Tasks In Less Time Research Articles

An At-Home Laparoscopic Curriculum for Junior Residents in Surgery, Obstetrics/Gynecology, and Urology.

Laparoscopic surgery requires significant training, and prior studies have shown that surgical residents lack key laparoscopic skills. Many educators have implemented simulation curricula to improve laparoscopic training. Given limited time for dedicated, in-person simulation center practice, at-home training has emerged as a possible mechanism by which to expand training and promote practice. There remains a gap in published at-home laparoscopic curricula employing embedded feedback mechanisms. We developed a nine-task at-home laparoscopic curriculum and an end-of-curriculum assessment following Kern's six-step approach. We implemented the curriculum over 4 months with first- to third-year residents. Of 47 invited residents from general surgery, obstetrics/gynecology, and urology, 37 (79%) participated in the at-home curriculum, and 25 (53%) participated in the end-of-curriculum assessment. Residents who participated in the at-home curriculum completed a median of six of nine tasks (interquartile range: 3-8). Twenty-two residents (47%) responded to a postcurriculum survey. Of these, 19 (86%) reported that their laparoscopic skills improved through completion of the curriculum, and the same 19 (86%) felt that the curriculum should be continued for future residents. Residents who completed more at-home curriculum tasks scored higher on the end-of-curriculum assessment (p = .009 with adjusted R 2 of .28) and performed assessment tasks in less time (p = .004 with adjusted R 2 of .28). This learner-centered laparoscopic curriculum provides guiding examples, spaced practice, feedback, and graduated skill development to enable junior residents to improve their laparoscopic skills in a low-stakes, at-home environment.

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered CRSs to deliver personalized recommendations by considering individual contexts. However, the impact of AI-based CRS on students’ course-selection decision-making process (inter alia, search and evaluation phases) is an open question. Understanding student perceptions and expectations of AI-based CRSs is key to optimizing their decision-making process in course selection. For this purpose, we employed speed dating with storyboards to gather insights from 24 students on five different types of AI-based CRS. The results revealed that students expected AI-based CRSs to play an assistive role in the search phase, helping them efficiently complete time-consuming search tasks in less time. Conversely, during the evaluation phase, students expected AI-based CRSs to play a leading role as a benchmark to address their uncertainty about course suitability, learning value, and serendipity. These findings underscore the adaptive nature of AI-based CRSs, which adjust according to the intricacies of students’ course-selection decision-making process, fostering fruitful collaboration between students and AI.

Andrioid mobile application aimed at getting personalized recommendations when selecting references

This article presents the results of the development of a mobile recommendation application to help library readers select references. One of the tasks of a librarian is to provide consultative assistance to the reader in selecting books. Working with a librarian can be replaced by the use of electronic library systems or special programs, which will allow you to solve the same task in less time. At the moment, there are websites and digital libraries that help the user to select books for further reading. They implement various algorithms that allow you to create personalized recommendations. Such systems are designed for electronic reading and are not linked to the databases of municipal library systems. Library readers can obtain recommendations when they choose literature for further reading using a mobile application. The paper considers the process of its creation: analysis of program requirements, design, implementation, testing and implementation. The application is developed for the Android operating system in the integrated development environment Android Studio in the Java programming language. The calculation of the technical and economic indicators of the project in its implementation has shown that the use of the application will provide sufficient economic effect. Its use can simplify the process of selecting books and significantly reduce the time to search for them, thus giving the librarian the opportunity to perform other tasks. The program does not require material costs for installation, which is an obvious advantage, and the functionality and system requirements allow its use on all mobile devices with the Android operating system.

Abstract 096: Effects Of Chronic Hypertension And Sex On Spatial Reference And Working Memory

