Task Hierarchy Research Articles

Youth and Service Work in Fast Food Restaurants: Some Structural and Cultural Considerations

The India Employment Report (2024) highlights the problem of youth unemployment and its insufficient absorption in the non-farm sectors and recommends the skilling of youth as a remedy for tardy economic growth and employability. In view of the proposed solution, I suggest that we inquire into the nature and conditions of non-farm work in alternative sectors and the experience of this work for young people. The issue is the adequacy of training and skilling as sole measures to counter insufficient absorption. My concern regarding the recommended solution is a short shrift it gives to structural and cultural factors as facilitators or barriers to absorption, and the retention of workers. This article is an inquiry into the organization and culture of service work in fast food industry– a non-farm sector–under conditions of globalization. The experience of service workers reveals the relation between service work and attitudes towards cleaning, meaning of toilet cleaning, gendered division of labour, hierarchy of tasks, ascriptive and unequal access to jobs, and dignity of labour. The experience of service work is not the same for girls and boys; it is fraught with different sorts of tensions for them. These insights question the efficacy of training and skilling as the talismanic measures for countering the problems under consideration. They make a strong case for the consideration of cultural and structural realities of India.

A hybrid maintenance approach for key components of the train bogie to optimize fleet availability

In the context of intelligent transport systems and the rapid development of the global economy, railways face increasing demand to become safer, more efficient, and more sustainable. As a critical structure of the railway vehicle, the bogie, designed to support the vehicle body and transmit dynamic loads through the wheels, houses several components that are vital for the reliability and safety of the train. The maintenance of such a structure is complex and can greatly benefit from hybrid approaches that leverage the potential of condition-monitoring data in proactively identifying faults or irregularities whilst ensuring appropriate intervals that maximize the availability of resources. This paper proposes a hybrid maintenance approach that combines preventive maintenance, condition-based monitoring, and predictive maintenance for the key components of the bogie, aiming to optimize costs while exploiting potential economic savings of maintenance decisions for different grouping strategies, therefore, maximizing fleet availability. The model aims to study the feasibility and measure the expected economic returns of shifting from a complete preventive maintenance program to a hybrid program incorporating predictive maintenance tasks in selected components of the bogie. A deterioration model, along with failure rate estimation, is proposed and integrated into the optimization formulation. A case study of a railway company operating in Catalonia, Spain, is presented. The framework demonstrates flexibility in tackling current maintenance practices, including hierarchy and periodicity of maintenance tasks according to the intervention type and flexible hazard rates with three stages to mimic the bathtub curve behaviour. The results show that shifting to a hybrid maintenance plan has great potential to reduce costs.

A SYSTEMATIC APPROACH TO ASSESSING THE ENVIRONMENTAL SAFETY OF A HIGH-TECH NATURAL AND TECHNICAL GEOSYSTEM

The practical application of system analysis procedures to the development of a methodology for assessing the level of environmental safety of a high-tech natural and technical geosystem (HNTG) is proposed. Using the structural scheme of material flows, it is shown that the ecological state of the HNTG is formed by microelectronics industries, vehicle and life support systems of the city, and the main anthropogenic load falls on the atmosphere. The factors determining the environmental safety of HNTG are presented in the form of a «tree» of causes of safety violations, and the priority of their consequences is determined by the method of expert assessments. It is determined that emissions from microelectronics industries are of the greatest importance for the ecological state of HNTG, which allows limiting the effect of the methodology by assessing the level of environmental safety of the atmosphere. It should contain criteria and indicators for evaluating all internal and external anthropogenic loads of the atmosphere of the HNTG in accordance with the structural target model – the tree of assessments and follow the hierarchy of tasks in the form of a tree of goals. Pollutants from microelectronics production exceeding the environmental technology intensity of HNTG require special attention to the assessment of their safety and the nature of their distribution in the atmosphere.

