6,139 research outputs found

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Real Estate Investment Trusts (REITs) Corporate Governance and Investment Decision-Making in the United Kingdom, South Africa and Nigeria

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    Adopting Real Estate Investment Trusts (REITs) has been relatively slow due to corporate governance issues and a limited understanding of investment decision-making processes. This study aims to enhance the performance of REITs by developing a Corporate Governance Scoring Framework and improving the investment decision-making process. A mixed-method research strategy was employed to gather data on investment decisionmaking processes and corporate governance in the UK, SA, and Nigeria from 2014-2019. Qualitative data was collected through semi-structured telephone interviews with key decision-makers in the three regimes and analysed using content and discourse analysis techniques. Quantitative data was obtained from the annual financial reports of listed REITs during the study period and analysed using OLS, fixed effects, and random effect models. The Integrated Corporate Governance Index (ICGI), a self-scoring framework, was used to measure the quality of corporate governance strength. The qualitative analysis identified four stages in the investment decision-making process: strategy, search, analysis and adjustment, and consultation or decision and review. The interviews revealed that the board, remuneration, and fee proxies were relevant factors across all three regimes, with audit and ownership also significant in the developing regimes of SA and Nigeria. The board's reputation, experience, and management role were highlighted as crucial during the decision-making process. Performance factors such as 'Operational Stability,' 'Tenant Quality,' 'Experience,' and metrics including 'Rental Income,' 'Dividend Payment,' and 'Yield' were identified. The quantitative analysis demonstrated that adherence to corporate governance codes was highest in the UK, followed by SA and Nigeria. Regression analysis results showed that a higher ICGI score improved return on assets (ROA) and return on equity (ROE) in the UK but not in SA and Nigeria. The index did not significantly impact firm value in the UK and pooled country analysis, but it led to better firm valuation in SA. In the Nigeria REIT regime, the ICGI harmed firm valuation. The study concluded that adherence to country-level corporate governance was more predictive of operational performance than firm valuation. In summary, this study contributes to the existing knowledge by providing insights into the investment decision-making processes of REITs and the importance of corporate governance in improving their performance. The developed Corporate Governance Scoring Framework offers a valuable tool for evaluating the quality of corporate governance in REITs, but further refinement is necessary to keep up with evolving policies

    Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

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    Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

