An optimal query assignment for wireless sensor networks

With the increased use of large-scale real-time embedded sensor networks, new control mechanisms are needed to avoid congestion and meet required Quality of Service (QoS) levels. In this paper, we propose a Markov Decision Problem (MDP) to prescribe an optimal query assignment strategy that achieves a trade-off between two QoS requirements: query response time and data validity. Query response time is the time that queries spend in the sensor network until they are solved. Data validity (freshness) indicates the time elapsed between data acquisition and query response and whether that time period exceeds a predefined tolerance. We assess the performance of the proposed model by means of a discrete event simulation. Compared with three other heuristics, derived from practical assignment strategies, the proposed policy performs better in terms of average assignment costs. Also in the case of real query traffic simulations, results show that the proposed policy achieves cost gains compared with the other heuristics considered. The results provide useful insight into deriving simple assignment strategies that can be easily used in practice

A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

An Optimal Query Assignment for Wireless Sensor Networks

A trade-off between two QoS requirements of wireless sensor networks: query waiting time and validity (age) of the data feeding the queries, is investigated. We propose a Continuous Time Markov Decision Process with a drift that trades-off between the two QoS requirements by assigning incoming queries to the wireless sensor network or to the database. To compute an optimal assignment policy, we argue, by means of non-standard uniformization, a discrete time Markov decision process, stochastically equivalent to the initial continuous process. We determine an optimal query assignment policy for the discrete time process by means of dynamic programming. Next, we assess numerically the performance of the optimal policy and show that it outperforms in terms of average assignment costs three other heuristics, commonly used in practice. Lastly, the optimality of the our model is confirmed also in the case of real query traffic, where our proposed policy achieves significant cost savings compared to the heuristics.Comment: 27 pages, 20 figure

Managing psychogenic pseudosyncope: Facts and experiences

Psychogenic pseudosyncope (PPS) is a common cause of apparent transient loss of conscious­ness (TLOC) with a dramatic impact on the quality of life. This review aims to give an overview of the definition, incidence, etiology, diagnosis, treatment, and prognosis of PPS based on a combination of literature data and personal experience. The limited literature on the subject suggests that PPS is relatively common but insufficiently recognized. PPS is probably similar to psychogenic nonepiteptic seizures (PNES), in which a long delay to diagnosis worsens the prognosis. A detailed history is of paramount importance for the diagnosis. The key feature in the history of patients with PPS is the occurrence of frequent, long attacks of apparent TLOC with closed eyes. The diagnosis is certain when a typical event is recorded during a tilt-table test with simultaneous blood pressure (BP), heart rate and video-electroencephalographic re­cordings. Home video and BP recording during an attack can be very useful. The diagnosis should be communicated to the patient in a way that is clear, understandable and does not cause offense. Although treatment options have not been investigated formally, the literature on PNES suggests that cognitive behavioral therapy is beneficial

The Effect of Humidity on the Knock Behavior in a Medium BMEP Lean-Burn High-Speed Gas Engine

The effects of air humidity on the knock characteristics of fuels are investigated in a lean-burn, high-speed medium BMEP engine fueled with a CH4 + 4.7 mole% C3H8 gas mixture. Experiments are carried out with humidity ratios ranging from 4.3 to 11 g H2O/kg dry air. The measured pressure profiles at non-knocking conditions are compared with calculated pressure profiles using a model that predicts the time-dependent in-cylinder conditions (P, T) in the test engine (“combustion phasing”). This model was extended to include the effects of humidity. The results show that the extended model accurately computes the in-cylinder pressure history when varying the water fraction in air. Increasing the water vapor content in air decreases the peak pressure and temperature significantly, which increases the measured Knock Limited Spark Timing (KLST); at 4.3 g H2O/kg dry air the KLST is 19 °CA BTDC while at 11 g H2O/kg dry air the KLST is 21 °CA BTDC for the same fuel. Excellent agreement is observed between the calculated knock resistance (using the Propane Knock Index, PKI) and the measured knock resistance (KLST) for the range in water content in air studied in this work. Since the effect of water on autoignition delay time is negligible, the observed increase in knock resistance of the fuel-air mixture is due a decrease in pressure and temperature of the end gas with increasing water content in as a result of changes in the mass burning rate, and thermophysical properties of the fuel-air mixture

The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database

In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5–89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN

Optimal Parameters of Deep Brain Stimulation in Essential Tremor:A Meta-Analysis and Novel Programming Strategy

