bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Lubrication Systems on the Fatigue Strength of Bolted Joints
Authors: Kraemer Fabian , Stähler Michael , Klein Marcus , Oechsner Matthias
Published: March 2022

The fatigue behavior of bolted joints is critical to failure for many applications due to the high notch effect. Among other parameters, the lifetime is based on the influence of the surface system, consisting of corrosion protection and lubricant. With the intention of considering the surface system in the design and dimensioning process of bolted joints, experimental investigations are carried out systematically for an exemplary selected basecoat and various lubricant systems. The basis is given by fatigue tests supported by selected methods of material analysis for the fractographic evaluation. A reproducible method to determine the crack initiation as well as the crack propagation is developed and used for the evaluation of lubricant systems. Defined damage can be reproducibly assigned on the basis of a critical frequency change rate of the resonance test machine. A high durability of the friction in the load-bearing contact (e.g., greases) reduces the stress and delays the crack initiation. Lubricants which are able to creep (e.g., oil) slow down the crack propagation and increase the lifetime, which is proved by a higher number of cycles to failure and an increased fatigue strength.
The Spatio-Temporal Influence of Atmospheric Circulations on Monthly Precipitation in Great Britain
Authors: West Harry , White Paul , Quinn Nevil , Horswell Michael
Published: March 2022

It has long been understood that the North Atlantic Oscillation (NAO) is a key driver of regional climate in Great Britain and across Europe. However, studies have also noted that there is spatio-temporal variability in NAO-rainfall signatures which arguably limits its practical inclusion in water management. In this study we quantify, at high spatio-temporal resolution, the influence of a broader set of atmospheric circulations on monthly precipitation. Using Standardised Precipitation Indices for the Integrated Hydrological Unit (IHU) Groups of Great Britain we apply univariate and multivariate regression models to understand the potential of five atmospheric circulation indices to explain precipitation variability. As far as we are aware this represents the first high spatial and temporal resolution analysis quantifying the influence of a broad set of atmospheric circulations, both individually and in combination. We highlight the influence of each circulation and establish that the NAO only partially explains precipitation variability, especially in the southern regions and during the summer months, where circulations, such as the East Atlantic Pattern, also have an important influence. In summary, we suggest that there is significant explanatory value in looking beyond the NAO when seeking to understand hydroclimatological variability in Great Britain, and there is potential for future work to explore how this understanding can translate into the practical application of atmospheric circulation indices in water management.
Signal Simulation of Dual-Polarization Weather Radar and Its Application in Range Ambiguity Mitigation
Authors: Dai Shaojun , Li Xuehua , Bu Zhichao , Chen Yajun , He Jianxin , Li Minghua , Xiong Maojie
Published: March 2022

In this paper, a dual-polarization weather radar echo signal simulation method is proposed for the evaluation of the performance enhancement of dual-polarization weather radar systems, the optimization of signal processing algorithms and the improvement of scanning strategies. The actual weather radar base data are used in the simulation as the reference weather scene, which avoids using a complex algorithm for weather modeling. Moreover, based on radar weather equations, the radar system parameters are added into the radar echo signal modeling to establish the relationship between the simulated echo signal and radar system. As a result, the final simulated echo signal not only shows both the time and frequency domain characteristics of the weather target, but also includes the effects of the important performance of the dual-polarization weather radar system. In addition, to evaluate the performance of range ambiguity mitigation using phase coding and batch working modes, two different simulation methods for the radar signal are established on the method above; one is for batch working mode with long-PRT (pulse repetition time) and short-PRT transmission and receiving, and the other is for phase-coded mode with phase-coded transmission and phase-uncoded receiving. Under the same weather scene, the observation of these two different methods of range ambiguity mitigation are simulated and compared. Results show that the performance of the phase coding mode for mitigating range ambiguity is better than that of the batch mode. Obviously, the simulation method can be used to directly show the observation of different algorithms for mitigation range ambiguity under the same weather process, and quickly compare and evaluate the algorithm’s performance, which is not possible for real radars.
Urban Climate Risk Mitigation via Optimal Spatial Resource Allocation
Authors: Nevat Ido , Mughal Muhammad Omer
Published: March 2022

