Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability and Risk Assessment

Fully updated and revised with more practical emphasis and more extensive use of case studies, this edition provides a comprehensive description of the main probabilistic methods employed in reliability and risk assessment, particularly fault tree analysis and failure mode, and effective analysis and their derivatives. Self-contained, each chapter focuses on a single subject or method and illustrates it with worked examples. The final chapter features four major case studies from recent projects to show how the methods have been applied to identify and quantify the risk and reliability characteristics against specific performance requirements. Focusing on the risk and reliability assessment of process plants, this book serves as a comprehensive reference to powerful analysis techniques for design, operation, and regulatory engineers involved directly with the safety and availability assessment of potentially hazardous process plants. It also serves as a text for undergraduate, graduate, and incompany courses and training. Contents: Introduction to Reliability and Risk Assessment; Reliability Mathematics; Qualitative Methods; Failure Mode and Effects Analysis; Quantific of Component Failure Probabilities; Reliability Networks; Fault Tree Analysis; Common Cause Failures; Maintainability; Markov Analysis; Simulation; Reliability Data Collection and Analysis; Risk Assessment; Case Study 1: Quantitative Safety Assessment of Ventilation Recirculation System in an Undersea Mine; Case Study 2: Failure Mode and Effects Criticality Analysis of Gas Turbine System; Case Study 3: In-Service Inspection of Structural Components (application to conditional maintenance of steam generators); Case Study 4: Business-interruption Risk Analysis. Appendix A: Expectation, Variance, and Standard Deviation. Appendix B: Standard Laplace Transforms; Glossary.
A critical systems thinking perspective for IS adoption

Information systems (IS) project management is a challenging task. Lack of user support and involvement are among the key reasons for IS/IT project failure. The established information technology adoption models—like TAM, TAM2 and similar—only consider technology adoption from an individual user’s viewpoint, highlighting key factors and their relationships, but they do not provide any mechanism to deal with multiple user perspectives and their roles in a holistic framework from a project management viewpoint. This chapter proposes a model for information system adoption based on critical systems thinking (CST) in an organisational context from a management perspective. The authors, considering IS adoption as a multiphase innovation project, argue that boundary considerations using a multiple stakeholder perspective (boundary critique) provide an alternative focus for IS adoption. The chapter uses the five basic phases or activities for information system development of the system development life cycle (SDLC). The model integrates the traditional SDLC with the ongoing process of ‘phase-stakeholder-identification’. The emerging systemic stakeholder networks are proposed to be applied with network mechanisms to influence stakeholders’ attitudes towards IS adoption. The study suggests that the proposed model has the capacity to serve as a roadmap for smoother IS adoption by facilitating organisational learning and change.
Surgical Treatment of Hashimoto’s Thyroiditis – Pros and Cons from the Perspective of Clinical Results

Patients with Hashimoto’s thyroiditis have hypothyroidism, goiter, and extrathyroidal lesions. Substitution therapy with thyroxine drugs in some cases is not effective. The work studies the feasibility of using surgical treatment – removal of the active site of autoimmune aggression. To study the effect of thyroidectomy on the quality of life of patients with Hashimoto’s thyroiditis with extrathyroidal manifestations. Sixty-one patients with Hashimoto’s thyroiditis were examined. Among them 29 patients were treated with drugs (control group) and 32 with surgery (main group). The quality of life was studied using the SF-36 questionnaire. TSH levels within euthyroidism were controlled and the level of antibodies to thyroperoxidase was determined. Two years after the surgical treatment, the studied quality of life indicators was improved significantly. The growth in individual indicators ranged from 34 (role-functioning, conditioned by emotional state) to 57 % (vital activity), and the overall health indicator increased by 52 %. The level of antibodies to peroxidase decreased almost to the norm. In the group of patients receiving drug treatment, overall quality of life indicators did not change significantly, but there was a tendency to worsen. The level of antibodies to peroxidase remained at high rates without significant changes. Surgical treatment of patients with Hashimoto’s thyroiditis improves the quality of life. The level of antibodies to thyroperoxidase after thyroidectomy is reduced to almost physiologically significant indicators.
Data Mining Approach to Detect Heart Diseases

