Executive Resources Research Articles

Task Scheduling Mechanisms for Fog Computing: A Systematic Survey

In the Internet of Things (IoT) ecosystem, some processing is done near data production sites at higher speeds without the need for high bandwidth by combining Fog Computing (FC) and cloud computing. Fog computing offers advantages for real-time systems that require high speed internet connectivity. Due to the limited resources of fog nodes, one of the most important challenges of FC is to meet dynamic needs in real-time. Therefore, one of the issues in the fog environment is the optimal assignment of tasks to fog nodes. An efficient scheduling algorithm should reduce various qualitative parameters such as cost and energy consumption, taking into account the heterogeneity of fog nodes and the commitment to perform tasks within their deadlines. This study provides a detailed taxonomy to gain a better understanding of the research issues and distinguishes important challenges in existing work. Therefore, a systematic overview of existing task scheduling techniques for cloud-fog environment, as well as their benefits and drawbacks, is presented in this article. Four main categories are introduced to study these techniques, including machine learning-based, heuristic-based, metaheuristic-based, and deterministic mechanisms. A number of papers are studied in each category. This survey also compares different task scheduling techniques in terms of execution time, resource utilization, delay, network bandwidth, energy consumption, execution deadline, response time, cost, uncertainty, and complexity. The outcomes revealed that 38% of the scheduling algorithms use metaheuristic-based mechanisms, 30% use heuristic-based, 23% use machine learning algorithms, and the other 9% use deterministic methods. The energy consumption is the most significant parameter addressed in most articles with a share of 19%. Finally, a number of important areas for improving the task scheduling methods in the FC in the future are presented.

Kebijakan Penerimaan Peserta Didik Baru (PPDB) Online di Sekolah pada Masa Pandemi Covid-19

Online education policy is a well-defined application of policies in education. Online policies are used as the basis for implementing a new educational acceptance system by way of online. The objective is to find out how the implementation of the new education policy is online and to look at the issues raised at the time of policies carried out in the school. Based on the implementation of the online PGDP on a predetermined principle, however, technical guidelines were established based on dictating the number 44 2019 as the receipt of the new trainee. Under the terms of technical PGDP instruction online has two pathways, i.e. 's zoning and non-zoning made up of affirmation, achievement, and the transfer of parental duties. The conclusion that the implementation of the policy of upper-school admissions online is already well under way of policy content, the benefits received, the changes expected, the location of decision and characteristic of existing institutions and regimes, and the power, interests, and strategies of the actors involved. But from the policy executive side, resources and compliance levels have not been working properly. Such things are hindering the success of the new learner's receiving policies online.

Toward a New Approach to Investigate the Role of Working Memory in Stereotype Threat Effects.

Stereotype threat arises when the activation of negative stereotypes about a group impairs performance of stigmatized individuals on stereotype relevant tasks. There is ample evidence that stereotype threat leads to performance detriments by consuming executive resources. Several studies indeed showed that working memory (WM) mediates stereotype threat effects among young adults. More recently, researchers have sought to understand whether the same mechanisms underlie age-based stereotype threat, but findings are mixed regarding the role of WM and some authors rather favor a motivational explanation based on regulatory fit. The present review critically appraises the empirical support for distinct forms of stereotype threat effects mediated by distinct mechanisms. We propose a novel approach based on one of the most recent WM models, the time-based resource sharing model, to evaluate the impact of stereotype threat on attentional resources in WM among both young and older adults.

Do emotionally salient cues improve prospective memory performance in children and adolescents with autism?

BackgroundProspective memory (PM) describes the ability to initiate and perform a planned action after a delay. Previous studies suggest reduced PM in autism spectrum disorder (ASD); especially when tasks put high demands on executive control resources. Increasing cue salience by presenting emotional cues improves PM performance in non-autistic populations. AimsTo explore whether children with ASD, whose processing of emotionally connoted information might differ from that of typically developing children, may also benefit from this type of salience in PM tasks. Methods and proceduresTwenty-five children with and 25 children without ASD completed a 1-back ongoing task into which an event-based PM task was embedded. Emotional salience of PM cues was varied. Outcomes and resultsChildren with ASD performed as well as children without ASD on the PM task and equally benefited from emotionally salient cues. Specifically, negative cues increased PM performance compared to neutral cues in both groups Conclusions and implicationsThe findings are consistent with the multiprocess framework which postulates that salient PM cues increase performance by promoting automatic intention retrieval and reducing executive control demands. Children with ASD seem to show similar comprehension and accessibility to emotional cues as typically developing children.

