Dartboard Research Articles

Roles and interplay of reinforcement-based and error-based processes during reaching and gait in neurotypical adults and individuals with Parkinson's disease.

From a game of darts to neurorehabilitation, the ability to explore and fine tune our movements is critical for success. Past work has shown that exploratory motor behaviour in response to reinforcement (reward) feedback is closely linked with the basal ganglia, while movement corrections in response to error feedback is commonly attributed to the cerebellum. While our past work has shown these processes are dissociable during adaptation, it is unknown how they uniquely impact exploratory behaviour. Moreover, converging neuroanatomical evidence shows direct and indirect connections between the basal ganglia and cerebellum, suggesting that there is an interaction between reinforcement-based and error-based neural processes. Here we examine the unique roles and interaction between reinforcement-based and error-based processes on sensorimotor exploration in a neurotypical population. We also recruited individuals with Parkinson's disease to gain mechanistic insight into the role of the basal ganglia and associated reinforcement pathways in sensorimotor exploration. Across three reaching experiments, participants were given either reinforcement feedback, error feedback, or simultaneously both reinforcement & error feedback during a sensorimotor task that encouraged exploration. Our reaching results, a re-analysis of a previous gait experiment, and our model suggests that in isolation, reinforcement-based and error-based processes respectively boost and suppress exploration. When acting in concert, we found that reinforcement-based and error-based processes interact by mutually opposing one another. Finally, we found that those with Parkinson's disease had decreased exploration when receiving reinforcement feedback, supporting the notion that compromised reinforcement-based processes reduces the ability to explore new motor actions. Understanding the unique and interacting roles of reinforcement-based and error-based processes may help to inform neurorehabilitation paradigms where it is important to discover new and successful motor actions.

SADO-Net: A spatial adaptive dart optimized network model for an automated brain tumor diagnosis using MRIs

Early detection of brain tumors is essential, and a biopsy is required to determine the type of tumors in the brain and is only possible after extensive brain surgery. Brain tumors can be identified and classified by clinicians with the aid of computational intelligence algorithms. Here, we developed an novel and intelligent automated system, known as, Spatial Adaptive Dart Optimized Network (SADO-Net) for the diagnosis of brain tumor using MRIs. This will allow medical professionals to identify tumors in their early stages with a high degree of accuracy. Before classifying the diseases, the preprocessed brain MRIs are segmented using the Spatial Pattern based Image Segmentation (SPISeg) technique. The type of brain tumor is then promptly and reliably identified using the Weight Optimized Deep Network (WODNet) Classification model. The adoption of the Darts Game based Optimization (DGO) method for feature reduction expedites the classification process. This work uses a range of metrics and popular public datasets such as BRATS 2018, BRATS 2019, BRATS 2020 and Figshare to compare and validate the performance of the proposed SADO-Net model in tumor identification. Based on the results, the SADO-Net model exhibits good performance, with an average accuracy of 99.2 % and a loss rate of 0.5 %.

Development of novel intrusion detection in Internet of Things using improved dart game optimizer‐derived optimal cascaded ensemble learning

AbstractBackground of the StudyInternet of things (IoT) industry has accelerated its development with the support of advanced information technology and economic expansion. A complete industrial foundation includes software, chips, electronic components, IoT services, integrated systems, machinery, and telecom operators, which the gradual improvement in the IoT industry system has formulated. As the exponential growth of IoT devices increases, the attack surface available to cybercriminals enables them to carry out potentially more damaging operations. As a result, the security sector has witnessed a rise in cyberattacks. Hackers use several methods to copy and modify the information in the IoT environment. Machine learning techniques are used by the intrusion detection (ID) model to determine and categorize attacks in IoT networks.ObjectivesThus, this study explores the ID system with the heuristic‐assisted deep learning approaches for effectively detect the attacks in the IoT. At first, the IoT data are garnered in benchmark resources. Then, the gathered data is preprocessed to perform data cleaning. Next, the data is transformed and fed to the feature extraction stage. The feature extraction is performed with the help of one‐dimensional convolutional neural network (1D‐CNN), where the features are extracted from the target‐based pooling layer. Then, these attained deep features are fed to the ID phase, where the cascaded ensemble learning (CEL) approach is adopted for detecting the intrusions. Here, the hyperparameter tuning is done with a new suggested improved darts game optimizer (IDGO) algorithm. Here, the main objective of the developed algorithm helps to maximize accuracy in ID.FindingsThroughout the experimental findings, the developed model provides 86% of accuracy. Thus, the finding of the developed model shows less detecting time and higher detection efficiency.

