Objective Of Task Research Articles

Throwing Strategy in a Dual-Motor-Task of Aiming at the Bullseye While Walking in Virtual Reality

Humans perform movement tasks using motor and cognitive skills. The optimum outcome of the task is dependent on how the motor and cognitive skills are combined. When a dual-task is performed, this combination becomes even more important as the user has to devote limited available attention to the competing tasks. In this study, a ball throwing task to a specified location was performed in a virtual environment while the participant walked on a treadmill performing a dual-motor-task. Fifteen healthy participants took part in the study. The task paradigm involved three conditions-warm-up treadmill walking, throwing a ball to the target while standing, and throwing a ball to the target while walking on a treadmill. The throwing strategy was investigated with quantifiable task-space performance and scoring in the game. The correlation between the task-space performance and the cognitive tests was evaluated in the single and the dual-motor-task. Results demonstrate that during the throwing activity in the single and the dual-motor tasks, the choice of orienting the throwing plane in space was pre-determined prior to releasing the ball in a projectile motion and was interconnected with the motor ability. However, in dual-tasking, once the throwing plane was selected, the variation of hit point localization in the target plane was correlated with the cognitive ability. The task was executed with an allocation of motor and cognitive abilities to control specific performance variables while dual-tasking. This strategy potentially helped participants to reduce the load on attentional capacity during dual-tasking. This study showcases an optimized utilization of skill interaction to achieve the overall task objective. The paradigm designed in this work can be utilized to improve specific motor and cognitive skills by performing only one dual-motor-task.

Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives; motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives’ motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space.

Anandamide and 2-arachidonoylglycerol differentially modulate autistic-like traits in a genetic model of autism based on FMR1 deletion in rats.

Autism spectrum disorder (ASD) has a multifactorial etiology. Major efforts are underway to understand the neurobiological bases of ASD and to develop efficacious treatment strategies. Recently, the use of cannabinoid compounds in children with neurodevelopmental disorders including ASD has received increasing attention. Beyond anecdotal reports of efficacy, however, there is limited current evidence supporting such an intervention and the clinical studies currently available have intrinsic limitations that make the interpretation of the findings challenging. Furthermore, as the mechanisms underlying the beneficial effects of cannabinoid compounds in neurodevelopmental disorders are still largely unknown, the use of drugs targeting the endocannabinoid system remains controversial. Here, we studied the role of endocannabinoid neurotransmission in the autistic-like traits displayed by the recently validated Fmr1-Δexon 8 rat model of autism. Fmr1-Δexon 8 rats showed reduced anandamide levels in the hippocampus and increased 2-arachidonoylglycerol (2-AG) content in the amygdala. Systemic and intra-hippocampal potentiation of anandamide tone through administration of the anandamide hydrolysis inhibitor URB597 ameliorated the cognitive deficits displayed by Fmr1-Δexon 8 rats along development, as assessed through the novel object and social discrimination tasks. Moreover, blockade of amygdalar 2-AG signaling through intra-amygdala administration of the CB1 receptor antagonist SR141716A prevented the altered sociability displayed by Fmr1-Δexon 8 rats. These findings demonstrate that anandamide and 2-AG differentially modulate specific autistic-like traits in Fmr1-Δexon 8 rats in a brain region-specific manner, suggesting that fine changes in endocannabinoid mechanisms contribute to ASD-related behavioral phenotypes.

Towards personalized persuasive dialogue generation for adversarial task oriented dialogue setting

