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This study investigates the efficacy of adapted boxing, specifically Rock Steady Boxing, in mitigating dopamine decline in individuals with Parkinson disease. The research involved 40 participants with confirmed diagnosis of Parkinson disease who underwent biweekly RSB sessions over an 8 week period. Training regimen included activation, core exercise, and a cooldown phase. The findings revealed a significant amelioration in depressive symptoms through the sessions. Assessment using the Beck Depression Inventory demonstrated a progressive decrease in scores associated with depressive symptoms, particularly affective, cognitive, and somatic symptoms. The reduction in more severe symptoms was accompanied by an increase in milder symptoms. Statistical analysis confirmed the significance of the reduction in depressive symptoms over time, suggesting that physical activity, particularly RSB, may contribute to enhancing the quality of life for individuals with Parkinson disease. The positive impact was observed in both motor and depressive symptoms, suggesting an overall beneficial effect of exercise training. It is important to note that six participants withdrew from the study due to organizational reasons, resulting in a reduction in the participant count from 40 to 34. Nonetheless, the overall results suggest that RSB could be an effective approach to addressing depression in Parkinson patients, providing a complementary treatment option to conventional pharmacological therapy., Comment: 17 pages, figures 22, master's thesis in exercise science

Stream processing is extensively used in the IoT-to-Cloud spectrum to distill information from continuous streams of data. Streaming applications usually run in dedicated Stream Processing Engines (SPEs) that adopt the DataFlow model, which defines such applications as graphs of operators that, step by step, transform data into the desired results. As operators can be deployed and executed independently, the DataFlow model supports parallelism and distribution, thus making streaming applications scalable. Today, we witness an abundance of SPEs, each with its set of operators. In this context, understanding how operators' semantics overlap within and across SPEs, and thus which SPEs can support a given application, is not trivial. We tackle this problem by formally showing that common operators of SPEs can be expressed as compositions of a single, minimalistic Aggregate operator, thus showing any framework able to run compositions of such an operator can run applications defined for state-of-the-art SPEs. The Aggregate operator only relies on core concepts of the DataFlow model such as data partitioning by key and time-based windows, and can only output up to one value for each window it analyzes. Together with our formal argumentation, we empirically assess how an SPE that only relies on such an operator compares with an SPE offering operator-specific implementations, as well as study the performance impact of a more expressive Aggregate operator by relaxing the constraint of outputting up to one value per window. The existence of such a common denominator not only implies the portability of operators within and across SPEs but also defines a concise set of requirements for other data processing frameworks to support streaming applications.

A new water-Cherenkov radiation detector, located at the Argentine Marambio Antarctic Base (64.24S-56.62W), has been monitoring the variability of galactic cosmic ray (GCR) flux since 2019. One of the main aims is to provide experimental data necessary to study interplanetary transport of GCRs during transient events at different space/time scales. In this paper we present the detector and analyze observations made during one full year. After the analysis and correction of the GCR flux variability due to the atmospheric conditions (pressure and temperature), a study of the periodicities is performed in order to analyze modulations due to heliospheric phenomena. We can observe two periods: (a) 1 day, associated with the Earth's rotation combined with the spatial anisotropy of the GCR flux; and (b) $\sim$ 30 days due to solar impact of stable solar structures combined with the rotation of the Sun. From a superposed epoch analysis, and considering the geomagnetic effects, the mean diurnal amplitude is $\sim$ 0.08% and the maximum flux is observed in $\sim$ 15 hr local time (LT) direction in the interplanetary space. In such a way, we determine the capability of Neurus to observe anisotropies and other interplanetary modulations on the GCR flux arriving at the Earth., Comment: to be published in Advances in Space Research

Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most effective way when only used individually, as sharing them provides even greater cost savings. Energy Peer-to-Peer (P2P) systems have thus been shown to be beneficial for prosumers and consumers through reductions in energy cost while also being attractive to grid or service providers. However, many practical challenges have to be overcome before all players could gain in having efficient and automated local energy communities; such challenges include the inherent complexity of matching together geographically distributed peers and the significant computations required to calculate the local matching preferences. Hence dedicated algorithms are required to be able to perform a cost-efficient matching of thousands of peers in a computational-efficient fashion. We define and analyze in this work a precise mathematical modelling of the geographical peer matching problem and several heuristics solving it. Our experimental study, based on real-world energy data, demonstrates that our solutions are efficient both in terms of cost savings achieved by the peers and in terms of communication and computing requirements. Our scalable algorithms thus provide one core building block for practical and data-efficient peer-to-peer energy sharing communities within large-scale optimization systems.

Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale stream processing applications. Given an application, SN parallelism instantiates several copies of each analysis task, making each instance responsible for a dedicated portion of the overall analysis, and relies on dedicated queues to exchange data among connected instances. On the one hand, SN parallelism can scale the execution of applications both up and out since threads can run task instances within and across processes/nodes. On the other hand, its lack of sharing can cause unnecessary overheads and hinder the scaling up when threads operate on data that could be jointly accessed in shared memory. This trade-off motivated us in studying a way for stream processing applications to leverage shared memory and boost the scale up (before the scale out) while adhering to the widely-adopted and SN-based APIs for stream processing applications. We introduce STRETCH, a framework that maximizes the scale up and offers instantaneous elastic reconfigurations (without state transfer) for stream processing applications. We propose the concept of Virtual Shared-Nothing (VSN) parallelism and elasticity and provide formal definitions and correctness proofs for the semantics of the analysis tasks supported by STRETCH, showing they extend the ones found in common Stream Processing Engines. We also provide a fully implemented prototype and show that STRETCH's performance exceeds that of state-of-the-art frameworks such as Apache Flink and offers, to the best of our knowledge, unprecedented ultra-fast reconfigurations, taking less than 40 ms even when provisioning tens of new task instances.

Streaming analysis is widely used in cloud as well as edge infrastructures. In these contexts, fine-grained application performance can be based on accurate modeling of streaming operators. This is especially beneficial for computationally expensive operators like adaptive stream joins that, being very sensitive to rate-varying data streams, would otherwise require costly frequent monitoring. We propose a dynamic model for the processing throughput and latency of adaptive stream joins that run with different parallelism degrees. The model is presented with progressive complexity, from a centralized non-deterministic up to a deterministic parallel stream join, describing how throughput and latency dynamics are influenced by various configuration parameters. The model is catalytic for understanding the behavior of stream joins against different system deployments, as we show with our model-based autoscaling methodology to change the parallelism degree of stream joins during the execution. Our thorough evaluation, for a broad spectrum of parameter, confirms the model can reliably predict throughput and latency metrics with a fairly high accuracy, with the median error in estimation ranging from approximately 0.1% to 6.5%, even for an overloaded system. Furthermore, we show that our model allows to efficiently control adaptive stream joins by estimating the needed resources solely based on the observed input load. In particular, we show it can be employed to enable efficient autoscaling, even when big changes in the input load happen frequently (in the realm of seconds)., Comment: part of the experimental campaign has been included in a different contribution

The LAGO (Latin American Giant Observatory) observatory is an experiment that spans over Latin America in a wide range of latitudes that gives different rigidity cut offs for the enter of cosmic rays in the atmosphere. The motivation of the Observatory is to study atmospheric radiation and space weather through the measurement of the secondary emission of low energy cosmic rays at ground level using Water Cherenkov Detectors (WCD). This work presents the contributions of the LAGO collaboration to the 2019 36th ICRC., Comment: International Cosmic Ray Conference 2019

Piecewise Linear Approximation (PLA) is a well-established tool to reduce the size of the representation of time series by approximating the series by a sequence of line segments while keeping the error introduced by the approximation within some predetermined threshold. With the recent rise of edge computing, PLA algorithms find a complete new set of applications with the emphasis on reducing the volume of streamed data. In this study, we identify two scenarios set in a data-stream processing context: data reduction in sensor transmissions and datacenter storage. In connection to those scenarios, we identify several streaming metrics and propose streaming protocols as algorithmic implementations of several state of the art PLA techniques. In an experimental evaluation, we measure the quality of the reviewed methods and protocols and evaluate their performance against those streaming statistics. All known methods have deficiencies when it comes to handling streaming-like data, e.g. inflation of the input stream, high latency or poor average error. Our experimental results highlight the challenges raised when transferring those classical methods into the stream processing world and present alternative techniques to overcome them and balance the related trade-offs., Comment: 12 pages+1 for references, 16 figures, 3 tables

