Task Periods Research Articles

Functional Near-Infrared Spectroscopy in Athletes Pre- and Post-Season Demonstrates Consistent Pattern of Frontal Cortical Activation with King-Devick Testing

Functional Near-Infrared Spectroscopy in Athletes Pre- and Post-Season Demonstrates Consistent Pattern of Frontal Cortical Activation with King-Devick Testing

CLASSIFICATION OF FORMS OF SHADOW ECONOMIC ACTIVITY FROM THE VIEWPOINT OF ENSURING ECONOMIC SECURITY

The article explores the problem of economic security and its important institutional threat – the shadow economy. The evolution of approaches to the problem of the shadow economy, external and internal conditions of functioning of the shadow economy are considered. The shadow schemes, which are used by some business entities to evade taxes, are revealed. The research objective – to explore the evolution of approaches to the problem of the shadow economy as an institutional threat to economic security, to reveal shadow schemes, by which business entities for the purpose of tax evasion, propose a classification of the shadow economy that can be used by fiscal and supervisory bodies in developing practical measures to reduce the impact Shadow economy on the economic security of the state. The methodological and theoretical basis for the article is general scientific and special methods of the theory of knowledge: system analysis and synthesis (in the study of the essence and conceptual provisions of the theory of economic security), synergistic representations (in the study of the shadow economy), logical generalization and the complex goal approach shadow economic activity), the program-target approach (in the formation of measures to counteract the shadow economy). Suggestions for the classification of shadow economic activities that various economists propose vary considerably depending on the period of research, goals and tasks that the authors consider to approach. The article examines the existing approaches to the analysis of the shadow economy, systematizes the external and internal conditions of its existence, proposes a classification of ways to transfer resources to the shadow turnover at the level of the business entity. Schemes of tax evasion are considered as a way of transferring resources to the shadow economy. Unified schemes of tax evasion are considered as an integral element of the shadow economy, as well as shadow economic activity in areas not related to taxation. Based on the analysis of existing schemes for the transfer of economic activity into the shadow turnover, it is proposed to classify the methods of economic evasion from social norms, rules, and obligations. Practical implications. The application of the proposed classification will greatly simplify the information interaction between those structures that oppose the shadow economy; this will allow more purposeful implementation of counteraction to shadow economic transactions, which are institutional threats to economic security.

Gamma EEG Correlates of Haptic Preferences for a Dial Interface

Consumers often develop preferences toward consumer electronics based not only on the visual appearance of a product, but also on its haptic interface. If consumers express a strong haptic preference for a consumer electronic product, they are more likely to purchase it. Hence, it is important to understand how consumers’ haptic preference for consumer electronics is formed. Conventional paper-based methods may not provide sufficient information for this purpose, because they provide post-event (i.e., after haptic experience) and environment-dependent (i.e., depending on the manner of asking questions, the person asking the questions, and so on.) data. Therefore, the present study investigated haptic preferences for consumer electronics using neural responses during haptic experiences, which provide the advantage of observing changes while the user is manipulating the product and obtaining environment-independent data. We measured neural responses using non-invasive electroencephalography (EEG). Eighteen volunteers participated in the study and manipulated a haptic dial knob that generates four different haptic profiles; during the manipulation, their EEG signals were recorded. After experiencing different haptic profiles, participants reported their level of preference for each profile. The analysis of EEG revealed that frontal gamma oscillations correlate with the level of haptic preferences, with oscillations becoming stronger with increasing haptic preference. The highest correlation between frontal gamma power and haptic preference was found in the early period of the dial task. Therefore, the frontal gamma oscillation of the EEG may represent a neural correlate of the haptic preference and provides a neural basis for understanding this preference in relation to consumer electronics.

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, body sensor networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (TPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus, this paper first proposes a novel joint TPC and duty-cycle adaptation-based framework for pervasive healthcare. Second, an adaptive energy-efficient transmission power control algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a feedback control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that the proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio by adjusting both the transmission power and the duty cycle unlike the conventional constant TPC and PTPC methods.

Changes in Spinal Muscle Oxygenation and Perfusion During the Biering-Sørensen Test: Preliminary Results of a Study Employing NIRS-Based Muscle Oximetry.