In recent years, task-oriented virtual assistants have gained huge popularity and demand in both research and industry communities. The primary aim of a task-oriented dialogue agent is to assist end-users in accomplishing a task successfully and satisfactorily. Existing virtual agents have acquired proficiency in assisting users in solving simple tasks such as restaurant bookings. However, they operate under the deterministic presumption that end-users will have a servable task objective, which makes them inadequate under adversarial situations such as goal unavailability. On the other hand, human agents accomplish users’ tasks even in many goal unavailability scenarios by persuading them towards a similar goal to the user’s proposed task. Motivated by the limitation, the current work proposes and builds a novel transformer-based context-aware personalized persuasive virtual assistant (CoPersUasive VA), which also serves end-users in task unavailability situations. The proposed CoPersUasive VA recognizes goal conflicts through user sentiment and identifies an appropriate persuasion strategy using ongoing dialogue context and user personality. Depending on users’ proposed goals, it finds a similar servable goal and persuades them with the identified persuasion strategy. The obtained experimental results and detailed post-analysis firmly establish that the proposed model effectively enhances the capability of task-oriented virtual assistants to deal with the task failures caused by goal unavailability. The obtained findings also suggest the crucial role of dialogue context in identifying an appropriate and appealing persuasion strategy. The proposed CoPersUasive model could easily be adapted to any other domain by fine-tuning the model on an underlying task. Furthermore, we developed a personalized persuasive multi-intent conversational dialogue corpus annotated with intent, slot, sentiment, and dialogue act for electronic domain.22The dataset and code will be made available to accelerate research towards the development of persuasive virtual sales assistants.

Evaluation of children's cognitive load in processing and storage of their spatial working memory.

Working memory performance affects children’s learning. This study examined objective (task performance), subjective (self-report), and physiological (pupil dilation) cognitive load (CL) while children completed a spatial working memory complex span task. Frist, 80 Taiwanese 11-year-olds (40 boys) who participated in Experiment 1 confirmed the suitability of the materials. Then, 72 Taiwanese 11-year-olds (35 boys) were assigned to high and low complexity groups to participate in Experiment 2 to test the study hypothesis. Children had to recall at the end of a dual-task list and answer two questions regarding the difficulty and mental effort involved in processing and storage. Their pupil diameters were recorded using an eye-tracker. Two-way mixed ANOVA found that the processing requirements and memory load reduced storage and aggravated the subjective CL of storage; the subjective CL of processing was higher under highly complex conditions. Stepwise regression analysis indicated that subjective CL of processing predicted memory performance in low CL conditions, and physiological CL of processing predicted it in high CL conditions.

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Human behaviours are guided by how confident we feel in our abilities. When confidence does not reflect objective performance, this can impact critical adaptive functions and impair life quality. Distorted decision-making and confidence have been associated with mental health problems. Here, utilising advances in computational and transdiagnostic psychiatry, we sought to map relationships between psychopathology and both decision-making and confidence in the general population across two online studies (N’s = 344 and 473, respectively). The results revealed dissociable decision-making and confidence signatures related to distinct symptom dimensions. A dimension characterised by compulsivity and intrusive thoughts was found to be associated with reduced objective accuracy but, paradoxically, increased absolute confidence, whereas a dimension characterized by anxiety and depression was associated with systematically low confidence in the absence of impairments in objective accuracy. These relationships replicated across both studies and distinct cognitive domains (perception and general knowledge), suggesting that they are reliable and domain general. Additionally, whereas Big-5 personality traits also predicted objective task performance, only symptom dimensions related to subjective confidence. Domain-general signatures of decision-making and metacognition characterise distinct psychological dispositions and psychopathology in the general population and implicate confidence as a central component of mental health.

Multi objective task scheduling based on hybrid metaheuristic algorithm for cloud environment

Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures.

Deep learning models fail to capture the configural nature of human shape perception

SummaryA hallmark of human object perception is sensitivity to the holistic configuration of the local shape features of an object. Deep convolutional neural networks (DCNNs) are currently the dominant models for object recognition processing in the visual cortex, but do they capture this configural sensitivity? To answer this question, we employed a dataset of animal silhouettes and created a variant of this dataset that disrupts the configuration of each object while preserving local features. While human performance was impacted by this manipulation, DCNN performance was not, indicating insensitivity to object configuration. Modifications to training and architecture to make networks more brain-like did not lead to configural processing, and none of the networks were able to accurately predict trial-by-trial human object judgements. We speculate that to match human configural sensitivity, networks must be trained to solve a broader range of object tasks beyond category recognition.

Cognitive Task Performance and Subjective Cognitive Symptoms in Individuals With Chronic Fatigue Syndrome or Fibromyalgia: A Cross-Sectional Analysis of the Lifelines Cohort Study.