    Managing people with technology : a sociomaterial perspective

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    The study highlights the disruptive influence of digital technologies on organizations, work structures, and the nature of work itself. While previous research has focused on the consequences of technology on HRM processes, there are limitations in understanding the complexity of technology and how it shapes HRM processes. The actual usage of technology by HRM actors is often overlooked, as well as the dynamic unfolding of e-HRM practices over time. This thesis adopts the sociomaterial perspective that recognizes the equal importance of human agency, material artifacts and social context in forming and reproducing e-HRM practices. Theories within the sociomaterial perspective view activities as dynamic and situated, which constitute and are constituted by people, actions, voices, gestures, tools, software, documents, infrastructure, hardware and other materiality. The key objective of the dissertation is to understand the role of technology in changing HRM practices and for HRM actors by shedding light on how the materiality of technology, social events, and human agency are intertwined in the HRM practice. The sociomaterial perspective is introduced in Paper 1, emphasizing the equal importance of human agency, material artifacts, and social context in shaping HRM practices. It recognizes the integral role of materiality, such as digital artifacts and physical spaces, in organizing social elements. Paper 2 applies the attention-based view to explore how technology influences the attentional engagement of line managers as HRM actors in remote performance evaluation. This offers a nuanced understanding of attention as both cognitive and context-dependent. In Paper 3, routine dynamics theory is employed to transform the conceptualization of HR roles, shifting from studying nominal roles to roles accomplished through routinized sequences of actions. These theoretical lenses align with the sociomaterial perspective and contribute to our understanding of the transformative impact of technology on HRM practices and the role of HR actors. The dissertation makes three main contributions to the research on HRM technology. It (1) theorizes HRM as sociomaterial practice and shows empirically the emergence nature of management practices around material artifacts, (2) addresses the lack of diversity of HRM actors in the literature, highlighting their agency in the enactment of technology, and (3) examines HR roles as dynamically produced and enacted through patterns of routines.Tämä väitöskirja korostaa digitaalisten teknologioiden disruptiivista vaikutusta organisaatioihin, työn rakenteisiin ja itse työn luonteeseen. Aikaisempi tutkimus on keskittynyt teknologian vaikutuksiin HRM-prosesseihin, mutta ymmärrys teknologian monimutkaisuudesta ja sen vaikutuksesta henkilöstöhallintoon on rajoittunutta. HRM-toimijoiden varsinainen teknologian käyttö jätetään usein huomioimatta, samoin kuin e-HRM-käytänteiden dynaaminen kehittyminen ajan myötä. Tässä väitöskirjassa käytetään sosiomateriaalista näkökulmaa, joka tunnistaa ihmisen toiminnan, materiaalisten artefaktien ja sosiaalisen kontekstin yhtäläisen merkityksen e-HRM-käytänteiden muodostumisessa ja toisintamises¬sa. Sosiomateriaaliseen näkökulmaan lukeutuvien teorioiden mukaan toiminnot nähdään dynaamisina ja tilannesidonnaisina, ja niihin kuuluu ja niitä muodostavat ihmiset, toimet, äänet, eleet, työkalut, ohjelmistot, asiakirjat, infrastruktuuri ja laitteisto. Väitöskirjan keskeinen tavoite on ymmärtää teknologian roolia HRM-käytänteissä ja HRM-toimijoille tuomalla selvyyttä siihen, miten teknologian materiaalisuus, sosiaaliset tapahtumat ja toimijuus kietoutuvat yhteen HRM-toiminnossa. Sosiomateriaalinen näkökulma esitellään ensimmäisessä artikkelissa, jossa korostuu toimijuuden, materiaalisten artefaktien ja sosiaalisen kontekstin yhtäläinen merkitys HRM-käytänteiden muovaamisessa. Siinä tunnistetaan materiaalisuu¬den kiinteä rooli, kuten digitaaliset artefaktit ja fyysiset tilat, sosiaalisten elementtien järjestämisessä. Toisessa artikkelissa sovelletaan huomiokeskeistä näkökulmaa (eng. attention-based view, ABV) tutkittaessa, miten teknologia vaikuttaa linjaesihenkilöiden huomion kiinnittämiseen HRM-toimijoina etäsuoriutumisenarvioinnissa. Tämä tarjoaa monisäikeisen ymmärryksen huomiosta sekä kognitiivisena että kontekstiriippuvaisena. Kolmannessa artikkelissa käytetään rutiinidynamiikan teoriaa muuttamaan käsitystä HR-rooleista e-HRM:ssä siirtymällä nimellisrooleista rooleihin, jotka saavutetaan rutiininomaisten toimien jaksojen kautta. Tämä väitöskirja tarjoaa kolme pääasiallista kontribuutiota HRM-teknologian tutkimukseen. Se (1) teoretisoi henkilöstöhallinnon olevan sosiomateriaalinen toiminto ja osoittaa empiirisesti johtamiskäytänteiden luonteen muodostuvan materiaalisista artefakteista, (2) käsittelee HRM-toimijoiden monimuotoisuuden puutetta kirjallisuudessa korostaen heidän toimijuuttaan teknologian toteuttamisessa, ja (3) tarkastelee HR-rooleja dynaamisesti tuotettuina ja toteutettuina rutiinien sarjoina.fi=vertaisarvioitu|en=peerReviewed

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    Aerial Network Assistance Systems for Post-Disaster Scenarios : Topology Monitoring and Communication Support in Infrastructure-Independent Networks

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    Communication anytime and anywhere is necessary for our modern society to function. However, the critical network infrastructure quickly fails in the face of a disaster and leaves the affected population without means of communication. This lack can be overcome by smartphone-based emergency communication systems, based on infrastructure-independent networks like Delay-Tolerant Networks (DTNs). DTNs, however, suffer from short device-to-device link distances and, thus, require multi-hop routing or data ferries between disjunct parts of the network. In disaster scenarios, this fragmentation is particularly severe because of the highly clustered human mobility behavior. Nevertheless, aerial communication support systems can connect local network clusters by utilizing Unmanned Aerial Vehicles (UAVs) as data ferries. To facilitate situation-aware and adaptive communication support, knowledge of the network topology, the identification of missing communication links, and the constant reassessment of dynamic disasters are required. These requirements are usually neglected, despite existing approaches to aerial monitoring systems capable of detecting devices and networks. In this dissertation, we, therefore, facilitate the coexistence of aerial topology monitoring and communications support mechanisms in an autonomous Aerial Network Assistance System for infrastructure-independent networks as our first contribution. To enable system adaptations to unknown and dynamic disaster situations, our second contribution addresses the collection, processing, and utilization of topology information. For one thing, we introduce cooperative monitoring approaches to include the DTN in the monitoring process. Furthermore, we apply novel approaches for data aggregation and network cluster estimation to facilitate the continuous assessment of topology information and an appropriate system adaptation. Based on this, we introduce an adaptive topology-aware routing approach to reroute UAVs and increase the coverage of disconnected nodes outside clusters. We generalize our contributions by integrating them into a simulation framework, creating an evaluation platform for autonomous aerial systems as our third contribution. We further increase the expressiveness of our aerial system evaluation, by adding movement models for multicopter aircraft combined with power consumption models based on real-world measurements. Additionally, we improve the disaster simulation by generalizing civilian disaster mobility based on a real-world field test. With a prototypical system implementation, we extensively evaluate our contributions and show the significant benefits of cooperative monitoring and topology-aware routing, respectively. We highlight the importance of continuous and integrated topology monitoring for aerial communications support and demonstrate its necessity for an adaptive and long-term disaster deployment. In conclusion, the contributions of this dissertation enable the usage of autonomous Aerial Network Assistance Systems and their adaptability in dynamic disaster scenarios

