Simple Maze Research Articles

Body image is associated with persistence. A study of the role of weight-related stigma.

The study replicates a preliminary report from 2019 on therelationship between body image and persistence. The aim of our study was to analyze the associations between body image, persistence, and body weight stereotypes. A total of 750 individuals were recruited for the study. The research was carried out in computer labs. The procedure consisted of psychological questionnaires (Persistence Scale, The Body Esteem Scale, Perceived Weight Stigma Questionnaire, Weight Bias Internalization Scale, Hospital Anxiety and Depression Scale, Formal Characteristics of Behavior - Temperament Inventory, and NEO-PI-R) and The Maze Test (a computer tool). After completing the Simple Maze Test, saliva samples were collected. Next, the subjects proceeded to the laboratory where anthropometric and body composition measurements were taken. The hormone levels (cortisol and dehydroepiandrosterone) in the collected saliva samples were analyzed via ELISA to determine stress. Body image and persistence are related variables. They are associated with the internalization of stereotypes and perceived stigma related to body weight. These associations are differentially shaped according to sex and the regularity of body weight. In women, a stronger association of these variables with body image was observed, while in men, the relationship with body image was weaker, with a stronger association shown by perceived weight-related stigma. In the group of participants with a BMI<18.5, there was no significant association between the internalization of stereotypes and the analyzed variables. This relationship appeared in the group of subjects with a normal body weight and was strongest in the group of participants who were overweight or obese. Perceived weight-related stigma was most strongly associated with body image in the group with BMI<18.5 kg/m2 and with persistence in the group with BMI>25 kg/m2. Body-related stigma affects not only overweight and obese individuals and its mechanisms may be shaped differently.

FF-RRT*: a sampling-improved path planning algorithm for mobile robots against concave cavity obstacle

The slow convergence rate and large cost of the initial solution limit the performance of rapidly exploring random tree star (RRT*). To address this issue, this paper proposes a modified RRT* algorithm (defined as FF-RRT*) that creates an optimal initial solution with a fast convergence rate. An improved hybrid sampling method is proposed to speed up the convergence rate by decreasing the iterations and overcoming the application limitation of the original hybrid sampling method towards concave cavity obstacle. The improved hybrid sampling method combines the goal bias sampling strategy and random sampling strategy, which requires a few searching time, resulting in a faster convergence rate than the existing method. Then, a parent node is created for the sampling node to optimize the path. Finally, the performance of FF-RRT* is validated in four simulation environments and compared with the other algorithms. The FF-RRT* shortens 32% of the convergence time in complex maze environment and 25% of the convergence time in simple maze environment compared to F-RRT*. And in a complex maze with a concave cavity obstacle, the average convergence time of Fast-RRT* in this environment is 134% more than the complex maze environment compared to 12% with F-RRT* and 34% with FF-RRT*. The simulation results show that FF-RRT* possesses superior performance compared to the other algorithms, and also fits with a much more complex environment.

Development of a test bench to solve a simple labyrinth

This paper deals with building an automatic test bench for detecting and solving a simple maze. There are many suitable sensors for detecting walls and ball, as find quick and stabile algorithm for deliver the ball to the target. Two servo motor are used to tilt the table of the maze. The first part of the paper shows the experiences of using OV7076 camera with Arduino Nano which was used for the first time to solve the problem. The final version of the maze solver contains a USB camera, an Arduino UNO platform, OpenCV and PyCharm software. Furthermore, paper reveals possibilities of decision-making-process and the direction of following research work.

7972 Enhancing Laparoscopic Education with Use of LaparAssist, a Hands-Free Device Designed to Direct Learners on a Laparoscopic Monitor