This study examined cognitive task performance and self-reported cognitive functioning in individuals with chronic fatigue syndrome (CFS) and fibromyalgia (FM) in a population-based sample and investigated the role of mood and anxiety disorders as well as severity of the physical symptoms. This study was performed in 79,966 participants (mean [standard deviation] age = 52.9 [12.6] years, 59.2% women) from the Lifelines general population. Symptoms consistent with the diagnostic criteria for CFS and FM were assessed using questionnaires. Two comparison groups were used: participants with self-reported medical disorders with well-defined pathophysiology (i.e., multiple sclerosis and rheumatic arthritis) and controls without these diseases. Objective task performance was based on the computerized CogState cognitive battery and subjective cognitive symptoms using the concentration subscale of the Checklist Individual Strength. Cognitive task performance was poorer in individuals with CFS versus controls without disease and controls with a medical disorder, although the severity of cognitive dysfunction was mild. Participants meeting the criteria for CFS ( n = 2461) or FM ( n = 4295) reported more subjective cognitive symptoms compared with controls without a medical disorder ( d = 1.53, 95% confidence interval [CI] = 1.49-1.57 for CFS; d = 1.25, 95% CI = 1.22-1.29 for FM) and participants with a medical disease ( d = 0.62, 95% CI = 0.46-0.79 for CFS; d = 0.75, 95% CI = 0.70-0.80 for FM). These differences remained essentially the same when excluding participants with comorbid mood or anxiety disorders or adjusting for physical symptom severity. Subjective cognitive symptoms and, to a lesser extent, suboptimal cognitive task performance are more prevalent in individuals with CFS or FM compared with controls without these conditions.

Multiple handlers, several owner changes and short relationship lengths affect horses’ responses to novel object tests

Despite numerous studies emerging on the human-horse relationship, significant gaps exist in the identification of the horse and handler factors that influence the quality of their relationship. Here, we explore key factors affecting human-animal relationships: the number of regular handlers an animal has, the length of the relationship with the handler, the number of owner changes, and the familiarity of the handler. A total of 76 horses participated in two novel object tasks (walking on novel surfaces and being touch with a novel object) to determine whether horses react differently to novel situations depending on whether they are handled by a familiar or an unfamiliar person. We observed that having multiple regular handlers negatively affected the horse reluctance towards novel surfaces and novel object. In horses used to be handled by multiple persons, 68% were showed reluctant behaviours towards the novel surfaces while 75% of the horses handled by only one person did not show reluctant behaviours. Similarly, 26% of the horses with multiple regular handlers refused to be touched with a novel object while only 13% of the horses with only one regular handler refused to be touched with the object. The relationship length between the horse and the familiar handler decreased the horse reluctance towards the novel surfaces and the novel object. The longer the relationship the less reluctant were the horses. Horses sold more than once were also more reluctant to the novel object. These horses had higher chances to refuse to be touched with the novel object than the horses still owned by their breeder or their first buyer. Finally, older horses (> 18 yo) had higher success at walking on the surface when led by someone familiar (87%) compared to led by someone unfamiliar (15%). Our findings suggest that the horse-human relationship may take time to develop as it is shaped by multiple factors involving the horse’s previous and current interactions with humans that affect their everyday life.

Towards an orthogonality constraint-based feature partitioning approach to classify veracity and identify stance overlapping of rumors on twitter