Decision makers (DMs) who are involved in urban planning are often required to allocate finite resources (say, money) to improve outdoor thermal comfort (OTC) levels in a region (e.g., city, canton, country). In this paper, for the first time, we address the following two questions, which are directly related to this requirement: (1) How can the statistical properties of the spatial risk profile of an urban area from an OTC perspective be quantified, no matter which OTC index the DM chooses to use? (2) Given the risk profile, how much and where should the DM allocate the finite resources to improve the OTC levels? We answer these fundamental questions by developing a new and rigorous mathematical framework as well as a new class of models for spatial risk models. Our approach is based on methods from machine learning: first, a surrogate model of the OTC index that provides both accuracy and mathematical tractability is developed via regression analysis. Next, we incorporate the imperfect climate model and derive the statistical properties of the OTC index. We present the concept of spatio-temporal aggregate risk (STAR) measures and derive their statistical properties. Finally, building on our derivations, we develop a new algorithm for spatial resource allocation, which is useful for DMs and is based on modern portfolio theory. We implemented the tool and used it to illustrate its operation on a practical case of the large-scale area of Singapore using a WRF climate model.
ErCas12a and T5exo-ErCas12a Mediate Simple and Efficient Genome Editing in Zebrafish
Authors: Han Bingzhou , Zhang Yage , Zhou Yang , Zhang Biao , Krueger Christopher J. , Bi Xuetong , Zhu Zuoyan , Tong Xiangjun , Zhang Bo
Published: March 2022

In zebrafish, RNA-guided endonucleases such as Cas9 have enabled straightforward gene knockout and the construction of reporter lines or conditional alleles via targeted knockin strategies. However, the performance of another commonly used CRISPR system, Cas12a, is significantly limited due to both the requirement of delivery as purified protein and the necessity of heatshock of injected embryos. To explore the potential of CRISPR/Cas12a-mediated genome editing and simplify its application in zebrafish, we took advantage of the recently reported mRNA-active ErCas12a and investigated its efficacy for the knockin of large DNA fragments, such as fluorescent reporter genes. For knockin via either microhomology-mediated end joining (MMEJ) or non-homologous end joining (NHEJ) pathways, ErCas12a-injected embryos with a brief heatshock displayed comparable knockin efficiency with Cas9 injection. Through the fusion of T5 exonuclease (T5exo) to the N-terminus of ErCas12a (T5exo-ErCas12a), we further demonstrated high efficiency gene knockout and knockin at a normal incubation temperature, eliminating the embryo-damaging heatshock step. In summary, our results demonstrate the feasibility of ErCas12a- and T5exo-ErCas12a-mediated genome manipulation under simplified conditions, and further expand the genome editing toolbox for various applications in zebrafish.
(Auto)Antibody Responses Shape Memory NK Cell Pool Size and Composition
Authors: Capuano Cristina , Pighi Chiara , Battella Simone , Pulcinelli Fabio , Santoro Cristina , Ferretti Antonietta , Turriziani Ombretta , De Federicis Davide , Fionda Cinzia , Sciumè Giuseppe , Galandrini Ricciarda , Palmieri Gabriella
Published: March 2022

In vivo establishment and long-term persistence of a heterogeneous memory or an adaptive NK cell pool represents a functional adaptation to human cytomegalovirus (HCMV) infection in humans. Memory NK cells are commonly identified by lack of the FcεRIγ signalling chain, variably associated to the preferential but not completely overlapping expression of the HLA-E receptor NKG2C and CD57 maturation marker. Although characterized by selective hyperresponsiveness to IgG stimulation, the impact of the CD16/antibody interaction in regulating the establishment/maintenance and size, and in determining the relative abundance of this population, is still under investigation. Memory NK cell subset ex vivo profile and in vitro responsiveness to CD16 stimulation was evaluated in HCMV+ healthy donors and in patients affected by immune thrombocytopenia (ITP), an antibody-mediated autoimmune disease. We identified the FcεRIγ− NKG2C+CD57+ memory NK cell subset, whose abundance is uniquely associated with anti-HCMV antibody levels in healthy seropositive donors, and which is significantly expanded in ITP patients. This fully mature memory subset robustly and selectively expands in vitro in response to mAb-opsonized targets or ITP-derived platelets and displays superior CD16-dependent IFNγ production. Our work identifies opsonizing antibodies as a host-dependent factor that shapes HCMV-driven memory NK cell compartment. We first demonstrate that chronic exposure to auto-antibodies contributes to the establishment/expansion of a highly specialized and unique memory NK cell subset with distinct CD16-dependent functional capabilities. We also identify the specific contribution of the lack of FcεRIγ chain in conferring to NKG2C+CD57+ memory cells a higher responsivity to CD16 engagement.
Effects of Vibrotactile Biofeedback Providing Real-Time Pressure Information on Static Balance Ability and Weight Distribution Symmetry Index in Patients with Chronic Stroke
Authors: Kim Ho , Kim Hongjun , Shin Won-Seob
Published: March 2022