<h3>Study Objective</h3> To determine if LaparAssist, a wireless foot-pedal activated glasses-mounted laser pointing device, is beneficial to medical trainees and performs non-inferiorly to standard pointing devices. <h3>Design</h3> A prospective observational study was performed on academic personnel, with a comparative follow-up study utilizing OB/GYN residents. Two tasks were designed: a maze task to determine pointing accuracy, and a point task measuring time of completion. <h3>Setting</h3> Participants stood 15˚ offset from perpendicular to a monitor that was 145 cm tall and 120 cm away while using the devices. Testing was performed in a prototyping lab and simulated operating room for academic personnel and residents, respectively. <h3>Patients or Participants</h3> Twenty-three academic personnel volunteered for the initial study. The follow-up comparative study utilized 10 resident volunteers. Participants took approximately 15 minutes to complete both tasks. Follow-up surveys were conducted. <h3>Interventions</h3> Participants were taken through a series of non-inferiority tests using LaparAssist, a laser pointer, and a computer mouse on a monitor. For the maze task, participants utilized each device to complete a simple maze. In the point task, participants pointed at randomly appearing dots. <h3>Measurements and Main Results</h3> For the maze task, participants were timed, and errors were recorded. Analysis demonstrated no significant difference in errors by the residents between LaparAssist and the laser pointer (p=0.05). For the point task, subjects were timed. Analysis demonstrated no significant difference in time by the residents between LaparAssist and a handheld laser or mouse. Overall, the residents performed the tasks faster than academic personnel. Survey results indicated no significant difference between devices in comfort and perceived performance for both academic personnel and residents. <h3>Conclusion</h3> LaparAssist allows laparoscopic surgeons to more clearly communicate with trainees without the use of occupied hands. Our data indicates LaparAssist can perform comparably to hand-controlled pointing devices.

The value of spatial experience and group size for ant colonies in direct competition.

Animals often search for food more efficiently with experience. However, the contribution of experience to foraging success under direct competition has rarely been examined. Here we used colonies of an individually foraging desert ant to investigate the value of spatial experience. First, we trained worker groups of equal numbers to solve either a complex or a simple maze. We then tested pairs of both groups against one another in reaching a food reward. This task required solving the same complex maze that one of the groups had been trained in, to determine which group would exploit better the food reward. The worker groups previously trained in the complex mazes reached the food reward faster and more of these workers fed on the food than those trained in simple mazes, but only in the intermediate size group. To determine the relative importance of group size versus spatial experience in exploiting food patches, we then tested smaller trained worker groups against larger untrained ones. The larger groups outcompeted the smaller ones, despite the latter's advantage of spatial experience. The contribution of spatial experience, as found here, appears to be small, and depends on group size: an advantage of a few workers of the untrained group over the trained group negates its benefits.

A New Paradigm for the Study of Cognitive Flexibility in Children and Adolescents: The "Virtual House Locomotor Maze" (VHLM).

Classical neuropsychological assessments are designed to explore cognitive brain functions using paper-and-pencil or digital tests. The purpose of this study was to design and to test a new protocol named the “Virtual House Locomotor Maze” (VHLM) for studying inhibitory control as well as mental flexibility using a visuo-spatial locomotor memory test. The VHLM is a simple maze including six houses using the technology of the Virtual Carpet Paradigm™. Ten typical development children (TD) were enrolled in this study. The participants were instructed to reach a target house as quickly as possible and to bear in mind the experimental instructions. We examined their planning and replanning abilities to take the shortest path to reach a target house. In order to study the cognitive processes during navigation, we implemented a spatio-temporal index based on the measure of kinematics behaviors (i.e., trajectories, tangential velocity and head direction). Replanning was tested by first repeating a path chosen by the subject to reach a given house. After learning this path, it was blocked imposing that the subject inhibited the learned trajectory and designed a new trajectory to reach the same house. We measured the latency of the departure after the presentation of each house and the initial direction of the trajectory. The results suggest that several strategies are used by the subjects for replanning and our measures could be used as an index of impulsivity.

A Generic Method for Classification of Player Behavior

Player classification allows for considerable improvements on both game analytics and game adaptivity. With this paper we aim at reversing the ad-hoc tendency in player classification methods, by proposing an approach to player classification that can be integrated across different games and genres and is particularly suited to be used by game designers. This paper describes our generic method of interaction-based player classification, which consists of three components: (i) intercepting player interactions, (ii) finding player types through fuzzy cluster analysis and (iii) classification using Hidden Markov Models (HMM). To showcase our method we developed Blindmaze, a simple web-based hidden maze game publicly available, featuring a bounded set of interactions. All data collected from a game is interaction-based, requiring minimal implementation effort from the game developers. It is concluded that our method makes player classification even more available by making it generic and re-usable across different games.

Geological Features for Geotourism in the Zanjan and Hamadan Area, Northern Iran

Iran is a land of complex geological past which reflects in its many natural wonders. There are more than 850 known caves in Iran with a total length not exceeding 1000 km, many of them not being properly explored. Two of the biggest and most famous caves are Ali-Sadr and Katale-Khor. The Katale-Khor cave has a simple maze anastomotic pattern. Ghar Ali-Sadr is an anastomotic type of cave located 75 km northwest of Hamedan in a village by the same name and circa 60 km south of Katale-Khor cave. Ali-Sadr cave is considered to be one of the most unique and scenic caves in the world. One of the features that makes Ali-Sadr cave special is a fact that it is the longest and the biggest water cave in the world (from the point of the boating course inside the cave). This natural wonders in Northern Iran are great base for developing a geotourism, as a form of tourism that specifically focuses on geology and landscape. Its purpose is to promote an understanding of earth sciences through appreciation and conservation of geodiversity. The best way to achieve this is through visits to geological features and locations.

