Planning Domain Research Articles

DEVELOPING INDICATORS FOR SCIENCE TEACHER COMPETENCY IN STEM-ESD LEARNING

Science, Technology, Engineering, and Mathematics (STEM) education focused on Education for Sustainability Development (ESD) can help students improve STEM skills and science procedures. STEM-ESD teachers are needed to acquire these competencies. This study aims to develop and determine the validity and reliability of STEM-ESD competency indicators for science teachers with Technological Pedagogical Content Knowledge (TPACK) standards. Descriptive research includes competency indicator development, indicator testing, and validation. This study employed Rasch modelling to assess 51 questionnaire items. This study included 108 science teachers. The results showed that STEM-ESD competencies were developed into 3 domains: planning, implementing, and professional development. The planning domain includes Content Knowledge (CK), Pedagogical Knowledge (PK), Technological Knowledge (TK), Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), and TPACK. Meanwhile, the implementation and professional development domain includes TPACK indicators. The results of testing with the Rasch model show that the competencies developed can measure what should be measured (with no other dimensions outside the construct) because the raw variance explained by measures is 69.7%, which is classified as a very good criterion. Then, the unexplained variance in the 1st-5th period shows results that are smaller than 15%. A total of 42 indicators were declared valid. Furthermore, the reliability results are shown by the Cronbach alpha result of 0.99 with a very good category. The development and validation of this indicator is expected to be a reference for teachers to know what competencies they must have in teaching STEM-ESD in science learning.

Large Language Models as Planning Domain Generators (Student Abstract)

The creation of planning models, and in particular domain models, is among the last bastions of tasks that require exten- sive manual labor in AI planning; it is desirable to simplify this process for the sake of making planning more accessi- ble. To this end, we investigate whether large language mod- els (LLMs) can be used to generate planning domain models from textual descriptions. We propose a novel task for this as well as a means of automated evaluation for generated do- mains by comparing the sets of plans for domain instances. Finally, we perform an empirical analysis of 7 large language models, including coding and chat models across 9 different planning domains. Our results show that LLMs, particularly larger ones, exhibit some level of proficiency in generating correct planning domains from natural language descriptions

An Eager Satisfiability Modulo Theories Solver for Algebraic Datatypes

Algebraic data types (ADTs) are a construct classically found in functional programming languages that capture data structures like enumerated types, lists, and trees. In recent years, interest in ADTs has increased. For example, popular programming languages, like Python, have added support for ADTs. Automated reasoning about ADTs can be done using satisfiability modulo theories (SMT) solving, an extension of the Boolean satisfiability problem with first-order logic and associated background theories. Unfortunately, SMT solvers that support ADTs do not scale as state-of-the-art approaches all use variations of the same lazy approach. In this paper, we present an SMT solver that takes a fundamentally different approach, an eager approach. Specifically, our solver reduces ADT queries to a simpler logical theory, uninterpreted functions (UF), and then uses an existing solver on the reduced query. We prove the soundness and completeness of our approach and demonstrate that it outperforms the state of the art on existing benchmarks, as well as a new, more challenging benchmark set from the planning domain.

Learning Planning Domains from Non-redundant Fully-Observed Traces: Theoretical Foundations and Complexity Analysis

Domain learning is the task of finding an action model that can explain given observed plan executions, so-called traces. It allows us to automate the identification of actions' preconditions and effects instead of relying on hand-modeled expert knowledge. While previous research has put forth various techniques and covers multiple planning formalisms, the theoretical foundations of domain learning are still in their infancy. We investigate the most basic setting, that is grounded classical planning without negative preconditions or conditional effects with full observability of the state variables. The given traces are assumed to be justified in the sense that either no single action or no set of actions can be removed without violating correctness of the plan. Furthermore, we might be given additional constraints in the form of a propositional logical formula. We show the consequences of these assumptions for the computational complexity of identifying a satisfactory planning domain.

