Miracle-dz/UniFAR-FTUs huggingface dataset:
A Proposal Model for Estimation of Project Success in Terms of Radial Based Neural Networks: A Case Study in Iran

For improving the conditions of project intended purpose and reaching high score in the project success, project Stakeholders (including employer, contractor, consultant and its users) try to comply with the implementation of project Critical Success Factors(CSFs) at the beginning of each project. This implementation is in two terms: economic and executive. Artificial neural networks are one of the new methods which have been developed to estimate and predict parameters by using inherent relationship among data. In this research, it tried to propose a model to determine the project success score by using radial basis neural networks. For reaching this purpose, firstly, the key indicators of project success (employer, contractor and consultant) among the main elements involved in the industry of macro-civil construction projects in Iran reviewed. Secondly, ten CSFs key project success indicators were recognized in five categories: (i) financial, (ii) interaction processes, (iii) manpower, (iv) contract settings and (v) characteristic nature of the project (based on conditions of the present research in Iran). Then, some projects were selected by random sampling of projects operated during the last 5 years in the country's Ministry of Energy. Among those projects, project information was collected by managers of large projects. After training the designed neural network, the project success model was provided based on an assessment of project objectives including factors of Scope, Time, Cost, and Quality of the projects. For facilitating other researches’ use, the applied equation of the model was presented as well. Outputs, calculated by the proposed model, were in good agreement with the actual number of projects assessed in Iran. The results of this study may be used as a tool in implementing projects for the rapid assessment of achieving project goals’ facilities.
Investigating the Dynamic Response of Hybrid-Electric Propulsion Systems for Flight Control Application

This thesis presents an initial analysis of the thrust response of hybrid-electric propulsion systems for UAVs and general aviation aircraft and the application for attitude control of fixed and rotary wing aircraft with distributed propulsion concepts. The work focuses on component modelling as well as the identification of scaling effects, design trade-offs and the sensitivity of the thrust response on component parameters. The objective is the consideration of transient requirements during the preliminary propulsion system design, which is shown in studies on component, system and aircraft level.
Virtual ethnography methodology for researching networked learning

This article describes the application of a qualitative method based on digital ethnography in research on networked learning in Higher Education. The proposed method tends towards a solution for the need of innovative techniques for the study of new forms of interaction emerging in computer supported collaborative learning (CSCL) and their effective uses. The method used is based on three phases: first, initial exploration and systematic collection, then organisation and comparison of data, finally analysis. The corpus is composed of various data (audio-visual recordings, images, texts): audio-visual and textual data are organised and compared. The results suggest that an analysis of the process in its development is possible. The interpretative course is built thanks to the effective situations of uses. The ethnographical methodology applied to the study of the technological environment brings a concrete visibility of the tracks of implementation and manners and gives the possibility of having access to the development of the non stop process. The taking into consideration of all the constituent elements allows to encircle the diverse activities of the actor. It permits to analyse phenomena that would be invisible with a quantitative method.
A Scalable Music Recommendation System

A content match based on filtering solution is proposed to handle large number of users and songs for search engine as a recommendation system is increasing important in online video application, news recommendation and electronic commerce especially in search engine. Our method could be applied to search engine like Google, Bing and Baidu to highly enhance user experience.
The design brief: inquiry into the starting point in a learning journey

Abstract The paper reviews an assessment regime for its capacity to engage university learners, enabling them to design radically new business offerings. We explore the effect of the briefing approach on defining the customer/offering match. This study is framed by participatory action research, where data draws on two distinctive module deliveries: one where the design brief asks learners to generate the offering first and then shape the customer segment. The second one supplies an archetype and asks learners to define customer first and then develop the offering. Our analysis reveals that learners’ engagement with the design brief prompts an emergence of five patterns of learners’ responses, leading to conclusions that the nature of design brief elements has an impact in shaping the overall learning. Moreover, going from customer to offering appears to generate better iterations between the two, overall leading to learners’ engagement with the process not simply seeking an outcome.
Clinical Utility in Adopting Race-free Kidney Donor Risk Index

