Objective Of Task Research Articles

Comparison of Optimal Drop Heights in 2 Drop-Jump Exercises for Basketball Athletes With Differing Strength Levels.

Previous research has primarily focused on bounce drop jump (BDJ) rather than countermovement drop jump (CDJ) in studying optimal drop heights for drop jump. This study explored the biomechanic parameters of both BDJ and CDJ and their relationship with performance across varying drop heights based on the percentage of squat-jump height (SJH). Twenty-four college basketball athletes were recruited and divided into low- and high-strength groups. Participants performed BDJ and CDJ at 50%, 75%, 100%, 125%, 150%, and 175% of SJH. The point or zone with the largest reactive strength index (RSI) corresponds to the optimal drop height for a drop jump. Optimal drop heights were determined to be 84.7% of SJH for BDJ low strength and 84.1% of SJH for BDJ high strength, with no significant difference between groups (P = .213). For CDJ, relative optimal drop heights were 104.6% of SJH for CDJ low strength and 106.1% of SJH for CDJ high strength, also with no significant difference (P = .717). Mechanical power with RSI exhibited a coefficient close to 1 (r = .98-.99), while average propulsive force demonstrated a very strong positive correlation with RSI (r = .72-.77). It is recommended that basketball athletes incorporate particular percentages of SJH into drop jump, with 75% to 100% of SJH for BDJ and 100% to 125% of SJH for CDJ, as these heights are associated with achieving an optimal RSI and mechanical power. Tailoring the inclusion of BDJ or CDJ in training should account for the specific demands of on-court positions and task objectives for basketball players.

The impact of noise exposure, time pressure, and cognitive load on objective task performance and subjective sensory overload and fatigue

ABSTRACT Introduction Sensory hypersensitivity (SHS) refers to an increased sensitivity to sensory stimuli, often leading to sensory overload and adversely affecting daily functioning and well-being. This study examined the effects of three situational triggers – noise, time pressure, and cognitive load – on task performance, sensory overload, and fatigue. Additionally, we sought to explore the associations between these effects and SHS, while accounting for other influencing factors such as personality, coping mechanisms, and anxiety. Method We experimentally tested 105 university students, employing a visuospatial task (the Paper Folding Test, PFT) under eight different conditions, manipulating the three situational triggers. The measured outcomes included task accuracy, average response time, sensory overload, and fatigue. Participants also completed several questionnaires: Highly Sensitive Person Scale (HSPS), Multi-Modal Evaluation of Sensory Sensitivity (MESSY), State and Trait Anxiety Index, Big Five Inventory, and COPE Easy. Results Our findings indicated that sensory overload increased as more situational triggers were introduced, with noise having the most significant impact. However, this increase in sensory overload did not correspond to changes in objective performance measures, such as accuracy and average response time on the PFT, which were primarily influenced by cognitive load (i.e. easy versus difficult items). Additionally, individuals with higher levels of SHS (HSPS and MESSY) reported greater overall sensory overload and fatigue. Nonetheless, the impact of the triggers on sensory overload and fatigue was not exclusive to those with high SHS, and neuroticism, conscientiousness, openness, and trait anxiety were significant predictors of SHS, more so than task-related outcomes. Conclusions Feelings of sensory overload may not necessarily impair cognitive performance, and the impact of situational triggers can be similar for individuals with and without SHS. This implies that the burden of SHS and overall sensory overload may be influenced by other underlying factors leading to an elevation of baseline sensory overload, warranting further investigation.

Self-reports map the landscape of task states derived from brain imaging

Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.

Enhancing cognitive functions in aged dogs and cats: a systematic review of enriched diets and nutraceuticals.

