External Interruptions Research Articles

The effects of online interruption pace and richness on task performance

ABSTRACT This study investigates the impact of online interruption pace and information richness on managerial task performance. A computer game simulating external interruptions online was used to expose participants to either a slow or rapid pace of interruptions and provide them with either lean or rich information during the task. Task performance was measured based on the points scored in the game. Results indicate that the pace of interruptions significantly affected task performance, with participants experiencing a slow pace of interruptions performing better than those experiencing a rapid one. Information richness did not significantly affect task performance. However, the interaction between the two variables significantly affected task performance, with optimal performance occurring with fewer interruptions and richer information. The growing prevalence of remote work and its potential for more frequent and varied interruptions calls for investigating a more complex model of interruption effects, helping managers better understand interruptions in work environments.

Association of external interruptions with increased medication administration duration and self-interruptions: A direct observational study: Empirical research quantitative.

To examine task duration and frequency of self-interruptions among study participants during externally interrupted compared with externally uninterrupted simulated medication administration. Interruptions are prevalent during nursing medication administration and can lead to inefficient, delayed, omitted and unsafe patient care. Interrupted nursing tasks are shown to take longer to complete compared to uninterrupted tasks; however, studies seldom indicate if the time spent in the interruption was included or excluded in the reported task duration. It is unknown if the time spent in the interruption leads to longer task completion times or if other factors, such as the time needed to re-engage in the primary task and/or self-interruptions, are involved. Little is known about associations between external interruptions and self-interruptions during nursing tasks. Self-interruptions are caused by an individual's own decision to stop an activity to attend to something else. Cross-sectional within-subjects design. This two-site study investigated task duration and frequencies of self-interruptions during externally interrupted and externally uninterrupted simulated medication administration. Data on medication administration duration, external interruption duration and self-interruptions were collected via direct observation from November 2019-February 2020. The time spent in the external interruption was deducted from the medication administration duration. Thirty-five participants were included in the study. The externally interrupted task had a significantly longer duration and significantly more frequent self-interruptions within-subjects compared to the externally uninterrupted task. Self-interruptions were most often due to forgotten supplies. The findings suggest that the time needed to re-engage with an externally interrupted task and/or self-interruptions may lead to longer task completion times. Researchers are encouraged to investigate mediators of interruptions that lead to longer task completion times and errors. Findings can be used to develop and implement interruption management strategies that aim to improve the safety and quality of patient care. Equator guidelines were followed using the STROBE reporting method. No patient or public involvement in this study. Educators and researchers can use the study findings to guide teaching methods and direct future studies. By gaining a better understanding of the mediators of interruptions that create longer task completion times and increase the risk for errors, tailored interruption management strategies that aim to improve the safety and quality of healthcare can be developed and implemented.

SAR optimization and Convolutional Neural Network based fault estimations and for auto-landing control model

During auto landing, the aircraft flies at a significantly low altitude and low speed. So the consequent accidents and flight crashes are highly possible. Several constrained space and foremost external interruption while landing is considered as one of the most complex phases of the aircraft. Therefore it is necessary to recover the aircraft from various major disturbances and to estimate the rate of fault during aircraft landing. In addition to this, for the effective design of an aircraft, it is essential in determining the fault that affects the aircraft. To overcome the issues, this article aim to propose a novel CNNLSTM-SAR-based fault estimation approach to estimate the fault rate from various state trajectories of the aircraft. Here we employed a Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) model integrated with the Search and Rescue (SAR) optimization algorithm to react instantaneously to the broad range of failures such as actuator failure and failure due to the wind. Then the performances of the proposed CNNLSTM-SAR based fault estimation approach are compared and the results demonstrated that the proposed approach provide a smooth landing with minimum fault and error.

The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).

The Design of OBE Blended Teaching Case Based on Rain Classroom—Taking MCS-51 Microcontroller External Interrupt Teaching as an Example

The Design of OBE Blended Teaching Case Based on Rain Classroom—Taking MCS-51 Microcontroller External Interrupt Teaching as an Example

MULTITASKING BEHAVIOR IN THE WORKPLACE: A SYSTEMATIC REVIEW

The multitasking behavior is burgeoning in today’s work environment which reflects capabilities of individual to manage multiple things simultaneously to attain the efficient work performance. Study examines effect of multitasking on human behavior in workplace and its impact upon work performance. A search strategy was formulated to conduct this literature review-based study, and different databases containing scholarly research material were explored to find related literature. Inclusion-exclusion criteria were defined & included studies fulfilled selection criteria. PRISMA-P (2015) guidelines were followed to complete study. After reviewing literature pertaining to subject, finding established the fact, internal and external interruption triggers the individuals’ working behavior towards multitasking at the workplace. Multitask working behavior exerts an impact on overall work performance particularly, on work quality, productivity, and working memory. In this linking, no prior literature-based evidence is available in this domain in particular context. The scope of study is confined to published literature on human multitasking and human performance, literature on system multitasking and media multitasking is not included.