Block-Level Goal Recognition Design

Existing works on goal recognition design (GRD) consider the underlying domain as a classical planning domain and apply modifications to the domain to minimize the worst case distinctiveness. In this paper, we propose replacing existing modifications with blocks, which group several closely related modifications together such that a block can modify a region in a search space with respect to some design constraints. Moreover, there could be blocks within blocks such that the design space becomes hierarchical for modifications at different levels of granularity. We present 1) a new version of pruned-reduce, a successful pruning rule for GRD, for block-level GRD, and 2) a new pruning rule for pruning some branches in both hierarchical and non-hierarchical design space. Our experiments show that searching in hierarchical design spaces greatly speeds up the redesign process.

Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract)

How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.

Sustainable Spatial Planning Based on Ecosystem Services, Green Infrastructure and Nature-Based Solutions

In the last decade, ecosystem services, that is, the goods and benefits provided by ecosystems to people, have gained significant importance in the planning domain, as a consequence of the growing scholarly awareness about, and interest in, the complex relationship between human well-being and nature [...]

Additive Manufacturing for Surgical Planning and Education: A Review

Additive manufacturing has been widely used in various industries, including the healthcare sector. Over the last few decades, AM has been playing an important role in the medical field in different areas, including surgical planning, implants, and educational activities. For surgical applications, AM can help surgeons practice and plan an operation until they are confident with the process. This can help to reduce operational risk and time. In addition, it can help to demonstrate the problem to other colleagues. AM has also been used to produce 3D models to teach students and doctors about human anatomy. This paper aims to comprehensively review the diverse applications of additive manufacturing within the domains of surgical planning and medical education. By focusing on the multifaceted roles played by AM in these critical areas, a contribution to the growing body of knowledge that underscores the transformative potential of this technology in shaping the future of healthcare practices is sought to be made.

Emotional Landscapes in Urban Design: Analyzing Color Emotional Responses of the Elderly to Community Outdoor Spaces in Yi Jie Qu

Addressing the emotional needs of the elderly in urban space design has increasingly become a vital concern. This study innovatively integrates emotional theories with the design of community outdoor spaces, thereby expanding the research on emotional categorization in urban spaces. At 8 community outdoor space sites in Yi Jie Qu, China, 330 elderly residents were randomly recruited to assess their color emotional responses (CER) to the color landscapes of these spaces. Based on the Affective Circumplex Model and Japanese Color Image Theory, a Color Emotion Circumplex was constructed to visually represent the overall emotional tendencies and significant positive emotions of the elderly. The second innovation of this research lies in exploring the driving factors behind positive emotional responses of the elderly, the primary user group of community outdoor spaces. We analyzed the significant differences in CER between autumn and winter scenes, employing variance analysis, correlation, and regression to investigate the substantial effects of individual factors and color characteristics on positive CER. The study discovered that the elderly exhibit a stronger CER towards clean and healthy emotions. Notably, CER was more pronounced in autumn scenes compared to winter. Furthermore, educational level, visit frequency, and color brightness positively influenced positive CER, whereas walking time from residence and the color area ratios of blue and gray negatively impacted CER. These findings not only provide a theoretical basis for age-friendly color design in community spaces, but also offer new perspectives and practical guidance for the international community planning and design domain. Our research underscores the importance of incorporating the emotional needs of the elderly into urban space design, offering novel theoretical and practical guidance for future urban planning and community design.

Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models.

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot's proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.

A review of unmanned aerial vehicle path planning techniques

The paper offers an exhaustive scholarly review of the algorithms and techniques employed in Unmanned Aerial Vehicle (UAV) path planning, categorizing these methodologies based on spatial dimensions, planning steps, and the nature of planning maps. It provides a critical evaluation of a plethora of algorithms, including random search methods, particle swarm algorithms, genetic algorithms, and A* algorithms, among others. The study elucidates the advantages and limitations of each algorithm, with a particular focus on their efficacy in real-time planning and navigation within complex three-dimensional environments. It underscores that while the domain of pre-flight path planning has reached a level of relative maturity, there exists a conspicuous gap in the literature concerning real-time obstacle avoidance and optimal path planning, particularly when constrained by limited computational resources. The paper thus serves as both a comprehensive review and a call for further research aimed at addressing these identified lacunae to ensure the generation of safe and feasible UAV trajectories.

