A local and global tour on MOMoT

Many model transformation scenarios require flexible execution strategies as they should produce models with the highest possible quality. At the same time, transformation problems often span a very large search space with respect to possible transformation results. Recently, different proposals for finding good transformation results without enumerating the complete search space have been proposed by using meta-heuristic search algorithms. However, determining the impact of the different kinds of search algorithms, such as local search or global search, on the transformation results is still an open research topic. In this paper, we present an extension to MOMoT, which is a search-based model transformation tool, for supporting not only global searchers for model transformation orchestrations, but also local ones. This leads to a model transformation framework that allows as the first of its kind multi-objective local and global search. By this, the advantages and disadvantages of global and local search for model transformation orchestration can be evaluated. This is done in a case-study-based evaluation, which compares different performance aspects of the local- and global-search algorithms available in MOMoT. Several interesting conclusions have been drawn from the evaluation: (1) local-search algorithms perform reasonable well with respect to both the search exploration and the execution time for small input models, (2) for bigger input models, their execution time can be similar to those of global-search algorithms, but global-search algorithms tend to outperform local-search algorithms in terms of search exploration, (3) evolutionary algorithms show limitations in situations where single changes of the solution can have a significant impact on the solution’s fitness.Ministerio de Economia y Competitividad TIN2015-70560-RJunta de Andalucía P12-TIC-186

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

The design and development of a knowledge-based lean six sigma maintenance system for sustainable buildings. The design and development of a hybrid Knowledge-based (KB)/Gauging Absence of Pre-requisites (GAP)/Analytic Hierarchy Process (AHP) model for implementing lean six sigma maintenance system in sustainable buildings' environment

The complexity of sustainable building maintenance environment requires managers to define and implement appropriate quality benchmark system suitable for this function. Lean Six Sigma (LSS) is one of the most effective process improvement and optimization philosophy that maintenance organisations can implement in their environment. However, literature review has shown that 90% of failures in LSS implementations are due to lack of readiness to change, the unawareness of the required benchmark organisation capabilities, and improper control of priorities. The contribution of the current research approach is in developing a hybrid Knowledge-Based (KB)/GAP/AHP System, consisting of three stages (Planning, Designing and Implementation) and containing over 2500 KB rules. The KB System can assist the decision-makers in identifying the obstacles behind the organisation readiness to change into a benchmark LSS maintenance environment. Thus the KB System will be used to achieve benchmark standards by determining the gap existing between the current environment and the benchmark goal, and then suggest a detailed plan to overcome these hurdles in a prioritised and structured manner, thus achieving cost benefits. To ensure its consistency and reliability, the KB System was validated in three Oman-based maintenance organisations, and one published case study for a UK-based organisation. The results from the validation were positive with the System output suggesting list of top priorities and action plans for achieving benchmark LSS standards for these organisations. The research concludes that the developed KB System is a consistent and reliable methodology for assisting decision-makers in designing, planning, and implementing LSS for benchmark sustainable building maintenance

Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in 3D

With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses.Mit der Entwicklung von Techniken zur Bestimmung der Chromosomen-Konforma- tion wissen wir jetzt, dass Chromatin in einer dreidimensionalen (3D) Struktur in- nerhalb des Zellkerns gepackt ist. Änderungen in der 3D-Chromatin-Architektur sind bereits mit Krankheiten wie Krebs in Verbindung gebracht worden. Daher ist ein besseres Verständnis dieser 3D-Konformation von Interesse, um einen tieferen Einblick in die komplexen, vielschichtigen Regulationsmechanismen des Genoms zu ermöglichen. Die in dieser Dissertation beschriebene Arbeit konzentriert sich im Wesentlichen auf die Entwicklung und Anwendung interpretierbarer maschineller Lernmethoden zur Vorhersage und Analyse von weitreichenden genomischen Inter- aktionen aus Chromatin-Interaktionsexperimenten. Im ersten Teil zeigen wir, dass die genetische Sequenzinformation an den genomis- chen Loci prädiktiv für die weitreichenden Interaktionen eines bestimmten Locus von Interesse (LoI) ist. Zum Beispiel kann die genetische Sequenzinformation an und um Enhancer-Elemente helfen, vorherzusagen, ob diese mit einer Promotorregion von Interesse interagieren. Dies wird durch die Erstellung von String-Kernel-basierten Support Vector Klassifikationsmodellen zusammen mit zwei neuen, intuitiven Visual- isierungsmethoden erreicht. Diese Modelle deuten auf eine mögliche allgemeine Rolle von kurzen, repetitiven Sequenzmotiven (”tandem repeats”) in der dreidimensionalen Genomorganisation hin. Die Erkenntnisse aus diesen Modellen sind jedoch immer noch grobkörnig. Zu diesem Zweck haben wir die maschinelle Lernmethode CoMIK (für Conformal Multi-Instance-Kernel) entwickelt, welche feiner aufgelöste Erkennt- nisse liefern kann. Beim Vergleich von Sequenzen mit variabler Länge in überwachten Lernszenarien kann CoMIK nicht nur die für die Klassifizierung wichtigen Merkmale identifizieren, sondern sie auch innerhalb der Sequenz lokalisieren. Diese genaue Identifizierung wichtiger Abschnitte der gesamten Sequenz kann dazu beitragen, de novo Einblick in jede Rolle zu gewinnen, die das dazwischen liegende Chromatin für weitreichende Interaktionen spielt. Obwohl CoMIK hauptsächlich nur genetische Se- quenzinformationen verwendet, kann es gleichzeitig auch andere Informationsquellen nutzen, beispielsweise zahlreiche funktionellen Genomdaten sofern verfügbar. Der zweite Teil beschreibt unsere Pipeline pHDee für die einfache Bearbeitung großer Mengen von 3D-Genomdaten. Wir haben die Pipeline zur Analyse von HiChIP- Experimenten zur Untersuchung von dreidimensionalen Architekturänderungen bei der seltenen Krebsart Ewing-Sarkom (EWS) verwendet, welche Jugendliche betrifft. Insbesondere werden HiChIP-Daten für zwei experimentelle Bedingungen, Doxycyclin- behandelt und unbehandelt, und für primäre Tumorproben analysiert. Wir zeigen, dass pHDee die Verarbeitung und einfache Integration großer Mengen der 3D-Genomik- Datenanalyse zusammen mit anderen datenintensiven Bioinformatik-Analysen erle- ichtert