Background. Recent events of racial injustice prompted us to study potential impact of removing race from kidney donor risk index (KDRI) calculator. Methods. We used Scientific Registry for Transplant Recipients data to analyze outcomes of 66 987 deceased-donor kidney transplants performed in the United States between 2010 and 2016. Graft failure (GF) was defined as death or return to dialysis or requiring repeat transplant. We compared original KDRI and a race-free KDRI (Black donor coefficient zeroed out in the KDRI formula) with respect to recategorization of perceived GF risk (based on KDPI categories: ≤20, 21–34, 35–85, ≥86)‚ risk discrimination (using the C statistic) and predictive accuracy (using Brier score), and GF risk prediction (using Cox regression on time-to-GF). We used logistic regression to study the impact of donor race on discard probability. Results. There were 10 949 (16.3% of recipients) GF, and 1893 (17% of GFs) were among recipients of kidneys from Black donors. The use of race-free KDRI resulted in reclassification of 49% of kidneys from Black donors into lower GF risk categories. The impact on GF risk discrimination was minimal, with a relative decrease in C statistic of 0.16% and a change in GF predictive accuracy of 0.07%. For a given recipient/donor combination, transplants from Black (compared with non-Black) donors are estimated to decrease predicted graft survival at 1-y by 0.3%–3%, and 5-y by 1%–6%. Kidneys from Black donors are significantly more likely to be discarded (odds ratio adjusted for KDRI except race = 1.24). We estimate that an equal discard probability for Black and non-Black donors would yield 70 additional kidney transplants annually from Black donors. Conclusions. Use of race-free KDRI did not impact GF risk discrimination or predictive accuracy and may lower discard of kidneys from Black donors. We recommend use of race-free KDRI calculator acknowledging the possibility of miscalculation of GF risk in small proportion of kidneys from Black donors.
Dynamic demand response in smart buildings using an intelligent residential load management system

In a smart grid environment, the utilities, which electric power distribution companies with different pricing strategies, are interested in demand side management (DSM) schemes to achieve reduction in peak demand. Utilities provide additional benefits and encourage the consumers to have direct interaction with the grid by actively participating in DSM programs. An intelligent residential load management system (IRLMS) is proposed for consumers to attain reduction in electricity bill and avail more incentive from the utility by responding to their DSM schemes. The IRLMS attempts to reduce the electricity bill and maintain the total load under maximum demand limit by scheduling the schedulable loads while considering the operating dynamics of non-schedulable loads. The IRLMS scheduler is built with two different techniques, namely priority-based scheduling algorithm and optimisation-based scheduling algorithm for different utility tariff methods such as flat rate tariff and real time pricing, respectively. The IRLMS model is implemented in MATLAB and the results illustrate significant benefits for both consumers and utility.
MOOClm: Learner Modelling for MOOCs

Massively Open Online Learning systems, or MOOCs, generate enormous quantities of learning data. Analysis of this data has considerable potential benefits for learners, educators, teaching administrators and educational researchers. How to realise this potential is still an open question. This thesis explores use of such data to create a rich Open Learner Model (OLM). The OLM is designed to take account of the restrictions and goals of lifelong learner model usage. Towards this end, we structure the learner model around a standard curriculum-based ontology. Since such a learner model may be very large, we integrate a visualisation based on a highly scalable circular treemap representation. The visualisation allows the student to either drill down further into increasingly detailed views of the learner model, or filter the model down to a smaller, selected subset. We introduce the notion of a set of Reference learner models, such as an ideal student, a typical student, or a selected set of learning objectives within the curriculum. Introducing these provides a foundation for a learner to make a meaningful evaluation of their own model by comparing against a reference model. To validate the work, we created MOOClm to implement this framework, then used this in the context of a Small Private Online Course (SPOC) run at the University of Sydney. We also report a qualitative usability study to gain insights into the ways a learner can make use of the OLM. Our contribution is the design and validation of MOOClm, a framework that harnesses MOOC data to create a learner model with an OLM interface for student and educator usage.
A project improvement system for effective management of construction projects