Lateral asymmetry of emotional arousal as a biomarker of attributional style during darts competitions.

According to the Multiple Arousal Theory, electrodermal activity (EDA) is not uniform across the body. However, the psychological meaning of a left or right-sided EDA dominance is still not clear. We explored EDA lateral asymmetry as a psychophysiological marker of optimistic and pessimistic attributional style regarding success and failure in a darts competition. Bilateral EDA pattern of 230 throws of a competing pair was measured by Obimon EDA including accelerometer measurements of movements. First, we confirmed that lateral asymmetry can be measured reliably based on EDA data from both wrists. Second, we assessed attributional styles related to lateral asymmetry based on 80 individual throws. We recorded participants' expectations regarding their upcoming performance, and their attribution of success and failure based on Seligman's definition as optimist (internal cause attributed to success, or external cause ascribed to failure) or pessimist. The ratio of optimist and pessimist attributions was significantly different for throws with right or left-sided EDA dominance (p = 0.001). Optimistic attribution characterized 84% of right dominant, while pessimist 63% of left-dominant EDA during throws. We replicated these findings on 50 throws from 10 more individuals (p = 0.034). All individuals were right-handed. We conclude that wrist EDA can be reliably measured during physical movements, such as in a darts game. Lateral EDA asymmetry is a consistent psychophysiological marker of the attitude toward success and failure in a competitive setting, suggesting that lateral asymmetry of emotional arousal may serve as a novel psychophysiological biomarker for attribution style. Results underlie the psychophysiological relevance of bilateral arousal assessment and provide evidence-based verification for the Multiple Arousal Theory.

Performance improvement of PV systems during dynamic partial shading conditions using optimization algorithms

PV power plants encounter varying levels of irradiance, temperature fluctuations, and partial shading because of the differences in sunlight conditions. Partial shading can cause an increase in the power loss, leading to a reduction in efficiency. Maximum Power Point Tracking (MPPT) is of utmost importance in the functioning of photovoltaic (PV) systems for electricity generation because it is indispensable for maximizing power extraction from PV modules, thereby increasing the overall power output. In situations where partial shading is present, the utilization of MPPT algorithms to achieve maximum power output becomes complex because of the existence of multiple distinct peak power points, each having a unique local optimum. To overcome this issue, a method is proposed that uses Darts Game Optimization (DGO), a game-based optimization process, to efficiently determine and extract the maximum power from various local optimal peaks. A population-based optimization method known as the Darts Game Optimization algorithm exists. In this approach, the optimization process begins by creating a population of random players. Then, the algorithm iteratively updates and improves the population to search for the best player or solution. In this study, the DGO algorithm was applied to the MPPT process for voltage optimization in the PV procedure. The DC-DC converter is utilized to capture the maximum available power, and the findings demonstrate that the DGO algorithm efficiently identifies the global maximum, resulting in accelerated convergence, reduced settling time, and minimized power oscillation. Through simulations, the feasibility and effectiveness of the DGO centered MPPT approach was confirmed and compared with MPPT algorithms relying on perturb and observe (P&O) and Particle Swarm Optimization (PSO). The simulation results offer compelling evidence that the DGO algorithm, as proposed in this study, proficiently traces the global maximum, thereby substantiating its practicality and efficiency.

The Effect of Dart Board Media in Discovery Learning Model toward The Motivation Learning

This quasy-experimental research aims to determine the effect of Dart Board media in the Discovery Learning Model toward the motivation learning. The research design used was pretest - posttest control group design. The population in this study were students of class XI SMA Negeri 1 Soppeng who studied chemistry totaling three classes. Sampling used was simple random technique so as to obtain class XI 9 as an experimental group and class XI 10 as a control group. Data collection was done by giving motivation questionnaire at the beginning and end of the meeting. The results of descriptive statistical analysis obtained the average learning motivation value of the experimental group pretest of 73,5 and posttest of 80,83, and the control group pretest of 73,90 and posttest of 77,25. The results of the inferential statistical analysis at a significant level of 0,05 obtained Zcount> Ztable (2,62 > 0,65), then H1 is accepted and H0 is rejected. Based on the test results it can be concluded that there is effect of Dart Board media in the Discovery Learning Model toward the motivation learning.

Hemispheric Lateralization in Older Adults Who Habitually Play Darts: A Cross-Sectional Study Using Functional Near-Infrared Spectroscopy.