    Machine learning enabled millimeter wave cellular system and beyond

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    Millimeter-wave (mmWave) communication with advantages of abundant bandwidth and immunity to interference has been deemed a promising technology for the next generation network and beyond. With the help of mmWave, the requirements envisioned of the future mobile network could be met, such as addressing the massive growth required in coverage, capacity as well as traffic, providing a better quality of service and experience to users, supporting ultra-high data rates and reliability, and ensuring ultra-low latency. However, due to the characteristics of mmWave, such as short transmission distance, high sensitivity to the blockage, and large propagation path loss, there are some challenges for mmWave cellular network design. In this context, to enjoy the benefits from the mmWave networks, the architecture of next generation cellular network will be more complex. With a more complex network, it comes more complex problems. The plethora of possibilities makes planning and managing a complex network system more difficult. Specifically, to provide better Quality of Service and Quality of Experience for users in the such network, how to provide efficient and effective handover for mobile users is important. The probability of handover trigger will significantly increase in the next generation network, due to the dense small cell deployment. Since the resources in the base station (BS) is limited, the handover management will be a great challenge. Further, to generate the maximum transmission rate for the users, Line-of-sight (LOS) channel would be the main transmission channel. However, due to the characteristics of mmWave and the complexity of the environment, LOS channel is not feasible always. Non-line-of-sight channel should be explored and used as the backup link to serve the users. With all the problems trending to be complex and nonlinear, and the data traffic dramatically increasing, the conventional method is not effective and efficiency any more. In this case, how to solve the problems in the most efficient manner becomes important. Therefore, some new concepts, as well as novel technologies, require to be explored. Among them, one promising solution is the utilization of machine learning (ML) in the mmWave cellular network. On the one hand, with the aid of ML approaches, the network could learn from the mobile data and it allows the system to use adaptable strategies while avoiding unnecessary human intervention. On the other hand, when ML is integrated in the network, the complexity and workload could be reduced, meanwhile, the huge number of devices and data could be efficiently managed. Therefore, in this thesis, different ML techniques that assist in optimizing different areas in the mmWave cellular network are explored, in terms of non-line-of-sight (NLOS) beam tracking, handover management, and beam management. To be specific, first of all, a procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in azimuth and elevation in non-line-of-sight mmWave communications based on a deep neural network is proposed. Moreover, along with the AOA and AOD prediction, a trajectory prediction is employed based on the dynamic window approach (DWA). The simulation scenario is built with ray tracing technology and generate data. Based on the generated data, there are two deep neural networks (DNNs) to predict AOA/AOD in the azimuth (AAOA/AAOD) and AOA/AOD in the elevation (EAOA/EAOD). Furthermore, under an assumption that the UE mobility and the precise location is unknown, UE trajectory is predicted and input into the trained DNNs as a parameter to predict the AAOA/AAOD and EAOA/EAOD to show the performance under a realistic assumption. The robustness of both procedures is evaluated in the presence of errors and conclude that DNN is a promising tool to predict AOA and AOD in a NLOS scenario. Second, a novel handover scheme is designed aiming to optimize the overall system throughput and the total system delay while guaranteeing the quality of service (QoS) of each user equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the reinforcement learning (RL) algorithm and optimization theory. An RL algorithm known as multi-agent proximal policy optimization (MAPPO) plays a role in determining handover trigger conditions. Further, an optimization problem is proposed in conjunction with MAPPO to select the target base station and determine beam selection. It aims to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. Third, a multi-agent RL-based beam management scheme is proposed, where multiagent deep deterministic policy gradient (MADDPG) is applied on each small-cell base station (SCBS) to maximize the system throughput while guaranteeing the quality of service. With MADDPG, smart beam management methods can serve the UEs more efficiently and accurately. Specifically, the mobility of UEs causes the dynamic changes of the network environment, the MADDPG algorithm learns the experience of these changes. Based on that, the beam management in the SCBS is optimized according the reward or penalty when severing different UEs. The approach could improve the overall system throughput and delay performance compared with traditional beam management methods. The works presented in this thesis demonstrate the potentiality of ML when addressing the problem from the mmWave cellular network. Moreover, it provides specific solutions for optimizing NLOS beam tracking, handover management and beam management. For NLOS beam tracking part, simulation results show that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2. Further, when it comes to the handover optimization part, the numerical results show the system throughput and delay are improved by 10% and 25%, respectively, when compared with two typical RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-learning (DQL). Lastly, when it considers the intelligent beam management part, numerical results reveal the convergence performance of the MADDPG and the superiority in improving the system throughput compared with other typical RL algorithms and the traditional beam management method
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