Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability of Extreme Learning Machines

The reliable application of machine learning methods becomes increasingly important in challenging engineering domains. In particular, the application of extreme learning machines (ELM) seems promising because of their apparent simplicity and the capability of very efficient processing of large and high-dimensional data sets. However, the ELM paradigm is based on the concept of single hidden-layer neural networks with randomly initialized and fixed input weights and is thus inherently unreliable. This black-box character usually repels engineers from application in potentially safety critical tasks. The problem becomes even more severe since, in principle, only sparse and noisy data sets can be provided in such domains. The goal of this thesis is therefore to equip the ELM approach with the abilities to perform in a reliable manner. This goal is approached in three aspects by enhancing the robustness of ELMs to initializations, make ELMs able to handle slow changes in the environment (i.e. input drifts), and allow the incorporation of continuous constraints derived from prior knowledge. It is shown in several diverse scenarios that the novel ELM approach proposed in this thesis ensures a safe and reliable application while simultaneously sustaining the full modeling power of data-driven methods.
Broad ligand specificity in the small multidrug resistance transporter EmrE

OF THE DISSERTATION Broad Ligand Specificity in the Small Multidrug Resistance Transporter EmrE by Emma Antonina Morrison Doctor of Philosophy in Biology and Biomedical Sciences Molecular Biophysics Washington University in St. Louis, 2014 Professor Katherine A. Henzler-Wildman, Chair EmrE, an E. coli small multidrug resistance transporter, effluxes a diverse range of toxic polyaromatic cations, thus imparting resistance to drug compounds of this type. Transporters must interconvert between inwardand outward-facing structures during the transport cycle to alternate access of its binding site between the two sides of the membrane. As a secondary active antiporter, EmrE must couple drug binding to this conformational exchange and its energy source, the proton gradient across the inner membrane. The transport cycle involves the import of two protons to drive the export of one substrate molecule. As a multidrug resistance transporter, EmrE has particularly broad specificity and a large variation in affinities for these transported substrates, yet all substrates must trigger the same conformational change between inwardand outward-facing states in order for transport to occur. My research focuses on this coupling between substrate binding and conformational exchange and the functional outcome of this coupling, transport. As one of the smallest transporters, EmrE serves as an ideal system to study the minimal requirements for multidrug recognition and transport. EmrE transports polyaromatic cations that
Hydrological peaks evaluation at chitral and tarbela stations, Pakistan, using combined Bayesian regularized neural network and signal difference average based variational mode decomposition method: a case study

Pakistan being an agricultural country highly depends on its natural water resources originate from the upper regions of Hindu Kush-Karakoram-Himalaya Mountains and nourish one of the world's largest Indus Basin irrigation system. This paper presents streamflow modelling and forecasting using signal difference average (SDA) based variational mode decomposition (VMD) combined with machine learning (ML) methods at Chitral and Tarbela stations on the Indus River network. For this purpose, VMD based; random forest (VMD-RF), gradient boosting machine (VMD-GBM) and Bayesian regularized neural network (VMD-BRNN) have been opted. Moreover, traditional time series flow model that is seasonal autoregressive integrated moving average (SARIMA) and classical decomposition approach with particle swarm optimization-based support vector regression (PSO-SVR) are considered as benchmark models for comparison. The results show that overall, VMD-BRNN performed best, followed by VMD-GBM and VMD-RF, whereas, SARIMA and PSO-SVR ranked last. Overall, SARIMA and PSO-SVR are failing to capture most of the peaks even during training period. Whereas, hybridization of VMD and ML methods has shown increased robustness of the models. The results show that the influential role of the high dimensional components and robustness on the river flow may be explored by most optimum SDA based VMD signals hybrid with BRNN method.
Deep Eutectic Solvents as an Alternate to Other Harmful Solvents

Solvents generally in liquid form, are used to dissolve, dilute, suspend any substances or extract other materials. More than one-third of the drugs listed in the various Pharmacopeias fall into the poorly water-soluble or water-insoluble categories. For more than 200 years, traditional solvents could be used as a solvent for substances that were insoluble in water. But the usage of these types of solvents should be decreased because these types of solvents are volatile, flammable, and often toxic. Also, the industrialist’s usages in different types of processes prove the risk for workers. In recent years, several solvents have been proposed to be the greener replacement for traditional solvents. Replacing hazardous chemicals with more environmentally friendly alternatives is a matter of current interest, in line with the philosophy of Green Chemistry. The use of nontraditional or nonconventional solvents such as supercritical fluids (SCFs) such as Carbon dioxide (CO2) and water, fluorous solvents, solventless reaction Ionic liquids (ILs) and their derivatives [polymeric ILs and magnetic ILs], and deep eutectic solvents (DESs) are alternatives for environmentally unfriendly traditional solvents. Among them, DES is a neoteric class of green solvents defined as a mixture of two or more compounds that are typically solid at room temperature, but when combined at a particular molar ratio, changes into a liquid at room temperature. It is assumed that eutectic mixtures show low volatility, have a broad liquid range, and are water-compatible, non-flammable, non-toxic, biocompatible, and eco-friendly. Eutectic solvents have been useful in several pharmaceutical fields, such as the increase of drug solubility, permeation, and absorption.
Detection of minimal residual disease in adult acute myeloid leukemia via CD25

