Miracle-dz/UniFAR-FTUs huggingface dataset:
D Breast Parametric Model for Surgery Planning-a Technical Review

Breast cancer is the most common cancer among females. Two main approaches are used as treatment: mastectomy, in which the cancerous breast is completely removed; and conservative treatment, in which the tumour is removed with margin of healthy tissue. To improve surgical approaches resulting less damage to dynamic shape of breast, it is worth to study the breast model to enable specialists having full comparison between the results of different treatments. The aim of this work is to study 3D reconstruction based on passive and active sensors. Also, it is aimed to study state of the art about parametric models to obtain breast shape. Such parametric model can enhance surgeons experience in order to perform better surgeries and patients to be more confident about the breast shape after treatment.
Vertical hydraulic generators experience with dynamic air gap monitoring

Dynamic air gap monitoring is the process of measuring the rotor-to-stator distance of a salient pole hydroelectric generator while it is in any of its operating modes. The main purposes for using dynamic air gap monitoring are to assist in minimizing loss of revenue through preventing the loss of rotor-to-stator air gap, and to aid in the maintenance and maintenance scheduling of large hydroelectric generators. The authors discuss the air gap measuring principle, the technique, and the equipment used. They also include several case studies in which dynamic air gap measurement data was analyzed and correlated to generator performance. >
On End-to-end Automatic Fact-checking Systems

The emergence of social media has aided the spread of nonfactual information across the internet, and organizations are combating disinformation by performing manual fact-checking. Due to the massive amount of online information, the automation of this process has recently gained great interest. Previous works have formulated several automatic fact-checking tasks, and explored machine learning and natural language processing approaches to the problems. In this thesis we follow this line of work, aim to build a fully-working automatic fact-checking system, and study methods for improving its fact-checking abilities. First, we introduce an end-to-end automatic fact-checking framework that integrates multiple previously studied subtasks to predict the factuality of given claims while providing supporting evidence. Next we explore the use of multi-task learning for improving factuality predictions. Finally, we devise methods for extracting temporal structure from news documents to aid the fact-checking process. Thesis Supervisor: James R. Glass Title: Senior Research Scientist
Size Identification via a Visual and Haptic Interfaces: Error Rates and Tolerances

How much perceptual error is involved in daily tasks of estimating object sizes? This paper shows the error rates (ER) and tolerances (TR) that occur when objects of different sizes (3.5, 5.5, and 7.5cm) are being identified by size under different grasping conditions (bare hand, gloved hand, hand in ringed glove, and force noise). The experiments in this paper offer separate visual and haptic information to participants to help them find the correct value of the indirect condition. The need for safety in the work environment requires accurate assessments of error rates and tolerances. Accurate values for both ER and TR can be used to enhance the performance of control algorithms. This paper presents three major findings: 1) the ER magnitude varies only with the size of the object, 2) wearing something on the hands affects the accuracy of the size identification, but has no effect on the ER; and 3) the TR value is generally about 4 times the ER value.
How does clickbait work: An eye-tracking method to discover people’s reactions

2 ACKNOWLEDGEMENTS. 3 LIST OF TABLES. 5 LIST OF FIGURES. 6 CHAPTER 1: Introduction. 7 CHAPTER 2: Literature Review. 12 CHAPTER 3: Methodology. 26 CHAPTER 4: Result. 40 CHAPTER 5: Discussion. 53 CHAPTER 6: Conclusion. 66 REFERENCES.68 APPENDIX A: Consent Form.74 APPENDIX B: Information Sheet.76 APPENDIX C: Debriefing Sheet.79
A Catalyst for Program Change and Improvement

