Multitasking Strategies Research Articles

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

In the dynamic global trade environment, accurately predicting trade values of diverse commodities is challenged by unpredictable economic and political changes. This study introduces the Meta-TFSTL framework, an innovative neural model that integrates Meta-Learning Enhanced Trade Forecasting with efficient multicommodity STL decomposition to adeptly navigate the complexities of forecasting. Our approach begins with STL decomposition to partition trade value sequences into seasonal, trend, and residual elements, identifying a potential 10-month economic cycle through the Ljung–Box test. The model employs a dual-channel spatiotemporal encoder for processing these components, ensuring a comprehensive grasp of temporal correlations. By constructing spatial and temporal graphs leveraging correlation matrices and graph embeddings and introducing fused attention and multitasking strategies at the decoding phase, Meta-TFSTL surpasses benchmark models in performance. Additionally, integrating meta-learning and fine-tuning techniques enhances shared knowledge across import and export trade predictions. Ultimately, our research significantly advances the precision and efficiency of trade forecasting in a volatile global economic scenario.

Time Scarcity and Student Performance: Instructional Strategies for Busy Adult Online Students

Adult online college students often suffer from time scarcity, which results in a drain on cognitive capacity and executive function, thus lowering their ability to plan, reason, and multitask. Busy students often engage in tunneling, ignoring everything but the most pressing concern. To support these students, educators should recommend timelines for task completion and divide larger assignments into smaller tasks. To reduce feelings of time scarcity, classrooms should have a predictable rhythm of regular assignments, a clear syllabus, meaningful assignments, and no busywork. Allocating points for preparatory tasks affirms their value and demonstrates respect for students’ time. Educators can help students build slack into their schedules so that they can better navigate disruptions by recommending multitasking strategies, suggesting rules of thumb, highlighting key points in instructions, and distinguishing essential from non-essential resources. Better understanding the challenges of the busy adult online student can help educators more effectively support their success.

Framework for dimensioning battery energy storage systems with applied multi-tasking strategies in microgrids

The shifting from the traditional centralized electric sector to a distributed and renewable system presents some challenges. Battery energy storage technologies have proven effective in relieving some aspects of this transition by facilitating load control and providing flexibility to non-dispatchable renewable production. Therefore, this paper investigates how to dimension battery energy storage systems with applied multi-tasking strategies in microgrids. To this end, it proposes a framework to accurately depict how BESS can be financially and technically feasible by deploying multi-tasking strategies that fit the system characteristics of a microgrid while providing arguments for the financial incentive. The framework development is based on the principles of the analytical approach and is conceptualized in a three-part funnel structure. This framework has been tested using the case study of Aeroe microgrid and resulted in a proposed battery energy storage configuration. Based on the findings, the BESS implementation contributes to improve load behavior and to increase internal production utilization. A sensitivity analysis was performed, to investigate the robustness of the configurations. Collectively, the framework has proven to provide feasible results within a wide range of parameters. This framework could help the preliminary investigation phase when analyzing future battery energy storage system investments.

Principle Centered Management and Multitasking Strategies in Fostering Organizational Effectiveness

This investigation tried to find out the relationship of principle-centered management, multitasking strategies and organizational effectiveness among the public elementary teachers of San Francisco District, Division of San Pablo City. School based management and teachers? performance serve as to dependent variables of the study and were observed carefully as to its possible relationship with principle centered management and multitasking strategies. The respondents of the study were one hundred eighty-six (186) teachers in San Francisco District for the school year 2021 ? 2022 using a survey questionnaire through google forms as the main instrument in gathering the data. Simple descriptive statistics like frequency, percent, mean and standard deviation were used to describe the person ? related factors and perceptions of the respondents while the Pearson ? product ? moment correlation coefficient was used to identify the relationship. Hypothesis was tested at 0.05 probability level. Majority of the respondents are female, teaching 5 years and below, and having Teacher I positions. Most of the respondent graduated their Bachelor?s degree with M.A. units. Respondents perceived that principle centered management as highly practiced particularly personal vision, personal leadership, personal management, interpersonal leadership, emphatic communication, creative cooperation and balanced self-renewal.

Multitasking strategies make the difference: Separating processing-code resources boosts multitasking efficiency when individuals prefer to interleave tasks in free concurrent dual tasking.

