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A digital twin of a pharmaceutical company’s internal supply chain is presented, along with a simulation-based rough cut capacity planning tool capable of giving estimates of the required monthly capacity for the different areas of the organization on the long-term. The work was a case study performed at a pharmaceutical company. The digital twin is delivered through a graphical user interface containing both its visualization and simulation tools. The proposed digital twin supplies accurate estimates of capacity needs to supply chain managers, giving the ability to easily visualize the resources required from different involved areas in the following 24 months.

A digital twin of pharmaceutical quality control laboratory was developed and employed as a benchmarking platform to estimate the performance of a new facility under alternative governance models. ...

In this paper, a new approach to the usage of 0-order Autonomous Learning Multi-Model (ALMMo-0) classifiers is proposed. ALMMo-0 classifiers are fully automatic and do not rely on any hyper-parameters. The creation of clouds relies on normalizing data points by their norm, which may remove an important degree of freedom from the data itself. The proposed approach consists of adding the initial radius of the clouds as an hyper-parameter, which makes it possible to skip the normalization step. This approach requires the search for the ideal value of the hyper-parameter. This way, upon training a set of models with different values for the initial radius, the user is expected to be able to choose from several models which range from more accurate to less complex. This approach was tested on three benchmark problems and compared to the results obtained using the original approach. Furthermore, this approach was also tested on a real dataset (Acute Kidney Injury). The obtained results enhance the versatility provided by the proposed method, successfully allowing the user to choose the model that fits better the design demands regarding accuracy, training time, and complexity.

ObjectiveGastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU.MethodsA machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates.ResultsThe optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all.ConclusionsThe potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

As the industry continues to pursue the reduction of downtimes and increases in the efficiency of resource usage, data appears as a valuable business asset, empowering intelligent automation solutions. In order to deal with current challenges in acquisition and management of data in industrial settings, this paper proposes a framework for intelligent automation. The traditional automation pyramid does not cope with today’s application requirements, as intelligent automation needs to exchange, in realtime, information with all layers of the pyramid, simultaneously. The middleware for intelligent automation provides interoperability through integration software along with cloud functionalities and big data warehousing. The middleware also provides decentralization by connecting to remote decision support systems. This paper presents two real implementation examples of the framework in the wood and the chemical industry. The framework successfully addresses the requirements of key intelligent automation features and data characteristics, supporting the development of support decision systems into automation modules.

This work presents a novel formulation for quality control laboratory scheduling considering both equipment and analysts as constraints. The problem is modelled as a dual-resource constrained flexible job shop problem. The formulation considers analyst tasks at different times during the processing of samples. The problem is formulated as a mixed integer linear programming model (MILP) aiming to minimise makespan. Two sets of instances for the scheduling problem are proposed. The first instance consists of a small example that illustrates the proposed formulation and is solved to optimality. The second instance mimics the real industrial problem and shows the challenges resulting from growing complexity.

Nowadays, there are many Deep Q-Learning (DQL) variants and Reinforcement Learning (RL) algorithms that have outperformed the original DQL algorithm using a simple Deep Q-Network (DQN) architecture on several RL applications. In this paper, we present an environment-oriented adapted DQN implementation on the OpenAI Gym CarRacing self-driving environment where a driver agent successfully learns difficult control policies from raw pixels using a deep convolutional neural network. By implementing multiple optimizations and environment-specific adaptations on the original DQL algorithm, we were able to improve on some of the limitations of the base DQN algorithm significantly on this platform. Our trained agent was therefore able to produce successful results and obtain higher convergence scores while being more time efficient and using less computational resources. Obtaining a promising average score over one hundred consecutive trials, the results achieved suggest that the proposed adapted DQN agent improves substantially on the base DQL algorithm. Our agent surpassed Double Deep Q-Network (DDQN) and multiple other approaches in the CarRacing RL environment.

Analysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit., Measurement(s) Blood Glucose • insulin (human) Technology Type(s) glucose analyzer • glucometer • Electronic Medical Record Factor Type(s) glucose readings • insulin inputs Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13564187

This paper proposes a flexible manufacturing system based on intelligent computational agents. A Multi-Agent System composed of 4 types of reactive agents was designed to control the operation of a real implementation in the Intelligent Automation Lab at Instituto Superior Tecnico. This implementation was based and constructed analogously to a known benchmark, AIP-PRIMECA. The agents were modelled using Petri nets and agent communications were defined through the combination of FIPA Interaction Protocols. The system was tested under the conditions of static and dynamic scenarios, having its performance validated whenever possible by comparison with results from a Potential Fields Approach in the same benchmark. Overall, the performance exhibited by the proposed MAS was slightly better and it is worth highlighting the simple behaviour of each agent and ability to respond in real-time to all the dynamic scenarios tested.

The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model-using only triage priorities-with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission.

Machine Learning for Phenotyping is composed of three chapters and aims to introduce clinicians to machine learning (ML). It provides a guideline through the basic concepts underlying machine learning and the tools needed to easily implement it using the Python programming language and Jupyter notebook documents. It is divided into three main parts: part 1—data preparation and analysis; part 2—unsupervised learning for clustering and part 3—supervised learning for classification.

The development of dynamic data-based Decision Support Systems (DSSs) along with the increasing availability of data in the industry, makes real-time data acquisition and management a challenge. Intelligent automation appears as a holistic combination of automation with analytics and decisions made by artificial intelligence, delivering smart manufacturing and mass customization while improving resource efficiency. However, challenges towards the development of intelligent automation architectures include the lack of interoperability between systems, complex data preparation steps, and the inability to deal with both high-frequency and high-volume data in a timely fashion. This paper contributes to industrial frameworks focused on the development of standardized system architectures for Industry 4.0, closing the gap between generic architectures and physical realizations. It proposes a platform for intelligent automation relying on a gateway or middleware between field devices, enterprise databases, and DSSs in real-time scenarios. This is achieved by providing the middleware interoperability, determinism, and automatic data structuring over an industrial communication infrastructure such as the OPC UA Standard over Time Sensitive Networks (TSN). Cloud services and database warehousing used to address some of the challenges are handled using fog computing and a multi-workload database. This paper presents an implementation of the platform in the pharmaceutical industry, providing interoperability and real-time reaction capability to changes to an industrial prototype using dynamic scheduling algorithms.

Machine Learning for Phenotyping is composed of three chapters and aims to introduce clinicians to machine learning (ML). It provides a guideline through the basic concepts underlying machine learning and the tools needed to easily implement it using the Python programming language and Jupyter notebook documents. It is divided into three main parts: part 1—data preparation and analysis; part 2—unsupervised learning for clustering and part 3—supervised learning for classification.

This article presents a model for courier services designed to guide a fleet of vehicles over a dynamic set of requests. Motivation for this problem comes from a real-world scenario in an ever-changing environment, where the time to solve such optimization problem is constrained instead of endlessly searching for the optimal solution. First, a hybrid method combining Ant Colony Optimization with Local Search is proposed, which is used to solve a given static instance. Then, a framework to handle and adapt to dynamic changes over time is defined. A new method pairing nearest neighbourhood search with subtractive clustering is proposed to improve initial solutions and accelerate the convergence of the optimization algorithm. Overall, the proposed strategy presents good results for the dynamic environment and is suitable to be applied on real-world scenarios.

Machine Learning for Phenotyping is composed of three chapters and aims to introduce clinicians to machine learning (ML). It provides a guideline through the basic concepts underlying machine learning and the tools needed to easily implement it using the Python programming language and Jupyter notebook documents. It is divided into three main parts: part 1—data preparation and analysis; part 2—unsupervised learning for clustering, and part 3—supervised learning for classification.