Circuit Design of Counting Electronic Scale Based on STC89C51 Single Chip Microcomputer

The main purpose of electronic scales is to measure, which can also be said to be for trading. Electronic scales have their relevant numerical measurement value restrictions when they are used. Therefore, they can only be used effectively within the measurement value range when they are used. As long as the weight does not exceed the measurement limit, they can be used at will. This measurement method greatly improves the measurement speed and the user's trading speed. STC89C51 single chip microcomputer as an 8-bit single chip microcomputer, first of all, the price is relatively low. The number of pins of 51 single chip microcomputer is up to 40. After subtracting some power supplies and special pins, 32 pins can be used, and all 32 pins can be used as input and output pins. Therefore, the performance price ratio of 51 single chip microcomputer is relatively high. At the same time, the application circuit of 51 single chip microcomputer is relatively simple, and only the crystal oscillator circuit and reset circuit are needed to achieve work. Besides, 51 single chip microcomputer also has serial communication, timer, external interrupt and other on-chip resources, which can meet the special requirements of some systems. Finally, in terms of programming, the 51 single-chip microcomputer can be used only after loading the header file, without configuring the input and output ports, and is also very convenient in programming. Therefore, STC89C51 single chip microcomputer is used as the basis and Proteus and KILL software are used to realize the design of electronic scales.

A connection between survival multistate models and causal inference for external treatment interruptions.

Recently, treatment interruptions such as a clinical hold in randomized clinical trials have been investigated by using a multistate model approach. The phase III clinical trial START (Stimulating Targeted Antigenic Response To non-small-cell cancer) with primary endpoint overall survival was temporarily placed on hold for enrollment and treatment by the US Food and Drug Administration (FDA). Multistate models provide a flexible framework to account for treatment interruptions induced by a time-dependent external covariate. Extending previous work, we propose a censoring and a filtering approach both aimed at estimating the initial treatment effect on overall survival in the hypothetical situation of no clinical hold. A special focus is on creating a link to causal inference. We show that calculating the matrix of transition probabilities in the multistate model after application of censoring (or filtering) yields the desired causal interpretation. Assumptions in support of the identification of a causal effect by censoring (or filtering) are discussed. Thus, we provide the basis to apply causal censoring (or filtering) in more general settings such as the COVID-19 pandemic. A simulation study demonstrates that both causal censoring and filtering perform favorably compared to a naïve method ignoring the external impact.

Integrated optimization of a regional integrated energy system with thermal energy storage considering both resilience and reliability

The regional integrated energy system (RIES) is widely adopted from the viewpoints of energy saving, emissions reduction and resilience enhancement. Especially, in case of external energy supply interruption caused by extreme events, a RIES can survive and ensure energy supply by multi-energy coupling and reserve capacity. Moreover, the resilience and reliability of a RIES can be further enhanced by employing energy storage equipment. In this study, an integrated optimization framework has been proposed for a RIES including thermal energy storage accounting for both resilience and reliability. Firstly, a rolling optimization model is developed to calculate the minimum capacity of backup thermal energy storage at each time. Following which, the day-ahead economic scheduling is carried out based on the constraint of backup energy. The Monte Carlo method based on the Markov process is used to simulate the system reliability. The reliability model can also reflect the increase of operation costs caused by backup thermal energy. As an illustrative example, a regional energy system located in Shanghai, China has been selected for analysis. According to the simulation results, the backup thermal energy storage improves both resilience and reliability of the RIES system, and ensures the load supply in case of external energy interruption or equipment failure. There are nine load-shedding periods with improved energy satisfaction rate under external energy interruptions in summer and five in winter. Furthermore, under 10,000 days of operation simulation, the supply interruptions due to equipment failure decrease from 47 to 15 in summer and 22 to 4 in winter.