Previous research has shown that individuals differ with respect to their preferred strategies in self-organized multitasking: They either prefer to work on one task for long sequences before switching to another (blocking), prefer to switch repeatedly after short sequences (switching), or prefer to respond almost simultaneously after processing the stimuli of two concurrently visible tasks (response grouping). In two experiments, we tested to what extent the choice of strategy and related differences in multitasking efficiency were affected by the between-resource competition (Wickens, 2002) of two tasks to be performed concurrently in a self-organized manner. All participants performed a set of dual tasks that differed with respect to the kind of stimuli (verbal vs. spatial) and/or responses (manual vs. vocal). The choice of strategy was hardly affected as most individuals persisted in their response strategy independent of the degree of resource competition. However, the efficiency of individuals preferring a switching or response-grouping strategy increased especially when the reduction in resource competition was response related (manual vs. vocal), leading even to considerable dual-tasking benefits under these circumstances. In contrast, individuals who preferred to block their responses did not achieve any considerable benefits (or costs) with either of the different dual tasks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Multitasking in Driving as Optimal Adaptation Under Uncertainty.

ObjectiveThe objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge.BackgroundMultitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment.MethodWe model the driver’s decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The supervisory level deploys attention based on per-task value estimates, which incorporate beliefs about risk. Model simulations are compared against human data collected in a driving simulator.ResultsHuman data show adaptation to the attentional demands of ongoing tasks, as measured in lane deviation and in-car gaze deployment. The predictions of our model fit the human data on these metrics.ConclusionMultitasking strategies can be understood as optimal adaptation under uncertainty, wherein the driver adapts to cognitive constraints and the task environment’s uncertainties, aiming to maximize the expected long-term utility. Safe and unsafe behaviors emerge as the driver has to arbitrate between conflicting goals and manage uncertainty about them.ApplicationSimulations can inform studies of conditions that are likely to give rise to unsafe driving behavior.

Does wearable device bring distraction closer to drivers? Comparing smartphones and Google Glass

BackgroundHead-up and wearable displays, such as Google Glass™, are sometimes marketed as safe in-vehicle alternatives to phone-based displays, as they allow drivers to receive messages without eye-off-the-road glances. However, head-up displays can still compromise driver performance (e.g., He et al., 2015b), as the distracting effect of interacting with any device will depend on the user's multitasking strategies. The present experiment examined drivers' interaction with a head-down smartphone display and a wearable head-up display. MethodParticipants performed a simulated driving task while receiving and responding to text messages via smartphone or the head-mounted display (HMD) on the Google Glass™. Incoming messages were signaled by an auditory alert, and responses were made vocally. ResultsWhen using Google Glass, participants' responses were quicker than that of smartphone, and the time to engage in a task did not vary according to lane-keeping difficulty. Results suggest that a willingness to engage more readily in distracting tasks may offset the potential safety benefits of wearable devices.

Flexibility of individual multitasking strategies in task-switching with preview: are preferences for serial versus overlapping task processing dependent on between-task conflict?

The prevalence and the efficiency of serial and parallel processing under multiple task demands are highly debated. In the present study, we investigated whether individual preferences for serial or overlapping (parallel) processing represent a permanent predisposition or depend on the risk of crosstalk between tasks. Two groups (n=91) of participants were tested. One group performed a classical task switching paradigm, enforcing a strict serial processing of tasks. The second group of participants performed the same tasks in a task-switching-with-preview paradigm, recently introduced by Reissland and Manzey (2016), which in principle allows for overlapping processing of both tasks in order to compensate for switch costs. In one condition, the tasks included univalent task stimuli, whereas in the other bivalent stimuli were used, increasing risk of crosstalk and task confusion in case of overlapping processing. The general distinction of voluntarily occurring preferences for serial or overlapping processing when performing task switching with preview could be confirmed. Tracking possible processing mode adjustments between low- and high-crosstalk conditions showed that individuals identified as serial processors in the low-crosstalk condition persisted in their processing mode. In contrast, overlapping processors split up in a majority adjusting to a serial processing mode and a minority persisting in overlapping processing, when working with bivalent stimuli. Thus, the voluntarily occurring preferences for serial or overlapping processing seem to depend at least partially on the risk of crosstalk between tasks. Strikingly, in both crosstalk conditions the individual performance efficiency was the higher, the more they processed in parallel.

Influences of Task Management Strategy on Performance and Workload for Supervisory Control

Individual differences between task management strategies in a dual task environment – as a possible contributing factor to failures in supervisory control – have not been heavily researched. The purpose of this study was to identify strategies utilized by human subjects operating in a dual task environment to determine the effect of task management strategy on performance and workload outcomes. Participants monitored 8 remote processes while simultaneously executing a cognitively demanding secondary task. Researcher observation and participant feedback were used to identify specifically how tasks were managed. Four competing strategies were identified: Balanced Interleaving, Balanced Multitasking, Adaptive Attack, and Adaptive Avoidance. Findings revealed that the Adaptive Attack and Balanced Multitasking strategies demonstrated more advantageous outcomes toward performance while also resulting in lower participant perceived workload; Balanced Interleaving showed marginally higher performance, but high workload; and Adaptive Avoidance resulted in both low workload and low performance.