Background We detected the expression of CD25 in patients with acute myeloid leukemia (AML) to value whether CD25 could be a promising marker for minimal residual disease (MRD). Methods Two hundred and twenty bone marrow (BM) specimens from 98 adult patients with AML after chemotherapy were detected using flow cytometry. The expression of CD25 was compared between MRD positive and negative subgroups. Results About 38% of patients with MRD were positive for CD25. The mean percentage of CD25-positive cell subpopulation was 58.68% relative to the whole MRD cluster (0.05%-100%). The mean fluorescence index ratio (MFIR) of CD25 in these cell subpopulations was approximately13-fold greater than that in normal myeloblasts. The detection sensitivity of CD25 was as high as 10 -4 . CD25 was also expressed on non-leukemic stem cells that were positive for CD34 and CD38. Conclusion CD25, as assessed by flow cytometry, is a promising marker for MRD in patients with AML.
Design of an independent front wheel steering system with optimal driver feedback

In this thesis, Independent Front Wheel Steering (IFWS) is studied. To implement an IFWS system, it is important to understand how dynamics of the front wheel and steering motor work. Furthermore it can be challenging to provide haptic feedback to the driver, especially when an active safety system is constantly adjusting the angle of the front wheel and causing interference. First a model of a front wheel steering system with a variable gear ratio between motor and front wheels is created. At low gear ratios, the simulations show an improvement of the response of the steering system to the tyre forces. A compromise should be made between a small motor and low gear ratio. A low gear ratio is desirable as it improves the accuracy of a direct current measurement (DCM), which is required for providing the driver with haptic feedback with road information. Then a virtual model (VM) based on the bicycle model is created which can estimate the actual tyre forces within a 16 % accuracy. The error in the VM is caused by the approximation of nonlinear vehicle dynamics. The DCM and the VM are compared to their reference tyre forces in a modified double lane change and a steady state corner simulation. A combiner of the DCM and VM (Combiner DCM and VM (CDV)) is proposed which can blend the two feedback sources. At low interference frequencies, the CDV shows good results. At high interference frequencies, presence of the inertia and damping of the steering system become more dominant compared to the tyre forces and causes a phase lead in the DCM. Towards the natural frequency of the steering system, the magnitude of the required motor current decreases. The result is a poor performance of the CDV at high interference frequencies. One of the recommendation for improving the CDV is installing a motor with a high constant torque value. The motor is coupled to the front wheel with a low gear ratio and will lower the inertia and damping of the steering system. This increases the eigenfrequency of the steering system and improves the bandwidth of the DCM. However the space requirements of a bigger motor can cause problems for implementation.
Exploring disaster risk reduction in the built environment

The purpose of this paper is to review the role of construction industry and built environment disciplines in disaster risk reduction. There is much evidence to show that every year natural disasters cause a substantial amount of damage throughout the whole world. This brought forward the importance of disaster risk reduction to prevent or mitigate the adverse impacts of disasters. The construction industry has a strong relationship with disaster management and therefore there is a high need in identifying how the construction industry and its related disciplines can contribute towards disaster risk reduction. Therefore the paper focuses on the role of the construction industry and the built environment professionals in disaster risk reduction. The literature review method has been used to address this emerging topic and therefore the paper is based on the academic literature, journal and conference papers and reports produced by various institutions.The review of literature reveals that there is a clear relationship between disaster risk reduction and the built environment disciplines. It has been identified that the construction industry and built environment professionals have a significant role to play in disaster risk reduction in both pre and post disaster situations. The risk reduction strategies can be incorporated in to all phases of construction projects, including planning, designing, construction and maintenance in addition to town and country planning and policy making.
An accurate analytical crosstalk model for RC interconnect

In recent times, crosstalk noise is playing a vital role in the performance of VLSI circuits, due to miniaturization of the feature size. In this paper, we propose an accurate, fast and simple closed form solution to estimate crosstalk noise between two adjacent wires, using RC interconnect model. The salient features of our proposed metric are minimization of computational overhead, elimination of adjustment to estimate the peak amplitude and pulse width of the noise waveform. Numerical calculations of our model are compared with SPICE simulation and other metrics by plotting the noise voltage verses time. It is evident from the experimental results that our crosstalk noise model in time domain is closely matched with SPICE simulation at a minimum estimation error of 0.62%.
Evaluations and utilizations of risk importances

