Performance Of Different Tasks Research Articles

Optimized Control of 3DOF Variable Stiffness Link Manipulator Using Feedback Linearization PD and Sliding Mode Approaches

Purpose: This study focuses on the control of a 3-degree-of-freedom (3DOF) variable stiffness flexible links manipulator, employing diverse control techniques to address the challenges associated with its inherent structural flexibilities. Variable Stiffness Link (VSL) manipulators offer enhanced adaptability and safety in various applications by dynamically adjusting their link stiffness. This feature allows them to optimize performance for different tasks, from delicate operations to robust industrial use. However, the variable stiffness introduces complex nonlinear dynamics, significantly complicating precise control and necessitating advanced control strategies. Methodology: The research investigates two advanced control methods: linearized feedback proportional-derivative (PD) control and sliding mode control (SMC). These techniques are employed for both position and trajectory control of the system, aiming to maintain precise joint angles while minimizing oscillations in the end effector. The controller designs for both linearized feedback PD and sliding mode control are presented, including stability analyses using Lyapunov theory. Experimental work is conducted to evaluate the effectiveness of both control strategies. Findings: The results demonstrate that both controllers achieve satisfactory performance in managing the complex dynamics of the flexible link system. The linearized feedback PD controller shows good tracking capabilities across the three joints while the sliding mode controller exhibits superior performance. Comparative analysis reveals that while both controllers effectively maintain stability and achieve precise trajectory tracking, the sliding mode controller displays marginally better performance in terms of steady-state errors and robustness to system nonlinearities. Unique contribution to theory, policy and practice: This research contributes to the advancement of control techniques for flexible manipulators, offering promising solutions for improving the performance and reliability of this manipulator for automation applications.

Влияние пола и когнитивного стиля на особенности движений глаз при навигации по карте

<p>We investigated the effects of sex and cognitive style on performance in the map navigation task with various label types (object labels, verbal labels, no labels) in 40 healthy participants (20 men, 20 women, age 24,3±0,8) using eye tracking. The cognitive styles (object, spatial, verbal) were assessed using a questionnaire developed by Blazhenkova and Kozhevnikov [2009]. Only men had a reduced number of fixations during route reproduction compared to route memorization; women had longer saccade durations than men. Participants with spatial cognitive style showed shorter saccade duration compared to object cognitive style. Participants with spatial and verbal cognitive styles had more fixations on maps with verbal labels than on the other maps. Analysis of questionnaire and the number of fixations has demonstrated the link between sex and cognitive style, but the effects of sex and cognitive style are not identical. Sex affects eye movements during the performance of different tasks (map familiarization, route memorization, and route reproduction), while cognitive style determines eye movements during navigation on maps with different types of labels.</p>

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

Use-wear analysis applied in a dissected palimpsest at the Middle Palaeolithic site of El Salt (eastern Iberia): working with lithic tools in a narrow timescale

Use-wear analyses are very useful to increase knowledge about the economic and subsistence dynamics carried out by Neanderthals. In general terms, functional results traditionally came from the analysis of tools belonging to stratigraphic units whose timescale refers to geological time. This is due to the fact that many Neanderthal sites are palimpsests of reiterated occupations over time, which must be dissected to approach us to human timescale. In the stratigraphic unit xa of El Salt (Alcoi, eastern Iberia), high temporal resolution archaeostratigraphic studies have been carried out. Diachronic material assemblages have been identified, allowing us to analyse more precisely the variability of Neanderthal behaviour over time. Amongst these assemblages, three have been selected (i.e. 5.3.1, 5.3.2 and 5.3.3) in order to analyse the lithic material functionality. The results obtained bring out the performance of different tasks within each analytical framework: woodworking in 5.3.1, woodworking and animal processing in 5.3.2, and butchering activity in 5.3.3. These results reflect the existence of a series of diachronic tasks carried out in overlapping activity areas. In this way, this work evidences flint use variability in a specific area of the site across time that could have been recognised only by means of high temporal resolution analytical frameworks.