We measured muscle perfusion in healthy subjects during the Biering-Sørensen (BS) test (the gold standard for evaluating back muscle endurance) using a commercial near-infrared spectroscopy (NIRS) device. We analysed data sets of five female healthy subjects (age: 34 ± 15years) who reached the maximum of 4 min during the BS test. Muscle oxygenation (SmO2) and perfusion ([tHb]) were measured using the Moxy NIRS device (Fortiori Design LLC, Hutchinson, USA). Probes were set unilaterally on M. longissimus, M. iliocostalis and M. multifidus. Additionally, mean arterial blood pressure (MAP), pulse pressure (PP), heart rate (HR), arterial oxygen saturation (SpO2) and lactate (pre, task, post) were measured. We observed (i) a large inter-subject variability in the SmO2 and [tHb] responses in the three muscles (i.e., SmO2 desaturations in the in M. longissimus across subjects during the task ranging from 1.1% to -56.6%), and (ii) a consistent response of the systemic signals in all subjects (i.e., increase in MAP, PP and HR). Lactate changes (post task minus task period) correlated with changes in PP and SmO2 of the multifidus muscle. Our preliminary results showed that during the BS test the response in the peripheral muscles was more variable than the central systemic response. A goal for future investigations is to explain this variability in the periphery by considering, for example, subject-specific changes in systemic cardiovascular activity, lactate and in the microvascular perfusion of muscle tissue.

Selective Suppression of Local Interneuron Circuits in Human Motor Cortex Contributes to Movement Preparation.

Changes in neural activity occur in the motor cortex before movement, but the nature and purpose of this preparatory activity is unclear. To investigate this in the human (male and female) brain noninvasively, we used transcranial magnetic stimulation (TMS) to probe the excitability of distinct sets of excitatory inputs to corticospinal neurons during the warning period of various reaction time tasks. Using two separate methods (H-reflex conditioning and directional effects of TMS), we show that a specific set of excitatory inputs to corticospinal neurons are suppressed during motor preparation, while another set of inputs remain unaffected. To probe the behavioral relevance of this suppression, we examined whether the strength of the selective preparatory inhibition in each trial was related to reaction time. Surprisingly, the greater the amount of selective preparatory inhibition, the faster the reaction time was. This suggests that the inhibition of inputs to corticospinal neurons is not involved in preventing the release of movement but may in fact facilitate rapid reactions. Thus, selective suppression of a specific set of motor cortical neurons may be a key aspect of successful movement preparation.SIGNIFICANCE STATEMENT Movement preparation evokes substantial activity in the motor cortex despite no apparent movement. One explanation for the lack of movement is that motor cortical output in this period is gated by an inhibitory mechanism. This notion was supported by previous noninvasive TMS studies of human motor cortex indicating a reduction of corticospinal excitability. On the contrary, our data support the idea that there is a coordinated balance of activity upstream of the corticospinal output neurons. This includes a suppression of specific local circuits that supports, rather than inhibits, the rapid generation of prepared movements. Thus, the selective suppression of local circuits appears to be an essential part of successful movement preparation instead of an external control mechanism.

Adolescent cannabis use and brain systems supporting adult working memory encoding, maintenance, and retrieval

Given prior reports of adverse effects of cannabis use on working memory, an executive function with a protracted developmental course during adolescence, we examined associations between developmental patterns of cannabis use and adult working memory (WM) processes. Seventy-five adults with longitudinal assessments of cannabis use (60 with reported use, 15 with no reported use) and prenatal drug exposure assessment completed a spatial WM task during fMRI at age 28. All subjects passed a multi-drug urine screen on the day of testing and denied recreational drug use in the past week. A fast event-related design with partial trials was used to separate the BOLD response associated with encoding, maintenance, and retrieval periods of the WM task. Behavioral results showed that subjects who began using cannabis earlier in adolescence had longer reaction times (RT) than those with later initiation. Cannabis age of onset was further associated with reduced posterior parietal cortex (PPC) encoding BOLD activation, which significantly mediated age of onset WM RT associations. However, cannabis age of onset brain-behavior associations did not differ between groups with a single reported use and those with repeated use, suggesting age of onset effects may reflect substance use risk characteristics rather than a developmentally-timed cannabis exposure effect. Within repeated cannabis users, greater levels of total cannabis use were associated with performance-related increases in dorsolateral prefrontal cortex (DLPFC) activation during maintenance. This pattern of significant results remained unchanged with inclusion of demographic and prenatal measures as covariates. Surprisingly, however, at the group level, cannabis users generally performed better than participants who reported never using cannabis (faster RT, higher accuracy). We extend previous investigations by identifying that WM associations with cannabis age of onset may be primary to PPC stimulus encoding activity, while the amount of cannabis use is associated with DLPFC maintenance processes. Poorer performance of participants who reported never using cannabis and the consistency of cannabis age of onset associations across single and repeated users limit interpretation of direct developmental effects of cannabis on WM in adulthood.