Guided Inquiry Learning Model in Chemistry Education: A Systematic Review

The abstractness of chemical concepts can be understood easily through learning chemistry using guided inquiry. This article uses the Systematic Literature Review (SLR) method to review eleven articles published from 2018 to 2023. Articles were systematically obtained from the online article databases ERIC, Scopus, and Google Schloar. The purpose of the review is to provide information to teachers and researchers in the field of chemistry education about the definition of guided inquiry models, the effect of guided inquiry models on chemistry learning outcomes, and how to apply guided inquiry models/strategies in chemistry learning. The review results show that the definition of various guided inquiry models involves students into a problem and confronts students with an investigation, and directs students to solve the problem of the problem. The effects of guided inquiry models on chemistry learning outcomes include improving metacognition, concept understanding, critical thinking skills, science process skills, learning outcomes, and creative thinking skills. Various chemistry learning models have also been applied in several learning models or strategies such as Guided Inquiry, Guided Inquiry-Based on Blended Learning, Flipped Classroom Based on Guided Inquiry Learning, Guided Inquiry Model Integrated with STEM, Guided Inquiry and Task Hierarchy Analysis Model in Cooperative Learning Strategy

Full-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods

Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what can be learned from measured vibration data. However, significant challenges remain in the tasks of identification, prediction and evaluation of full-scale structures. A significant aid in the roadmap to the application of cutting-edge methods to the demands of in-service engineering structures, is the development of comprehensive benchmark datasets. With the aim of developing a useful and worthwhile benchmark dataset for structural dynamics, an extensive testing campaign is presented here. This recent campaign was performed on a decommissioned BAE system Hawk T1A aircraft at the Laboratory for Verification and Validation (LVV) in Sheffield. The aim of this paper is to present the dataset, providing details on the structure, experimental design, and data acquired. The collected data is made freely and openly available with the intention that it serve as a benchmark dataset for challenges in full-scale structural dynamics. Here, the details pertaining to two test phases (frequency and time domain) are presented. So as to ensure that the presented dataset is able to function as a benchmark, some baseline-level results are additionally presented for the tasks of identification and prediction, using standard approaches. It is envisaged that advanced methodologies will demonstrate superiority by favourable comparison with the results presented here. Finally, some dataset-specific challenges are described, with a view to form a hierarchy of tasks and frame discussion over their relative difficulty.

Unifying the Generalized Jacobian Matrix and prioritized task hierarchies, with application to free-floating VMSs

Free-floating vehicle manipulator systems (VMSs), such as those in space or underwater, experience a coupling effect between the motion of the manipulator arm and the vehicle base, since the motion of the joints induces a motion of the base relative to the system's center of gravity (CG). In this work a control framework is proposed that takes this coupling effect into account, selecting the motion of the CG as the highest priority task in a dynamically consistent task hierarchy in order to reduce the need for counteracting disturbances while controlling the position of the manipulator workspace. The proposed approach generalizes previous works using the Generalized Jacobian matrix to allow the completion of several prioritized tracking tasks. Control allocation is performed in a manner ensuring that the thrusters are used only for controlling the overall position of the VMS, while the joints are used for tasks requiring higher accuracy. The set in which all tracking error dynamics are zero is shown to be uniformly asymptotically stable, and the performance of the proposed control method is validated in a simulation study.

A Comprehensive of the Guided Inquiry Learning Model in Education: A Review

The abstract nature of the topics is easily understood while learning chemistry through guided inquiry. This article employs the A Review (SLR) technique to review eleven papers released between 2018 and 2023 that deal with teaching chemistry using various guided inquiry models. Three online article databases-ERIC, Scopus, and Google Scholar-were used to retrieve articles methodically. The information in this review will be helpful to educators and researchers who work in chemical education in understanding guided inquiry models, how they impact chemical learning outcomes, and how to apply them to improve students' comprehension of chemistry. The review's conclusions show how various guided inquiry strategies immerse students in a problem, offer them an investigation to work through and provide guidance on how to solve the problem. Guided inquiry models influence chemistry learning outcomes by enhancing metacognition, concept understanding, critical thinking skills, science process skills, learning outcomes, creative thinking abilities, and reducing misconceptions. Many learning models or strategies, including guided inquiry, guided inquiry-based on blended learning, guided inquiry-based on the flipped classroom, guided inquiry models integrated with STEM, and guided inquiry and task hierarchy analysis models in cooperative learning strategies, have also used different chemistry learning models.