Chronic hypertension is a primary risk factor of vascular dementia. The hippocampus is a brain region critical to learning and memory and is highly susceptible to hypoxia/ischemia. Here, we investigated how chronic hypertension affects two different types of memory function, and whether one sex is more susceptible to hypertension-induced memory impairment. Memory function in adult male and female SHR (spontaneously hypertensive rats) and normotensive Wistar rats was tested using two behavioral tasks (n=6/group). The radial arm maze (RAM) tested reference memory that is largely hippocampal-dependent by measuring the time it took to retrieve rewards from three baited arms during daily 10 min sessions. A continuous y maze task was used to calculate alternation index and determine working memory that is cortico-hippocampal-dependent. Data are mean±SEM and comparisons made via a two-way ANOVA with a post-hoc Tukey test. In the RAM task, female rats had steeper learning curves than males as they completed the task in less time with each session (Fig 1). There was a significant interaction between sex and hypertension in the y maze task (F (1,20) =13.26, p=0.002). Working memory was impaired in female SHR, with a lower alternation index (index=0.60±0.03) than Wistar rats (index=0.74±0.03). However, male SHR that had a higher alternation index of 0.75±0.02 compared to 0.58±0.6 in male Wistar rats. Overall, females had stronger reference memory than males regardless of hypertension. Working memory was differentially affected in male and female SHR, suggesting sex differences in the susceptibility of different brain regions and types of memory to hypertension-induced dysfunction.

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning.

In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%.

Development of Energy- Efficient Load Balancing Scheduling Algorithm in Real-time Systems

Real- time systems are considered as one of the most important topics that have attracted the attention of researchers in various scientific and technical fields due to their widespread in many fields of communication, informatics, operating systems and automatic control systems. The basic idea in real- time systems is the execution of a set of tasks that are assigned to CPUs, within a time constraint associated with each of them, and this time constraint is called a deadline. Task scheduling in real- time systems is the main factor in determining the success or failure of the system. Therefore, many studies have imposed suggestions and hypotheses for improving scheduling, according to the underlined platform, whether it is single- processor, multi- processor, or multi- core processor. Researchers have recently focused on studying and providing solutions to scheduling problems, taking into account energy consumption since energy consumption today plays an important role in determining the efficiency and reliability of the system. Energy consumption can be saved either statically by turning the processor into sleep mode or dynamically by changing the frequency of the processor cores and thus reducing power consumption. However, a slight increase in frequency leads to executing more tasks in less time and thus can lead to an increase in the efficiency of the real- time system by reducing tasks which may miss its deadline at the time of execution. In this research, a new method has been proposed to control the processor frequency in an optimal manner that makes a balanced trade- off between power consumption and execution of tasks in real- time systems.

Cost-Aware Resource Optimization for Efficient Cloud Application in Smart Cities

In this generation of smart computing environment, every device in the system and multple system are interconnected to each other, which allows users to view, analyze data, and make smart decisions. Smart cities are one example of smart environments where every device is connected and computing is performed on the cloud. In such a situation, the system requires an efficient system to handle huge requests and deliver data. Cloud computing plays an essential role in solving this issue but suffers from resource optimization, cost optimization, and load balancing. This work is aimed at solving the issue of resource and cost optimization in cloud infrastructure to provide a high service rate and sustainable infrastructure to cloud applications in smart cities. The proposed model is inspired by artificial neural networks and nature-inspired algorithm to reduce execution cost, average start time, and finish time and to make the system power efficient at the same time to improve the utilization of the system. The result shows that the proposed model completes more tasks in the least time and execution cost as compared to the existing models. This showcases the smart cloud applications are cost efficient and can complete the tasks in less time.

Modelling and analysis of agriculture three in one machine using solar power

India is one of the countries in the world as an agriculture-based where 70% of the population relies on the results of farming. However, as the population grows, the farm is distributed among the family and their relatives and as a result, only two acre farms are owned to the average farmer in India. Farmers are also economically poor, which prevents them from purchasing tractors and other expensive equipment, forcing them to rely on traditional farming methods. Bullocks, horses, and buffalo are commonly used by Indian farmers in their farming operations. Comparatively with other nations in the globe but it does not meet the necessary energy needs to agriculture field. In this situation, we need to replace such technology developed modern equipments as a substituent to compensate the human and animal efforts. As a result, we are designing this equipment to meet all of these requirements as well as to address the labour shortage. All of our mechanical components, such as DC motors and DC pumps, are powered by solar energy in this equipment. A 12 V battery is used to store the energy. Ploughing, seed sowing, and spraying machinery are all rolled into one in our proposal. Using this equipment, one may complete a variety of farming tasks in less time and at a lower cost.