The concept of construction productivity began in the early 20th century with a series of time and motion studies to improve bricklaying operations. A substantial number of subsequent studies focused on repetitive tasks and automation of the manufacturing operations and subsequently benefited manufacturing more than the construction industry. There has been a lack of studies for productivity improvement in the construction industry. Building upon the available body of knowledge as it related to productivity of manufacturing and construction industries, this study focused on developing a project improvement system for effective management of construction projects. Development of the system included a review of literature, where an inventory of productivity related factors was found and interpolated into a primary productivity evaluation checklist. A survey of construction practitioners was conducted to rank and determine the degree of influence of various factors on construction productivity. A field study gathered information for the refinement of the primary productivity evaluation checklist. The review of literature, together with the findings of survey and field study, led to the development of the Project Improvement System (PIS). Finally, the PIS was tested and evaluated in a case study of a construction project to ensure its workability, suitability, and influence. Out of 36 productivity factors gathered from the literature, the survey identified 30 factors as high, 4 factors as moderate, and 2 factors as low with respect to their degree of influence on productivity. This information provided guidance for the field study to refine the primary productivity evaluation checklist. The survey revealed that executives, project managers, and superintendents of general contracting firms have similar perceptions of the degree of influence of productivity factors in construction and view productivity as a concept
Embryonic transcription factor upregulation during normal lactation and breast oncogenesis

In addition to their physiological role during pregnancy/lactation, mammary stem cells (MaSCs) are primary targets of malignant transformation, giving rise to invasive breast carcinomas. To pinpoint the molecular switches that transform these cells, we must understand the basic machinery that controls their self‐renewal. We non‐invasively accessed lactation‐associated MaSCs via breastmilk. A cell population was identified expressing the pluripotency transcription factors (TFs) Oct4, Sox2 and Nanog, which generated monolayers and spheroids in 2D and 3D culture, respectively, expressing these TFs. Culture in differentiation conditions resulted in differentiation into cells originating from the three germ layers. Examination of normal resting and lactating human breast tissues revealed that these TFs are upregulated during lactation. Lentivirally‐transduced Oct4‐overexpressing mammary cells from the resting gland bypassed cellular senescence and generated subcutaneous tumors with colonization capabilities when injected in nude mice. Examination of different types of breast tumors showed expression of these TFs only in invasive basal tumors with lactating features. It is concluded that these embryonic TFs play important regulatory roles in the normal expansion of the breast during pregnancy/lactation, but also act as oncogenic targets in certain types of breast cancers.
An Enhanced Time-Delay Control using a Second-Order Time-Delay Estimation for Robot Manipulators

