Miracle-dz/UniFAR-FTUs huggingface dataset:
A handbook of software and systems engineering - empirical observations, laws and theories

Preface List of abbreviations 1. Introduction 2. Requirements definition, prototyping and modeling 3. System design and specification 4. System construction and composition 5. Validation and static verification 6. Testing or dynamic verification 7. System manufacturing, distribution, and installation 8. System administration, evolution and maintenance 9. Project management and business analysis 10. User skills, motivation, and satisfaction 11. Technology, architecture, and industry capabilities 12. Measurements, experiments, and empirical research 13. Conclusions and challenges Appendix 1: Summary of laws, hypotheses, and conjectures Appendix 2: Biographies of pioneers and key contributors Appendix 3: On-going projects/research groups References Index
HOTSPOT REMEDIATION USING GERMANIUM SELF COOLING TECHNOLOGY

Title of Thesis: HOTSPOT REMEDIATION USING GERMANIUM SELF COOLING TECHNOLOGY Horacio Cesar Nochetto, Masters Of Science, 2011 Thesis Directed By: Professor Avram Bar-Cohen, Department of Mechanical Engineering Localized thermoelectric “self cooling” in semiconductor material s is among the most promising approaches for the remediation of on-chip hot spots resulting from the shrinking feature sizes and faster switching speeds of nanoelectronic component s. Self cooling in a germanium chip is investigated, using 3-dimensional, thermal-ele ctric, coupled numerical simulations, for a range of systems and geometric param eters. The results suggest that localized cooling, associated with the introduction of an elec tric current on the back surface of a germanium chip, can effectively reduce the hot spot temperature rise on the active side of the chip. It was found that self cooling i n a 100μm thick chip could provide between 3.9oC and 4.5oC hotspot temperature reduction. When using a germanium layer above an electrically insulated silicon layer, s elf-cooling was found to yield an additional 1oC to 2o C temperature reduction. A streamlined computational tool is developed to facilitate the identification of optimal cooli ng parameters.
High frequency characteristics of a magnetically insulated transmission line oscillator oscillating in HEM 11 mode

Coaxial disk-loaded waveguide has been widely used in magnetically insulated transmission line oscillators (MILO). Although TM 00 mode is assumed to be its main mode, the asymmetric mode might be excited by slight asymmetry of the structure, asymmetric emission of the beam, or both. So the research on asymmetric mode in coaxial disk-loaded waveguide is necessary. The general eigen equation with both symmetric and asymmetric modes in coaxial disk-loaded slow-wave structures is theoretically analyzed. Then, a HEM 11 mode working MILO is designed and a tentative experiment is carried out to test its performance. Employing an electron beam of about 480 kV and 39 kA, HEM 11 mode working MILO generates a high power microwave output of about 1.2 GW with 40 ns pulse duration at 1.98GHz. The power conversion efficiency is about 6.5% and the generated microwave mode is TE 11 in the coaxial output region.
Reliability modeling and prediction using classical and Bayesian approach

Purpose – The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.Design/methodology/approach – The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.Findings – For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of fai.
Active Deformation Control for a Magnetically Levitated Planar Motor Mover

This article describes a method for the active control of the deformations on a magnetically levitated moving-magnet planar motor. The planar motor under consideration is comprised of a stator on a double coil array configuration and a mover with permanent magnets, and it is designed to perform positioning tasks with nanometer level of accuracy. Due to the spatially asymmetric, nonuniform force distribution on the magnet plate, mechanical deformations are induced to the mover, which can severely hinder the desired positioning accuracy. The proposed method overcomes this challenge by properly shaping the force distribution on the moving magnet plate, which is enabled by the presence of multiple coils interacting with the mover, corresponding to an “overactuation” scheme. As a consequence, the independent control of elementary deformation shapes (modes) is achieved. The proposed overactuation scheme is experimentally validated on a planar motor prototype, proving the efficiency of the proposed method during both standstill and motion.
AN ON-BOARD SYSTEM FOR DETECTING LAPSES OF ALERTNESS IN CAR DRIVING

