A novel ranking procedure for forecasting approaches using Data Envelopment Analysis

To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreement on which approach should be used. Generally forecasting measures represent ratios or percentages providing an overall image of how well fitted the forecasting technique is to the observations. This paper proposes a multiplicative Data Envelopment Analysis (DEA) model in order to rank several forecasting techniques. We demonstrate the proposed model by applying it to the set of yearly time series of the M3 competition. The usefulness of the proposed approach has been tested using the M3-competition where five error measures have been applied in and aggregated to a single DEA score.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Coastal modelling studies for forecasting and remediation solutions

The coastal area along the Emilia-Romagna (ER), in the Italian side of the northern Adriatic Sea, is considered to implement an unstructured numerical ocean model with the aim to develop innovative tools for the coastal management and a forecasting system for the storm surge risk reduction. The Adriatic Sea has been the focus of several studies because of its peculiar dynamics driven by many forcings acting at basin and local scales. The ER coast is particularly exposed to storm surge events. In particular conditions, winds, tides and seicehs may combine and contribute to the flooding of the coastal area. The global sea level rise expected in the next decades will increase even more the hazard along the ER and Adriatic coast. Reliable Adriatic and Mediterranean scale numerical ocean models are now available to allow the dynamical downscaling of very high-resolution models in limited coastal areas. In this work the numerical ocean model SHYFEM is implemented in the Goro lagoon (named GOLFEM) and along the ER coast (ShyfER) to test innovative solutions against sea related coastal hazards. GOLFEM was succesfully applied to analyze the Goro lagoon dynamics and to assess the dynamical effects of human interventions through the analysis of what-if scenarios. The assessment of storm surge hazard in the Goro lagoon was carried out through the development of an ensemble storm surge forecasting system with GOLFEM using forcing from different operational meteorological and ocean models showing the fundamental importance of the boundary conditions. The ShyfER domain is used to investigate innovative solutions against storm surge related hazard along the ER coast. The seagrass is assessed as a nature-based solution (NBS) for coastal protection under present and future climate conditions. The results show negligible effects on sea level but sensible effects in reducing bottom current velocity.

Urban air quality: new high-resolution modeling approaches and forecasting tools for citizens

Air pollution is one of the greatest health risks in the world. At the same time, the strong correlation with climate change, as well as with Urban Heat Island and Heat Waves, make more intense the effects of all these phenomena. A good air quality and high levels of thermal comfort are the big goals to be reached in urban areas in coming years. Air quality forecast help decision makers to improve air quality and public health strategies, mitigating the occurrence of acute air pollution episodes. Air quality forecasting approaches combine an ensemble of models to provide forecasts from global to regional air pollution and downscaling for selected countries and regions. The development of models dedicated to urban air quality issues requires a good set of data regarding the urban morphology and building material characteristics. Only few examples of air quality forecast system at urban scale exist in the literature and often they are limited to selected cities. This thesis develops by setting up a methodology for the development of a forecasting tool. The forecasting tool can be adapted to all cities and uses a new parametrization for vegetated areas. The parametrization method, based on aerodynamic parameters, produce the urban spatially varying roughness. At the core of the forecasting tool there is a dispersion model (urban scale) used in forecasting mode, and the meteorological and background concentration forecasts provided by two regional numerical weather forecasting models. The tool produces the 1-day spatial forecast of NO2, PM10, O3 concentration, the air temperature, the air humidity and BLQ-Air index values. The tool is automatized to run every day, the maps produced are displayed on the e-Globus platform, updated every day. The results obtained indicate that the forecasting output were in good agreement with the observed measurements.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.

