Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomic Force Microscopy Nanoindentation Method on Collagen Fibrils

Atomic Force Microscopy nanoindentation method is a powerful technique that can be used for the nano-mechanical characterization of bio-samples. Significant scientific efforts have been performed during the last two decades to accurately determine the Young’s modulus of collagen fibrils at the nanoscale, as it has been proven that mechanical alterations of collagen are related to various pathological conditions. Different contact mechanics models have been proposed for processing the force–indentation data based on assumptions regarding the shape of the indenter and collagen fibrils and on the elastic or elastic–plastic contact assumption. However, the results reported in the literature do not always agree; for example, the Young’s modulus values for dry collagen fibrils expand from 0.9 to 11.5 GPa. The most significant parameters for the broad range of values are related to the heterogeneous structure of the fibrils, the water content within the fibrils, the data processing errors, and the uncertainties in the calibration of the probe. An extensive discussion regarding the models arising from contact mechanics and the results provided in the literature is presented, while new approaches with respect to future research are proposed.
Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure

Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure.
Load effect and fatigue damage of bridges under combined actions of traffic and wind: a case study

Study of load effects on bridges with large windward sides has to consider not only passing vehicles but also wind actions. This paper concerns the method to calculate actual load effect and fatigue damage under combined actions of traffic and wind, taking one of the steel rods of Anyang Bridge as an example project. Firstly, internal force time‐history of the rod was simulated based on measured data of traffic load and wind field. Then, extreme values of internal force of the rod were extrapolated using an improved method based on level crossing, and stress spectra of three fatigue‐prone details on the rod were gained using the rain‐flow method. Finally, the rod was checked under ultimate limit state and fatigue limit state, respectively. It was found that characteristic values of internal force of the rod under combined actions of traffic and wind increased from +5 849/–4 063 kN (considering vehicle actions alone) to +6 350/–5 466 kN and the maximum damage degree of fatigue‐prone details increased from 0.61 (considering vehicle actions alone) to 0.63. These results suggest that wind load contributes a significant increase to internal force characteristic values of the rod but a small increase to fatigue damage. However, high stress amplitudes due to wind load do not allow infinite life design. The actual load level is much higher than that prescribed in the design code. Hence, wind actions should be fully considered.
Qlik Sense® Cookbook

Over 80 step-by-step recipes to tackle the everyday challenges faced by Qlik Sense developersAbout This BookCreate dynamic dashboards for business intelligenceDiscover the techniques and practices for Qlik SenseDelve into a broad range of topics covering the Qlik Sense backend and the all-important user interfaceWho This Book Is ForThe book is for everybody who have been exposed to Qlik Sense and want to start using it actively for business intelligence. Anybody with prior knowledge of its sister product, QlikView, will also benefit from this book. Familiarity with the basics of business intelligence is a prerequisite.What You Will LearnSource, load, preview, and distribute your organization's informationMaximize the effect of visualizations for different types of dataGet to grips with the new visualization functions and add bespoke features not covered in the standard optionsLeverage scripting tools, techniques, and best practices that once implemented, never stop adding valueMake your user interface more advanced and intuitive with various custom objects and indicatorsExplore various tips and tricks of navigation for the Qlik Sense frontendDevelop creative extensions for your Qlik Sense dashboardIn DetailThis book is an excellent guide for all aspiring Qlik Sense developers. It will take you through the basics, right through to the use of more advanced functions. With the recipes in this book, you will be empowered to create fully featured desktop applications in Qlik Sense.Starting with a quick refresher on obtaining data from data files and databases, this book moves on to the more refined features of Qlik Sense, including visualization, scripting, and set analysis. The tips and tricks provided will help you to overcome challenging situations while developing your applications in Qlik Sense. This and more will help you to deliver engaging dashboards and reports efficiently.By the end of the book, you will be an expert user of Qlik Sense and will be able to use its features effectively for business intelligence in an enterprise environment.Style and approachThis book is the perfect mix of essential theory combined with real-life examples, with clear explanations of sophisticated Qlik Sense concepts. Each recipe begins with a task-based problem and the answers are provided in simple steps. This book follows an intuitive problem and solution approach that is easy to follow.
Improved double Fourier series on a sphere and its application to a semi-implicit semi-Lagrangian shallow-water model