An AI assistant to help review and improve causal reasoning in epidemiological documents

Drawing sound causal inferences from observational data is often challenging for both authors and reviewers. This paper discusses the design and application of an Artificial Intelligence Causal Research Assistant (AIA) that seeks to help authors improve causal inferences and conclusions drawn from epidemiological data in health risk assessments. The AIA-assisted review process provides structured reviews and recommendations for improving the causal reasoning, analyses and interpretations made in scientific papers based on epidemiological data. Causal analysis methodologies range from earlier Bradford-Hill considerations to current causal directed acyclic graph (DAG) and related models. AIA seeks to make these methods more accessible and useful to researchers. AIA uses an external script (a “Causal AI Booster” (CAB) program based on classical AI concepts of slot-filling in frames organized into task hierarchies to complete goals) to guide Large Language Models (LLMs), such as OpenAI's ChatGPT or Google's LaMDA (Bard), to systematically review manuscripts and create both (a) recommendations for what to do to improve analyses and reporting; and (b) explanations and support for the recommendations. Review tables and summaries are completed systematically by the LLM in order. For example, recommendations for how to state and caveat causal conclusions in the Abstract and Discussion sections reflect previous analyses of the Study Design and Data Analysis sections. This work illustrates how current AI can contribute to reviewing and providing constructive feedback on research documents. We believe that such AI-assisted review shows promise for enhancing the quality of causal reasoning and exposition in epidemiological studies. It suggests the potential for effective human-AI collaboration in scientific authoring and review processes.

Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation

Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we propose a novel approach to enhance HRL performance through topologically sorted potential calculation for reward machines. By leveraging the topological structure of the task hierarchy, our method efficiently determines potentials for different sub-goals. This topological sorting enables the agent to prioritize actions leading to the accomplishment of higher-level goals, enhancing the learning process. To assess the efficacy of our approach, we conducted experiments in the grid-world environment with OpenAI-Gym. The results showcase the superiority of our proposed method over traditional HRL techniques and reward machine-based reinforcement learning approaches in terms of learning efficiency and overall task performance.

Opportunity, hierarchy, and awareness: an ethnographic exploration across rehabilitation units of interprofessional practice in nutrition and mealtime care

ABSTRACT Interprofessional practice is increasingly cited as necessary in the delivery of high-quality nutrition and rehabilitation services. However, there is limited evidence available exploring the factors which influence interprofessional practice in subacute rehabilitation nutrition services. Our ethnographic study explored collaborative activities, influential factors and staff attitudes related to interprofessional practice in nutrition care. Fifty-eight hours of ethnographic field work were undertaken from September 2021—April 2022, across three subacute rehabilitation units, with a total of 165 patients, support persons and staff participating. Overall, 125 unique participants were observed and 77 were interviewed. We generated three themes through reflexive thematic analysis. First, the potential opportunities for interprofessional practice at mealtimes, as influenced by communication, role clarity and reciprocity. Second, hierarchy of nutrition roles and tasks impedes interprofessional practice, where the perceived lower importance of nutrition care to other clinical roles and physical therapies influences staff practice. Third, the mystery of nutrition care roles and systems in rehabilitation, which exposes gaps in the awareness of different team members regarding nutrition care roles and systems, hindering interprofessional practice. Our findings highlight the opportunity for embedded, innovative models of care and staff education to enhance interprofessional practice in nutrition and mealtimes.

Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.

Constrained Reinforcement Learning in Hard Exploration Problems

One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are dependent on the policy. Recent works in constrained RL have developed methods that ensure constraints are enforced even at learning time while maximizing the overall value of the policy. Unfortunately, as demonstrated in our experimental results, such approaches do not perform well on complex multi-level tasks, with longer episode lengths or sparse rewards. To that end, we propose a scalable hierarchical approach for constrained RL problems that employs backward cost value functions in the context of task hierarchy and a novel intrinsic reward function in lower levels of the hierarchy to enable cost constraint enforcement. One of our key contributions is in proving that backward value functions are theoretically viable even when there are multiple levels of decision making. We also show that our new approach, referred to as Hierarchically Limited consTraint Enforcement (HiLiTE) significantly improves on state of the art Constrained RL approaches for many benchmark problems from literature. We further demonstrate that this performance (on value and constraint enforcement) clearly outperforms existing best approaches for constrained RL and hierarchical RL.

Enhancement of Non-Linear Deep Learning Model by Adjusting Confounding Variables for Bone Age Estimation in Pediatric Hand X-rays.

