Abstract
The iHandU system is a wearable device that quantitatively evaluates changes in wrist rigidity during Deep Brain Stimulation (DBS) surgery, allowing clinicians to find optimal stimulation settings that reduce patient symptoms. Robotic accuracy is also especially relevant in DBS surgery, as accurate electrode placement is required to increase effectiveness and reduce side effects. The main goal of this work is to integrate the advantages of each system in a closed-loop system between an industrial robot and the iHandU system. For this purpose, a comparative analysis of a Leksell stereotactic frame and neuro-robotic system accuracies was performed using a lab-made phantom. The neuro-robotic system reached 90% of trajectories, while the stereotactic frame reached all trajectories. There are significant differences in accuracy errors between these trajectories (p < 0.0001), which can be explained by the high correlation between the neuro-robotic system errors and the distance from the trajectory to the origin of the Leksell coordinate system (?=0.72). Overall accuracy is comparable to existing neuro-robotic systems, achieving a deviation of (1.0 ± 0.5) mm at the target point. The accuracy of DBS electrode positioning and stimulation parameters choice leads to better long-term clinical outcomes in Parkinson's disease patients. Our neuro-robotic system combines real-time feedback assessment of the patient's symptomatic response and automatic positioning of the DBS electrode in a specific brain area.

Abstract
There is a widespread social awareness for the need of environment protection and sustainable systems in different areas of human activity. In particular, the catering industry is responsible for a significant share of sewage systems pollution, due to daily leaks of food remnants containing Fat, Oil and Grease (FOG). This work focuses on building a combined IoT monitoring solution to automate the remote management of industrial FOG-Separators, aiming to prevent or reduce leakage of FOG and food debris into sewer systems. The proposed solution adopted the use of custom-made in-premises sensor motes integrating two particular sensors: an in-the-house developed conductivity sensor, built specifically to distinguish levels of water and FOG in industrial FOG-Separators; an off-the-shelf turbidity sensor integrated to assess the amount of water debris. Briefly, this work has four major fold contributions: i) design and implementation of a combined IoT sensing solution; ii) most significant was the development, test, and integration of the capacity-based sensor coupled to local sensor motes, for assessing Water/FOG levels; iii) assessing and profiling edge motes energy autonomy; iv) finally, deploying the combined IoT architecture to validate the entire process of monitoring and scheduling the maintenance of industrial FOG-Separators. © 2022 IEEE.

Abstract
Water loss is one of the factors that most affect a concessionaire's financial sustainability. Early detection of any anomaly in water consumption is very valuable. This article aims to carry out a preliminary study to detect change points in consumption associated with water meter malfunction. The dataset is composed of water consumption measurements of two different companies (a hotel and a hospital) located in the north of Portugal, obtained during a complete year. Different methods were implemented in order to study its effectiveness in the detection of change points in the time series related to a sharp decrease in water consumption. Results suggest that the Seasonal Decomposition of Time Series by Loess method (STL) and the combination of several breakpoint detection methods is a suitable approach to be implemented in a software system, in order to help the company in anomaly detection and in the decision-making process of substituting the water meters.

Abstract
The integrated power and natural gas system (IPGS) is a promising technique against extreme weather. In this letter, a valley-shaped resilience model is proposed to evaluate the performance of IPGSs under extreme weather and inter-energy assistances between the electric power sector and the natural gas sector. Furthermore, a quantification metrics framework for IPGS resilience evaluation is raised. Novel metrics are proposed, especially the distortion rate (Theta) and linepack effect (Delta t*) for quantifying coupling tightness and assistance from natural gas transient in resilience perspective respectively. Evaluation on different restoration strategies is conducted to validate the effectiveness of the proposed metrics framework.

Abstract
- A patient's ability to recall and retrieve health information contributes to a better health management. HealthTalks was developed to address these issues by recording a summary of a medical appointment, uttered by the physician, and transcribing it. For each appointment, the user can also take free-text notes. Nowadays, search engines have become a ubiquitous part of everyone's life and are expected on most applications. Here, we describe the development of a search engine for HealthTalks. The app's characteristics demand a lightweight and offline engine, which requires a specific solution rather than an existing library or service. Our approach combines SQLite's Full-Text Search 4 module, which includes ngram indexing, with traditional information retrieval techniques to rank the documents. We created a test collection with summaries of clinical appointments (our documents), information needs, search queries, and relevance assessments for an initial search engine evaluation. Using this test collection, we assessed performance using NDCG@10, the first rank position of a totally relevant result, and query latency. Results are promising, with an average NDCG of 0.97. The median rank position of the first relevant result varies between 1.9 and 1.95, depending on the use of 4-gram character tokenization, an aspect that did not significantly affect the results. We expect this work to be useful for future developments of full-text search engines in mobile environments.

Abstract
Refactoring code manually can be complex. Several refactoring tools were developed to mitigate the effort needed to create more readable, adaptable, and maintainable code. However, most of them continue to provide late feedback, assistance, and support on how developers should improve their software. That's where the concept of Live Refactoring comes in. We believe the immediate and continuous suggestion of refactoring candidates to the code will help reduce this problem. Therefore, we prototyped a Live Refactoring Environment that identifies, recommends, and applies Extract Method refactorings. We carried out an empirical experiment that showed us that our approach helped developers reach better code, with more quality, improving their refactoring experience.