This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually.The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

This work introduces a scheduling technique using the Artificial Bee Colony (ABC) algorithm for static and dynamic environments. The ABC algorithm combines different initial populations and generation of new food source methods, including a moving operations technique and a local search method increasing the variable neighbourhood search that, as a result, improves the solution quality. The algorithm is validated and its performance is tested in a static environment in 9 instances of Flexible Job Shop Problem (FJSP) from Brandimarte dataset obtaining in 5 instances the best known for the instance under study and a new best known in instance mk05. The work also focus in developing tools to process the information on the factory through the development of solutions when facing disruptions and dynamic events. Three real-time events are considered on the dynamic environment: jobs cancellation, operations cancellation and new jobs arrival. Two scenarios are studied for each real-time event: the first situation considers the minimization of the disruption between the previous schedule and the new one and the second situation generates a completely new schedule after the occurrence. Summarizing, six adaptations of ABC algorithm are created to solve dynamic environment scenarios and their performances are compared with the benchmarks of two case studies outperforming both.

The reduction of non-technical losses is a significant part of the total potential benefits resulting from implementations of the smart grid concept. This paper proposes a data-based method to detect sources of theft and other commercial losses. Prototypes of typical consumption behavior are extracted through clustering of data collected from smart meters. A distance-based novelty detection framework classifies new data samples as malign if their distance to the typical consumption prototypes is significant. The proposed method works on the space of four different indicators of irregular consumption, enabling the easy interpretation of results. A use case based on real data is presented to evaluate the method. The threat model considers sixteen different possible types of changes in consumption pattern that result from non-technical losses, including attacks and defects present since the first day of metering. The proposed clustering-based novelty detection method for identification of non-technical losses, using the Gustafson-Kessel fuzzy clustering algorithm, achieves a true positive rate of 63.6% and false positive rate of 24.3%, outperforming other state-of-the-art unsupervised learning methods.

Energy efficiency is today the main issue for any yeast production company. Portuguese yeast companies are not an exception. Purely replacing some equipment with more energy efficient ones surely contributes to decrease the overall energy consumption, but it is not enough. A yeast production process needs to be characterized and analyzed as a distributed and resource-constrained system. To achieve that in a Portuguese yeast factory, a suitable solution to obtain a dynamic resource-constrained process scheduling was pursued. A genetic algorithm (GA) based scheduling system was used to scheduling optimization of factory’s fermentation units to minimize their specific cost at yeast production. Numerical simulations were first effectuated for calibration and validation of the yeast production model developed. A 2.29% reduction in the electricity cost per ton and per week of yeast production was achieved, which means about 7500 euros/year if the level of optimization is maintained.

In this paper, a new topology of a spherical machine, denominated as a shell-like spherical induction machine, which was initially studied in recent published papers, is now designed and optimized for a powered-wheel application. The main objective is to perform an optimization design for the power/volume ratio maximization. The multiobjective/multiconstraint evolutionary algorithm NSGA-II (elitist nondominated sorting genetic algorithm) was used as the optimization tool. This optimization tool provides us a first design and physical insight to be further used in more complex 3-D finite-element analysis (FEA) tools. Thus, the shell-like spherical induction machine electromagnetic and thermal analytical models are first developed. To validate the thermal model, a small-scale prototype was built using a new soft magnetic material as the stator and rotor magnetic core. Tests to the prototype were carried out in our laboratory, measuring its electromagnetic torque and temperature, comparing them with the FEA and analytical results. Then, the optimization problem is defined by describing the objective functions, decision variables, and constraints for the powered-wheel application. Finally, the optimization results are analyzed using the Pareto front approach, showing the evolution of each objective function and decision variable.

Introduction Co-management between internists and surgeons of selected patients is becoming one of the pillars of modern clinical management in large hospitals. Defining the patients to be co-managed is essential. The aim of this study is to create a decision tool using real-world patient data collected in the preoperative period, to support the decision on which patients should have the co-management service offered. Methods Data was collected from the electronic clinical health records of patients who had an International Classification of Diseases, 9th edition (ICD–9) code of colorectal surgery during the period between January 2012 and October 2014 in a 200 bed private teaching hospital in Lisbon. ICD–9 codes of colorectal surgery [48.5 and 48.6 (anterior rectal resection and abdominoperineal resection), 45.7 (partial colectomy), 45.8 (Total Colectomy), and 45.9 (Bowel Anastomosis)] were used. Only patients above 18 years old were considered. Patients with more than one procedure were excluded from the study. From these data the authors investigated the construction of predictive models using logistic regression and Takagi-Sugeno fuzzy modelling. Results Data contains information obtained from the clinical records of a cohort of 344 adult patients. Data from 398 emergent and elective surgeries were collected, from which 54 were excluded because they were second procedures for the same patients. Four preoperative variables were identified as being the most predictive of co-management, in multivariable regression analysis. The final model performed well after being internally validated (0.81 AUC, 77% accuracy, 74% sensitivity, 78% specificity, 93% negative predictive value). The results indicate that the decision process can be more objective and potentially automated. Conclusions The authors developed a prediction model based on preoperative characteristics, in order to support the decision for the co-management of surgical patients in the postoperative ward setting. The model is a simple bedside decision tool that uses only four numerical variables.

