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Existing literature presents multiple perspectives on quality within organizational contexts. Although these perspectives may differ, they universally emphasize the importance of meeting customer expectations regarding products/services. Consequently, organizations are dedicated to addressing customer requirements to foster elevated satisfaction levels. This study aims to assess customer satisfaction in water laboratories and develop a predictive model using artificial neural networks to improve service quality. A methodology was devised, integrating principles from thermodynamics with logic programming for knowledge representation and reasoning. Data were collected from 412 participants of both genders, aged 22 to 79 years old, using a questionnaire covering six specific areas, i.e., customer service, quality of service provided, support documentation, technical support, billing and payment, and online services and tools. While customer opinions were largely positive, the study identified areas for improvement, including clarity and effectiveness in responses to inquiries, reliability of results, clarity of analysis reports, usefulness of test interpretation guidelines, inclusion of legal information, billing options, and online services. Differences in satisfaction were noted based on socio-demographic factors such as age and academic qualifications. The findings offer a framework (an ANN-based model) for future evaluations and improvements in services, highlighting the importance of addressing specific customer needs to enhance satisfaction. [ABSTRACT FROM AUTHOR]

Biotechnology-based technologies have the potential to act as catalysts for economic development by fostering innovation, creating new job opportunities, stimulating industry growth, and promoting environmental sustainability. This study aims to evaluate the perceptions of the Portuguese population regarding the economic impacts of using these technologies in areas such as the environment, energy resources, agriculture, industry, and health. For this purpose, a questionnaire was developed and distributed in Portugal to a sample consisting of 559 individuals of both genders, aged between 16 and 82 years old. The findings suggest that, although there is a higher perception of the economic impact of these technologies, participants reveal difficulties in perceiving impacts on health, industry, and energy resources. Moreover, metrics for quantifying participants' overall perception and improvement potential are provided. These metrics are particularly important as they enable the formation of participant groups with similar characteristics, facilitating the development of tailored intervention strategies. Additionally, a model based on artificial neural networks was presented to predict the perceptions of the Portuguese population regarding the economic impacts of using the mentioned technologies. The proposed model performs well, achieving accuracy rates of 93.0% for the training set and 90.9% for the test set. [ABSTRACT FROM AUTHOR]

In today's world, the importance of preserving the environment has become increasingly evident. As a result, more sustainable solutions and techniques are being developed to combat environmental destruction. Higher education institutions are now including environmental themes in their technological courses to promote sustainable behavior and indirectly enhance environmental literacy among the population. This study aims to evaluate the level of literacy to biotechnological solutions for environmental sustainability in four areas, namely Air Pollution, Aquatic Pollution, Global Warming, and Energy Resources. A questionnaire was developed and distributed to a sample consisting of 471 individuals of both genders, age range between 15 and 78 years old, to collect data characterizing the sample and assess their literacy in environmental issues. The questionnaire was distributed in Portugal, and the participants were asked to indicate their level of agreement with several statements related to the aforementioned environmental themes. The findings suggest that literacy regarding biotechnological solutions for environmental sustainability is influenced by age group and academic qualifications. The age group above 65 years old is the one with the lowest levels of literacy, exhibiting frequencies of response I don't know exceeding 50% in 10 out of the 22 issues present in the questionnaire. The findings also suggest that the levels of literacy are higher in the thematic areas of Global Warming and Aquatic Pollution and lower in the thematic areas of Air Pollution and Energy Resources, with lower levels of literacy in the issues that have not been widely disseminated by the media. Additionally, a model based on Artificial Neural Networks was presented to predict literacy to biotechnological solutions for environmental sustainability. The proposed model performs well, achieving accuracy rates of 90.8% for the training set and 86.6% for the test set. [ABSTRACT FROM AUTHOR]

A number of guidelines for Psychosocial Risk Management in organizations have been proposed in recent decades; however, some reviews on the subject also highlights that the terms Stress and Psychosocial Risks (PRs) are not mentioned explicitly in most pieces of legislation, leading to lack of clarity on the terminology used. To improve the way of dealing with this type of vulnerability and to allow organizations to successfully manage PRs , this work proposes and characterizes a workable problem-solving method in which the PRs can be evaluated for the entropy they generate within the organization. The analysis and development of such a system is based on a series of logical formalisms for Knowledge Representation and Reasoning that are grounded on Logic Programming , complemented with an Artificial Neural Networ k approach to computing. [ABSTRACT FROM AUTHOR]

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%). [ABSTRACT FROM AUTHOR]

Kidney renal failure means that one's kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient's history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9-94.2 %, respectively. [ABSTRACT FROM AUTHOR]

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Wild edible mushrooms Amanita ponderosa Malençon and Heim are very appreciated in gastronomy, with high export potential. This species grows in some microclimates, namely in the southwest of the Iberian Peninsula. The results obtained demonstrate that A. ponderosa mushrooms showed different inorganic composition according to their habitat and the molecular data, obtained by M13-PCR, allowed to distinguish the mushrooms at species level and to differentiate the A. ponderosa strains according to their location. Taking into account, on the one hand, that the characterisation of different strains is essential in further commercialisation and certification process and, on the other hand, the molecular studies are quite time consuming and an expensive process, the development of formal models to predict the molecular profile based on inorganic composition comes to be something essential. In the present work, Artificial Neural Networks (ANNs) were used to solve this problem. The ANN selected to predict molecular profile based on inorganic composition has a 6-7-14 topology. A good match between the observed and predicted values was observed. The present findings are wide potential application and both health and economical benefits arise from this study. [ABSTRACT FROM AUTHOR]

