MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key challenge of coordinating heterogeneous preferences and requirements from multiple participants while preserving their privacy and minimizing financial costs for the aggregator. The participant agents use a novel Disjunctively Constrained Knapsack Problem optimization to curtail or shift the requested household appliances based on the selected demand reduction. Through case studies with electricity data from $25$ households, the proposed approach effectively reduced energy consumption's Peak-to-Average ratio (PAR) by $14.48$% compared to the original PAR while fully preserving participant privacy. This approach has the potential to significantly improve the efficiency and reliability of the electricity grid, making it an important contribution to the management of renewable energy resources and the growing electricity demand.Comment: 8 pages, IEEE Belgrade PowerTech, 202

Implementation of paediatric precision oncology into clinical practice: The Individualized Therapies for Children with cancer program ‘iTHER’

iTHER is a Dutch prospective national precision oncology program aiming to define tumour molecular profiles in children and adolescents with primary very high-risk, relapsed, or refractory paediatric tumours. Between April 2017 and April 2021, 302 samples from 253 patients were included. Comprehensive molecular profiling including low-coverage whole genome sequencing (lcWGS), whole exome sequencing (WES), RNA sequencing (RNA-seq), Affymetrix, and/or 850k methylation profiling was successfully performed for 226 samples with at least 20% tumour content. Germline pathogenic variants were identified in 16% of patients (35/219), of which 22 variants were judged causative for a cancer predisposition syndrome. At least one somatic alteration was detected in 204 (90.3%), and 185 (81.9%) were considered druggable, with clinical priority very high (6.1%), high (21.3%), moderate (26.0%), intermediate (36.1%), and borderline (10.5%) priority. iTHER led to revision or refinement of diagnosis in 8 patients (3.5%). Temporal heterogeneity was observed in paired samples of 15 patients, indicating the value of sequential analyses. Of 137 patients with follow-up beyond twelve months, 21 molecularly matched treatments were applied in 19 patients (13.9%), with clinical benefit in few. Most relevant barriers to not applying targeted therapies included poor performance status, as well as limited access to drugs within clinical trial. iTHER demonstrates the feasibility of comprehensive molecular profiling across all ages, tumour types and stages in paediatric cancers, informing of diagnostic, prognostic, and targetable alterations as well as reportable germline variants. Therefore, WES and RNA-seq is nowadays standard clinical care at the Princess Máxima Center for all children with cancer, including patients at primary diagnosis. Improved access to innovative treatments within biology-driven combination trials is required to ultimately improve survival

Implementation of paediatric precision oncology into clinical practice: The Individualized Therapies for Children with cancer program ‘iTHER’

iTHER is a Dutch prospective national precision oncology program aiming to define tumour molecular profiles in children and adolescents with primary very high-risk, relapsed, or refractory paediatric tumours. Between April 2017 and April 2021, 302 samples from 253 patients were included. Comprehensive molecular profiling including low-coverage whole genome sequencing (lcWGS), whole exome sequencing (WES), RNA sequencing (RNA-seq), Affymetrix, and/or 850k methylation profiling was successfully performed for 226 samples with at least 20% tumour content. Germline pathogenic variants were identified in 16% of patients (35/219), of which 22 variants were judged causative for a cancer predisposition syndrome. At least one somatic alteration was detected in 204 (90.3%), and 185 (81.9%) were considered druggable, with clinical priority very high (6.1%), high (21.3%), moderate (26.0%), intermediate (36.1%), and borderline (10.5%) priority. iTHER led to revision or refinement of diagnosis in 8 patients (3.5%). Temporal heterogeneity was observed in paired samples of 15 patients, indicating the value of sequential analyses. Of 137 patients with follow-up beyond twelve months, 21 molecularly matched treatments were applied in 19 patients (13.9%), with clinical benefit in few. Most relevant barriers to not applying targeted therapies included poor performance status, as well as limited access to drugs within clinical trial. iTHER demonstrates the feasibility of comprehensive molecular profiling across all ages, tumour types and stages in paediatric cancers, informing of diagnostic, prognostic, and targetable alterations as well as reportable germline variants. Therefore, WES and RNA-seq is nowadays standard clinical care at the Princess Máxima Center for all children with cancer, including patients at primary diagnosis. Improved access to innovative treatments within biology-driven combination trials is required to ultimately improve survival