Model‐based user experience‐focused roadmapping

AbstractTechnology roadmapping is an important yet challenging strategic process involving inter‐disciplinary knowledge and requiring key decision‐makers participation. To improve and facilitate this activity, a concept of model‐based technology roadmapping has been proposed in the literature as an application of model‐based systems engineering practices to the strategic planning domain. At the same time, many modern complex technical systems are required to be not only functional, efficient, and reliable, but also useful, usable, and desirable, which shifts the focus of planning to the user experience (UX). In this paper, we developed a Domain‐specific language that uniformly represents key concepts related to the four levels essential for strategic planning in the user‐oriented business context: market, UX design, engineering design, and technology. A collaborative interactive software was developed to support the proposed methodology. As a case study, a sustainable transformation of the automotive sector was tested and implemented in the software.

Analysis of Investors' Investment Choices for Oil Companies

This paper delves into the significance of oil within the realms of energy supply, transportation, and the chemical industry, all while exploring the unique challenges encountered by diverse categories of investors. Employing a multifaceted approach encompassing risk analysis, profitability assessment, and market ratio analysis, this study meticulously dissects the financial records of select oil companies. Consequently, it formulates insightful recommendations aimed at enhancing investor decision-making processes and fostering stronger corporate investor relations. The primary findings of this research emphasize the intricate interplay of numerous factors that sway investors' choices, necessitating a comprehensive evaluation that takes into account risk, profitability, and prevailing market conditions. This study not only identifies these influences but also offers specific, actionable guidance to navigate them effectively. The implications of this research extend to the practical domains of investment decision-making and corporate strategic planning, thus underscoring its intrinsic value in shaping both individual investment portfolios and the strategic directions of oil enterprises.

Availability of STEAM Approach Requirements among Intermediate-Stage Mathematics Teachers and Their Attitudes towards It

The present research aimed at investigating the availability of the requirements of the STEAM approach among intermediate-stage teachers and their attitudes towards the STEAM approach in public schools of Amman city, Jordan. This was a cross-sectional study that recruited a sample of 294 intermediate-stage mathematics teachers. A researcher-developed questionnaire was used in this study. It comprised 46 items (30 items for the availability of the STEAM approach requirements and 16 items for teachers' attitudes towards the STEAM approach). The data gathered in this study was analyzed using the SPSS. The results showed that the requirements of the STEAM education approach were high. Exploring the domains of STEAM requirements revealed that the implementation domain was the highest available requirement (3.90±0.54), followed by the planning domain (3.81±0.68) was available at a high degree, and lastly the assessment domain (3.11±0.76) at a moderate degree. In addition, the results showed that the enrolled mathematics teachers had positive attitudes toward using the STEAM education approach in teaching mathematics. It was found the most positive attitudes were within the desire to apply the strategy domain (4.18±0.60), followed by the motivation and self-learning domain (4.06±0.83), thinking and problem-solving domain (3.66±1.08) and the last one was collaboration and communication domain (3.21±0.59). The study concluded that STEAM approach requirements are available to a high degree as perceived by intermediate-stage mathematics teachers and they had positive attitudes towards using the STEAM approach in teaching mathematics in the public schools in Amman city, Jordan. Keywords: STEAM, requirements, attitudes, intermediate stage, mathematics, Jordan

Maximizing Retirement Savings: Strategic Forecasting of Employees’ Provident Fund (EPF) Dividends

