Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind Turbine Blade Icing Fault Prediction Based on SCADA Data by XGBoost

: To deal with the icing problem of wind turbine blades, the traditional classification methods are introduced firstly in this paper, and the XGBoost model based on monitoring and data acquisition (SCADA) system is introduced to estimate the icing conditions of blades. Meanwhile, the generation process of the XGBoost model is introduced in detail. Finally, the superiority of the XGBoost model is verified by experiments. The results show that XGBoost has higher precision and efficiency than other methods.
Global well-posedness of NLS with a rough potential below the energy norm

We show that in the presence of a rough external potential, a 3d cubic defocusing NLS is globally well-posed in H^s for s>5/6. The proof is based on the approach of Colliander-Keel-Staffilani-Takaoka-Tao, called the I-method, but in order to deal with a rough potential, we modify harmonic analysis tools by spectral theory.
EVALUATION OF CLASSIFICATION ALGORITHMS ON LOCKY RANSOMWARE USING WEKA TOOL

The ongoing danger of ransomware has led to a struggle between creating and identifying novel approaches. Although detection and mitigation systems have been created and are used widely, they are always evolving and being updated due to their reactive nature. This is because harmful code and its behavior can frequently be altered to evade detection methods. In this study, we present a classification method that combines static and dynamic data to improve the precision of locky ransomware detection and classification. We trained supervised machine learning algorithms using cross-validation and used a confusion matrix to observe accuracy, enabling a systematic comparison of each algorithm. In this work, supervised algorithms such as the decision tree algorithm resulted in an accuracy of 97%, naïve baiyes 95%, random tree 63%, and ZeorR 50%.
Guava Leaf Extract Suppresses Fructose Mediated Non-Alcoholic Fatty Liver Disease in Growing Rats

Purpose Fructose is highly lipogenic, and its unhindered ingestion by children and adolescents is understood to induce hypertriglyceridemia and non-alcoholic fatty liver disease (ped-NAFLD) that is till date managed symptomatically or surgically. The aim of the present study was to investigate the potential of hydroethanolic extract of leaves of Guava (PG-HM) to suppress the alterations in the hepatic molecular signals due to unrestricted fructose (15%) drinking by growing rats. Methods Weaned rats (4 weeks old) in control groups had ad libitum access to fructose drinking solution (15%) for four (4FDR) or eight (8FDR) weeks, ie, till puberty or early adulthood, respectively, while treatment groups (4PGR, 8PGR) additionally received PG-HM (500 mg/kg, po). Results The PG-HM suppressed ped-NAFLD through hepatic signalling pathways of 1) leptin-insulin (Akt/FOX-O1/SREBP-1c), 2) hypoxia-inflammation (HIF-1ɑ/VEGF, TNF-ɑ), 3) mitochondrial function (complexes I–V), 4) oxidative stress (MDA, GSH, SOD) and 5) glycolysis/gluconeogenesis/de novo lipogenesis (hexokinase, phosphofructokinase, ketohexokinase, aldehyde dehydrogenase). Parri passu, the insulin sensitizing effect of PG-HM and its ethyl acetate fraction (PG-EA) was elucidated using HepG2 cells grown in media enhanced with fructose. Further, in murine hepatocytes cultured in fructose-rich media, PG-HM (35 µg mL-1) outperformed Pioglitazone (15 µM) and Metformin (5 mM), to suppress hepatic insulin resistance. Conclusion This study established that hydroethanolic extract of leaves of Guava (PG-HM) has potential to suppress hepatic metabolic alteration for the management of the pediatric NAFLD.
Using digital technology to enhance youth participatory evaluation

The aim of this study is to demonstrate the challenges encountered in youth participatory evaluation and how digital technology may assist in overcoming these challenges. The study explores the meaning of youth participatory evaluation and examines the challenges faced in utilizing the approach. We argue that using digital technology can help overcome many challenges of youth participatory evaluations, with the added benefit that such technology may also attract and influence young people to engage more fully with evaluation processes. These findings show that digital technology helps decrease adultism, increase adults’ capacity to engage with youth, and respond to and empower youth. Some practical guidelines and challenges are also discussed.
Empathetic Leadership and Follower Workplace Engagement: Mediating Role of Climate for Inclusion