Constant improvement of vehicle technologies towards more efficient powertrains and reduced pollutant emissions, frequently leads to the increase of the vehicle or fuel costs, compromising ...

A simulation model of pharmaceutical quality control laboratories was employed as a benchmarking platform to estimate the performance of a new facility under alternative governance models. Key performance metrics, such as sample processing time and utilization rates of analytical staff and equipment were computed and analyzed under alternative governance scenarios, in order to evaluate solutions to be implemented in practice. Two frameworks were compared, leading to the conclusion that higher efficiency can be achieved under free-for-all governance, without the need to procure additional resources.

Data mining in medical databases often involves the comparison of time series, which represent the evolution of a physiological variable. Temporal misalignment of physiological variables can conceal the discovery of patterns and trends shared between different patients. To address this problem, this paper proposes the mixed fuzzy clustering (MFC) algorithm with the dynamic time-warping (DTW) distance. We developed the MFC algorithm by 1) incorporating the DTW distance into the standard fuzzy c-means to handle misaligned time series; 2) introducing a new dimension into the spatiotemporal clustering algorithm to handle P time-variant features; and 3) incorporating unsupervised learning of cluster-dependent attribute weights. The algorithm is designed to simultaneously cluster time-variant and time-invariant data. We demonstrate the advantages of the proposed algorithm in four synthetic datasets and in two real-world applications in intensive care units. The first application is the classification of patients who will need the administration of vasopressors, and the second is the classification of patients with a high risk of mortality. Time-variant features consist of physiological variables collected with different sampling rates at different points in time. Time-invariant features consist of patients’ demographics and score records. The performance is evaluated using cluster validity measures, showing that the proposed algorithm outperforms fuzzy c-means.

This paper presents an optimization model to identity the best set of retrofitting measures applied to four different regions of Portugal; Lisbon, Porto, Braganca and Evora. The main objective is to maximize the annual energy savings, expressed in Euros, while minimizing the initial investment expressed also in Euros, considering the retrofit strategies as decisions variables and evaluating their effect in the usage of space heating and cooling and costs, depending on the chosen technology. A multi-objective genetic algorithm coded in MATLAB © to solve the optimization problem. Different energy efficiency measures are considered, such as the improvement of U-values in external walls and windows through the application of insulation materials, the change of lighting fixtures and refrigerator model to reduce the electricity consumption, and the usage of PV panels for self-consumption, and the use of Heat Pumps for space heating and cooling. The fitness function is subjected to different constraints related with the fulfillment of the demand, the physical limitation of household space, the capacity to comply with the heating and cooling needs, and the non-negativity nature of the variables. The best set of retrofitting options are presented, the budget to implement them and the amount of achieved energy savings.

Collective photovoltaic self-consumption is an extension of traditional, single user self-consumption, whose objective is to maximize the share of local generated energy that is consumed at the generation point with multiple consumers sharing a photovoltaic system. With demand-side management, consumption profiles can be adapted to generation profiles which can be posed as an optimization problem. In this work, the problem of scheduling uninterruptible shiftable devices is addressed from a multiagent perspective considering two different approaches. On one hand, a centralized architecture assumes that a single agent has the capacity to solve all agents problems. On the other, a new partially distributed architecture is proposed to solve the same problem by distributing decision-making among agents using an heuristic algorithm with a virtual dynamic tariff as coordination mechanism. A computational experiment is set up in order to test increasing numbers of agents. Results show that the centralized approach, although finding the optimal solution, shows running times that are two orders of magnitude greater than the decentralized method, which is able to find high quality solutions at lower time cost but with increased monetary cost. This trade-off becomes more relevant as the number of agents increase.