This study embarks on an analytical drive to project future dividends of the Employees Provident Fund (EPF) by harnessing the historical dividend data spanning from 1952 to 2023. With a comprehensive methodology that fuses both descriptive and inferential statistical approaches, the study conducts an in-depth examination of the fluctuations and trends in EPF dividend rates across seventy years. Utilizing EXCEL statistical tools, such regression models, it examines into identifying the pivotal factors that have historically influenced EPF dividends, thereby enabling the prediction of their future trajectories. This endeavor aims to refine the strategies employed in financial planning, ensuring they are more aligned with predicted outcomes. The study uncovers a pattern of volatility in EPF dividends, marked by significant highs and lows which correspond with the changing tides of the economic landscape. The development and application of a predictive model, deemed most effective through this study, shed light on potential future trends of EPF dividends. This model is instrumental in proposing a strategic approach for EPF contributors, tailored to navigate the complexities of investment planning with foresight. Moreover, the significance of this study transcends the boundaries of dividend prediction. It lays down a comprehensive framework that deepens the understanding of how retirement fund dividends are influenced by economic fluctuations. This contribution is invaluable for investors and policymakers alike, providing them with the foresight needed to make informed decisions that could preemptively address the challenges posed by future economic shifts. In essence, this paper makes a profound contribution to the domain of financial planning and analysis by advocating for a shift towards a more proactive and informed management of retirement savings.

Community Engagement in Urban Planning: A Catalyst for Sustainable Development

The domain of urban planning occupies a seminal position in the delineation of our urban landscapes, wielding considerable influence over the trajectory of our cities. A burgeoning acknowledgment underscores the imperative integration of robust community participation in the intricate tapestry of urban development. This exposition articulates the indispensable nexus between community engagement and the multifaceted intricacies of urban planning, elucidating its profound implications for the realization of urban environments characterized by sustainability, inclusivity, and resilience.

The time variable in the Territorial Digital Twin: The case of Guadalajara (Spain)

The growing importance of Territorial Digital Twins (TDT) in the built environment is becoming increasingly apparent. The TDT can play a crucial role in helping governments make informed decisions based on a thorough understanding and interpretation of the evolution of both the physical territory and its urban, social, and economic development. This study develops the Territorial Digital Twin of Guadalajara (Spain), within the knowledge domain of urban planning and architectural and natural heritage. It allows the analysis of the evolution of the city, using the variable time, taking as origin an unpublished flight of Guadalajara from 1934. To be able to count on the widest possible time span, it is essential to recover the unpublished documentation that remains in historical archives. In this sense, aerial images have proved to be invaluable source for the recovery of historical heritage and territorial analysis. During the study, different techniques of image georectification have been combined, and a historical vector graphic base with topology has been created, plus an associated database accompanied by files with town planning and heritage information. As a result, the changes produced in the urban planning of the city are analysed and shown on the open-source website developed for the consultation of Territorial Digital Twin by all the stakeholders (collectivity, public bodies, university, investigation bodies, etc).

Assesing Smart Growth Implementation and Its Impact on Urban Sprawl: A Content and GIS-Based Analysis

Smart growth (SG) is a set of policies and regulations implemented to mitigate the expansion of urban sprawl. SG is a planning approach that prioritizes creating compact, mixed-use, and high-density urban areas to foster sustainable regional development. Nevertheless, it is important to note that both theoretically and practically SG is still undergoing refinement to ensure its suitability and applicability within the context of land control regulations in each location. The Bodetabek region in Indonesia is seeing rapid growth. Nevertheless, amidst this exponential expansion, the extent to which the local government has embraced SG concepts in its regional development remains uncertain. The primary aim of this study is to assess the extent to which SG concepts have been implemented in the domain of urban planning within the Bodetabek region. The methods of this paper are divided into several stages. Firstly, an examination is conducted to determine the extent to which SG concepts have been incorporated into the planning policy of the Bodetabek area. Subsequently, an assessment is made of the urban sprawl that has occurred in the Bodetabek area for 20 years. Lastly, an analysis is performed to investigate the pattern of urban expansion in the Bodetabek. The findings in this research show that the SG idea has been implemented in the Bodetabek region. The correlation between the proportion of SG content in Bodetabek local laws and land cover changes is evident. There is an inverse relationship between the proportion of SG content in local legislation and the extent of urban development in the Bodetabek area. Naturally, these findings provide preliminary evidence or indicators that can serve as a reminder of the potential of SG to curb urban sprawl, effectively aligning with established theoretical frameworks.

