LTLf and LDLf Monitoring: A Technical Report

Runtime monitoring is one of the central tasks to provide operational decision support to running business processes, and check on-the-fly whether they comply with constraints and rules. We study runtime monitoring of properties expressed in LTL on finite traces (LTLf) and in its extension LDLf. LDLf is a powerful logic that captures all monadic second order logic on finite traces, which is obtained by combining regular expressions and LTLf, adopting the syntax of propositional dynamic logic (PDL). Interestingly, in spite of its greater expressivity, LDLf has exactly the same computational complexity of LTLf. We show that LDLf is able to capture, in the logic itself, not only the constraints to be monitored, but also the de-facto standard RV-LTL monitors. This makes it possible to declaratively capture monitoring metaconstraints, and check them by relying on usual logical services instead of ad-hoc algorithms. This, in turn, enables to flexibly monitor constraints depending on the monitoring state of other constraints, e.g., "compensation" constraints that are only checked when others are detected to be violated. In addition, we devise a direct translation of LDLf formulas into nondeterministic automata, avoiding to detour to Buechi automata or alternating automata, and we use it to implement a monitoring plug-in for the PROM suite

Thermal Comfort and Climatic Potential of Ventilative Cooling in Italian Climates

The chapter describes several climate-correlated variables and suitable key performance indicators (KPIs) to define the local ventilative cooling potential. Furthermore, a methodology is presented to verify potential correlations between climate KPIs and indoor comfort parameters. The latter values are calculated by adopting dynamic energy simulations (EnergyPlus) and comfort models – both Fanger (ISO 7730) and the recently updated EU adaptive comfort approach (EN 16798-1) – considering a sample building unit. Simulations are run by using a parametric-enabling tool developed by the research unit to check correlations and is part of work performed for the PRELUDE project, co-funded by the EU, Horizon 2020 research and innovation programme under grant agreement No 958345. The approach is applied to the whole Italian territory considering typical yearly (hourly defined) meteorological conditions for all municipalities (about 8000 data points). Strong connections between climate and building KPIs are underlined together with the high potential of ventilative cooling in reducing discomfort and energy needs in the Italian territory

Development of a Particle-In-Cell code with Structured Adaptive Mesh Refinement for Plasma Focus devices breakdown simulation

The aim at simulating the breakdown phase of a Plasma Focus (PF) discharge follows the need to fully understand the dynamics of such device, in order to retrieve useful information for the design and optimization of the machine itself. PFs are compact devices able to generate, accelerate, compress and confine a plasma by means of strongly varying electric and magnetic fields. In the final phase of the discharge, the generated plasma collapses in a high density region (the focus) where nuclear reactions occur. The choice of the gases composing the plasma tunes the nuclear reactions in order to characterize the device as a possible neutron-free Short-Life Radioisotopes (SLRs) generator for PET (f.i. 18F and 15O), as well as a neutrons or collimated-electrons-beams source for radio-therapy applications. An electrostatic-collisional Particle-In-Cell (PIC) code for Plasma Focus devices (es-cPIF) has already been developed to investigate the breakdown phenomenon and the formation of the plasma seed, the preliminary plasma spot, within the device: the exact knowledge of the phase space distribution function (strongly deviating from the Maxwellian equilibrium one) is a fundamental basis indeed for the whole discharge simulation. In order to extend the present simulations towards the complete evolution of the plasma seed into a running plasma sheath, the code is being re-structured for strong parallelization and inclusion of Structured Adaptive Mesh Refinement (SAMR) capabilities. In this paper the development frame as well as the software design architecture are presented together with the features that will be provided by the new SAMRes-cPIF code

Simulated Versus Monitored Building Behaviours: Sample Demo Applications of a Perfomance Gap Detection Tool in a Northern Italian Climate

Green building technologies and design-correlated choices may significantly contribute to supporting the transition toward net energy flows in the built environment. Nevertheless, large discrepancies are underlined between standard simulated and monitored building behaviours requiring approaches able to simply correlate real building behaviours and simulated ones to further support coherent certification and/or optimization. The paper focusses on the application of a semi-automatic methodology to compare and evaluate thermal behaviours of buildings considering monitored and simulated data. The approach is based on a new Python tool developed by the authors, able to manage EnergyPlus inputs and perform multi-source KPIs calculations. The mentioned tool is used here to support semi-automatic model verifications of real weather data by optimizing model parameters to fit monitored behaviours. The approach is applied in this chapter to two demo buildings, a municipality school and a residential unit, located in the Turin metropolitan area of Piedmont, in Northwest Italy

Energy simulation platform supporting building design and management

The paper describes specific usage scenarios of an innovative platform, interfacing users, monitoring, and simulations for building energy simulations and the computation of key performance indicators to support, in an interoperable and open vision, design and management choices exploiting the enabling capabilities of ICT in architecture. The modularity of the proposed solution allows the development of pre-defined usage scenarios for professionals: impact of modifications in technological design choices, model calibration, and performance gap between simulated and monitored building data. The paper faces some new architectural usage scenarios of the tool, considering its enabling capabilities, and focuses on the tool’s components developed and tested in the EU H2020 E-DYCE project

Recurrence of Mycosis Fungoides on Multiple Melanocytic Nevi: A Case Report and Review of the Literature

