Parallel Planning Research Articles

Effectiveness of Basic Physics II Practicum Guidelines Based On Science Process Skills

The Jambi University Physics Education Study Program has developed a Basic Physics II practicum guidelines based on science process skills that valid and reliable. However, there has not been a large scale implementation to determine the effectiveness of the guidelines. The purpose of this study was to determine the mastery of student science process skills after using the Basic Physics II practicum guidelines based on science process skills on refraction subject mater. This research is a quantitative research type of pre-experimental design with the One-Shot Case Study. The research sample was the Physics Education second year students of Jambi University, amounting to 87 people. Data collection is done by observing using observation sheets about science process skills. Data were analyzed using descriptive statistics. The results showed that students were very good at mastering science process skills after using practicum guidelines based on science process skills. Percentage of mastery of science process skills for refraction practicum on parallel plan glass including: observation 64.84%; experimental planning: 64.84%; prediction 61.54%; conducting experiment 53.85%; measuring 49.45% and variabel identification 28.57%. On average, students were very good at mastering science process skills in refraction practicums in prisms with their respective percentages: planning experiments 61.54%; prediction 58.24%; observation 48.35%; conducting experiment 45.05%; measuring 39.56% and identifying variables 32.97%.

Parallel planning: a new motion planning framework for autonomous driving

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as &#x201C parallel planning &#x201D is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality. A deep planning model which combines a convolutional neural network &#x0028 CNN &#x0029 with the Long Short-Term Memory module &#x0028 LSTM &#x0029 is developed to make planning decisions in an end-to-end mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers. Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder &#x0028 VAE &#x0029 and a generative adversarial network &#x0028 GAN &#x0029 is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

Productivity and Superficial Quality of an Aspherical Die Using CAD/CAM

Nowadays, it is hard to imagine modern industry without the manufacturing and design software aided by computer, known as CAD/CAM, once the search for excellence in complex geometry parts, reduced time, quality assurance and interchangeability is increasing. Within this quest, the CAD/CAM platform has become a significant factor in manufacturing, especially on complex surfaces parts, mainly because of superficial quality, reliability and product competitiveness. Defined as surface within one or more non-flat surfaces or non-square, usually represented by parametric models, the freeform surfaces are very complex, requiring the control of three or more axes simultaneously, making the CAM tool indispensable. Tests were carried out by milling of aspherical cavities, on VP100 steel samples, with cemented carbide ball nose inserts with TiAlN coating. Two cutting strategies – zig-zag and spiral – and two finishing (CAM) strategies – parallel plans and Z level – were also investigated. Final Surface quality, tool productivity and tool wear were related to machining time, surface roughness (Ra, Rz, Rku and Rsk) and tool wear (VBmax). Results showed that the chosen strategy (tool path) it is the main factor to productivity. Keywords: CAD/CAM; Superficial Quality; Milling; Finishing

Approaches to Speed up Data Processing in Relational Databases

The increasing of data volumes and the tightening of requirements by the time of data processing actualize the problem of finding methods for optimizing data structures and queries in databases. This article presents a set of methods that can help speed up the processing of data. These include the method of restructuring a completed database, a method for obtaining a parallel query plan, and methods for optimizing queries. The presented methods can be used both in a complex and independently of each other. At the end of the article, are shown the results of the experiments, which were carried out on a test database for approbation of optimization methods for a parallel query plan.

Advising Alternatives: A Case Study

Given a rapidly changing labor market, advisors use parallel planning and alternative advising to teach students adaptability. Whereas parallel planning proactively prepares students for future transitions through considering multiple pathways, alternative advising occurs after students fail to make degree progress or secure admission and, consequently, must change their goals. This article integrates research that can help frame an advising curriculum, pedagogy, and learning outcomes as well as a case study that demonstrates such learning outcomes as coping with loss, managing anxiety, and restoring self-efficacy. Through the process described, students discover hope, resiliency, and a renewed focus on their goals.

Parallel Motion Planning: Learning a Deep Planning Model against Emergencies

To handle the issue of preventing emergencies for motion planning in autonomous driving, we present a novel parallel motion planning framework. Artificial traffic scenes are firstly constructed based on real traffic scenes. A deep planning model which can learn from both real and artificial scenes is developed and used to make planning decisions in an end-to-end mode. To prevent emergencies, a generative adversarial networks (GAN) model is designed and learns from the artificial emergencies from artificial traffic scenes. During deployment, the well-trained GAN model is used to generate multiple virtual emergencies based on the current real scene, and the well-trained planning model simultaneously makes different planning decisions for both virtual scenes and the current scenes. The final planning decision is made by comprehensively analyzing observations and virtual emergencies. Through parallel planning, the planner can timely make rational decision without a large number of calculations when an emergency occurs.

