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Safe and infinite resource scheduling using energy timed automata
No full textWe study the existence of infinite and safe schedules for resource-dependent real-time systems, in the setting of multiple continuous resources. Specifically, we explore the multi-variable extension of Energy Timed Automata, where variables are bounded by polyhedra in Rn. We ask the question of whether there exist infinite runs satisfying such boundary constraints and show how schedules can be synthesized by characterising these runs as limit sets using quantifier elimination for linear real arithmetic. We show that for linear limit sets, it is possible to characterise such infinite runs. Additionally, we relate this to an earlier decidability result for single-variable Energy Timed Automata that are flat and segmented, and show constructively that there exist flat and segmented multi-variable Energy Timed Automata that give rise to non-linear limit sets. Lastly, we solidify our framework and method with a case study. Specifically, a multi-agent extension of an industrial case concerned with oil tanks, originally provided by the HYDAC company.</p
Impact of avatar features and presence on treatment outcomes in virtual reality-assisted therapy for auditory hallucinations
No full textCompressing High-Frequency Time Series Through Multiple Models and Stealing from Residuals
No full textWind turbines are equipped with high-quality sensors that generate vast volumes of high-frequency time series. The time series are ingested on the edge and transferred to the cloud for later analytics. This process is complicated by challenges like low network bandwidth and high cloud storage costs. ModelarDB was proposed as a solution to efficiently manage time series across the entire pipeline by using so-called models for lossless or error-bounded lossy compression of time series. However, ModelarDB’s compression can be further improved through: 1) avoiding models that only represent few values by storing residuals (i.e., values that models fail to compress) explicitly with them; 2) exploiting error bounds even more through preprocessing; and 3) timestamp compression specialized for regular and irregular time series. We propose the multi-model compression method Fauna which uses 1) the novel model fitting method Platypus; 2) PMC and Swing for compressing values and; 3) the novel Macaque for compressing residuals and timestamps. Platypus is a model fitting method that uses different models for specialized compression of values and residuals. We then evaluate state-of-the-art lossless compression methods for 32-bit floats and propose preprocessing methods to add support for error-bounded compression. We present Macaque that includes MacaqueV and MacaqueTS. MacaqueV modifies Facebook Gorilla’s lossless compression method for 32-bit floats (GorillaV) and combines it with our novel preprocessing methods to now also enable error-bounded lossy compression. MacaqueTS is a lossless compression method for timestamps. Using only Platypus reduces ModelarDB’s storage use by up to 1.8x and significantly simplifies using the system. While also up to 7x better for lossless compression, ModelarDB with Fauna uses up to 2.5x less storage than ModelarDB and up to 14.5x, 7.2x, 17.5x and 14.2x less storage than ClickHouse, Apache IoTDB, Apache Parquet and TimescaleDB, respectively, with a realistic 1% error bound
Advances in diagnosis, classification, and management of pain in Parkinson's disease
No full textWith over 10 million people affected worldwide, Parkinson's disease is the fastest-growing neurological disorder. More than two-thirds of people with Parkinson's disease live with chronic pain, which can manifest in various stages of the disease, substantially affecting daily activities and quality of life. The Parkinson's disease Pain Classification System overcomes the limitations of previous classification systems by distinguishing between pain related to Parkinson's disease and unrelated pain, while also incorporating clinical and pathophysiological (mechanistic) descriptors such as nociceptive, neuropathic, and nociplastic pain. This system provides a framework for accurate diagnosis and mechanism-based therapy. Alongside the appropriate classification of pain, consideration of treatment approaches that include non-invasive (pharmacological and non-pharmacological) and invasive strategies tailored to specific types of pain will refine and inform research trials and clinical practice when it comes to treating pain in Parkinson's disease
Machine Learning-Driven Cellular-Satellite Multi-Connectivity for Monitoring Livestock Transport in Rural Areas