Using a local cross-cultural health serviceprogramasaframework,theauthorsdescribetheprocessofanevaluabilityassessment(EA)and illustrate how it can be a catalyst for pro-gram change. An EA is a process thatimproves evaluation. The key product was alogic model, which traces the links betweenobjectives, activities, and outcomes. Four keyinsights emerged. First, the distinction of whowasincludedandexcludedinthetargetpopu-lation, originally ambiguous, was clearlydefined. Second, through the development ofthe logic model, staff members were able toanalyzetheirgoalsandassumptionsandcriti-cally explore possible gaps between expectedoutcomes and activities. Third, the EAenabledreflectiononandclarificationofbothprocess and outcome measures. Finally,global goals were pared down to better matchthe project capacity. Developing anevaluability assessment was a cost-effectiveway to collaborate with staff to develop aclearer, more evaluable project.
RELEVANCE OF IMMANUEL KANT’S PHILOSOPHY OF TRANSCENDENTALISM ON IMPLEMENTATION OF CURRICULUM IN SECONDARY SCHOOLS IN MERU-SOUTH SUB COUNTY, KENYA

vi TABLE OF CONTENTS . vii LIST OF TABLES . x LIST OF FIGURES . xi ABBREVIATIONS AND ACRONYMS . xii CHAPTER ONE: INTRODUCTION . 1 1.1 Background of the Study . 1 1.2 Statement of the Problem . 5 1.3 Purpose of the Study . 5 1.4 Objectives of the Study . 5 1.5 Research Questions . 5 1.6 Significance of the Study . 6 1.7 Scope of the Study . 6 1.8 Limitations of the Study. 6 1.9 Assumptions of the Study . 6 1.10 Operational Definition of Terms . 8 CHAPTER TWO: LITERATURE REVIEW . 10 2.1 Kant‟s Transcendentalism Philosophy and Curriculum Organization . 10 2.2 Kant‟s Transcendentalism and Aims of Education . 12 2.3 The Scope of the Curriculum . 12 2.4 Kant Transcendentalism and the Role of Teacher . 13 2.5 Curriculum Implementation . 14 2.6 Kant‟s Probing Method . 19 2.7 Kant‟s Experiential Learning . 22 2.8 Kant‟s Experimentation Method . 25 2.9 Conventional Methods of Curriculum Implementation . 28
Active Learning for Social Media Analysis in Crisis Situations

: Social media has become an important open communication medium during crisis. This has motivated much work on social media data analysis for crisis situations using machine learning techniques but has mostly been carried out by traditional techniques. Those methods have shown mixed results and are criticized for being unable to generalize beyond the scope of the designed study. Since every crisis is special, such retrospect models have little value. In contrast, active learning shows very promising results by learning in noisy environments such as image classification and game playing. It has, therefore great potential to play a significant role in the future social media analysis in noisy crisis situations. This position paper proposes an approach to improve the social media analysis in crisis situations to achieve better understanding and decision support during a crisis. In this approach, we aim to use active learning to extract features and patterns related to the text and concepts available in crisis related social media posts and use them to provide an overview of the crisis.
LINE LOSS ANALYSIS OF LOW-VOLTAGE DISTRIBUTION SYSTEM: A BIG-DATA METHOD

Line loss in power distribution system occupies one fifth to one quarter of the total power loss in power systems. Different from the line loss in transmission system, distribution line loss is closely related to the load characteristics and economic types of power users. And the proportion of line loss caused by management problems is significant. Hence the traditional physical model of line loss is not suitable for distribution line loss analysis. To analyze line loss of low-voltage distribution system accurately, a big-data method based on MSApriori association rule mining algorithm is proposed in this paper. Based on the operational data of more than 20,000 distribution stations and the detailed data of 1.8 million power users in Shanghai, a line loss analysis method based on big data is proposed in this paper. The relationship between the line loss and its influencing factors is analyzed from the perspectives of user type, user number, power supply volume, reactive power shortage, and transformer type. The association factors obtained from the analysis are helpful to further guide the reduction of line loss.
Design/Research

