Abstract
Explanations of a model's biases or predictions are essential to medical image analysis. Yet, explainable machine learning approaches for medical image analysis are challenged by needs to preserve privacy of patient data, and by current trends in deep learning to use unsustainably large models and large datasets. We propose DeepFixCX for explainable and privacy-preserving medical image compression that is nimble and performant. We contribute a review of the field and a conceptual framework for simultaneous privacy and explainability via tools of compression. DeepFixCX compresses images without learning by removing or obscuring spatial and edge information. DeepFixCX is ante-hoc explainable and gives privatized post hoc explanations of spatial and edge bias without accessing the original image. DeepFixCX privatizes images to prevent image reconstruction and mitigate patient re-identification. DeepFixCX is nimble. Compression can occur on a laptop CPU or GPU to compress and privatize 1700 images per second of size 320 x 320. DeepFixCX enables use of low memory MLP classifiers for vision data; permitting small performance loss gives end-to-end MLP performance over 70x faster and batch size over 100x larger. DeepFixCX consistently improves predictive classification performance of a Deep Neural Network (DNN) by 0.02 AUC ROC on Glaucoma and Cervix Type detection datasets, and can improve multi-label chest x-ray classification performance in seven of 10 tested settings. In all three datasets, compression to less than 5% of original number of pixels gives matching or improved performance. Our main novelty is to define an explainability versus privacy problem and address it with lossy compression.This article is categorized under:Fundamental Concepts of Data and Knowledge > Explainable AICommercial, Legal, and Ethical Issues > Security and PrivacyFundamental Concepts of Data and Knowledge > Big Data Mining

Abstract
This work proposes a sensor that utilizes a transmission scheme for measuring glucose aqueous solutions based on surface plasmon resonance. A comparison between the performance of two sensors with similar lengths and different diameters is performed. The first sensor comprises a multimode optical fiber with a diameter of 400 µm and a 10 mm middle section of the cladding removed. The second sensor is similar, except that the fiber has a diameter of 600 µm. The sensors were evaluated for their performance in measuring glucose concentrations ranging from 0.0001 to 0.5000 g/mL. The 400 µm sensor demonstrated high sensitivity however, the sensor with a diameter of 600 µm attained a slightly higher maximum sensitivity of 322.0 nm/(g/mL).

Abstract
The growing use of technology and social media has resulted in the emergence of digital influencers, a new profession capable of changing the mentalities and behaviours of those who follow them. This study arises to better understand the potential impact digital influencers might have on the Portuguese population’s purchase behaviour and patterns, and for this purpose, seven hypotheses were formulated. An online questionnaire was conducted to respond to these theoretical assumptions and collected data from 175 respondents. A total of 129 valid answers were considered. It was possible to conclude that purchase intention does not necessarily translate into a purchase action. It was also concluded that the relationship between social network use and the purchase of products/services recommended by influencers is only significant for Instagram. Furthermore, individuals’ Generation is not significantly linked with purchasing a product/service recommended by influencers. Furthermore, a small percentage of respondents have also identified themselves as impulsive shoppers and perceived Instagram as their favourite social network. With the results of this study, it is also possible to state that the influencer’s opinion was classified as the last factor considered in the purchase decision process. Additionally, there is a weak negative association between purchasing a product/service recommended by influencers with sponsorship disclosure and remunerated partnership, which decreases credibility and discourages purchasing.

Abstract
Lightning hit a transmission powerline outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1m households. The power outage raised questions about the ability of the GB electricity grid to withstand rapid changes in frequency caused by outages and surges on the network. Grid inertia has been changing in recent years due to the emergence of renewable generation as a significant contributor to the energy mix. As part of climate change mitigation efforts, there has been an acceleration in the deployment of distributed renewable generation replacing conventional thermal power plants in grids across the world. As a result, there has been a change in the aggregate and regional inertial capacity, with consequences for the stability of these networks and their ability to withstand large variations in frequency. Measures to mitigate the consequences of this change to grid stability need to be evaluated and the level of investment required to prevent a reoccurrence of an event such as that of 9 August quantified. Simulating frequency events on the GB grid using a single-bus model involves a system of differential equations representing the overall generation and load present at the time. The standard model based on the swing equation assumes unlimited capacity in aggregated resources, the availability of these services for the duration of the frequency excursion and a homogeneous response without local variation. In simulating the effect of outages on the GB Grid frequency on 9 August 2019 and other events in the period 2018--2019, the effect of limiting these services to the capacity of resources engaged during the event is examined. Taking resource limitations into account enables the approximation of the frequency trace for documented network perturbations. Enhancing this model so that it represents a networked grid using an algebraic differential system of equations facilitates the simulation of the effects of localized variation in inertia and frequency response services on the propagation of transients across a network. Using this model, the effects of varying responses to transients can be investigated, and grids of varying scales and topologies can be compared to determine differences in their response to outages. The propagation of disturbances across domains within the network that have different frequency response characteristics can thereby be examined with a view to drawing conclusions about the optimal deployment of frequency response services, and their relative cost-effectiveness in delivering a stable supply as the proportion of renewable generation in the energy mix grows. The model is demonstrated to be generalizable by its application to simulating an outage on the Italian grid, with the results compared to similar results on that network. This demonstrates the facility of applying the model to examining power systems of different topologies and characteristics, and evaluating plans for their migration to zero-carbon generation. Insight is gained into the responses of various characteristics of the grid and how they interact with unplanned generation imbalances. Using this adapted model, events on the GB grid are examined to validate the influence of these features and evaluate the anticipated response to similar events in the future using energy-mix scenario projections. With the effectiveness of the model validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

Abstract
The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

Abstract
We present an imperative refinement language for the development of backtracking programs and discuss its semantic foundations. For expressivity, our language includes prospective values and preference - the latter being a variant of Nelson's biased choice that backtracks from infeasibility of a continuation. Our key contribution is to examine feasibility-preserving refinement as a basis for developing backtracking programs, and several key refinement laws that enable compositional refinement in the presence of non -monotonic program combinators.