The light detection and ranging (LiDAR) technology allows to sense surrounding objects with fine-grained resolution in a large areas. Their data (aka point clouds), generated continuously at very high rates, can provide information to support automated functionality in cyberphysical systems. Clustering of point clouds is a key problem to extract this type of information. Methods for solving the problem in a continuous fashion can facilitate improved processing in e.g. fog architectures, allowing continuous, streaming processing of data close to the sources. We propose Lisco, a single-pass continuous Euclidean-distance-based clustering of LiDAR point clouds, that maximizes the granularity of the data processing pipeline. Besides its algorithmic analysis, we provide a thorough experimental evaluation and highlight its up to 3x improvements and its scalability benefits compared to the baseline, using both real-world datasets as well as synthetic ones to fully explore the worst-cases.

Data streaming relies on continuous queries to process unbounded streams of data in a real-time fashion. It is commonly demanding in computation capacity, given that the relevant applications involve very large volumes of data. Data structures act as articulation points and maintain the state of data streaming operators, potentially supporting high parallelism and balancing the work between them. Prompted by this fact, in this work we study and analyze parallelization needs of these articulation points, focusing on the problem of streaming multiway aggregation, where large data volumes are received from multiple input streams. The analysis of the parallelization needs, as well as of the use and limitations of existing aggregate designs and their data structures, leads us to identify needs for proper shared objects that can achieve low-latency and high throughput multiway aggregation. We present the requirements of such objects as abstract data types and we provide efficient lock-free linearizable algorithmic implementations of them, along with new multiway aggregate algorithmic designs that leverage them, supporting both deterministic order-sensitive and order-insensitive aggregate functions. Furthermore, we point out future directions that open through these contributions. The paper includes an extensive experimental study, based on a variety of aggregation continuous queries on two large datasets extracted from SoundCloud, a music social network, and from a Smart Grid network. In all the experiments, the proposed data structures and the enhanced aggregate operators improved the processing performance significantly, up to one order of magnitude, in terms of both throughput and latency, over the commonly-used techniques based on queues.

The Latin American Giant Observatory (LAGO) is an extended cosmic ray observatory composed by a network of water-Cherenkov detectors spanning over different sites located at significantly different altitudes (from sea level up to more than $5000$\,m a.s.l.) and latitudes across Latin America, covering a huge range of geomagnetic rigidity cut-offs and atmospheric absorption/reaction levels. This detection network is designed to measure the temporal evolution of the radiation flux at ground level with extreme detail. The LAGO project is mainly oriented to perform basic research in three branches: high energy phenomena, space weather and atmospheric radiation at ground level. LAGO is built and operated by the LAGO Collaboration, a non-centralized collaborative union of more than 30 institutions from ten countries. These are the contributions of the LAGO Collaboration to the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The Netherlands, Comment: 9 proceedings, the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The Netherlands; in PoS(ICRC2015)