Binning-Based Silhouette Approach to Find the Optimal Cluster Using K-Means

Clustering is one of the critical parts of machine learning algorithms. K-Means clustering is the standard technique that various data analysts use for clustering the data among the various clusters. Even though the K means clustering algorithm can work effectively, there is a need to tune the value of K according to the dataset under consideration. The process of tuning for the value of k requires the execution of the K-means algorithm with different values of k. The values of k with the best cluster quality based on specific metrics are selected. The elbow method and silhouette coefficient is the most popular approach for selecting the number of clusters. However, both approaches are time-consuming, as they have to execute K-means for each value of k to find a good score. This approach is iterative. This paper proposed a divide and conquer approach that performs the same task in less time. From the proposed approach, the task has been completely 2.5 times faster in comparison to the iterative method at the cost of some memory required to store the results.

A new model of inexpensive portable homemade PERC Mentor (IPHOM) and its validation

BackgroundPercutaneous nephrolithotomy (PCNL) is a complex surgery and has a flat learning curve. Due to this and the ethical issues, trainees do not get enough hands on exposure. Virtual simulator is very expensive and bulky. Animal model requires legal clearance. This inexpensive portable homemade PERC Mentor (IPHOM) teaches all the major aspects of PCNL surgery. This article has shown the way to make this model and its validation study.MethodsIPHOM can be made at home with carton box, ball bearings, LED torch and some hospital wastes. After a short demonstration of IPHOM, 14 residents and 4 urologists were given 8 tasks to perform on it followed by 15-min supervised practice exercise on day 0 and day 1. Their performance was reassessed on day 2 and 3. Response to 17 feedback points was recorded on a seven-point Likert scale.ResultsThere was significant difference between the performance of expert and novice on day 0. Expert completed all the tasks in less time and no. of attempts. The time for tract dilatation and duration of radiation exposure were significantly less in the expert group. The performance of both expert and novice improved on day 2 and 3, but the improvement was significantly more in novice. Response to the feedback points showed no difference between expert and novice (p > .05).ConclusionsWe have found that training on IPHOM has improved the concept and skills of PCNL in residents. The simplicity and low cost of the model make it constructible at home.

Developing smart devices with automated Machine learning Approach: A review

In the current era, Artificial Intelligence is playing a major role in smart devices. Apart from incorporating intelligence in the devices, it has extended its range beyond that. Now new AI development tools such as automated machine learning (AutoML) approach are increasing rapidly because of its performance on smart devices. AutoML is used to design a machine learning model with self-adapting capability which can perform the task in less time with advanced skills. This approach does not need any explicit coding and the data science experts but this can build smart sensor codes that will get executed automatically on the microcontrollers embedded within the IoT devices. Due to these capabilities, the developers can build efficient smart devices with real-time responsiveness, security, and data privacy without the help of machine learning experts. In this paper, we will show how AutoML will be used to create smart de-vices. The objective of this paper is to develop the skills that will help to develop smart edge devices which include planning, design-ing, implementation, and the evaluation of the smart IoT applications.

UsabilityZero: Can a Bad User Experience Teach Well?

Interfaces with good usability help their users complete more tasks in less time and with less effort, which gives them greater satisfaction. Given the vast array of options available to users today, usability is an important interface feature that may lead to the commercial success or failure of a software system. Despite its importance, few educational tools are available to help usability teachers and students. Knowing how to measure interface usability is one of the basic concepts that students should learn when they study the theme. This paper presents UsabilityZero, a web application to support the teaching of usability concepts to undergraduate students. By using UsabilityZero, students interact with a system displaying a reduced usability interface and, later, with the same system exhibiting an increased usability interface. Considering the use of UsabilityZero by 64 students, the differences between the interface with reduced and increased usability were: (i) 61.5% decrease in the number of clicks; (ii) 62.2% decrease in the time to perform tasks; (iii) 92.9% effectiveness increase; and (iv) a 277.3% satisfaction increase. During their experience with UsabilityZero, students learn how to measure efficiency, effectiveness, and satisfaction of user interfaces. After using the application, Information Systems and Computer Science students who had never been in touch with the subject could identify key usability aspects. The students’ perception of efficiency, effectiveness, and satisfaction as usability measures was higher than 80%. Also, they could identify some usability criteria and understand how measurements change when some of them are present in the interface design. As a result, over 92% of these students said they recognized the importance of usability to the quality of a software product, and 79% declared that their experience with the application would contribute to their professional lives.