The predictive power of risk elicitation tasks

This work reports the results of two online experiments with a general-population sample examining the performance of different tasks for the elicitation of risk attitudes. First, I compare the investment task of Gneezy and Potters (1997), the standard choice-list method of Holt and Laury (2002), and the multi-alternative procedure of Eckel and Grossman (2002) and evaluate their performance in terms of the number of correctly-predicted binary decisions in a set of out-of-sample lottery choices. There are limited differences between the tasks in this sense, and performance is modest. Second, I included three additional budget-choice tasks (selection of a lottery from a linear budget set) where optimal decisions should have been corner solutions, and find that a large majority of participants provided interior solutions instead, casting doubts on people’s understanding of tasks of this type. Finally, I investigate whether these two results depend on cognitive ability, numerical literacy, and education. While optimal choices in budget-choice tasks are related to numerical literacy and cognitive ability, the predictive performance of the risk-elicitation tasks is unaffected.

A Meta Reinforcement Learning-Based Task Offloading Strategy for IoT Devices in an Edge Cloud Computing Environment

Developing an effective task offloading strategy has been a focus of research to improve the task processing speed of IoT devices in recent years. Some of the reinforcement learning-based policies can improve the dependence of heuristic algorithms on models through continuous interactive exploration of the edge environment; however, when the environment changes, such reinforcement learning algorithms cannot adapt to the environment and need to spend time on retraining. This paper proposes an adaptive task offloading strategy based on meta reinforcement learning with task latency and device energy consumption as optimization targets to overcome this challenge. An edge system model with a wireless charging module is developed to improve the ability of IoT devices to provide service constantly. A Seq2Seq-based neural network is built as a task strategy network to solve the problem of difficult network training due to different dimensions of task sequences. A first-order approximation method is proposed to accelerate the calculation of the Seq2Seq network meta-strategy training, which involves quadratic gradients. The experimental results show that, compared with existing methods, the algorithm in this paper has better performance in different tasks and network environments, can effectively reduce the task processing delay and device energy consumption, and can quickly adapt to new environments.

The impact of prior and current information of a new nest on relocation dynamics in an Indian ant

AbstractAnimals are known to acquire and store information of their environment in order to enhance their performance in different tasks like foraging, migration and breeding. Having information regarding nesting sites would be particularly useful when they have to relocate. In the current study, we asked if having information of a new nest prior to or at the onset of relocation has a positive impact on the ability of ant colonies to relocate. We performed five sets of experiments on a ponerine ant species Diacamma indicum that recruits nestmates solely by tandem running. By analyzing data of 4756 unique ants across 61 colonies, we found that, access to information of the new nest for 1 day prior to the relocation did not give any significant advantage as compared to no prior information of the new nest. However, access to information for 7 days prior to relocation resulted in 50% increase in the number of tandem leaders and reduction of the transport time by an average of 49%. Our data on providing information at the onset of relocation indicates that introducing explorers to the new nest did not aid the process of relocation but when information of a new nest was self‐acquired, there was a reduction in discovery time by 50% and also in the transport time per unit distance for relocation into a new nest. Having information regarding only the surroundings and not the nest at the onset of a relocation did not give any significant advantages. Further, the manner in which work was organized during the relocation process was not significantly influenced by prior information across any of the treatments. Our findings suggest that information of new nest is important and when such information is self‐acquired at the onset of relocation or is available for a long enough duration, it has a positive influence in the process of relocation in this ant species.

Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.

Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.

How is entrepreneurship as generic and professional competences diverse? Some reflections on the evaluations of university students' generic competences (students of education and bioeconomics)

Generic competences have an interdisciplinary nature, which indicates their usability in different disciplines, situations, and contexts in the performance of different tasks. Generic competencies are thus considered from two perspectives, daily life and professional activity, that are equally important, implying that generic competences are necessary for individuals to successfully adapt to change and live meaningful and productive lives. Entrepreneurship competences can be observed from two perspectives: generic competencies viewed from the perspective of the individual's personal experience and professional competencies viewed from the perspective of the individual's professional experience. In this article, it will be observed from both perspectives to see its performance in diverse contexts and to clarify distinctions between these contexts. The present study aimed to shed light on how specific university study disciplines with a professional focus (educational sciences and bioeconomics) support the development of a specific generic competence (entrepreneurship competencies). The Specific Research Questions of This Article Are: (1) What Entrepreneurship Competences Emerge Among Latvian Bioeconomics and Educational Science Students? (2) How Do Entrepreneurship Competences Differ Between Bioeconomics and Educational Science Students? (3) How Are Entrepreneurship Competences Correlated With Each Other? Data for the study were gathered by using the online survey platform QuestionPro. The questionnaire was filled in by 135 students, of whom 82 were from the field of educational sciences and 53 from the field of bioeconomics. The study presents a comparison of entrepreneurship competence's self-assessments of bachelor's, master's, and doctoral students of bioeconomics and educational sciences. Despite the fact that entrepreneurship is more linked to economics, the results show that, in two out of three main areas of entrepreneurship competences, students of educational sciences self-assessed their entrepreneurship competences as higher than students of bioeconomics.