Exercise training integrating physical and cognitive activities is gaining attention because of its potential benefits for brain health. This study focuses on exercise training using a dart game called Wellness Darts. Wellness Darts is a sport involving throwing darts and walking to pull them out of the board, memorizing the score, and subtracting this from the total score, thus requiring the simultaneous performance of two tasks: exercise and calculation. This is expected to maintain and improve cognitive function, and whether this continual darts training affects brain function is of great interest. Before conducting the longitudinal study revealing its effect on brain function, we aimed to cross-sectionally confirm the difference in hemispheric lateralization between expert and non-expert players. Functional near-infrared spectroscopy (fNIRS) was used to measure brain activity for three groups: an expert older group who practiced darts continually, a non-expert older control group, and a non-expert younger control group. Their brain activity patterns were quantified by the lateralization index (LI) and compared between groups. The results showed that the younger and the expert older groups had significantly higher LI values than the non-expert older group, and there was no difference between the expert older and the younger groups. Our results suggest that the Wellness Darts game possibly promotes hemispheric lateralization.

Evaluating the impact of a darts game intervention on cognitive function in older adults with and without mild cognitive impairment: a pilot study.

The current study investigated the relationship between the characteristics of a darts game, including the throwing motion toward a target, and mild cognitive impairment (MCI). To this end, we examined the associations between cognitive function and mental health, and the shift in center of gravity while throwing darts. In a preliminary investigation, a 1-month dart game intervention was conducted among older individuals living in the community. The participants were divided into the non-MCI and MCI groups, and the relationship between center of gravity movement during throwing and the presence of dementia was examined. The intervention lasted for 1 month and was tested on healthy older individuals (aged ≥ 65 years) recruited from the community. The Japanese version of the Montreal Cognitive Assessment and the Trail Making Test was used to assess cognitive function. Mental health was evaluated using the Kessler Psychological Distress Scale and the Subjective Well-being Inventory. The center of pressure was analyzed to determine the center of gravity shift during dart throwing. The analysis of factors influencing the determination of the MCI score during the intervention revealed a tendency for the center of gravity shift to be associated as a protective factor in the non-MCI group, although this association did not reach statistical significance (odds ratio = 0.942, p = 0.084). In the MCI group, a significant effect of age was observed in the MCI score (odds ratio = 1.539, p = 0.007). The current findings suggest that conducting center of gravity shift testing could potentially provide a helpful tool for predicting early decline in cognitive function.

Efektivitas Media Dart Board Modifikasi terhadap Kemampuan Kognitif dalam Mengenal Buah-buahan bagi Anak Usia Dini

The lack of growth in children's cognitive capacities to identify objects around them, such as the fact that children still struggle to recognize fruit, particularly its color, shape, and size, is what inspired this research. The research purpose is to evaluate the impact of dart board media on young children's cognitive development with regard to fruit comprehension. This study employs quasi-experimental techniques along with a quantitative approach. This study included every student at the Pembina Pasaman State Kindergarten, which had 115 students spread across six classes. Using a purposive sample technique, the sample of 17 kids from classes B6 and B1 was selected. Tests were used to collect the data, and tests for homogeneity, hypothesis testing, normality, and effect size were used to analyze the data. Therefore, the pre-test.

Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

Quantum Computing with Dartboards.

We present a physically appealing and elegant picture for quantum computing, using rules constructed for a game of darts. A dartboard is used to represent the state space in quantum mechanics, and the act of throwing the dart is shown to have close similarities to the concept of measurement or collapse of the wave function in quantum mechanics. The analogy is constructed in arbitrary dimensional spaces, that is, using arbitrary dimensional dartboards, and for such arbitrary spaces this also provides us a "visual" description of uncertainty. Finally, connections between qubits and quantum computing algorithms are also made, opening the possibility to construct analogies between quantum algorithms and coupled dart throws.

Optimal resilient microgrids formation based on darts game theory approach and emergency demand response program for cyber-physical distribution networks considering natural disasters

In this paper, novel multi-objective microgrids (MGs) formation method-based darts game theory optimization algorithm is proposed. The main objectives are to minimize the total losses, amount of load-shedding, and total restoration cost while ensuring a variety of topological and electrical constraints. The network graph theory represents the formed MG, and the non-linear equations of power flow and loss calculations are adopted in the MG formation model. In the proposed model, the DRERs include diesel generators, fuel cells, micro-turbines, and renewables wind and PV units with energy storage systems. The demand response program includes curtailable loads to improve load profile reduction during peaks. To assess the system's resiliency under extremely inevitable incidents, resiliency metrics are applied. To validate the effectiveness of the proposed approach, the modified IEEE 33-bus test system is utilized. Single-fault and multiple faults disaster case studies are performed with and without considering the emergency demand response program (EDRP). The results show the effect of EDRP in minimizing system losses, load-shedding, restoration cost, and improving the resiliency metrics after natural disasters. Finally, the analytic hierarchy process (AHP) model indicates the most efficient operation scenario while achieving the system objectives considering the problem alternatives.