The consequences of fake news and rumors have adversely affected social and political stability worldwide. Many such incidents have been reported, which resulted in mass chaos with the loss of lives and property. In response, many researchers have developed models for the veracity detection of rumors on social media. The recent advances in veracity detection models incorporate the use of Deep Neural Networks (DNN) over statistical and traditional machine learning based models. Current veracity detection approaches leverage powerful DNN models such as Transformer, Adversarial Networks, Graph Convolutional Networks (GCN), Variational Autoencoder (VAE) etc., along with exploiting the intuition of Multi-task learning (MTL) approach. In addition, most of these aforesaid well-known models rely on auxiliary (additional) information to a good extent. Presently, these recent models’ dependence on (1) auxiliary information and multiple tasks restrain productivity and incur cost on resources. Moreover, (2) the structural constraints of these models put a limiting effect on model deliverance. These two shortcomings of the recent models result in poor resource utilization and unstructured feature organization of the objective task, resulting in a compromised output of the model. In this paper, we present an efficient Segregated Non-overlapping and Collectively exhaustive DNN model (SeNoCe) which mitigates the effects of poor utilization of resources and enhances the model performance without the aid of auxiliary information or tasks which incur good manual efforts and costs. SeNoCe is capable of utilizing the fine-granularity of implicit features as attention for task identification. SeNoCe reports a major performance improvement over the state-of-the-art techniques on standard benchmark metrics across two real-world rumor datasets. It records a minimum of 24.06−12.8%, 53.1−49.2% improvement in terms of Macro F and Accuracy, respectively over the best performing state-of-the-art.

Social influences in the digital era: When do people conform more to a human being or an artificial intelligence?

The spread of artificial intelligence (AI) technologies in ever-widening domains (e.g., virtual assistants) increases the chances of daily interactions between humans and AI. But can non-human agents influence human beings and perhaps even surpass the power of the influence of another human being? This research investigated whether people faced with different tasks (objective vs. subjective) could be more influenced by the information provided by another human being or an AI. We expected greater AI (vs. other humans) influence in objective tasks (i.e., based on a count and only one possible correct answer). By contrast, we expected greater human (vs. AI) influence in subjective tasks (based on attributing meaning to evocative images). In Study 1, participants (N = 156) completed a series of trials of an objective task to provide numerical estimates of the number of white dots pictured on black backgrounds. Results showed that participants conformed more with the AI's responses than the human ones. In Study 2, participants (N = 102) in a series of subjective tasks observed evocative images associated with two concepts ostensibly provided, again, by an AI or a human. Then, they rated how each concept described the images appropriately. Unlike the objective task, in the subjective one, participants conformed more with the human than the AI's responses. Overall, our findings show that under some circumstances, AI can influence people above and beyond the influence of other humans, offering new insights into social influence processes in the digital era.

Information-Based Sensor Control for Multitask Planning via Multi-Bernoulli Filter

In multitarget tracking, the issue of sensor control is a challenging problem in theoretical analysis and calculation. In this paper, we study the sensor control strategies for multitask planning based on information criteria and propose two sensor control strategies according to the two different task objectives. Initially, we propose a sensor control strategy to improve the overall multitarget tracking performance within the partially observed Markov decision process (POMDP) framework, where the reward function is calculated by the Bhattacharyya distance between the prior and the posterior multitarget densities. In this strategy, we present a target-oriented multi-Bernoulli (TOMB) particle sampling method to approximate the multitarget density and then derive the solution of Bhattacharyya distance in detail. Subsequently, as another important contribution of this paper, we propose a threat-based sensor control strategy, which is still solved under the information theory where the goal is to prioritize multiple threat targets and then to track preferentially the maximum threat target. These strategies are finally used to optimize the sensor trajectory for range-bearing multitarget tracking.

A Reconfigurable Multirobot Cooperation Workcell for Personalized Manufacturing

Most robotic systems designed for mass manufacturing are optimized for a specific type of product. They generally lack the ability to adapt to low-volume customized products. In this article, we present a system based on a modular design for manufacturing personalized medical stent graft implants. The concept is based on learning-by-demonstration by integrating real-time 3-D vision, multirobot collaboration, and personalization to guide the robots to learn and execute tasks continuously with adaptation to different implant geometry. The system is optimized to generate customized and collision-free paths for efficient object manipulation and task completion. We show that the system is generalizable to different stent graft designs and the proposed multirobot system can seamlessly work together with high efficiency without collisions. The results have also suggested its usability for other manipulation tasks, especially for flexible production of customized products where bimanual or multirobot cooperation is required. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this article is the problem of automatic sewing of personalized stent grafts (a tailor-made artificial vessel). Existing personalized stent grafts are mostly hand sewn, which is time consuming and often undersupplied. Automating such process can significantly improve the production and this requires a sewing system that can handle different designs. This article suggests a new schema to design a robotic system to handle personalized designs. The first methodology is modularized design to separate the task into a repetitive part and a personalized part, each handled by a module. The second methodology is to find the best relative pose between the modules such that the robots can complete their task within their working space and with minimum motion. This ensures that a stent graft can be sewn feasibly and with the lowest cost. Computational results show this approach can find optimal solution for different personalized stent grafts and preliminary on robot experiment verifies that this approach is feasible. Please note that this approach is not limited to sewing personalized stent graft. The schema can be applied to solve similar problem of customized product motion planning and system design.

