Abstract
Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them.

Abstract
The increasing number of Distributed Energy Resources (DERs) provides new opportunities for increased interactions between prosumers and local distribution companies. Aggregating large numbers of prosumers through Home Energy Management Systems (HEMS) allows for easier control and coordination of these interactions. With the contribution of the dedicated end-users in fulfilling the required flexibility during the day, the network operator can easily handle the power mismatches to avoid fluctuations in the load-generation side. The bi-level optimization allows for a more comprehensive and systematic assessment of flexibility procurement strategies. By considering both the network operator's objectives and the preferences and capabilities of end-users, this approach enables a more nuanced and informed decision-making process. Hence, this article presents a bi-level optimization model to examine the potential for several groups of prosumers to offer flexibility services to distribution companies. The model is applied to the IEEE 33 bus test system and solved through distributed optimization techniques. The model considers various DERs, including Battery Energy Storage Systems (BESS). Results show that the groups of aggregated consumers can provide between +/- 7 to +/- 29 kW flexibility in each interval, which is significant. Furthermore, the aggregators' flexibility capacity is closely linked to the demand at each node.

Abstract
Port wine plays a crucial role in the Douro region in Portugal, providing significant economic support and international recognition. The efficient and sustainable management of the wine sector is of utmost importance, which includes the verification of abandoned vineyard plots in the region, covering an area of approximately 250,000 hectares. The manual analysis of aerial images for this purpose is a laborious and resource-intensive task. However, several artificial intelligence (AI) methods are available to assist in this process. This paper presents the development of AI models, specifically deep learning models, for the automatic detection of abandoned vineyards using aerial images. A private image database was expanded, containing a larger collection of images with both abandoned and non-abandoned vineyards. Multiple AI algorithms, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), were explored for classification. The results, particularly with the ViTs approach, achieved high accuracy and demonstrated the effectiveness of automatic detection, with the ViT models achieving an accuracy of 99.37% and an F1-score of 98.92%. The proposed AI models provide valuable tools for monitoring and decision-making related to vineyard abandonment. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
This research study presents an optimized approach for charging electric vehicles (EVs) in existing residential multi dwelling buildings. The proposed solution tackles the problem in two distinct, but complementary ways. First it takes advantage, in a novel way, of the existing electrical infrastructure by taping directly into the main feeder of the building, second it distributes the power in real time by leveraging in an optimized methodology. The aim of this methodology is to minimize the discrepancy between the desired and final state of charge (SOC) of EVs by the end of each charging session. To achieve this, the method leverages on commuting and charging preferences of EV owners, as well as the electrical infrastructure of residential buildings. To dynamically adjust the charging power for each EV in real-time, an optimized charging management system is employed. This system solves a non-linear minimization optimization problem that considers various parameters, including the initial SOC of each EV, the desired final SOC, the available charging time, and the available charging power. To assess the effectiveness of the proposed methodology, comparative analysis was conducted against a baseline methodology commonly used in practice. The results show that the optimized approach significantly outperforms the non-optimized methods, particularly in high demand scenarios. In these scenarios, the optimized methodology allows for a 200% increase in the supplied energy to the buildings' EV fleet, as well as more than doubling the range made available to users when compared to traditional approaches. In conclusion, this research work offers a robust and effective solution for charging EVs in residential buildings.

Abstract

Abstract
The development and use of digital marketing strategies by entrepreneurs is a key element of success for innovative projects. Moreover, effective execution of marketing intervention in what is referred to as the digital customer journey is essential to achieving business success. Under this paradigm, the present study aims to identify the use of digital marketing activities by entrepreneurs in their projects at each phase of the customer journey. The research bridges a gap in in the existing literature, first by a systematic review of literature using the statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance using alternating least squares (HOMALS) framework programmed in the R language. Based on the results of this analysis, 13 digital marketing techniques are identified along with their use across the five phases of the digital customer journey that are linked to technology transfer and adoption: awareness, engagement, conversion, loyalty, and advocacy. Furthermore, different applications of digital marketing techniques by entrepreneurs are discussed, and new applications for each phase are proposed. The results reveal that entrepreneurs lack knowledge about the customer journey, the use of the awareness phase, and the knowledge of Big Data tools to boost innovation. Finally, the main digital marketing strategies are appropriately classified for each phase of the customer journey, and 16 questions for future research in this research area are proposed.