Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.

An Assessment of Pharmacological Properties of Schinus Essential Oils: A Soft Computing Approach

Plants of genus Schinus are native South America and introduced in Mediterranean countries, a long time ago. Some Schinus species have been used in folk medicine, and Essential Oils of Schinus spp. (EOs) have been reported as having antimicrobial, anti-tumoural and anti-inflammatory properties. Such assets are related with the EOs chemical composition that depends largely on the species, the geographic and climatic region, and on the part of the plants used. Considering the difficulty to infer the pharmacological properties of EOs of Schinus species without a hard experimental setting, this work will focus on the development of an Artificial Intelligence grounded Decision Support System to predict pharmacological properties of Schinus EOs. The computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters to the handling of incomplete, unknown, or even self-contradictory information. New clustering methods centered on an analysis of attribute’s similarities were used to distinguish and aggregate historical data according to the context under which it was added to the Case Base, therefore enhancing the prediction process.

An Evaluative Model to Assess the Organizational Efficiency in Training Corporations

In an organisation any optimization process of its issues faces increasing challenges and requires new approaches to the organizational phenomenon. Indeed, in this work it is addressed the problematic of efficiency dynamics through intangible variables that may support a different view of the corporations. It focuses on the challenges that information management and the incorporation of context brings to competitiveness. Thus, in this work it is presented the analysis and development of an intelligent decision support system in terms of a formal agenda built on a Logic Programming based methodology to problem solving, complemented with an attitude to computing grounded on Artificial Neural Networks. The proposed model is in itself fairly precise, with an overall accuracy, sensitivity and specificity with values higher than 90 %. The proposed solution is indeed unique, catering for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in a quantitative or qualitative arrangement.

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.

Age Prediction through Pelvis X-Ray Images - A Case Based approach to Problem Solving

It is well known that the dimensions of the pelvic bones depend on the gender and vary with the age of the individual. Indeed, and as a matter of fact, this work will focus on the development of an intelligent decision support system to predict individual’s age based on pelvis’ dimensions criteria. On the one hand, some basic image processing technics were applied in order to extract the relevant features from pelvic X-rays. On the other hand, the computational framework presented here was built on top of a Logic Programming approach to knowledge representation and reasoning, that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

Analysis of Dyscalculia Evidences through Artificial Intelligence Systems

Dyscalculia is usually perceived of as a specific learning difficulty for mathematics or, more appropriately, arithmetic. Because definitions and diagnoses of dyscalculia are in their infancy and sometimes are contradictory. However, mathematical learning difficulties are certainly not in their infancy and are very prevalent and often devastating in their impact. Co-occurrence of learning disorders appears to be the rule rather than the exception. Co-occurrence is generally assumed to be a consequence of risk factors that are shared between disorders, for example, working memory. However, it should not be assumed that all dyslexics have problems with mathematics, although the percentage may be very high, or that all dyscalculics have problems with reading and writing. Because mathematics is very developmental, any insecurity or uncertainty in early topics will impact on later topics, hence to need to take intervention back to basics. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work will focus on the development of a Decision Support System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, grounded on a Case-based approach to computing, that allows for the handling of incomplete, unknown, or even self-contradictory information.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).

Response of tropical maize to supplemental irrigation strategies.

Water is one of the most important factors influencing crop production in rainfed cropping systems. In tropical regions, supplemental irrigation reduces the risk of yield losses associated to water deficit due to insufficient rainfall. Water deficit in regions with irregularities in rainfall may be overcome with the use of supplemental irrigation, a technique based on the application of water at amounts below the crop?s evapotranspiration (ETc). We investigated the potential of supplemental irrigation as a strategy to increase yield of maize grown under tropical conditions. We used the CSM-CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT) to simulate irrigation strategies of maize in six counties in the state of Minas Gerais, Brazil. Our results indicate significant differences on simulated crop yield in response to supplemental irrigation. As a consequence, water productivity was improved with reductions of 10% and 15% of full irrigation depths in one of the six counties while in two the water productivity was higher when full irrigation was applied.

Simulações no CROPGRO/DSSAT do carbono no solo cultivado com soja sob plantio direto e preparo convencional, em clima subtropical do sul do Brasil.

Objetivou-se avaliar o potencial do modelo CROPGRO, inserido no DSSAT v.4,0 (Decision Support System for Agrotechnology Transfer) para simular o carbono no solo, no sistema plantio direto. Os dados foram coletados na Estação Experimental da Universidade Federal do Rio Grande do Sul (EEA/UFRGS), em Eldorado do Sul, durante o ano agrícola 2003/04, num delineamento em faixas, em Argissolo Vermelho distrófico típico. A semeadura da soja (cv. Fepagro RS10 - ciclo longo) ocorreu em 20/11/03 para uma população inicial em torno de 300 mil plantas ha-1. Foram utilizados dois sistemas de manejo do solo: preparo convencional (PC) e sistema plantio direto (PD) irrigados (I) e não irrigados (NI). Foram inseridos no DSSAT dados edáficos, meteorológicos diários e da cultura. Adotou-se o método Ceres, no CROPGROSoja para simular o teor de carbono (C) no solo. As simulações mostraram que há maior estoque de C em plantio direto irrigado em relação ao preparo convencional, demonstrando sensibilidade do CROPGRO-Soja ao manejo do solo. Os mais elevados resíduos de C em solo sob plantio direto evidenciam mitigações de emissões desse gás para a atmosfera em cultivos na região estudada.