Aberrant Spatial and Temporal Prefrontal Activation Patterns in Medication-Naïve Adults with ADHD.

Previous near-infrared spectroscopy (NIRS) studies using a verbal fluency task (VFT) have consistently reported that adults with attention-deficit hyperactivity disorder (ADHD) showed significantly smaller oxygenated-hemoglobin [oxy-Hb] activations in the prefrontal cortex (PFC) compared to those in healthy controls (HC). Despite this consistent evidence of brain dysfunction in ADHD, ADHD is currently diagnosed based only on subjective clinical and scoring measures, which are often unreliable. Hence, it is necessary to establish objective neuroimaging biomarkers for ADHD. While most NIRS studies have utilized averaged [oxy-Hb] values during the whole task period for group comparisons, we used a cluster-based non-parametric randomization test to compare the [oxy-Hb] time-course changes with a 0.1-s time resolution between drug-naïve adults with ADHD and HC, which may provide us with more details regarding abnormal prefrontal activation patterns in ADHD. A total of 101 participants, consisting of 63 drug-naïve adult individuals with ADHD and 38 HC, were included in this study. We identified that adults with ADHD showed significantly smaller [oxy-Hb] activations than those in HC at spatially and temporally connected clusters located in the bilateral PFC (more prominent on the left) and temporal brain region (more prominent on the left). We further found that aberrant [oxy-Hb] activation differs according to the time period during the task or according to brain location. Our findings indicate more detailed aberrant prefrontal and temporal activation patterns of ADHD compared with those in previous studies, possibly representing a biological marker for ADHD.

Formal Verification of a Timing Enforcer Implementation

A timing enforcer is a scheduler that not only allocates CPU cycles to threads, but also uses timers to enforce time budgets. An approach for verifying safety properties of timing enforcers at the source code level is presented. We assume that the enforcer is implemented as a set of “enforcer” functions that are executed atomically on critical system-level events, such as the arrival and departure of jobs, and triggering of timers. The key idea is to express the safety property as an invariant, and prove that it is inductive across all the enforcer functions. A formal semantics of timing enforcers is presented, including the semantics of functions used to read the system clock and set timers. Using this semantics, the verification approach is presented, and its soundness proved. Further, the approach also takes into consideration the periodicity of tasks. It is validated by proving the correctness of the enforcement of CPU cycle budgets for tasks by the Zero-Slack Rate Monotonic ( zsrm ) scheduler, which is implemented in C as a Linux kernel module. The inductiveness of the necessary zsrm invariants is proved by expressing them as function contracts using the acsl specification language, and verifying the contracts using the frama-c tool.

Imperial College near infrared spectroscopy neuroimaging analysis framework.

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.

A Novel Algorithm for Solving Job-Shop Scheduling Problem

Of late, Scheduling optimization is the highly significant hassles in the job shop. The supreme advantage of the job shop scheduling is that the conclusion period of the entire tasks is cutback to the minimum possible. The job shop scheduling problem takes its origin from the conventional job shop scheduling problem, which is equipped with extensive accessibility of machines for all the entire tasks to be completed. Taking into account the two phases of the issue, two diverse definitions such as total flexibility and partial flexibility are envisaged to segregate the diverse accessibility data of machines. It is cheering that the efficiency has been scaled up by means of the opposite genetic algorithm which has proved its mettle as an effectual and proficient mechanism for successfully addressing the pointed issue of job-shop scheduling. Moreover, it is effectively employed to find out the least optimal make-span time with the optimal solution. DOI: http://dx.doi.org/10.5755/j01.mech.23.4.14055

Dynamics of EEG functional connectivity during statistical learning

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning.

Harmonicity-Aware Task Partitioning for Fixed Priority Scheduling of Probabilistic Real-Time Tasks on Multi-Core Platforms

The uncertainty due to performance variations of IC chips and resource sharing on multi-core platforms have significantly degraded the predictability of real-time systems. Traditional deterministic approaches based on the worst-case assumptions become extremely pessimistic and thus unpractical. In this article, we address the problem of scheduling a set of fixed-priority periodic real-time tasks on multi-core platforms in a probabilistic manner. Specifically, we consider task execution time as a probabilistic distribution and study how to schedule these tasks on multi-core platforms with guaranteed Quality of Service (QoS) requirements in terms of deadline-missing probabilities. Moreover, it is a well-known fact that the relationship among task periods, if exploited appropriately, can significantly improve the processor utilization. To this end, we present a novel approach to partition real-time tasks that can take both task execution time distributions and their period relationships into consideration. From our extensive experiment results, our proposed methods can greatly improve the schedulability of real-time tasks when compared with existing approaches.

