Literatura académica sobre el tema "Variabili Smartness"

Investing in digital transformation turns out to be a strategic action to tackle contemporary issues and to improve competitiveness for enterprises. The high variability of options in the digital transformation process enforces a higher complexity level in configuring and setting up objectives and goals based on cities’ needs; hence, a systematic approach is required to assist decision makers for better and sustainable transformation. A reference model is described in this paper to support decision makers with comprehensive assessment data for digital transformation cities transport. The proposed reference model assesses the cities based on digital transformation of transport services to assist policy makers for better decisions in transforming the Mobility 4.0. The proposed model in this study functions as a knowledge-based systematic framework for assessing the capabilities of the cities, diagnosing their needs under given circumstances and identifying the best fitting workflow for digital transformation of urban transportation systems and related services. The reference model takes on board a group of smart city indices with respective assessment criteria in determining a smartness level of transportation components. A conceptual 4-tier smartness scale has been proposed to establish a consistent assessment subject to cities circumstances in many respects. The reference model has been formalised into a mathematical model to characterise the assessments. The mathematical model encompasses strategic assessments by experts to identify priorities of investments in the digitalization process, which are aligned with strategic goals and policies of cities’ management.

Background: Among the various types of pharmaceuticals, vaccines have a special place. However, in the case of HIV, nearly after 40 years of its discovery, an effective vaccine still is not available. The reason lies in several facts mainly the variability and smartness of HIV as well as the complexity of the interaction between HIV and immune responses. A robust, effective, and longterm immunity is undoubtedly what a successful preventive vaccine should induce in order to prevent the infection of HIV. Failure of human trials to this end has led to the idea of developing therapeutic vaccines with the purpose of curing already infected patients by boosting their immune responses against the virus. Nevertheless, the exceptional ability of the virus to escape the immune system based on the genetically diverse envelope and variable protein products have made it difficult to achieve an efficient therapeutic vaccine. Objective: We aimed at studying and comparing different approaches to HIV therapeutic vaccines. Methods: In this review, we summarized the human trials undergoing on HIV therapeutic vaccination which are registered in the U.S. clinical trial database (clinicaltrials.gov). These attempts are divided into different tables, according to the type of formulation and application in order to classify and compare their results. Result/Conclusion: Among several methods applied in studied clinical trials which are mainly divided into DNA, Protein, Peptide, Viral vectors, and Dendritic cell-based vaccines, protein vaccine strategy is based on Tat protein-induced anti-Tat Abs in 79% HIV patients. However, the studies need to be continued to achieve a durable efficient immune response against HIV-1.

Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between−1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden the CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well.

Tesi di dottorato di ricerca inerente la ricerca degli elementi di misurazione delle incidenze delle intelligenze artificiali applicate ai contesti urbanistici finalizzati alla definizione della chiave interpretativa e valutativa di contesti con smartness definibili comunemente Smart City. La ricerca intende non solo approfondire la letteratura e le interpretazioni più consuete secondo cui è possibile parlare di città integrata nel binomio AI e progettazione urbana (Smart City), ma si muove all'interpretazione e riconoscimento di quelle condizioni di sussistenza di smartness e variabili sostantive che permettono di misurare l'intelligenza urbana e parametrarla in molteplici contesti urbanistico - territoriali.