A coupled ocean-atmosphere system for short term forecasting in the southern european seas

This coupled model combines two state-of-the-art numerical models, NEMO for the oceanic component and WRF for the atmospheric component and implements them at an appropriate resolution. The oceanic model has been implemented starting from the Mediterranean Forecasting System with a resolution of 1/24° and the domain was extended to exactly match the grid of a newly implemented atmospheric model for the same area. The uncoupled ocean model has been validated against SST observed data, both in the simulation of an extreme event and in the short-term forecast of two seasonal periods. A new setup of the model was successfully tested in which the downward radiative fluxes were prescribed from atmospheric forecasts. Various physical schemes, domain, boundary, and initial conditions were tested with the atmospheric model to obtain the best representation of medicane Ianos. The heat fluxes calculated by the uncoupled models were compared to determine which setup gave the best energy balance between the components of the coupled model. The coupling strategy used is the traditional one, where the ocean is driven by the surface stress, heat fluxes, and radiative fluxes computed in the atmospheric component, which in turn receives the SST and surface currents. As expected, the overall skills of the coupled model are slightly degraded compared to the uncoupled models, even though the positioning and timing of the cyclone at the time of the landfall is enhanced. The mean heat fluxes do not change compared to the uncoupled model, whereas the pattern of the shortwave radiation and latent heat is changed. Moreover, the two energy fluxes are larger in absolute values than those calculated with the MFS formulas. The fact that they have opposite signs give raise to a compensation error that limits the overall degradation of the coupled simulation.

Sales Forecasting e Demand Planning all'interno del processo di Sales & Operations Planning: il caso Cefla s.c.

Il presente elaborato di tesi è stato realizzato coerentemente con quanto osservato in Cefla s.c., azienda italiana composta attualmente da 4 Business Unit che operano a livello internazionale in settori distinti. I temi trattati riguardano nel dettaglio la Business Unit Medical Equipment, la quale realizza prodotti a supporto del professionista sanitario in tutte le fasi della sua attività, comprendendo riuniti odontoiatrici, apparecchiature per l’imaging e radiologia digitale e sistemi di sterilizzazione. L’obiettivo di questo elaborato è quello di descrivere l’attuale processo di Sales & Operations Planning all’interno di questa divisione dell’azienda e contribuire alla progettazione del piano per la sua strutturazione, reso necessario dalla situazione di forte criticità che Cefla s.c. è stata costretta ad affrontare. Vengono quindi descritte le problematiche che caratterizzano i processi interni all’azienda allo stato attuale, la cui valutazione è stata supportata da consulenti esterni, al fine di evidenziare gli aspetti più critici ed elaborare proposte di miglioramento. Queste ultime sono distinte in funzione delle diverse figure coinvolte che hanno contribuito alla loro realizzazione e ai sottoprocessi interessati e che costituiscono il Sales & Operations Planning: Sales Forecasting, Demand Planning e Supply Planning. In particolare, vengono approfonditi i processi che riguardano la previsione della domanda, in quanto per essi è stato possibile collaborare nell’elaborazione di proposte di miglioramento mirate. Visti i tempi medio lunghi che caratterizzano le soluzioni proposte all’azienda si è cercato di contribuire con la progettazione di proposte quick-win in ambito di Sales Forecasting e Demand Planning. Infine, si è tentato di quantificare i costi sostenuti da Cefla s.c. per far fronte alla situazione di criticità affrontata tramite valutazioni economiche e KPI, potendo così stimare l’impatto dato dall’implementazione di proposte di miglioramento.

Imported Malaria In A Non-endemic Area: The Experience Of The University Of Campinas Hospital In The Brazilian Southeast.

Although malaria in Brazil almost exclusively occurs within the boundaries of the Amazon Region, some concerns are raised regarding imported malaria to non-endemic areas of the country, notably increased incidence of complications due to delayed diagnoses. However, although imported malaria in Brazil represents a major health problem, only a few studies have addressed this subject. A retrospective case series is presented in which 263 medical charts were analysed to investigate the clinical and epidemiological characterization of malaria cases that were diagnosed and treated at Hospital & Clinics, State University of Campinas between 1998 and 2011. Amongst all medical charts analysed, 224 patients had a parasitological confirmed diagnosis of malaria. Plasmodium vivax and Plasmodium falciparum were responsible for 67% and 30% of the infections, respectively. The majority of patients were male (83%) of a productive age (median, 37 years old). Importantly, severe complications did not differ significantly between P. vivax (14 cases, 9%) and P. falciparum (7 cases, 10%) infections. Severe malaria cases were frequent among imported cases in Brazil outside of the Amazon area. The findings reinforce the idea that P. vivax infections in Brazil are not benign, regardless the endemicity of the area studied. Moreover, as the hospital is located in a privileged site, it could be used for future studies of malaria relapses and primaquine resistance mechanisms. Finally, based on the volume of cases treated and the secondary complications, referral malaria services are needed in the non-endemic areas of Brazil for a rapid and efficient and treatment.

