Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, ...generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.
Breast reconstruction plays a fundamental role in the therapeutic process of breast cancer treatment and breast implants represents the leading breast reconstruction strategy. Breast Implant ...Associated-Anaplastic Large Cell Lymphoma (BIA-ALCL), locoregional recurrence in the skin flap, and skin flap necrosis are well-known complications following mastectomy and immediate breast reconstruction (IBR). We report a case of locoregional cancer recurrence in the mastectomy flap mimicking BIA-ALCL, in a patient who underwent 6 breast procedures in four facilities across 15 years including immediate breast reconstruction with macrotextured breast implants. Despite the rate and onset of the disease, clinicians should be aware of BIA-ALCL. Due to the risk of false negative results of fine needle aspiration, clinical suspicion of BIA-ALCL should drive clinicians' choices, aside from cytological results. In the present case, surgical capsulectomy of the abnormal periprosthesic tissue revealed locoregional recurrence.
Coronavirus-19 (COVID-19) pandemic outbreak is currently having a huge impact on medical resource allocation. Breast Cancer (BC) patients are concerned both with BC treatment and COVID-19. This study ...aimed to estimate the impact of anxiety among patients, caused by the spreading of COVID-19.
Between the 16th of January and the 20th of March 2020, we retrospectively enrolled 160 patients. Eighty-two patients with a suspected breast lesion (SBL) were divided into two groups: PRE-COVID-19-SBL and POST-COVID-19-SBL. Seventy-eight BC patients were divided into PRE-COVID-19-BC and POST-COVID-19-BC. Patient characteristics including age, marital status, SBL/BC diameter, personal and family history of BC, clinical stage and molecular subtype were recorded. Procedure Refusal (PR) and Surgical Refusal (SR) were also recorded with their reason.
BC and SBL analysis showed no difference in pre-treatment characteristics (p>0.05). Both POST-COVID-19-SBL and POST-COVID-19-BC groups showed higher rates of PR and SR (p=0.0208, p=0.0065 respectively). Infection risk represented primary reason for refusal among POST-COVID-19 patients.
COVID-19-related anxiety could affect patients' decision-making process.
Extraordinary restrictions aimed to limit Sars-CoV-2 spreading; they imposed a total reorganization of the health-system. Oncological treatments experienced a significant slowdown. The aim of our ...multicentric retrospective study was to evaluate screening suspension and surgical treatment delay during COVID-19 and the impact on breast cancer presentation.
All patients who underwent breast surgery from March 11, 2020 to May 30, 2020 were evaluated and considered as the Lockdown group. These patients were compared with similar patients of the previous year, the Pre-Lockdown group.
A total of 432 patients were evaluated; n=223 and n=209 in the Lockdown and Pre-lockdown-groups, respectively. At univariate analysis, waiting times, lymph-nodes involvement and cancer grading, showed a statistically significant difference (p<0.05). Multivariate analysis identified waiting-time on list (OR=1.07) as a statistically significant predictive factor of lymph node involvement.
Although we did not observe a clinically evident difference in breast cancer presentation, we reported an increase in lymph node involvement.
Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy ...systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning.
Reverse power flow, defined as the continuous flow of electricity in a direction opposite to the normal direction of the power flow in a grid, typically occurs in microgrids when the energy generated ...by the distributed electric power plants exceeds the local load demand. This phenomenon imposes several risks related to inefficient operation or damage of equipment, grid instability, and energy losses. In order to reduce reverse power flow in microgrids and support energy autonomy, we introduce a forecast-driven framework. The framework builds upon deep learning models that forecast the electricity produced (photovoltaic systems) and consumed by the microgrid and an optimization algorithm that schedules its shiftable loads (electric vehicles) based on said forecasts. We conduct an ablation study to evaluate the effect that optimized scheduling and energy storage has on the autonomy of the microgrid, also investigating the impact of different capacities of batteries and sizes of electric vehicle fleets. Our results suggest that forecast-driven load shifting can significantly reduce reverse power flow, especially for relatively larger amounts of shiftable loads. Moreover, we find that electricity storage can complement load shifting, further improving its beneficial effect. Nevertheless, these improvements are subject to forecast accuracy and storage abilities.
•A forecast-driven solution to the microgrid’s shiftable loads scheduling problem.•Two Deep Learning models to predict the PV production and the microgrid consumption.•A heuristic algorithm that schedules the Electric Vehicle charging sessions.•A rule-based method that automates the charging and discharging of batteries.
Despite the large amount of clinical data available of Coronavirus-19 (COVID-19), not many studies have been conducted about the psychological toll on Health Care Workers (HCWs).
In this multicentric ...descriptive study, surveys were distributed among 4 different Breast Cancer Centers (BCC). BCCs were distinguished according to COVID-19 tertiary care hospital (COVID/No-COVID) and district prevalence (DP) (High vs. Low). DASS-21 score, PSS score and demographic data (age, sex, work) were evaluated.
A total of 51 HCWs were analyzed in the study. Age, work and sex did not demonstrate statistically significant values. Statistically significant distribution was found between DASS-21-stress score and COVID/No-COVID (p=0.043). No difference was found in the remaining DASS-21 and PSS scores, dividing the HCWs according to COVID-19-hospital and DP.
Working in a COVID-19-hospital represents a factor that negatively affects psychosocial well-being. However, DP seems not to affect the psychosocial well-being of BCC HCWs. During the outbreak, psychological support for low risk HCWs should be provided regardless DP.
SARS-CoV-2 pandemic imposed extraordinary restriction measures and a complete reorganization of the Health System. The aim of the study was to evaluate the impact of COVID-19 on emergency surgical ...department accesses.
Patients admitted to surgical emergency departments was retrospectively recorded during the Lockdown (March 11, 2020-May 3, 2020) and compared with the same number of days in 2019 and immediately before Lockdown (January 16, 2020-March 10, 2020). Diagnoses, priority levels, modes of patient's transportation, waiting times and outcomes were analysed.
During the lockdown phase, we ob-served a reduction in the access to emergency surgical departments of 84.45% and 79.78%, com-pared with the Pre-Lockdown2019 and Pre-Lockdown2020 groups, respectively. Patient's transportation, hospitalization and patients discharge with indications to an outpatient visit, waiting and total times exhibited a significant difference during the lockdown (p<0.005).
We observed a reduction of surgical emergency accesses during the lockdown. Implementing the use of the regional systems and preventing overcrowding of emergency departments could be beneficial for reducing waiting times and improving the quality of treatments for patients.
PDF