A comparative analysis of algorithms for satellite operations scheduling

Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Implementation of Digital Technologies on Beverage Fermentation

In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years. Fermented beverages have been one of the most researched industries to implement these technologies to assess product composition and improve production processes and product quality. This Special Issue (SI) is focused on the latest research on the application of digital technologies on beverage fermentation monitoring and the improvement of processing performance, product quality and sensory acceptability

Toward a Holistic Undergraduate Curricular Model in Design Thinking

This dissertation explores the emerging subject of Design Thinking amidst the increasingly accelerating evolution of the subject. As the term design thinking is becoming more popular and is being taught in diverse disciplines in higher education, both in and beyond the traditional realms of design such as graphic design, a question arises as to what exactly it is and how it could or should be taught. This dissertation combines reflective practice and action research to contribute to the understanding and subsequent proposal of a holistic approach in the teaching of Design Thinking. A review of literature published on the subject from the 1980s onwards brought forth some common themes and concerns that have been addressed, initially from the disciplines of engineering and architecture, and later on in product development and business. In the course of the reflective journey, the guiding vision of using design to create a better future for mankind took shape. Reflecting on and combining themes and strategies found in literature, a number of selected ideas were refined to be implemented in a pilot teaching project. The selected ideas were organized and integrated in the planning and implementation of the pilot course, a 200-level course in Design Thinking offered in a trans-disciplinary, technology-oriented undergraduate program. Within the context of action research, data was collected throughout the course, including personal observations and notes, assignments, tests, discussion forums, surveys and evaluations. The data was then analyzed and reflected upon. Since the pilot teaching project in the fall term of 2009, the subject of Design Thinking has burgeoned, resulting in a significant number of publications stemming simultaneously from isolated disciplines within the course of a year. New insights gained from this literature, along with the findings gathered from the pilot teaching project, converged to furnish suggestions for potential future directions in practice and research. This dissertation is illustrated with 18 sample slides used in the pilot course, 45 artworks collected from students, 6 tables and 1 diagram

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Comparative Evaluation of Conventional and Innovative Biotechnologies for Odour Abatement in Wastewater Treatment Plants

Como resultado de una legislación ambiental cada vez más estricta, del acercamiento de las zonas residenciales a las Estaciones Depuradoras de Aguas Residuales (EDARs) y del aumento de las expectativas ciudadanas con respecto a los estándares de calidad ambiental exigidos a las compañías que explotan estas EDARS, el número de quejas por contaminación odorífera ha crecido de manera substancial en los últimos años. En la presente tesis se realiza una comparación sistemática de la eficacia de diferentes sistemas biológicos (tanto convencionales como innovadores: biofiltros, biofiltros percoladores, sistemas de difusión en lodos activos, bioreactores de membrana y sistemas bifásicos) en el tratamiento de emisiones odoríferas, centrándose en la fracción más hidrofóbica de estas emisiones. Además, se evalúa la influencia de parámetros clave en el rendimiento de desodorización del proceso, la estabilidad y las dinámicas microbianasDepartamento de Ingeniería Química y Tecnología del Medio Ambient