Training with visual and auditory biofeedback, in patients with stroke, improved balance ability and asymmetric posture. We developed a new biofeedback training device to prevent falls and improve balance ability in patients with stroke. This device corrects motion errors by collecting the pressure information of patients in real-time. This randomized crossover study aimed to investigate the effect of this biofeedback training on the static balance ability and weight distribution symmetry index in 24 patients with chronic stroke. Pressure sensor-based vibrotactile biofeedback, visual biofeedback providing posture information, and standing without biofeedback were randomly applied for 1 d each with 24 h washout intervals to minimize adaptation. The static balance ability was measured for each biofeedback training type, and the weight distribution symmetry index was calculated using the collected weight-bearing rate data. The static balance ability and weight distribution symmetry index differed significantly according to the type of biofeedback training used. Post-hoc analysis revealed significant differences in the order of newly developed vibrotactile biofeedback, visual biofeedback, and standing without biofeedback. These findings provide evidence that pressure sensor-based vibrotactile biofeedback improves static balance ability and weight support rates by proposing better intervention for patients with chronic stroke in the clinical environment.
A Novel Electrochemical Sensing Platform for the Sensitive Detection and Degradation Monitoring of Methylene Blue
Authors: Hayat Mazhar , Shah Afzal , Nisar Jan , Shah Iltaf , Haleem Abdul , Ashiq Muhammad Naeem
Published: March 2022

Methylene blue is a toxic dye that is extensively used as a colorant in textile industries. Industrial effluent containing methylene blue, when drained into water bodies without proper treatment, poses a serious threat to aquatic and human lives. In order to protect the biocycle, various methods have been established to detect and remove hazardous dyes from aqueous systems. Electrochemical methods are preferred, owing to their characteristic features of simplicity, portability, potential selectivity, cost effectiveness, and rapid responsiveness. Based on these considerations, an electrochemical sensor consisting of amino-group-functionalized, multi-walled carbon nanotubes (NH2-fMWCNTs) immobilized on a glassy carbon electrode (GCE) was developed for the sensitive detection of methylene blue in aqueous solutions. The performance of the designed sensor was analyzed by electrochemical impedance spectroscopy, cyclic voltammetry, and square wave voltammetry. The developed sensing tool demonstrated promising features of sensitivity, selectivity, stability, fast responsiveness, and the ability to work with a very small volume of the analyte, i.e., in microliters, for analysis. Amino groups rich in electrons provide a negative charge to multi-walled carbon nanotubes, which significantly enhances the electrocatalytic activity of NH2-fMWCNTs for cationic dyes such as methylene blue. Using the designed sensing platform, a linear calibration plot with a limit of detection of 0.21 nM was obtained for methylene blue under optimized conditions. The designed sensor was also employed to monitor the extent and kinetics of the degradation of methylene blue. Titania nanoparticles were used for photocatalytic degradation, and the kinetics of degradation was monitored by both UV-Visible spectroscopic and electrochemical methods. The results revealed more than 95% removal of methylene blue in a time span of just 30 min.
Biomarkers Associated with Cardiovascular Disease in COVID-19
Authors: Kaufmann Christoph C. , Ahmed Amro , Burger Achim Leo , Muthspiel Marie , Jäger Bernhard , Wojta Johann , Huber Kurt
Published: March 2022