Abstract JAE's White Issue is long overdue. The notion that architectural experimentation requires a rigorous feedback between design and research has been at play in design pedagogy since at least the 1960s and 1970s, which included the seminal research studio by Robert Venturi, Denise Scott Brown, and Steven Izenour, Learning from Las Vegas 1; the rise of operations research and systems engineering; the urban networks research of Shadrach Woods, the Smithsons, Constantinos Doxiadis, or Kenzo Tange; and the structure and materials research of Buckminster Fuller, Frei Otto, and Robert Le Ricolais, to name but a few. Concerns about the deterministic instrumentalism of systems thinking led to a new wave of humanism and composition, seen especially in the postmodernist uses of both historical examples and junkspace vernaculars as a Disneyesque brand image. The work of Venturi and Scott Brown, ironically, was to lead the way on the heels of the “duck vs. decorated shed” conclusions of Learning from Las Vegas.2 Unlike most postmodernists, however, Venturi and Scott Brown did not abandon research, and their understanding of the relationships between pop culture, branding, and marketing techniques remains sophisticated and timely.
Impact of Lean Manufacturing Practices on Operational and Business Performance: Evidence from Sri Lankan Food Industry

Lean is a strategy that aims to achieve smooth production flow by eliminating waste and hence helps manufacturing firms realize their time, cost, and quality goals. Food manufacturers not only have limited ability to offer uniform quality but also a greater chance of generating wastes due to the perishable nature of materials. This study investigates the degree to which the Sri Lankan food sector is practicing lean manufacturing and examines the direct and indirect relationships between lean manufacturing, business performance, and operational performance. Data were collected through a questionnaire distributed to managers who have lean experience in the food industry. Using a sample of 86, the hypotheses were tested applying Partial Least Square Structural Equation Modelling with SmartPLS software. Developing a new construct to represent four lean manufacturing practices, namely, Just-in-time (JIT), Total Quality Management (TQM), Total Preventive Maintenance (TPM), and Human Resource Management (HRM), this study empirically confirms that lean manufacturing has a significant positive impact on both operational and business performance. Furthermore, operational performance partially mediates the relationship between lean manufacturing and business performance. The findings offer valuable implications for mangers, particularly in the food industry to achieve sustainability through practicing lean in their manufacturing and supply chain operations.
Towards Real-Time Blob Detection in Large Images with Reduced Memory Cost

—We propose a method for real-time blob detection in large images by exploiting parallelism in computation which can be easily obtained in a specialized hardware (multi-core platforms, FPGA, ASIC). In this method, image is divided into blocks of equal size to which a maximally stable extremal regions (MSER) blob detector is applied in parallel. Parallelism provides a great speed-up of the algorithm, but the system is then unable to detect all blobs detected by original MSER detection algorithm. Our approach is over 20 times more memory efficient than original algorithm if large images are processed due to processing of smaller image blocks. Although it has some limitations, this method can find its place in many applications like medical imaging if the real time performance is needed, as well as in the video surveillance or in wide area motion imagery (WAMI). Since there is often need for image registration and alignment in these applications, we explored possibilities to use detected blobs for feature-based image alignment as well.
A THEORETICAL ANALYSIS OF STUDENTS’ ACADEMIC ACHIEVEMENT AND ASSOCIATED FACTORS

The academic performance of student is influenced by several factors. Studies have been conducted in the field of educational data mining to find out what all the factors are that have an effect on a student's academic performance. There are many factors that can have an effect on a student's academic success, but our study aims to find the main factors that can have an effect on a student's academic performance. Predicting student performance becomes more difficult due to the large volume of information in academic databases. The responsibility of the teacher increases, she must be attentive to the activities and behavior of the student. To facilitate the work of a teacher, this study attempted to identify some factors that affect the academic performance of students. Our study could bring benefits and impacts to students, educators/teachers, and tutoring institutions. The factors investigated in this study are: student base knowledge, socio-economic status, college/school environment, environment pollution, teachers’ support, parental/family support, friend circle and health.
Transient Conductive and Radiative Heat Transfer through a Porous Layer with One Boundary Subject to a Time-Varying Temperature Condition