An efficient approach for the multiprocessor non-preemptive strictly periodic task scheduling problem

Abstract Strict periodicity constraint is of great importance since it concerns some hard real-time systems where missing deadlines leads to catastrophic situations. However, the problem of schedulability analysis for non-preemptive strictly periodic tasks on a multiprocessor platform is even more intractable than the one with the common periodicity. In order to implement such systems, designers need effective tools based on fast and near-optimal solutions. This paper presents a schedulability analysis which results mainly in a, two versions, task assignment and start-time calculation algorithm. The first one targets the harmonic task periods case while the second one targets the non-harmonic task periods case. Each version is based on a sufficient uniprocessor schedulability test. In addition, for the non-harmonic case which is the most intractable, the uniprocessor sufficient schedulability test uses the strictly periodic task utilization factor. This factor stands for the fraction of time spent to execute a task while its strict periodicity and the ones of the already scheduled tasks are met. As a result, an efficient and easily implementable scheduling algorithm is proposed which begins by assigning tasks to processors then attributes a start-time to every task in such a way that strict periodicity and deadline constraints are met. The effectiveness of the proposed scheduling algorithm, in both versions, has been shown by a performance evaluation and comparisons with an optimal and a similar suboptimal solution.

Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury.

Changes in functional network connectivity following traumatic brain injury (TBI) have received increasing attention in recent neuroimaging literature. This study sought to understand how disrupted systems adapt to injury during resting and goal-directed brain states. Hyperconnectivity has been a common finding, and dedifferentiation (or loss of segregation of networks) is one possible explanation for this finding. We hypothesized that individuals with TBI would show dedifferentiation of networks (as noted in other clinical populations) and these effects would be associated with cognitive dysfunction. Graph theory was implemented to examine functional connectivity during periods of task and rest in 19 individuals with moderate/severe TBI and 14 healthy controls (HCs). Using a functional brain atlas derived from 83 functional imaging studies, graph theory was used to examine network dynamics and determine whether dedifferentiation accounts for changes in connectivity. Regions of interest were assigned to one of three groups: task-positive, default mode, or other networks. Relationships between these metrics were then compared with performance on neuropsychological tests. Hyperconnectivity in TBI was most commonly observed as increased within-network connectivity. Network strengths within networks that showed differences between TBI and HCs were correlated with performance on five neuropsychological tests typically sensitive to deficits commonly reported in TBI. Hyperconnectivity within the default mode network (DMN) during task was associated with better performance on Digit Span Backward, a measure of working memory [R2(18) = 0.28, p = 0.02]. In other words, increased differentiation of networks during task was associated with better working memory. Hyperconnectivity within the task-positive network during rest was not associated with behavior. Negative correlation weights were not associated with behavior. The primary hypothesis that hyperconnectivity occurs through dedifferentiation was not supported. [corrected]. Instead, enhanced connectivity post injury was observed within network. Results suggest that the relationship between increased connectivity and cognitive functioning may be both state (rest or task) and network dependent. High-cost network hubs were identical for both rest and task, and cost was negatively associated with performance on measures of psychomotor speed and set-shifting.

Visual short term memory related brain activity predicts mathematical abilities.

Previous research suggests visual short-term memory (VSTM) capacity and mathematical abilities are significantly related. Moreover, both processes activate similar brain regions within the parietal cortex, in particular, the intraparietal sulcus; however, it is still unclear whether the neuronal underpinnings of VSTM directly correlate with mathematical operation and reasoning abilities. The main objective was to investigate the association between parieto-occipital brain activity during the retention period of a VSTM task and performance in mathematics. The authors measured mathematical abilities and VSTM capacity as well as brain activity during memory maintenance using magnetoencephalography (MEG) in 19 healthy adult participants. Event-related magnetic fields (ERFs) were computed on the MEG data. Linear regressions were used to estimate the strength of the relation between VSTM related brain activity and mathematical abilities. The amplitude of parieto-occipital cerebral activity during the retention of visual information was related to performance in 2 standardized mathematical tasks: mathematical reasoning and calculation fluency. The findings show that brain activity during retention period of a VSTM task is associated with mathematical abilities. Contributions of VSTM processes to numerical cognition should be considered in cognitive interventions. (PsycINFO Database Record