Coronavirus disease-19 (COVID-19) emerged late December 2019 in the city of Wuhan, China and has since spread rapidly all over the world causing a global pandemic. While the respiratory system is the primary target of disease manifestation, COVID-19 has been shown to also affect several other organs, making it a rather complex, multi-system disease. As such, cardiovascular involvement has been a topic of discussion since the beginning of the COVID-19 pandemic, primarily due to early reports of excessive myocardial injury in these patients. Treating physicians are faced with multiple challenges in the management and early triage of patients with COVID-19, as disease severity is highly variable ranging from an asymptomatic infection to critical cases rapidly deteriorating to intensive care treatment or even fatality. Laboratory biomarkers provide important prognostic information which can guide decision making in the emergency department, especially in patients with atypical presentations. Several cardiac biomarkers, most notably high-sensitive cardiac troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), have emerged as valuable predictors of prognosis in patients with COVID-19. The purpose of this review was to offer a concise summary on prognostic cardiac biomarkers in COVID-19 and discuss whether routine measurements of these biomarkers are warranted upon hospital admission.
HPLC-PDA-ESI-HRMS-Based Profiling of Secondary Metabolites of Rindera graeca Anatomical and Hairy Roots Treated with Drought and Cold Stress
Authors: Naliwajski Marcin Robert , Wileńska Beata , Misicka Aleksandra , Pietrosiuk Agnieszka , Sykłowska-Baranek Katarzyna
Published: March 2022

To cope with environmental harmful conditions, plant cells developed adaptive strategy that involves production of a wide variety of complex secondary metabolites. The spectrum and quantity of biosynthesized compounds in specific plant species is determined by its genotype, tissue, developmental and physiological stage and environmental factors. This phenomenon was used to exploit the potential of anatomical and hairy root cultures of Rindera graeca to produce bioactive compounds. Cultivated in vitro roots were subjected to abiotic stresses i.e., drought or coldness. Next the extract profiling was performed using HPLC-PDA-ESI-HRMS method, as well quantitative determination of caffeic, rosmarinic and lithospermic B acids, that were present in all root extracts. Phenolic acids, flavonoids and iridoids represent the major groups of compounds detected in chemical profiles growing under various conditions roots. The highest number of phytochemicals was determined in roots subjected to coldness. Lithospermic B acid proved to be the most abundant compound in all investigated extracts. Among applied abiotic stress factors it was demonstrated that coldness affected to the most secondary metabolites production. The results of current study suggest that root cultures of R. graeca could serve as a new and abundant source of lithospermic B acid.
Multifunctional Mesoporous Silica Nanoparticles for Oral Drug Delivery
Authors: Sreeharsha Nagaraja , Philip Manish , Krishna Sivadas Swathi , Viswanad Vidya , Sahu Ram Kumar , Shiroorkar Predeepkumar Narayanappa , Aasif Afzal Haq , Fattepur Santosh , Asdaq Syed Mohammed Basheeruddin , Nair Anroop B. , Attimarad Mahesh , Venugopala Katharigatta N.
Published: March 2022

Nanotechnology has transformed engineering designs across a wide spectrum of materials and applications. Mesoporous Silica Nanoparticles (MSNs) are one of the new fabrications of nanostructures as medication delivery systems. MSNs have pore sizes varying from 2 to 50 nm, making them ideal for a variety of biological applications. They offer unique characteristics such as a tunable surface area, well-defined surface properties, and the ability to improve drug pharmacokinetic characteristics. Moreover, they have the potential to reduce adverse effects by delivering a precise dose of medications to a specific spot rather than the more frequent systemic delivery, which diffuses across tissues and organs. In addition, the vast number of pores allow drug incorporation and transportation of drugs to various sites making MSNs a feasible platform for orally administered drugs. Though the oral route is the most suitable and convenient platform for drug delivery, conventional oral drug delivery systems are associated with several limitations. Surpassing gastrointestinal barriers and the low oral bioavailability of poorly soluble medicines pose a major challenge in the pharmaceutical industry. This review provides insights into the role of MSNs and its mechanism as an oral drug delivery system.
Quantifying Computed Tomography of Basalt Fiber-Reinforced Concrete under Unconfined Compression
Authors: Jiao Huazhe , Chen Xi , Yang Xiaolin , Wang Yunfei , Yang Yixuan , Chen Xinming , Yang Liuhua , Yu Jianxin
Published: March 2022