Although inclusive climate is an emerging approach in empathetic leadership to enhance workplace outcomes, limited research has shown how empathetic leaders create climate for inclusion. The basic aim of current study was to investigate the role of empathetic leaders to promote a climate for inclusion and to examine the effect on workplace outcomes. Survey method was used to collect data of female teachers from various universities. Data revealed that Empathetic leadership plays a vital role to create an inclusive climate for female teachers to engage at their workplace. Findings supported the research hypothesis and research model and established a connection between empathetic leadership, climate for inclusion and follower workplace engagement.
Research on Temperature Control of Incubator Based on Chaotic Particle Swarm Optimization and Fuzzy PID

: In the process of performance testing of precision devices in the aerospace field, it is difficult to apply the traditional PID control to the high-precision incubators, which is unable to achieve the ideal control accuracy. For this problem, fuzzy PID is optimized by using nonlinear piecewise Logistic chaos mapping to initiate particle group, optimize the parameters of ACPSO-Fuzzy-PID and introduce shrinkage factor to ensure the convergence of the algorithm, making the overall and local search capability of particle group more efficient. Firstly, the algorithm was tested by standard functions, and finally, PID, fuzzy PID, PSO-Fuzzy-PID, ACPSO-Fuzzy-PID were contrast simulated by SIMULINK at a constant temperature of 75℃. Simulation results show that the ACPSO-Fuzzy-PID control system has faster response time, less overshoot, shorter adjustment time, temperature control fluctuation more stable and stronger anti-disturbance ability, which significantly improves the temperature control accuracy of the incubator in the aerospace field.
Loneliness and Pain Catastrophizing Among Individuals with Chronic Pain: The Mediating Role of Depression

Purpose Loneliness increased during the COVID-19 pandemic and social distancing guidelines, potentially exacerbating negative cognitions about pain. The present study investigated the longitudinal relationship between loneliness, assessed during the early weeks of the pandemic, and pain catastrophizing, assessed after living in the pandemic for approximately 1 year, among chronic pain patients. We also examined whether severity of depressive symptoms mediated this association. Methods This prospective longitudinal study recruited individuals with chronic pain (N=93) from Massachusetts using an online convenience sampling method via the platform Rally. Participants completed an initial survey early after the onset of social distancing (4/28/20–6/17/20; Time 1) and a follow-up survey 1 year later (5/21/21–6/7/21; Time 2). Participants completed validated assessments of loneliness (T1), pain catastrophizing (T2), and depression (T2). Spearman correlations and Mann–Whitney U-tests were used to explore associations among psychosocial, pain, and participant characteristics. A mediation analysis was conducted to test whether the association between loneliness and pain catastrophizing was mediated by depression. Results Participants had a mean age of 40.6 years and were majority female (80%) and White (82%). Greater loneliness was associated with subsequent higher pain catastrophizing (b=1.23, 95% CI [0.03, 2.44]). Mediation analysis showed a significant indirect effect (b=0.57, 95% CI [0.10, 1.18) of loneliness (T1) on catastrophizing (T2) through depression (T2) while accounting for several important covariates. The direct effect of loneliness on catastrophizing was no longer significant when depression was included in the model (b=0.66, 95% CI [−0.54, 1.87]). Conclusion Findings suggest that greater loneliness during the pandemic was associated with higher pain catastrophizing 1 year later, and severity of depression after living in the pandemic mediated this association. As loneliness, depression, and catastrophizing can all be modified with behavioral interventions, understanding the temporal associations among these variables is important for the employment of future empirically supported treatments.
Liquid Crystal Droplet-Based Biosensors: Promising for Point-of-Care Testing

