Safety Climate Assessment: The Implementation of Psychological Fatigue Indicators in Airline Fatigue Risk Management Systems

In commercial aviation, safety is of paramount importance and an indispensable prerequisite for building extensive, dependable air transportation networks. Several aspects affect airline safety, from training to operations and organizational conditions. The critical importance of aircrew fatigue has long been identified; in 2016, nearly 20% of all accidents under investigation by the US NTSB had it listed as one of the probable causes or contributing factors. This study aims to evaluate the utility of adding psychological fatigue elements to the fatigue self-report and survey tools currently used by airlines. Research goals include identifying psychological fatigue markers in airline fatigue survey forms, analyzing these instruments to determine their sensitivity in identifying fatigue that is not exclusively attributed to physiological factors, and investigating potential improvements of these instruments for use in commercial aviation. The authors examined the effect of psychological and physiological fatigue factors on the aviation industry's overall state and dynamics, especially given the consequences of the recent pandemic. Analysis indicates that incorporating psychological items to existing fatigue reporting and survey instruments significantly changes the measured fatigue levels, suggesting that measuring physiological fatigue alone may not be sufficient to accurately determine overall fatigue levels. The proposed implementation of psychological Fatigue Indicators identifies critical and significant risks related to the current Fatigue Risk Management System, especially in atypical periods when the aviation industry is experiencing substantial disruptions and psychological fatigue is prevalent

A comparative analysis of job satisfaction among military and airline pilots:During, and post COVID-19

This research aimed to examine the impact of the COVID-19 pandemic on the Job Satisfaction of military and airline pilots, in order to identify factors that influence the sustainability of aviation operations during global economic shocks, through the prism of industrial and organizational (I/O) psychology. The primary focus was on identifying factors linked to pilots' Job Satisfaction differentiations over the time course of the pandemic, and proposing measures to mitigate their negative effects. Survey Job Satisfaction data were collected from 203 pilots during the COVID-19 pandemic in March 2021, and were subsequently measured in a quasi-identical sample of 205 pilots after the pandemic in May 2023. During the pandemic, the results indicate that airline pilots experienced an acute decrease in Job Satisfaction due to the disruptive nature of COVID-19. This decline was primarily attributed to factors such as pay cuts and reduced promotion prospects, which consequently resulted in feelings of limited job security and future uncertainty. Importantly, a substantial recovery of airline pilots JS was observed following the end of the pandemic. In contrast, military pilots' levels remained relatively constant over the duration of the pandemic, as military organizations typically received government provided financial security. To improve commercial sustainability, it is recommended that airline companies enhance their preparedness for future crises by minimizing the financial impact experienced by pilots. Additionally, effective communication strategies should be implemented to address and alleviate uncertainty among pilots. It is crucial to prevent adverse psychological conditions among pilots, as they play a critical role in maintaining flight safety.</p

Arax: A Runtime Framework for Decoupling Applications from Heterogeneous Accelerators

Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as TensorFlow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available resources elastically during application execution, and (c) reducing the required programming effort. In this paper, we present Arax, a runtime system that decouples applications from heterogeneous accelerators within a server. First, Arax maps application tasks dynamically to available resources, managing all required task state, memory allocations, and task dependencies. As a result, Arax can share accelerators across applications in a server and adjust the resources used by each application as load fluctuates over time. dditionally, Arax offers a simple API and includes Autotalk, a stub generator that automatically generates stub libraries for applications already written for specific accelerator types, such as NVIDIA GPUs. Consequently, Arax applications are written once without considering physical details, including the number and type of accelerators. Our results show that applications, such as Caffe, TensorFlow, and Rodinia, can run using Arax with minimum effort and low overhead compared to native execution, about 12% (geometric mean). Arax supports efficient accelerator sharing, by offering up to 20% improved execution times compared to NVIDIA MPS, which supports NVIDIA GPUs only. Arax can transparently provide elasticity, decreasing total application turn-around time by up to 2x compared to native execution without elasticity support