With advancements in veterinary care and the growing recognition of pets as integral member of the family, the lifespans of dogs and cats have significantly increased, leading to a higher prevalence of age-related conditions, including cognitive dysfunction syndrome (CDS). CDS adversely impacts pets' quality of life and presents emotional and practical challenges for owners. Given its similarities to Alzheimer's disease in humans, CDS has gained attention as a target for nutrition-based interventions aimed at preserving cognitive function. This systematic review evaluates the efficacy of enriched diets and nutraceuticals in improving cognition in aging companion animals. A literature search was conducted using PubMed, CAB Abstracts, Web of Science, and Dimensions to identify clinical trials published in English that investigated the effects of enriched diets or nutraceuticals on cognitive functions in aged cats or dogs. Study quality was assessed using a modified CAMARADES checklist. A total of 30 studies (27 canine and 2 feline trials) published between 2002 and 2023 were reviewed. Studies on enriched diets generally demonstrated higher methodological quality compared to those on supplements. Omega-3 fatty acids showed cognitive benefits in aging pets, especially at higher doses, while antioxidants from plant extracts and products and vitamins E and C alone were less effective but remain essential for stabilizing omega-3 fatty acids. Other supplements, including S-adenosyl methionine, medium-chain triglycerides, homotaurine, and apoaequorin, also showed promise. However, future studies must standardize protocols, include robust control groups, and utilize both objective tasks and subjective questionnaires to strengthen conclusions.

Maternal touch in object- and nonobject-oriented play interactions: A longitudinal study at 7 and 12 months.

Social touch is a crucial part of how mothers interact with their infants, with different touch types serving distinct purposes in these exchanges. However, there is still a limited understanding of how mothers' touch behavior adapts to specific interactive tasks, particularly throughout infancy. To address this gap, we observed mother-infant dyads at 7 and 12 months during three structured social play tasks: (a) play with objects, (b) play without objects, and (c) play with a difficult object. Using an adapted version of the Ordinalized Maternal Touch Scale, we categorized every touch performed by the mother. The effect of the infant's age and play tasks on the proportion of time mothers touch their infants was evaluated using Bayesian beta mixed models, taking into account both the total quantity and the Ordinalized Maternal Touch Scale touch categories. Results showed that (a) the frequency of maternal touch is prevalent in dyadic interactions and lowered in triadic object play; (b) mothers used affectionate, static, and playful touch categories more often in dyadic play tasks; (c) in triadic play task, mothers used object-mediated touch more frequently; (d) the total frequency of maternal touch decreased across infant age, which was primarily due to a decrease in static and object-mediated touch; and (e) maternal touch varies depending on the complexity of object play task. Our findings suggested that the developmental trajectory of maternal touch behavior is modulated by the infant's evolving needs and the different challenges in object versus nonobject play tasks. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A matter of complexity? The role of the dorsal posterior parietal cortex in processing changes in spatial information across time.

The posterior parietal cortex (PPC) is an associative neocortical region that integrates multiple streams of information and is implicated in spatial cognition and decision making. In some cases, however, the PPC is not required for these functions. One possibility is that the PPC is recruited when spatial complexity is high. Yet, few studies of PPC function have explicitly manipulated environmental complexity, complexity of spatial changes, or the temporal structure of spatial tasks. To examine whether task complexity recruits PPC function, we tested rats with neurotoxic damage to the dorsal PPC on a series of tasks varying in spatial and temporal complexity. Recognition memory was first assessed in standard exploration tasks, including object recognition, object location, and object in place, as well as a more complex object task in which spatial changes occurred across multiple delays. Spatial navigation was assessed in the circular hole board maze (Barnes maze), and temporal processing was assessed in a temporal order task. PPC damage spared performance on standard recognition memory tasks but caused deficits on tasks involving changes in object configuration or multiple changes across time. PPC damage spared acquisition on the Barnes maze but impaired retention and decreased efficiency of search strategies. PPC damage did not impact temporal order memory. Overall, these results suggest that the PPC is necessary when spatial complexity of the task increases attentional and long-term memory demands. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation.

For more efficient generalization to unseen domains (classes), most Few-shot Segmentation (FSS) would directly exploit pre-trained encoders and only fine-tune the decoder, especially in the current era of large models. However, such fixed feature encoders tend to be class-agnostic, inevitably activating objects that are irrelevant to the target class. In contrast, humans can effortlessly focus on specific objects in the line of sight. This paper mimics the visual perception pattern of human beings and proposes a novel and powerful prompt-driven scheme, called "Prompt and Transfer" (PAT), which constructs a dynamic class-aware prompting paradigm to tune the encoder for focusing on the interested object (target class) in the current task. Three key points are elaborated to enhance the prompting: 1) Cross-modal linguistic information is introduced to initialize prompts for each task. 2) Semantic Prompt Transfer (SPT) that precisely transfers the class-specific semantics within the images to prompts. 3) Part Mask Generator (PMG) that works in conjunction with SPT to adaptively generate different but complementary part prompts for different individuals. Surprisingly, PAT achieves competitive performance on 4 different tasks including standard FSS, Cross-domain FSS (e.g., CV, medical, and remote sensing domains), Weak-label FSS, and Zero-shot Segmentation, setting new state-of-the-arts on 11 benchmarks.