The development of biosensing platforms has been impressively accelerated by advancements in liquid crystal (LC) technology. High response rate, easy operation, and good stability of the LC droplet-based biosensors are all benefits of the long-range order of LC molecules. Bioprobes emerged when LC droplets were combined with biotechnology, and these bioprobes are used extensively for disease diagnosis, food safety, and environmental monitoring. The LC droplet biosensors have high sensitivity and excellent selectivity, making them an attractive tool for the label-free, economical, and real-time detection of different targets. Portable devices work well as the accessory kits for LC droplet-based biosensors to make them easier to use by anyone for on-site monitoring of targets. Herein, we offer a review of the latest developments in the design of LC droplet-based biosensors for qualitative target monitoring and quantitative target analysis.
Distributed Intelligent Transportation System Data Management and Processing Platform

: In recent years, with the rapid development of China's road construction and the continuous improvement of people's living standards, the number of urban motor vehicles has continued to grow year after year. Motor vehicles have become one of the main means of transportation for urban residents. At the same time, due to the increasing amount of traffic information data, a single server platform can not meet the reception and storage of massive traffic data, nor can it meet the needs of rapid query of massive traffic data. Therefore, this paper will use the distributed system to process data to solve the above problems, so as to ensure the real-time transmission and storage of massive traffic data. This paper discusses and analyzes the characteristics of distributed intelligent transportation system and the design of communication system data management and processing platform. Through the behavior decision algorithm of distributed transportation system, the flow data of distributed intelligent transportation system are tested and analyzed. The experimental results show that in the time period from 6:00 to 8:00, from 12:00 to 14:00 and from 16:00 to 18:00 when the traffic data reaches the peak, the amount of data processed by each communication server is very different, and the difference data amount can reach 60000 traffic data. The statistical results of this traffic data amount can show that the ratio of the data amount sent by each front-end bayonet every moment is not equal to the ratio of the data amount of each front-end bayonet every day, which is enough to show that the data management and processing capacity and efficiency of the distributed intelligent transportation system have been greatly improved.
Is the effectiveness of government bonds as a diversifier of equity risk weakened after the Covid-19 crisis?†

In this paper, we examine how the relationship between the US stock index and the US Treasury market has changed after the start of the Covid-19 crisis. For that purpose, we compute upside and downside correlations between the index and interest rates of different maturities. Our main findings are as follows: first, we document the correlation asymmetry. The downside correlation is higher than the upside correlation before the crisis but the opposite is true after the start of the crisis for all maturities. The magnitude of the change is large for short-term maturities but small for long-term maturities. It indicates that the benefit of holding the short-term government bonds as a diversifier for stock investors is weakened after the start of the Covid-19 crisis. We also study the economic impact of ignoring the correlation asymmetry and report that the cost increased after the start of the crisis in general.
An Improved Magnetic Transduction Scheme and Electronic Processing for Sensing Rotary-Shaft Position Over Full-Circle Range

Angular position of a through-shaft (TS) is an important measurand whose value needs to be accurately estimated in a number of industrial scenarios. This article brings forward the design, analysis, and hardware-based evaluation of a new magnetic transduction module and associated electronics for TS angle (TSA) estimation. The proposed transduction module enables noncontact and full-circle operation using an intelligent arrangement of magnetic disks and two Hall sensors. This easy-to-fabricate module is analyzed and its (nonlinear) characteristics are derived. Optimal specifications for the transducer are also ascertained using this analysis. Furthermore, a simple electronic processor, implementing new linearization function, is employed to realize an automated TS sensing system. This system retains all merits of magnetic sensor and reluctance techniques and eliminated many issues of the previous TS sensor systems. A hardware prototype of the transducer and electronic processor is built and tested. Test results show that the developed system can measure TSA over 360° range with error <0.8%. This article, in its final part, discusses the comparison of the developed scheme with prior art as well as the recommendations for fabricating an equivalent industrial model of the proposed system.
Studies in the Glossopteris flora of India - 4. Glossopteris, Gangamopteris and Palaeovittaria from the Raniganj Coalfield