Sustained Attention as Measured by Reaction Time Variability Is a Strong Modulator for the P600, but Not the N400.

The functional significance of the two prominent language-related ERP components N400 and P600 is still under debate. It has recently been suggested that one important dimension along which the two vary is in terms of automaticity versus attentional control, with N400 amplitudes reflecting more automatic and P600 amplitudes reflecting more controlled aspects of sentence comprehension. The availability of executive resources necessary for controlled processes depends on sustained attention, which fluctuates over time. Here, we thus tested whether P600 and N400 amplitudes depend on the level of sustained attention. We reanalyzed EEG and behavioral data from a sentence processing task by Sassenhagen and Bornkessel-Schlesewsky [The P600 as a correlate of ventral attention network reorientation. Cortex, 66, A3-A20, 2015], which included sentences with morphosyntactic and semantic violations. Participants read sentences phrase by phrase and indicated whether a sentence contained any type of anomaly as soon as they had the relevant information. To quantify the varying degrees of sustained attention, we extracted a moving reaction time coefficient of variation over the entire course of the task. We found that the P600 amplitude was significantly larger during periods of low reaction time variability (high sustained attention) than in periods of high reaction time variability (low sustained attention). In contrast, the amplitude of the N400 was not affected by reaction time variability. These results thus suggest that the P600 component is sensitive to sustained attention whereas the N400 component is not, which provides independent evidence for accounts suggesting that P600 amplitudes reflect more controlled and N400 amplitudes reflect more automatic aspects of sentence comprehension.

Genetic Algorithm-Based Task Scheduling in Cloud Computing Using MapReduce Framework

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

An improved pathfinder algorithm using opposition-based learning for tasks scheduling in cloud environment

Cloud computing (CC) has become a major topic of study in recent years, providing a cost-effective deployment framework for hosting and running workflows. It is considered as the leading model for distributed computing due to its elasticity, speed, and pay-per-use model. Running scientific applications in the cloud environment with a number of criteria remains a great challenge for researchers, because it requires a robust infrastructure with high computing performance, communication and storage capabilities. Most previous works designed for cloud environments examine the fixed number of resources and aims only to reduce runtime. This has forced scientists to develop the workflow scheduling (WFS) optimization algorithms that strike the right balance between the three main qualities of service (QoS) parameters: time, cost and resource utilization. Due to its substantial implications for the front end and back end of the cloud research business, resource utilization is undoubtedly an important factor that should be taken into consideration. When efficient resource usage is performed in the cloud, good load balancing is obtained. To address the preceding problems, we propose in this paper an innovative workflow scheduling technique in the cloud employing the Pathfinder algorithm (PFA) and the oppositional-based learning algorithm (OBL). The proposed scheduler focused on three objectives (i.e., total execution time, cost, and resource utilization). Simulation tests are carried out using the workflowsim toolkit to evaluate the effectiveness of the suggested approach. The results of the experiments proved that the proposed OBLPFA algorithm outperforms other algorithms called (Particle Swarm technique (PSO), dragonfly (DFA), the Arithmetic Optimization Technique (AOA), the Reptile Search technique (RSA), the Aquila Optimization method (AO), and Lion Optimization method (LOA)) in terms of total execution time, cost, and resource utilization.

Mindfulness-Based Stress Reduction, Cognitive Behavioral Therapy, and Acupuncture in Chronic Low Back Pain: Protocol for Two Linked Randomized Controlled Trials.

BackgroundNonpharmacologic mind-body therapies have demonstrated efficacy in low back pain. However, the mechanisms underlying these therapies remain to be fully elucidated.ObjectiveIn response to these knowledge gaps, the Stanford Center for Low Back Pain—a collaborative, National Institutes of Health P01–funded, multidisciplinary research center—was established to investigate the common and distinct biobehavioral mechanisms of three mind-body therapies for chronic low back pain: cognitive behavioral therapy (CBT) that is used to treat pain, mindfulness-based stress reduction (MBSR), and electroacupuncture. Here, we describe the design and implementation of the center structure and the associated randomized controlled trials for characterizing the mechanisms of chronic low back pain treatments.MethodsThe multidisciplinary center is running two randomized controlled trials that share common resources for recruitment, enrollment, study execution, and data acquisition. We expect to recruit over 300 chronic low back pain participants across two projects and across different treatment arms within each project. The first project will examine pain-CBT compared with MBSR and a wait-list control group. The second project will examine real versus sham electroacupuncture. We will use behavioral, psychophysical, physical measure, and neuroimaging techniques to characterize the central pain modulatory and emotion regulatory systems in chronic low back pain at baseline and longitudinally. We will characterize how these interventions impact these systems, characterize the longitudinal treatment effects, and identify predictors of treatment efficacy.ResultsParticipant recruitment began on March 17, 2015, and will end in March 2023. Recruitment was halted in March 2020 due to COVID-19 and resumed in December 2021.ConclusionsThis center uses a comprehensive approach to study chronic low back pain. Findings are expected to significantly advance our understanding in (1) the baseline and longitudinal mechanisms of chronic low back pain, (2) the common and distinctive mechanisms of three mind-body therapies, and (3) predictors of treatment response, thereby informing future delivery of nonpharmacologic chronic low back pain treatments.Trial RegistrationClinicalTrials.gov NCT02503475; https://clinicaltrials.gov/ct2/show/NCT02503475International Registered Report Identifier (IRRID)PRR1-10.2196/37823