On a dart game of Niedermaier

We analyze a game introduced by Andy Niedermaier, where m players take turns throwing a dart at a dartboard. A player is eliminated unless his dart lands closer to the center than all previously thrown darts, in which case he goes to the back of the line, until only one player remains. Using generating functions, we determine the distribution of the number of throws in the game, and we obtain a recursive formula to compute the probability that each player wins.

Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

SummaryVarious significant methodologies have been developed in classifying chronic kidney disease (CKD). But still, there emerge certain drawbacks, including high storage space requirements, increased diagnosis time, high cost of computation, and degraded accuracy. Hence in the proposed research work, Ebola deep wavelet extreme learning machine (EDWELM) is proposed for the precise classification of CKD and non‐CKD. Initially, the accessed data are preprocessed by transforming categorical to numerical values and replacing missing values with median to remove unwanted distortions. In the feature selection process, the hybrid methodology of Darts game and Battle royale optimization called Darts Battle game optimizer is carried out to choose the most discriminative features for improving classification accuracy. The final step undertaken is the classification process, which is one of the important data mining applications for distinguishing the data classes. In the proposed EDWELM classification method, ELM based autoencoder, wavelet neural network, and Ebola optimization search algorithm are carried out for effective CKD classification. From the CKD dataset, the performance is analyzed to various terms like accuracy, F1 score, precision, recall, kappa, and balanced score. The accuracy of 99.83% is attained by the proposed EDWELM classification method, which is comparatively better than the existing approaches.

운동학습의 OPTIMAL 이론이 다트 던지기 정확성, 자기-효능감, 정서적 경험에 미치는 영향

Purpose: This study was to investigate the influence of OPTIMAL theory(Wulf & Lewthwaite, 2016) on dart throwing accuracy. Methods: A total of 24 participants were randomly assigned into two groups (optimized vs. yoked). The dart throwing task was to throw the dart pin towards the bulls-eye of the dart board from 2m apart. The accuracy scores were determined by 10 scales of centric circles around the bulls-eye. The experiment was proceeded in the order of 1. pre-test 2. practice phase 3. retention test 4. transfer test. The dependent variables of this study were confirming the application level of OPTIMAL theory, dart throwing accuracy, self-efficacy, and emotional experiences(positive affect & negative affect). Results: First, the high application level of OPTIMAL theory were confirmed in the practice phase. Second, optimized group performed better in the practice phase and showed significant difference in the retention & transfer test. Third, optimized group showed significantly higher self-efficacy and positive affect. Conclusion: The OPTIMAL theory could induce positive learning effect in dart throwing accuracy, self-efficacy, and positive affect. These beneficial results imply that OPTIMAL theory must considered when organizing the motor-learning program.

The Effectiveness of the Combination of Quartet and Dart Games for Teaching Speaking

This study investigates the use of the combination of Quartet and Dart games for teaching speaking. The main problem in teaching speaking of English as foreign language is how to stimulate and motivate students to speak. There are some media to teach speaking. This study proposes an alternative way for minimizing some problems that make the students hesitant to speak by using the combination of Quartet and Dart games. Quartet is a card game. There are totally 32 cards divided into 4 theme cards. Every card provides a brief description that will make the students read the description while waiting for the turn. The goal of the game is to collect a set of cards as many as possible. Dart game is an activity that is designed to challenge students to engage with the texts. The students are supposed to speak and to interpret the information carefully based on the Dart game. By using Quartet game, students will be able to speak well. It makes them speak based on the Quartet card. Quartet cards facilitate them with spoken vocabularies, grammar and structure of the text. Therefore, Dart game makes the students ready when they are going to express their ideas based on the instruction of Dart board. The objectives of this research are to find out whether the combination of Quartet and Dart games is effective to teach speaking to Junior High School‟s students and to find out whether the students are able to speak well by using the combination of Quartet and Dart games. The population was the eighth grade students of SMPS Yayasan Johan Sentosa Kabupaten Kampar in the academic year of 2014/2015. The number of population was 312 and sample was 36 students of VIII B for the try-out class and 37 students of VIII A as the experimental class. I used pre-experimental design in this research. When conducting the research, I used oral performance test to collect the data. The study showed that after the students were given treatment by using the combination of Quartet and Dart game, there was an increasing score from the pre- test and the post- test result about 72.86%. The tvalue was 27.69 and t-table was 1.69. Since the t-value is higher than the t-table, it can be concluded that the working hypothesis is accepted. It means that the combination of Quartet and Dart games is effective to improve students‟ speaking skills.