3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision

Estimation of distance from objects in real‐world scenes is an important topic in several applications such as navigation of autonomous robots, simultaneous localization and mapping (SLAM), and augmented reality (AR). Even though there is a technology for this purpose, in some cases, this technology has some disadvantages. For example, GPS systems are susceptible to interference, especially in places surrounded by buildings, under bridges or indoors; alternatively, RGBD sensors can be used, but they are expensive, and their operational range is limited. Monocular vision is a low‐cost suitable alternative that can be used indoor and outdoor. However, monocular odometry is challenging because the object location can be known up a scale factor. Moreover, when objects are moving, it is necessary to estimate the location from consecutive images accumulating error. This paper introduces a new method to compute the distance from a single image of the desired object, with known dimensions, captured with a monocular calibrated vision system. This method is less restrictive than other proposals in the state‐of‐the‐art literature. For the detection of interest points, a Region‐based Convolutional Neural Network combined with a corner detector were used. The proposed method was tested on a standard dataset and images acquired by a low‐cost and low‐resolution webcam, under noncontrolled conditions. The system was tested and compared with a calibrated stereo vision system. Results showed the similar performance of both systems, but the monocular system accomplished the task in less time.

Effects of hidden layer sizing on CNN fine-tuning

Some applications have the property of being resilient, meaning that they are robust to noise (e.g. due to error) in the data. This characteristic is very useful in situations where an approximate computation allows to perform the task in less time or to deploy the algorithm on embedded hardware. Deep learning is one of the fields that can benefit from approximate computing to reduce the high number of involved parameters thanks to its impressive generalization ability. A common approach is to prune some neurons and perform an iterative re-training with the aim of both reducing the required memory and to speed-up the inference stage. In this work we propose to face CNN size reduction from a different perspective: instead of reducing the network weights or look for an approximated network very close to the Pareto frontier, we investigate whether it is possible to remove some neurons only from the fully connected layers before the network training without substantially affecting the network performance. As a case study, we will focus on “fine-tuning”, a branch of transfer learning that has shown its effectiveness especially in domains lacking effective expert-designed features. To further compact the network, we apply weight quantization to the convolutional kernels. Results show that it is possible to tailor some layers to reduce the network size, both in terms of the number of parameters to learn and required memory, without statistically affecting the performance and without the need for any additional training. Finally, we investigate to what extent the sizing operation affects the network robustness against adversarial perturbations, a set of approaches aimed at misleading deep neural networks.

The effects of reading on pixel vs. paper: a comparative study

ABSTRACT The aim of this study was to examine the effects that reading on screens (using digital devices with different screen sizes) and on paper have on reading time and comprehension. The study group was comprised of126 secondary students from a private school. To ensure balance, the participants were assigned into test groups, formed according to the size of the reading device, based on class levels, gender, and average school grade. In the data collection process, a narrative text was transferred to both print and electronic media. Access to the text was provided on paper and three digital devices. There were no time limitations. After reading, participants were given comprehension tests that consisted of twenty multiple-choice questions. Female participants’ on-screen comprehension scores were found to be relatively higher compared to their paper comprehension scores. They also acquired better scores as the screen size increased. Conversely, male participants had higher comprehension scores on tablets and smart phones. Participants who read the text on smart phones and tablets completed the tasks in less time compared to other groups. However, the effects of reading modality on comprehension and reading time were not found to be statistically significant in either the male or female groups.

Experienced surgeons versus novice surgery residents: Validating a novel knot tying simulator for vessel ligation

BackgroundVessel ligation with a knot is one of the most fundamental tasks surgeons must master. We developed a simulator designed to enable novices to acquire and refine gentle knot tying capabilities. MethodsA bench-top, knot-tying simulator with computer-acquired assessment was tested on expert surgeons and surgery residents at an academic medical center during the years 2016 to 2018. Each participant tied a total of 8 knots in different settings (superficial versus deep) and techniques (1-handed versus 2 hands). The simulator measured vertical forces and task completion time. ResultsFifteen experienced surgeons and 30 surgery residents were recruited. The expert group exerted considerably less total force during placement of the knots than the novice residents (3.8 ± 2.0 vs 9.2 ± 6.1 N, respectively; P = .0005) and the peak force exerted upward was less in the expert group (1.31 ± 0.6 vs 1.75 ± 0.84 N; P = .02). The experts also completed the task in less time (10.9 ± 3.4 vs 18.3 ± 7.2 seconds; P = 0.001). ConclusionThe simulator can offer residency programs a low-cost, bench-top platform to train and assess objectively the knot-tying capabilities of surgery residents.