The macroscopic aspects of adding basalt fiber (BF) to concrete has been the subject of studies that document great increases in compression strength. Research into the microscopic aspects of the reinforcement mechanism is still in an exploratory stage, and the quantitative analysis and visual observation of fibers in the concrete matrix are difficult. In this paper, the reinforcement effect of fiber on concrete is studied by means of computed tomography (CT) scanning technology and digital image processing (DIP) technology, combined with the macro-mechanical properties obtained from the unconfined compression strength test, and quantitative analysis from the micro point of view. At the same time, the fiber visualization is realized with the help of Avizo. The results show that with the increase in fiber dosage, the peak stress of concrete first increases and then decreases. When the fiber dosage is 3 kg/m3, the peak stress is 44.4 MPa, which is 41.85% higher than that of ordinary concrete. Additionally, the proportion of macropores is the least, which is the best fiber dosage. It is found that when the fiber dosage is 3 kg/m3, the angular distribution of φ is relatively uniform, and the uniform distribution of fibers forms a dense network structure, which significantly increases the peak stress of concrete. However, when the fiber dosage is too high, it will lead to the accumulation of fibers and produce macropores, and these excess fibers mainly appear in the horizontal direction and do not contribute to the compression strength.
Oxidation and Microstructural Behaviors at 1200 °C of 32.5 wt.% Cr–Containing Co–Based Alloys Strengthened by HfC Carbides
Authors: Berthod Patrice
Published: March 2022

Two alloys based on cobalt, designed to be reinforced by HfC and containing chromium with content beyond 30 wt.%, were produced by casting. They were subjected to a 46 h–long isothermal exposure at 1200 °C in synthetic air with thermogravimetric monitoring of the oxidation progress. In the as–cast state, the two alloys contain high quantities of script–like shaped HfC carbides. Both of them demonstrated a much better behavior than previous similar alloys containing only 25 wt.% Cr. After 46 h at 1200 °C, the morphology of the carbides had almost not evolved. The control of the creep behavior at 1200 °C showed that these oxidation–resistant alloys are, additionally, as creep–resistant as the 25 wt.% Cr containing previous alloys.
Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG
Authors: Kwon Joon-myoung , Jo Yong-Yeon , Lee Soo Youn , Kang Seonmi , Lim Seon-Yu , Lee Min Sung , Kim Kyung-Hee
Published: March 2022

Background: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF). Methods: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG. Results: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913–0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively. Conclusions: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.
COVID-19 Infection Complicated by Disseminated Intravascular Coagulation during Pregnancy—Two Cases Report
Authors: Skalska-Świstek Małgorzata , Huras Hubert , Jaworowski Andrzej Piotr , Świstek Rafał , Kołak Magdalena
Published: March 2022

Coagulopathies are one of the obstetric complications affecting the period of pregnancy, childbirth, and puerperium. One of the more severe and complex disorders of the haemostatic system is the disseminated intravascular coagulation syndrome (DIC), in which generalised activation of the coagulation system and activation of inflammatory cells occurs. DIC syndrome was observed in patients whose pregnancy was complicated by SARS-CoV-2 infection. Both the course of these cases and literature review indicate that particular notice should be paid to laboratory parameters of the coagulation system, closely monitoring the well-being of the foetus and, in the situation of acute DIC development, it is advised to deliver a baby and initiate intensive therapy.
Implementation of Whole School Restorative Approaches to Promote Positive Youth Development: Review of Relevant Literature and Practice Guidelines
Authors: Mas-Expósito Laia , Krieger Virginia , Amador-Campos Juan Antonio , Casañas Rocío , Albertí Mònica , Lalucat-Jo Lluís
Published: March 2022

Positive youth development highlights the promotion of skills through engaging and caring settings and building opportunities for bidirectional and constructive relationships. Whole School Restorative Approaches (WSRA) promote school community relationships and social and emotional skills which are core components for positive youth development. To our knowledge, the literature reviews on the implementation of restorative practice approaches in schools do not focus specifically on WSRA and are not comprehensive. This study carries out a systematic review of the empirical evidence on the effectiveness of WSRA and develops some evidence-based practice guidelines on their effects. Thirteen studies met the inclusion criteria and we developed 10 evidence-based practice guidelines on the implementation of WSRA. There were many studies with WSRA showing positive results related to improving social and emotional skills and behavior. The highest level of scientific evidence is for secondary education where two WSRA are recommended. Although our review suggests that more rigorous methodological research is necessary, it also points out that the evidence available on WSRA implementation is encouraging. They could be used through all educational stages to deal with diversity and inclusion while promoting a school culture of peace based on the positive management of relationships and conflicts.
Inverse Analog Filters: History, Progress and Unresolved Issues
Authors: Senani Raj , Bhaskar Data Ram , Raj Ajishek
Published: March 2022