Play Like the Pros? Solving the Game of Darts as a Dynamic Zero-Sum Game

The game of darts has enjoyed great growth over the past decade with the perception of darts moving from that of a pub game to a game that is regularly scheduled on prime-time television in many countries such as the United Kingdom, Germany, the Netherlands, and Australia, among others. It involves strategic interactions between two players, but to date, the literature has ignored these interactions. In this paper, we formulate and solve the game of darts as a dynamic zero-sum game (ZSG), and to the best of our knowledge, we are the first to do so. We also estimate individual skill models using a novel data set based on darts matches that were played by the top 16 professional players in the world during the 2019 season. Using the fitted skill models and our ZSG problem formulation, we quantify the importance of playing strategically—that is, taking into account the score and strategy of one’s opponent—when computing an optimal strategy. For top professionals, we find that playing strategically results in an increase in win probability of just 0.2%–0.6% over a single leg but as much as 2.2% over a best-of-31-legs match. Summary of Contribution: Dynamic zero-sum games (ZSGs) are of considerable interest, as they arise in many applications including sports, the management of communication networks, interdiction games, and heads-up poker—an important topic in modern artificial intelligence. In this study we consider the game of darts, which is growing increasingly popular around the world today. We formulate the game of darts as a ZSG and solve it iteratively by formulating each player’s best-response problem as a stochastic shortest-path (SSP) problem. We then solve these SSPs using standard dynamic programming methods. In solving the ZSG, we are able to accurately quantify the importance of top professionals playing strategically.

Cognitive Support Tools for Pre-Performance Routine in Darts Game

This paper describes two types of a cognitive support tool for a pre-performance routine (PPR) in darts game. PPRs entail the performance of determined motions before an action and are often executed in sports for the purpose of removing stress or raising concentration. The concentration-stabilizing phenomenon was discovered by the previous research and it determined that the phenomenon appears more conspicuous in the case of experts and PPRs. A tool using a simple brainwaves sensor has been designed and shows us the current status of concentration and notifies us of the concentration-stabilizing phenomenon on a tablet computer. Another tool has been developed on a smart watch with a heart rate sensor. The smart watch indicated heartbeat as a “beep” sound to a user. It was designed based on a result that indicated that darts game scores tend to improve by throwing immediately after a heartbeat. The effectiveness of the tools was verified in several experiments.

PENGARUH MODEL PEMBELAJARAN ADVANCE ORGANIZER BERBANTUAN MEDIA DART BOARD TERHADAP KEMAMPUANBERPIKIR KRITIS SISWA PADA MATERI LAJU REAKSI

This research aimed at seeing the effect of Advance Organizer (AO) learning model with Dart Board media toward student critical thinking ability on Reaction Rate lesson at the eleventh grade of Natural Science of Islamic Integrated Senior High School of Al-Ittihad Pekanbaru. It was a quasi-experimental research with pretest-posttest design. The subjects of this research were the eleventh-grade students of Natural Science in the Academic Year of 2019/2020. Purposive sampling technique was used in this research, so there were 2 sample classes—the eleventh-grade students of Pa Natural Science (experimental group) and the students of Pi Natural Science (control group). The data were collected through observation, preliminary data test that was homogeneity test, and final data tests that were pretest and posttest. Independent sample t-test with SPSS 21 was used to know and analyze the effect of AO learning model with Dart Board media toward student critical thinking ability. Based on the result of processing the final data, it was obtained that the score of Sig. (2-tailed) 0.000 was lower than 0.05 (0.000<0.05). It could be concluded that H a was accepted and H 0 was rejected. It showed that there was an effect of AO learning model with Dart Board media toward student critical thinking ability at the eleventh grade of Natural Science of Islamic Integrated Senior High School of Al-Ittihad Pekanbaru. To know the effect of AO learning model with Dart Board media toward student critical thinking ability, it was done the calculation of determination coefficient (r 2 ), and it was obtained the coefficient of effect 54.43%.