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The paper’s conceptual framework is the United Nations Agenda 2030 and the long-term Sustainable Development Goals . Scientific work on environmental issues has underlined the urgency and profoundness of the transformations needed to achieve the COP21 global warming thresholds in the short time left. Despite the systemic character of sustainability , most suggested innovation policies do not consider the advantages of an integrated view of environmental and social issues. The paper explores this possibility by analysing the chances of minor centres (small towns and peripheral communities) to combine these challenges in sustainable development models. Transformative innovation steps inspired by the responsible innovation approach are vital instruments to reach that goal. The paper’s conjecture about the minor centres is supported by analysing three main courses in the sustainability route: the conversion to renewable energy, the circular economy, and the digitalisation process. The analysis offers innovation hints for the responsible development of plans such as the Next Generation EU , launched to support Europe’s economic revival in the post-pandemic phase.

Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction is essential to match supply and demand and ensure grid stability. However, the PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning and deep learning, to predict day-ahead PV power accurately. Machine learning models need a rich historical dataset that includes years of PV power outputs to capture hidden patterns between essential variables to predict day-ahead PV power production accurately. Therefore, this study presents a framework based on the transfer learning method to use reliable trained deep learning models of old PV plants in newly installed PV plants in the same neighborhoods. The numerical results show the effectiveness of transfer learning in day-ahead PV prediction in newly established PV plants where a sizable historical dataset of them is unavailable. Among all nine models presented in this study, the LSTM models have better performance in PV power prediction. The new LSTM model using the inadequate dataset has 0.55 mean square error (MSE) and 47.07% weighted mean absolute percentage error (wMAPE), while the transferred LSTM model improves prediction accuracy to 0.168 MSE and 32.04% wMAPE.

Purpose Our study aimed to describe recovery of gustatory dysfunction (GD) and olfactory dysfunction (OD) in COVID-19 patients, and to analyze variables associated with early or late recovery. Methods Telephone surveys were administered during an 18-month follow-up after COVID-19 diagnosis. One hundred and thirty-two included patients rated olfactory and gustatory function at each follow-up. Results One hundred and twenty-nine patients reported GD, of whom 91 (70.5%) reported severe GD, and 99 patients reported OD, of whom 84 (84.9%) reported severe OD. Seventy-two/129 (55.8%) and 52/99 (52.5%) patients reported an improvement in GD and in OD during the first 7 days from the onset, respectively. At 3-month follow-up, 110/120 patients (85.3%) recovered from GD, while 80/99 patients (80.8%) recovered from OD. At 18-month follow-up, a total of 120/129 patients (93.0%) recovered from GD and 86/99 patients (86.9%) recovered from OD; while 10 patients (7.0%) still reported GD and 13 patients (13.1%) still reported OD. Severe GD/OD at presentation were associated with late complete recovery of taste/smell ( p  = 0.019 and p  = 0.034, respectively). Improvement over the first 7 days from onset was significantly associated with faster recovery ( p  < 0.001). Conclusions More than 80% of patients reported complete recovery of olfactory/gustatory function in the first 3 months after symptom onset. At 18-month follow-up, patients reporting complete recovery of gustatory and olfactory function were 93% and 87%, respectively. Severity of chemosensory dysfunction at the onset was negatively correlated to recovery, and improvement of taste and/or smell function within the first 7 days from symptom onset was significantly associated with early resolution.

Purpose To underline discrepancies between the Union for International Cancer Control (UICC) and the American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) classifications in oral cavity cancer. Methods Comparison between the UICC and AJCC TNM classifications of oral cavity cancer in their 8th editions and following versions. Results The most important update was the introduction of the depth of infiltration (DOI), which reflects the proximity of the tumor to the underlying lymphovascular tissues and was associated to the presence of nodal metastases. Since the first publication of the 8th edition of the AJCC Cancer Staging Manual on March 30, 2017, two further versions have been published, while the UICC TNM classification was left unchanged until a document containing modifications to the 8th edition of the UICC TNM Classification of Malignant Tumours was published online on October 6, 2020. Conclusion Different versions of the TNM classification can be confounding for the scientific community. Citing the 8th edition of the UICC TNM Classification of Malignant Tumours or the AJCC Cancer Staging Manual without specifying the precise version used for classification may be insufficient. Clinicians and researchers are invited to always refer to the latest update of each classification.

