Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Efficient heuristics for the parallel blocking flow shop scheduling problem

We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them –especially the IGA– perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft

A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimize total flowtime

This paper focuses on the blocking flow shop scheduling problem with the objective of total flowtime minimisation. This problem assumes that there are no buffers between machines and, due to its application to many manufacturing sectors, it is receiving a growing attention by researchers during the last years. Since the problem is NP-hard, a large number of heuristics have been proposed to provide good solutions with reasonable computational times. In this paper, we conduct a comprehensive evaluation of the available heuristics for the problem and for related problems, resulting in the implementation and testing of a total of 35 heuristics. Furthermore, we propose an efficient constructive heuristic which successfully combines a pool of partial sequences in parallel, using a beam-search-based approach. The computational experiments show the excellent performance of the proposed heuristic as compared to the best-so-far algorithms for the problem, both in terms of quality of the solutions and of computational requirements. In fact, despite being a relative fast constructive heuristic, new best upper bounds have been found for more than 27% of Taillard’s instances.Ministerio de Ciencia e Innovación DPI2013-44461-P/DP

Heuristics for the distributed blocking Ffow shop scheduling problem

Postprint (published version

New efficient constructive heuristics for the hybrid flowshop to minimise makespan: A computational evaluation of heuristics

This paper addresses the hybrid flow shop scheduling problem to minimise makespan, a well-known scheduling problem for which many constructive heuristics have been proposed in the literature. Nevertheless, the state of the art is not clear due to partial or non homogeneous comparisons. In this paper, we review these heuristics and perform a comprehensive computational evaluation to determine which are the most efficient ones. A total of 20 heuristics are implemented and compared in this study. In addition, we propose four new heuristics for the problem. Firstly, two memory-based constructive heuristics are proposed, where a sequence is constructed by inserting jobs one by one in a partial sequence. The most promising insertions tested are kept in a list. However, in contrast to the Tabu search, these insertions are repeated in future iterations instead of forbidding them. Secondly, we propose two constructive heuristics based on Johnson’s algorithm for the permutation flowshop scheduling problem. The computational results carried out on an extensive testbed show that the new proposals outperform the existing heuristics.Ministerio de Ciencia e Innovación DPI2016-80750-

Efficient heuristics for the hybrid flow shop scheduling problem with missing operations

In this paper, we address the hybrid flowshop scheduling problem for makespan minimisation. More specifically, we are interested in the special case where there are missing operations, i.e. some stages are skipped, a condition inspired in a realistic problem found in a plastic manufacturer. The main contribution of our paper is twofold. On the one hand we carry out a computational analysis to study the hardness of the hybrid flowshop scheduling problem with missing operations as compared to the classical hybrid flowshop problem. On the other hand, we propose a set of heuristics that captures some special features of the missing operations and compare these algorithms with already existing heuristics for the classical hybrid flowshop, and for the hybrid flowshop problem with missing operations. The extensive computational experience carried out shows that our proposal outperforms existing methods for the problem, indicating that it is possible to improve the makespan by interacting with the jobs with missing operations.Ministerio de Ciencia e Innovación DPI2016-80750-

Survival and disinfection of SARS-Cov-2 in environment and contaminated surface

The detection of SARS-Cov-2 in the sewage and water resources has increased the awareness among the people about the possibility survival of SARS-Cov-2 in the environment and the potential to transmit into the human through food chain or water resources. Moreover, the surface contaminated by the virus need to be disinfected frequently by using an effective disinfectant, the current chapter discussed the efficiency of the most traditional treatment process of the sewage and wastewater, and their role in the elimination of the virus as well as the sterility assurance level concept. Moreover, the chemical disinfectant used currently and their temporary efficiency has been reviewed

Moderating effects of cross-cultural dimensions on the relationship between persuasive smartphone application's design and acceptance-loyalty

Applying persuasive system design to different cultures has been a focus of many researchers as the global medium of communication has been centered within Smartphone via applications (apps). This is due to the vast proliferation of the Smartphone and the personal attachment of users to this device in various cultures. This led designers to search for ultimate ways to target users in specific regions of the world. The basic purpose of this study was to determine the relevance of cross-cultural factors to persuasive technologies, and the acceptance and loyalty of Smartphone apps. This was achieved by examining the moderating effects of Hofstede’s six cross-cultural dimensions on the relationship between Oinas-Kukkonen and Harjumaa’s Persuasive System Design (PSD), and acceptance and loyalty. By evaluating elements of persuasive systems design and cross-cultural dimensions, from user’s perspective, against a globally popular application like WhatsApp, an instrument was devised to investigate the cross-cultural adoption and continued use of Smartphone apps. Using this instrument, surveys were conducted for this research study to identify the influencing factors that have motivated the users from Malaysia, Netherlands, Germany, and the Kingdom of Saudi Arabia to adopt and continue using this application on a daily basis. These surveys, which included responses from 488 participants, further investigated if there is one cross-cultural dimension that has more moderating effects per country. The findings indicate an agreement among WhatsApp users from all four countries about their reasons for adopting and using this app, namely: social influence (93.7 percent), reliability (83.4 percent), dialog-support via feedback (76.4 percent), ease of use (90.5 percent) and small cost (87.7 percent). The results put new perspective that the gap among cultures is narrowing. Persuasive design strategies are particularly relevant to cultures across the globe. This study can aid the research community in investing efforts into enhancing the persuasive design framework for Smartphone apps

A scheduling theory framework for GPU tasks efficient execution

Concurrent execution of tasks in GPUs can reduce the computation time of a workload by overlapping data transfer and execution commands. However it is difficult to implement an efficient run- time scheduler that minimizes the workload makespan as many execution orderings should be evaluated. In this paper, we employ scheduling theory to build a model that takes into account the device capabili- ties, workload characteristics, constraints and objec- tive functions. In our model, GPU tasks schedul- ing is reformulated as a flow shop scheduling prob- lem, which allow us to apply and compare well known methods already developed in the operations research field. In addition we develop a new heuristic, specif- ically focused on executing GPU commands, that achieves better scheduling results than previous tech- niques. Finally, a comprehensive evaluation, showing the suitability and robustness of this new approach, is conducted in three different NVIDIA architectures (Kepler, Maxwell and Pascal).Proyecto TIN2016- 0920R, Universidad de Málaga (Campus de Excelencia Internacional Andalucía Tech) y programa de donación de NVIDIA Corporation