STPC-Net: Learn Massive Geo-Sensory Data as Spatio-Temporal Point Clouds

Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data have irregular space and time coordinates. To represent geo-sensory data, there have been extensive research efforts using time sequences, grid-like images, and graph signals. However, there still lacks a proper representation that can describe both the mobile and stationary geo-sensory data without the information-losing discretization in spatial and temporal dimensions. In this paper, we propose to represent massive geo-sensory data as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatio-temporal point clouds</i> (STPC), and present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STPC-Net</i> , a novel deep neural network for processing STPC. STPC leverages the original irregular space-time coordinates, and STPC-Net captures intra-sensor and inter-sensor correlations from STPC. In this way, STPC-Net learns the key information of STPC, and overcomes challenges in data irregularity. Experiments using real-world datasets show that STPC-Net achieves state-of-the-art performance in different tasks on both mobile and stationary geo-sensory data. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhengchuanpan/STPC-Net</uri> .

Generative models improve radiomics performance in different tasks and different datasets: An experimental study

PurposeRadiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. MethodsWe used two datasets of low dose CT scans – NSCLC Radiogenomics and LIDC-IDRI – as test datasets for two tasks – pre-treatment survival prediction and lung cancer diagnosis. We used encoder-decoder networks and conditional generative adversarial networks (CGANs) trained in a previous study as generative models to transform low dose CT images into full dose CT images. Radiomic features extracted from the original and improved CT scans were used to build two classifiers – a support vector machine (SVM) and a deep attention based multiple instance learning model – for survival prediction and lung cancer diagnosis respectively. Finally, we compared the performance of the models derived from the original and improved CT scans. ResultsDenoising with the encoder-decoder network and the CGAN improved the area under the curve (AUC) of survival prediction from 0.52 to 0.57 (p-value < 0.01). On the other hand, the encoder-decoder network and the CGAN improved the AUC of lung cancer diagnosis from 0.84 to 0.88 and 0.89 respectively (p-value < 0.01). Finally, there are no statistically significant improvements in AUC using encoder-decoder networks and CGAN (p-value = 0.34) when networks trained at 75 and 100 epochs. ConclusionGenerative models can improve the performance of low dose CT-based radiomics in different tasks. Hence, denoising using generative models seems to be a necessary pre-processing step for calculating radiomic features from low dose CTs.

THE ROLE OF CONTROL PROCESSES IN REGULATING SELF- EFFICACY IN TASKS WITH HIGH PREDICTABILITY

The study's objective is to describe how cognitive control mechanisms influence performance in different tasks. We believe that popular resource models have disadvantages when explaining one's effectiveness in a particular task. We argue that the limitations on one's performance happen not because of limited cognitive resources but rather due to logically based restrictions. One of the restrictions is rooted in one's need for consistency in their representations of the world and themselves. We infer that having a specific expectation about self-efficacy in a particular task dramatically influences the outcome: cognitive control mechanisms finetune one's performance to agree with one's expectations on the efficiency of said performance. In the study, we tested how regularities in stimuli influence one's performance. In the experiment (23 subjects), we used the Musical Stroop test. For part of the trials, we used melodies as regularities, while in other trials, we presented fillers - random sets of notes. The results show that melodies reduce interference and have a faster response time than in the filler condition. We argue that it is due to the role of cognitive control mechanisms that maintain self-efficacy. We discuss the implication of the proposed approach.

