Abstract
Optical fiber sensors were implemented to measure in-situ temperature variations in an oscillatory flow crystallizer operating in continuous. The sensors were fabricated by cleaved in the middle 8 mm-length fiber Bragg gratings, forming tips with a Bragg grating of 4 mm inscribed at the fiber ends. The geometry of the sensors fabricated, with a diameter of 125 mu m, allowed the temperature monitorization of the process flow, inside the crystallizer, at four different points: input, two intermediate points, and output. The results revealed that the proposed technology allows to perform an in-situ and in line temperature monitorization, during all the crystallization process, as an alternative to more expensive and complex technology.

Abstract
Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Abstract

Abstract
Background Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. Objective This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. Method We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. Results We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. Discussion We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Conclusion Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.

Abstract
This study addresses the critical need to enhance patient's experience with healthcare mobile applications. With the exponential growth of healthcare apps over the past decade, it is imperative to understand the patients' perceptions of what is lacking and how healthcare mobile applications can be improved in terms of design, features, and communication between patients and providers, such as doctors or hospitals. This research was conducted in four phases: gathering insights into User-Interface (UI) and User-Experience, constructing a patient-focused survey, experimenting with various UI designs, and statistically analyzing survey responses about hospital mobile applications. Key findings from 82 responses highlighted the necessity to redesign both hospital app transaction processes and the apps themselves in terms of functionality and UI. Technological innovations like chatbots were underutilized due to the lack of affective computing in developing these features and a reported lack of user awareness. Regarding UI preferences, respondents favored larger text, less bold text, and blue as the primary color. Future developments should include direct communication with doctors and self-check-in features. Addressing these areas can significantly enhance patient satisfaction and engagement with healthcare mobile applications, particularly hospital apps.

Abstract