Influence of envelope design in the optimization of the operational energy costs of a multi-family building

The international efforts for improving energy efficiency in buildings and reducing their environmental impact also constitute a challenge for working against the risk of energy poverty. The work aims to test a methodology for optimizing the operational costs of the different flats of a multi-family building for social housing. The method combines the use of TRNSYS building energy simulation program with GenOpt Generic Optimization program in a so-called simulation-based optimization method. A typical floor of a real case study building was modeled and the energy costs for heating and cooling due to the variation of design variables related to the building envelope was studied. The optimization led to reduce the total operational costs of the flats by the range 17%-23%. The different share of heating, cooling, ventilation and DHW in the total operational costs was studied and resulted differences in energy rating and costs between flats were analyzed

Influence of envelope design in the optimization of the energy performance of a multi-family building

In Europe, the recast of the Directive on the Energy Performance of Building and the consequent Zero Energy Buildings objective that has to be reached for all new buildings by 2020, lead designers to re-think building design as a complex optimization problem aimed at identifying the most effective strategies to improve building performance.These strategies can help reducing not only the climate change effect, but also the risk of energy poverty for low-income households. This work is intended to apply a simulation-based optimization methodology for optimizingthe energy performance of a multi-family building for social housing. The method combines the use of TRNSYS® withGenOpt®. A typical floor of a real case study was modeled and the impacts of the variation of several design parameters on the heating and cooling demand wereassessed.The optimization lead to reduce the primary energy demand of a floor by 36%. The resulted differences in performance and energy rating between flats were analyzed

Memorization test and resting state EEG components in mild and subjective cognitive impairment

BACKGROUND: Mild (MCI) and Subjective Cognitive Impairment (SCI) are conditions at risk of developing Alzheimer's disease (AD). Differential between normal aging at early stages can be really challenging; available biomarkers need to be combined and can be quite invasive and expensive. OBJECTIVE: The aim of this pilot study is to examine possible EEG alterations in MCI and SCI compared to controls, analyzing if a cognitive task could highlight early AD hallmarks. METHOD: We recruited 11 MCI, 8 SCI and 7 healthy subjects as controls (CS), all matched for age and education. Neuropsychological assessment and EEG recording, at resting state and during a mental memory task, were performed. Classical spectral measures and nonlinear parameters were used to characterize EEGs. RESULTS: During cognitive task, \u3b1-band power reduction was found predominantly in frontal regions in SCI and CS, diffused to all regions in MCI; moreover, decreased EEG complexity was found in SCI compared to controls. The \u3b1 -band power attenuation restricted to frontal regions in SCI during a free recall task (involving frontal areas), suggests that MCI patients compensate for encoding deficit by activating different brain networks to perform the same task. Furthermore, EEG complexity reduction - that has been found already in SCI - could be a possible early hallmark of AD. CONCLUSION: This study draws attention on the importance of nonlinear approach in EEG analysis and the potential role of cognitive task in highlighting EEG alterations at very early stages of cognitive impairment; EEG could therefore have a practical impact on dementia diagnosis

A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images

Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022. The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels. Automated detection can in fact save time and resources compared to manual inspection. Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid. However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations. In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy. The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category. We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques. Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand. Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of scales of the installations and the sparsity of the annotations, considerably improving upon the baseline results. Last, we introduce a robust and efficient post-processing polygonization algorithm that is tailored to PV panels. This algorithm converts the rough raster predictions into cleaner and more precise polygons for practical use. Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels. Instance segmentation techniques are well-suited for estimating the localization of panels, while semantic solutions excel at surface delineation. We also demonstrate the effectiveness of our ad-hoc solutions and post-processing algorithm, which can provide an improvement up to +10% on the final scores, and can accurately convert coarse raster predictions into usable polygons

Effect of Transcranial Magnetic Stimulation (TMS) on Parietal and Premotor Cortex during Planning of Reaching Movements