로봇 매니퓰레이터는 매우 비선형적이며(highly nonlinear), 관절 별 동역학이 서로 강하게 연관된(strongly coupled)된 플랜트이다. 따라서, 로봇의 움직임을 정확히 제어하기 위해서는 매우 복잡한 비선형제어기가 필요하게 된다. 그러나 실제로 이런 제어기를 설계한다는 것은 어려울 뿐만 아니라, 제어기의 구현 측면에서도 비효율적이다. 따라서 좀 더 간단한 구조를 가지면서도 플랜트의 비선형성이나 외란(disturbance)에 강인한 제어기법이 요구 되었다. 이러한 요구에 부응할 수 있는 제어기법으로서 시간지연을 이용한 제어기법(Time Delay Control:TDC)이 제안되었다. TDC 는 플랜트의 예측 못한 외란이나 잘 모르는 비선형 동역학을 추정하기 위해 시간지연추정(Time Delay Estimation: TDE)을 이용하는 제어기이다. 이 제어기는 지금까지 개발된 고등 제어 알고리즘에 비하여 단순한 구조를 지니면서도, 외란과 매개변수의 변동에 대한 우수한 강인성을 보인다. 하지만, TDE 기법의 이용으로, 시간 지연된 입력과 출력을 이용하기 때문에, 시간지연 보다 빠른 동역학 특성, 소위 Hard Nonlinearity 로 분류되는 정지 마찰력(static friction)이나 쿨롱마찰력(coulomb friction)이 플랜트에 존재할 경우, TDC 의 제어 성능이 저하되는 현상을 보여준다. 이러한 시간지연추정 기법의 사용시, 생기는 시간지연추정 오차에 의해서 시간지연제어기의 성능을 저하시킨 것이다. 이 시간지연추정 오차를 보상하여 제어 성능을 더욱 향상시킨 제어기로는 향상된 시간지연제어(Enhanced Time Delay Control: ETDC)가 제안되었다. 향상된 시간지연제어기법은 전반적으로 시간지연제어기법에 비해, 좋은 성능을 보여준다. 하지만, 향상된 시간지연제어기법은 기존의 시간지연제어기법에 비해 채터링(chattering)같은 현상을 보여준다. 따라서 향상된 시간지연제어기법의 이러한 문제를 해결하기 위해, 이차 시간지연추정을 이용한 향상된 시간지연제어(Enhanced Time-Delay Control using a Second-Order Time Delay Estimation)를 이 논문에서 제안한다. 제안한 제어기법은 시간지연제어기법과 향상된 시간지연제어기법의 중간 정도의 특성을 가지고 있다. 제안한 제어기법에서는 채터링과 같은 문제를 유발하지 않는 시간지연제어기법의 특성을 가지면서도, 향상된 시간지연제어기법의 뛰어난 제어성능을 보여준다. 제안하는 제어기의 설계 방법은 기존의 향상된 시간지연 제어에서 사용한 시간지연추정 오차보상항을 시간지연추정오차에 관하여 유도한 다음 그 유도된 시간지연추정오차 항과 어떠한 게인과 결합함으로써 설계 할 수 있다. 그 게인은 특성게인(characteristic gain)이라 명명되었으며, 이 게인의 범위는 0 과 1 사의 값을 가진다. 이 게인이 0 으로 가깝게 설정이 되었을 때, 제안한 제어기의 특성은 기존의 시간지연제어기법과 가까운 특성을 보여준다. 하지만 그 게인값을 1 에 가깝게 설정을 할 경우, 제안한 제어기는 향상된 시간지연제어기법의 특성을 보여준다. 따라서 이 게인을 적절히 선정하여 제안한 제어기법을 적용하면, 기존의 시간지연제어기보다는 월등한 제어성능을 보여주면서, 향상된 시간지연제어기의 문제점인 채터링 문제점을 완화시킨 제어성능을 나타낸다. 이 제안한 제어기법은, 주파수응답특성해석을 통하여 실제로 기존의 시간지연제어기법과 향상된 시간지연제어기법의 특성을 가진 것을 증명한다. 1 자유도 로봇 매니퓰레이터와 2 자유도 매니퓰레이터의 시뮬레이션실험으로 향상된 시간지연제어기와 성능을 비교하였다. 또한, 실제 6 축 로봇실험을 통하여 제안한 제어기법의 특성을 보여주기 위한 실험과 더불어, 향상된 시간지연제어기법과 비교실험을 통하여 제안한 제어기법의 우수한 제어성능을 증명한다. ⓒ 2013 DGIST
The weakening of branch predictor performance as an inevitable side effect of exploiting control independence

Many algorithms are inherently sequential and hard to explicitly parallelize. Cores designed to aggressively handle these problems exhibit deeper pipelines and wider fetch widths to exploit instruction-level parallelism via out-of-order execution. As these parameters increase, so does the amount of instructions fetched along an incorrect path when a branch is mispredicted. Many of the instructions squashed after a branch are control independent, meaning they will be fetched regardless of whether the candidate branch is taken or not. There has been much research in retaining these control independent instructions on misprediction of the candidate branch. This research shows that there is potential for exploiting control independence since under favorable circumstances many benchmarks can exhibit 30% or more speedup. Though these control independent processors are meant to lessen the damage of misprediction, an inherent side-effect of fetching out of order, branch weakening, keeps realized speedup from reaching its potential. This thesis introduces, formally defines, and identifies the types of branch weakening. Useful information is provided to develop techniques that may reduce weakening. A classification is provided that measures each type of weakening to help better determine potential speedup of control independence processors. Experimentation shows that certain applications suffer greatly from weakening. Total branch mispredictions increase by 30% in several cases. Analysis has revealed two broad causes of weakening: changes in branch predictor update times and changes in the outcome history used by branch predictors. Each of these broad causes are classified into more specific causes, one of which is due to the loss of nearby correlation data and cannot be avoided. The classification technique presented in this study measures that 45% of the weakening in the selected SPEC CPU
Ca2+ pulsars: spatially restricted, IP3R‐mediated Ca2+ release important for endothelial function