TMS over dorsolateral prefrontal cortex affects the timing of motor imagery but not overt action: Further support for the motor-cognitive model

The Motor-Cognitive model suggests a functional dissociation between motor imagery and overt action, in contrast to the Functional Equivalence view of common processes between the two behaviours. According to the Motor-Cognitive model, motor imagery differs from overt action primarily through the use of executive resources to monitor and elaborate a motor image during execution, which can result in a lack of correspondence between motor imagery and its overt action counterpart. The present study examined the importance of executive resources in motor imagery by using TMS to impair the function of the dorsolateral prefrontal cortex while measuring the time to complete imagined versus overt actions. In two experiments, TMS over the dorsolateral prefrontal cortex slowed motor imagery but did not affect overt actions. TMS over the same region also interfered with performance of a mental calculation task, though it did not reliably affect less demanding cognitive tasks also thought to rely on executive functions. Taken together, these results were consistent with the Motor-Cognitive model but not with the idea of functional equivalence. The implications of these results for the theoretical understanding of motor imagery, and potential applications of the Motor-Cognitive model to the use of motor imagery in training and rehabilitation, are discussed.

Software and hardware co-design and implementation of intelligent optimization algorithms

In order to improve the operating efficiency of the algorithm, some intelligent optimization algorithms are considered to be implemented on hardware. However, the existing design scheme has the problem of poor versatility. Therefore, this paper proposes a general software–hardware co-design scheme of intelligent optimization algorithms. In the design scheme, the initialization module and fitness module of the algorithm are deployed on the Advanced RISC Machines (ARM) for execution to increase the flexibility of the program. The update module of the algorithm is deployed on the Field Programmable Gate Array (FPGA) for execution to realize the hardware acceleration. The data between ARM and FPGA is transferred through Advanced eXtensible Interface (AXI) bus. In this paper, the PSO, BA, WOA, GWO, CMAES and EO algorithms are implemented with the proposed design scheme. And the six algorithms are tested on thirteen benchmark functions of different types. The experimental results prove the feasibility of the design scheme. In addition, by comparing with software and other implementation methods in execution time, resource occupancy and convergence, the effectiveness and superiority of the proposed scheme are proved.

A Single-Pass and One-Round Message Authentication Encryption for Limited IoT Devices

In this work, we propose three efficient variants of a message authentication encryption (MAE) algorithm, which is based on the dynamic key-dependent concept and dynamic operation mode to reach a high level of security. These variants consist of a single pass and a single round, in addition to the use of common operations for the encryption and authentication processes to reduce the required execution time and resources. Accordingly, the proposed scheme outperforms the existing solutions that are based on the static approach with multiple rounds. Furthermore, to reduce the overhead associated with the regeneration of the dynamic key and the corresponding cryptographic primitives, we propose a simple, yet effective update process. In such a scheme, even when the same plaintext is processed, it will be encrypted and authenticated using different cryptographic primitives (substitution and permutation tables in addition to round keys), which guards against the existing cryptanalysis techniques. The experimental results show that the proposed MAE variants are more efficient than the counter with cipher block chaining message authentication code (CCM), Galois message authentication code (GMAC), offset codebook mode (OCB), and the Chacha20-poly1305. The best performance is achieved with the third MAE variant that presents a high throughput with an enhancement of at least 373% compared to CCM, 90% compared to GCM, 23% compared to OCB, and 22% compared to Chacha20-poly1305.

Influence of body mobility on attention networks in school-aged prematurely born children: A controlled trial.