Respiratory Viruses Are Continuously Detected In Children With Chronic Tonsillitis Throughout The Year.

To evaluate the oscillations on the viral detection in adenotonsillar tissues from patients with chronic adenotonsillar diseases as an indicia of the presence of persistent viral infections or acute subclinical infections. Cross-sectional prospective study. Tertiary hospital. The fluctuations of respiratory virus detection were compared to the major climatic variables during a two-year period using adenoids and palatine tonsils from 172 children with adenotonsillar hypertrophy and clinical evidence of obstructive sleep apnoea syndrome or recurrent adenotonsillitis, without symptoms of acute respiratory infection (ARI), by TaqMan real-time PCR. The rate of detection of at least one respiratory virus in adenotonsillar tissue was 87%. The most frequently detected viruses were human adenovirus in 52.8%, human enterovirus in 47.2%, human rhinovirus in 33.8%, human bocavirus in 31.1%, human metapneumovirus in 18.3% and human respiratory syncytial virus in 17.2%. Although increased detection of human enterovirus occurred in summer/autumn months, and there were summer nadirs of human respiratory syncytial virus in both years of the study, there was no obvious viral seasonality in contrast to reports with ARI patients in many regions of the world. Respiratory viruses are continuously highly detected during whole year, and without any clinical symptomatology, indicating that viral genome of some virus can persist in lymphoepithelial tissues of the upper respiratory tract.

Effect Of Composite Containing An Iodonium Salt On The Bond Strength Of Brackets To Bovine Enamel.

This study investigated the effect of the incorporation of an iodonium salt in experimental composites, on the bond strength of metallic brackets bonded to bovine teeth. Two hundred and seventy bovine teeth were embedded in self-curing acrylic resin and divided into 18 groups (n=15), according to the experimental composite with an iodonium salt at molar concentrations 0 (control), 0.5, or 1%; the light-activation times (8, 20 and 40 s); and the storage times (10 min or 24 h). Metallic brackets were fixed on the tooth surface using experimental composites. Photoactivation was performed with a quartz-tungsten-halogen light-curing unit curing unit for 8, 20 and 40 s. The specimens were stored in distilled water at 37 °C for 10 min or 24 h and submitted to bond strength test at 0.5 mm/min. The data were subjected to three-way ANOVA and Tukey's test (α=0.05). The Adhesive Remnant Index (ARI) was used to classify the failure modes. The shear bond strengths (MPa) at 10 min for light-activation times of 8, 20 and 40 s were: G1 - 4.6, 6.9 and 7.1; G2 - 8.1, 9.2 and 9.9; G3 - 9.1, 10.4 and 10.7; and at 24 h were: G1 - 10.9, 11.1 and 11.7; G2 - 11.8, 12.7 and 14.2; G3 - 12.1, 14.4 and 15.8. There was a predominance of ARI score 3 for groups with 10 min storage time, and ARI score 2 for groups with 24 h storage time. In conclusion, the addition of iodonium salt (C05 and C1) to the experimental composite may increase the bond strength of brackets to bovine enamel using reduced light exposure times.

Genetic Diversity Of Giardia Duodenalis: Multilocus Genotyping Reveals Zoonotic Potential Between Clinical And Environmental Sources In A Metropolitan Region Of Brazil.