This paper reports the results of an analysis on transient heat transfer through a layer of porous material subject to a time-varying temperature boundary condition. The purpose of the analysis was to study the radiation effect on transient heat transfer through porous building materials such as thermal insulations. The physical situation considered was that of a planar porous material with one boundary held at a fixed temperature and the other subjected to a cyclic temperature with a 24-hour period. Two heat transfer models were used in the analysis. They were (1) a purely conductive model which accounted for radiation through the use of an apparent thermal con ductivity, (2) a combined conductive and radiative model which included the two- flux equations to account for radiation in a more rigorous manner. The governing equations were solved numerically using a finite-difference approximation. The ac cumulated heat flows at both boundaries of the porous material were obtained. Ef fects due to parameters such as the boundary emissivities and the extinction coeffi cient of the porous material were illustrated. The results predicted by the two heat transfer models were compared.
SHAPE probing reveals human ribosomal RNAs are largely unfolded in solution.

Chemical probing experiments, coupled with empirically-determined free energy change relationships, can enable accurate modeling of the secondary structures of diverse and complex RNAs. A current frontier lies in modeling large and structurally heterogeneous transcripts, including complex eukaryotic RNAs. To validate and improve on experimentally driven approaches for modeling large transcripts, we obtained high-quality SHAPE data for the protein-free human 18S and 28S ribosomal RNAs (rRNAs). To our surprise, SHAPE-directed structure models for the human rRNAs poorly matched accepted structures. Analysis of predicted rRNA structures based on low-SHAPE and low-entropy (lowSS) metrics revealed that, whereas about 75% of E. coli rRNA sequences form well-determined lowSS secondary structure, only about 40% of the human rRNAs do. Critically, regions of the human rRNAs that specifically fold into well-determined lowSS structures were modeled to high accuracy using SHAPE data. This work reveals that eukaryotic rRNAs are more unfolded than are those of prokaryotic rRNAs, and indeed are largely unfolded overall, likely reflecting increased protein dependence for eukaryotic ribosome structure. Further, those regions and sub-structures that are well-determined can be identified de novo and successfully modeled by SHAPE-directed folding.
ENSEMBLE CLASSIFICATION BASED MICROARRAY GENE RETRIEVAL SYSTEM

Data mining plays an important role in the process of classifying between the normal and the cancerous samples by utilizing microarray gene data. As this classification process is related to the human lives, greater sensitivity and specificity rates are mandatory. Taking this challenge into account, this work presents a technique to classify between the normal and cancerous samples by means of efficient feature selection and classification. The process of feature selection is achieved by Information Gain Ratio (IGR) and the selected features are forwarded to the classification process, which is achieved by ensemble classification. The classifiers being employed to attain ensemble classification are k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The performance of the proposed approach is analysed with respect to three different datasets such as Leukemia, Colon and Breast cancer in terms of accuracy, sensitivity and specificity. The experimental results prove that the proposed work shows better results, when compared to the existing techniques.
Super-Resolution on Depth-Map Images

—Breast Cancer Conservative Treatment (BCCT) is nowadays the standard procedure to treat breast tumours. Initial assessment of the aesthetical outcome was performed by visual inspection and has many limitations. The Breast Cancer Conservative Treatment.cosmetic results (BCCT.core) software presents an objective evaluation of the cosmetic result, using frontal images of the patient. This system can be improved by the incorporation of tri-dimensional (3D) data, ideally acquired through a low-cost solution. A Kinect™ device can render a depth-map of a scene with relative low resolution. Resolution enhancement through Super-Resolution (SR) techniques is necessary before further processing. This paper presents a brief comparative study of state-of-the-art Super-Resolution algorithms performance applied on the captured Depth images from the Kinect™.
PoPulation Genetics of Wood froGs ( Lithobates syLvaticus ) in an altered forested ridGetoP Wetland ecosystem in aPPalachia