Presuming a nature in the context of resilience

Over the last decades, the debate on climate change has brought back the everlasting discussion on the conceptualization of nature and the delimitation of our relationship with it. The emergence of „destruction“ has been instrumental in transforming moral evil to natural, viz the transition from the mechanistic-instrumental view of nature to a romantic one, where the superiority of “logos” over nature is now reversed. In the context of this conceptual shift, the rhetoric of „security“ was raised, and today is mainly expressed through the mechanisms of „mechanistic resilience“, namely the persistence in an ideal, almost a metaphysical equilibrium state of functioning of all biotic and abiotic systems. However, at the same time and in the context of ecological science, in recent decades, parallel transformations have also occurred in the notion of “resilience”. The latter is no longer defined on the basis of maintaining a balance, but rather adapting to lasting change (part of which is the “destruction”) which is recognized as a structural element of all natural and non- natural processes. If faith in the equilibrium tried to respond to a “revengeful nature” or a nature perceived as danger then which nature responds to adaptation? Accepting the latter as the new state of optimum functioning means that we must accept a new notion of evil that stems from the theory of resilience but ultimately expands to the „construction“ of a nature

G-Safe: Safe GPU Sharing in Multi-Tenant Environments

Modern GPU applications, such as machine learning (ML) frameworks, can only partially utilize beefy GPUs, leading to GPU underutilization in cloud environments. Sharing GPUs across multiple applications from different users can improve resource utilization and consequently cost, energy, and power efficiency. However, GPU sharing creates memory safety concerns because kernels must share a single GPU address space (GPU context). Previous GPU memory protection approaches have limited deployability because they require specialized hardware extensions or access to source code. This is often unavailable in GPU-accelerated libraries heavily utilized by ML frameworks. In this paper, we present G-Safe, a PTX-level bounds checking approach for GPUs that limits GPU kernels of each application to stay within the memory partition allocated to them. G-Safe relies on three mechanisms: (1) It divides the common GPU address space into separate partitions for different applications. (2) It intercepts and checks data transfers, fencing erroneous operations. (3) It instruments all GPU kernels at the PTX level (available in closed GPU libraries) fencing all kernel memory accesses outside application memory bounds. We implement G-Safe as an external, dynamically linked library that can be pre-loaded at application startup time. G-Safe's approach is transparent to applications and can support real-life, complex frameworks, such as Caffe and PyTorch, that issue billions of GPU kernels. Our evaluation shows that the overhead of G-Safe compared to native (unprotected) for such frameworks is between 4\% - 12\% and on average 9\%

Civil society and institutional practices towards democratization of urban planning: case studies on three German cities

Social and urban movements, citizens' initiatives and their collective reactions co-produce a wide and differentiated space of multiple and even conflictual features, origins and intentions, where local and global issues, from social cohesion and solidarity at neighbourhood scale reclaiming the public space or the implications of economic crisis, are intertwined. Contemporary approaches on spatial planning tend to incorporate new methodological tools to cope with the movements of civil society, a series of procedures and practices usually described as bottom-up, informal or non-institutional, that originate from multiple synergies of distinct trajectories

Interactive and Urgent HPC: Challenges and Opportunities

As a broader set of applications from simulations to data analysis and machine learning require more parallel computational capability, the demand for interactive and urgent high performance computing (HPC) continues to increase. This paper overviews the progress made so far and elucidates the challenges and opportunities for greater integration of interactive and urgent HPC policies, techniques, and technologies into HPC ecosystems.Comment: 10 pages, submitted to IEEE CiSE journa

Perioperative structure and process quality and safety indicators: a systematic review

BACKGROUND: Clinical indicators assess healthcare structures, processes, and outcomes. While used widely, the exact number and level of scientific evidence of these indicators remains unclear. The aim of this study was to evaluate the number, type, and evidence base of clinical process and structure indicators currently available for quality and safety measurement in perioperative care. METHODS: We performed a systematic review searching Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Google Scholar, and System for Information in Grey Literature in Europe databases for English language human studies in adults (age >18) published in the past 10 years (January 2005–January 2016). We also included professional and governmental body publications and guidelines describing the development, validation, and use of structure and process indicators in perioperative care. RESULTS: We identified 43 860 journal articles and 43 relevant indicator program publications. From these, we identified a total of 1282 clinical indicators, split into structure (36%, n=463) and process indicators (64%, n=819). The dimensions of quality most frequently addressed were effectiveness (38%, n=475) and patient safety (29%, n=363). The majority of indicators (53%, n=675) did not have a level of evidence ascribed in their literature. Patient-centred metrics accounted for the fewest published clinical indicators. CONCLUSIONS: Despite widespread use, the majority of clinical indicators are not based on a strong level of scientific evidence. There may be scope in setting standards for the development and validation process of clinical indicators. Most indicators focus on the effectiveness, safety, and efficiency of care