Analytics and optimization for emergency healthcare processes

This thesis deals with the analysis and management of emergency healthcare processes through the use of advanced analytics and optimization approaches. Emergency processes are among the most complex within healthcare. This is due to their non-elective nature and their high variability. This thesis is divided into two topics. The first one concerns the core of emergency healthcare processes, the emergency department (ED). In the second chapter, we describe the ED that is the case study. This is a real case study with data derived from a large ED located in northern Italy. In the next two chapters, we introduce two tools for supporting ED activities. The first one is a new type of analytics model. Its aim is to overcome the traditional methods of analyzing the activities provided in the ED by means of an algorithm that analyses the ED pathway (organized as event log) as a whole. The second tool is a decision-support system, which integrates a deep neural network for the prediction of patient pathways, and an online simulator to evaluate the evolution of the ED over time. Its purpose is to provide a set of solutions to prevent and solve the problem of the ED overcrowding. The second part of the thesis focuses on the COVID-19 pandemic emergency. In the fifth chapter, we describe a tool that was used by the Bologna local health authority in the first part of the pandemic. Its purpose is to analyze the clinical pathway of a patient and from this automatically assign them a state. Physicians used the state for routing the patients to the correct clinical pathways. The last chapter is dedicated to the description of a MIP model, which was used for the organization of the COVID-19 vaccination campaign in the city of Bologna, Italy.

Adapting fruit production to climate change: how to increase intrinsic fruit quality and save water through deficit irrigation scheduling.

Fruit crops are an important resource for food security, since more than being nutrient they are also a source of natural antioxidant compounds, such as polyphenols and vitamins. However, fruit crops are also among the cultivations threatened by the harmful effects of climate change This study had the objective of investigating the physiological effects of deficit irrigation on apple (2020-2021), sour cherry (2020-2021-2022) and apricot (2021-2022) trees, with a special focus on fruit nutraceutical quality. On each trial, the main physiological parameters were monitored along the growing season: i) stem and leaf water potentials; ii) leaf gas exchanges; iii) fruit and shoot growth. At harvest, fruit quality was evaluated especially in terms of fruit size, flesh firmness and soluble solids content. Moreover, it was performed: i) total phenolic content determination; ii) anthocyanidin concentration evaluation; and iii) untargeted metabolomic study. Irrigation scheduling in apricot, apple and sour cherry is surely overestimated by the decision support system available in Emilia-Romagna region. The water stress imposed on different fruit crops, each during two years of study, showed as a general conclusion that the decrease in the irrigation water did not show a straightforward decrease in plant physiological performance. This can be due to the miscalculation of the real water needs of the considered fruit crops. For this reason, there is the need to improve this important tool for an appropriate water irrigation management. Furthermore, there is also the need to study the behaviour of fruit crops under more severe deficit irrigations. In fact, it is likely that the application of lower water amounts will enhance the synthesis of specialized metabolites, with positive repercussion on human health. These hypotheses must be verified.

Spatial dynamics of team sports exposed by Voronoi diagrams

Team sports represent complex systems: players interact continuously during a game, and exhibit intricate patterns of interaction, which can be identified and investigated at both individual and collective levels. We used Voronoi diagrams to identify and investigate the spatial dynamics of players' behavior in Futsal. Using this tool, we examined 19 plays of a sub-phase of a Futsal game played in a reduced area (20 m(2)) from which we extracted the trajectories of all players. Results obtained from a comparative analysis of player's Voronoi area (dominant region) and nearest teammate distance revealed different patterns of interaction between attackers and defenders, both at the level of individual players and teams. We found that, compared to defenders, larger dominant regions were associated with attackers. Furthermore, these regions were more variable in size among players from the same team but, at the player level, the attackers' dominant regions were more regular than those associated with each of the defenders. These findings support a formal description of the dynamic spatial interaction of the players, at least during the particular sub-phase of Futsal investigated. The adopted approach may be extended to other team behaviors where the actions taken at any instant in time by each of the involved agents are associated with the space they occupy at that particular time.

Spatial multicriteria decision analysis for the siting of on-shore wind power in Kemiönsaari

Wind power is a low-carbon energy production form that reduces the dependence of society on fossil fuels. Finland has adopted wind energy production into its climate change mitigation policy, and that has lead to changes in legislation, guidelines, regional wind power areas allocation and establishing a feed-in tariff. Wind power production has indeed boosted in Finland after two decades of relatively slow growth, for instance from 2010 to 2011 wind energy production increased with 64 %, but there is still a long way to the national goal of 6 TWh by 2020. This thesis introduces a GIS-based decision-support methodology for the preliminary identification of suitable areas for wind energy production including estimation of their level of risk. The goal of this study was to define the least risky places for wind energy development within Kemiönsaari municipality in Southwest Finland. Spatial multicriteria decision analysis (SMCDA) has been used for searching suitable wind power areas along with many other location-allocation problems. SMCDA scrutinizes complex ill-structured decision problems in GIS environment using constraints and evaluation criteria, which are aggregated using weighted linear combination (WLC). Weights for the evaluation criteria were acquired using analytic hierarchy process (AHP) with nine expert interviews. Subsequently, feasible alternatives were ranked in order to provide a recommendation and finally, a sensitivity analysis was conducted for the determination of recommendation robustness. The first study aim was to scrutinize the suitability and necessity of existing data for this SMCDA study. Most of the available data sets were of sufficient resolution and quality. Input data necessity was evaluated qualitatively for each data set based on e.g. constraint coverage and attribute weights. Attribute quality was estimated mainly qualitatively by attribute comprehensiveness, operationality, measurability, completeness, decomposability, minimality and redundancy. The most significant quality issue was redundancy as interdependencies are not tolerated by WLC and AHP does not include measures to detect them. The third aim was to define the least risky areas for wind power development within the study area. The two highest ranking areas were Nordanå-Lövböle and Påvalsby followed by Helgeboda, Degerdal, Pungböle, Björkboda, and Östanå-Labböle. The fourth aim was to assess the recommendation reliability, and the top-ranking two areas proved robust whereas the other ones were more sensitive.