OSCE: An Effective Tool of Assessment for Medical Students

Background: To assess the competency of a student different tools are used. Since its introduction in 1975 by Dr. Harden and his team, OSCE has gained tremendous strides to assess the clinical competencies. Since 1975 onward OSCE has been very successfully used to assess the clinical competencies of medical student globally. OSCE is an assessment tool in which student is observed for performance of different tasks at specified stations. In the current study perception of medical students about OSCE examination was done which shall give room for positive criticism and further improvement of the system where ever required. Objective: To expedite view of final year MBBS students of Azra Naheed College about OSCE Material and Method Study design: Quantitative, cross sectional study. Settings: Azra Naheed College, Lahore. Duration: Six months i.e. 1st July2020 to 31st December 2020 Data Collection procedure: After an informed consent and appropriate briefing, the questionnaire was distributed among the final year medical students of Azra Naheed Medical College. Questionnaire developed by Russell et al was used. Results: Out of 148 students who participated in the study, 66(45%) students were females and 82(55%) were male. Majority of the students were satisfied with the quality of the exam. Consensus about the quality of exam was that, 29.7% were aware about the nature of the exam, 52.7% were satisfied that the syllabus taught was asked in the exam, 58.1% were satisfied about the time allocation for each station. Majority i.e. 60% considered OSCE an exam of practical nature which is not biased by gender or ethnicity. More than 50% of the students were satisfied with the standard of the exam. At the same time more than 50% students considered essay exam the easiest format of assessment. However, OSCE was considered to be fairest form of assessment 73%. 68.9% perceived that learning is enhanced by MCQs rather than other formats of assessment. Conclusion: To conclude this study, it is very much clear that the perception of students about OSCE as an assessment tool was very encouraging, as it not only provided them the opportunity to highlight their weaknesses but also helped them to perform well in the exam, manage time during exam and to overcome them stress which influenced their results. Key words: OSCE, Objective, Examinations, Clinical skills, qualitative analysis

Proposal for space-borne quantum memories for global quantum networking

Global-scale quantum communication links will form the backbone of the quantum internet. However, exponential loss in optical fibres precludes any realistic application beyond few hundred kilometres. Quantum repeaters and space-based systems offer solutions to overcome this limitation. Here, we analyse the use of quantum memory (QM)-equipped satellites for quantum communication focussing on global range repeaters and memory-assisted (MA-) QKD, where QMs help increase the key rate by synchronising otherwise probabilistic detection events. We demonstrate that satellites equipped with QMs provide three orders of magnitude faster entanglement distribution rates than existing protocols based on fibre-based repeaters or space systems without QMs. We analyse how entanglement distribution performance depends on memory characteristics, determine benchmarks to assess the performance of different tasks and propose various architectures for light-matter interfaces. Our work provides a roadmap to realise unconditionally secure quantum communications over global distances with near-term technologies.

СПОСІБ РОЗРОБКИ ІЗОЕФЕКТИВНОЇ ГЕТЕРОГЕННОЇ СИСТЕМИ НА ОСНОВІ МАШИННОГО НАВЧАННЯ ДЛЯ ЗАДАЧІ ДИСКРЕТНОГО ПЕРЕТВОРЕННЯ ФУР’Є

In this paper, the isoefficiency of MPP systems and heterogeneous CPU-GPU systems on the problem of discrete Fourier transform is considered. The development of parallel applications as its goal can not only reduce execution time, but also provide opportunities to solve problems of a larger dimension. The peculiarity of algorithm parallelization includes the efficient use of hardware while increasing the dimension of the problem is an important characteristic of parallel computing. However, currently heterogeneous systems have not been researched extensively to determine isoefficiency characteristics and build application-specific systems around said method, although there are articles that show potential using isoefficiency to design the system and using heterogeneous approach to accelerate performance of different tasks. Discrete Fourier Transform algorithm lets build systems that discretize analogue and digital signals and it can serve as a benchmark to test different systems. Algorithms suited for MPP systems can use analytical approach to find out issoefficiency function and to determine how scaling the system or changing the size of the task will change its performance metrics. One of the most popular approaches to linking up processing units in MPP systems is using hypercube topology. MPP system that is connected using this topology will be analyzed. CPU-GPU heterogeneous system will be analyzed using an approach based on polynomial regression. Due to the nature of heterogeneous systems, analytic approach used in MPP system is impossible. Predictive model based on polynomial regression will use modelling results from using CPU and GPU separately to estimate how much time it will take for heterogeneous system to finish the task. To ensure accuracy of the experiment, several systems will be used to model the task. Using this approach, resulting issoefficient heterogeneous system will be analyzed using performance metrics s

