No description available.

No description available.

No description available.

No description available.

Диссертация посвящена использованию данных цифровых информационных моделей в ГК «Кортрос» для оценки себестоимости строительства на этапах проектирования и подготовки к контрактации строительно-монтажных работ. В рамках исследования рассмотрен инструмент бизнес-аналитики — BI-платформа как метод определения стоимости строительства. А также написан скрипт для автоматизации получения себестоимости строительства с использованием классификационных таблиц. В результате исследования авторами сделаны выводы об использовании аналитики данных информационных моделей на ранних стадиях работы над инвестиционно-строительным проектом для прогнозирования стоимости строительства, а также о BI-платформах как инструментах ее определения. / The dissertation is devoted to the use of these digital information models in the Kortros Group of Companies to estimate the cost of construction at the design stages and preparation for the contracting of construction and installation works. As part of the study , a business intelligence tool, the BI platform, is considered as a method for determining the cost of construction. A script has also been written to automate the production of construction costs using classification tables. As a result of the study, the authors made conclusions about the use of data analytics of information models at the early stages of work on an investment and construction project to predict the cost of construction, as well as about BI-platforms as tools for determining it.

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.

Corruption is a social evil that resonates far and deep in societies, eroding trust in governance, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. It is a multidimensional and complex societal malady that occurs in various forms and contexts. As such, any effort to combat corruption must be accompanied by a thorough examination of the attributes that might play a key role in exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that influence corruption, using machine learning techniques, neural network analysis, and time series causal relationship analysis and aggregated data from 113 countries from 2007 to 2017. The results suggest that improvements in technological readiness, human development index, and e-governance index have the most profound impacts on corruption reduction. This dissertation discusses corruption at each phase of infrastructure systems development and engineering ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical efficiency measurement methods to measure infrastructure inefficiencies, and to rank infrastructure administrative jurisdictions at the state level. An efficiency frontier is developed using optimization and the highest performing jurisdictions are identified. The dissertation’s framework could serve as a starting point for governmental and non-governmental oversight agencies to study forms and contexts of corruption and inefficiencies, and to propose influential methods for reducing the instances. Moreover, the framework can help oversight agencies to promote the overall accountability of infrastructure agencies by establishing a clearer connection between infrastructure investment and performance, and by carrying out comparative assessments of infrastructure performance across the jurisdictions under their oversight or supervision.

<p>Professional engineers think and act in distinctive ways when addressing engineering problems. Students need to develop this reasoning or engineering thinking during their education. Unfolding the undergraduate students’ thinking is a necessary step in designing experiences and teaching materials that foster not only their understanding of engineering concepts but also their learning to think as professional engineers. While there are previous studies about the students' thinking in other disciplines, more research is needed in engineering. This three-study dissertation aims to further our comprehension of undergraduate students’ engineering thinking using an adapted version of the Engineering Habits of Mind (EHoM) model. Specifically, the dissertation’s studies work together to continue the research that addresses the question:<em> What are the characteristics of undergraduate students</em>’ <em>engineering thinking?</em></p> <p><br></p> <p>The first study used naturalistic inquiry to holistically explore the cognition associated with the EHoM of senior chemical engineering students when improving a chemical plant. The analysis of students’ interactions showed that their redesign process followed an iterative co-evolution of the problem and solution spaces. Furthermore, they treated the task as a socio-technical problem considering engineering and non-engineering factors. In addition, while exploring problem and solution entities, they used multiple representations to communicate ideas but had difficulties translating symbolic representations into more physical, concrete representations. Regardless the technical issues and time constraints, the students completed the conceptual redesign and communicated their proposal to the client.</p> <p><br></p> <p>The second study used qualitative content analysis to examine first-year engineering students’ ideation as a cognitive skill associated with the EHoM of problem finding and creative problem solving. Particularly, it focused on students’ ideation of questions and recommendations when doing data analytics to help improve a client’s enterprise. The analysis of students’ reports showed that they expanded the problem space of the task by bringing additional information that was not provided. They asked questions focused on performing statistical analysis of the dataset and requesting information about the company’s business model. At the end of their data analytics, students made high- and low-quality recommendations considering their alignment with a specific problem, robust evidence, and the client’s needs. </p> <p><br></p> <p>The third study used qualitative descriptive research to investigate undergraduate participants' cognitive competencies within engineering systems thinking at the International Genetically Engineered Machine (iGEM) competition. These competencies are associated with the EHoM of problem finding, creative problem solving, systems thinking, and visualization. Mainly, the study focused on analyzing the evidence of cognitive competencies documented in the publicly available participants’ wikis where they registered their design process. Results showed that iGEM teams developed solutions with biological systems interacting with other systems and used concepts and tools from multiple disciplines. They also cooperated with stakeholders, which helped them analyze their system from multiple lenses. Moreover, depending on their upfront task, they fluidly represented their systems from structural, behavioral, and functional perspectives. </p> <p><br></p> <p>The final chapter of this dissertation presents an overarching discussion across the studies. The findings and implications will support curriculum designers, instructors, and other interested readers to prepare learning environments that promote undergraduate students’ engineering thinking. Furthermore, they may guide future efforts to continue exploring the students' thinking process when addressing engineering problems. </p>

No description available.