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

When information processing is demanding: the importance of performance feedback

Working memory is a limited component of the cognitive system that requires attentional control to store information despite internal and external interruptions actively. There is a prolonged understanding that working memory capacity is a trait variable; and emerging evidence indicates that it possesses state variable qualities (i.e. momentary oscillations). Corresponding variations have been discovered in WM-consuming situations such as stress, anxiety, and intrusive thoughts. The current study investigated such phenomena using false feedback as a manipulation to investigate transient changes in working memory capacity. Participants performed a highly correlated working memory task, preceded by manipulating either negative or positive feedback, and then completed a second closely correlated working memory task. The results indicated that the manipulation affected participants’ performance on a subsequent second working memory task. Our results indicated that it is feasible to modulate the level of working memory capacity accessible on a limited basis. The findings of present study are pertinent to additional domains such as (social, educational, professional) in which information load may lead to stress and decrease working memory capacity and processing speed. The current research demonstrates the importance of feedback to enhance working memory capacity. Our study also recommends that the information load can be handled more efficiently to promote learning through different strategies.

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence was segmented according to the recognition results. Secondly, the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method, and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors, and the monitoring sequence correlation was further used to verify the assessment. Finally, the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.

Theta Dynamics Contribute to Retrieving Motor Plans after Interruptions in the Primate Premotor Area.

To achieve a behavioral goal, we often need to maintain an internal action plan against external interruption and thereafter retrieve the action plan. We recently found that the maintenance and updating of motor plans are reflected by reciprocal changes in the beta and gamma power of the local field potential (LFP) of the primate medial motor areas. In particular, the maintenance of the immediate motor plan is supported by enhanced beta oscillations. However, it is unclear how the brain manages to maintain and retrieve the internal action plan against interruptions. Here, we show that dynamic theta changes contribute to the maintenance of the action plan. Specifically, the power of the theta frequency band (4–10 Hz) of LFPs increased before and during the interruption in the dorsal premotor areas in two monkeys. Without theta enhancement before the interruption, retrieval of the internal action plan was impaired. Theta and beta oscillations showed distinct changes depending on the behavioral context. Our results demonstrate that immediate and suspended motor plans are supported by the beta and theta oscillatory components of LFPs. Motor cortical theta oscillations may contribute to bridging motor plans across behavioral interruptions in a prospective manner.

Patterns of triggers for on-task and off-task behaviors: university students in independent study

ABSTRACT With the rapid development of mobile devices and web-based technologies, it becomes common for students to switch between different tasks during study time. However, it remains unclear how the transition between on-task and off-task states happens and how digital devices affect the process. This study examines college students’ independent study in the library to delineate the pattern of on-task and off-task behavior, and identify the triggers for students’ engagement or disengagement. Through video recordings, the researchers captured the behaviors of over 266 students at the library. Observations were taken and used to double-check and confirm the behaviors. The study adopted the behavioral sequences method to scrutinize behavioral transition and explored gender differences. The results indicate that on-task behaviors tended to decline as the learning duration increased, whereas off-task behaviors tended to increase when students interacted with personal devices. As the most used device, smartphones posed persistent interference for learners, leading to reduced focus during independent study time. Moreover, male students were more likely to be influenced by external interruptions, whereas female students were more prone to the influence of internal interruptions. These findings can contribute to the development of research-based guidelines for improving students’ attention and self-adjusting during self-study time.

Emergency Physicians' Workstation Design: An Observational Study of Interruptions and Perception of Collaboration During Shift-End Handoffs.

Frequent external interruptions and lack of collaboration among team members are known to be common barriers in end-of-shift handoffs between physicians in the emergency department. In spite of being the primary location for this crucial and cognitively demanding task, workstations are not designed to limit barriers and support handoffs. The purpose of this study is to examine handoff characteristics, actual and perceived interruptions, and perceived collaboration among emergency physicians performing end-of-shift handoffs in physician workstations with varying levels of enclosures-(a) open-plan workstation, (b) enclosed workstation, and (c) semi-open workstation. Handoff and interruption characteristics were captured through in-person observations of 60 handoffs using an iPad-based tool. Additionally, physicians participating in the handoffs responded to a survey pertaining to their perception of interruptions and collaboration with clinicians during each phase. Other organizational and demographic data were obtained from the hospital database, surveys, and observations. Physicians working in the open workstation experienced a significantly higher number of interruptions/hour (18.08 int/hr) as compared to the semi-open (13.62 int/hr) and enclosed workstations (11.41 int/hr). Most physicians perceived that they were interrupted in the semi-open and open workstations. In addition, majority of physicians in the enclosed pod perceived high collaboration with clinicians involved in and present in the workstation during handoff. This correlational study showed positive outcomes experienced by physician working in the enclosed workstation as compared to the open and semi-open workstations.

