational measure in ocean-going vessels sailing at slow speeds. It can help climate mitigation efforts by cutting down marine fuel consumption and consequently reducing CO2 and other Greenhouse Gas Emissions (GHG). Due to climate change both the European Union (EU) and the International Maritime Organization (IMO) are analysing the inclusion of international shipping in the EU Emissions Trading System (ETS) in the near future or alternatively implementing a carbon tax. The paper proposes a methodology to decide the optimal speed of a vessel taking into account its characteristics and the factors that determine its economic results. The calculated cash flow can be used in valuation models. The methodology is applied for a case study for any container ship in a range from 2000 to 20,000 Twenty-foot Equivalent Units (TEU) on a leg of a round trip from Shanghai to Rotterdam. We calculate how speed reduction, CO2 emissions and ship owner’s earnings per year may vary between a business-as-usual scenario and a scenario in which shipping is included in the ETS. The analysis reveals that the optimal speed varies with the size of the vessel and depends on several variables such as marine fuel prices, cargo freight rates and other voyage costs. Results show that the highest optimal speed is in the range of 5500–13,000 TEUs whether or not the ETS is applied. As the number of TEUs transported in a vessel increases emissions per TEU decrease. In an established freight rate market, the optimal speed fluctuates by 1.8 knots. Finally, the medium- and long-term expectations for slow steaming are analysed based on future market prices.
Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation
Authors: Sanz-Gorrachategui Iván , Pastor-Flores Pablo , Bono-Nuez Antonio , Ferrer-Sánchez Cora , Guillén-Asensio Alejandro , Bernal-Ruiz Carlos
Published: November 2021

Battery parameters such as State of Charge (SoC) and State of Health (SoH) are key to modern applications; thus, there is interest in developing robust algorithms for estimating them. Most of the techniques explored to this end rely on a battery model. As batteries age, their behavior starts differing from the models, so it is vital to update such models in order to be able to track battery behavior after some time in application. This paper presents a method for performing online battery parameter tracking by using the Extremum Seeking (ES) algorithm. This algorithm fits voltage waveforms by tuning the internal parameters of an estimation model and comparing the voltage output with the real battery. The goal is to estimate the electrical parameters of the battery model and to be able to obtain them even as batteries age, when the model behaves different than the cell. To this end, a simple battery model capable of capturing degradation and different tests have been proposed to replicate real application scenarios, and the performance of the ES algorithm in such scenarios has been measured. The results are positive, obtaining converging estimations both with new and aged batteries, with accurate outputs for the intended purpose.
Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
Authors: Kothe Anna-Katharina , Kuptel Alexander , Seidl Roman
Published: November 2021

A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO2 abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM.
High Voltage Redox-Meditated Flow Batteries with Prussian Blue Solid Booster
Authors: Ostrander John , Younesi Reza , Mogensen Ronnie
Published: November 2021

This work presents Prussian blue solid boosters for use in high voltage redox-mediated flow batteries (RMFB) based on non-aqueous electrolytes. The system consisted of sodium iodide as a redox mediator in an acetonitrile catholyte containing solid Prussian blue powder. The combination enabled the solid booster utilization in the proposed systems to reach as high as 66 mAh g−1 for hydrated Prussian blue and 110 mAh g−1 for anhydrous rhombohedral Prussian blue in cells with an average potential of about 3 V (vs. Na+/Na). Though the boosted system suffers from capacity fading, it opens up possibilities to develop non-aqueous RMFB with low-cost materials.
Spatial Model of Optimization Applied in the Distributed Generation Photovoltaic to Adjust Voltage Levels
Authors: Cararo José A. G. , Caetano Neto João , Vilela Júnior Wagner A. , Reis Márcio R. C. , Wainer Gabriel A. , dos Santos Paulo V. , Calixto Wesley P.
Published: November 2021

The main objective of this work is to develop a methodology for analyzing the quality of the voltage level in the distribution power grid to identify and reduce the violations of voltage limits through the proposition of optimal points for the allocation of photovoltaic distributed generation. The methodology uses the geographic location of the power grid and its consumers to perform the grouping and classification in spatial grids of 100 × 100 m using the average annual consumption profile. The generated profiles, including the grid information, are sent to the photovoltaic distributed generation allocation algorithm, which, using an optimization process, identifies the geographic location, the required installed capacity, and the minimum number of photovoltaic generation units that must be inserted to minimize the violations of voltage limits, respecting the necessary restrictions. The entire proposal is applied in a real feeder with thousands of bars, whose model is validated with measurements carried out in the field. Different violations of voltage limits scenarios are used to validate the methodology, obtaining grids with better voltage quality after the optimized allocation of photovoltaic distributed generation. The proposal presents itself as a new tool in the work of adapting the voltage of the distribution power grid using photovoltaic distributed generation.
Remediation of Annular Gas Migration along Cemented Wellbores Using Reactive Mineral Fluids: Experimental Assessment of Sodium Bicarbonate and Sodium Silicate-Based Solutions
Authors: Wolterbeek Timotheus K. T. , Hangx Suzanne J. T.
Published: November 2021