Abstract. One way to reduce the computational cost of a spectral model using spherical harmonics (SH) is to use double Fourier series (DFS) instead of SH. The transform method using SH usually requires O(N3) operations, where N is the truncation wavenumber, and the computational cost significantly increases at high resolution. On the other hand, the method using DFS requires only O(N2log N) operations. This paper proposes a new DFS method that improves the numerical stability of the model compared with the conventional DFS methods by adopting the following two improvements: a new expansion method that employs the least-squares method (or the Galerkin method) to calculate the expansion coefficients in order to minimize the error caused by wavenumber truncation, and new basis functions that satisfy the continuity of both scalar and vector variables at the poles. Partial differential equations such as the Poisson equation and the Helmholtz equation are solved by using the Galerkin method. In the semi-implicit semi-Lagrangian shallow-water model using the new DFS method, the Williamson test cases and the Galewsky test case give stable results without the appearance of high-wavenumber noise near the poles, even without horizontal diffusion and without a zonal Fourier filter. In the Eulerian advection model using the new DFS method, the Williamson test case 1, which simulates a cosine bell advection, also gives stable results without horizontal diffusion but with a zonal Fourier filter. The shallow-water model using the new DFS method is faster than that using SH, especially at high resolutions and gives almost the same results, except that very small oscillations near the truncation wavenumber in the kinetic energy spectrum appear only in the shallow-water model using SH.
A review of recent advances on the properties of polypropylene - carbon nanotubes composites

Polypropylene (PP) has a wide range of engineering applications in automobile, biomedical, energy, machine parts, electronic packaging etc. However, PP lacks some desired engineering properties such as good thermal properties and mechanical strength. For instance, PP is known for its low melting temperature, high flammability and low heat resistance. This has resulted in continuous improvement in various properties of PP via modification of its matrix. One of the ways this has been achieved is by incorporation of foreign bodies in form of reinforcements in the PP matrix. The recent discovery of carbon nanotubes (CNTs) has further paved the ways for advancing the properties of PP via the development of PP-CNTs composites. Such composites have not only shown improved engineering properties but retain the lightweight and flexibility of PP. The advanced engineering properties recorded by various studies using PP-CNTs composites are due to the good properties of PP in conjunction with the excellent properties of CNTs such as high thermal conductivity, strength, electron mobility and formation of conductivity networks in the PP matrix. Although the development of PP-CNTs composites faces challenges of high agglomeration and incompatibility of CNTs in the matrix, the moves towards addressing the hurdles and achievements recorded so far are encouraging. Therefore, this review investigated the contribution of CNTs on various advanced engineering properties of PP-CNTs composites. Various results drawn from previously published literature within the decade were tabulated for future academic references and industrial purposes. Current hurdles faced by PP-CNTs composites, future prospect and their advanced engineering applications were discussed.
Development of boost converter based on switched-capacitor circuits

A new type of switching-mode power supply, which contains no magnetic devices, is developed. By avoiding inductors or transformers, the DC/DC power converter presents high power density, small size and low weight. The controlled energy conversion is realized by using switched capacitors (SC). The switching frequency is fixed and the driving signals of the externally-controlled switches are dictated by a PWM feedback circuit. The circuit is simulated by applying the averaged state-space method. Both simulation and experimental results prove that the new boost regulator presents high efficiency, small output voltage ripple and good regulation for large changes in the input voltage and/or load. The SC-based power supplies can be manufactured in IC technology for small powers. <<ETX>>
Metal-Organic Framework Assembled on Oriented Nanofiber Arrays for Field-Effect Transistor and Gas Sensor-Based Applications

Metal–organic framework (MOF) films are essential for numerous sensor and device applications. However, metal-organic framework materials have poor machinability due to their predominant powder-like nature, and their presence as the active layer in a device can seriously affect the performance and utility of the device. Herein, active layers of field-effect transistor (FETs) devices and chemiresistor gas sensors with high performance were constructed by loading Cu3(HITP)2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene) in situ-axial anchoring on oriented nanofiber arrays prepared via electrospinning. The strong interaction between polar groups on the polymer chains and metal ions promotes the nucleation of Cu3(HITP)2, steric hindrance makes particles of Cu3(HITP)2 with uniform size, morphology, and good crystallinity during nucleation by liquid phase epitaxial growth (LPE). Influences of differently-oriented Cu3(HITP)2 NFAs-based FETs on the electrical properties were studied, optimally oriented Cu3(HITP)2 NFAs-based FETs showed good mobility of 5.09 cm2/V·s and on/off ratio of 9.6 × 103. Moreover, excellent gas sensing response characteristics were exhibited in sensing volatile organic compounds (VOCs). Chemiresistor gas sensors with high response value, faster response and recovery are widely suited for VOCs. It brings new inspirations for the design and utilization of electrically conductive MOFs as an active layer for FETs and sensor units for chemiresistor gas sensors.
Capturing GreenTech-related Commercial Activities of Listed Companies