A large amount of magnetized plasma is frequently ejected from the Sun as coronal mass ejections (CMEs). Some of these ejections are detected in the solar wind as magnetic clouds (MCs) that have flux rope signatures. Magnetic clouds are structures that typically expand in the inner heliosphere. We derive the expansion properties of MCs in the outer heliosphere from one to five astronomical units to compare them with those in the inner heliosphere. We analyze MCs observed by the Ulysses spacecraft using insitu magnetic field and plasma measurements. The MC boundaries are defined in the MC frame after defining the MC axis with a minimum variance method applied only to the flux rope structure. As in the inner heliosphere, a large fraction of the velocity profile within MCs is close to a linear function of time. This is indicative of} a self-similar expansion and a MC size that locally follows a power-law of the solar distance with an exponent called zeta. We derive the value of zeta from the insitu velocity data. We analyze separately the non-perturbed MCs (cases showing a linear velocity profile almost for the full event), and perturbed MCs (cases showing a strongly distorted velocity profile). We find that non-perturbed MCs expand with a similar non-dimensional expansion rate (zeta=1.05+-0.34), i.e. slightly faster than at the solar distance and in the inner heliosphere (zeta=0.91+-0.23). The subset of perturbed MCs expands, as in the inner heliosphere, at a significantly lower rate and with a larger dispersion (zeta=0.28+-0.52) as expected from the temporal evolution found in numerical simulations. This local measure of the expansion also agrees with the distribution with distance of MC size,mean magnetic field, and plasma parameters. The MCs interacting with a strong field region, e.g. another MC, have the most variable expansion rate (ranging from compression to over-expansion).

Observations of magnetic clouds (MCs) are consistent with the presence of flux ropes detected in the solar wind (SW) a few days after their expulsion from the Sun as coronal mass ejections (CMEs). Both the \textit{in situ} observations of plasma velocity profiles and the increase of their size with solar distance show that MCs are typically expanding structures. The aim of this work is to derive the expansion properties of MCs in the inner heliosphere from 0.3 to 1 AU.We analyze MCs observed by the two Helios spacecraft using \textit{in situ} magnetic field and velocity measurements. We split the sample in two subsets: those MCs with a velocity profile that is significantly perturbed from the expected linear profile and those that are not. From the slope of the \textit{in situ} measured bulk velocity along the Sun-Earth direction, we compute an expansion speed with respect to the cloud center for each of the analyzed MCs. We analyze how the expansion speed depends on the MC size, the translation velocity, and the heliocentric distance, finding that all MCs in the subset of non-perturbed MCs expand with almost the same non-dimensional expansion rate ($\zeta$). We find departures from this general rule for $\zeta$ only for perturbed MCs, and we interpret the departures as the consequence of a local and strong SW perturbation by SW fast streams, affecting the MC even inside its interior, in addition to the direct interaction region between the SW and the MC. We also compute the dependence of the mean total SW pressure on the solar distance and we confirm that the decrease of the total SW pressure with distance is the main origin of the observed MC expansion rate. We found that $\zeta$ was $0.91\pm 0.23$ for non-perturbed MCs while $\zeta$ was $0.48\pm 0.79$ for perturbed MCs, the larger spread in the last ones being due to the influence of the environment conditions on the expansion.

Magnetic Clouds (MCs) are a particular subset of Interplanetary Coronal Mass Ejections (ICMEs), forming large scale magnetic flux ropes. In this work we analyze the evolution of a particular MC (observed on March 1998) using {\it in situ} observations made by two spacecraft approximately aligned with the Sun, the first one at 1 AU from the Sun and the second one at 5.4 AU. We study the MC expansion, its consequent decrease of magnetic field intensity and mass density, and the possible evolution of the so-called global ideal-MHD nvariants. We describe the magnetic configuration of the MC at both spacecraft using different models and compute relevant global quantities (magnetic fluxes, helicity and energy) at both helio-distances. We also track back this structure to the Sun, in order to find out its solar source. We find that the flux rope is significantly distorted at 5.4 AU. However, we are able to analyze the data before the flux rope center is over-passed and compare it with observations at 1 AU. From the observed decay of magnetic field and mass density, we quantify how anisotropic is the expansion, and the consequent deformation of the flux rope in favor of a cross section with an aspect ratio at 5.4 AU of $\approx 1.6$ (larger in the direction perpendicular to the radial direction from the Sun). We quantify the ideal-MHD invariants and magnetic energy at both locations, and find that invariants are almost conserved, while the magnetic energy decays as expected with the expansion rate found. The use of MHD invariants to link structures at the Sun and the interplanetary medium is supported by the results of this multispacecraft study. We also conclude that the local dimensionless expansion rate, that is computed from the velocity profile observed by a single spacecraft, is very accurate for predicting the evolution of flux ropes in the solar wind., Comment: 16 two-column pages, 8 figures. Accepted for publication in A&A