This report presents approaches for utilizing Probabilistic Risk Analyses (PRA's) to determine risk importances. Risk importances are determined for design features, plant operations, and other factors that can affect risk. PRA's can be used to identify the importances of risk contributors or proposed changes to designs or operations. The objective of this report is to serve as a handbook and guide in evaluating and applying risk importances. The utilization of both qualitative risk importances and quantitative risk importances is described in this report. Qualitative risk importances are based on the logic models in the PRA, while quantitative risk importances are based on the quantitative results of the PRA. Both types of importances are among the most robust and meaningful information a PRA can provide. A wide variety of risk importance evaluations are described including evaluations of the importances of design changes, testing, maintenance, degrading environments, and aging. Specific utilizations are described in inspection and in reliability assurance programs, however the general approaches have widespread applicability. The role of personal computers and decision support programs in applying risk importance evaluations is also described.
Detecting and tracking human motion in variance-based radio tomography imaging

Currently, few methods exist to accurately model a human motion inside a monitored area. Most of the approaches that exist depend on some kind of boolean data from sensors that tell the presence or absence of person a at a given instant of time near a particular sensor. Using that information, some systems can then track a person across the area at different timestamps. Furthermore, for most existing approaches, the accuracy drops rapidly as the number of persons in the image increases. The sensors used in such settings are usually expensive. Not much work has been done to build a similar system based on inexpensive radio sensors. As there is no way for our radio sensors to provide information as to whether a person is present at a location, we need to extract it from the data using computer vision and machine learning techniques. However, it is not easy in such a system to model the noise component accurately. Therefore, we provide a probabilistic model to decide whether a detected blob is noise or an actual person. In our work, we exploit the fact that images do not change by much between successive timeframes and use this to detect and track multiple persons in a monitored area with a reasonably high accuracy. We use location and count of persons in historical images, and their similarity with the current image to calculate the new locations and count.
Listening to your audience: developing an educational tool to assist with recreation planning on private lands

The Georgia Forestry Commission in conjunction with the U.S. Forest Service instituted the Forest Stewardship Program (FSP) in �99�. A growing component of the FSP is recreation, which is the component least understood by stewardship planners. A survey was developed to better understand the needs of planners with regard to developing forest recreation plans, and the results will be used to create an Internet- based educational resource. Less than half of the forest planners surveyed had experience with writing recreation plans, and only 3� percent had previously written aesthetic plans. The results indicate a strong desire for a database of fact sheets related to all facets of natural resources. Based on the survey findings, the planned tutorial-style Website was changed to a fact sheet database for stewardship planners.
Development and study of a self-recording sticky belt.

Onder andere wordt ingegaan op het praktisch gebruik en enige resultaten hierbij van de installatie, waarmee men in een proefopstelling door middel van een lijmband, waarop de zaden terechtkomen, de werking van precisiezaaimachines kan nagaan
Emerging Technologies And The Future Of Geotechnical Instrumentation

This paper discusses the results of two recent case studies in which high-tech prototype instruments were used. The first case study describes the results of a ground-based hyperspectral imaging tool used to map the geology of an open-pit mine highwall. The second case study outlines the use of interferometric synthetic aperture radar and how this type of radar can be used in a variety of geotechnical situations. These case studies are part of an on-going research project at the National Institute for Occupational Safety and Health’s (NIOSH) Spokane Research Laboratory. The project objective is to implement engineering controls and design methods in order to reduce the number of injuries and fatalities associated with slope failures at mining operations. Project personnel are hopeful that these new developments in technology will lead to better geotechnical monitoring and design in slope stability and other important areas of rock mechanics.
The Science Classroom Profile: Design, Development, and Use

A vehicle primarily for unmanned operation is equipped with a video camera pointed up, and progressively records in an on-board computer memory the observed locations of points derived from overhead features such as pre-existing overhead lights in a building during a manually driven teaching mode trip along a desired course. On subsequent unmanned trips, the vehicle directs itself along the chosen course by again observing the overhead features, deriving points from them and comparing the locations of those points to the locations of the points that were recorded during the teaching mode trip. It then corrects its steering to bring the errors between the observed locations and the recorded locations to zero, thereby directing the vehicle to automatically follow the course that was followed during the teaching mode trip.
Prediction of drug distribution in rat and human