Globally, heart diseases are the number one cause of death. About 80% of deaths occurred in low- and middle income countries. If current trends are allowed to continue, by 2030 an estimated 23.6 million people will die from cardiovascular disease (mainly from heart attacks and strokes). The healthcare industry gathers enormous amounts of heart disease data which, unfortunately, are not “mined” to discover hidden information for effective decision making. The reduction of blood and oxygen supply to the heart leads to heart disease. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques which will be useful for medical practitioners to take effective decision. The objective of this research work is to predict more accurately the presence of heart disease with reduced number of attributes. Originally, thirteen attributes were involved in predicting the heart disease. Thirteen attributes are reduced to 11 attributes. Three classifiers like Naive Bayes, J48 Decision Tree and Bagging algorithm are used to predict the diagnosis of patients with the same accuracy as obtained before the reduction of number of attributes. In our studies 10-fold cross validation method was used to measure the unbiased estimate of these prediction models.
Energy storage as an essential part of sustainable energy systems

Energy supply is an intricate task that provides a reliable energy service to consumers throughout the year. Import dependencies, seasonal differences in energy supply and use, and daily fluctuations in consumption require a sophisticated management of energy resources and conversion, or energy distribution and resource intermittency in order to guarantee continuous energy services throughout all sectors. Therein, energy storage plays a critical role. Energy storage balances the daily fluctuations and seasonal differences of energy resource availability, which results from physical, economical or geo-political constraints. A strongly variable energy demand through day and night also requires energy to be stored in adequate amounts. In particular, short- and mid-term storage levels out or buffers energy output gaps or overflows. Energy is mostly stored in between conversion steps from primary to secondary energy and secondary to final energy. Often rechargeable systems are used to refill the storage capacity when energy demand is low and energy services are not needed. Primary storage such as large crude oil and natural gas storage tanks are essential for the functioning of a country's energy system. Storage of water in reservoirs behind dams is valuable for selling hydropower electricity at the right time or in seasons of increased demand. Secondary or final storage systems, for instance in tanks or in batteries, are crucial for emergency situations, uninterrupted industrial production, long-distance mobility or to secure energy services at home. Storage systems are engineered to hold adequate amounts of mechanical, thermo-physical, electro-chemical or chemical energy for prolonged periods of time. Energy storage systems should be quickly chargeable and should have a large energy storage capacity, but at the same time should also have high rates of recovery and high yields of energy regain. Final energy in factories or households is often stored in tanks as chemical energy in the form of heating oil or natural gas. Thermo-physical energy in the form of steam, hot or cold water, or thermo-oils is also used. For some special applications or for safety reasons energy may be stored electrochemically in batteries or physically in the form of pressurized air. Other storage systems are related to electricity and apply mechanical storage in the form of spinning turbines or flywheels, physical storage in the form of water in reservoirs in highland terrains, or electrostatic storage in super-capacitors. Research is extensive in the area of energy storage since an increase of new renewable energy technologies such as wind and solar is expected to increase fluctuations and deviations from grid parameters. These need too be balanced out using reserve power capacities, grid level power storage capabilities, distributed generation units connected to the grid, and possibly appropriate new grid architectures.
Experimental automated localization of small damages of a steel beam using local modal filters

This work investigates the experimental application of a damage localization technique based on local modal filters on a 3.78 meters long steel I-beam equipped with 20 piezoelectric (PVDF) sensors, and excited with an electro-dynamic shaker. A small damage responsible of a small shift of the eigenfrequencies (less than 2%) is introduced at different locations by fixing a stiffener. By following the guidelines established in previous numerical studies, the modal filters are applied on five local filters in order to locate damage, and a new feature extraction procedure is proposed. Hotelling T2 control charts are used to locate automatically all the damage positions correctly. The results obtained with this experimental application of modal filters based on strain measurements confirm the first experimental observations that were previously obtained on a small scale set-up, and show the real interest of this very simple method for output-only non-model based automated damage localization of real structures.
Design and manufacturing of personalized implants and standardized templates for cranioplasty applications

Cranial (skull) defects are treated by the cranioplasty technique, which is required to protect underlying brain, correct major aesthetic deformities, or both. This research was aimed at investigating the technical solutions for cranioplasty treatments for the ASEAN countries based on available production and material technologies in this region. Solutions for design and manufacturing of personalized cranioplasty implants and a new concept of standardized templates (SDTs) for cranioplasty applications are presented. SDTs are made in mass production, based on the reverse engineering and rapid tooling techniques; they are used for preparing cranioplasty implants both pre- and intra-operatively. With the development of the design support database and program interfacing between the Medical Image Processing (MIP) and Computer Aided Design (CAD) system, the design time for the personalized cranioplasty implant was minimized to half a day. Through the use of SDTs, surgeons have a new solution to prepare implants in which the required skills are reduced. The cost of implants made by proposed solutions is acceptable for the ASEAN region.
Persuasion Wars as a Useful Tool for Teaching Social Influence: A Replication With Direct Measure of Pedagogical Effectiveness