The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P1 (pH and conductivity), P2 (nitrate and manganese) and P3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P2 and included in group P3, for which the sampling frequency is lower, based on the chemical parameters included in group P1. In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R² values varying in the range 0.9960-0.9989 for the training set and 0.9993-0.9952 for the test set. [ABSTRACT FROM AUTHOR]

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions. [ABSTRACT FROM AUTHOR]

Human performance, in all its different dimensions, is a very complex and interesting topic. In this paper we focus on performance in the workplace which, asides from complex is often controversial. While organizations and generally competitive working conditions push workers into increasing performance demands, this does not necessarily correlates positively to productivity. Moreover, existing performance monitoring approaches (electronic or not) are often dreaded by workers since they either threat their privacy or are based on productivity measures, with specific side effects. We present a new approach for the problem of performance monitoring that is not based on productivity measures but on the workers' movements while sitting and on the performance of their interaction with the machine. We show that these features correlate with mental fatigue and provide a distributed architecture for the non-intrusive and transparent collection of this data. The easiness in deploying this architecture, its non-intrusive nature, the potential advantages for better human resources management and the fact that it is not based on productivity measures will, in our belief, increase the willingness of both organizations and workers to implement this kind of performance management initiatives. [ABSTRACT FROM AUTHOR]

Fatigue, especially in its mental form, is one of the most worrying health problems nowadays. It affects not only health but also motivation, emotions and feelings and has an impact both at the individual and organizational level. Fatigue monitoring and management assumes thus, in this century, an increased importance, that should be promoted by private organizations and governments alike. While traditional approaches are mostly based on questionnaires, in this paper we present an alternative one that relies on the observation of the individual's interaction with the computer. We show that this interaction changes with the onset of fatigue and that these changes are significant enough to support the training of a neural network that can classify mental fatigue in real time. The main outcome of this work is the development of non-invasive systems for the continuous classification of mental fatigue that can support effective and efficient fatigue management initiatives, especially in the context of desk jobs. [ABSTRACT FROM AUTHOR]

Abstract: Although Artificial Neural Networks (ANNs) are important data mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input–output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms. [Copyright &y& Elsevier]

Abstract: The combined effect of incubation time (IT) and aspartic acid concentration (AA) on the predicted biomass concentration (BC), Bacillus sporulation (BS) and anti-fungal activity of compounds (AFA) produced by Bacillus amyloliquefaciens CCMI 1051, was studied using Artificial Neural Networks (ANNs). The values predicted by ANN were in good agreement with experimental results, and were better than those obtained when using Response Surface Methodology. The database used to train and validate ANNs contains experimental data of B. amyloliquefaciens cultures (AFA, BS and BC) with different incubation times (1–9days) using aspartic acid (3–42mM) as nitrogen source. After the training and validation stages, the 2–7-6–3 neural network results showed that maximum AFA can be achieved with 19.5mM AA on day 9; however, maximum AFA can also be obtained with an incubation time as short as 6days with 36.6mM AA. Furthermore, the model results showed two distinct behaviors for AFA, depending on IT. [ABSTRACT FROM AUTHOR]

Summary: Objective: The main intensive care unit (ICU) goal is to avoid or reverse the organ failure process by adopting a timely intervention. Within this context, early identification of organ impairment is a key issue. The sequential organ failure assessment (SOFA) is an expert-driven score that is widely used in European ICUs to quantify organ disorder. This work proposes a complementary data-driven approach based on adverse events, defined from commonly monitored biometrics. The aim is to study the impact of these events when predicting the risk of ICU organ failure. Materials and methods: A large database was considered, with a total of 25,215 daily records taken from 4425 patients and 42 European ICUs. The input variables include the case mix (i.e. age, diagnosis, admission type and admission from) and adverse events defined from four bedside physiologic variables (i.e. systolic blood pressure, heart rate, pulse oximeter oxygen saturation and urine output). The output target is the organ status (i.e. normal, dysfunction or failure) of six organ systems (respiratory, coagulation, hepatic, cardiovascular, neurological and renal), as measured by the SOFA score. Two data mining (DM) methods were compared: multinomial logistic regression (MLR) and artificial neural networks (ANNs). These methods were tested in the R statistical environment, using 20 runs of a 5-fold cross-validation scheme. The area under the receiver operator characteristic (ROC) curve and Brier score were used as the discrimination and calibration measures. Results: The best performance was obtained by the ANNs, outperforming the MLR in both discrimination and calibration criteria. The ANNs obtained an average (over all organs) area under the ROC curve of 64, 69 and 74% and Brier scores of 0.18, 0.16 and 0.09 for the dysfunction, normal and failure organ conditions, respectively. In particular, very good results were achieved when predicting renal failure (ROC curve area of 76% and Brier score of 0.06). Conclusion: Adverse events, taken from bedside monitored data, are important intermediate outcomes, contributing to a timely recognition of organ dysfunction and failure during ICU length of stay. The obtained results show that it is possible to use DM methods to get knowledge from easy obtainable data, thus making room for the development of intelligent clinical alarm monitoring. [Copyright &y& Elsevier]

Summary: Objective: This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. Materials and methods: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires 17 static variables (e.g. serum sodium), which are collected within the first day of the patient’s admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13,164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. Results: The event based models predicted the outcome more accurately than the currently used SAPS II model (), with ROC areas within the ranges 83.9–87.1% (ANN) and 82.6–85.2% (LR) versus 80% (LR SAPS II). When using the same inputs, the ANNs outperform the LR (improvement of 1.3–2%). Conclusion: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data. [Copyright &y& Elsevier]