Prediction of Emergency Capsule Egress Performance

INTRODUCTION: Critical mission tasks for Martian exploration have been identified and include specific duties that astronauts will have to perform despite any adverse effects of chronic microgravity. Specifically, astronauts may have to perform an emergency capsule egress upon return to Earth, which places specific demands on compromised cardiovascular and neuromuscular systems. Therefore, the purpose of this project was to determine the relationship between cardiorespiratory fitness and simulated capsule egress time.METHODS: There were 15 subjects who volunteered for this study. Vo2peak and peak power output (PPO) were determined on cycle and rowing ergometers. Critical power (CP) was determined by a 3-min all-out rowing test. Subjects then performed an emergency capsule egress on a mock-up of NASA's Orion space capsule. Peak metabolic data were compared between the cycling and rowing tests. Pearson's correlation was used to identify relationships between egress time and Vo2peak, PPO, and CP.RESULTS: Vo2peak, Vco2peak, and minute ventilation were not different between cycling and rowing tests. Cycling elicited a greater PPO than the rowing test. Egress time was negatively correlated to rowing PPO (r = -0.60), but not cycling or rowing Vo2peak, cycling PPO, or CP.CONCLUSIONS: Rowing PPO/kg correlates with egress time. Although individuals with higher PPO/kg were able to finish the task in less time, individuals with low fitness levels (Vo2peak ≤ 20 ml · kg-1 · min-1) could complete the egress within 2 mins. These results suggest that cardiorespiratory fitness should not limit emergency egress and that this can be assessed using rowing exercise.Alexander AM, Sutterfield SL, Kriss KN, Hammer SM, Didier KD, Cauldwell JT, Dzewaltowski AC, Barstow TJ, Ade CJ. Prediction of emergency capsule egress performance. Aerosp Med Hum Perform. 2019; 90(9):782-787.

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones.

We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology.

Construct validity of eoSim – a low-cost and portable laparoscopic simulator

Purpose: To examine the construct validity of the low-cost, portable laparoscopic simulator eoSim using motion analysis.Material and methods: Novice and experienced surgeons (≤ 100 and >100 laparoscopic procedures performed, respectively) completed four tasks on the eoSim using the SurgTrac software: intracorporeal suture and tie, tube ligation, peg capping and precision cutting. The following metrics were recorded: Time to complete task, distance traveled, handedness (left- versus right hand use), time off-screen, distance between instrument tips, speed, acceleration and motion smoothness.Results: Compared to novices (n = 22), experienced surgeons (n = 14) completed tasks in less time (p ≤ .025), except when performing peg capping (p = .052). On all tasks, they also scored lower on the distance metric (p ≤ .001). Differences in handedness (left hand compared between groups, right hand compared between groups) were found to be significant for three tasks (p ≤ .025). In general, the experienced group made greater use of their left hand than the novice group.Conclusion: The eoSim can differentiate between experienced and novice surgeons on the tasks intracorporeal suture and tie, tube ligation and precision cutting, thus providing a convenient method for surgical departments to implement testing of their surgeons’ basic laparoscopic skills.

Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

Automated Storage and Retrieval Systems (AS/RS) offer several advantages such as gaining space, reducing human errors, and completing more storing and retrieving tasks in less time to large size warehouses. Machines included multi-shuttle systems are designed to carry multiple loads in one cycle on the purpose of develop the performance. This paper deals with a quadruple command cycle location assignment method in unit-load dual-shuttle AS/RS. Also, it is shown how to solve location assignment problem using Particle Swarm Optimization (PSO) with the objective of shortest travel distance therewithal shortest travel time. Binary Coded Genetic Algorithm (BGA) and Real Coded Genetic Algorithm (RGA) are developed to validate result obtained. At the end, ten numerical examples for two scenarios designed small size and large size are presented to prove the implementation of the proposed method. Results showed that PSO accomplished to find optimal solution for shortest travel distance of AS/RS machine.