Background Persuasion Wars are exercises for teaching social influence that have not been directly assessed with the use of students’ knowledge gain as an indicator. Objective This study aimed to establish the extent to which these exercises led to students’ knowledge increase in comparison to traditional teaching method. Method In a between subject design, 142 undergraduate students participated in two exercises: Persuasion Wars or Article Analysis. Pre-test and post-test measurements of students’ knowledge were collected to compute knowledge gain scores. At the end of the study, an exercise evaluation survey was distributed among students. Results Both teaching methods increased students’ knowledge about social influence. Regardless of the topic, students from Persuasion War group gained more knowledge when compared to students from Article Analysis group. Students assessed both teaching methods to a similar extent. Conclusion Persuasion Wars may serve as a useful teaching method in familiarizing students with the basic concepts of social influence despite being rated similarly to a more traditional activity. Teaching implications To improve the teaching outcomes of classes concerning basic topics of social influence, teachers should consider the use of Persuasion Wars exercises as well as should incorporate the use of a direct assessment of pedagogical effectiveness to avoid misleading predictions.
A Robust Input Modification Approach for High Tracking Control Performance of Flexible Joint Robot

Recently, there are increasing demands from industries for improved robot control system which can achieve given tasks more quickly, accurately, and vibrationlessly. An observer based state feedback control was developed in our previous study for the suppression of residual vibration of a robot [1]. The control was very effective in suppressing the inherent vibration of a flexible joint robot. However it is necessary to enhance the control scheme to meet the high tracking control performance at high speed.
Simulation approaches in transportation analysis : recent advances and challenges

Part I. Simulation Models and Their Application/State of the Art: Application of a Simulation-Based Traffic Assignment Model. The DRACULA Dynamic Network Microsimulation Model. Dynamic Network Simulation with AIMSUN. Microscopic Traffic Simulation: Models and applications. - Part II. Applications of Transport Simulation: The Art of the Utilization of Traffic Simulation Models: How do we make them reliable tools? Absorbing Markov Process OD Estimation and a Transportation Network Simulation Model. Simulating Travel Behaviour using Location Positioning Data Collected with a Mobile Phone System. - Part III. Representing Traffic Dynamics: Simulation of the Autobahn Traffic in North Rhine-Westphalia. Data and Parking Simulation Models. Saga of Traffic Simulation Models in Japan. A Study of Feasibility of Integrating Probe Vehicle Data into a Traffic State Estimation Problem using Simulated Data. - Part IV. Representing User Behavior. Consistency of Traffic Simulation and Travel Behavior Choice Theory: Driver's Route Choice Behavior and its Implications on Network Simulation and Traffic Assignment. An Overview of PCATS/DEBNetS Micro-Simulation System: Its Development, Extension, and Application to Demand Forecasting.
Comparison of coupled DEM-CFD and SPH-DEM methods in single and multiple particle sedimentation test cases

In this paper, the capability of two major methods for modelling two-phase flow systems, coupled discrete element method and computational fluid dynamics (DEM-CFD) and smoothed particle hydrodynamics and discrete element method (SPH-DEM), is investigated. The particle phase is modelled using the discrete element method DEM, while the fluid phase is described using either a mesh-based (CFD) or a mesh-less (SPH) method. Comparisons are performed to address algorithmic differences between these methods using a series of verification test cases, prior to its application to more complex systems. The present study describes a comprehensive verification for the fluid-particle simulations with -two different test cases: single particle sedimentation and sedimentation of a constant porosity block. In each case the simulation results are compared with the corresponding analytical solutions showing a good agreement in each case.
Entry, Exit, and Productivity of Indonesian Electronics Manufacturing Plants

We study the link between plant turnover and productivity using Indonesian plant-level data for the period of 1990-95. First, we compare productivity differentials among incumbents, entrants, and exiting plants by constructing the Farrell technical efficiency index using data envelopment analysis. We test the significance of these differentials using Simar and Wilson (1998) bootstrap algorithm and Li’s (1996) nonparametric test of closeness between unknown distributions. We find that the incumbent plants are on average the most productive group in every year of the estimation period. Also, the new plants are relatively less productive than the exiting plants in the early years. However, they are more productive than the exiting plants in the later years. Second, and more importantly, we estimate the productivity change during the study period using the Malmquist productivity change index and decompose the change to see if the differences in measured productivity change among the three groups of plants come from differences in the efficiency change or the technical change. Since the existing literature rarely distinguishes between these two different components, little is known whether exiting plants are less productive because of their inability to catch up to the current frontier or to adopt a better technology. Similarly, not much known whether entrants’ ability to survive come from their being equipped with a ’better’ technology or being able to catch up to the current frontier. Our findings indicate that although new plants enter with relatively lower productivity levels, they exhibit the highest productivity change during the early years. In addition, we find entrants’ high productivity growth in the early period is due to a movement toward the frontier, while in the later period is due to an upward shift of the technology frontier. Exiting plants, on the other hand, exhibit the lowest productivity change during the early years when entrants experience high productivity change.
Using EM Algorithm to identify defective parts per million on shifting production process