Real-time uniprocessor scheduling with fewer preemptions

In this paper, we propose a simple, but effective scheduling framework for EDF and RM, which reduces the number of preemptions by simply introducing a dummy task. We first observe useful preemption behavior under EDF and RM, leading to an interesting finding: an effective way to reduce the number of preemptions is to prevent jobs of a task with the smallest task period from preempting other jobs upon their release. To achieve this, we add a dummy task that invokes its job only when a newly-released job of the task with the smallest task period has a higher priority than the currently-executing job. Then, the currently-executing job can continue its execution without getting preempted by inheriting the priority of the dummy job. Since adding the dummy task can make a schedulable task set unschedulable, we propose how to set the dummy task’s parameters without compromising schedulability. In addition to the negligible overhead of this framework due to its simplicity, it holds an important property that does not increase the number of preemptions of any task set, compared to the original scheduling algorithm, which has not been achieved by existing studies. We also demonstrate via simulation that the proposed framework effectively reduces the number of preemptions under EDF and RM.

Medial Prefrontal-Medial Temporal Theta Phase Coupling in Dynamic Spatial Imagery.

Hippocampal-medial prefrontal interactions are thought to play a crucial role in mental simulation. Notably, the frontal midline/medial pFC (mPFC) theta rhythm in humans has been linked to introspective thought and working memory. In parallel, theta rhythms have been proposed to coordinate processing in the medial temporal cortex, retrosplenial cortex (RSc), and parietal cortex during the movement of viewpoint in imagery, extending their association with physical movement in rodent models. Here, we used noninvasive whole-head MEG to investigate theta oscillatory power and phase-locking during the 18-sec postencoding delay period of a spatial working memory task, in which participants imagined previously learned object sequences either on a blank background (object maintenance), from a first-person viewpoint in a scene (static imagery), or moving along a path past the objects (dynamic imagery). We found increases in 4- to 7-Hz theta power in mPFC when comparing the delay period with a preencoding baseline. We then examined whether the mPFC theta rhythm was phase-coupled with ongoing theta oscillations elsewhere in the brain. The same mPFC region showed significantly higher theta phase coupling with the posterior medial temporal lobe/RSc for dynamic imagery versus either object maintenance or static imagery. mPFC theta phase coupling was not observed with any other brain region. These results implicate oscillatory coupling between mPFC and medial temporal lobe/RSc theta rhythms in the dynamic mental exploration of imagined scenes.

Current direction in TMS revisited: Behavior

Introduction: A recent experiment investigated for the first time whether the excitatory synaptic inputs to corticospinal neurons (CSNs) activated by posterior-anterior (PA) and anterior-posterior (AP) currents participated in different types of behaviour. Putatively preconditioning the excitability of PA- and AP-inputs affected learning of a ballistic thumb acceleration task and a visuomotor adaptation task, respectively. The implication is that different inputs to CSNs subserve different forms of motor learning. Here we used a more direct approach to examining the functional role of PA- and AP-inputs, by testing whether MEPs evoked by AP and PA pulses were modulated differently during the warning and reaction period of various reaction time tasks (choice, simple and Go/No Go).

Parental management of peers and autonomic nervous system reactivity in predicting adolescent peer relationships.

The present study examined sympathetic and parasympathetic indices of autonomic nervous system reactivity as moderators of the prospective association between parental management of peers via directing of youths' friendships and peer adjustment in a sample of typically developing adolescents. Participants included 246 adolescents at Time 1 (T1) [47% boys; 66% European American (EA), 34% African American (AA)] and 226 adolescents at Time 2 (T2; 45% boys; 67% EA, 33% AA). Adolescents were approximately 16 and 17 years old at T1 and T2, respectively. To address study aims, a multiinformant, multimethod longitudinal design was utilized. Skin conductance level (SCL) and respiratory sinus arrhythmia (RSA) were measured during a baseline period and challenge task (star-tracing). Reactivity was computed as a difference score between the task and baseline period. Results from path models revealed that higher levels of mother-reported parental directing predicted decreases in adolescent-reported peer rejection and friends' deviant behavior from T1 to T2 at relatively low levels of physiological arousal in response to challenge (i.e., low SCL reactivity, RSA augmentation). Further, exploratory analyses indicated that directing was associated with decreases in friends' deviant behavior and peer rejection particularly among boys who exhibited lower levels of physiological arousal, but increases in friends' deviant behavior among boys who exhibited higher levels of arousal reflected in RSA withdrawal only. Overall, findings are consistent with prior studies revealing the benefits of parental behavioral control for underaroused youth, contributing to the growing literature on the interplay of parenting and physiological factors in the adolescent peer domain. (PsycINFO Database Record