Many methods exist in the literature for the prediction of pharmacokinetic parameterswhich describe drug distribution in rat and human, such as tissue-to-plasma partitioncoefficients (Kps) and volume of distribution (Vss). However, none of these methods makeuse of the in vivo information obtained at the early stages of the drug developmentprocess in the form of plasma concentration vs. time profiles. The overall aim of thepresented study was to improve upon an existing Kp prediction method by making use ofthe distribution information contained within this experimental data. Chapter 2 shows thatKp values can be successfully obtained experimentally, but that this process is expensiveand time-consuming. Chapter 3 compares six Kp prediction methods taken from theliterature for their ability to predict the Kp values of 80 drugs. The Rodgers et al. modelwas found to be the most accurate, with over 77% of predictions within 3-fold ofexperimental values. This Chapter also discusses the Vss prediction ability of some ofthese methods, with the Poulin & Theil and Rodgers et al. models shown to be the mostaccurate predictors for rat Vss and human Vss respectively. Chapter 4 investigates therelationship between muscle Kp and the Kps of all other tissues, to show that experimentalmuscle Kp can be used as a surrogate from which all other non-adipose Kp values can bepredicted. However, the predictions made using this method were shown to be lessaccurate than predictions made by the Rodgers et al. model for the same dataset of drugs.A relationship was identified between muscle Kp and tumour Kp in rat, suggesting apotential way to predict tumour Kp in the future. In Chapter 5, a novel method is developedwhereby Kp predictions made by the Rodgers et al. model are updated using priorinformation obtained from the in vivo concentration-time profile. These updated values arethen used within a physiologically-based pharmacokinetic (PBPK) model and are shown inChapter 6 to generate improved predictions for other pharmacokinetic parameters such asVss and clearance in both rat and human. 100% of human Vss predictions made by themost accurate of the novel methods presented here were within 3-fold of experimentalvalues, compared to 68.8% of predictions made by the Rodgers et al. model. The workpresented here has highlighted the need for a more accurate method for the prediction ofKp values, and has addressed this need by generating a model which improves upon themost accurate Kp prediction method currently found in the literature. This will lead to anincrease in confidence in the use of predicted pharmacokinetic parameters within PBPKmodelling.
USING A DESIGN LED DISRUPTIVE INNOVATION APPROACH TO DEVELOP NEW SERVICES: PRACTICING INNOVATION IN TIMES OF DISCONTINUITY

Organisations face increasing competition from new firms in emerging markets and their past superior products may no longer provide competitive advantage in markets based on different cost and value differentials. A shift in design practices from product solutions to health services which are accessible and affordable by all is required. This paper explores a design led approach to innovation to assist medical device companies develop new services and experiences and reshape their notions of the nature, development and deployment of health care services. This approach uses design tools and methodologies that are grounded in the authentic understandings of stakeholder experiences, to assist an organisation create a vision of likely future health care scenarios. Through this process, organisations can explore the complexities in the delivery of future health care services in new and emerging markets allowing them to tailor product and service solutions which focus on being accessible and affordable by all. The industry based case study for the design of health services in carried out in emerging economies. The contribution of this work in advancing research into design innovation and future research directions are also presented.
Self-Consolidating Concrete Robustness in Continuous Production Regarding Fresh and Hardened State Properties

Due to its advantages over conventional concrete, there has been a considerable increase worldwide in the number of applications using self-consolidating concrete (SCC). Engineers have been encouraged to study the stability of different SCC mixture designs in terms of robustness further because of good degrees of reliability associated with large-scale SCC production. SCC mixture robustness is its properties' stability when errors affect original mixture design in weighing the constituents. Six different SCC mixture designs were selected for SCC robustness evaluation. After they were applied with variations, large scale production errors were simulated, and there was evaluation of their properties in fresh and hardened states. Analysis of variance was used to analyze how such errors influenced concrete properties. Additionally, there was comparison of statistical distributions of real data errors obtained from several Spanish concrete producers and tolerances generally accepted by codes. Traditional concrete tolerances generally have also proven to be valid for SCC. Water and fines content weighing errors are admissible up to ±6% and are of capital importance.
Soft-Error-Aware SRAM for Terrestrial Applications

Soft errors such as single-node upset (SNU) and multiple-node upset (MNU) have become a major problem for SRAMs in aircraft and terrestrial applications. In this letter, a novel low-cost and write enhancement soft-error-aware-14T (LWS14T) SRAM cell is proposed to provide sufficient protection against soft errors. The obtained observations demonstrate that the LWS14T can recover not only from SNUs but also from MNUs irrespective of the upset polarity. Furthermore, compared to considered RHBD cells, the LWS14T has reduced the cost in terms of power and delay. Also, the LWS14T gives the better critical charge, better stability, and better writability.