The name Glossopteris was instituted by Brongniart for the large tongue-shaped leaves, which were dominant in the Lower Gondwana flora. So far about 40 species of this genus have been described by various authors. From India alone as many as 18 species are known, majority of which were described by Feistmantel. Since most of the species were founded before the cuticular technique was adopted by the palaeobotanists, species were created on the external characters alone, such as the shape, size and venation of the leaf. However, no two authors agree about the distinctive characters of any particular species and such examples are numerous in the literature. While Feistmantel gave new specific names to the leaves on very minor differences, Arber did not agree in multiplicity of the names. He, in fact, merged several of the Glossopteris species and reduced their number to 13 (ARBER, 1905, pp. 47, 48). In the present work, cuticles of 14 Indian species of Glossopteris are described. Glossopteris indica (ZElLLER, 1896) and Glossopteris angustifolia (SAHNI, 1923) were repeated in order to find any new details. Glossopteris angustifolia did not reveal any new features worthy of note. The present study, therefore, confirm the observations of Professor Sahni.
Simultaneous SARS-CoV-2 Genome Sequencing of 384 Samples on an Illumina MiSeq Instrument through Protocol Optimization

In the present study, we propose a high-throughput sequencing protocol using aNextera XT Library DNA kit on an Illumina MiSeq instrument. We made major modifications to this library preparation in order to multiplex 384 samples in a single Illumina flow cell. To validate our protocol, we compared the sequences obtained with the modified Illumina protocol to those obtained with the GridION Nanopore protocol. For the modified Illumina protocol, our results showed that 94.9% (357/376) of the sequences were interpretable, with a viral genome coverage between 50.5% and 99.9% and an average depth of 421×. For the GridION Nanopore protocol, 94.6% (356/376) of the sequences were interpretable, with a viral genome coverage between 7.0% and 98.6% and an average depth of 2123×. The modified Illumina protocol allows for gaining EUR 4744 and returning results of 384 samples in 53.5 h versus four times 55.5 h with the standard Illumina protocol. Our modified MiSeq protocol yields similar genome sequence data as the GridION Nanopore protocol and has the advantage of being able to handle four times more samples simultaneously and hence is much less expensive.
Memory optimization at Edge for Distributed Convolution Neural Network

Internet of Things (IoT) edge intelligence has emerged by optimizing the deep learning (DL) models deployed on resource‐constraint devices for quick decision‐making. In addition, edge intelligence reduces network overload and latency by bringing intelligent analytics closer to the source. On the other hand, DL models need a lot of computing resources. As a result, they have high computational workloads and memory footprint, making it impractical to deploy and execute on IoT edge devices with limited capabilities. In addition, existing layer‐based partitioning methods generate many intermediate results, resulting in a huge memory footprint. In this article, we propose a framework to provide a comprehensive solution that enables the deployment of convolutional neural networks (CNNs) onto distributed IoT devices for faster inference and reduced memory footprint. This framework considers a pretrained YOLOv2 model, and a weight pruning technique is applied to the pre‐trained model to reduce the number of non‐contributing parameters. We use the fused layer partitioning method to vertically partition the fused layers of the CNN and then distribute the partition among the edge devices to process the input. In our experiment, we have considered multiple Raspberry Pi as edge devices. Raspberry Pi with a neural computing stick is a gateway device to combine the results from various edge devices and get the final output. Our proposed model achieved inference latency of 5 to ∼$$ \sim $$ 7 seconds for 3×3$$ 3\times 3 $$ to 5×5$$ 5\times 5 $$ fused layer partitioning for five devices with a 9% improvement in memory footprint.
Higher grade hybrid model of layered superconductors

A hybrid discrete-continuous model of layered superconductors with interlayer Josephson couplings of arbitrary range is constructed. The conditions required by gauge invariance and thermodynamical stability of the model are determined. Some important special cases, in particular transition to the classic Lawrence-Doniach model, are discussed. The conditions for presence of alternating solutions and the posibilities to describe such states of a superconductor by the continuum or bi-continuum models are examined. The enhancement of superconductivity caused by the presence of higher order Josephson couplings is shown.
Establishing model uncertainty of SLS reinforced concrete crack models applied to load-induced cracking

