Abstract
Accurate localization is a critical task in underwater navigation. Typical localization methods use a set of acoustic sensors and beacons to estimate relative position, whose geometric configuration has a significant impact on the localization accuracy. Although there is much effort in the literature to define optimal 2D or 3D sensor placement, the optimal sensor placement in irregular and constrained 3D surfaces, such as autonomous underwater vehicles (AUVs) or other structures, is not exploited for improving localization. Additionally, most applications using AUVs employ commercial acoustic modems or compact arrays, therefore the optimization of the placement of spatially independent sensors is not a considered issue. This article tackles acoustic sensor placement optimization in irregular and constrained 3D surfaces, for inverted ultra-short baseline (USBL) approaches, to improve localization accuracy. The implemented multi-objective memetic algorithm combines an evaluation of the geometric sensor's configuration, using the Cramer-Rao Lower Bound (CRLB), with the incidence angle of the received signal. A case study is presented over a simulated homing and docking scenario to demonstrate the proposed optimization algorithm.

Abstract
This paper presents the goals and components of a quantitative energy balance assessment framework to define Positive Energy Districts (PEDs) flexibly in three important contexts: the context of the district's density and local renewable energy supply (RES) potential, the context of a district's location and induced mobility, and the context of the district's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious, to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition-system boundaries, balancing weights, and balance targets-and argues how they can be linked to the definition goals in detail. In particular, we specify three levels of system boundaries and argue their individual necessity: operation, mobility, and embodied energy and emissions. It argues that all three pillars of PEDs, energy efficiency, onsite renewables, and energy flexibility, can be assessed with the single metric of a primary energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the surrounding energy system, which we identify as a context factor. Three examples of such context factors, each corresponding to the balance target of one of the previously defined system boundaries, operation, mobility, and embodied emissions, are presented: density (as a context for operation), sectoral energy balances and location (as a context for mobility), and an outlook on personal emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

Abstract
PurposeRecent years witnessed an exponential growth of the Portuguese real estate market. This growth has generated the need to implement effective management control tools to allow companies to improve their planning and monitoring of activities. Drawing on the agency and goal-setting theories, this paper explores the impact of companies' participative budgeting processes on employee performance in the real estate industry.Design/methodology/approachFor this purpose, a questionnaire was developed and a sample of 116 employees that participate in the budgeting process of real estate organizations collected, with data analyzed using structural equation modelling.FindingsThe results show that participation in the budgeting process has an impact on employees' performance through budget goal commitment, trust and job satisfaction. However, no statistical support was found for the role of budgetary slack in this process.Research limitations/implicationsThis study was conducted in a single industry and is based on self-reported measures of employees that participate in the budgeting process of their organizations.Practical implicationsThe findings highlight the need for real estate organizations to involve their staff in the elaboration of budgets, contributing to a higher level of commitment to established goals, job satisfaction, trust and performance. Real estate organizations should provide adequate working conditions, foster their employees' autonomy and recognize their work.Originality/valueThe findings encourage real estate companies to extend the participation in the budget process to employees and, ultimately, to mitigate the probability of budget failure.

Abstract
Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.

Abstract
The complexity of automotive supply chain management presents significant challenges, demanding rigorous coordination of tasks to ensure efficiency in its operations. This chapter examines the potential benefits of adopting blockchain technology in supply chain management. The study employs a case study methodology, focusing on a prominent European automotive company that, compared to its major competitors, appears hesitant to integrate blockchain into its supply chain management practices. The analysis relies on secondary data and utilizes a range of strategic analysis tools to gain deeper insights into the advantages that blockchain technology could offer in supply chain management. Based on the findings, this chapter offers 12 strategic recommendations pertaining to blockchain technology, which can aid the company in addressing various management issues by enhancing security, efficiency, and technological advancements. © 2024, IGI Global. All rights reserved.

Abstract
Deep Learning has reached human-level performance in several medical tasks including clas-sification of histopathological images. Continuous effort has been made at finding effective strategies to interpret these types of models, among them saliency maps, which depict the weights of the pixels on the classification as an heatmap of intensity values, have been by far the most used for image classification. However, there is a lack of tools for the systematic evaluation of saliency maps, and existing works introduce non-natural noise such as random or uniform values. To address this issue, we propose an approach to evaluate the faithfulness of the saliency maps by introducing natural perturbations in the image, based on oppose-class substitution, and studying their impact on evaluation metrics adapted from saliency models. We validate the proposed approach on a breast cancer metastases detection dataset PatchCamelyon with 327,680 patches of histopathological images of sentinel lymph node sections. Results show that GradCAM, Guided-GradCAM and gradient-based saliency map methods are sensitive to natural perturbations and correlate to the presence of tumor evidence in the image. Overall, this approach proves to be a solution for the validation of saliency map methods without introducing confounding variables and shows potential for application on other medical imaging tasks.