Achieving zonal isolation along wellbores is essential for upholding the containment integrity of subsurface reservoirs and preventing fluid seepage to the environment. The sealing performance of Portland cements conventionally used to create barriers can be severely compromised by defects like fractures or micro-annuli along casing–cement–rock interfaces. A possible remediation method would be to circulate reactive fluids through compromised cement sections and induce defect clogging via mineral precipitation. We assess the sealing potential of two prospective fluids: sodium bicarbonate and sodium silicate solutions. Reactive flow-through experiments were conducted on 6-m-long cemented steel tubes, bearing ~20-μm-wide micro-annuli, at 50 °C and 0.3–6 MPa fluid pressure. For the sodium bicarbonate solution (90 g/kg-H2O), reactive flow yielded only a minor reduction in permeability, with values remaining within one order. Injection of sodium silicate solution (37.1 wt.%, SiO2:Na2O molar ratio M= 2.57) resulted in a large decrease in flow rate, effectively reaching the setup’s lower measurement limit in hours. However, this strong sealing effect can almost certainly be attributed to gelation of the fluid through polymerisation, rather than defect clogging via mineral precipitation. For both fluids investigated, the extent of solids precipitation resulting from single-phase injection was less than anticipated. This shortfall is attributed to ineffective/insufficient liberation of Ca-ions from the alkaline phases in the cement.
Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
Authors: Kajewska-Szkudlarek Joanna , Bylicki Jan , Stańczyk Justyna , Licznar Paweł
Published: November 2021

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.
Eco-Dimensioning Approach for Planar Transformer in a Dual Active Bridge (DAB) Application
Authors: de Freitas Lima Glauber , Rahmani Boubakr , Rio Maud , Lembeye Yves , Crebier Jean-Christophe
Published: November 2021

Power electronics converters are traditionally designed regarding efficiency, power density, cost, and reliability figures of merit. Today, with the extreme spread of power electronic applications in our modern societies, together with the earth limits in terms of materials resources, it is important to consider the ecological impact of the converter not only during its usage, but over its whole life cycle. This article introduces an eco-dimensioning methodology for analyzing and accounting for the energy consumption over the entire converter life. The analysis is applied on a small DC-DC converter considering the main components dual active bridge (DAB) converter. The planar transform is one of the key elements modeled in this article, including material and manufacturing conditions. The traditional and eco-dimensioning approaches are carried out and compared in order to emphasize the possible consequences on total energy cost.
Yellow Mealworm and Black Soldier Fly Larvae for Feed and Food Production in Europe, with Emphasis on Iceland
Authors: Thrastardottir Runa , Olafsdottir Hildur Thora , Thorarinsdottir Ragnheidur Inga
Published: November 2021

Insects are part of the diet of over 2 billion people worldwide; however, insects have not been popular in Europe, neither as food nor as a feed ingredient. This has been changing in recent years, due to increased knowledge regarding the nutritional benefits, the need for novel protein production and the low environmental impact of insects compared to conventional protein production. The purpose of this study is to give an overview of the most popular insects farmed in Europe, yellow mealworm, Tenebrio molitor, and black soldier fly (BSF), Hermetia illucens, together with the main obstacles and risks. A comprehensive literature study was carried out and 27 insect farming companies found listed in Europe were contacted directly. The results show that the insect farming industry is increasing in Europe, and the success of the frontrunners is based on large investments in technology, automation and economy of scale. The interest of venture capital firms is noticeable, covering 90% of the investment costs in some cases. It is concluded that insect farming in Europe is likely to expand rapidly in the coming years, offering new proteins and other valuable products, not only as a feed ingredient, but also for human consumption. European regulations have additionally been rapidly changing, with more freedom towards insects as food and feed. There is an increased knowledge regarding safety concerns of edible insects, and the results indicate that edible insects pose a smaller risk for zoonotic diseases than livestock. However, knowledge regarding risk posed by edible insects is still lacking, but food and feed safety is essential to put products on the European market.
Biscuit Contaminants, Their Sources and Mitigation Strategies: A Review
Authors: Pasqualone Antonella , Haider Noor N. , Summo Carmine , Coldea Teodora Emilia , George Saher S. , Altemimi Ammar B.
Published: November 2021