Construction of a Multi-View Deep Learning Model for the Severity Classification of Acute Pancreatitis.

Acute pancreatitis (AP) is a prevalent pathological condition of abdomen characterized by sudden onset, high incidence and complex progression. Timely assessment of AP severity is crucial for informing intervention decisions so as to delay deterioration and reduce mortality rates. Existing AP-related scoring systems can only assess current condition of patients and utilize only a single type of clinical data, which is of great limitation. Therefore, it is imperative to establish more accurate and data-compatible methods for predicting the severity of AP. The artificial intelligence (AI) algorithm based on artificial neural network (ANN) allow for the adaptive feature extraction for objective task through its internal complex network, instead of the hand-crafted methods commonly used in traditional machine learning (ML) algorithms. In this study, we delve into the final severity classification prediction of newly admitted AP patients, using deep learning (DL) algorithm to develop multi-view models, incorporated with patients' demographic information, vital signs, AP-related laboratory indexes and admission computed tomography (CT) images. The pancreatitis database in the platform of Clinical Data Research Center of Acute Abdominal Surgery at the First Affiliated Hospital of Dalian Medical University was used to gather AP cases. Deep neural network (DNN) and convolutional neural network (CNN) were utilized to construct models. The DNN prediction models with clinical data as input, the CNN prediction models with admission CT as input, and the multi-view models combining the two inputs were respectively established to predict the severity of AP. DL models for AP severity classification based on clinical indexes, imaging data and merged data were constructed. The multi-view model based on merged data offered more accurate prediction of the final severity classification of AP, with an overall accuracy rate of 80.26% (95% confidence interval (CI): 79.58%-80.94%). The constituent accuracy rates for mild acute pancreatitis, moderately severe acute pancreatitis, and severe acute pancreatitis were 91.69% (95% CI: 87.80%-95.57%), 64.90% (95% CI: 58.85%-70.95%), and 75.56% (95% CI: 68.58%-82.53%), respectively. The multi-view models using clinical indexes and imaging data as input outperform single-view models in AP severity prediction.

Affective Influences on the Intensity of Mental Effort: 25 Years of Programmatic Research.

This article highlights the systematic impact of experienced and implicit affect on the intensity of mental effort. The key argument is that both consciously experienced affect and implicitly activated affect knowledge can influence responses in the cardiovascular system reflecting effort intensity by informing individuals about task demand-the key variable determining resource mobilization. According to the motivational intensity theory, effort rises with experienced demand as long as success is possible and the necessary effort is justified. Twenty-five years of programmatic research have provided clear evidence that both consciously experienced affect and implicitly activated affect knowledge systematically influence the intensity of effort. Importantly, affect's impact on effort is moderated by task context variables, like objective task difficulty, incentive, and other general boundary conditions.

Real-time, accurate, and open source upper-limb musculoskeletal analysis using a single RGBD camera — An exploratory hand-cycling study

Biomechanical biofeedback may enhance rehabilitation and provide clinicians with more objective task evaluation. These feedbacks often rely on expensive motion capture systems (∼$100000), which restricts their widespread use, leading to the development of computer vision-based methods. These methods are subject to large joint angle errors, considering the upper limb, and exclude the scapula and clavicle motion in the analysis. Our open-source approach offers a user-friendly solution for high-fidelity upper-limb kinematics using a single consumer-grade depth-sensing camera (∼$500) and includes semi-automatic skin marker labeling. Real-time biomechanical analysis, ranging from kinematics to muscle force estimation, was conducted on eight participants performing a hand-cycling motion to demonstrate the applicability of our approach on the upper limb. Markers were recorded by the depth-sensing camera and an optoelectronic camera system, considered as a reference. Muscle activity and external load were recorded using eight electromyography sensors and instrumented hand pedals, respectively. Bland-Altman analysis revealed significant agreements in the 3D markers’ positions between the two motion capture methods, with errors averaging 3.3 ± 3.9 mm. The error propagation was low for the biomechanical analysis, with joint angle differences, for example, below 5° when comparing both systems. Biofeedback from the depth-sensing camera was provided at 68 Hz. Our study introduces a novel method for using a depth-sensing camera as a low-cost motion capture solution. Results from healthy participants suggest its potential for accurate kinematic reconstruction and comprehensive upper-limb biomechanical studies. Further investigation is needed to explore its clinical applications in pathological populations.