Analysis of the circumstances surrounding motorway accidents clearly points to drowsiness at the steering wheel as one of the main causes. It is therefore not hard to understand the growing interest shown by the car makers in rapidly developing a system for detecting lapses of alertness. Renault's Departement Biomedical de l'Automobile (France) recently organized a series of tests to validate such a system. Twenty-one tests were performed on a driving track at night. Numerous lapses of alertness were obtained, leading to drivers often dozing off and leaving the road. In this paper the results are presented of the physiological recordings (electroencephalogram (EEG), electro-oculogram), and the behavioural recordings (analysis of the video film of the driver's face). It is shown how a physiological reference of the driver's level of alertness from this data is established. The correlation between the results provided by the EEG and those obtained by behavioural analysis is discussed. A multi-sensor approach, based on the analysis of driving behaviour, and the respiratory signal and the automatic processing of the film of the driver's face are dealt with. The study of the driver's reactions based on mechanical signals collected on the vehicle is also briefly described. (A) For the covering abstract of the conference see IRRD 894848.
Multi-scale Modeling of Bone Remodeling

The number of hip operations performed by inserting implants has continuously increased in recent years. In Germany 110 673 femoral head fractures of female persons were diagnosed in the year 2004. To minimize the surgical intervention, the femoral head is saved whenever possible and fixated by screws or nails. Thereby, the induced stiffness change leads to an adaptation of the material with respect to the new loading situation. The very same adaptation can cause a repeated failure of the femoral head. The present work introduces a numerical model, which is able to anticipate the adaptation process so that it is possible to make a statement about the failure probability in advance. The model considers the anisotropy of the bone material as well as the influence of its microstructure. The model will be implemented into two different Finite Element codes. Finally, the capability of the model is demonstrated on some numerical examples. Die Zahl der Huftoperationen, bei denen Implantate eingesetzt werden, ist in den letzten Jahren kontinuierlich gestiegen. Allein in Deutschland wurde im Jahr 2004 bei 110 673 weiblichen Personen eine Oberschenkelhalsfraktur festgestellt. Um den chirurgischen Eingriff so gering wie moglich zu halten, wird — wenn moglich — der Femurkopf erhalten und mittels Schrauben oder Nageln fixiert. Die dadurch implizierte Steifigkeitsanderung geht einher mit einer Anpassung des Materials bezuglich der neuen Belastungssituation. Eben diese Anpassung kann jedoch zu einem abermaligen Versagen fuhren. Die vorliegende Arbeit stellt ein numerisches Modell vor, das in der Lage ist, die Anpassung zu antizipieren, so dass sich bereits im Vorfeld Aussagen uber die Versagenswahrscheinlichkeit machen lassen. Das Modell berucksichtigt dabei sowohl die Anisotropie als auch die Mikrostruktur des Knochenmaterials. Dieses Modell wird in zwei unterschiedliche Finite Elemente Codes umgesetzt. Abschliesend demonstrieren einige Beispiele die Leistungsfahigkeit des Modells.
Evaluation of Bluetooth for Traffic Stream Travel Time Estimation Under Indian Conditions

Travel time estimation has been a high interest topic in traffic operation and management for years. Travel time information is more useful to the user than other parameters such as speed, flow, density, headway etc. However, travel time being spatial in nature, requires a spatial sensor for its measurement. The spatial sensors available for such a data collection include GPS, AVI, mobile phones, Bluetooth, etc. These can measure the travel time directly; however techniques like GPS and AVI require participation of the user for data collection, which is difficult especially in private vehicles. Use of mobile phones is another possibility, which has lesser location accuracy since it uses triangulation to find the location of vehicles. Bluetooth, Zigbee, and wifi sensors are other possibilities and are reported as possible source of travel time data from western countries. They have the advantage of no participation requirement and reasonably good location accuracy. However, the disadvantage is that they can capture only a small percentage of the entire traffic stream. The present study explores Bluetooth sensor as a traffic data source under Indian conditions. The first part of the study checks the penetration rate of Bluetooth to check the possibility of using it as a traffic data source under Indian conditions. The second part of the study analysed and identified the type of vehicles being represented by Bluetooth. This is important under Indian conditions, where several different types of vehicles are sharing the same roadway. And the last part of the study presents the stream travel time estimation from these selected classes of vehicles being represented by Bluetooth data. This involved the use of a weighted linear regression model. The results of modeling are promising and show potential for real time ITS applications.
The effectiveness of product placements: the influence of the likeability of the programme