Finding more than one path through a simple maze with a quantum walk

We study quantum walks through chains consisting of two and three star graphs. The first star has a distinguished vertex labelled START and the last has one labelled END. There are multiple paths between these two vertices, and the object is to find these paths. We show that a quantum walk can do this with a quantum speedup.

Validation of a Vision-Guided Mobility Assessment for RPE65-Associated Retinal Dystrophy

PurposeTo validate a vision-guided mobility assessment for individuals affected by RPE65-associated retinal dystrophy (RPE65-RD).MethodsIn this comparative cross-sectional study, 29 subjects, comprising 19 subjects with RPE65-RD and 10 normally-sighted subjects undertook three assessments of mobility: following a straight line, navigating a simple maze, and stepping over a sidewalk “kerb.” Performance was quantified as the time taken to complete each assessment, number of errors made, walking speed, and percent preferred walking speed, for each assessment. Subjects also undertook assessments of visual acuity, contrast sensitivity, full-field static perimetry, and age-appropriate quality of life questionnaires. To identify the most relevant metric to quantify vision-guided mobility, we investigated repeatability, as well as convergent, discriminant, and criterion validity. We also measured the effect of illumination on mobility.ResultsWalking speed through the maze assessment best discriminated between RPE65-RD and normally-sighted subjects, with both convergent and discriminant validity. Walking speed also approached statistical significance when assessed for criterion validity (P = 0.052). Subjects with RPE65-RD had quantifiably poorer mobility at lower illumination levels. A relatively small mean difference (−0.09 m/s) was identified in comparison to a relatively large repeatability coefficient (1.10 m/s).ConclusionsWe describe a novel, quantifiable, repeatable, and valid assessment of mobility designed specifically for subjects with RPE65-RD. The assessment is sensitive to the visual impairment of individuals with RPE65-RD in low illumination, identifies the known phenotypic heterogeneity and will furthermore provide an important outcome measure for RPE65-RD.Translational RelevanceThis assessment of vision-guided mobility, validated in a dedicated cohort of subjects with RPE65-RD, is a relevant and quantifiable outcome measure for RPE65-RD.

Hot minnows could struggle to navigate as temperatures rise

Climate change is going to get uncomfortable for many, but for ectotherms (so-called ‘cold blooded’ animals), which depend on their surroundings to maintain a healthy body temperature, the consequences could be disastrous. For example, warmer temperatures raise the metabolic rate for basic life support, leaving less spare energy for animals to go about their daily activities, in addition to impacting the energy use of the brain. But no one knows how these changes might affect the ability of ectotherms to negotiate their surroundings. For example, could higher temperatures produce bold super-smart fish or timid dunderheads that struggle with the simplest task? Libor Závorka from Shaun Killen's lab at the University of Glasgow, UK, nipped to the nearby River Kelvin to catch young minnows (Phoxinus phoxinus) before raising them at different temperatures (14 and 20°C) for 8 months to find out how climate change might affect their ability to navigate their surroundings.Measuring the fish's oxygen consumption while they lazed about and after a chase in the tank (to get their heart racing) it was clear that the fish that had grown up in extreme heat (20°C) used more energy to keep their bodies ticking over. Yet, they were able to increase the amount of energy they consumed when exercising hard, so they had as much spare energy as fish currently residing at current River Kelvin temperatures (14°C) for day-to-day activities. But when the team tested the minnows’ ability to home in on a bloodworm snack secluded in a simple maze, they realised that the fish that had been raised at 20°C struggled to secure their tasty reward, despite having larger brains. The hot fish raised in conditions that could be more common in the future simply weren't as good at navigating as modern-day fish, even though their larger brains could have made them smarter.All in all, it seems probable that minnows will be able to keep active as temperatures rise in the future, but Killen and his colleagues are concerned that they may struggle to explore their surroundings successfully, ‘probably affecting fitness and ecological interactions’, says Závorka.