This research is focused on the prediction of ICU readmissions using fuzzy modeling and feature selection approaches. There are a number of published scores for assessing the risk of readmissions, but their poor predictive performance renders them unsuitable for implementation in the clinical setting. In this work, we propose the use of feature engineering and advanced computational intelligence techniques to improve the performance of current models. In particular, we propose an approach that relies on transforming raw vital signs, laboratory results and demographic information into more informative pieces of data, selecting a subset of relevant and nonredundant variables and applying fuzzy ensemble modeling to the featureengineered data for deriving important nonlinear relations between variables. Different criteria for selecting the best predictor from the ensemble and novel evaluation measures are explored. In particular, the area under the sensitivity curve and area under the specificity curve are investigated. The ensemble approach combined with feature transformation and feature selection showed increased performance, being able to predict early readmissions with an AUC of 0.77 0.02. To the best of our knowledge, this is the first computational intelligence technique allowing the prediction of readmissions in a daily basis. The high balance between sensitivity and specificity shows its strength and suitability for the management of the patient discharge decision making process.

Laboratories are critical components in drug manufacturing, and inefficiencies in laboratory management can have a major impact on the overall supply chain service level. The aim of this paper is to build a Discrete Event Simulation model of a Quality Control laboratory. To achieve this objective, a generic framework for information treatment and organization was built. In particular, information coming from different databases was organized into a single one that was used as input to a discrete event simulation model. The proposed model represents in detail the work flow of a quality control laboratory and it is intended as a support tool for planning, scheduling and decision making. The model was validated using real data, and it proved to be effective in the estimation of performance parameters such as, system throughput, equipment usage rate, system responsiveness and tasks processing times. Furthermore, the simulation model was tested with an alternative scheduling policy to evaluate how modifications on the system may improve its performance.

The fourth industrial revolution, Industrie 4.0, motivates research to adapt concepts from Internet of Things and Cyber-Physical Systems to automation. Focus is placed on integrating all data sources, from sensors and actuators to management software, to achieve interoperability of heterogeneous systems. To assure data integration, a data collection architecture that guarantees data access from all systems, including legacy devices, is needed. This allows companies to gradually evolve to Industrie 4.0 deployments while still keeping their mission-critical systems online. A middleware-based architecture is a valid answer to the presented challenges. Numerous communication technologies can act as a middleware, all with different advantages and disadvantages. A technology-independent implementation ensures that a lock-in to a specific technology is prevented and makes the architecture more robust and adequate for various use-cases. This work contributes with a concept of a middleware-based data collection architecture, which is independent of specific communication protocols. Therefore, a protocol-agnostic communication interface is developed. A technology comparison summarizes relevant protocols in the field of industrial automation. The concept is subsequently implemented for a case-study and compared to a peer-to-peer legacy approach. Results show that the concept is feasible, as well as that it reduces complexity and effort for implementation and migration of such communication systems. Future work should focus on further developing the concepts, in order to support additional communication protocols and verify the suitability of the architecture in scenarios with a multitude of technologies.

This paper proposes a decision support system based on fuzzy clustering, fuzzy modeling and fuzzy fingerprints, to provide personalized therapy for critically ill patients. It is hypothesized that the ‘collective experience’ from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the Intensive Care Unit (ICU). The validity of the proposed systems will be tested using the case study of patients admitted to the ICU who then develop acute kidney injury (AKI); Two-thirds of patients with AKI require renal support therapy. Generalized severity scoring systems have consistently performed poorly for patients with AKI.

Nowadays, industry is being forced to produce smaller and more diverse batches, increasing the complexity of internal supply chains. Data has become a valuable asset, supporting the development of intelligent automation solutions. Decision support systems, which leverage data, require the automation pyramid to be more flexible, as information needs to be exchanged simultaneously and in real-time with all automation layers. This paper proposes a framework for intelligent automation to deal with current challenges in acquisition and management of data in industrial settings, towards feeding decision support systems. It frames the topic within the scope of internal supply chains, addressing the framework impact on work practices within the organisation. Two real industrial implementation cases are examined, in the wood and chemical industries. Results help practitioners address the most impactful challenges affecting the performance of internal supply chains, by developing systems which are faster, more flexible, efficient and with improved quality.