Intracellular Ca2+ is essential for endothelial functions such as the control of vascular tone. Using confocal microscopy, endothelial Ca2+ signaling was investigated in cut‐open mesenteric arteries from mice expressing the Ca2+ indicator GCaMP2 exclusively in the endothelium. Spontaneous and local Ca2+ release (“Ca2+ pulsars”) from endothelial IP3 receptors (IP3R) have been identified and are also present in pressurized arteries. The Ca2+ pulsars show distinct kinetics from ryanodine receptor–dependent Ca2+ sparks and from IP3R‐mediated Ca2+ puffs. The endothelium‐dependent vasodilator acetylcholine increased the frequency of Ca2+ pulsars. The Ca2+ pulsars are localized in the endothelial projections through the internal elastic lamina to the vascular smooth muscle. Immunocytochemistry also identified IP3R and KCa3.1 channels in the myoendothelial projections. While block of KCa3.1 channels with ChTX depolarized the endothelial membrane by ≈10 mV, inhibition of Ca2+ pulsars with CPA (10 μM) prevented ChTX‐induced depolarization. Moreover, exposure to CPA induced a depolarization of the endothelium similar to ChTX (≈11 mV). Our results suggest a novel IP3R‐dependent mechanism regulating endothelium‐smooth muscle communication.
Fingerprinting mobile devices: A short analysis

The variety of possibilities for customizing web browser, combined with the capacity of a remote server to query information without requesting any permission, has created a new kind of privacy issue called browser fingerprinting. Previous studies demonstrated the effectiveness of fingerprinting to uniquely identify browsers running on laptops and desktops. Meanwhile, the majority of browsing activities have switched from laptops to mobile devices. Yet, there is currently a lack of systematic analysis of browser fingerprinting on mobile devices. In this paper, we describe browser fingerprinting technologies and summarize previous observations on laptops. Then, we present preliminary observations on mobile devices, highlighting differences with previous results, and challenges for future work.
Comparative analysis of steel telecommunication tower

The purpose of this paper is to minimise the cost of steel telecommunication lattice self-supported towers by using V shaped hot rolled steel profiles. Comparative analyses were carried out for different tower heights using different configuration of V shaped and L shaped hot rolled steel profiles. An elastic 3D truss finite element model was used to determine the axial forces and stresses in each member under wind and various antenna load cases. Strength and serviceability limit states were controlled according to ANSI/EIA/TIA-222-G (addendum 2) during design stage. For the cost optimisation, both production, manufacturing, installation and material costs were addressed. During manufacturing stage, there are many obstacles to manufacture V shaped steel profiles, but the results indicate that the minimum cost solution corresponds to V shaped steel profiles.
Time after Time: Longitudinal Trends in Nostalgic Listening

Nostalgia was once considered a medical disease but is now understood as a beneficial, identity-affirming emotion. Yet what induces feelings of nostalgia is not fully understood. Are nostalgic feelings prompted by changes in a person’s life, and do certain events prompt nostalgia across whole populations? In this paper, we analyze when people listen to nostalgic music. We use data from a large, cross-national survey to train a classifier to detect which tracks are nostalgic for individual listeners. We then analyze a comprehensive dataset of listening histories from a 5.5 year period. Despite it being a complex concept, we were able to predict nostalgic listening with relatively high precision. We compare our results across listeners from four countries to understand how consistent behavior toward nostalgic music is. We find people listen to nostalgic music more often as they age. We also find people tend to listen to nostalgic music consistently in their day-to-day lives. We do not find evidence that listening to personally nostalgic music increases in response to particular traditions, seasons, or events. However, we do find traditions and events can affect how much “back catalog” music people listen to. These trends are consistent across the national contexts we studied. Our results advance prior findings about nostalgia and the life course, and demonstrate a novel methodological consideration for studies of nostalgic listening.
Telemarketing outcome prediction using an Ensemblebased machine learning technique

Business organisations often use telemarketing, which is a form of direct marketing strategy to reach a wide range of customers within a short time. However, such marketing strategies need to target an appropriate subset of customers to offer them products/services instead of contacting everyone as people often get annoyed and disengaged when they receive pre-emptive communication. Machine learning techniques can aid in this scenario to select customers who are likely to positively respond to a telemarketing campaign. Business organisations can use their CRM-based customer information and embed machine learning techniques in the data analysis process to develop an automated decisionmaking system, which can recommend the set of customers to be communicated. A few works in the literature have used machine learning techniques to predict the outcome of telemarketing, however, the majority of them used a single classifier algorithm or used only a balanced dataset. To address this issue, this article proposes an ensemble-based machine learning technique to predict the outcome of telemarking, which works well even with an imbalanced dataset and achieves 90.29% accuracy.