The scientific literature is rich in investigations on the presence of various contaminants in biscuits, and of articles aimed at proposing innovative solutions for their control and prevention. However, the relevant information remains fragmented. Therefore, the objective of this work was to review the current state of the scientific literature on the possible contaminants of biscuits, considering physical, chemical, and biological hazards, and making a critical analysis of the solutions to reduce such contaminations. The raw materials are primary contributors of a wide series of contaminants. The successive processing steps and machinery must be monitored as well, because if they cannot improve the initial safety condition, they could worsen it. The most effective mitigation strategies involve product reformulation, and the use of alternative baking technologies to minimize the thermal load. Low oxygen permeable packaging materials (avoiding direct contact with recycled ones), and reformulation are effective for limiting the increase of contaminations during biscuit storage. Continuous monitoring of raw materials, intermediates, finished products, and processing conditions are therefore essential not only to meet current regulatory restrictions but also to achieve the aim of banning dietary contaminants and coping with related diseases.
Preparation of Specific Nanobodies and Their Application in the Rapid Detection of Nodularin-R in Water Samples
Authors: Yang Jinyi , Si Rui , Wu Guangpei , Wang Yu , Fang Ruyu , Liu Fei , Wang Feng , Lei Hongtao , Shen Yudong , Zhang Qi , Wang Hong
Published: November 2021

Nanobodies have several advantages, including great stability, sensibility, and ease of production; therefore, they have become important tools in immunoassays for chemical contaminants. In this manuscript, nanobodies for the detection of the toxin Nodularin-r (NOD-R), a secondary metabolite of cyanobacteria that could cause a safety risk for drinks and food for its strong hepatotoxicity, were for the first time selected from an immunized Bactrian camel VHH phage display library. Then, a sensitive indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) for NOD-R, based on the nanobody N56 with great thermostability and organic solvent tolerance, was established under optimized conditions. The results showed that the limit of detection for NOD-R was 0.67 µg/L, and the average spike recovery rate was between 84.0 and 118.3%. Moreover, the ic-ELISA method was validated with spiked water sample and confirmed by UPLC–MS/MS, which indicated that the ic-ELISA established in this work is a reproducible detection assay for nodularin residues in water samples.
Discovery of 178 Giant Radio Galaxies in 1059 deg2 of the Rapid ASKAP Continuum Survey at 888 MHz
Authors: Andernach Heinz , Jiménez-Andrade Eric F. , Willis Anthony G.
Published: November 2021

We report the results of a visual inspection of images of the Rapid ASKAP Continuum Survey (RACS) in search of extended radio galaxies (ERG) that reach or exceed linear sizes on the order of one Megaparsec. We searched a contiguous area of 1059 deg2 from RAJ = 20h20m to 06h20m, and −50∘<DecJ<−40∘, which is covered by deep multi-band optical images of the Dark Energy Survey (DES) and in which previously only three ERGs larger than 1 Mpc had been reported. For over 1800 radio galaxy candidates inspected, our search in optical and infrared images resulted in hosts for 1440 ERG, for which spectroscopic and photometric redshifts from various references were used to convert their largest angular size (LAS) to projected linear size (LLS). This resulted in 178 newly discovered giant radio sources (GRS) with LLS >1 Mpc, of which 18 exceed 2 Mpc and the largest one is 3.4 Mpc. Their redshifts range from 0.02 to ∼2.0, but only 10 of the 178 new GRS have spectroscopic redshifts. For the 146 host galaxies, the median r-band magnitude and redshift are 20.9 and 0.64, while for the 32 quasars or candidates these are 19.7 and 0.75. Merging the six most recent large compilations of GRS results in 458 GRS larger than 1 Mpc, so we were able to increase this number by ∼39% to 636.
Injectable and Antioxidative HT/QGA Hydrogel for Potential Application in Wound Healing
Authors: Ren Yikun , Zhang Dan , He Yuanmeng , Chang Rong , Guo Shen , Ma Shanshan , Yao Minghao , Guan Fangxia
Published: November 2021

Hydrogels have gained a niche in the market as wound dressings due to their high water content and plasticity. However, traditional hydrogel wound dressings are difficult to fully adapt to irregular-shaped wound areas. Additionally, excessive reactive oxygen species (ROS) accumulated in the damaged area impede the wound healing process. Therefore, hydrogels with injectable and antioxidant properties offer promising qualities for wound healing, but their design and development remain challenges. In this study, HT/QGA (tyramine-grafted hyaluronic acid/gallic acid-grafted quaternized chitosan) hydrogels with injectable and antioxidant properties were prepared and characterized. This hydrogel exhibited excellent injectability, favorable antioxidant activity, and good biocompatibility. Moreover, we evaluated the therapeutic effect of HT/QGA hydrogel in a full-thickness skin injury model. These results suggested that HT/QGA hydrogel may offer a great potential application in wound healing.
SARS-CoV-2 and Pre-Tamponade Pericardial Effusion. Could Sotos Syndrome Be a Major Risk Factor?
Authors: Citoni Barbara , Digilio Maria Cristina , Capolino Rossella , Gagliardi Maria Giulia , Campana Andrea , Drago Fabrizio , Calcagni Giulio
Published: November 2021