No full textEmerging domains such as wireless industrial control, vehicular communications, smart grids, and augmented reality demand low latency, high throughput, and high reliability from wireless communication systems. Unfortunately, single connectivity (SC) communications frequently fail to fulfill these stringent requirements. To address these challenges, employing a multi-connectivity (MC) solution appears to be a promising technique. In this paper, in the context of Horizon Europe COMMECT project, we seek to develop a multi-connectivity solution that intelligently integrates cellular and satellite networks for the purpose of monitoring livestock transport in rural regions where 5G coverage is limited. Multi-connectivity can be helpful for meeting EU regulations requiring seamless communication between transport units and the operational center to ensure animal welfare during transit. To achieve this, we employ machine learning (ML) models within a Classification and Regression framework in the proposed multi-connectivity solution. The ML models process radio-related key performance indicators (KPIs) as inputs to estimate network throughput and latency. The outputs of the model are used to decide whether to continue with the cellular link or activate the backup satellite link in the multi-connectivity setup, ensuring an almost uninterrupted connection. This capability is particularly crucial in regions where 5G coverage is limited, and maintaining a reliable connection is essential. To evaluate the proposed framework, we used a hybrid emulation setup based on experimental data collected in the northern part of Denmark. The emulation results demonstrate that the MC solution significantly outperforms the cellular SC. Although our solution is designed for livestock transport monitoring, it can be adapted for other applications, such as precision farming, in areas with insufficient 5G availability.</p
Neuroanatomy and lesion networks of central poststroke pain
No full textIdentifying lesion sites associated with central poststroke pain (CPSP) may facilitate targeted screening for early symptoms, possibly even paving the way for preventive measures and earlier treatment initiation. Here, we test the hypothesis that damage to a nociceptive pathway extending from the brainstem to the cerebral cortex, and including white matter tracts, is associated with CPSP. We investigated the lesion locations of 72 patients with CPSP relative to poststroke comparison subjects without pain (n = 123), divided into a discovery and independent validation data set. The study included three main analyses: (1) we compared lesion intersection with our a priori region of interest (ROI) between groups with and without CPSP, (2) we performed lesion-symptom mapping to evaluate whether lesions associated with CPSP localize to the a priori ROI, and (3) we used lesion network mapping to infer the broader structural and functional connectivity patterns associated with CPSP lesions. CPSP lesions overlapped the nociceptive pathway ROI to a greater extent than comparison lesions. Lesion-symptom mapping identified a CPSP-associated region overlapping with the ventrocaudal thalamus and adjacent white matter, which was located mostly within the a priori ROI. Lesion network mapping demonstrated that lesions associated with CPSP disrupt nodes and tracts of the nociceptive pathway ROI. Interestingly, the CPSP lesion network results demonstrated connectivity to intereffector nodes of the primary motor cortex, providing a novel link between CPSP and the somato–cognitive action network. Together, these findings indicate that CPSP can be conceptualized as a lesion-associated network disruption of the nociceptive pathway and somato–cognitive action network
Decentralized Reinforcement Learning for Adaptive Power Sharing in Hybrid DC Microgrids
No full text This paper proposes a decentralized voltage control strategy for islanded DC microgrids that replaces conventional droop control with a reinforcement learning (RL)-based approach. Using a Deep Deterministic Policy Gradient (DDPG) agent, the controller learns to generate real-time voltage references based solely on local measurements, eliminating the need for inter-unit communication. Compared to droop control, the proposed method reduces power sharing error from +30% to +8% and halves bus voltage deviation under high line impedance scenarios. The framework adapts to dynamic load and network conditions, offering a scalable and resilient control solution for next-generation microgrids.</p
Clinical practice guideline for nonpharmacological prevention and management of patient agitation in the adult intensive care unit (CALM ICU)