School Scientific Argumentation Enriched by Digital Technologies: Results With Pre- and in-Service Science Teachers

This article focuses on the integration of argumentation and digital technologies in science teacher education. We present theoretical reflections, results of empirical research, and description of innovative experiences with pre- and in-service biology teachers. Regarding argumentation, we analyse what defending a claim in science entails for teachers, the teaching strategies they (would) use in the classroom in relation to argumentation, the relevance that they attribute to the performance of different tasks for arguing, the subject content where argumentation fits more suitably, and the reasons they would consider in favour of arguing in science classes. For the analysis of educational practices that involve use of technologies, we adopt a perspective that looks at their complexity and recognises teachers as their creative authors. We discuss the way in which teachers (would) use technologies in their practice, their expectations around this, and the foreseen results with students. Finally, we examine three innovative educational experiences already implemented, using argument maps, web-based inquiry, and a digital game. We look into the possibilities that these technological resources offer for teachers to analyse the argumentation levels reached by their students.

Testing Multi-Task Cancer Evolution: How Do We Test Ecological Hypotheses in Cancer?

Recently several authors described a family of models, according to which different cancer types and subtypes fall within a space of selective trade-offs between archetypes that maximize the performance of different tasks: cell division, biomass and energy production, lipogenesis, immune interaction, and invasion and tissue remodeling. On this picture, inter- and intratumor heterogeneity can be explained in part as a product of these selective trade-offs in different cancers, at different stages of cancer progression. The aim of this Perspective is to critically assess this approach. I use this case study to consider more generally both the advantages of using ecological models in the context of cancer, and the challenges facing testing of such models.

Microtubule organization across cell types and states

Encircling and traversing the cell are architectural struts and dynamic intracellular highways made of cylindrical polymers called microtubules. Built from structurally asymmetric subunits of αβ-tubulin heterodimers, microtubules have an inherent structural polarity with a slow-growing minus end and a comparatively dynamic plus end that grows and shrinks. Thus, a key feature of microtubules is that each polymer is polarized, allowing for the execution of cellular tasks that are directional in nature. For example, microtubules build polarized highways allowing directional intracellular transport, generate directional force such as in chromosome alignment and segregation, provide structural support for cell shape, and assemble into highly ordered polar structures like centrioles and cilia. The output of these microtubule-based functions is the performance of different tasks, including establishment and maintenance of cellular polarity, secretion and absorption, cell-cell communication, migration, mechanical resiliency, and mitosis. Different cells accomplish these functions by using distinct sites within the cell called microtubule-organizing centers (MTOCs) to build cell-specific microtubule arrangements. While the specific requirement for microtubules in many in vivo cell types is unknown, disrupting even a subset of microtubule-supported functions is often lethal and is associated with many diseases (e.g., cancer and neuropathies), suggesting that specific patterns of microtubule organization are likely important for cellular function in vivo. This Primer focuses on how differentiated animal and plant cells use distinct MTOCs to generate specific microtubule arrangements, how those arrangements support cellular functions, and how cells rearrange their microtubules to accommodate changing cellular tasks.

Investigation of dynamics of a pipe robot with vibrational drive and unsymmetric with respect to the direction of velocity of motion dissipative forces

In all fields of human activities intensification of use of robots and their systems takes place. In agriculture this process takes place too slowly if compared with some domains of machine building industry. Here the problem of creation of robots of various structures and for performance of different tasks is solved by using one coordinate mechanical blocks with vibrational mechanical drives used for this purpose. This block in a separate case is a robot moving in a pipe. Dynamics of such a block is represented as a nonlinear system and because of the nonlinearity of a block it is possible to obtain different laws of motions and their parameters. The investigation is performed by analytical – graphical method. This enables to choose most suitable parameters of the investigated system.

Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.