The purpose of this work is to investigate whether news flows can be used to effectively capture financial success of green commercial activities conducted by listed companies. The authors employ a large, cross-sectoral, global dataset, consisting of 97,954 articles from 10 online magazines, mentioning over 344 different firms that are part of more than 286 listed companies, covering the years 2004–2017 and over 32 countries. The notifications focus on GreenTech-related activities performed by companies. The authors conduct event studies to calculate abnormal returns and text analytics to gather (hyper-) textual features. Finally, the authors analyse the relationship between returns and features using ordinary least squares (OLS) regression models. Results indicate that textual features extracted from web notifications significantly provide new market information. Thus, news flow is found to serve as a reliable measure to reflect the financial success of green activities for future research on listed companies. Features, such as multimedia elements turn out to not provide new market information, while readability and sentiment measures do. The authors extend the growing literature on GreenTech by proposing the novel combination of textual and event study analysis in order to enable research on green commercial activities conducted by listed companies.
Hierarchical integrated maintenance planning for automated manufacturing systems

In this research, we develop an innovative hierarchal approach to maintenance planning in complex, automated manufacturing systems. Strategic, tactical, and in-process planning are addressed. At the strategic planning level, we develop an analytical methodology, based on Mean Value Analysis, that provides the maintenance planner with a capability to rapidly and accurately determine optimal preventive maintenance intervals considering production costs and interrelationships associated with machine breakdown. The algorithm produced consistent estimates within 5% of true system performance over a wide range of Weibull nonhomogeneous failure distributions. In addition to development and validation of the algorithm, an important contribution of the current work has been the quantification of model performance through response surface methods, permitting users of the model to estimate expected error levels for particular applications. At the tactical planning level, we develop a predictive model which utilizes proportional hazard modeling to analyze machine condition. The predictive maintenance system determines whether a machine failure appears to be pending, and if so, schedules a repair and determines the optimal priority level to assign. The predictive model demonstrated a remarkably good capability to identify repair requirements in order to prevent failures. In the majority of datasets tested, 100% of failures were prevented. Robust performance was demonstrated across a variety of failure characteristics. At the in-process level, we develop an innovative neural network system which actively performs machine diagnostics and discovers trends in sensory data. Upon confirmation or correction of a diagnosis, the network can adapt to incorporate this new knowledge. We have performed an in-depth analysis and testing of the performance of the neural network as a diagnostic tool for machining equipment. This analysis led to the development of a number of preprocessing techniques, network modifications, and a knowledge extraction procedure which improve the network's diagnostic capabilities. The network is robust to noise, can generalize from training examples, has excellent classification accuracy, and rapid, real-time operation. The three levels have been integrated in a hierarchal planning system with adaptive capability. The system is capable of performing consistent optimized automated maintenance planning in complex manufacturing environments.
Supervised Work Experience

Abstract The benefits to students of an industrial placement or supervised work experience (SWE) as an integral part of undergraduate degree programmes have long been accepted. Employers use SWE as an opportunity to assess the capability of students prior to offering them permanent employment on completion of their studies. Likewise, students use SWE to review an employer's ability to provide them with the relevant post-graduation experience to enable them to progress to professional qualification. Also, during SWE they assess the construction industry in terms of its working environment and as a long-term career. The findings are presented of an eight-year study into the ability of organizations within the construction industry to provide appropriate learning environments during SWE. Construction organizations are perceived to be supportive in terms of personal and informal support provided by colleagues, and to a lesser extent working practices, but less supportive in terms of the more formal support given by managers, specifically in the use of appraisal systems. Construction organizations need to accurately assess their ability to provide an effective learning environment in order to attract potential employees to a career in construction and to retain students within the industry after SWE.
Coocclusion of Helicoverpa armigera Single Nucleopolyhedrovirus (HearSNPV) and Helicoverpa armigera Multiple Nucleopolyhedrovirus (HearMNPV): Pathogenicity and Stability in Homologous and Heterologous Hosts