Graphical abstractDisplay Omitted HighlightsProbabilistic fuzzy systems (PFS) are used to predict mortality of septic shock patients.PFS models are compared with Takagi-Sugeno fuzzy models and logistic regression models.The methods are tested using ICU patients with abdominal septic shock.PFS models increase the transparency of the learned system using fuzzy rules.By providing estimates for the mortality risk, PFS help clinical decision making. Mortality scores based on multiple regressions are common in critical care medicine for prognostic stratification of patients. However, to be used at the point of care, they need to be both accurate and easily interpretable. In this work, we propose the application of one existent type of rule base system using statistical information - probabilistic fuzzy systems (PFS) - to predict mortality of septic shock patients. To assess its accuracy and interpretability, these models are compared to methodologies previously proposed in this domain: Takagi-Sugeno fuzzy models and logistic regression models. The methods are tested using a retrospective cohort study including ICU patients with abdominal septic shock. Regarding accuracy, PFS models are comparable to fuzzy modeling and logistic regression. In terms of interpretability, results indicate that PFS models increase the transparency of the learned system (using fuzzy rules), but at the same time, provide additional means for validating the fuzzy classifier using expert knowledge (from physicians in this paper). By providing accurate and interpretable estimates for the mortality risk, results suggest the usefulness of PFS to develop scores for critical care medicine.

Emergency department triage is the first point in time when a patient’s acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome—mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients’ chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency–inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model—a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients’ age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.

Motivation Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage. Objectives The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation. Methods We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems. Results From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient's prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage. Conclusions In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.

Up to two thirds of critically ill patients in Intensive Care Units (ICU) around the world develop Acute Kidney Injury (AKI). This complication is characterized by a reduction of kidney function and is intrinsically linked to an increased risk of mortality. AKI is also connected to higher costs in healthcare due to longer hospital stay times and ICU readmissions for affected patients. The present study developed a model capable of predicting which patients are at a larger risk of death and therefore allow for a more efficient use of hospital resources. Information regarding several physiological parameters of patients with AKI as well as data regarding their survival was used for the creation of a fuzzy model. The model achieved AUC values of up to 0.92 when using mean values for time series and analyzing a subset of patients with AKI at its most advanced stage. When analysing a general population of ICU patients, the algorithm can achieve AUC values of 0.86. This value can be compared with values obtained in similar studies of 0.85 and surpasses values obtained in traditional risk scores such as APACHE II and SOFA. A model such as this, shows the potential of using this type of predictive applications for scheduling human resources and managing monetary resources on critical patients according to their relative need for attention and treatment.

The oscillating-water-column (OWC) is widely regarded as the simplest and most reliable type of wave energy converter. An OWC device comprises a partly submerged hollow structure composed of an aperture below the water surface and an air chamber above the free surface. The oscillating motion of the internal free water surface produced by the incident waves compresses and expands the air in the chamber. The pressure between the air chamber and the atmosphere can be used to drive a turbine connected directly to an electrical generator. Usually the turbines used in OWCs are of the self-rectifying type, i.e., they rotate always in the same sense independently of the flow direction. The aim of this paper is to assess the potential use of experimental data collected in the Mutriku breakwater wave power plant, located in the north of Spain, to perform short-term prediction of the pressure inside the air chamber by a Nonlinear Autoregressive with eXogenous inputs (NARX) Artificial Neural Network (ANN). The NARX model is explored and tuned for a one-step and multi-step-ahead prediction. The results showed that the model is able to capture the OWC system dynamics for a few-steps ahead with a good performance.