The Big Problem With Little Interruptions to Classroom Learning

Narrative accounts of classroom instruction suggest that external interruptions, such as intercom announcements and visits from staff, are a regular occurrence in U.S. public schools. We study the frequency, nature, duration, and consequences of external interruptions in the Providence Public School District (PPSD) using original data from a district-wide survey and classroom observations. We estimate that a typical classroom in the PPSD is interrupted more than 2,000 times per year and that these interruptions and the disruptions they cause result in the loss of between 10 and 20 days of instructional time. Several findings suggest that there exists substantial scope for reducing interruptions. Administrators appear to systematically underestimate the frequency and negative consequences of interruptions. Furthermore, interruptions vary widely across schools and are largely caused by school staff. Schools might reduce disruptions to the learning environment by creating a culture that prioritizes instructional time, instituting better communication protocols, and addressing the challenges posed by student tardiness.

Managing power supply interruptions: a bottom-up spatial (frontier) model with an application to a Spanish electricity network

In December 2013, a new electricity law was approved in Spain as part of an electricity market reform including a new remuneration scheme for distribution companies. This remuneration scheme was updated in December 2019, and the new regulatory framework introduced a series of relevant modifications that aim to encourage regulated firms to reduce their power supply interruptions using a benchmarking approach. While some managerial decisions can prevent electricity power supply interruptions, other managerial decisions are more oriented toward mitigating the consequences of these interruptions. This paper examines the second type of decision using a unique dataset referring to the power supply interruptions of a Spanish distribution company network between 2013 and 2019. We focus our analysis on the effect which grid automatization has on restoration times, the relative efficiency of the maintenance staff, and the importance of its location. We estimate a bottom-up spatial model and a stochastic frontier model to examine both external and internal power supply interruptions at municipal level. While our frontier model is standard, our spatial model differs from a conventional one in that it is developed from scratch using the information of each individual power supply interruption.

Performance Analysis of Visible Light Communication (Vlc) with and Without Consider the External Noise Using Mach Zehnder (Mz) Modulator

Visible light communication is a high-speed developing technique with LEDs for both lighting and data communication. In this paper, examined the visible light communication performance over Mach Zehnder Modulation via Optisystem simulation tool. Quality factor and BER values of different link distance are predicted by optisystem simulation tool. The planned system can support 5 Gbps data rate up to 50 meter of link distance with a Quality factor of 15.71 for without any external noise interruption. In the case of external noise influence, the planned VLC system performance is also investigated through simulation tool. Consider the external noise interruption, the designed system can support 2Gbps data rate up to 20-meter link distance of Quality factor of 11.935. Quality factor for the system without any external noise influence is better compared to the system with external noise influence.

Non-genetically Editing Dysbiotic Microbiome Using Probiotic-specific Prebiotics for Treatments of Human Skin Diseases

External interruption in the balance of microbes in the microbiome can lead to a dysbiotic microbiome which has been linked to a variety of human diseases. Antibiotics have been widely used for treatments of infectious diseases in humans, but they are generally not pathogen specific and run a risk of yielding antibiotic-resistant microbes. Inappropriate use of antibiotics may disrupt the homeostasis of human microbiome, causing dysbiotic microbiome and abolishing natural and self-curative of human body. Here, we review a new approach by using bacterial specific prebiotics to amplify the fermentation activity of probiotic bacteria against pathogens and keep the microbiome balanced for optimal health. The pros and cons of using skin prebiotic as a new modality for improvement of skin health are compared to other treatments including antibiotics, vaccines, bacteriophages, genetically modified/engineering bacteria and live skin probiotic bacteria.

Scheduling periodic and aperiodic tasks with time, energy harvesting and precedence constraints on multi-core systems

This paper deals with the real-time scheduling problem of multi-core systems powered by renewable energy harvested from environment. They handle two types of software tasks which are mapped to cores statically and not allowed to migrate. A task can be periodic which may depend on other tasks’ results, or aperiodic which is added to the system to cope with external interruptions. The uncertainty of energy availability in energy harvesting systems makes real-time scheduling more challenging because energy constraints can be violated to ensure real-time performance. A novel scheduling strategy is proposed to effectively compute deadlines allowing for tasks and messages to meet related constraints. This method consists of two phases, (i) the first one defines different time slots each of which is characterized by energy and frequency parameters to cope with the energy availability issue, and (ii) the second one calculates the deadlines ensuring real-time system feasibility by considering the invocation of aperiodic task execution and task precedence constraints. The originality of the current work compared with related studies is that it deals with multi-core, periodic and aperiodic tasks, dependency, energy harvesting, and real-time aspects simultaneously.