In medicine, confounding variables in a generalized linear model are often adjusted; however, these variables have not yet been exploited in a non-linear deep learning model. Sex plays important role in bone age estimation, and non-linear deep learning model reported their performances comparable to human experts. Therefore, we investigate the properties of using confounding variables in a non-linear deep learning model for bone age estimation in pediatric hand X-rays. The RSNA Pediatric Bone Age Challenge (2017) dataset is used to train deep learning models. The RSNA test dataset is used for internal validation, and 227 pediatric hand X-ray images with bone age, chronological age, and sex information from Asan Medical Center (AMC) for external validation. U-Net based autoencoder, U-Net multi-task learning (MTL), and auxiliary-accelerated MTL (AA-MTL) models are chosen. Bone age estimations adjusted by input, output prediction, and without adjusting the confounding variables are compared. Additionally, ablation studies for model size, auxiliary task hierarchy, and multiple tasks are conducted. Correlation and Bland-Altman plots between ground truth and model-predicted bone ages are evaluated. Averaged saliency maps based on image registration are superimposed on representative images according to puberty stage. In the RSNA test dataset, adjusting by input shows the best performances regardless of model size, with mean average errors (MAEs) of 5.740, 5.478, and 5.434months for the U-Net backbone, U-Net MTL, and AA-MTL models, respectively. However, in the AMC dataset, the AA-MTL model that adjusts the confounding variable by prediction shows the best performance with an MAE of 8.190months, whereas the other models show the best performances by adjusting the confounding variables by input. Ablation studies of task hierarchy reveal no significant differences in the results of the RSNA dataset. However, predicting the confounding variable in the second encoder layer and estimating bone age in the bottleneck layer shows the best performance in the AMC dataset. Ablations studies of multiple tasks reveal that leveraging confounding variables plays an important role regardless of multiple tasks. To estimate bone age in pediatric X-rays, the clinical setting and balance between model size, task hierarchy, and confounding adjustment method play important roles in performance and generalizability; therefore, proper adjusting methods of confounding variables to train deep learning-based models are required for improved models.

Mapping cortical activations underlying covert and overt language production using high-density diffuse optical tomography

Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., “silent”) language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., “out loud”) language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces.

Parsing-Based Recognition of Hierarchical Plans Using the Grammar Constraint

Plan recognition is the problem of recognizing a goal task and an agent’s plan based on the observed actions. Plan recognition techniques can be employed in multiagent systems, behaviour recognition, computer security, and other fields related to artificial intelligence. Hierarchical task networks (HTN) describe the decomposition hierarchy of tasks in planning problems. In HTN plan recognition, a prefix of the plan (actions observed so far) is given as an input, and the aim is to find a task that decomposes into a sequence of actions with the given prefix. In this paper, we show how the performance of parsing-based HTN plan recognition can be improved by restricting possible suffixes of the given prefix based on generalized arc consistency of a corresponding context-free grammar.

Assessing Mathematical Higher-Order Thinking Skills: An Analysis of Turkish University Entrance Examinations

ABSTRACT Internationally, mathematics education reform has been directed toward characterizing educational goals that go beyond topic/content/skill descriptions and develop students’ problem solving. The Revised Bloom’s Taxonomy and MATH (Mathematical Assessment Task Hierarchy) Taxonomy characterize such goals. University entrance examinations have been seen as one way of accomplishing these goals and influence learning, teaching, and assessment in mathematics. The present study analyzed mathematics items (N = 1077) in Turkish university entrance examinations in 1998-2013 and objectives (N = 621) in mathematics curricula in 2005, 2011, and 2013 to determine the extent to which they represent the dimensions/categories of these taxonomies and the degree to which items are aligned with objectives in terms of reflecting the dimensions/categories of these taxonomies. The findings reveal that the items demand, to a large extent, automated computational skills; this is also evident in the relevant mathematics curricula. Implications for practice are discussed and could play a role in reforming assessment.

Design of mechanisms for ensuring the execution of tasks in project planning

This paper reports an analysis of aspects of the project planning stage. The object of research is the decision-making processes that take place at this stage. This work considers the problem of building a hierarchy of tasks, their distribution among performers, taking into account restrictions on financial costs and duration of project implementation. Verbal and mathematical models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed. Such indicators of the project implementation process efficiency were introduced as the time, cost, and cost-time efficiency. In order to be able to apply these criteria, the tasks of estimating the minimum value of the duration of the project and its minimum required cost were considered. Appropriate methods have been developed to solve them. The developed iterative method for assessing the minimum duration of project implementation is based on taking into account the possibility of simultaneous execution of various tasks. The method of estimating the minimum cost of the project is to build and solve the problem of Boolean programming. The values obtained as a result of solving these problems form an «ideal point», approaching which is enabled by the developed iterative method of constructing a hierarchy of tasks based on the method of sequential concessions. This method makes it possible to devise options for management decisions to obtain valid solutions to the problem. According to them, the decision maker can introduce a concession on the value of one or both components of the «ideal point» or change the input data to the task. The models and methods built can be used when planning projects in education, science, production, etc.