Helicoverpa armigera single nucleopolyhedrovirus (HearSNPV) is a virulent pathogen of lepidopterans in the genera Heliothis and Helicoverpa, whereas Helicoverpa armigera multiple nucleopolyhedrovirus (HearSNPV) is a different virus species with a broader host range. This study aimed to examine the consequences of coocclusion of HearSNPV and HearMNPV on the pathogenicity, stability and host range of mixed-virus occlusion bodies (OBs). HearSNPV OBs were approximately 6-fold more pathogenic than HearMNPV OBs, showed faster killing by approximately 13 h, and were approximately 45% more productive in terms of OB production per larva. For coocclusion, H. armigera larvae were first inoculated with HearMNPV OBs and subsequently inoculated with HearSNPV OBs at intervals of 0–72 h after the initial inoculation. When the interval between inoculations was 12–24 h, OBs collected from virus-killed insects were found to comprise 41–57% of HearSNPV genomes, but the prevalence of HearSNPV genomes was greatly reduced (3–4%) at later time points. Quantitative PCR (qPCR) analysis revealed the presence of HearSNPV genomes in a small fraction of multinucleocapsid ODVs representing 0.47–0.88% of the genomes quantified in ODV samples, indicating that both viruses had replicated in coinfected host cells. End-point dilution assays on ODVs from cooccluded mixed-virus OBs confirmed the presence of both viruses in 41.9–55.6% of wells that were predicted to have been infected by a single ODV. A control experiment indicated that this result was unlikely to be due to the adhesion of HearSNPV ODVs to HearMNPV ODVs or accidental contamination during ODV band extraction. Therefore, the disparity between the qPCR and end-point dilution estimates of the prevalence of mixed-virus ODVs likely reflected virus-specific differences in replication efficiency in cell culture and the higher infectivity of pseudotyped ODVs that were produced in coinfected parental cells. Bioassays on H. armigera, Spodoptera frugiperda and Mamestra brassicae larvae revealed that mixed-virus OBs were capable of infecting heterologous hosts, but relative potency values largely reflected the proportion of HearMNPV present in each mixed-virus preparation. The cooccluded mixtures were unstable in serial passage; HearSNPV rapidly dominated during passage in H. armigera whereas HearMNPV rapidly dominated during passage in the heterologous hosts. We conclude that mixed-virus coocclusion technology may be useful for producing precise mixtures of viruses with host range properties suitable for the control of complexes of lepidopteran pests in particular crops, although this requires validation by field testing.
Nonlinear ultrasonic measurements in nuclear reactor environments

Several Department of Energy Office of Nuclear Energy (DOE-NE) programs, such as the Fuel Cycle Research and Development (FCRD), Advanced Reactor Concepts (ARC), Light Water Reactor Sustainability, and Next Generation Nuclear Power Plants (NGNP), are investigating new fuels, materials, and inspection paradigms for advanced and existing reactors. A key objective of such programs is to understand the performance of these fuels and materials during irradiation. In DOE-NE’s FCRD program, ultrasonic based technology was identified as a key approach that should be pursued to obtain the highfidelity, high-accuracy data required to characterize the behavior and performance of new candidate fuels and structural materials during irradiation testing. The radiation, high temperatures, and pressure can limit the available tools and characterization methods. In this thesis, two ultrasonic characterization techniques will be explored. The first, finite amplitude wave propagation has been demonstrated to be sensitive to microstructural material property changes. It is a strong candidate to determine fuel evolution; however, it has not been demonstrated for in-situ reactor applications. In this thesis, finite amplitude wave propagation will be used to measure the microstructural evolution in Al-6061. This is the first demonstration of finite amplitude wave propagation at temperatures in excess of 200 °C and during an irradiation test. Second, a method based on contact nonlinear acoustic theory will be developed to identify compressed cracks. Compressed cracks are typically transparent to ultrasonic wave propagation; however, by measuring harmonic content developed during finite amplitude wave propagation, it is shown that even compressed cracks can be characterized. Lastly, piezoelectric transducers capable of making these measurements are developed. Specifically, three piezoelectric sensors (Bismuth Titanate, Aluminum Nitride, and Zinc Oxide) are tested in the Massachusetts Institute of Technology Research reactor to a fast neutron fluence of 8.65x10 n/cm. It is demonstrated that Bismuth Titanate is capable of transduction up to 5 x10 n/cm, Zinc Oxide is capable of transduction up to 6.27 x10 n/cm, and Aluminum Nitride is capable of transduction up to 8.65x x10 n/cm.