Abstract
Recent studies have highlighted the potential of supply chain visibility (SCV) to improve companies' performance, support the decision-making process and increase the resilience and sustainability of supply chains (SC). However, the definition and the approaches to manage visibility are not consensual, especially when it comes to its assessment. This paper studies the influence that some characteristics of the information shared among SC partners have on SCV and provides a model that allows to assess the level of visibility. Focus group research was used to discuss the visibility assessment model with academic and practical experts and case studies were conducted to evaluate the implementation of the model in real contexts. The results support the applicability of the model in complex SCs and show that the visibility indices proposed can help managers to identify the SC partners where improvement actions are needed. This study contributes to literature and practice by means of a model to assess the visibility throughout the SC by considering different types and characteristics of the information shared.

Abstract
Problem definition: Marketplace platforms such as Amazon or Farfetch provide a convenient meeting point between customers and suppliers and have become an important element of e-commerce. This sales channel is particularly interesting for suppliers that sell seasonal goods under a tight time frame because they provide expanded reach to potential customers even though it entails lower margins. In this dyadic relationship, a supplier needs to optimize when to share inventory with the platform, and the platform needs to set the right commission structure during the season. Academic/practical relevance: We characterize supplier participation into the platform in a dynamic setting and link it to inventory levels, demand rates, time left in the season, and commission structure. This directly drives the commission structure decision made by the platform. We, thus, provide a framework to evaluate platform commission fee policies, taking into account supplier responses. Methodology: We use an optimal control framework with limited inventory supply and a stochastic demand process. We study the conditions under which the supplier accepts participation and use the platform as a sales channel. We also study the optimal commission structure that the platform should employ and the supplier procurement response. Results: We find that suppliers only participate if inventory is high relative to the time left to sell the items. As a result, the platform can only offer limited supply at the beginning of the season. Given this behavior, we find that the platform and the system are always better off with flexible pricing via fully dynamic commissions, which hurts the supplier the most (better off with less flexible commission fees). Interestingly, when the inventory decision is contingent on the platform pricing policy, the platform often finds it beneficial to commit to a static fee to incentivize the supplier to stock up, highlighting that inability to commit to fixed commissions may destroy value through double marginalization effects. Managerial implications: Our work suggests that short-term profit for the platform is maximized with fully dynamic commission fees at the expense of supplier profit. If inventory is endogenous, suppliers can retaliate by reducing their commitment at the start of the season. Despite the increased revenue obtained with the fully dynamic commission fee, the lost sales from the inventory drop incentivize the platform to opt for supplier-friendly commission fees, which are better for long-term-profit.

Abstract
The goal of this article is to introduce the fundamental notions and concepts of stability analysis for linear and nonlinear systems in the context of electronic power conversion. Power electronic circuits have strong nonlinear behavior in their essence; often we need to linearize them to understand their properties and study their stability with the applied control laws. We present different concepts of stability (internal, input-output, Lyapunov-based), observability and controllability, as well as practical tests to check these properties. We then apply these tests in the context of a single power converter example, a DC/DC boost converter. © 2023 Elsevier Inc. All rights reserved.

Abstract
Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic within that domain. This work establishes the fundamental constraint levels required to achieve realism and introduces the adaptative perturbation pattern method (A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on pattern sequences that are independently adapted to the characteristics of each class to create valid and coherent data perturbations. The proposed method was evaluated in a cybersecurity case study with two scenarios: Enterprise and Internet of Things (IoT) networks. Multilayer perceptron (MLP) and random forest (RF) classifiers were created with regular and adversarial training, using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and untargeted attacks were performed against the classifiers, and the generated examples were compared with the original network traffic flows to assess their realism. The obtained results demonstrate that A2PM provides a scalable generation of realistic adversarial examples, which can be advantageous for both adversarial training and attacks.

Abstract
Teleoperation of autonomous mobile robots (AMR) is relevant in logistics operations to automate repetitive tasks that often result in injuries to the operator. This paper presents an overview of the systems involved in the current teleoperation scheme where these AMRs are present as well as some works and advances that have been done in the high-level teleoperation field.

Abstract
The water content in an agricultural crop is of crucial importance and can either be estimated through proximal or remote sensing techniques, allowing better irrigation scheduling and avoiding extreme water stress periods. However, the current climate change context is increasing the use of eco-friendly practices to reconcile water management and thermal protection from sunburn. These approaches aim to mitigate summer stress factors (high temperature, high radiation, and water shortage) and improve the plants' thermal efficiency. In this study, data from unmanned aerial vehicles (UAVs) were used to monitor the efficiency of foliar kaolin application (5%) in a commercial vineyard. Thermal infrared imagery (TIR) was used to compare the canopy temperature of grapevines with and without kaolin and to compute crop water stress and stomatal conductance indices. The gas exchange parameters of single leaves were also analysed to ascertain the physiological performance of vines and validate the UAV-based TIR data. Generally, plants sprayed with kaolin presented a lower temperature compared to untreated plants. Moreover, UAV-based data also showed a lower water stress index and higher stomatal conductance, which relate to eco-physiological measurements carried out in the field. Thus, the suitability of UAV-based TIR data proved to be a good approach to monitor entire vineyards in regions affected by periods of heatwaves, as is the case of the analysed study area.