Coordinated Motion Planning: Reconfiguring a Swarm of Labeled Robots with Bounded Stretch

We develop constant-factor approximation algorithms for minimizing the execution time of a coordinated parallel motion plan for a relatively dense swarm of homogeneous robots in the absence of obstacles. In our first model, each robot has a specified start and destination on the square grid, and in each round of coordinated parallel motion, every robot can move to any adjacent position that is either empty or simultaneously being vacated by another robot. In this model, our algorithm achieves constant stretch factor: if every robot starts at distance at most $d$ from its destination, then the total duration of the overall schedule is $O(d)$, which is optimal up to constant factors. Our result holds for distinguished robots (each robot has a specific destination), identical (unlabeled) robots, and most generally, classes of different robot types (where each destination specifies a required type of robot). We also show that finding the optimal coordinated parallel motion plan is NP-hard, justifying approximation algorithms. In our second model, each robot is a unit-radius disk in the plane, and robots can translate continuously in parallel subject to not intersecting, i.e., having disk centers at $L_2$-distance at least $2$. We prove the same result---constant-factor approximation algorithm to minimizing execution time via constant stretch factor---when the pairwise $L_{\infty}$-distance between disk centers is at least $2\sqrt{2}=2.8284\dots$. On the other hand, for $N$ densely packed disks at distance at most $2+\delta$ for a sufficiently small $\delta>0$, we prove that a stretch factor of $\Omega(N^{1/4})$ is sometimes necessary (when densely packed), while a stretch factor of $\mathcal{O}(N^{1/2})$ is always possible.

Social and Physical Planning: Two Approaches to Territorial Production in Socialist Yugoslavia between 1955 and 1963

By introducing workers’ self-management in the 1950s, socialist Yugoslavia aimed to decentralize socio-economic planning and gradually translate into practice the Marxist-Leninist theory of the ‘withering away of the State’. Although the new planning model was intended to provide a more balanced distribution of economic opportunities across the socially diverse federal territory, in practice it prioritized exponential economic growth, contributing to a rural exodus and hyper-concentration of workforce, management capacities and resources in the urban agglomerations. As a direct response to the severe consequences of post-war urbanisation in Yugoslavia, experts from the Urban Planning Institute of Croatia developed the first Yugoslavian methodology of regional planning. With a reference to Hilberseimer’s theoretical work on regional planning, the methodology aimed to integrate rural and urban areas into a coherent regional space with a greater degree of socio-economic independence from the urban centres. This paper provides a critical overview of social and regional planning in Yugoslavia between the introduction of the communal system in 1955 and the new constitution in 1963. By following the two parallel yet interwoven planning discourses, the paper analyses the transition of the State ideology and political economy into the spatial realm. The comparison of two discourses reveals the ambiguous relationship between social and regional planning and the strategic attempts of urban planners to negotiate their ideological positions within the evolving political system. The regional plan for the Krapina district is the first manifestation of the new planning methodology, which intended to reconcile contradictions between city and countryside, centre and periphery, centralisation and decentralisation.

The Best Scenario for Nonparticipatory Conditions

Entering the 3rd millennium, the 7th Secretary General of the United Nations proclaimed the global actions as the result of the synergy between 3 main pillars of the society including the governments with the key role in the field of politics and law, business entities in the field of economy and community-based organizations in the arena of civil society. WHO states: “A National Cancer Control Program (NCCP) is a public health program designed to reduce the number of cancer cases and deaths and improve quality of life of cancer patients.” It has also stated: “If national leaders decide to create a new or updated cancer control plan, then the cancer control planning process can start with broad participatory support. All key stakeholders should be involved early in the planning stages, and national leadership is needed throughout the process.” The key question is “Do national cancer control plans include the main stakeholders according to Mr. Annan's recommendation?” The short answer is “No.” This presentation will address the fundamental question that in the situation where the governments, especially in low- and middle-income countries, are the only policy makers in the field of health, while there exists active community-based organizations with proven advocacy reach operating with complete reliance on public donations and social assets, what is the main role of these organizations now that their capacities are not being used in national cancer control planning: 1. Be only an executor of these plans? 2. Design strategic plans in parallel with the national programs? 3. Allocate their resources to fill the gaps which are not addressed by the governments?

Technical Note: Acceleration of online adaptive replanning with automation and parallel operations.

