Abstract
Telecommunication operators compete not only for new clients, but, above all, to maintain current ones. The modelling and prediction of the top-up behaviour of prepaid mobile subscribers allows operators to anticipate customer intentions and implement measures to strengthen customer relationship. This research explores a data set from a Portuguese operator, comprising 30 months of top-up events, to predict the top-up monthly frequency and average value of prepaid subscribers using offline and online multi-target regression algorithms. The offline techniques adopt a monthly sliding window, whereas the online techniques use an event sliding window. Experiments were performed to determine the most promising set of features, analyse the accuracy of the offline and online regressors and the impact of sliding window dimension. The results show that online regression outperforms the offline counterparts. The best accuracy was achieved with adaptive model rules and a sliding window of 500,000 events (approximately 5 months). Finally, the predicted top-up monthly frequency and average value of each subscriber were converted to individual date and value intervals, which can be used by the operator to identify early signs of subscriber disengagement and immediately take pre-emptive measures.

Abstract
The Hoffmann reflex is a is a neurophysiological test that provides insight into the functioning of the human nervous system. It is commonly used in clinical and research settings to evaluate the modulation of the monosynaptic spinal reflex. This paper focus the analysis of the Hoffmann reflex in the trapezius muscle, a muscle of particular interest for researchers and clinicians due to its importance in upper limb function and dynamic stability. However, the Hoffmann reflex analysis of this muscle bring some challenges as the need of applicating burst of electrical square impulses in each current intensity. A web-based smart dashboard, implemented in Python, which allows the user to visualize and analyze the Hoffmann reflex using various signals acquired through a constant current stimulator. The dashboard provides an intuitive and user-friendly interface that facilitates the selection of muscle signals of interest, analysis cycles, and start and end points for the signals. The visualizations offered by the dashboard, including overlapped and mean signal graphics, provide valuable insights into the Hoffmann reflex and its properties. Preliminary experiments with field experts and physiotherapists have yielded positive feedback on the usefulness of this tool, as they seek to gain a deeper understanding of the Hoffmann reflex, and we plan to further improve its capabilities in the future by employing machine learning techniques to automate the reflex detection. © 2023 ITMA.

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.