This paper traces the history of the evolution of inverse analog filters (IAF) and presents a review of the progress made in this area to date. The paper, thus, presents the current state-of-the art of IAFs by providing an appraisal of a variety of realizations of IAFs using commercially available active building blocks (ABB), such as operational amplifiers (Op-amp), operational transconductance amplifiers (OTA), current conveyors (CC) and current feedback operational amplifiers (CFOA) as well as those realized with newer active building blocks of more recent origin, such as operational transresistance amplifiers (OTRA), current differencing buffered amplifiers (CDBA) and variants of current conveyors which, although not available as off-the-shelf ICs yet, can be implemented as complementary metal–oxide–semiconductors (CMOS) or be realized in discrete form using other commercially available integrated circuits (IC). In the end, some issues related to IAFs have been highlighted which need further investigation.
High-Performance Magnetoinductive Directional Filters
Authors: Voronov Artem , Syms Richard R. A. , Sydoruk Oleksiy
Published: March 2022

Multiport magnetoinductive (MI) devices with directional filter properties are presented. Design equations are developed and solved using wave analysis and dispersion theory, and it is shown that high-performance directional filters can be realised for use both in MI systems with complex, frequency-dependent impedance and in conventional systems with real impedance. Wave analysis is used to reduce the complexity of circuit equations. High-performance MI structures combining directional and infinite rejection filtering are demonstrated, as well as multiple-passband high-rejection filtering. A new method for improving filtering performance through multipath loss compensation is described. Methods for constructing tuneable devices using toroidal ferrite-cored transformers are proposed and demonstrated, and experimental results for tuneable MI directional filters are shown to agree with theoretical models. Limitations are explored, and power handling sufficient for HF RFID applications is demonstrated, despite the use of ferrite materials.
Forecasting Crude Oil Prices with a WT-FNN Model
Authors: Wang Donghua , Fang Tianhui
Published: March 2022

In order to improve the accuracy of forecasting crude oil prices, a new crude oil price forecasting method is introduced in the paper that is a combination of the FNN model and the stochastic time effective function—namely, the WT-FNN model. The FNN model keeps track of the historical values of crude oil prices and predicts future crude oil prices, and the stochastic time effective function gives greater weight to recent information and smaller weight to old information, thus making the prediction of crude oil prices more reasonable. We selected the daily data of Brent crude oil prices from 4 January 2000 to 30 September 2021 as research objects and then used the WT-FNN model to train and predict the research objects. By comparing it to the benchmark model, we found that the predictive effect of the WT-FNN model was better than the FNN model and the no-change model. The results also passed a robustness test.
Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages
Authors: Owolabi Olukunle O. , Sunter Deborah A.
Published: March 2022

Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO.
A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling
Authors: Anagnostis Athanasios , Moustakidis Serafeim , Papageorgiou Elpiniki , Bochtis Dionysis
Published: March 2022

Modelling of thermal energy storage (TES) systems is a complex process that requires the development of sophisticated computational tools for numerical simulation and optimization. Until recently, most modelling approaches relied on analytical methods based on equations of the physical processes that govern TES systems’ operations, producing high-accuracy and interpretable results. The present study tackles the problem of modelling the temperature dynamics of a TES plant by exploring the advantages and limitations of an alternative data-driven approach. A hybrid bimodal LSTM (H2M-LSTM) architecture is proposed to model the temperature dynamics of different TES components, by utilizing multiple temperature readings in both forward and bidirectional fashion for fine-tuning the predictions. Initially, a selection of methods was employed to model the temperature dynamics of individual components of the TES system. Subsequently, a novel cascading modelling framework was realised to provide an integrated holistic modelling solution that takes into account the results of the individual modelling components. The cascading framework was built in a hierarchical structure that considers the interrelationships between the integrated energy components leading to seamless modelling of whole operation as a single system. The performance of the proposed H2M-LSTM was compared against a variety of well-known machine learning algorithms through an extensive experimental analysis. The efficacy of the proposed energy framework was demonstrated in comparison to the modelling performance of the individual components, by utilizing three prediction performance indicators. The findings of the present study offer: (i) insights on the low-error performance of tailor-made LSTM architectures fitting the TES modelling problem, (ii) deeper knowledge of the behaviour of integral energy frameworks operating in fine timescales and (iii) an alternative approach that enables the real-time or semi-real time deployment of TES modelling tools facilitating their use in real-world settings.
Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor
Authors: Lalik Krzysztof , Kozak Jakub , Podlasek Szymon , Kozek Mateusz
Published: March 2022

Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).
Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes
Authors: Loyola-Fuentes Jose , Pietrasanta Luca , Marengo Marco , Coletti Francesco
Published: March 2022

Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes.
Energy Systems Analysis and Modelling towards Decarbonisation
Authors: Fragkos Panagiotis , Siskos Pelopidas
Published: March 2022

The Paris Agreement establishes a process to combine Nationally Determined Contributions with the long-term goal of limiting global warming to well below 2 °C and even to 1 [...]
Optimization-Based Capacitor Balancing Method with Customizable Switching Reduction for CHB Converters
Authors: Galván Luis , Gómez Pablo J. , Galván Eduardo , Carrasco Juan M.
Published: March 2022

This paper presents a method for switching reduction in cascaded H-bridge converters. Given the wide applicability of this topology, it would be especially desirable to increase its efficiency with switching losses reduction techniques. Since this type of converter requires voltage balancing methods, several modulation methods consider the possibility of combining the balancing and switching reduction goals together. In this paper, a previously disclosed optimization-based balance method was modified further to consider the switching losses in its objective function. Each commutation was penalized in proportion to the phase current and the module voltage, thus avoiding commutations that would produce the most losses but tolerating low-losses commutations. The structure of the original method was maintained so that the algorithm could be applied with minimal change. The results show that it is possible to reduce the switching up to 14% without any noticeable drawback and up to 22% at the cost of a greater DC-link ripple. It is also possible to selectively reduce the effective switching frequency of only some modules, making it significantly low. This extends the adaptability of the converter, possibly allowing hybrid converters with modules of different transistor technologies.
A Sensitivity-Based Three-Phase Weather-Dependent Power Flow Algorithm for Networks with Local Voltage Controllers
Authors: Pompodakis Evangelos E. , Ahmed Arif , Alexiadis Minas C.
Published: March 2022

Local voltage controllers (LVCs) are important components of a modern distribution system for regulating the voltage within permissible limits. This manuscript presents a sensitivity-based three-phase weather-dependent power flow algorithm for distribution networks with LVCs. More specifically, the proposed algorithm has four distinct characteristics: (a) it considers the three-phase unbalanced nature of distribution systems, (b) the operating state of LVCs is calculated using sensitivity parameters having accelerated convergence, (c) it considers the precise switching sequence of LVCs based on their reaction time delays, and (d) the nonlinear influence of weather variations in the power flow is also taken into consideration. Simulations and validation results presented indicate that the proposed approach outperforms other existing algorithms with respect to the accuracy and speed of convergence, thus making it a promising power flow tool for accurate distribution system analysis.
A Multivariate Approach to Study the Bacterial Diversity Associated to the Wooden Shelves Used for Aging Traditional Sicilian Cheeses
Authors: Gaglio Raimondo , Busetta Gabriele , Gannuscio Riccardo , Settanni Luca , Licitra Giuseppe , Todaro Massimo
Published: March 2022

The present study was carried to correlate the microbial diversity of the biofilms developed on the wooden boards used for aging traditional Sicilian cheeses with cheese typology. To this end, the microbial diversity of the shelves in contact with the cheeses PDO Pecorino Siciliano, PDO Piacentinu Ennese, and TAP Caciocavallo Palermitano, during ripening, was evaluated by a multivariate statistical approach. The shelf biofilms of this study were previously analyzed for their microbial composition, but no correlation between biodiversity and cheese type was investigated. Canonical discriminant analysis confirmed a cheese typology effect on the microbial loads of the wooden shelves investigated. Regarding the plate count data, the centroids of different cheeses were statistically distant from one another. This analysis also showed a good graphic separation of data regarding bacterial order operational taxonomy units (OTUs). Thus, the microbiological differences imputed to the cheese typologies were not affected by the environmental conditions of the facilities. Furthermore, wooden shelf lactic acid bacteria (LAB) were investigated for their ability to inhibit the main dairy pathogens. Although inhibitors were mainly enterococci, P. pentosaceus WS287 and W. paramesenteroides WS581 showed the highest inhibition activity, indicating their possible application to control the undesired bacteria in situ.
PRIAS: An Intelligent Analysis System for Pesticide Residue Detection Data and Its Application in Food Safety Supervision
Authors: Chen Yi , Dou Haifeng , Chang Qiaoying , Fan Chunlin
Published: March 2022