Virtual reality simulations in robotic surgery training: a systematic review and meta-analysis.

To compare VR simulations with other training methods regarding improvements in objective assessment scores and task completion times. A database search was conducted on 20 May 2024 across Central, MEDLINE, EMBASE, Web of Science, and Scopus. Included were randomised controlled trials comparing VR simulations to other training methods, assessing objective scores and task times. The Risk of Bias-2 tool was used for bias assessment. Eighteen studies were included. VR significantly improved objective scores (n = 339, SMD 1.04, 95% CI 0.40-1.69, P = 0.002) and reduced task completion times (n = 357, SMD of -1.08, 95% CI of as -2.05 to -0.12, P = 0.03) compared to no additional training. VR was as effective as dry lab training for improving scores (n = 213, SMD -0.47, 95% CI -1.34 to 0.41, P = 0.30) and task times (n = 98, SMD -0.37, 95% CI -1.51 to 0.78, P = 0.53). However, one study found wet lab training significantly reduced task times compared to VR (n = 20, SMD of 1.7, 95% CI of 0.65-2.76, P = 0.002). No significant differences were found when VR alone was compared to VR with expert advice. VR is more effective than no additional training and is as effective as dry lab training in robotic skill acquisitions. Therefore, VR is effective in enhancing robotic surgery skills and warrants an expanded role in surgical training programmes.

Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.

To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources. Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy. 20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality. The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

Comparing modelling approaches for a generic nuclear waste repository in salt

This paper contains a comparison of five modelling approaches for a simplified nuclear waste repository in a domal salt formation. It is the result of a four-year collaboration between five international teams on Task F of the DECOVALEX-2023 project on performance assessment modelling. The primary objectives of Task F are to build confidence in the models, methods, and software used for performance assessment (PA) of deep geologic nuclear waste repositories, and/or to bring to the fore additional research and development needed to improve PA methodologies. This work demonstrates how these objectives are accomplished through staged development and comparison of the models and methods used by participating teams in their PA frameworks. Participating teams made a wide range of model assumptions, ranging from compartmentalized networks to full 3D models of the salt formation and repository. Despite differences in the modelling strategies, all models indicate that salt compaction and diffusion of radionuclides in brine are key processes in the repository. For the isothermal spent nuclear fuel and vitrified waste scenario with multiple early failures considered, all models indicate little of the disposed radionuclides will migrate beyond the repository seal over the 100,000-year simulations. In general, the model output quantities have the largest differences over the short term and near the waste. Disparities between the models are believed to be due to differing simplifications from the conceptual model.

Evaluating Visual Perception of Object Motion in Dynamic Environments

Precisely understanding how objects move in 3D is essential for broad scenarios such as video editing, gaming, driving, and athletics. With screen-displayed computer graphics content, users only perceive limited cues to judge the object motion from the on-screen optical flow. Conventionally, visual perception is studied with stationary settings and singular objects. However, in practical applications, we---the observer---also move within complex scenes. Therefore, we must extract object motion from a combined optical flow displayed on screen, which can often lead to mis-estimations due to perceptual ambiguities. We measure and model observers' perceptual accuracy of object motions in dynamic 3D environments, a universal but under-investigated scenario in computer graphics applications. We design and employ a crowdsourcing-based psychophysical study, quantifying the relationships among patterns of scene dynamics and content, and the resulting perceptual judgments of object motion direction. The acquired psychophysical data underpins a model for generalized conditions. We then demonstrate the model's guidance ability to significantly enhance users' understanding of task object motion in gaming and animation design. With applications in measuring and compensating for object motion errors in video and rendering, we hope the research establishes a new frontier for understanding and mitigating perceptual errors caused by the gap between screen-displayed graphics and the physical world.