The activation of the superior parietal lobule (SPL) and premotor cortex (PM) has been investigated using transcranial magnetic stimulation (TMS) during planning of reaching movements under visual guidance. A facilitory effect was found when TMS was delivered on the parietal cortex at about half of the time from sight of the target to hand movement, independently of target location in space. Furthermore, at the same stimulation time, a similar facilitory effect was found in PM, which is probably related to movement preparation. This data contributes to the understanding of cortical dynamics in the parieto-frontal network, and suggests that it is possible to interfere with the planning of reaching movements at different cortical points within a particular time window. Since similar effects may be produced at similar times on both the SPL and PM, parallel processing of visuomotor information is likely to take place in these regions

Altered Modulation of Silent Period in Tongue Motor Cortex of Persistent Developmental Stuttering in Relation to Stuttering Severity

Motor balance in developmental stuttering (DS) was investigated with Transcranial Magnetic Stimulation (TMS), with the aim to define novel neural markers of persistent DS in adulthood. Eleven DS adult males were evaluated with TMS on tongue primary motor cortex, compared to 15 matched fluent speakers, in a "state" condition (i.e. stutterers vs. fluent speakers, no overt stuttering). Motor and silent period thresholds (SPT), recruitment curves, and silent period durations were acquired by recording tongue motor evoked potentials. Tongue silent period duration was increased in DS, especially in the left hemisphere (P<0.05; Hedge's g or Cohen's dunbiased = 1.054, i.e. large effect size), suggesting a "state" condition of higher intracortical inhibition in left motor cortex networks. Differences in motor thresholds (different excitatory/inhibitory ratios in DS) were evident, as well as significant differences in SPT. In fluent speakers, the left hemisphere may be marginally more excitable than the right one in motor thresholds at lower muscular activation, while active motor thresholds and SPT were higher in the left hemisphere of DS with respect to the right one, resulting also in a positive correlation with stuttering severity. Pre-TMS electromyography data gave overlapping evidence. Findings suggest the existence of a complex intracortical balance in DS tongue primary motor cortex, with a particular interplay between excitatory and inhibitory mechanisms, also in neural substrates related to silent periods. Findings are discussed with respect to functional and structural impairments in stuttering, and are also proposed as novel neural markers of a stuttering "state" in persistent DS, helping to define more focused treatments (e.g. neuro-modulation)

Outcome of cardiac surgery in patients with low preoperative ejection fraction

Background: In patients undergoing cardiac surgery, a reduced preoperative left ventricular ejection fraction (LVEF) is common and is associated with a worse outcome. Available outcome data for these patients address specific surgical procedures, mainly coronary artery bypass graft (CABG). Aim of our study was to investigate perioperative outcome of surgery on patients with low pre-operative LVEF undergoing a broad range of cardiac surgical procedures. Methods: Data from patients with pre-operative LVEF ≤40 % undergoing cardiac surgery at a university hospital were reviewed and analyzed. A subgroup analysis on patients with pre-operative LVEF ≤30 % was also performed. Results: A total of 7313 patients underwent cardiac surgery during the study period. Out of these, 781 patients (11 %) had a pre-operative LVEF ≤40 % and were included in the analysis. Mean pre-operative LVEF was 33.9 ± 6.1 % and in 290 patients (37 %) LVEF was ≤30 %. The most frequently performed operation was CABG (31 % of procedures), followed by mitral valve surgery (22 %) and aortic valve surgery (19 %). Overall perioperative mortality was 5.6 %. Mitral valve surgery was more frequent among patients who did not survive, while survivors underwent more frequently CABG. Post-operative myocardial infarction occurred in 19 (2.4 %) of patients, low cardiac output syndrome in 271 (35 %). Acute kidney injury occurred in 195 (25 %) of patients. Duration of mechanical ventilation was 18 (12-48) hours. Incidence of complications was higher in patients with LVEF ≤30 %. Stepwise multivariate analysis identified chronic obstructive pulmonary disease, pre-operative insertion of intra-aortic balloon pump, and pre-operative need for inotropes as independent predictors of mortality among patients with LVEF ≤40 %. Conclusions: We confirmed that patients with low pre-operative LVEF undergoing cardiac surgery are at higher risk of post-operative complications. Cardiac surgery can be performed with acceptable mortality rates; however, mitral valve surgery, was found to be associated with higher mortality rates in this population. Accurate selection of patients, risk/benefit evaluation, and planning of surgical and anesthesiological management are mandatory to improve outcome