Real-time outage management in active distribution networks using reinforcement learning over graphs.

Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.

Polymorphisms in Cytokine Receptor and Regulator Genes are Associated with Levels of Exercise in Women Prior to Breast Cancer Surgery.

Background: Little is known about the genetic characteristics associated with exercise in women undergoing breast cancer surgery. Purpose: In a sample of women who were evaluated prior to breast cancer surgery (n = 310), we evaluated for differences in demographic and clinical characteristics between patients who did and did not exercise on a regular basis and evaluated for associations between polymorphisms in genes for pro- and anti-inflammatory cytokines, their receptors, and their transcriptional regulators. Methods: Patients completed an investigator-developed exercise questionnaire. Based on the recommended level of exercise (≥150 minutes/week), survivors were classified into no exercise (NoEx), less exercise (LessEx), or recommended exercise (RecEx) groups. Candidate gene analyses were done to identify relationships between polymorphisms and exercise group membership (i.e., NoEx vs. RecEx). Only 23.5% of the total sample met the recommendations for regular exercise. Results: Compared to the RecEx group (n = 78), patients in the NoEx group (n = 120) had less education; were less likely to report being White or Asia/Pacific Islander; more likely to report a lower household income; had a higher body mass index (BMI), had a poorer functional status; had a higher comorbidity burden; were more likely to self-report high blood pressure; and were more likely to have received neoadjuvant chemotherapy. Polymorphisms in IFNGR1 and NFKB1 were associated with membership in the NoEx group. Conclusions: While they warrant replication, our findings suggest that variations in cytokine-related genes may play a role in exercise behavior, and that clinicians need to assess for barriers to regular exercise and educate patients on its benefits.

Risk Factors Associated With Chemotherapy-Induced Nausea in the Week Before the Next Cycle and Impact of Nausea on Quality of Life Outcomes.

CONTEXT: Despite current advances in antiemetic treatments, between 19% and 58% of oncology patients experience chemotherapy-induced nausea (CIN). OBJECTIVES: Aims of this post hoc exploratory analysis were to determine occurrence, severity, and distress of CIN and evaluate for differences in demographic and clinical characteristics, symptom severity, stress; and quality of life (QOL) outcomes between oncology patients who did and did not report CIN in the week before chemotherapy (CTX). Demographic, clinical, symptom, and stress characteristics associated with CIN occurrence were determined. METHODS: Patients (n = 1296) completed questionnaires that provided information on demographic and clinical characteristics, symptom severity, stress, and QOL. Univariate analyses evaluated for differences in demographic and clinical characteristics, symptom severity, stress, and QOL scores between the two patient groups. Multiple logistic regression analysis was used to evaluate for factors associated with nausea group membership. RESULTS: Of the 1296 patients, 47.5% reported CIN. In the CIN group, 15% rated CIN as severe and 23% reported high distress. Factors associated with CIN included less education; having childcare responsibilities; poorer functional status; higher levels of depression, sleep disturbance, evening fatigue, and intrusive thoughts; as well as receipt of CTX on a 14-day CTX cycle and receipt of an antiemetic regimen that contained serotonin receptor antagonist and steroid. Patients in the CIN group experienced clinically meaningful decrements in QOL. CONCLUSION: This study identified new factors (e.g., poorer functional status, stress) associated with CIN occurrence. CIN negatively impacted patients' QOL. Pre-emptive and ongoing interventions may alleviate CIN occurrence in high-risk patients.

La medicina centrada en el paciente como método clínico / Patient-centered medicine as a clinical method

El modelo biomédico tradicional centrado en la enfermedad, con su énfasis en la tecnología, en los últimos años viene siendo cuestionado; en su lugar, se propugna el Modelo biopsicosocial, más integral, sistémico y holístico, que se centra en la persona como un ser biológico, psicológico y social. Para responder adecuadamente las necesidades y expectativas de los pacientes surge el método clínico centrado en el paciente (MCCP) desarrollado por Stewart y Brown (1995). Para ejemplificarlo, en este artículo tomamos un caso clínico, desarrollando los cuatro componentes de la MCCP: la exploración de la dolencia, la enfermedad y la salud; el entendimiento de la persona como un todo, la búsqueda de un espacio común para definir problemas, metas y roles que se adoptarán en el encuentro de la consulta y finalmente el desarrollo de la relación médico paciente.

The traditional biomedical model centered in the disease with emphasis on technological advances is being questioned in recent years. Instead of this model, a bio-psycho-social more integral, systemic and holistic model that is centered in the patient is proposed. To better answer the needs and expectations of the patients, Stewart and Brown (1995) developed a patient-centered clinical method (PCCM). To describe this method, we present a clinical case and explain the components of the method: exploring the ailment, integration of the concept of disease and health, understanding the patient as a whole with search of common ground to define problems, aims and roles during medical consultation, and finally the physician-patient relationship.