The programming of deep brain stimulation (DBS) parameters for tremor is laborious and empirical. Despite extensive efforts, the end-result is often suboptimal. One reason for this is the poorly understood relationship between the stimulation parameters' voltage, pulse width, and frequency. In this study, we aim to improve DBS programming for essential tremor (ET) by exploring a new strategy. At first, the role of the individual DBS parameters in tremor control was characterized using a meta-analysis documenting all the available parameters and tremor outcomes. In our novel programming strategy, we applied 10 random combinations of stimulation parameters in eight ET-DBS patients with suboptimal tremor control. Tremor severity was assessed using accelerometers and immediate and sustained patient-reported outcomes (PRO's), including the occurrence of side-effects. The meta-analysis showed no substantial relationship between individual DBS parameters and tremor suppression. Nevertheless, with our novel programming strategy, a significantly improved (accelerometer p = 0.02, PRO p = 0.02) and sustained (p = 0.01) tremor suppression compared to baseline was achieved. Less side-effects were encountered compared to baseline. Our pilot data show that with this novel approach, tremor control can be improved in ET patients with suboptimal tremor control on DBS. In addition, this approach proved to have a beneficial effect on stimulation-related complications

Two-year effectiveness of a stepped-care depression prevention intervention and predictors of incident depression in primary care patients with diabetes type 2 and/or coronary heart disease and subthreshold depression; data from the Step-Dep cluster randomized controlled trial

Introduction Major depressive disorders (MDD), diabetes mellitus type 2 (DM2) and coronary heart disease (CHD) are leading contributors to the global burden of disease and often co-occur. Objectives To evaluate the two-year effectiveness of a stepped-care intervention to prevent MDD compared to usual care and to develop a prediction model for incident depression in DM2 and/or CHD patients with subthreshold depression. Methods Data of 236 Dutch primary care DM2/CHD patients with subthreshold depression (Patient Health Questionnaire 9 (PHQ-9) score ≥6, no current MDD according to the Mini International Neuropsychiatric Interview (DSM-IV criteria)), who participated in the Step-Dep trial were used. A PHQ-9 score of ≥10 at minimally one measurement during follow-up (at 3, 6, 9, 12 and 24 months) was used to determine the cumulative incidence of MDD. Potential demographic and psychological predictors were measured at baseline via web-based self-reported questionnaires and evaluated using a multivariable logistic regression model. Model performance was assessed with the Hosmer–Lemeshow test, Nagelkerke’s R2 explained variance and Area Under the Receiver Operating Characteristic curve (AUC). Bootstrapping techniques were used to internally validate our model. Results 192 patients (81%) were available at two-year follow-up. The cumulative incidence of MDD was 97/192 (51%). There was no statistically significant overall treatment effect over 24 months of the intervention (OR 1.37; 95% CI 0.52; 3.55). Baseline levels of anxiety, depression, the presence of >3 chronic diseases and stressful life-events predicted the incidence of MDD (AUC 0.80 interquartile range (IQR) 0.79-0.80; Nagelkerke’s R2 0.34 IQR 0.33-0.36). Conclusion A model with four factors predicted depression incidence during two-year follow-up in patients with DM2/CHD accurately, based on the AUC. The Step-Dep intervention did not influence the incidence of MDD. Future depression prevention programs should target patients with these four predictors present, and aim to reduce both anxiety and depressive symptoms

Acute effects of adaptive Deep Brain Stimulation in Parkinson's disease

Background: Beta-based adaptive Deep Brain Stimulation (aDBS) is effective in Parkinson's disease (PD), when assessed in the immediate post-implantation phase. However, the potential benefits of aDBS in patients with electrodes chronically implanted, in whom changes due to the microlesion effect have disappeared, are yet to be assessed. Methods: To determine the acute effectiveness and side-effect profile of aDBS in PD compared to conventional continuous DBS (cDBS) and no stimulation (NoStim), years after DBS implantation, 13 PD patients undergoing battery replacement were pseudo-randomised in a crossover fashion, into three conditions (NoStim, aDBS or cDBS), with a 2-min interval between them. Patient videos were blindly evaluated using a short version of the Unified Parkinson's Disease Rating Scale (subUPDRS), and the Speech Intelligibility Test (SIT). Results: Mean disease duration was 16 years, and the mean time since DBS-implantation was 6.9 years. subUPDRS scores (11 patients tested) were significantly lower both in aDBS (p=<.001), and cDBS (p = .001), when compared to NoStim. Bradykinesia subscores were significantly lower in aDBS (p = .002), and did not achieve significance during cDBS (p = .08), when compared to NoStim. Two patients demonstrated re-emerging tremor during aDBS. SIT scores of patients who presented stimulation-induced dysarthria significantly worsened in cDBS (p = .009), but not in aDBS (p = .407), when compared to NoStim. Overall, stimulation was applied 48.8% of the time during aDBS. Conclusion: Beta-based aDBS is effective in PD patients with bradykinetic phenotypes, delivers less stimulation than cDBS, and potentially has a more favourable speech side-effect profile. Patients with prominent tremor may require a modified adaptive strategy