The wide adoption of environmentally friendly solutions for transportation, such as Electric Vehicles (EVs), is crucial to reducing greenhouse gases and mitigate the effects of climate change. To meet the growing demand of EVs, enough Charging Stations (CSs) must be deployed. In this study, the Ultra-Fast Charging (UFC) technology is investigated, and a method is proposed to locate the minimum indispensable UFC infrastructure to enable a nationwide travel, considering both infrastructure costs and power losses. To address the location problem, first the average electric range of the EVs currently on the market is analyzed to estimate the maximum allowable distance between two consecutive CS. In the assessment of the driving range all the factors which influence the energy consumption are considered. The CSs are then located within the existing Service Areas (SAs) to save infrastructure costs while meeting the maximum distance constraint between charging stations. Then, a cost comparison is performed between the economic impact of power losses and the savings from reduced infrastructure costs. The methodology is applied to the Italian highway network. Results show that installing charging infrastructure within existing SAs is more cost-effective than placing them near Medium Voltage (MV) cabins.

The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure.

Human pluripotent stem cells (hPSCs) constitute a valuable model to study the complexity of early human cardiac development and investigate the molecular mechanisms involved in heart diseases. The differentiation of hPSCs into cardiac lineages in vitro can be achieved by traditional two-dimensional (2D) monolayer approaches or by adopting innovative three-dimensional (3D) cardiac organoid protocols. Human cardiac organoids (hCOs) are complex multicellular aggregates that faithfully recapitulate the cardiac tissue’s transcriptional, functional, and morphological features. In recent years, significant advances in the field have dramatically improved the robustness and efficiency of hCOs derivation and have promoted the application of hCOs for drug screening and heart disease modeling. This review surveys the current differentiation protocols, focusing on the most advanced 3D methods for deriving hCOs from hPSCs. Furthermore, we describe the potential applications of hCOs in the pharmaceutical and tissue bioengineering fields, including their usage to investigate the consequences of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV2) infection in the heart.

Considering the proposed strict new constraints of public authorities, decarbonization has become a key trend in recent years. Although several countries have started the process of decarbonization through the introduction of electric vehicles in their public services, for many countries, especially developing countries, transportation is still a hard sector to decarbonize. The presence of obsolete and polluting vehicles discourages citizens from using public transport and thus incentivizes the use of private vehicles, which create traffic congestion and increase emissions. Based on these considerations, this paper aimed to implement a simulation for a public service in Eritrea, evaluating whether it is possible to take a long trip using an electric minibus. A case study is implemented highlighting the barriers of electrifying transportation in this area, producing results on fuel consumption and service reliability. In the case study, four scenarios are presented to estimate the service. The scenarios evaluate the possibility to perform from three to five recharges. Fewer charges mean longer charging time, leading to a 2 h charging phase in Scenario 1, while recharging more than twice along the route will lead to shorter 30 min charges, as in Scenario 3. The case study also highlights the relevance of the slope in electric vehicle performance, as reported for the case of Asmara–Massawa travel (Econs= 6.688 kWh). Finally, an environmentally sustainable solution, such as a 92 kWh/day photovoltaic plant, is proposed to power the service.

The paper is mostly devoted to applications of a novel optimal control theory for perturbed sweeping/Moreau processes to two practical dynamical models. The first model addresses mobile robot dynamics with obstacles, and the second one concerns control and optimization of traffic flows. Describing these models as controlled sweeping processes with pointwise/hard control and state constraints and applying new necessary optimality conditions for such systems allow us to develop efficient procedures to solve naturally formulated optimal control problems for the models under consideration and completely calculate optimal solutions in particular situations.

Background In the past, sporadic demonstrations of the existence of a subarachnoid subdiaphragmatic cistern have been published. The aim of this study was to evaluate the anatomical characteristics of the subdiaphragmatic cistern of the pituitary gland. Methods After a complete review of the literature published on the topic, we report anatomical observations of the subdiaphragmatic cistern and its relationship to the pituitary gland and to the chiasmatic cistern. Ten cadaveric heads were studied using different techniques and surgical methods (plastination, plastic casts of the subarachnoid spaces, microscopic and transsphenoidal endoscopic approaches). Moreover, 3-T magnetic resonance images of ten healthy volunteers were analyzed to investigate the presence and anatomical variability of the subdiaphragmatic cistern. Results By means of our qualitative radioanatomic study, we found that the roof of the subdiaphragmatic cistern is formed by the diaphragma sellae, the floor by the superior face of the pituitary gland, the lateral walls by the arachnoidea extending laterally through the medial walls of the cavernous sinus, and the medial walls by the infundibular stem. The subdiaphragmatic cistern communicates by means of the ostium of the diaphragm with the chiasmatic cistern. Conclusion We confirmed the existence of the subdiaphragmatic cistern. The overused term “suprasellar cistern” refers more to a complex of cisterns, formed by the subdiaphragmatic cistern, below the diaphragma sella, and by the chiasmatic cistern, above it, in direct communication with the lamina terminalis and carotid cisterns.