Sparsity through evolutionary pruning prevents neuronal networks from overfitting

Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. However, still the networks fail – in contrast to our brain – to develop general intelligence in the sense of being able to solve several complex tasks with only one network architecture. This could be the case because the brain is not a randomly initialized neural network, which has to be trained from scratch by simply investing a lot of calculation power, but has from birth some fixed hierarchical structure. To make progress in decoding the structural basis of biological neural networks we here chose a bottom-up approach, where we evolutionarily trained small neural networks in performing a maze task. This simple maze task requires dynamic decision making with delayed rewards. We were able to show that during the evolutionary optimization random severance of connections leads to better generalization performance of the networks compared to fully connected networks. We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches.

Neuromodulated multiobjective evolutionary neurocontrollers without speciation

Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Neuromodulation has been used to facilitate unsupervised learning by adapting neural network weights. Multiobjective evolution of neural network topology and weights has been used to design neurocontrollers for autonomous robots. This paper presents a novel multiobjective evolutionary neurocontroller with unsupervised learning for robot navigation. Multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with neuromodulated learning. NEAT-MODS is an NSGA-II based multiobjective neurocontroller that uses two conflicting objectives. The first rewards the robot when it moves in a direct manner with minimal turning; the second objective is to reach as many targets as possible. NEAT-MODS uses speciation, a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. The effectiveness of the design is demonstrated using a series of experiments with a simulated robot traversing a simple maze containing target goals. It is shown that when neuromodulated learning is combined with multiobjective evolution, better-performing neural controllers are synthesized than by evolution alone. Secondly, it is demonstrated that speciation is unnecessary in neuromodulated neuroevolution, as neuromodulation preserves topological innovation. The proposed neuromodulated approach is found to be statistically superior to NEAT-MODS alone when applied to solve a multiobjective navigation problem.

Communication-Free Multi-Agent Coordination in an Unknown Environment

Abstract We propose a communication-free control protocol for the coordination of multi-agent systems of autonomous vehicles. We consider a maze solving problem in which two agents must search an unknown maze as fast and as securely as possible. We formulate this problem using a discrete-event systems framework and present a coordination strategy for two agents to search a simple maze that involves no communication, thereby eliminating any cyber-security threats. Our strategy involves two agents having reverse priority decision-making policies, while searching the maze with a depth-first search control policy. Under our definition of inefficiency, which is a function of the number of dead end states that are searched by both agents, we show that our method achieves the minimum inefficiency cost, and thus performs as well as any communication-based solution.

Is body image associated with perseverance? A study among people with normal and increased body weight.

BackgroundThe extreme lack of perseverance in obese and overweight people is mainly associated with difficulties in holding back irrelevant thoughts or memories. The aim of this study was to analyze the relationship between body image and perseverance.MethodsThe research was carried out amongst a cohort of 135 people with normal and overweight/obese body weight. The criterion determining body image in the study participants was an index in the form of a subjective assessment of body weight (too low/correct/too high). Perseverance was determined using a website with simple maze tasks. For the assessment of one’s nutritional status, the applied body mass index (BMI) was utilized. For the statistical analysis, the Student’s t-test and the r-Pearson correlation were used.ResultsRespondents with a normal BMI were more persistent in performing the tasks, in comparison with those who possessed a BMI above the normal range. There was a positive correlation between the average time spent on one task and the BMI. People in either group of normal BMI or with an overweight BMI, who assessed their body weight as too high, had lower results of analyzed perseverance indicators than those who assessed their body weight as normal.ConclusionThis is the first study analyzing the relationship between body image with perseverance. Our results showed that perseverance is associated with body image. There was a correlation between the distractor resistance index and BMI. Lower results of analyzed perseverance indexes were observed in people perceiving their body weight as too high and not necessarily overweight.

A method for rapid testing of social learning in vampire bats

Designing experiments on social learning using an untested behaviour or species requires baseline knowledge of how the animals will perform. We conducted a pilot study of a procedure for rapidly testing social learning in the highly social common vampire bat (Desmodus rotundus) using a simple maze. To create demonstrators, we allowed captive bats to learn to exit a three-dimensional maze, which reunited them with their colony as a reward. We then filmed naive bats in the same maze, comparing their ability to exit the maze before, during and after the addition of a trained demonstrator. The presence of a demonstrator increased the exit rates of naive bats, presumably by attracting the attention of the naive bats to the maze exit. Four of the five naive bats that exited in the presence of a demonstrator retained the ability to exit without the demonstrator. No naive bat exited during trials without a potential demonstrator present. This experimental procedure appears to be a promising approach for efficient tests of social learning in vampire bats because maze difficulty can be manipulated to adjust learning rates and each trial requires only 15 min.

