A fault tree-based approach for aviation risk analysis considering mental workload overload

Many lives and aircrafts have been lost due to human errors associated with mental workload overload (MWLOL). Human errors are successfully considered in existing Fault Tree Analysis (FTA) methods. However, MWLOL is considered through Performance Shaping Factors indirectly and its information is hidden in FT construction, which is not conducive to analyze the root causes of human errors and risks. To overcome this difficulty, we develop a risk analysis method where Multiple Resources Model (MRM) is incorporated into FTA methods. MRM analyzes mental workload by estimating the resources used during performing concurrent tasks, probably including abnormal situation handling tasks introduced by basic events in FT. Such basic events may cause MWLOL and then trigger corresponding human error events. A MWLOL gate is proposed to describe MWLOL explicitly and add these new relationships to traditional FT. This new method extends previous FTA methods and provides a more in-depth risk analysis. An accident, a helicopter crash in Maryland, is analyzed by the proposed method.

Exhaustion of the Executive Control Capacity Eliminates Retrieval Induced Forgetting

Retrieval Induced Forgetting (RIF) demonstrates that retrieval of information can lead to forgetting of related information. The standard RIF paradigm involves studying a certain number of category-exemplar pairs; thereafter, half of the exemplars from half of the categories are retrieved. Finally, all studied pairs are recalled. RIF is revealed when unretrieved exemplars from the retrieved categories are more poorly recalled than exemplars from the unretrieved categories. One explanation for RIF asserts that inhibition prevents interference from the exemplars of the same category during the interpolated retrieval practice phase, which leads to forgetting of these items at final recall. An ongoing debate concerns whether this inhibition requires executive control or whether it is automatic. If inhibition in RIF involves executive control, then a task that will exhaust this limited capacity should reduce or eliminate the RIF effect. The effects of concurrent tasks during the retrieval practice phase have been shown to reduce or eliminate RIF, however, to our knowledge, the effects of prior tasks on RIF has not been investigated. In the present study, in one condition, we conducted an exhaustive inhibition task before the retrieval practice phase and compared this condition to the one in which the prior task was non-exhaustive. Results showed that the RIF effect was eliminated when the prior task was exhaustive. The results supported the executive control view for the inhibition mechanism behind RIF and further showed that exhaustion of the executive control capacity can impair inhibition in subsequent tasks.

Distributed in-memory data management for workflow executions

Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel execution control design is to manage workflow data for efficient executions while enabling user steering support. Data access for high scalability is typically transaction-oriented, while for data analysis, it is online analytical-oriented so that managing such hybrid workloads makes the challenge even harder. In this work, we present SchalaDB, an architecture with a set of design principles and techniques based on distributed in-memory data management for efficient workflow execution control and user steering. We propose a distributed data design for scalable workflow task scheduling and high availability driven by a parallel and distributed in-memory DBMS. To evaluate our proposal, we develop d-Chiron, a WMS designed according to SchalaDB’s principles. We carry out an extensive experimental evaluation on an HPC cluster with up to 960 computing cores. Among other analyses, we show that even when running data analyses for user steering, SchalaDB’s overhead is negligible for workloads composed of hundreds of concurrent tasks on shared data. Our results encourage workflow engine developers to follow a parallel and distributed data-oriented approach not only for scheduling and monitoring but also for user steering.

Effects of Dynamic Model Errors in Task-Priority Operational Space Control

SUMMARY Control algorithms of many Degrees-of-Freedom (DOFs) systems based on Inverse Kinematics (IK) or Inverse Dynamics (ID) approaches are two well-known topics of research in robotics. The large number of DOFs allows the design of many concurrent tasks arranged in priorities, that can be solved either at kinematic or dynamic level. This paper investigates the effects of modeling errors in operational space control algorithms with respect to uncertainties affecting knowledge of the dynamic parameters. The effects on the null-space projections and the sources of steady-state errors are investigated. Numerical simulations with on-purpose injected errors are used to validate the thoughts.

OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles

A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

Deglutition Impairment during Dual Task in Parkinson Disease Is Associated with Cognitive Status

Introduction Dysphagia is a relevant symptom in Parkinson disease (PD), and its pathophysiology is poorly understood. To date, researchers have not investigated the effects of combined motor tasks on swallowing. Such an assessment is of particular interest in PD, in which patients have specific difficulties while performing two movements simultaneously. Objective The present study tested the hypothesis that performing concurrent tasks could decrease the safety of swallowing in PD patients as visualized using fiberoptic endoscopic evaluation of swallowing (FEES). Methods A total of 19 patients and 19 controls matched by age, gender, and level of schooling were compared by FEES under two conditions: isolated swallowing and dual task (swallowing during non-sequential opposition of the thumb against the other fingers). The two tasks involved volumes of food of 3 mL and 5 mL. The PD subjects were classified according to the Hoehn & Yahr (H&Y) Scale, the Mini Mental State Examination (MMSE), and the Montreal Cognitive Assessment (MoCA). The FEES assessment was performed according to the Boston Residue and Clearance Scale (BRACS). Results The data showed a significant worsening of swallowing in the dual task assessment for both volumes (3 mL: p ≤ 0.001; 5 mL: p ≤ 0.001) in the PD group. A correlation between the MoCA and dual-task swallowing of 3 mL was also found. Conclusion These findings suggest that additional tasks involving manual motor movements result in swallowing impairment in patients with PD. Moreover, these data highlight the need to further evaluate such conditions during treatment and assessment of PD patients.

Cloud computing based load balancing algorithm for erlang concurrent traffic

<p>The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced. There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.</p>