School-aged prematurely born children (PC) have a higher risk of academic difficulties, which may be partly explained by attention difficulties. It has been suggested that children’s attentional performance might be influenced by their body posture and spontaneous body motion. The aim of this study (ClinicalTrials.gov – NCT 03125447) was to test the influence of three body mobility conditions on the three functions of attention (alertness, orienting, and executive control) among school-aged PC vs. term-born children (TC). Notably, 21 PC and 21 TC performed the Attention Network Test for Children in three body mobility conditions, namely, sitting and standing imposed fixed postures and a free-to-move condition. The children’s median reaction times were compared between trials (1) with and without alerting cues, (2) with valid and invalid orienting cues, and (3) with and without distracting information, to calculate the performance of alertness, orienting, and executive control, respectively. Results showed that with distracting information, PC exhibited significantly slower responses in the standing-still posture than in the sitting-still posture (1,077 ± 240 vs. 1,175 ± 273 ms, p < 0.05), but not TC. No difference was observed with the free-to-move condition. PC and TC did not significantly differ in alertness or orienting, regardless of body mobility condition. These data suggest that PC must use executive resources to stand still and maintain position, which impairs their performance during executive tasks. We speculate that these results may be related to less developed postural control and motor inhibition in PC.

Evaluation of Fast Sample Entropy Algorithms on FPGAs: From Performance to Energy Efficiency.

Entropy is one of the most fundamental notions for understanding complexity. Among all the methods to calculate the entropy, sample entropy (SampEn) is a practical and common method to estimate time-series complexity. Unfortunately, SampEn is a time-consuming method growing in quadratic times with the number of elements, which makes this method unviable when processing large data series. In this work, we evaluate hardware SampEn architectures to offload computation weight, using improved SampEn algorithms and exploiting reconfigurable technologies, such as field-programmable gate arrays (FPGAs), a reconfigurable technology well-known for its high performance and power efficiency. In addition to the fundamental disclosed straightforward SampEn (SF) calculation method, this study evaluates optimized strategies, such as bucket-assist (BA) SampEn and lightweight SampEn based on BubbleSort (BS-LW) and MergeSort (MS-LW) on an embedded CPU, a high-performance CPU and on an FPGA using simulated data and real-world electrocardiograms (ECG) as input data. Irregular storage space and memory access of enhanced algorithms is also studied and estimated in this work. These fast SampEn algorithms are evaluated and profiled using metrics such as execution time, resource use, power and energy consumption based on input data length. Finally, although the implementation of fast SampEn is not significantly faster than versions running on a high-performance CPU, FPGA implementations consume one or two orders of magnitude less energy than a high-performance CPU.

Insights on COVID-19, School Reopening Procedures, and Mental Wellness: Pilot Interviews With School Employees.

ABSTRACTBACKGROUNDThe COVID‐19 pandemic has caused interruptions to the K‐12 US school landscape since spring 2020.METHODSIn summer 2020, we completed a pilot study utilizing interviews (n = 13) with school staff (ie, nurses, educators) from across the United States. We aimed to understand the status of school operation and re‐entry plans after the primary period of school closure, along with resources needed for students and staff during the COVID‐19 pandemic.RESULTSAll interviewees described their school's re‐entry plan as complete or in‐development. Ten plans included strategies to meet students' mental health needs. Only 3 clearly planned for staff mental health resources. Interviews suggest gaps in planning and execution of mental health resources for school staff, a group already vulnerable to stress, anxiety, and burnout.IMPLICATIONS FOR SCHOOL HEALTHSeveral school staff mental health resources were developed as a result of the pandemic, though ongoing impacts necessitate integration of these supports into school operation plans. This is particularly important as schools continue to navigate periods of altered operation in response to elevated community COVID‐19 infection rates.CONCLUSIONSAs schools implement strategies to support students, similar consideration should be given to the adults in the school environment who teach and support school‐aged children.

Adaptive Recommendation Method of IoT Apps and Ideological and Political Teaching Resources

In view of the current poor nature of online ideological and political teaching and the deficient impact of the executive teaching resource recommendation, this paper advances the examination on the versatile recommendation technique for online ideological and political teaching resources under the school enterprise cooperation mode, gathers and deals with the ideological and political teaching data in view of the teaching qualities of the school enterprise cooperation mode, and develops the assessment calculation of teaching data the board. At last, through tests, it is affirmed that the versatile recommendation technique for online ideological and political teaching resources under the school enterprise cooperation mode has high practicability and fully meets the research requirements.

Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks.

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study’s goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.