Pericarditis with pericardial effusion in SARS CoV-2 infection is a well-known entity in adults. In children and adolescents, only a few cases have been reported. Here, we present here a case of a 15-year-old girl affected by Sotos syndrome with pre-tamponed pericardial effusion occurred during SARS-CoV-2 infection. A possible relation between SARS-CoV-2 pericarditis and genetic syndromes, as a major risk factor for the development of severe inflammation, has been speculated. We emphasize the importance of active surveillance by echocardiograms when SARS-CoV-2 infection occurs in combination with a genetic condition.
COVID-19 Anti-Vaccine Sentiments: Analyses of Comments from Social Media
Authors: Wong Li Ping , Lin Yulan , Alias Haridah , Bakar Sazaly Abu , Zhao Qinjian , Hu Zhijian
Published: November 2021

Purpose: This study analyzed the insights and sentiments of COVID-19 anti-vaccine comments from Instagram feeds and Facebook postings. The sentiments related to the acceptance and effectiveness of the vaccines that were on the verge of being made available to the public. Patients and methods: The qualitative software QSR-NVivo 10 was used to manage, code, and analyse the data. Results: The analyses uncovered several major issues concerning COVID-19 vaccine hesitancy. The production of the COVID-19 vaccine at an unprecedented speed evoked the fear of skipping steps that would compromise vaccine safety. The unknown long-term effects and duration of protection erode confidence in taking the vaccines. There were also persistent concerns with regard to vaccine compositions that could be harmful or contain aborted foetal cells. The rate of COVID-19 death was viewed as low. Many interpreted the 95% effectiveness of the COVID-19 vaccine as insufficient. Preference for immunity gains from having an infection was viewed as more effective. Peer-reviewed publication-based data were favoured as a source of trust in vaccination decision-making. Conclusions: The anti-COVID-19 vaccine sentiments found in this study provide important insights for the formulation of public health messages to instill confidence in the vaccines.
Minimally Invasive Two-Staged Surgery in the Treatment of Large Cystic Lesions of the Jaw
Authors: Irimia Andreea , Moraru Liliana , Ciubotaru Diana Alina , Caruntu Constantin , Farcasiu Alexandru-Titus , Caruntu Ana
Published: November 2021

Background: Cystic lesions of the jaw are commonly found in clinical practice. Large, expansive cysts raise challenges for the clinician from both diagnostic and surgical perspectives. The aim of our work is to present a combined, two-staged surgical approach in histologically confirmed non-aggressive cystic lesions of the jaw. Methods and Results: We report the case of an extensive mandibular cyst, associating a high risk of bone fracture, that is treated in the initial stage by cystic decompression through marsupialization with concomitant histological diagnostic confirmation, followed in the second stage by radical excision and mandibular reconstruction with titanium mesh, with the purpose of prevention for oro-cystic chronic fistula formation. Conclusions: Large odontogenic mandibular cysts imply a meticulously conducted assessment and treatment. Marsupialization should be taken into consideration for the treatment of large cystic lesions, followed by secondary enucleation, with minimal risks for the patient. The soft tissue healing process can be optimized with the use of titanium meshes, as an alternative for other reconstructive techniques, in the management of large cystic lesions.
Psychological Well-Being of Malaysian University Students during COVID-19 Pandemic: Do Religiosity and Religious Coping Matter?
Authors: Che Rahimi Aisyah , Bakar Raishan Shafini , Mohd Yasin Mohd Azhar
Published: November 2021

The COVID-19 pandemic and the restrictions imposed that changed the teaching and learning activities may add a psychological impact to the existing academic stress faced by university students. Past studies have associated low levels of psychological disorder with high religiosity and positive religious coping (RC). This study aimed to determine the level of psychological disorder among university students in Malaysia during the COVID-19 pandemic and measure their association with religiosity and religious coping (RC). An online cross-sectional survey was conducted between March and June 2020 involving 450 students. The survey instruments consisted of sociodemographic proforma, Duke University Religious Index (DUREL) for religiosity, Brief RCOPE Scale for RC and General Health Questionnaire-12 (GHQ-12) for psychological disorder; 36% of the participants experienced psychological disorder. Younger age, being a Muslim, living in the Green/Yellow zone and higher negative RC were significantly associated with psychological disorder. Higher positive RC was found to be protective against psychological disorder. However, the level of religiosity had no significant association with psychological disorder. In conclusion, the level of psychological disorder among university students has been high during the pandemic. Measures and interventions focusing on positive RC and reducing negative RC are recommended to improve the psychological well-being.