Full text linkBackground Agitation affects 32–70% of adult patients in the intensive care unit (ICU) and is associated with disruption of life-saving treatment, prolonged hospitalisation, and psychological trauma. While nonpharmacological interventions are increasingly encouraged to reduce the reliance on sedatives, existing guidelines predominantly focus on pharmacological management. This contributes to inconsistent practices and underutilisation of effective person-centred nonpharmacological alternatives. Objective The objective of this study was to develop evidence-based recommendations for the nonpharmacological prevention and management of patient agitation in the adult ICU. Method The clinical practice guideline for non-pharmacological prevention and management of patient agitation in the adult ICU (CALM ICU) was developed following the Australian National Health and Medical Research Council Guidelines for Guidelines and the Danish Health Authority’s manual on guideline development. The process included stakeholder consultation with ICU clinicians, researchers, patients, and family members on the initial scope of the guideline, a systematic review and an umbrella review, a three-round modified Delphi study involving 114 participants from Denmark and Australia, and finally, stakeholder and methodological reviews of the draft guideline. The Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess the certainty of the evidence. Results The guideline offers 14 recommendations, including four conditional recommendations and 10 consensus recommendations. These address early and systematic assessment, identifying and treating underlying causes of agitation, prioritising nonpharmacological interventions, and using multicomponent interventions. The recommendations also include using de-escalation strategies, reorientation, promoting sleep, adjusting stimuli, supporting comfort and relaxation, encouraging mobilisation, and involving family members. The guideline also includes two additional recommendations highlighting the importance of fundamental person-centred care and organisational support for ICU staff. Conclusions The CALM ICU guideline provides the best available evidence for reducing patient agitation through nonpharmacological strategies. It should be integrated into standard ICU care and serve as a foundation for education and practice. Further research is needed to strengthen the evidence base and explore implementation in diverse ICU settings. This guideline has been endorsed by the Australian College of Critical Care Nurses
Painless construction of unconditional bases for anisotropic modulation and Triebel-Lizorkin type spaces
No full textWe construct smooth localized orthonormal bases compatible with anisotropic Triebel-Lizorkin and Besov type spaces on Rd. The construction is based on tensor products of so-called univariate brushlet functions that are based on local trigonometric bases in the frequency domain, and the construction is painless in the sense that all parameters for the construction are explicitly specified. It is shown that the associated decomposition system form unconditional bases for the full family of Triebel-Lizorkin and Besov type spaces, including for the so-called α-modulation and α-Triebel-Lizorkin spaces. In the second part of the paper we study nonlinear m-term approximation with the constructed bases, where direct Jackson and Bernstein inequalities for m-term approximation with the tensor brushlet system in α-modulation and α-Triebel-Lizorkin spaces are derived. The inverse Bernstein estimates rely heavily on the fact that the constructed system is non-redundant.</p
The organisation of evaluations:The influence of the ministry of finance on evaluation systems
No full textBackground:Despite increasing scholarly interest in the organisation of evaluations within different countries’ political-administrative landscapes, not much attention has hitherto been paid to the consequences of a specific institutional set-up for the function of evaluations within government.Aims and objectives:This article investigates how the organisational anchorage of policy evaluations within central administration shapes the function those evaluations primarily serve.Methods:We focus on the role of ministries of finance for coordinating countries’ evaluation systems, and study its influence in Denmark and the Netherlands through a combination of document analysis and interviews with centrally placed civil servants.Findings:Our analysis shows how the ministries of finance come to influence the evaluation activities of the whole central administration by constituting a specific economic outlook on evaluation, which (1) narrows down the applied evaluation methods and criteria; (2) inserts the ministry of finance as primary evaluation user; and hereby (3) furthers accountability rather than learning as the main function of evaluations within central administration. In both countries, the result is that the ministry of finance’s main role in the evaluation systems favours somewhat defensive qualities, where evaluations are primarily used for control and piecemeal changes in policies, rather than fundamental revisions or reflections on the appropriateness of specific policies.Discussion and conclusions:Our findings indicate that the influence of evaluation systems is not only dependent on the degree of institutional anchorage of evaluation activities, but also very much a matter of whom the evaluation systems is centred around