Calprotectin and Imbalances between Acute-Phase Mediators Are Associated with Critical Illness in COVID-19

The trajectory from moderate and severe COVID-19 into acute respiratory distress syndrome (ARDS) necessitating mechanical ventilation (MV) is a field of active research. We determined serum levels within 24 h of presentation of 20 different sets of mediators (calprotectin, pro- and anti-inflammatory cytokines, interferons) of patients with COVID-19 at different stages of severity (asymptomatic, moderate, severe and ARDS/MV). The primary endpoint was to define associations with critical illness, and the secondary endpoint was to identify the pathways associated with mortality. Results were validated in serial measurements of mediators among participants of the SAVE-MORE trial. Levels of the proinflammatory interleukin (IL)-8, IL-18, matrix metalloproteinase-9, platelet-derived growth factor (PDGF)-B and calprotectin (S100A8/A9) were significantly higher in patients with ARDS and MV. Levels of the anti-inflammatory IL-1ra and IL-33r were also increased; IL-38 was increased only in asymptomatic patients but significantly decreased in the more severe cases. Multivariate ordinal regression showed that pathways of IL-6, IL-33 and calprotectin were associated with significant probability for worse outcome. Calprotectin was serially increased from baseline among patients who progressed to ARDS and MV. Further research is needed to decipher the significance of these findings compared to other acute-phase reactants, such as C-reactive protein (CRP) or ferritin, for the prognosis and development of effective treatments.     

An extended nonparametric homogeneously weighted moving average sign control chart

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted moving average (DHWMA) control chart based on the sign statistic is proposed for monitoring the location parameter of an unknown and continuous distribution. The performance of the proposed chart is measured through the run-length distribution and its associated characteristics by performing Monte Carlo simulations. The DHWMA sign chart is compared with other nonparametric sign charts, such as the homogeneously weighted moving average, generally weighted moving average (GWMA), double GWMA, and triple exponentially weighted moving average sign charts, as well as the traditional DHWMA chart. The results indicate that the proposed chart performs just as well as and in some cases better than its competitors, especially for small shifts. Finally, two examples are provided to show the application and implementation of the proposed chart.     

Deploying AL-FEC protection with online algorithms for multicast services over cellular networks

Reliability control is a key concern on the evolution of mobile multicast services. To this direction, the use of forward error correction (FEC) on the application layer is widely adopted in several mobile multicast standards. FEC is a feedback free error control method, where the transmitter introduces in advance redundant information within the source data to enable receivers recovering arbitrary data erasures. On multicast delivery where retransmission-based error recovery methods are not efficient, the most suitable error control method is the use of application layer forward error correction (AL-FEC) codes. In this work, we introduce novel AL-FEC deployment policies over mobile multicast environments utilizing online algorithms. We aim at the efficient application of AL-FEC protection with RaptorQ codes over multicast delivery in the context of competitive analysis. We provide a competitiveness analysis model of AL-FEC application over mobile multicast environments. Furthermore, we propose two online algorithms adjusting the introduced redundancy of AL-FEC protection according to several FEC encoding parameters and constraints of mobile multicast delivery.     

Performance evaluation of monitoring IoT systems using LoRaWan

The proliferation of smart devices, or even better, IoT devices, has led to the widespread development of applications that take advantage of these devices. Of particular interest is the precise localization of such a device. However, these use cases become extremely difficult when connectivity to end-devices is required even in areas where the signal is too low or different technologies co-exist for the transmission of the data. In this research work, we study LoRaWan and Wi-Fi as two possible candidates for data transmission. We are particularly focused on the study of the above technologies in terms of performance as well as application development that can be used as rescue monitoring systems. For this reason, we start by describing LoRa as an ideal low power and long-distance communication protocol on the IoT devices compared to the Wi-Fi network. We perform various simulations in terms of time on air transmission, bit error rate by changing important metrics to study the behavior of the whole mechanism. Based on our simulations, the main findings highlight that the contribution of a spreading factor and bandwidth optimizations can be applied to real hardware for real search and rescue (SAR) cases giving improved results in case of coverage and battery extension applications. As a continuation of our research, we developed a monitor application that collects and visualizes data from end-nodes (wearables). These data are processed gateway and network server to The Things Network (TTN) for further analysis. The proposed solution can be used in different rescue monitor scenarios such as identifying and find individuals of vulnerable groups or those belonging to group of people with a high probability of being lost. The purpose of the above solution is to overcome monitor problems on SAR cases, compare with WiFi and suggest a module supporting both technologies in order to be used in real experiments.