Fish school search (FSS) algorithm has inspired several adaptations for multi-objective problems or binary optimization. However, there is no particular proposition to solve both problems simultaneously. The proposed multi-objective approach binary fish school search (MOBFSS) aims to solve optimization problems with two or three conflicting objective functions with binary decision input variables. MOBFSS is based on the dominance concept used in the multi-objective fish school search (MOFSS) and the threshold technique deployed in the binary fish school search (BFSS). Additionally, the authors evaluate the proposal for feature selection for classification in well-known datasets. Moreover, the authors compare the performance of the proposal with a state-of-art algorithm called BMOPSO-CDR. MOBFSS presents better results than BMOPSO-CDR, especially for datasets with higher complexity.

This chapter presents two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and time invariant features. The mixed fuzzy clustering (MFC) algorithm is used for determining the parameters of Takagi-Sugeno fuzzy models (FMs) in two different ways: (1) MFC FM, where the antecedent fuzzy sets are determined based on the partition matrix generated by the mixed fuzzy clustering algorithm; (2) FCM–UMFC FM, where the input features are transformed using MFC and the antecedent fuzzy sets are derived using fuzzy c-means (FCM). The fuzzy modeling approaches are tested on four health care applications for the classification of critically ill patients: administration of vasopressors in pancreatitis and pneumonia patients, mortality in septic shock and early readmissions. Both approaches increase the performance of Takagi-Sugeno based on FCM, in all datasets. In particular, the best performer, FCM–UMFC FM, achieves notable improvements in the four datasets.

The knowledge of the service life and durability of building components is paramount to sustainable analysis and decision making since it allows a more rational management of the maintenance of building and provides data for life cycle analysis procedures. Nevertheless, predicting the service life of a building or its components is a complex process with which a number of variables are associated. The main difficulties associated with service life prediction are related to the complexity of the degradation phenomena and to the lack of understanding of degradation factors and mechanisms. This paper aims at establishing a model for the service life prediction of rendered facades using a Takagi–Sugeno fuzzy model. The models proposed include the variables that influence the degradation of rendered facades (render age, render type, building height, facade orientation, exposure to damp, and facade protection level). In this study, the degradation condition of 100 case studies located in Portugal is analyzed based only on in situ visual inspections. The proposed models are able to describe appropriately the degradation of rendered facades and to predict the service life of the sample analyzed.

Left ventricular ejection fraction (LVEF) constitutes an important physiological parameter for the assessment of cardiac function, particularly in the settings of coronary artery disease and heart failure. This study explores the use of routinely and easily acquired variables in the intensive care unit (ICU) to predict severely depressed LVEF following ICU admission. A retrospective study was conducted. We extracted clinical physiological variables derived from ICU monitoring and available within the MIMIC II database and developed a fuzzy model using sequential feature selection and compared it with the conventional logistic regression (LR) model. Maximum predictive performance was observed using easily acquired ICU variables within 6 hours after admission and satisfactory predictive performance was achieved using variables acquired as early as one hour after admission. The fuzzy model is able to predict LVEF ≤ 25% with an AUC of 0.71 ± 0.07, outperforming the LR model, with an AUC of 0.67 ± 0.07. To the best of the authors’ knowledge, this is the first study predicting severely impaired LVEF using multivariate analysis of routinely collected data in the ICU. We recommend inclusion of these findings into triaged management plans that balance urgency with resources and clinical status, particularly for reducing the time of echocardiographic examination., MIT-Portugal Program, Portuguese Science and Technology Foundation (Project LAETA 2015-202, Reference UID/EMS/50022/2013), Portuguese Ministry of Education and Science (Portuguese Science and Technology Foundation Grant SFRH/BD/51028/2010), Portuguese Science and Technology Foundation (IC4U-Decision Support System for Preventing ICU Readmissions PTDC/EMS-SIS/3220/2012)

Intensive care treatment presents unique challenges in the medical world. When treating patients, their wide variety leave care providers with few past examples to draw on. Instead of operating in a pure knowledge discovery capacity, decision support systems can be developed to help predict short-term and long-term patient outcome, based upon available data. One area in which generalized severity scoring systems have consistently performed poorly is among patients admitted intensive care units (ICU) who then develop acute kidney injury. Urine output is used to guide fluid resuscitation and is one of the criteria for the diagnosis of acute kidney injury. This paper provides an example application for predicting short-term critical kidney function in an intensive care unit. Feature construction is performed to extract important aspects of the clinical evolution of the patient. Feature selection is performed on several patient features. Classifiers based on support vector machines and Takagi-Sugeno fuzzy models are developed to predict short-term drops in patient urine output rate. Both types of models showed comparable results, with an AUC of 78%. This shows potential in using similar classifiers to build an ICU decision support system with the goal of predicting short-term complication in the patient and augment current guidelines by anticipating treatment.