The objective of this research is to analyse the influence of likeability of the programme on the effectiveness of product-placements and TV-commercials. Four different experimental groups were exposed to different TV-soap-series, with combinations of commercials and product- placements. Results indicate that a more appreciated programme can significantly enhance the effectiveness of product-placements but no commercials. The effectiveness of TV-commercials is significantly higher than for product-placements. No differences are found for attitude toward the brand between commercials and product-placements after watching the programmes. More generally, the attitude toward the well known brands studied seems not to be affected by different viewing experiences.
Stress and Coping Strategies in Public Speaking: Comparative Case Studies of Japanese and Malaysian Undergraduates

Public speaking competency is one of the core skills that is essential for personal and professional growth. Students who display effective public speaking skills are able to get their messages across while projecting confidence, clarity, and conviction which enhance their job prospects for future employability. Conversely, students’ failure to cope with the stress faced in preparing for public speaking may affect their speaking effectiveness. This comparative case study explored the stress and coping strategies among Japanese and Malaysian undergraduates in two universities to understand the similarities, differences, and patterns across these two groups that share a common focus. Study participants were selected through a purposive sampling technique in which relevant data was collected using semistructured interviews. Data gathered were then analysed thematically to identify the stressors and coping strategies in public speaking across the two groups. Results indicate similar stressors experienced by participants which are external speech stress factors, resource deficit, and anticipatory speech anxiety in public speaking. Personal, social, and academic-oriented strategies were the participants’ strategies to cope with the stress. These results suggest that similar stressors are faced by undergraduate students in performing public speaking, as well as and coping strategies used. This shows that the stress of public speaking is a prevalent occurrence and that institutional intervention can be developed by tertiary institutions to minimise its detrimental effects.
Underwater 3D vision, ranging and range gating

Abstract: An impressive number of laser-based sensors for 3D vision for terrestrial applications have been developed so far, making it a mature and expanding market worth billions of euros. When it comes to the subsea environment the presence of water, combined with the demand to operate at depth, acts as game-changing factors and considerable scientific and technological challenges arise. This chapter concentrates on selected techniques for subsea 3D vision and ranging using laser-based sensors, with priority given to projects where the efforts have been focused on developing devices ready to be deployed in real scenarios.
A survey of decontamination processes applicable to DOE nuclear facilities

The objective of this survey was to select an appropriate technology for in situ decontamination of equipment interiors as part of the decommissioning of U.S. Department of Energy nuclear facilities. This selection depends on knowledge of existing chemical decontamination methods. This report provides an up-to-date review of chemical decontamination methods. According to available information, aqueous systems are probably the most universally used method for decontaminating and cleaning metal surfaces. We have subdivided the technologies, on the basis of the types of chemical solvents, into acid, alkaline permanganate, highly oxidizing, peroxide, and miscellaneous systems. Two miscellaneous chemical decontamination methods (electrochemical processes and foam and gel systems) are also described. A concise technical description of various processes is given, and the report also outlines technical considerations in the choice of technologies, including decontamination effectiveness, waste handing, fields of application, and the advantages and limitations in application. On the basis of this survey, six processes were identified for further evaluation. 144 refs., 2 tabs.
Reliable internet routing