Although some research has been performed, serviceability limit state (SLS) concrete crack models have yet to be calibrated fully in probabilistic terms in structural design standards. This is partly due to the fact that the SLS is generally not the critical limit state in structural design. However, in some specialist structures such as water retaining structures, the SLS such as cracking is the limiting design criterion, specifically the crack width required to control leakage and thus requires a proper probabilistic analysis. In probabilistic crack models, the reliability of the crack model is determined by the performance function whereby the design limiting crack width is greater than the estimated crack width calculated using the appropriate design crack model. As cracking in concrete is a random mechanism with a high degree of variability and crack models tend to be at least semi-empirical with inherent uncertainty, model uncertainty of the crack model is significant and is applied in the reliability model as a random variable. A database was established of both short and long term cracking experimental data for the tension load case to quantify model uncertainty. However, the data for long term cracking is limited which meant that the model uncertainty in this case was not definitively established. This paper discusses the determination of model uncertainty for tension cracking, which could be extrapolated to other models where data is limited.
Potential benefits of climate change for potatoes in the United States

Potatoes are a mainstay of human diets and 4 million metric tons are produced annually in the United States. Simulations of future crop production show that climate change is likely to reduce the yields of the major grain crops around the world, but the impacts on potato production have yet to be determined. A model ensemble consisting of five process-based and one statistical model was used to estimate the impact of climate change on fully irrigated, well-fertilized potato crop across the USA under the RCP 8.5 scenario of high emissions. Results indicate that increasing temperature will reduce potato yields, but this will be mostly compensated by elevated atmospheric CO2. Yields are predicted to decline with climate change in the current highest-yielding areas, which might experience the highest rises in growing season temperature during short hot summers. Simulated yields increase slightly elsewhere in the southern regions of the USA. Planting potatoes earlier as adaptation to avoid hot summers might improve yields in most regions. Water use by the potato crop is predicted to decline despite higher temperatures, due to a shorter growing season and increased water use efficiency under elevated atmospheric CO2. With higher yields in many regions, crop uptake for (nitrogen + phosphorus + potassium) NPK fertilizer will increase, despite the reduced concentration of nutrients in potatoes due to a growth stimulus from elevated atmospheric CO2. With earlier planting, by 2050 water use will decline by 11.7%, NPK fertilizer uptake will increase by 10.4%, and yields of slightly less nutritious potatoes will increase by 14.9% nationally.
Role of the Cysteine in R3 Tau Peptide in Copper Binding and Reactivity

Tau is a widespread neuroprotein that regulates the cytoskeleton assembly. In some neurological disorders, known as tauopathies, tau is dissociated from the microtubule and forms insoluble neurofibrillary tangles. Tau comprises four pseudorepeats (R1–R4), containing one (R1, R2, R4) or two (R3) histidines, that potentially act as metal binding sites. Moreover, Cys291 and Cys322 in R2 and R3, respectively, might have an important role in protein aggregation, through possible disulfide bond formation, and/or affecting the binding and reactivity of redox-active metal ions, as copper. We, therefore, compare the interaction of copper with octadeca-R3-peptide (R3C) and with the mutant containing an alanine residue (R3A) to assess the role of thiol group. Spectrophotometric titrations allow to calculate the formation constant of the copper(I) complexes, showing a remarkable stronger interaction in the case of R3C (log Kf = 13.4 and 10.5 for copper(I)-R3C and copper(I)-R3A, respectively). We also evaluate the oxidative reactivity associated to these copper complexes in the presence of dopamine and ascorbate. Both R3A and R3C peptides increase the capability of copper to oxidize catechols, but copper-R3C displays a peculiar mechanism due to the presence of cysteine. HPLC-MS analysis shows that cysteine can form disulfide bonds and dopamine-Cys covalent adducts, with potential implication in tau aggregation process.