Performance of Strategies for the Iterated Prisoner’s Dilemma in a Natural Environment

In nature, all species have their own behaviors and strategies for survival. Some species survive and reproduce, while others become extinct. This paper proposes a model to simulate these strategies and test their performance. Natural behavior is represented as strategies for the iterated Prisoner's Dilemma (IPD). Agents wielding one of ten common IPD strategies are deployed in a natural spatial environment with biologically realistic conditions, where they continuously play Prisoner's Dilemma games. If the payoffs are well enough, agents are able to reproduce. The harshness of the environment is determined by three factors. The cost of living directly controls the climate and age limitation and energy limitation affect an agent's ability to reproduce. Another influencing factor is evolution, which gives agents the option to adopt different strategies in later stages. Harsh environments are defined by high costs of living, high reproduction costs and low life expectancy. Results show that cooperative strategies are more likely to survive and reproduce in harsh environments. Moreover, evolution is in the advantage of cooperative strategies, because many unsuccessful defectors evolve into cooperators

MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

Distribution System Operators (DSOs) are responsible preventing grid congestion, while accounting for growing demand and the intermittent nature of renewable energy resources. Incentive-based demand response programs promise real-time flexibility to relieve grid congestion. To include residential consumers in these programs, aggregators can financially incentivize participants to reduce their energy demand and make aggregated energy reduction available to DSOs. A key challenge for aggregators is to coordinate heterogeneous preferences from multiple participants while preserving their privacy. This thesis proposes MARL-iDR: a decentralized Multi-Agent Reinforcement Learning approach to an incentive-based demand response program. The approach respects participants' privacy and preferences and makes decisions in real-time when deployed. The aggregator and each participant are controlled by Deep Reinforcement Learning agents that learn to maximize their reward. The aggregator agent learns a policy that dispatches suitable incentives to participants based on total energy demand and a target reduction, while minimizing financial costs. The participant agent learns to respond to these incentives by reducing consumption to a fraction of the original demand. The participant agents curtail or shift requested household appliances based on the selected consumption reduction using a novel Disjunctively Constrained Knapsack Problem optimization, while minimizing residents' dissatisfaction. A case study with real-world electricity data from 25 households demonstrates the capability to induce demand-side flexibility. The approach is compared to the case without demand response and to a centralized myopic baseline approach. A 9% reduction of the Peak-to-Average ratio (PAR) was achieved compared to the original PAR (no demand response)

Gevolgen van restrictieve maatregelen door COVID-19 uitbraak op eenzaamheid en sociale behoeften van bewoners, naasten en vrijwilligers in verpleeghuizen

Due to the (privacy) sensitive nature of the data and contractual limitations, the data are stored in a secure location at Tilburg University and cannot be shared. A codetree was created to analyse the data of this qualitative study and consists of main codes and subcodes, regarding the topics of this project. The codetree is available. Topic of the research: This qualitative study reported on the consequences of the COVID-19 restrictive measures in nursing homes, from the perspective of residents, close relatives, and volunteers. The focus was on social needs, negative consequences such as loneliness, resilience and moral judgement concerning the measures. Main research questions: 1. What was the impact of the restrictive measures on experienced loneliness and social needs of residents, close relatives, and volunteers in nursing homes? 2. Which resources were used by residents, close relatives, and volunteers to deal with the restrictive measures and what helped them to minimize the consequences of the measures? 3. How has the ban on in-person visits been judged by residents, close relatives, and volunteers in retrospect? 4. What are lessons learned