Effects of attention on visual discrimination and awareness: Evidence from eye-specific attention.

The role of attention in visual awareness is a foundational issue for understanding consciousness, but remains highly controversial. Therefore, in two experiments, we investigated whether and how attention modulates visual awareness using a monocular cuing paradigm. Although observers are not aware of which eye received a cue, a monocular cue can attract eye-specific attention to a cued eye. In Experiment 1, we found that eye-specific attention enhanced subjective visual awareness (i.e., awareness reports) as well as objective task performance (i.e., orientation discrimination) of a Gabor target. Importantly, attention enhanced visual awareness only when a decision for orientation discrimination was correct, suggesting that the effect of attention on visual awareness is closely associated with perceptual evidence for orientation discrimination. In Experiment 2, we showed that the modulatory effect of attention on visual awareness was based on contrast gain, which amplified the effective contrast of an attended stimulus, and did not differ from that on orientation discrimination performance qualitatively and quantitatively. Our findings suggest that attention influences visual awareness by amplifying perceptual evidence on which visual awareness and objective performance are commonly based. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ASE

The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only enable humans to perform complex tasks, but also provide powerful priors for guiding their behaviors when learning new tasks. This is in stark contrast to what is common practice in physics-based character animation, where control policies are most typically trained from scratch for each task. In this work, we present a large-scale data-driven framework for learning versatile and reusable skill embeddings for physically simulated characters. Our approach combines techniques from adversarial imitation learning and unsupervised reinforcement learning to develop skill embeddings that produce life-like behaviors, while also providing an easy to control representation for use on new downstream tasks. Our models can be trained using large datasets of unstructured motion clips, without requiring any task-specific annotation or segmentation of the motion data. By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills. We show that a single pre-trained model can be effectively applied to perform a diverse set of new tasks. Our system also allows users to specify tasks through simple reward functions, and the skill embedding then enables the character to automatically synthesize complex and naturalistic strategies in order to achieve the task objectives.

A Versatile Affordance Modeling Framework Using Screw Primitives to Increase Autonomy During Manipulation Contact Tasks

Recent studies utilizing Affordance Templates to perform remote contact manipulation tasks with mobile manipulators have demonstrated their usefulness for modeling complex tasks allowing robots to work in uncertain environments. These efforts largely fall into the &#x201C;supervised autonomy&#x201D; paradigm where a user commands high-level actions and supervises the robot&#x2019;s execution, but is not responsible for the low-level execution details such as controlling the endpoint/joints, managing contact forces, or avoiding collisions. In this work, we present a novel formulation for modeling affordances that features the versatility and generality of screw theory. We also propose a generic framework and algorithm for executing manipulation contact tasks. To thoroughly evaluate the performance of the proposed formulation and framework, we performed various sets of experiments using our mobile manipulator. We showed that this framework is generic enough to successfully manipulate a variety of articulated objects. Moreover, by performing a set of manipulation tasks on a wheel valve as our case study, we demonstrated that our approach lowers task duration (about 5 times) and applied forces and torques significantly (about 2.5 and 3 times, respectively) when compared to direct teleoperation. Further, by performing 90 trials, we show robust performance of the proposed screw-based framework even when there is significant error in the estimated position and orientation (i.e., about 10&#x00A0;cm and 0.35&#x00A0;rd) of the task objects.

Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment

Convolutional neural networks have achieved success in resolving object identification problems. This study contributes a suitable new approach to herb identification for educational and research purposes based on a small dataset and small-sized images. Two self-collected Thai herb datasets with either plain or natural environment backgrounds were used for experimentation to realize this objective. The plain background dataset includes 4,400 images of 11 leaf types, and the natural dataset contains 1,620 images of nine leaf types. The images were divided into a training set containing 75% of the images and a separate test set with the remaining 25%. The experiments included five-fold cross-validation applied to the training set; the InceptionV3, MobileNetV2, ResNet50V2, VGG16, and Xception convolutional neural network models RMSprop and Adam optimizers. Further, dropout rates of 0.3, 0.5, and 0.7 were considered along with five and ten epochs. Transfer learning was applied using pre-trained weights. The model with the best outcome, based on the average accuracy of the cross-validation results on both datasets (the plain background dataset was 94.55%, and the natural dataset was 90.37%), was the VGG16 with the RMSprop optimizer, which exhibited a dropout rate of 0.5 over ten epochs. The model achieved 96.64% and 92.00% accuracy on the plain background training and test sets, and 99.59% and 91.36% on the natural environment training and test sets, respectively. The results show that the method has a high potential for objective tasks and application in identifying herbs based on visual leaf information.

Real Power Loss Reduction by Accipitridae Optimization Algorithm

In this paper, Accipitridae Optimization Algorithm (AOA) is used to solve the power loss lessening problem. Authorizing a satisfactory magnitude of reactive power is effervescent for the steadfast operation of power transmission structures, as the power meagreness may tip to staid power catastrophe and leading power upheavals. Loss reduction problem is intended as one of the prominent conditions for compassionate and financial action of edifice. It is exceptional by appropriate union of the network apparatus castoff to deal with up the power flow with the dispassionate of decreasing the losses and evolution the power outline of the assemblage. Plentiful previous studies unravelled the power problem as the optimization objective tasks premeditated noticeably. Real power loss reduction, Voltage stability expansion and voltage deviation minimization are the key objectives of the work. Hunting methods of Accipitridae are imitated to design the Accipitridae Optimization Algorithm (AOA). Accipitridae does hunting in a single-mode on the prey. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae is formulated. As soon as the prey spot is precise then Accipitridae is geared up for landing and assault, the Accipitridae move down perpendicularly with a preface assault to determine the prey response. AOA initiates the upgrading measures by engendering an arbitrary solution. In the course of the curve of reiteration, the explore stratagem of the AOA is the rational location of the most excellent optimal solution. The authenticity of the Accipitridae Optimization Algorithm (AOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Voltage stability escalated with minimization of power deviance. Then the proportion of actual power loss lessening is amplified.

Impact of background music on the performance of laparoscopy teams

BackgroundAcoustic conditions in the operating room have different impacts on surgeon’s performance. Their effects on the performance of surgical teams are not well documented. We investigated if laparoscopic teams operating under pleasant acoustic conditions would perform better than under noisy conditions.MethodsWe recruited 114 surgical residents and built 57 two-person teams. Each team was required to perform two laparoscopic tasks (object transportation and collaborative suturing) on a simulation training box under music, neutral, and noisy acoustic conditions. Data were extracted from video recordings of each performance for analysis. Task performance was measured by the duration of time to complete a task and the total number of errors, and objective performance scores. The measures were compared over the three acoustic conditions.ResultsA music environment elicited higher performance scores than a noisy environment for both the object transportation (performance score: 66.3 ± 8.6 vs. 57.6 ± 11.2; p < 0.001) and collaborative suturing tasks (78.6 ± 5.4 vs. 67.2 ± 11.1; p < 0.001). Task times in the music and noisy environments was subtracted to produce a music-noisy difference time. Pearson correlation coefficient analysis showed a significant negative relationship between the team experience score and the music-noisy difference time on the object transportation (r = − 0.246, p = 0.046) and collaborative suturing tasks (r = − 0.248, p = 0.044).ConclusionsAs to individuals, music enhances the performance of a laparoscopy team while a noisy environment worsens performance. The negative correlation between team experience and music-noisy difference time suggests that laparoscopy teams composed of experienced surgeons are less likely affected by an acoustic distraction than the noisy teams. Team resistance to acoustic distraction may lead to a new way for assessing team skills.