Emotion Regulation and Strategic Decision Making: A Depletion of Resources Perspective

A growing body of evidence shows that making strategic decisions is emotionally draining and requires managers to devote significant effort to regulating emotions. However, theoretical models of strategic decision have not kept pace with these developments and largely overlook the role of emotion regulation. In this paper, we adopt a resource depletion perspective to examine how executives’ emotion regulation capabilities influence strategic decision processes and outcomes in dynamic environments. We propose that executive emotion regulation resources are vital for developing and sustaining novel representations of strategic opportunities, searching for distant solutions, and utilizing social systems for evaluating and selecting alternatives. Hence, the depletion of regulatory resources inhibits these processes and reduces the effectiveness of strategic decision making. By identifying interdependencies among executive emotion regulation capacity and the cognitive and social resources required for strategic decision making, our analysis extends understanding of the requirements for making strategic decisions in dynamic environments. We discuss implications for research on strategic decision making, executive job demands and dynamic managerial capabilities.

Adaptive-Learning-Based Vehicle-to-Vehicle Opportunistic Resource-Sharing Framework

With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.

The effect of transcranial direct current stimulation on the interplay between executive control, behavioral variability and mind wandering: A registered report

Mind wandering (MW) is a mental phenomenon humans experience daily. Yet, we lack a complete understanding of the neural basis of this pervasive mental state. Over the past decade there has been an increase in publications using transcranial direct current stimulation (tDCS) to modulate the propensity to mind wander, but findings are diverse, and a satisfactory conclusion is missing. Recently, Boayue et al. (2020) reported successful reduction of mind wandering using high-definition tDCS (HD-tDCS) over the dorsolateral prefrontal cortex, providing preliminary evidence for the efficacy of HD-tDCS in interfering with mind wandering. The current study is a high-powered, pre-registered direct replication attempt of the effect found by Boayue et al. (2020). In addition, we investigated whether the effects of HD-tDCS on mind wandering would be prolonged and assessed the underlying processes of mind wandering using electroencephalography (EEG) and pupillometry during a finger-tapping random sequence generation task that requires the use of executive resources. We failed to find any evidence of the original effect of reduced MW during and after stimulation. When combining our data with the data from Boayue et al. (2020), the original effect of reduced MW caused by HD-tDCS disappeared. In addition, we observed increased occipital alpha power as task duration increased and increased midfrontal theta power preceding response patterns signaling high executive function use. Finally, tonic and phasic pupil size decreased as task duration increased yet, phasic responses were increased, while tonic responses were reduced preceding reports of MW. Additionally phasic pupil size also showed a tendency to be increased during periods of high executive function use. Importantly, none of the EEG or pupil measures were modulated by HD-tDCS. We conclude that HD-tDCS over the dorsolateral prefrontal cortex does not affect MW propensity and its neural signatures. Furthermore, we recommend that previously reported effects of tDCS on mind wandering and other cognitive functions should only be accepted after a successful pre-registered replication.

Mental health conditions and unsafe driving behaviors: A naturalistic driving study on ADHD and depression

Introduction: Road injuries remain a persistent public health concern across the world. The task of driving is complicated by mental health conditions, which may affect drivers’ executive functioning and cognitive resource allocation. This study examines whether attention-deficit/hyperactivity disorder (ADHD) and depression are associated with unsafe driving behaviors. Method: Generalized linear mixed models were employed to estimate the association of self-reported ADHD and depression with 18 unsafe driving behavior types found prior to at-fault crashes and near-crashes using a large-scale naturalistic driving dataset. Driver demographics, cognitive traits, environmental factors, and driver random effects were included to reduce confounding and biases. Results: Controlling for other covariates, people with self-reported ADHD were more likely to have performed improper braking or stopping (OR = 4.89, 95% CI 1.82–13.17) prior to an at-fault crash or near-crash, while those with self-reported depression did not have a significant association with any unsafe driving behavior. Conclusions: After accounting for demographic, cognitive, and environmental covariates, individuals with ADHD and depression were not prone to purposefully aggressive or reckless driving. Instead, drivers with self-reported ADHD may unintentionally execute unsafe driving behaviors in particular driving scenarios that require a high level of cognitive judgment. Practical Applications: These findings can inform the curriculum design of driver’s education programs that help learners with mental health conditions gain practice in certain road scenarios, for example, more practice on preemptively reducing speed instead of making sudden brakes and smooth turning on curved roads for students with ADHD. Furthermore, specific advanced driver assistance systems may prove particularly helpful for drivers with ADHD, such as detection of leading objects and curve speed warning.