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.
Oxamide (OXA) and azodicarbonamide (ADA) are among the known burning rate suppressants used in composite solid rocket propellants. Much research has been carried out to understand mechanism of ...suppression but literature about the action of OXA and ADA on the combustion characteristics of propellant is still scarce. Here, a systematic study on coolant-based propellants has been undertaken spanning from thermal analyses of ingredients to a variety of burning processes of the corresponding propellants. Thermal gravimetric analysis and differential thermal analysis on individual coolants are carried out to study their behaviour with temperature. It was noticed that the thermal decomposition of OXA exhibits only endothermic effects, whereas that of ADA presents both endothermic and exothermic effects. Successive experiments on solid propellant looking at burning rate characterization, condensed combustion product collection and visualization, pressure deflagration limit and thermochemical analysis gave a greater insight and enabled better understanding of the action of coolants during combustion. It is proposed that OXA and ADA are acting on both the condensed and gas phases. Also, the nature of coolant is a key parameter, which affects the burning rate pressure index. Increase of agglomerate size and of pressure deflagration limit was obtained in the coolant-based propellants, confirming the trend given in the literature.
Transportation is one of the sectors with the highest CO2 emissions, accounting for 23% globally and significantly contributing to climate change. To address this challenge, the authorities have ...proposed new stringent policies that lead to decarbonization. From this perspective, this work proposes a multi-scenario analysis for the electrification of a fleet of private users. The scenarios differ on the type of charging mode adopted: slow charging (charging modes 1 and 2) and fast charging (charging modes 3 and 4). The model aims to identify the percentage of potential users who can shift from Internal Combustion Engine (ICE) to Electric Vehicles (EVs) in different scenarios. Furthermore, the model will highlight the average expenditure of users for charging, highlighting how the cost of energy could be a driver for the electrification of the sector. Finally, the model will allow us to evaluate the savings of up to 220 tons of CO2/year thanks to the electrification of the sector with Long Range vehicles, in best case scenario. The use of a multi-scenario analysis allowed several possible electrification solutions to be explored, highlighting the strengths and weaknesses of the charging mode used, supported by quantitative results. This data-driven approach allows us to identify optimal locations for public charging stations in region of northern Italy region, where the data was sourced, which will help to encourage the switch to EVs.
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.
Abstract
We present the discovery of 13 new widely separated T dwarf companions to M dwarf primaries, identified using Wide-field Infrared Survey Explorer/NEOWISE data by the CatWISE and Backyard ...Worlds: Planet 9 projects (hereafter BYW). This sample represents an ∼60% increase in the number of known M + T systems, and allows us to probe the most extreme products of binary/planetary system formation, a discovery space made available by the CatWISE2020 catalog and the BYW effort. Highlights among the sample are WISEP J075108.79-763449.6, a previously known T9 thought to be old due to its spectral energy distribution, which was found by Zhang et al. (2021b) to be part of a common proper motion pair with L34-26 A, a well-studied young M3 V star within 10 pc of the Sun; CWISE J054129.32-745021.5 B and 2MASS J05581644-4501559 B, two T8 dwarfs possibly associated with the very fast-rotating M4 V stars CWISE J054129.32745021.5 A and 2MASS J05581644-4501559 A; and UCAC3 52-1038 B, which is among the widest late-T companions to main-sequence stars, with a projected separation of ∼7100 au. The new benchmarks presented here are prime JWST targets, and can help us place strong constraints on the formation and evolution theory of substellar objects as well as on atmospheric models for these cold exoplanet analogs.
As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data ...accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.
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.
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.
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
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.
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