     

A data locality methodology for matrix–matrix multiplication algorithm

Matrix-Matrix Multiplication (MMM) is a highly important kernel in linear algebra algorithms and the performance of its implementations depends on the memory utilization and data locality. There are MMM algorithms, such as standard, Strassen–Winograd variant, and many recursive array layouts, such as Z-Morton or U-Morton. However, their data locality is lower than that of the proposed methodology. Moreover, several SOA (state of the art) self-tuning libraries exist, such as ATLAS for MMM algorithm, which tests many MMM implementations. During the installation of ATLAS, on the one hand an extremely complex empirical tuning step is required, and on the other hand a large number of compiler options are used, both of which are not included in the scope of this paper. In this paper, a new methodology using the standard MMM algorithm is presented, achieving improved performance by focusing on data locality (both temporal and spatial). This methodology finds the scheduling which conforms with the optimum memory management. Compared with (Chatterjee et al. in IEEE Trans. Parallel Distrib. Syst. 13:1105, 2002; Li and Garzaran in Proc. of Lang. Compil. Parallel Comput., 2005; Bilmes et al. in Proc. of the 11th ACM Int. Conf. Super-comput., 1997; Aberdeen and Baxter in Concurr. Comput. Pract. Exp. 13:103, 2001), the proposed methodology has two major advantages. Firstly, the scheduling used for the tile level is different from the element level’s one, having better data locality, suited to the sizes of memory hierarchy. Secondly, its exploration time is short, because it searches only for the number of the level of tiling used, and between (1, 2) (Sect. 4) for finding the best tile size for each cache level. A software tool (C-code) implementing the above methodology was developed, having the hardware model and the matrix sizes as input. This methodology has better performance against others at a wide range of architectures. Compared with the best existing related work, which we implemented, better performance up to 55% than the Standard MMM algorithm and up to 35% than Strassen’s is observed, both under recursive data array layouts.     

The acceptance and use of computer based assessment

The effective development of a computer based assessment (CBA) depends on students’ acceptance. The purpose of this study is to build a model that demonstrates the constructs that affect students’ behavioral intention to use a CBA. The proposed model, Computer Based Assessment Acceptance Model (CBAAM) is based on previous models of technology acceptance such as Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Usage of Technology (UTAUT). Constructs from previous models were used such as Perceived Usefulness, Perceived Ease of Use, Computer Self Efficacy, Social Influence, Facilitating Conditions and Perceived Playfulness. Additionally, two new variables, Content and Goal Expectancy, were added to the proposed research model. Data were collected from 173 participants in an introductory informatics course using a survey questionnaire. Partial Least Squares (PLS) was used to test the measurement and the structural model. Results indicate that Perceived Ease of Use and Perceived Playfulness have a direct effect on CBA use. Perceived Usefulness, Computer Self Efficacy, Social Influence, Facilitating Conditions, Content and Goal Expectancy have only indirect effects. These eight variables explain approximately 50% of the variance of Behavioural Intention.     

Optimization of heuristic search using recursive algorithm selection and reinforcement learning

