Abstract
Objective: This study aims to present the developmental stages of a Mobile App Prototype designed to enhance patient participation in the rehabilitation process after hip replacement. Methods: To ensure effective interaction between the system and the end user, a User-Centered Design methodology was followed, encompassing three phases: Requirements gathering, Prototyping, and Evaluation. Usability tests were conducted to assess the usability of the developed system. Results: The RehabApp for mobile devices was created, and the testing results were positive. Users expressed satisfaction with the outcome, deeming it a valuable tool for their recovery. This outcome demonstrates the high receptiveness of these technologies in the healthcare sector, making it a project that can readily be expanded into other areas of rehabilitation. Conclusion: This study demonstrated the potential of the RehabApp in the rehabilitation process after hip replacement surgery. This accomplishment was realized by ensuring the active participation of patients, potential users, and healthcare professionals throughout the app's development. Innovation: The RehabApp is a mobile application to provide users with all the necessary knowledge, enabling them to undergo a smoother and safer rehabilitation. Feedback from both patients and healthcare professionals played a crucial role in refining the app's features and addressing usability concerns.

Abstract
Portable X-Ray Fluorescence (XRF) has become increasingly popular where traditional laboratory methods are either impractical, time consuming, and/or too costly. While the Limit of Detection (LOD) is generally poorer for XRF compared to laboratory-based methods, recent advances have improved XRF LODs and increased its potential for field-based studies. Portable XRF can be used to screen food products for toxic elements such as lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), manganese, (Mn), zinc (Zn), and strontium (Sr). In this study, 23 seafood samples were analyzed using portable XRF in a home setting. After XRF measurements were completed in each home, the same samples were transferred to the laboratory for re-analysis using microwave-assisted digestion and Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS). Four elements (Mn, Sr, As, and Zn) were quantifiable by XRF in most samples, and those results were compared to those obtained by ICP-MS/MS. Agreement was judged reasonable for Mn, Sr, and As, but not for Zn. Discrepancies could be due to (1) the limited time available to prepare field samples for XRF, (2) the heterogeneous nature of real samples analyzed by XRF, and (3) the small beam spot size (similar to 1 mm) of the XRF analyzer. Portable XRF is a cost-effective screening tool for public health investigations involving exposure to toxic metals. It is important for practitioners untrained in XRF spectrometry to (1) recognize the limitations of portable instrumentation, (2) include validation data for each specific analyte(s) measured, and (3) ensure personnel have some training in sample preparation techniques for field-based XRF analyses.

Abstract

Abstract
Multi-attribute decision making (MADM) methods provide a structured approach for decision making considering various criteria and subjective preferences. A range of MADM methods have been applied in the context of renewable energy. The main benefits of MADM methods are their ability to include stakeholder's interests and increase participation, to account for a broader set of sustainability criteria that go beyond cost-efficiency including environment, social, technical and economic criteria, and provide opportunities to increase energy technology acceptance. This contribution provides an overview of the most applied MADM methods in renewable energy system analysis. It offers a summary of the calculation basics, suggests a novel classification scheme of MADM methods based on their mathematical foundation, and reviews applications related to renewable energy topics. Finally, future research directions in this field are identified. © The Institution of Engineering & Technology 2024.

Abstract
The telecommunications industry is particularly competitive and characterized by very high churn rates. The literature on the topic is vast, but studies on the determinants of churn behavior are dispersed, failing to provide a comprehensive view of the state of the art. Based on this research gap, this article aims to contribute to developing the literature on customer churn in the telecommunications sector by summarizing the current state of research, and identifying the main determinants of churn and switching intentions. It provides a systematic literature review (SLR) of 37 articles on the topic published between 1999 and 2022. The results reveal the existence of two research streams. The first, in which the studies are based on surveys examining the alleged intentions of subscribers to change operators, with criteria such as satisfaction and attitudes as predictors, and the second, dealing with subscribers' actual switching behavior and relating this to behaviors and characteristics extracted from internal customer management systems. All independent variables used to explain switching intention or real churn were mapped. It was found that age, gender, satisfaction, switching costs and barriers, and service quality are the most important determinants highlighted by the literature. Our study also outlines some insightful practical implications, which could be extended to other service sectors. The paper ends with a research agenda for future studies according to the gaps detected by the study's results. Among its limitations, this research excludes papers related to predictive models and studies not in English.

Abstract
Accurate detection and differentiation of grapevine canopies from other vegetation, along with individual grapevine row identification, pose significant challenges in precision viticulture (PV), especially within irregularly structured vineyards shaped by natural terrain slopes. This study employs aerial imagery captured by unmanned aerial vehicles (UAVs) and introduces an image processing methodology that relies on the orthorectified raster data obtained through UAVs. The proposed method adopts a data-driven approach that combines visible indices and elevation data to achieve precise grapevine row detection. Thoroughly tested across various vineyard configurations, including irregular and terraced landscapes, the findings underscore the method's effectiveness in identifying grapevine rows of diverse shapes and configurations. This capability is crucial for accurate vineyard monitoring and management. Furthermore, the method enables clear differentiation between inter-row spaces and grapevine vegetation, representing a fundamental advancement for comprehensive vineyard analysis and PV planning. This study contributes to the field of PV by providing a reliable tool for grapevine row detection and vineyard feature classification. The proposed methodology is applicable to vineyards with varying layouts, offering a versatile solution for enhancing precision viticulture practices.