Zhang equivalency of inequation-to-inequation type for constraints of redundant manipulators

In solving specific problems, physical laws and mathematical theorems directly express the connections between variables with equations/inequations. At times, it could be extremely hard or not viable to solve these equations/inequations directly. The PE (principle of equivalence) is a commonly applied pragmatic method across multiple fields. PE transforms the initial equations/inequations into simplified equivalent equations/inequations that are more manageable to solve, allowing researchers to achieve their objectives. The problem-solving process in many fields benefits from the use of PE. Recently, the ZE (Zhang equivalency) framework has surfaced as a promising approach for addressing time-dependent optimization problems. This ZEF (ZE framework) consolidates constraints at different tiers, demonstrating its capacity for the solving of time-dependent optimization problems. To broaden the application of ZEF in time-dependent optimization problems, specifically in the domain of motion planning for redundant manipulators, the authors systematically investigate the ZEF-I2I (ZEF of the inequation-to-inequation) type. The study concentrates on transforming constraints (i.e., joint constraints and obstacles avoidance depicted in different tiers) into consolidated constraints backed by rigorous mathematical derivations. The effectiveness and applicability of the ZEF-I2I are verified through two optimization motion planning schemes, which consolidate constraints in the velocity-tier and acceleration-tier. Schemes are required to accomplish the goal of repetitive motion planning within constraints. The firstly presented optimization motion planning schemes are then reformulated as two time-dependent quadratic programming problems. Simulative experiments conducted on the basis of a six-joint redundant manipulator confirm the outstanding effectiveness of the firstly presented ZEF-I2I in achieving the goal of motion planning within constraints.

An integrated approach to decision-making variables on urban water systems using an urban water use (UWU) decision-support tool

In response to pressing global challenges like climate change, rapid population growth, and an urgent need for sustainable infrastructure, cities face an immediate and crucial necessity to transition swiftly toward an integrated approach to managing urban water resources. This shift is not merely an option but an imperative, driven by the rapidly evolving urban landscape. In addressing this imperative, a crucial decision support tool that has emerged as an asset in the domain of urban water planning and management is the Urban Water Use (UWU) tool. This tool offers an integrated approach for strategic planning, promoting urban water conservation and environmental health through the investigation of interventions in urban infrastructure under different scenarios. In this study, the latest version of this UWU tool was deployed in a case study conducted in Almirante Tamandaré, Brazil. The objective was to evaluate how an integrated decision-making approach concerning urban water systems influences the efficiency and effectiveness of interventions, ultimately contributing to achieve widespread adoption, accessibility, and relevance of urban water services. The refined UWU tool evaluates a spectrum of measures across diverse scenarios, incorporating various drivers, focusing on the stakeholders' visions for the locality. These visions are composed of sustainability indicators, specifying different sets of target values and importance weights for each indicator. The approach followed in this study demonstrates how the effectiveness indexes can vary based on stakeholders' perception. Measures under Water Sensitive Urban Design and Water Demand Management strategies were deployed to simulate the response of urban water systems under three distinct scenarios, embracing the complexities of social dynamics and of climate change. The findings of the study emphasize that realizing a desired vision through selected measures relies significantly on the adoption of an integrated approach within the decision-making process. The stakeholders' perception of how indicators should be weighted while defining the vision was found to significantly impact the effectiveness range of these measures.