Discovering plasticity rules that organize and maintain neural circuits.

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Oja's rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

Decreased Knee Extensor Torque During Single-Limb Stance: A Computer Simulation Study of Compensations and Consequences

Background/Objectives: For over 50 years, it has been suggested that the plantar flexors and hip extensors can compensate for weak knee extensors and prevent collapse of the leg during a single-limb stance. However, the effects of these compensations have not been studied thoroughly. The purpose of this computer simulation study was to determine, for a given posture, the hip and ankle net joint torque (NJT) required to prevent leg collapse due to systematic decreases in knee NJT and to determine the effect of these compensations on the horizontal ground reaction force. Methods: Single-limb stance was simulated using a static, multisegmented model in eight different postures. For each posture, the knee NJT was systematically decreased. The ankle and knee NJT necessary to prevent lower extremity collapse, along with any net horizontal ground reaction forces, were then calculated. Results: Decreases in knee NJT required linear increases in ankle and hip NJT to prevent the limb from collapsing. There were greater increases in ankle NJT compared to hip NJT, resulting in posteriorly-directed horizontal ground reaction forces. While the magnitudes were different, these findings applied to all postures simulated. Conclusions: For a given posture, ankle and hip NJTs can compensate for a decrease in knee NJT. However, this resulted in a horizontal ground reaction force, which was in the posterior direction for all the postures examined. This horizontal ground reaction force would induce an acceleration on the body’s center of mass that, if not accounted for, could have deleterious effects on achieving a task objective.

The effects of moderate prenatal alcohol exposure on performance in object and spatial discrimination tasks by adult male rats

Exposure to alcohol during pregnancy produces Fetal Alcohol Spectrum Disorders, which in its most severe form is characterized by physical dysmorphology and neurobehavioral alterations. Moderate prenatal alcohol exposure (mPAE) is known to produce deficits in discrimination of spatial locations in adulthood. However, the impact of mPAE on higher-order sensory representations, such as discrimination of perceptually similar stimuli, is currently unknown. In the present study, we tested the hypothesis that mPAE would disrupt performance on hippocampal-sensitive tasks that require discrimination between perceptually similar objects or discrimination between spatial locations in a radial arm maze. Here we report that male mPAE rats exhibited intact performance on three types of object discrimination tasks: one in which rats discriminated between distinct toy objects, a second in which discrimination was made between distinct and similar LEGO objects, and a mnemonic similarity task in which rats discriminated between randomly presented LEGO objects that varied in similarity with a learned object. Although adult male mPAE rats performed similarly to control rats on all three object discrimination tasks, they showed deficits when tested in a radial arm maze spatial discrimination task. Specifically, male mPAE rats expressed a significantly higher number of working memory errors (returns to previously visited arms) and were more likely to use non-spatial strategies during training. Together, the findings of the present study support the conclusion that mPAE produces specific deficits in the online processing of spatial information and executing spatial navigation strategies, but spares the ability to discriminate between perceptually similar stimuli.

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

The Remedial Values of Adaptive Ball Types for Children with Autism Spectrum Disorders

Previous studies in the area of motor impairments in children with autism spectrum disorders (ASDs) have warranted an urgent need to review interventions that support fundamental movement skills (FMS). This paper intends to investigate the effectiveness of adaptive ball types used within Physical Education (PE) on the object control proficiency (a subgroup of FMS), in children with ASDs. Following pilot work, 12 children (aged 14.70 ± 2.70) performed four object control tasks (throwing, catching, kicking and soccer style dribbling), using three different ball types (the Developmental Ball, an underweight ball and a control ball). A repeated measures ANOVA was run to statically analyse performance scores. The ANOVA indicated that the type of ball used (f(2,22) 22.798, p<0.001), activity undertaken (f(3,33)= 12.377, p<0.01) and the interaction between the two (f(6,66)=-70.163, p<0.01) ultimately, had a significant effect on motor proficiency. Moreover, the Developmental Ball proved to be consistently beneficial across the object control skills, with post hoc t-tests showing strong significance against the underweight ball in kicking (t(11)=-3,031, p<0. 033) and soccer style dribbling (t(11)=-8.603, p<0.016). This paper concludes Skogstad’s creation to be an invaluable blueprint for the development of fundamental object control skills, in children with ASDs.