Seizure prediction : ready for a new era

Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin

Sonic interactions in virtual environments

This book tackles the design of 3D spatial interactions in an audio-centered and audio-first perspective, providing the fundamental notions related to the creation and evaluation of immersive sonic experiences. The key elements that enhance the sensation of place in a virtual environment (VE) are: Immersive audio: the computational aspects of the acoustical-space properties of Virutal Reality (VR) technologies Sonic interaction: the human-computer interplay through auditory feedback in VE VR systems: naturally support multimodal integration, impacting different application domains Sonic Interactions in Virtual Environments will feature state-of-the-art research on real-time auralization, sonic interaction design in VR, quality of the experience in multimodal scenarios, and applications. Contributors and editors include interdisciplinary experts from the fields of computer science, engineering, acoustics, psychology, design, humanities, and beyond. Their mission is to shape an emerging new field of study at the intersection of sonic interaction design and immersive media, embracing an archipelago of existing research spread in different audio communities and to increase among the VR communities, researchers, and practitioners, the awareness of the importance of sonic elements when designing immersive environments

Intelligent automatic operational modal analysis

Operational modal analysis methods have been proven especially useful to identify existing structures and infrastructures under serviceability conditions. However, the installation of sensing systems for monitoring continuously an ever larger number of existing constructions has motivated significant efforts towards the automation of the available methods. Within this framework, the present paper introduces a new paradigm for the automatic output-only modal identification of linear structures under ambient vibrations, namely the intelligent automatic operational modal analysis (i-AOMA). It exploits the covariance-based stochastic subspace (SSI-cov) algorithm for the output-only identification of the modal parameters and its workflow consists of two main phases. Initially, quasi-random samples of the control parameters for the SSI-cov algorithm are generated. Once the SSI-cov algorithm is performed for each sample, the corresponding stabilization diagrams are processed in order to prepare a database for training the intelligent core of the i-AOMA method. This is a machine learning technique (namely a random forest algorithm) that predicts which combination of the control parameters for the SSI-cov algorithm is able to provide good modal estimates. Afterward, new quasi-random samples of the control parameters for the SSI-cov algorithm are generated repeatedly until a statistical convergence criterion is achieved. If the generic sample is classified as feasible by the intelligent core of the i-AOMA method, then the SSI-cov algorithm is performed. Finally, stable modal results are distilled from the stabilization diagrams and relevant statistics are computed to evaluate the uncertainty level due to the variability of the control parameters. The proposed i-AOMA method has been applied to identify the modal features of the Al-Hamra Firduos Tower, an iconic 412.6 m tall building located in Kuwait City (Kuwait). The final results well agree with a previous experimental study, and it was also possible to identify two new vibration modes of the structure. The implemented open-source Python code is made freely available

Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications

Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Measurement, optimisation and control of particle properties in pharmaceutical manufacturing processes

Previously held under moratorium from 2 June 2020 until 6 June 2022.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material

The impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining

The technological progress in terms of increasing computational power and growing virtual space to collect data offers great potential for businesses to benefit from data mining applications. Data mining can create a competitive advantage for corporations by discovering business relevant information, such as patterns, relationships, and rules. The role of the human user within the data mining process is crucial, which is why the research area of domain knowledge becomes increasingly important. This thesis investigates the impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining. Domain knowledge is defined as methodological, data and business know-how. The thesis investigates the topic from a new perspective by shifting the focus from a one-sided approach, namely a purely analytic or purely theoretical approach towards a target group-oriented (researcher and practitioner) approach which puts the methodological aspect by means of a scientific guideline in the centre of the research. In order to ensure feasibility and practical relevance of the guideline, it is adapted and applied to the requirements of a practical business case. Thus, the thesis examines the topic from both perspectives, a theoretical and practical perspective. Therewith, it overcomes the limitation of a one-sided approach which mostly lacks practical relevance or generalisability of the results. The primary objective of this thesis is to provide a scientific guideline which should enable both practitioners and researchers to move forward the domain knowledge-driven research for variable derivation on a corporate basis. In the theoretical part, a broad overview of the main aspects which are necessary to undertake the research are given, such as the concept of domain knowledge, the data mining task of classification, variable derivation as a subtask of data preparation, and evaluation techniques. This part of the thesis refers to the methodological aspect of domain knowledge. In the practical part, a research design is developed for testing six hypotheses related to domain knowledge-driven variable derivation. The major contribution of the empirical study is concerned with testing the impact of domain knowledge on a real business data set compared to the impact of a standard and randomly derived data set. The business application of the research is a binary classification problem in the domain of an insurance business, which deals with the prediction of damages in legal expenses insurances. Domain knowledge is expressed through deriving the corporate variables by means of the business and data-driven constructive induction strategy. Six variable derivation steps are investigated: normalisation, instance relation, discretisation, categorical encoding, ratio, and multivariate mathematical function. The impact of the domain knowledge is examined by pairwise (with and without derived variables) performance comparisons for five classification techniques (decision trees, naive Bayes, logistic regression, artificial neural networks, k-nearest neighbours). The impact is measured by two classifier performance criteria: sensitivity and area under the ROC-curve (AUC). The McNemar significance test is used to verify the results. Based on the results, two hypotheses are clearly verified and accepted, three hypotheses are partly verified, and one hypothesis had to be rejected on the basis of the case study results. The thesis reveals a significant positive impact of domain knowledge-driven variable derivation on classifier performance for options of all six tested steps. Furthermore, the findings indicate that the classification technique influences the impact of the variable derivation steps, and the bundling of steps has a significant higher performance impact if the variables are derived by using domain knowledge (compared to a non-knowledge application). Finally, the research turns out that an empirical examination of the domain knowledge impact is very complex due to a high level of interaction between the selected research parameters (variable derivation step, classification technique, and performance criteria)