Melanocytic nevi represent a widespread cutaneous finding. Nevertheless, the presence of mycosis fungoides and melanocytic nevi in the same location is an extremely rare event. We report the case of a patient affected by mycosis fungoides and treated with PUVA therapy, with complete remission of the disease. Eight years after therapy discontinuation, he presented epidermal scaling and an erythematous perinevic halo on 3 old melanocytic lesions, the clinical aspect of which was highly suggestive for Meyerson nevi. The histological and immunohistochemical examination of an excised melanocytic lesion revealed histological features consistent with the diagnosis of mycosis fungoides superimposed on junctional melanocytic nevi. The finding of patches of mycosis fungoides superimposed on melanocytic nevi is a rare event; the confounding clinical appearance with eczematous changes around a pre-existing nevus may recall the halo dermatitis known as Meyerson phenomenon; this highlights the importance of clinical and histological examination to make the correct diagnosis of dermatological diseases

Informed classification of sweeteners/bitterants compounds via explainable machine learning

Perception of taste is an emergent phenomenon arising from complex molecular interactions between chemical compounds and specific taste receptors. Among all the taste perceptions, the dichotomy of sweet and bitter tastes has been the subject of several machine learning studies for classification purposes. While previous studies have provided accurate sweeteners/bitterants classifiers, there is ample scope to enhance these models by enriching the understanding of the molecular basis of bitter-sweet tastes. Towards these goals, our study focuses on the development and testing of several machine learning strategies coupled with the novel SHapley Additive exPlanations (SHAP) for a rational sweetness/bitterness classification. This allows the identification of the chemical descriptors of interest by allowing a more informed approach toward the rational design and screening of sweeteners/bitterants. To support future research in this field, we make all datasets and machine learning models publicly available and present an easy-to-use code for bitter-sweet taste prediction

A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements

Nutritional surveillance in Tuscany: maternal perception of nutritional status of 8-9 y-old school-children

Introduction. Overweight and obesity in the developmental age has become a public health problem. For this reason, prevention projects must be developed in advance with the aim to involve not only children, but their parents as well. Our objective is to evaluate the accuracy of the mothers? perceptions of adolescent nutritional status. Methods. Cross-sectional study. We selected a statistical sample of 3,076 subjects (1,583 males, 1,493 females), 8-9 y-old school-children of 164 3rd-grade elementary school classes from throughout Tuscany, as well as their mothers. The mothers? information was gathered via self-administered questionnaires, while the children were given an eating behaviour survey under the supervision of qualified personnel. Mothers? education level (self-reported) height and weight were collected; children?s height and weight were measured. The former were asked how they perceived their children?s body image. Results. A correlation exists between the mothers? perceptions of the nutritional state of their children via the silhouettes and the BMI classes of the children, which is equal to 80% with a k-Cohen for agreement equal to 0.58 (SE = 0.02; P : 0.0001). However, no correlation exists between the mothers? responses to the question ?In your opinion, is your child ??? and the child?s actual BMI class (the exact percentage correlation is equal to 75%, with a k-Cohen for agreement equal to 0.43 SE = 0.014; P : 0.0001). Discussion. Mothers have an accurate perception of the nutritional status of their children, correctly choosing the silhouette that corresponds to the child?s BMI profile without variation by gender. We can assume that mothers in our sample have a good concept about healthy nutritional status

Nutritional Surveillance in Tuscany: eating habits at breakfast, mid-morning and afternoon snacks among 8-9 y-old children

Introduction.The prevalence of overweight and obesity in children is rapidly increasing in many countries. For that it has been interesting to investigate the eating habits of 8-9 y-old Tuscany children by paying attention to their meals frequency per day and their food choices in total and in relation to children?s Body Mass Index (BMI) classes. In addition we considered some environment factors that could affect the children eating behaviours, such as mother?s BMI and their education level. Methods. A statistical sample of 3,076 (1,583 males, 1,493 females), 8-9 year-old school-children was collected; weight and height were measured using standardized personnel and instruments. BMI classes were calculated using Cole et al.?s cutoff for children and adolescents. In order to evaluate the consumption frequency of individual meals and various foods, a Food Frequency Questionnaire (FFQ) was used, which was completed by the children themselves at school. A self-administered questionnaire revealed the weight and height of parents and their educational levels. Three educational levels were established: high, medium and low. Results. The results showed that 92.3% of children ate breakfast from 4-7 times a week, the vast majority at home, while only 3% declared consuming breakfast never or almost never. The most preferred breakfast consisted of milk and biscuits for all children?s BMI classes. 95.9% of children reported having mid-morning snack at school; fruit juice and tea are the most frequently consumed liquid foods, and pizza, salami sandwiches and pre-packaged snacks are the most frequently consumed solid foods in all BMI classes. 93.6% ate afternoon snack for the most part at home, even if 12% of children reported consuming it elsewhere; fruit juice and tea with pizza, sandwiches and pre-packaged snacks are still the most highly consumed foods by all children?s BMI classes. The consumption frequency of breakfast (P inf. 0.001), mid-morning (P inf. 0.05) and afternoon snack (P inf. 0.05) of 8-9 y-old Tuscany children decrease with increase the children?s BMI classes. The same tendency may be noted for the consumption frequency of breakfast in relation to mother?s BMI (P inf. 0.05) and their education level (P inf. 0.05). This data strengthens the thesis that some home environments can affect the children?s eating behaviours. Conclusion. No substantial differences in food choices at the meals analyzed were determined among normal weight, over weight and obese children. Children of normal weight had a greater tendency to consume meals more regularly. Mother?s BMI and their education level can have influence on children?eating behaviours