Abstract
Automated Program Repair (APR) is an area of research focused on the automatic generation of bug-fixing patches. Current APR approaches present some limitations, namely overfitted patches and low maintainability of the generated code. Several works are tackling this problem by attempting to come up with algorithms producing higher quality fixes. In this experience paper, we explore an alternative. We believe that by using existing low-cost APR techniques, fast enough to provide real-time feedback, and encouraging the developer to work together with the APR inside the IDE, will allow them to immediately discard proposed fixes deemed inappropriate or prone to reduce maintainability. Most developers are familiar with real-time syntactic code suggestions, usually provided as code completion mechanisms. What we propose are semantic code suggestions, such as code fixes, which are seldom automatic and rarely real-time. To test our hypothesis, we implemented a Visual Studio Code extension (named pAPRika), which leverages unit tests as specifications and generates code variations to repair bugs in JavaScript. We conducted a preliminary empirical study with 16 participants in a crossover design. Our results provide evidence that, although incorporating APR in the IDE improves the speed of repairing faulty programs, some developers are too eager to accept patches, disregarding maintenance concerns.

Abstract
A non-parametric identification algorithm is proposed to identify Linear Time Periodic (LTP) systems. The period is unknown and can be any real positive number. The system is modelled as an ARX Linear Parameter Varying (LPV) system with a virtual scheduling signal consisting of two orthogonal sinusoids (a sine and a cosine) with a period equal to the system period. Hence, the system parameters are polynomial functions of the scheduling vector. As these polynomials may have infinite degree, a non-parametric model is adopted to describe the LPV system. This model is identified by a Gaussian Process Regression (GPR) algorithm where the system period is a hyperparameter. The performance of the proposed identification algorithm is illustrated through the identification of a simulated LTP continuous system described by a state-space model. The ARX-LTP discrete-time model estimated in the noiseless case was taken as the true model. Copyright (C) 2021 The Authors.

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AbstractIn this study, the geochemistry and mineralogy of regoliths formed on Fogo Island (Cape Verde), a polygenic stratovolcano built during the Quaternary, are used to assess the geomorphological factors that control the early stages of basalt weathering. Fogo Island soils are mainly derived from relatively homogenous silica-undersaturated basaltic rocks. However, a discernible exotic component is recognised in areas most exposed to prevailing winds by ratios on non-mobile elements that are hosted in different amounts by basaltic rocks (e.g., Th, Sc and Ti). Weathering extent is evidenced by a relative depletion in mobile elements (e.g., Na, Ca, Mg) and an enrichment in non-mobile elements (e.g., Ti, Fe, Sc, Al), the decomposition of the most labile minerals (olivines), and the enrichment in secondary components (phyllosilicates and some Fe-oxides, such as hematite-goethite), along with quartz supplied from non-volcanic areas. It depends on bedrock age and type (pyroclastic deposits vs. lava-flows). In particular, soils covering older volcanic units tend to be more affected by chemical alteration than those overlying younger units. In addition, more intense weathering is observed in locations characterised by a combination of moderate elevation, slopes with low gradient and relatively high rainfall. The present investigation shows that even in low humidity environments recently formed basalt are affected by weathering, with the extent of chemical decomposition being mainly determined by the age of surface exposure and local orographic/climatic features.

Abstract
Geophysical techniques can be successfully applied towards the detection of buried explosive devices, the ground-penetrating radar (GPR) being an example of one such method. This technology works through emission and reception of electromagnetic radio waves being thus able to detect the presence of a subsurface object fundamentally due to reflections from contrasting electromagnetic properties between the object and the surrounding medium (e.g., soil). Many factors can influence the success of a GPR survey (e.g., target type, soil type, environmental conditions, GPR antenna frequency, data processing techniques), being essential to know and understand their likely effects before performing GPR studies, mainly in real cases. In this paper, through the analysis of case studies related to the use of GPR technology towards the detection of buried explosive devices, we intend to arrange and layout the main prior knowledge that a forensic geophysical expert must have when dealing with this type of fieldwork.