Network routing algorithms responsible for selecting paths to destinations have a profound impact on network reliability experienced by the network users. Unfortunately, performance of state-of-the-art routing algorithms often falls short of users' expectations. (i) The flexibility with which operators of independently administered networks can choose their routing policies allows them to make selections that are "conflicting" and may lead to route oscillations. Oscillating routes have a negative impact on performance experienced by the user, and also cause overloading of the routers with control messages. (ii) Interdomain routing in the Internet is based on trust. As a result, false route announcements can be made by a malicious network operator. Such false announcements can be made even without knowledge of the network operator, e.g., due to accidentally misconfigurations or router hijacking. False route announcements may lead to denial of service, or worse yet, traffic can be intercepted without detection of both the sender and recipient. (iii) Even if network routes are stable and secure, unexpected equipment failures may cause performance degradation. It is difficult to pre-configure current routing protocols with all possible failures in mind, and not enough flexibility is offered to balance load in the network evenly. This thesis addresses these three challenging problems. (i) We provide a new theoretical model of interdomain routing and derive the necessary and sufficient conditions that determine which policy combinations lead to route oscillations. Moreover, we also provide a practical polynomial-time algorithm that allows network operators to verify the existence of such conflicts. (ii) To secure routing against malicious attacks, we offer a new secure routing protocol that, unlike earlier attempts, is incrementally deployable. Our solution can protect both participants and non-participants if as few as 5–10 independently administered domains deploy our solution. (iii) To handle traffic engineering in the presence of failures, we propose a new architecture that optimizes load balancing for a wide range of failure scenarios. Our architecture supports flexible splitting of traffic over multiple precomputed paths, with efficient path-level failure detection and automatic load balancing over the remaining paths. Collectively, the contributions of the dissertation provide tools that improve routing reliability and as a result network performance perceived by the user.
R

Nanoparticle scaffolds can impart multiple benefits onto immobilized enzymes including enhanced stability, activity, and recoverability. The magnitude of these benefits is modulated by features inherent to the scaffold–enzyme conjugate, amongst which the size of the nanoscaffold itself can be critically important. In this review, we highlight the benefits of enzyme immobilization on nanoparticles and the factors affecting these benefits using quantum dots and gold nanoparticles as representative materials due to their maturity. We then review recent literature on the use of these scaffolds for enzyme immobilization and as a means to dissect the underlying mechanisms. Detailed analysis of the literature suggests that there is a “sweet-spot” for scaffold size and the ratio of immobilized enzyme to scaffold, with smaller scaffolds and lower enzyme:scaffold ratios generally providing higher enzymatic activities. We anticipate that ongoing studies of enzyme immobilization onto nanoscale scaffolds will continue to sharpen our understanding of what gives rise to beneficial characteristics and allow for the next important step, namely, that of translation to large-scale processes that exploit these properties.
Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.
Synchronization phenomena in many oscillators coupled by resistors as a ring

In this article, we investigate the synchronization phenomena in N oscillators coupled by resistors as a ring. Since the system tends to minimize the energy consumed by the coupling resistors, the phase shift must be /spl plusmn//spl pi/ and /spl plusmn/(N-1)/spl pi//N, for the case of N is even and odd respectively. In addition, only for the case of N is odd, we confirmed that the oscillation was stopped in some range of the coupling resistors. It is interesting that this phenomenon occurs for parameter values which are not too large or too small. These interesting phenomena are confirmed by both of circuit experiments and numerical calculations, and are analyzed by the averaging method theoretically.
Lung Cancer Detection Using Chi-Square Feature Selection and Support Vector Machine Algorithm

Lung Cancer is the most general type of disease in theworld ofcancer. It affects the lungs of the human body. So, the prediction of lung cancer at its earlier stage is difficult. It is the deadliest cause of death in both men and women. Its symptoms are harder to recognize in the initial stages.Machine learning algorithms have made the prediction and detection of lung cancereasier. Chi-square is used for feature selection to select the relevant features in the lung cancer dataset. Different Machine Learning algorithms are used to predict Lung Cancer.The algorithmsutilized in the proposed work are SVM and Random Forest. We have compared these algorithms with and without feature selection (Chi-square). SVM is identified as the best algorithm in the proposed work due to its accuracy and less execution time for detecting the model. The key objective of this paper is to enhance the accuracy and reduce the execution time of the classifier.
What Drives Bank Peformance?

Focusing on some key metrics of bank performance, such as revenues and loan charge-off rates, we estimate the fraction of the observed variation in these metrics that can be attributed to changes in economic conditions. Macroeconomic factors can explain the preponderance of the fluctuations in charge-off rates. By contrast, bank-specific, idiosyncratic factors account for a sizable share of the variation in bank revenues. These results point to importance of bank-specific business models as a driver of performance.