Limited by various human interventions and manual operations, the routine practice of online adaptive replanning (OLAR) is time consuming and impractical with the current planning technology. To accelerate OLAR and to minimize human efforts, the methods and software tools are developed to automate OLAR workflow and to use parallel processing during OLAR. Speedy online adaptive replanning SOLAR, a software tool, was developed to automate the OLAR workflow and to implement parallel operation in plan generation and contour editing. The SOLAR tool is designed to automate and monitor the operation of OLAR on multiple client workstations and to allow parallel manual contour editing and plan generation with multiple workstations. The performance of the SOLAR tool was tested on selected prostate and pancreatic cancer cases. The SOLAR system has been tested in the clinical environment. With the automation and parallel operations, the operation time for OLAR can be reduced by 70%, allowing OLAR to be completed within 10min for the tested prostate cancer cases and within 15min for the pancreatic cancer cases. The SOLAR system generated superior plans compared to the standard repositioning method. SOLAR software was developed to accelerate online adaptive replanning workflow with automation and parallel operations. By reducing the time and human intervention, thus, reducing potential human error, the SOLAR solution would improve the practicality of OLAR.

Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models

This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.

A qualitative study of health care professionals\u2019 views and experiences of paediatric advance care planning

BackgroundGood end-of-life care planning is vital to ensure optimal care is provided for patients and their families. Two key factors are open and honest advance care planning conversations between the patient (where possible), family, and health care professionals, focusing on exploring what their future wishes are; and the development of an advance care plan document. However, in paediatric and neonatal settings, there has been little research to demonstrate how advance care planning conversations take place. This study explored health care professionals’ views and experiences of paediatric advance care planning in hospitals, community settings and hospices.MethodsA qualitative methodology was employed using purposive sampling of health care professionals involved in the end-of-life care for children aged 0–18 years known to the hospital palliative care team, and had died at least three months before, but less than 18 months prior to the study. Ethics committee approval was obtained for the study. Located in the North of England, the study involved three hospitals, a children’s hospice, and community services. Data were collected using semi-structured, digitally recorded, telephone interviews. All interviews were transcribed verbatim and subjected to thematic analysis.ResultsTwenty-one health care professionals participated, including generalist paediatric staff as well as specialist palliative care staff.Two themes were generated from the study: The timing of planning conversations, including waiting for the relationship with the family to form; the introduction of parallel planning; avoiding a crisis situation. Secondly, supporting effective conversations around advance care planning, including where to have the conversation; introducing the conversation; and how to approach the topic encompassing the value of advance care planning and documentation for families.ConclusionThe timing of when to start the advance care planning conversations remains an issue for health care professionals. The value of doing it in stages and considering the environment where the conversations are held was noted. Timely planning was seen as vital to avoid difficult conversations at a crisis point and for co-ordination of care. Good advance care planning is to provide the best person-centred care for the child and experience for the family.

Massively Parallel Implementation of IPSA on GPU Architecture for Multi-Criteria Optimization

Current implementation of inverse planning simulated annealing (IPSA) algorithm for clinical uses requires the physicists to manually adjust the input potentials used to minimize the cost function during the optimization process. This workflow produces a single plan which might not encapsulate all the plan goals set forth by the physicians. On the fly adjustment of the potentials are feasible and can be fast but are time-limited due to the patient waiting for treatment and can be dependent on the optimization skill of the user. The purpose of this work is to present a graphics processing unit (GPU) based implementation of IPSA (gIPSA) which can generate multiple plans in parallel for high dose rate (HDR) brachytherapy. Thus a Pareto Front of equally-optimal plans can be generated for each patient from which the user can chose the most suitable option depending on the clinical situation.

Contour parallel tool path planning based on conformal parameterisation utilising mapping stretch factors

Parameterisation-based methods for planning tool paths on mesh surfaces have been developing for years. The issue of existing mapping deformation which results in machining error has not been sufficiently addressed. And it still needs particular 3D geometric operations when planning iso-scallop tool path. To handle these, an effective approach to directly generating the iso-scallop paths on the parametric domain utilising anisotropic mapping stretch factors is proposed. The conformal parameterisation algorithm, Angle Based Flattening, is first implemented for the mapping between the spatial mesh and the planar mesh. Then a general method to convert 3D path parameters into 2D, which involves the direction mapping and length mapping based on the mapping deformation analysis, is presented. After that, a non-uniform offsetting method is proposed for retrieving the valid 2D offset paths. Finally, the iso-scallop paths on the parametric domain can be generated using the converted 2D path parameters and the present non-uniform offsetting method, and then the corresponding tool paths are obtained by inverse mapping. Simulation and experimental results are given to validate the feasibility and effectiveness of the proposed methods.