Recognition of a Virtual Maze Scene Using Simulated Prosthetic Vision.

A real-time psychological physics experimental platform was built to aid in discovering the best real-time image processing strategy for the limited number of electrodes, and in providing the most useful prosthetic implant visual information. A number of maze pathfinder tasks were performed at low resolution to simulate use by blind individuals in performing daily visual activities. In this study, simple (5 rows × 5 columns), medium (8 × 8), and complex (11 × 11) maze models were created in 3DMAX. The models were constructed by using Unity to build virtual scenes for real-time pixel processing, including binarization, color inversion, and matching to templates with three different resolutions (16 × 16, 24 × 24, 32 × 32). Subjects completed maze pathfinding tasks using 45° and 60° views of the labyrinth at the 32 × 32 resolution to determine the optimal viewing angle. The time required to find the maze entrance and complete the maze were analyzed along with the rate of maze completion (accuracy) at various resolutions. In the first experiment, the average time required to find the entrance and the average maze pathfinding duration were significantly longer at 60°. Therefore, the 45° view provided the best perspective. In the second experiment, the angle was fixed to 45°. As the maze difficulty increased, the average time needed to find the maze entrance decreased, but the average time required to complete the maze increased. When the difficulty of the maze was fixed, the time required to find the maze entrance and solve the maze decreased when the resolution increased. The accuracy with which the maze path was identified increased as well. The average maze pathfinding time at 24 × 24 was significantly less than at 16 × 16. A similar trend was observed when the average maze pathfinding times at 32 × 32 and 24 × 24 were compared. At 32 × 32, the average pathfinding accuracy was 100%. This indicates that 32 × 32 is an effective resolution for maze pathfinding.

Effects of cerebrolysin on nerve growth factor system in the aging rat brain.

Background:Aging is associated with some cognitive decline and enhanced risk of development of neurodegenerative diseases. It is assumed that altered metabolism and functions of neurotrophin systems may underlie these age-related functional and structural modifications. CerebrolysinTM (CBL) is a neuropeptide mixture with neurotrophic effects, which is widely used for the treatment of stroke and traumatic brain injury patients. It is also evident that CBL has an overall beneficial effect and a favorable benefit-risk ratio in patients with dementia. However, the effects of CBL on cognition and brain neurotrophin system in normal aging remain obscure.Objective:The aim of the present study was to examine the age-related modifications of endogenous neurotrophin systems in the brain of male Wistar rats and the effects of CBL on learning and memory as well as the levels neurotrophins and their receptors.Methods:Old (23–24 months) and young (2–3 months) male Wistar rats were used for the study. A half of animals were subjected to CBL course (2.5 ml/kg, 20 i.p. injections). Behavior of rats was studied using the open field test and simple water maze training. The contents of NGF and BDNF were studied using enzyme-linked immunosorbent assay; the expression of neurotrophin receptors was estimated by Western-blot analysis.Results:CBL treatment did not affect general status, age-related weight changes, general locomotor activity as well as general brain histology. In a water maze task, a minor effect of CBL was observed in old rats at the start of training and no effect on memory retention was found. Aging induced a decrease in neurotrophin receptors TrkA, TrkB, and p75NTR in the neocortex. CBL counteracted effects of aging on neocortical TrkA and p75NTR receptors and decreased expression of proNGF without influencing overall NGF levels. BDNF system was not significantly affected by CBL.Conclusion:The pro-neuroplastic “antiaging” effects of CBL in the neocortex of old animals were generally related to the NGF rather than the BDNF system.

ROS プラットフォームにおける移動ロボットの自律走行のための 動作獲得学習および走行シミュレーション

This paper describes a system for learning and autonomous navigation of mobile robots on ROS (Robot Operating System) platform. Applying learning of autonomous navigation in the ROS platform will increase availability for autonomous navigation in actual environments, because the ROS platform is often utilized for navigation system in actual environments such as Tsukuba Challenge. Especially, in this study, a learning system based on Deep Q-Networks, that is effective for learning tasks in high-dimensional state spaces, is introduces. At first, simple maze problems as simplified robot navigation environment are solved. Then, the maze problems are applied to simulated robot navigation tasks on ROS platform. Learning results show that the robot obtained a policy to reach any destinations in the maps of the ROS simulator.

Towards psychologically adaptive brain–computer interfaces

Objective. Brain–computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.