Simulated firefighting task performance and physiology under very hot conditions

PURPOSE: To assess the impact of very hot (45°C) conditions on the performance of, and physiological responses to, a simulated firefighting manual-handling task compared to the same work in a temperate environment (18°C). METHODS: Ten male volunteer firefighters performed a 3-h protocol in both 18°C (CON) and 45°C (VH). Participants intermittently performed 12 × 1-min bouts of raking, 6 × 8-min bouts of low-intensity stepping, and 6 × 20-min rest periods. The area cleared during the raking task determined work performance. Core temperature, skin temperature, and heart rate were measured continuously. Participants also periodically rated their perceived exertion (RPE) and thermal sensation. Firefighters consumed water ad libitum. Urine specific gravity (USG) and changes in body mass determined hydration status. RESULTS: Firefighters raked 19% less debris during the VH condition. Core and skin temperature were 0.99 ± 0.20 and 5.45 ± 0.53°C higher, respectively, during the VH trial, and heart rate was 14-36 beats.min(-1) higher in the VH trial. Firefighters consumed 2950 ± 1034 mL of water in the VH condition, compared to 1290 ± 525 in the CON trial. Sweat losses were higher in the VH (1886 ± 474 mL) compared to the CON trial (462 ± 392 mL), though both groups were hydrated upon protocol completion (USG < 1.020). Participants' average RPE was higher in the VH (15.6 ± 0.9) compared to the CON trial (12.6 ± 0.9). Similarly, the firefighers' thermal sensation scores were significantly higher in the VH (6.4 ± 0.5) compared to the CON trial (4.4 ± 0.4). CONCLUSIONS: Despite the decreased work output and aggressive fluid replacement observed in the VH trial, firefighters' experienced increases in thermal stress, and exertion. Fire agencies should prioritize the health and safety of fire personnel in very hot temperatures, and consider the impact of reduced productivity on fire suppression efforts.

Processes of task performance as measured by the assessment of motor and process skills (AMPS): a predictor of work-related outcomes for adults with schizophrenia?

Objective: To determine whether the processes of task performance as measured by the Assessment of Motor and Process Skills (AMPS) would discriminate between the employment levels of adults with schizophrenia. Participants: Twenty adults with schizophrenia who were engaged either in competitive employment, supported employment, prevocational training, or nonvocational activities, participated in this exploratory study. Methods: Each participant completed the AMPS, the Positive and Negative Syndrome Scale (PANSS), the Addiction Severity Index (ASI), and theWorker Role Interview (WRI) to gather data about their occupational performance, symptoms, drug / alcohol use, and psychosocial / environmental factors that might influence their work related outcomes. Results: Analysis revealed a moderate correlation between the level of employment and the global scores of the process skills scale in the AMPS. Conclusions: This should be seen as preliminary evidence that beyond the basic cognitive functions, processes of task performance may also be a predictor of work related outcomes for this population. The results also highlighted the importance of considering personal causation and worker roles when assessing the work capacities of these clients. Finally, findings supported the four levels of employment used in this study, which appeared to form a continuum from nonvocational activities, prevocational training, supported employment, through to competitive employment.

Procedural learning deficits in specific language impairment (SLI): a meta-analysis of serial reaction time task performance

Meta-analysis and meta-regression were used to evaluate whether evidence to date demonstrates deficits in procedural memory in individuals with specific language impairment (SLI), and to examine reasons for inconsistencies of findings across studies. The Procedural Deficit Hypothesis (PDH) proposes that SLI is largely explained by abnormal functioning of the frontal-basal ganglia circuits that support procedural memory. It has also been suggested that declarative memory can compensate for at least some of the problems observed in individuals with SLI. A number of studies have used Serial Reaction Time (SRT) tasks to investigate procedural learning in SLI. In this report, results from eight studies that collectively examined 186 participants with SLI and 203 typically-developing peers were submitted to a meta-analysis. The average mean effect size was .328 (CI95: .071, .584) and was significant. This suggests SLI is associated with impairments of procedural learning as measured by the SRT task. Differences among individual study effect sizes, examined with meta-regression, indicated that smaller effect sizes were found in studies with older participants, and in studies that had a larger number of trials on the SRT task. The contributions of age and SRT task characteristics to learning are discussed with respect to impaired and compensatory neural mechanisms in SLI.

Automating performance and energy consumption analysis for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system is often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems is very important, which is the focus of this paper. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Performance modeling and analysis of heterogeneous meta-computing systems interconnection networks

The overall performance of a distributed system often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems is very important, which is the focus of this paper. In particular, we address the problem of interconnection networks performance modeling for heterogeneous meta-computing systems. We consider the meta-computing system as a typical multi-cluster system. Since the heterogeneity is becoming common in such systems, we take into account network as well as cluster size heterogeneity to propose the model. To this end, we present an analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems–which is the focus of this paper–is very important. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrate that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions. On the basis of the validated model, we propose an adaptive assignment function based on the existing heterogeneity of the system to minimize multi-user environment overhead.

Fighting fires and fatigue: sleep, physical activity, and physical task performance

 During bushfires, firefighters often work long shifts with little sleep. Grace’s research showed that although firefighters’ sleep is restricted, it does not adversely affect their ability to perform physical tasks. This contrasts the cognitive impairments associated with sleep loss and could prompt revision of fatigue management guidelines in physically-demanding occupations.

Cultural intelligence (CQ) in virtual, cross-cultural interactions: generalizability of measure and links to personality dimensions and task performance

Previous studies have focused on investigating CQ in face-to-face contexts but very few have assessed CQ in virtual, cross-cultural interactions. This study highlights the relevance of cultural intelligence (CQ) as an intercultural capability in cross-cultural communications that are virtual. This two-study research (study 1: n = 274; study 2: n = 223) conducted in call centers in the Philippines (a) assesses the generalizability of the four-factor CQ model (i.e., cognitive, metacognitive, motivational and behavioral CQ) as applied in the virtual context and (b) tests the relationship between CQ, personality dimensions (i.e., openness to experience and extraversion) and supervisor’s ratings of task performance. Study 1 results show that the structural validity of the four-factor CQ model was supported with minor issues in some ofthe items indicating the need to modify the CQ measure when utilized in the virtual context. Study 2 results show that CQ is positively and significantly related to openness to experience and extraversion. In addition, results show that CQ predicts task performance highlighting the importance of developing CQ among call center representatives and other working professionals who virtually engage and interact with clients and customers from culturally diverse backgrounds.

StressCloud: a tool for analysing performance and energy consumption of cloud applications

Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption. A demonstration video can be accessed at: https://www.youtube.com/watch?v=0l4-a-CNtVQ.

Automated analysis of performance and energy consumption for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.