Giardia duodenalis is a flagellate protozoan that parasitizes humans and several other mammals. Protozoan contamination has been regularly documented at important environmental sites, although most of these studies were performed at the species level. There is a lack of studies that correlate environmental contamination and clinical infections in the same region. The aim of this study is to evaluate the genetic diversity of a set of clinical and environmental samples and to use the obtained data to characterize the genetic profile of the distribution of G. duodenalis and the potential for zoonotic transmission in a metropolitan region of Brazil. The genetic assemblages and subtypes of G. duodenalis isolates obtained from hospitals, a veterinary clinic, a day-care center and important environmental sites were determined via multilocus sequence-based genotyping using three unlinked gene loci. Cysts of Giardia were detected at all of the environmental sites. Mixed assemblages were detected in 25% of the total samples, and an elevated number of haplotypes was identified. The main haplotypes were shared among the groups, and new subtypes were identified at all loci. Ten multilocus genotypes were identified: 7 for assemblage A and 3 for assemblage B. There is persistent G. duodenalis contamination at important environmental sites in the city. The identified mixed assemblages likely represent mixed infections, suggesting high endemicity of Giardia in these hosts. Most Giardia isolates obtained in this study displayed zoonotic potential. The high degree of genetic diversity in the isolates obtained from both clinical and environmental samples suggests that multiple sources of infection are likely responsible for the detected contamination events. The finding that many multilocus genotypes (MLGs) and haplotypes are shared by different groups suggests that these sources of infection may be related and indicates that there is a notable risk of human infection caused by Giardia in this region.

Saphenous vein graft bypass in the treatment of giant cavernous sinus aneurysms: report of two cases

Two cases of giant intracavernous aneurysms treated by high flow bypass with saphenous vein graft between the external carotid artery (ECA) and branches of the middle cerebral artery (MCA) are presented. Very often these aneurysms are unclippable because they are fusiform or have a large neck. Occlusion of the internal carotid artery (ICA) is the treatment of choice in many cases. This procedure has however a high risk of brain infarction. Revascularization of the brain by extra-intracranial anastomosis between the superficial temporal artery (STA) and branches of the MCA is frequently performed. This procedure provides however a low flow bypass and brain infarction may occur. We report two cases of giant cavernous sinus aneurysms treated by high flow bypass and endovascular balloon occlusion of the ICA. Immediate high flow revascularization of MCA branches was achieved and the patients showed no ischemic events. Follow-up of 8 and 14 months after operation shows patency of the venous graft and no neurological deficits. Angiographic control examination showed complete aneurysm occlusion in both cases.

Metformina interage com o treinamento físico diminuindo a glicemia e aumentando o armazenamento de glicogênio em ratos diabéticos

INTRODUCTION: Like in humans, lower amounts of glycogen are present in tissues of diabetic rats. However, training or drugs that lower glycemia can improve the metabolic control. Metformin increased glycogen while decreased glycemia in normal rats stressed by exercise. OBJECTIVE: In this work we investigated if regular exercise and metformin effects improve the metabolism of diabetic rats. METHODS: Alloxan diabetic Wistar rats treated with metformin (DTM) or not (DT) were trained. Training consisted of 20 sessions of 30 min, 5 days a week. Sedentary diabetic rats served as control (SD and SDM). Metformin (5.6 µg/g) was given in the drinking water. After 48 h resting, glucose (mg/dl) and insulin (ng/mL) was measured in plasma and glycogen (mg/100 mg of wet tissue) in liver, soleus and gastrocnemius. RESULTS: Glycemia decreased in DM group from 435±15 to 230±20, in DT group to 143±8.1 and in DTM group to 138±19 mg/dl. DM group had proportional increase in the hepatic glycogen from 1.69±0.22 to 3.53±0.24, and the training increased to 3.36 ± 0.16 mg/100 mg. Metformin induced the same proportional increase in the muscles (soleus from 0.21±0.008 to 0.42±0.03 and gastrocnemius from 0.33±0.02 to 0.46±0.03), while the training promoted increase on gastrocnemius to 0,53 ± 0,03, only. A high interaction was observed in liver (glycogen increased to 6.48±0.34). CONCLUSION: Very small oral doses of metformin and/or, partially restored glycemia in diabetic rats and decreased glycogen in tissues. Its association with an exercise program was beneficial, helping lower glycemia further and increase glycogen stores on liver of diabetic rats.

Familial glioblastoma

The authors describe a family with three members affected by glioblastoma. The proband patient, a 7 year-old girl, developed a rare complication, a pulmonary metastasis. Chromosomal analysis of her peripheral blood lymphocytes showed a normal karyotype (46, XX), without structural abnormalities. Cytogenetic study of the tumor cells disclosed several abnormalities: 46, XX, 7q - / 46, XX, -2, 4p-, 7p-, +15/ 46, XX. Some aspects about genetics of glial neoplasms are discussed.