An asynchronous parallel disassembly planning based on genetic algorithm

Disassembly is one of the most crucial remanufacturing activities. Disassembly sequence planning (DSP) is a combinatorial optimization problem and has been studied by many researchers. Conventional DSP techniques focus on sequential disassembly planning (SDP) in which only one manipulator is used to remove a single part or subassembly at a time such that it is inefficient when disassembling large or complex products. Recently, parallel disassembly has attracted some interest as it employs several manipulators to remove multiple components simultaneously. However, most of the work to date focuses on parallel disassembly techniques which require synchronization between manipulators, i.e., they must start their tasks simultaneously. This simplifies the modeling and analysis efforts but fails to fully realize the benefits of parallel disassembly. In this work, we propose asynchronous parallel disassembly planning (aPDP) which eliminates the synchronization requirement. In addition to precedence constraints, aPDP becomes highly operation time-dependent. To deal with this, we design an efficient encoding and decoding strategy for the disassembly process. In this paper, a metaheuristic approach, based on a genetic algorithm, is developed to solve the aPDP problem. The proposed algorithm is applied to four products which require disassembly processes of varying complexity, and the results are compared with two methods reported in literature. It is suggested that the proposed approach can identify faster disassembly processes, especially when solving large-scale problems.

Froid

For decades, RDBMSs have supported declarative SQL as well as imperative functions and procedures as ways for users to express data processing tasks. While the evaluation of declarative SQL has received a lot of attention resulting in highly sophisticated techniques, the evaluation of imperative programs has remained naïve and highly inefficient. Imperative programs offer several benefits over SQL and hence are often preferred and widely used. But unfortunately, their abysmal performance discourages, and even prohibits their use in many situations. We address this important problem that has hitherto received little attention. We present Froid, an extensible framework for optimizing imperative programs in relational databases. Froid's novel approach automatically transforms entire User Defined Functions (UDFs) into relational algebraic expressions, and embeds them into the calling SQL query. This form is now amenable to cost-based optimization and results in efficient, set-oriented, parallel plans as opposed to inefficient, iterative, serial execution of UDFs. Froid's approach additionally brings the benefits of many compiler optimizations to UDFs with no additional implementation effort. We describe the design of Froid and present our experimental evaluation that demonstrates performance improvements of up to multiple orders of magnitude on real workloads.

Challenges to discussing palliative care with people experiencing homelessness: a qualitative study

ObjectivesTo explore the views and experiences of people who are homeless and those supporting them regarding conversations and approaches to palliative careSettingData were collected between October 2015 and October 2016...

Parallel Planning Mechanisms as a "Recipe for Disaster"

This note offers a critical reflection of the recent landmark decision in City of Johannesburg Metropolitan Municipality v Gauteng Development Tribunal which lay to rest the negative consequences of employing the DFA procedures of the Development Facilitation Act 67 of 1995 (DFA) alongside those of the provincial Ordinances to establish townships (or to use DFA parlance, “land development areas”). The welcome and timely decision in City of Johannesburg Metropolitan Municipality v Gauteng Development Tribunal has declared invalid chapters V and VI of the DFA. Moreover, it has formalised planning terminology in South Africa, delineated the boundaries of “municipal planning” and “urban planning and development” as listed in Schedules 4 and 5 of the Constitution of the Republic of South Africa, 1996 and, in the process, clarified the structure of planning law. This note examines the decision of the SCA and focus on the role it will clearly have in reforming some of the law relating to planning. It considers the facts of the case, uncertainties around terminology, the structure of planning in South Africa, the content of municipal planning, the role of the DFA and the consequences of the declaration of invalidity by the SCA.

Encoding Domain Transitions for Constraint-Based Planning

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using don’t cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

FUNCTIONAL COORDINATION OF METHODS OF ORGANIZATION OF ADAPTIVE DATA COLLECTION PROCESSES AND METHODS OF SPATIAL SELF-ORGANIZATION OF MOBILE AGENTS

The way of functional coordination of methods of organization adaptive data collection processes and methods of spatial self-organization of mobile agents by parallel execution of the corresponding data collection processes and the process of motion control of a mobile agent using the proposed protocol of their interaction and the algorithm of parallel execution planning is proposed. The method allows to speed up the calculations in the decision block of the mobile agent by an average of 40.6%. Key words: functional coordination, adaptive data collection process, spatial self-organization, mobile agents