Pesticide residue is a prominent factor that leads to food safety problems. For this reason, many countries sample and detect pesticide residues in food every year, which generates a large amount of pesticide residue data. However, the way to deeply analyze and mine these data to quickly identify food safety risks is still an unresolved issue. In this study, we present an intelligent analysis system that supports the collection, processing, and analysis of detection data of pesticide residues. The system is first based on a number of databases such as maximum residue limit standards for the fusion of pesticide residue detection results; then, it applies a series of statistical methods to analyze pesticide residue data from multiple dimensions for quickly identifying potential risks; it uses the Apriori algorithm to mine the implicit association in the data to form pre-warning rules; finally, it applies Word document automatic generation technology to automatically generate pesticide residue analysis and pre-warning reports. The system was applied to analyze the pesticide residue detection results of 42 cities in mainland China from 2012 to 2015. Application results show that the system proposed in this study can greatly improve the depth, accuracy and efficiency of pesticide residue detection data analysis, and it can provide better decision support for food safety supervision.
Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis
Authors: Shimaoka Nanako , Okamoto Shogo , Akiyama Yasuhiro , Yamada Yoji
Published: March 2022

Sensory responses dynamically change while eating foods. Temporal dominance of sensations (TDS) methods record temporal evolution and have attracted attention in the last decade. ISO 13299 recommends that different levels of attributes are investigated in separate TDS trials. However, only a few studies have attempted to link the dynamics of two different levels of sensory attributes. We propose a method to link the concurrent values of dominance proportions for primary- and multi-sensory attributes using canonical correlation analysis. First, panels categorized several attributes into primary- and multi-sensory attributes. Primary-sensory attributes included sweet, sour, fruity, green, watery, juicy, aromatic, and light. Multi-sensory attributes included refreshing, fresh, pleasurable, rich/deep, ripe, and mild. We applied the TDS methods to strawberries using these two categories of attributes. The obtained canonical correlation model reasonably represented the relationship between the sensations in a reductive manner using five latent variables. The latent variables couple multiple primary- and multi-sensory responses that covary. Hence, the latent variables suggest key components to comprehend food intake experiences. We further compared the model based on the dominance proportions and the time-derivatives of the dominance proportions. We found that the former model was better in terms of the ease of interpreting the canonical variables and the degree to which the canonical variables explain the dominance proportions. Thus, these models help understand and leverage the sensory values of food products.
Morphologies of Wolf–Rayet Planetary Nebulae Based on IFU Observations
Authors: Danehkar Ashkbiz
Published: March 2022

Integral field unit (IFU) spectroscopy of planetary nebulae (PNe) provides a plethora of information about their morphologies and ionization structures. An IFU survey of a sample of PNe around hydrogen-deficient stars has been conducted with the Wide Field Spectrograph (WiFeS) on the ANU 2.3-m telescope. In this paper, we present the Hα kinematic observations of the PN M 2-42 with a weak emission-line star (wels), and the compact PNe Hen 3-1333 and Hen 2-113 around Wolf–Rayet ([WR]) stars from this WiFeS survey. We see that the ring and point-symmetric knots previously identified in the velocity [N ii] channels of M 2-42 are also surrounded by a thin exterior ionized Hα halo, whose polar expansion is apparently faster than the low-ionization knots. The velocity-resolved Hα channel maps of Hen 3-1333 and Hen 2-113 also suggest that the faint multipolar lobes may get to a projected outflow velocity of ∼100 ± 20 km s−1 far from the central stars. Our recent kinematic studies of the WiFeS/IFU survey of other PNe around [WR] and wels mostly hint at elliptical morphologies, while collimated outflows are present in many of them. As the WiFeS does not have adequate resolution for compact (≤6 arcsec) PNe, future high-resolution spatially-resolved observations are necessary to unveil full details of their morpho-kinematic structures.