Abstract

Abstract
Deep learning approaches have become popular in many different areas, including sentiment analysis (SA), because of their competitive performance. However, the downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. In contrast, previous approaches that used sentiment lexicons can do that, but their performance is normally not high. To leverage the strengths of both approaches, we present a neuro-symbolic approach that combines deep learning (DL) and symbolic methods for SA tasks. The DL approach uses a pre-trained language model (PLM) to construct sentiment lexicon. The symbolic approach exploits the constructed sentiment lexicon and manually constructed shifter patterns to determine the sentiment of a sentence. Our experimental results show that the proposed approach leads to promising results with the additional advantage that sentiment predictions can be accompanied by understandable explanations.

Abstract
The potential of hyperspectral UV–VIS–NIR reflectance for the in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325–1075 nm) of twenty kiwi plants were obtained in vivo and in situ with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal over 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV–VIS–NIR reflectance for the non-destructive discrimination of bacterial canker on kiwi leaves. © 2023 by the authors.

Abstract
This paper presents a general comparison between network-secure and network-free optimization frameworks to manage flexible multi-energy resources. Both frameworks were implemented in a test case that includes electricity, gas, and heat distribution networks. Several potential scenarios for the decarbonization of the multi-energy system were simulated. The economic, technical, and environmental impacts were compiled. The network-secure framework is highly recommended to avoid service disruptions due to network violations, but its implementation comes with a price - overall operational costs increase, sometimes substantially.

Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

Abstract
An attempt has been made to address the difficulty of identifying and measuring the benefits derived from investment projects and capturing capital gains for an organization, focusing on developing and implementing a management model and realizing benefits for a leading company in its activity sector. Thus, the objective is to understand how it is possible to achieve the expected benefits of an investment project: A model characterized as generalist was developed (applied to all areas of the company), with the objective of optimizing the realization of benefits, measuring them and thus create value for the organization. Among the methods used, we highlight, in a first phase, the research of some existing Frameworks, which later enabled the development of a proposed framework, validated internally using the existing Business Intelligence platform. Subsequently, based on a satisfaction questionnaire about the framework proposed to users, data related to its development and implementation were collected, with the aim of understanding its acceptance among the users and employees of the company. With the data from this questionnaire, an artifact was developed: a PowerBI dashboard that reflects the benefits identified and captured. In summary, the artifact made it possible to identify, measure, and achieve the benefits generated by the project in question, but also to motivate its use in other existing investment projects, by adapting it to each of the other ones. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.