Use of Bloom's Taxonomy in the ESP final exam preparation as the way to avoid the feelings of anxiety in the war period

Using the methodology of qualitative research, a series of the English language classes on the developing of asking questions skills was held with the 4th-year students of the educational and professional program "Physics" in 2019, 2020 and 2021. The findings of the study made it possible to introduce an innovative part of the speaking exam format that was developed and implemented in 2021 and 2022. The impetus for this implementation was the tense social and political situation that arose in connection with the threat of COVID 19 and the beginning of military operations in Ukraine, which forced teachers to adapt the educational process to the new reality. In this regard, the principles of students’ autonomy and critical thinking have become of primary importance. It was highlighted that questioning plays an important role in the critical thinking development of the students. The analysis of the literature on the use of B. Bloom's Taxonomy and its updated version showed that the researchers made a great contribution to the development of teachers to use questions of different cognitive levels of Bloom’s taxonomy to outline the goals and assess the knowledge of schoolchildren or higher education students. Considering that asking questions is very important for students as future physicists, our research emphasized a student-centered approach. An extended structure of the speaking exam for the 4th year students of the Faculty of Physics, which was called a prepared speech, was developed. The principles of autonomy, scientific approach, academic integrity, critical thinking, creativity and hierarchy of tasks were defined. According to them, an algorithm of tasks for prepared speech has been developed. Based on the observation and the students’ survey, conclusions were drawn regarding the high level of motivation of students to perform the proposed structure of tasks. Achievement orientation helped students to overcome the anxiety caused not only by the exam, but primarily by the war. Asking questions they gained a deeper understanding of the physical subject, new vision of the phenomenon and developed the necessary professional skills of critical thinking. These facts show the viability of the innovative exam format in the war period.

UVMS task-priority planning framework for underwater task goal classification optimization.

This paper presents a task prioritization strategy based on a generic underwater task goal classification transformation for multitasking underwater operational tasks: attitude control, floating manipulation, collision-free motion, especially optimizing trajectory of the end-effector of an underwater vehicle manipulator system (UVMS) in a complex marine environment. The design framework aims to divide the complex underwater operational tasks into UVMS executable generic task combinations and optimize the resource consumption during the whole task. In order to achieve the corresponding underwater task settings, the system needs to satisfy different task scheduling structures. We consider the actual application scenarios of the operational goals and prioritize and define each category of task hierarchy accordingly. Multiple tasks simultaneously enable fast adaptation to UVMS movements and planning to complete UVMS autonomous movements. Finally, an underwater vehicle manipulator system implements the task prioritization planning framework for a practical scenario with different constraints on different goals. We quickly and precisely realize the interconversion of different tasks under goal constraints. The autonomous motion planning and real-time performance of UVMS are improved to cope with the increasing operational task requirements and the complex and changing practical engineering application environments.

A hierarchical reinforcement learning method for missile evasion and guidance

This paper proposes an algorithm for missile manoeuvring based on a hierarchical proximal policy optimization (PPO) reinforcement learning algorithm, which enables a missile to guide to a target and evade an interceptor at the same time. Based on the idea of task hierarchy, the agent has a two-layer structure, in which low-level agents control basic actions and are controlled by a high-level agent. The low level has two agents called a guidance agent and an evasion agent, which are trained in simple scenarios and embedded in the high-level agent. The high level has a policy selector agent, which chooses one of the low-level agents to activate at each decision moment. The reward functions for each agent are different, considering the guidance accuracy, flight time, and energy consumption metrics, as well as a field-of-view constraint. Simulation shows that the PPO algorithm without a hierarchical structure cannot complete the task, while the hierarchical PPO algorithm has a 100% success rate on a test dataset. The agent shows good adaptability and strong robustness to the second-order lag of autopilot and measurement noises. Compared with a traditional guidance law, the reinforcement learning guidance law has satisfactory guidance accuracy and significant advantages in average time and average energy consumption.