Logistic regression and Takagi-Sugeno fuzzy models are sequentially trained with categorical and numerical data in an ensemble-based multistage scheme. In the first stage, a logistic regression model is used to transform the binary feature space into a numerical feature that is used to train a second stage of models consisting of an ensemble of two Takagi-Sugeno fuzzy models. In the ensemble, one model is trained in the space of numerical features and first stage prediction values. The other model is trained only with samples that were classified with a low degree of confidence by the first stage model, in the space of numerical variables. The final output is given by the average of the ensemble predictions at second stage. This scheme was devised under the hypothesis that separating binary from numerical features in the modeling process would increase the performance of a single model using both types of features together. The proposed multistage approach is used to solve a clinical classification problem in a Portuguese hospital. The problem consists of predicting comanagement signalling based on patient clinical data, including diagnosis, procedures, comorbidities and numerical scores, collected before surgery. The multistage performed better in the comanagement dataset, and in 2 out of 5 benchmark datasets.

Closed-loop supply chain (CLSC) design implies the modelling of the forward and the reverse flows of products in an integrated way. This paper introduces nonlinear dimensioning factors in the design of CLSC and uses ant colony optimization to optimize the design of the supply chain. The proposed algorithm is called SCAnt-NLDesign. The modelled nonlinear dimensioning factors are: cost variations in transportation distances between facilities (tapering principle), scale economies related to transported quantities, and scale economies regarding the facilities' capacity. Results show that the proposed SCAnt-NLDesign algorithm reduced the total cost in 44 %, when compared to a linear formulation of a CLSC. Note also that a mixed integer linear programming implementation of the nonlinear CLSC was not able to get closed to the optimal solution, given worse results than the linear CLSC.

Two new methods for the optimization of probabilistic fuzzy classifiers are proposed. Probabilistic fuzzy systems are specially attractive due to their explicit and simultaneous modelling of two kinds of uncertainty, namely vagueness in linguistic terms (fuzziness) and probabilistic uncertainty. The current method uses the maximization of the likelihood with the stochastic gradient descent, which not only converges to local minima but also does not guarantee the minimization of the misclassification error. The proposed methods address this specific problem by incorporating global search techniques. The first algorithm proposed is a genetic algorithm with simple crossover and mutation operations. The other is a first generation memetic algorithm which combines the genetic algorithm with the stochastic gradient descent. A total of five benchmarks were used to compare the three algorithms. The results show that the proposed methods have an average relative improvement of 2% and 6% for the accuracy with the genetic and memetic algorithms, respectively.

In large scale systems of embodied agents, such as robot swarms, the ability to flock is essential in many tasks. However, the conditions necessary to artificially evolve self-organised flocking behaviours remain unknown. In this paper, we study and demonstrate how evolutionary techniques can be used to synthesise flocking behaviours, in particular, how fitness functions should be designed to evolve high-performing controllers. We start by considering Reynolds' seminal work on flocking, the boids model, and design three components of a fitness function that are directly based on his three local rules to enforce local separation, cohesion and alignment. Results show that embedding Reynolds' rules in the fitness function can lead to the successful evolution of flocking behaviours. However, only local, fragmented flocking behaviours tend to evolve when fitness scores are based on the individuals' conformity to Reynolds' rules. We therefore modify the components of the fitness function so that they consider the entire group of agents simultaneously, and find that the resulting behaviours lead to global flocking. Furthermore, the results show that alignment need not be explicitly rewarded to successfully evolve flocking. Our study thus represents a significant step towards the use of evolutionary techniques to synthesise collective behaviours for tasks in which embodied agents need to move as a single, cohesive group. info:eu-repo/semantics/publishedVersion