The objective of this project is to determine whether utilizing an EM Algorithm to fit a Gaussian mixed model distribution model provides needed accuracy in identifying the number of defective parts per million when the overall population is made up of multiple independent runs or lots. The other option is approximating using standard software tools and common known techniques available to a process, industrial or quality engineer. These tools and techniques provide methods utilizing familiar distributions and statistical process control methods widely understood. This paper compares these common methods with an EM Algorithm programmed in R using a dataset of actual measurements for length of manufactured product.
Classification of Plants Leaf Diseases using Convolutional Neural Network

Agriculture is one of the most important professions in many countries, including Iraq, as the Iraqi financial system depends on agricultural production and great attention should be paid to concerns about agricultural production. Because plants are exposed to many diseases and monitoring plant diseases with the help of specialists in the agricultural region can be very expensive. There is a need for a system capable of automatically detecting diseases. The aim of the research proposed is to create a model that classifies and predicts leaf diseases in plants. This model is based on a convolution network, which is a kind of deep learning. The dataset used in this study called (Plant Village) was downloaded from the kaggle website. The dataset contains 34,934 RGB images, and the deep CNN model can efficiently classify 15 different classes of healthy and diseased plants using the leaf images. The model used techniques to augment data and dropout. The Soft max output layer was used with the categorical cross-entropy loss function to apply the CNN model proposed with the Adam optimization technique. The results obtained by the proposed model were 97.42% in the training phase and 96.18% in the testing phase.
Navigation algorithms with applications to unmanned helicopters

This thesis deals with the design, the analysis, and the implementation of new navigation algorithms. The main focus lies on integrated navigation Systems based on inertial measurementdata combined with external navigation aids. The new concept of model based INS/GPS navigation is introduced and discussed. Here, the mathematical model of the craft is taken into account in order to improve the navigation Performance. The inertial navigation error dynamics are derived in a conceptually new way, allowing a more detailed Interpretation of the navigation error model. The results are compared with former derivations. Likewise, the error dy¬ namics of model based navigation are developed. Various concepts of im¬ plementationare presented. Simulation results as well as real flight data reveal the characteristics of the derived navigation algorithms. The navigation analysis has been motivated by the development of an un¬ manned, autonomously flying helicopter. Subsequent to the introduction on unmannedaerial vehicles, further insights are given on rotary-wing aircraft. This includes several aspectsof the mechanical, the electrical, and the Software environment. The major development progress is presented from the engineering point of view. Numerous flight experiments demonstrate the successful development of the autopilot system.
Radiolocation using AM broadcast signals

I have designed, built, and evaluated a passive radiolocation system that uses only signals of opportunity, that is, signals that exist for purposes other than radiolocation. The system estimates the relative position vector between a base station, which is a navigation receiver at a known location, and a rover, which is like the base station but free to move about. The relative position vector, called the baseline vector, is determined by multilateration from observations of the carrier phases of signals received from AM broadcast stations. This system determines the horizontal components of the baseline with about ten-meter uncertainties for baseline lengths up to about 35 kilometers. The navigation receivers are implemented as software radios on standard Intel-based personal computers. The signals received by a one-meter vertical whip antenna are bandpass-filtered, amplified, and digitized. The entire AM band is digitized so simultaneous observation of all available signals is achieved. All further processing of the signals, including carrier-phase determination, is implemented in software run on the personal computer. The base station and rover record observed phase, frequency, and amplitude data on their local hard drives; and navigation algorithms are implemented in post-realtime. The interpretation of a carrier-phase observation in terms of position is ambiguous because one cycle of a carrier wave is virtually indistinguishable from the next. Previous attempts at signal-of-opportunity navigation using carrier phase sidestepped the ambiguity problem by requiring that the initial position of the rover be known and that phase variations be tracked without interruption. I designed and implemented an ambiguity-function method that enables the phase ambiguity to be resolved instantaneously without position initialization or signal-tracking continuity. I encountered several impediments to AM-broadcast-based radiolocation that, if not dealt with appropriately, reduce positioning accuracy, reduce ambiguity-resolution robustness, or both. AM transmitter position uncertainty directly causes receiver positiondetermination uncertainty. Since the error in published antenna positions sometimes exceeds 100 meters, I conducted sub-meter-accuracy geodetic surveys of 29 Boston-area