The traditional approach to computational problem solving is to use one of the available algorithms to obtain solutions for all given instances of a problem. However, typically not all instances are the same, nor a single algorithm performs best on all instances. Our work investigates a more sophisticated approach to problem solving, called Recursive Algorithm Selection, whereby several algorithms for a problem (including some recursive ones) are available to an agent that makes an informed decision on which algorithm to select for handling each sub-instance of a problem at each recursive call made while solving an instance. Reinforcement learning methods are used for learning decision policies that optimize any given performance criterion (time, memory, or a combination thereof) from actual execution and profiling experience. This paper focuses on the well-known problem of state-space heuristic search and combines the A* and RBFS algorithms to yield a hybrid search algorithm, whose decision policy is learned using the Least-Squares Policy Iteration (LSPI) algorithm. Our benchmark problem domain involves shortest path finding problems in a real-world dataset encoding the entire street network of the District of Columbia (DC), USA. The derived hybrid algorithm exhibits better performance results than the individual algorithms in the majority of cases according to a variety of performance criteria balancing time and memory. It is noted that the proposed methodology is generic, can be applied to a variety of other problems, and requires no prior knowledge about the individual algorithms used or the properties of the underlying problem instances being solved.     

Flow on the Internet: a longitudinal study of Internet addiction symptoms during adolescence

Internet Addiction (IA) constitutes an excessive Internet use behavior with a significant impact on the user’s well-being. Online flow describes the users’ level of being absorbed by their online activity. The present study investigated age-related, gender, and flow effects on IA in adolescence. The sample comprised 648 adolescents who were assessed twice at age 16 and 18 years. IA was assessed using the Internet Addiction Test and online flow was assessed using the Online Flow Questionnaire. A three-level hierarchical model estimated age-related, gender, and online flow effects on IA symptoms and controlled for clustered random effects. IA symptoms decreased over time (for both genders) with a slower rate in males. Online flow was associated with IA symptoms and this remained consistent over time. Findings expand upon the available literature suggesting that IA symptoms could function as a development-related manifestation at the age of 16 years, while IA-related gender differences gradually increase between 16 and 18 years. Finally, the association between online flow and IA symptoms remained stable independent of age-related effects. The study highlights individual differences and provides directions for more targeted prevention and intervention initiatives for IA.     

Improved transmission line model for high-frequency modelling of through silicon vias

Accurate and reliable models can support Through Silicon Via (TSV) testing methods and improve the quality of 3D ICs. A model for expressing resistance and inductance of TSVs at frequencies up to 50 GHz is proposed. It is based on the two-parallel transmission cylindrical wires model, known also as the Transmission Line Model and improved through the fitting to ANSYS Q3D simulation results. The proximity effect between neighbouring TSVs that alters the paths through which current flows is empowered at high frequencies. The consideration of the dependence of the proximity effect on frequency for calculating TSV resistance and inductance is the main contribution of this work. Additionally, the modelling of resistance is extended to accurately correspond to a TSV in an array. The proposed models are in good agreement with the simulator results with an average error below 2% and 5.4% for the resistance and the inductance, respectively. The maximum error is 3% and 9.1%, respectively. In the case of the resistance of a TSV in an array, the maximum error is 4.7%. As long as the coefficients of the proposed equations have been extracted, the time for resistance and inductance calculation based on the presented models is negligible, compared to the time-consuming EM simulation.     

Coastal Aquifer Management Based on the Joint use of Density-Dependent and Sharp Interface Models

In pumping optimization of coastal aquifers, the evaluation of the objective function and constraints using density-dependent models is overwhelmed by complex and time-consuming numerical simulations. To address those cases where the available density-dependent model runs are very limited, due to excessive computational burden, an efficient optimization strategy is developed. The proposed methodology uses an efficient sharp interface model jointly with a complex density-dependent model in an evolutionary optimization algorithm. While most evaluations are based on the sharp interface model, the density-dependent model is selectively called to evaluate promising solutions and to improve the predictions of the sharp interface model through the adaptive modification of the saltwater-freshwater density ratio. The method is tested for pumping optimization problems in confined and unconfined coastal aquifers with multiple pumping wells. The optimal solutions are compared to those obtained by density-dependent as well as by sharp interface optimization alone. Under a very restrictive computational budget, the best feasible solution is attained in less than 25 density-dependent model runs for two optimization problems of 10 and 20 decision variables. The results indicate that this optimization method leads to good feasible solutions and that an improved estimation of optimal pumping rates can be achieved within a limited computational budget. The method could also stand as an efficient preliminary exploration of the optimal search space, to provide good feasible starting points for the implementation of more comprehensive methods of coastal aquifer management.