—Natural wetlands are important for maintaining regional amphibian biodiversity and their loss and degradation are a major cause of amphibian population declines. Population genetic data can be useful to conservation efforts by providing information on genetic variation, effective population size, and spatiotemporal patterns of gene flow. On the Cumberland Plateau in Daniel Boone National Forest, Kentucky, USA, ridgetops contain natural and constructed wetlands interspersed on the landscape. Our study objective was to determine population genetic diversity and structure of Wood Frogs (Lithobates sylvaticus) across this altered ecosystem and to evaluate what local and landscape factors influence patterns of genetic variation. We analyzed genetic data for nine microsatellite DNA loci from 20–26 egg clutches at five randomly selected natural wetlands across a 12.2-km landscape. Overall, we found considerable variability in genetic profiles of populations: observed heterozygosity = 0.581–0.719, expected heterozygosity = 0.736–0.780, FIS = 0.069–0.252, and allelic richness = 8.83–11.95. Three populations exhibited a signature of a population bottleneck. There was support for three genetic clusters, and overall FST was 0.054 ± 0.022 standard error. Geographic distance significantly correlated to genetic distance (P < 0.05) and explained 42% of the variation in genetic distance among populations. Our study provides insight into current status and conservation needs of Wood Frogs in ridgetop wetland systems. Despite the abundance of constructed wetlands and potential for population sinks, genetic diversity is still relatively high, although this might be due to the recency of constructed wetlands on the landscape and the inherent lag time of genetic response.
An Improved Moving Particle Semi-Implicit Method for Free Surface Flows

The objective of this study is to make some improvements to the original Moving Particle Semi-implicit method (MPS) for free surface flows. Compared to traditional mesh methods, MPS is feasible to simulate surface flows with large deformation, however, during the simulation; the pressure oscillation is quite violent, duo to misjudgment of surface particles as well as particles gathering together. To modify this problem, a new arc method is applied to judge free surface particles, and a collision model is introduced to avoid particles from gathering together. Hydrostatic pressure and classical dam break are investigated by original and improved MPS. The results verify that improved MPS method is more effective for free surface flows.
Frequency shift calculations for quartz resonators

A technique has been developed to calculate frequency shifts for quartz resonators arising from mechanical stresses. This solution technique utilizes finite-element analysis. The solution technique is general in that any combination of mount and resonator geometries may be modeled. The frequency perturbation calculation includes movement of the mode to any position in the general vicinity of the resonator center. Experimental results for AT- and SC-cut resonators subjected to diametric force and inertial loading verify the accuracy of frequency shifts calculated using the proposed solution technique. Minimum acceleration sensitivities corresponding to optimum mode locations and mount orientations were calculated for ideal SC-cut crystal units. Simple mount imperfections were modeled to determine what affect these might have on resonator performance. <<ETX>>
Energy Storage Based Adaptive Control of Disturbed Load Interconnected Power Systems

A simple interconnected power system with periodic load disturbance is considered, an energy storage based adaptive controller is proposed to output tracking and disturbance completely rejection for the system. Since the periodic load disturbance is uncertain with both amplitude and frequency, by constructing a new exosystem, the output tracking and disturbance rejection problem is solved with output regulation theory. By designing an internal model for the complicated system and introducing an appropriate transformation, the output regulation problem is changed into the stabilization problem of a new augmented system. With the help of adaptive control technique, the designed dynamic energy storage controller can solve stabilization problem of the augmented systems as well as the output tracking and disturbance rejection problem for the original system. Finally, the simulation results show the effectiveness of the proposed control strategy.
MODEL CHECKING PLANS FOR FLEXIBLE MANUFACTURING SYSTEMS

Abstract In this work an approach to analyze non-deterministic execution plans for flexible manufacturing systems is presented. This approach consists of modeling the system using coloured Petri nets and then, by simulation, discover the possible executions for the system. After, model checking is used to prove that the model behavior do not deviate from the planning. Based on this approach it is possible to model a production system based on cells and to predict and to prove its behavior before the system is developed. Therefore, the analyzed model can be used to develop flexible and dependable production systems to meet the market requirements of quantity, diversity, and quality.