The multifunctional use of urban greenspace

This paper calls attention to the critical role of greenspaces in cities, while it overviews the many functions they provide. From a theoretical perspective, the utility function of urban greenspaces concerns multiple dimensions. Temporal, spatial and social aspects clearly need a taxonomic approach, which is also described in this study. Thus, the prominent goal of this paper is to highlight the importance of the multifunctional use of urban green areas. It is next argued that multitasking strategies may enhance the use of farmland within urban areas and that this may turn out to be a win—win situation, provided urban planners are able to understand the different motivations of local communities. These basic issues are essential in identifying and mapping out attractive developments for modern urban greenspaces.

A Cognitive Constraint Model of the Effects of Portable Music-Player Use on Driver Performance

We describe an approach to modeling strategic variations in how people might select media content from an Apple iPod portable music player while driving. An experiment was conducted to determine the time required to select a target from a list using the scroll wheel on the iPod. It was found that a linear model accurately predicted the time to scroll through a list to a target. This model was then used in conjunction with a previously reported steering control model to derive a priori predictions for dual-task performance over the entire range of possible multitasking strategies. From this set of strategies, we then focused on identifying the fastest and the safest strategies for completing both a simple selection task and also a more complex selection task. It was found that the model predictions bracketed the observed human data from a recent study that investigated the effects of using an iPod while driving. Moreover, the analysis suggests that in order to compensate for the inherent risks of using devices that demand longer interaction episodes to complete a task, people might adjust their multitasking strategy by giving more time up to steering control while completing the secondary task.

A high-capacity LC/MS system for the bioanalysis of samples generated from plate-based metabolic screening.

HPLC/MS is a linear technique characterized by serial injection and analysis of individual samples. Parallel-format high-throughput screens for druglike properties present a significant analytical challenge. Analysis speed and system ruggedness are key requirements for bioanalysis of thousands of samples per day. The tasks involved in LC/MS analysis are readily divided into three areas, sample preparation/liquid handling, LC/MS method building/sample analysis, and data processing. Several automation and multitasking strategies were developed and implemented to minimize plating and liquid handling errors, reduce dead times within the analysis cycle, and allow for comprehensive review of data. Delivering multiple samples to multiple injectors allows the autosampler time to complete its wash cycles and aspirate the next set of samples while the previous set is being analyzed. A dual-column chromatography system provides column cycling and peak stacking and allows rapid throughput using conventional LC equipment. Collecting all data for a compound into a single file greatly reduces the number of data files collected, increases the speed of data collection, allows rugged and complete review of all data, and provides facile data management. The described systems have analyzed over 40 000 samples per month for two years and have the capacity for over 2000 samples per instrument per day.

Performance of level 3 BLAS kernels in a dynamically partitioned data-flow environment

The Dynamically Partitioned Data-Flow (DPDF) model is based on an original analysis concept of the data dependency graph at the instruction level. Instead of a breadth first analysis, as in a classical Data-Flow Model, we execute instructions along data-dependent paths. As a consequence, data locality can be exploited by reusing results between the execution of consecutive instructions. In addition, the different paths are not statically defined but arise from a dynamical partitioning of the graph. This model presents the advantage to support very small cost dynamic scheduling and multitasking strategies. In order to study the efficiency of this new model, a first architecture has been defined. This architecture is currently limited to a single processor with one serial processing unit but four graph analyzing units (called prefetch units). Each of these prefetch units is able to build dynamically its own execution path inside the Data-Flow graph of an application. The efficiency of this architecture is studied on a numerical benchmark composed of a subset of the Livermore loops and of three routines of the Level 3 BLAS (GEMM, SYRK and TRSM). Our goal in these experimentations is to demonstrate the ability of the four prefetch units to feed the ALU.

Multitasking: Experience with applications on a CRAY X-MP

On the multiprocessor vector-supercomputer CRAY X-MP, parallelism—beyond vectorization—can be exploited on the programming language level by two multitasking strategies: macrotasking and, more recently, microtasking. In this paper, multitasking results and experiences are presented which have been gained by applying these two implemented modes to linear-algebra and non-numerical algorithms as well as to a large fluid-flow simulation code. While comparing the concepts and realizations of macrotasking and microtasking, the features, tools, and problems of multitasking programming and the potential user benefit of these parallel processing techniques are discussed.