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In this article, the author talks about Sustainability Tracking, Assessment and Rating System or "STARS," a tool currently available that aims to help a campus answer the "how" and "how hard" questions. Created by AASHE (the Association for the Advancement of Sustainability in Higher Education), STARS presents guidelines and suggestions (based on industry experience) for becoming more sustainable and allows colleges and universities to assess their own efforts. And because STARS is explicitly designed to evolve as experience increases, its range of best practices seems likely to become the de facto standard for how to achieve sustainability within higher education. By enabling the correlation of practices reported under STARS with actual greenhouse gas (GHG) emission reductions reported under the American College and University Presidents Climate Commitment (PCC), AASHE is setting the stage for the creation of an empirical model of sustainable campus management. But more important from a campus sustainability planning perspective is the fact that participating in STARS forces sustainability administrators to engage in a far wider range of conversations than is necessary simply to complete a GHG inventory. The perspective that is gained by working within the STARS system can help sustainability administrators recognize what conversations are likely to be productive on their particular campus. In this article, the author suggests a few possible examples.

The concept of continuous planning (CP) refers to the periodic revisions of master plans in response to uncertainties faced by long-term urban planning. In this paper, we analyse CP practices in two European cities – Stavanger (Norway) and Belgrade (Serbia) and present how they cater for innovative planning tools which respond to the need to balance between both stability and change in long-term planning. We argue that in spite of its limitations, the CP approach adjusted to the local context, contributes to the certainty and stability of urban communities. [ABSTRACT FROM AUTHOR]

Academic libraries are under continued pressure to provide quality services and resources in support of the educational needs of their institutions while experiencing shrinking budgets and calls for greater accountability. Since 2010, the University of Texas at Austin Libraries (“UT Libraries”) has been embedding in its organization a data-driven and data-supported continuous planning process that utilizes the Balanced Scorecard. The collection and analysis of assessment and performance data have been integrated into both the planning and evaluation phases of its strategy development. This article will provide a case study of how UT Libraries is employing assessment activities and continuous planning to transform itself through increased accountability and transparency, while empowering all staff to participate in charting the future of the organization. [ABSTRACT FROM PUBLISHER]

The article considers the construction and implementation of financial strategies based on the approach of continuous financial planning.

This paper describes the experience of one university in developing a Shared Vision and planning strategically for achieving it. At Indiana University Northwest, strategic planning is an ongoing process that is moving the campus toward its vision for the future and the long-term outcomes derived from it. Unlike traditional strategic outcomes that are finitely measurable, IU Northwest's outcomes are aspirational and include such things as civility, diversity, and engagement. This paper describes a journey from organizational fragmentation to integrated, vision-based planning and accreditation processes. [ABSTRACT FROM AUTHOR]

The Casper's internal working that provides such critical reasoning capabilities to robotic explorers is presented. The Three Corner Sat and the Autonomous Sciencecraft Constellation missions are described and the Casper's contribution to each is highlighted.

The success of processes executed in uncertain and changing environments is reliant on the dependable use of relevant information to support continuous planning at runtime. At the core of this planning is a model which, if incorrect, can lead to failures and, in critical processes such as evacuation and disaster relief operations, to harm to humans. Obtaining reliable and timely estimations of model parameters is often difficult, and considerable research effort has been expended to derive methods for updating models at run-time. Typically, these methods use data sources such as system logs, run-time events and sensor readings, which are well structured. However, in many critical processes, the most relevant data are produced by human participants to, and observers of, the process and its environment (e.g., through social media) and is unstructured. For such scenarios we propose COPE, a work-in-progress method for the continuous planning of critical processes involving humans and carried out in uncertain, changing environments. COPE uses a combination of runtime natural-language processing (to update a stochastic model of the target process based on unstructured data) and stochastic model synthesis (to generate Pareto-optimal plans for the process). Preliminary experiments indicate that COPE can support continuous planning effectively for a simulated evacuation operation after a natural disaster. [ABSTRACT FROM AUTHOR]

This article introduces a Bayesian learning approach for planning continuously evolving leagile project and portfolio baselines. Unlike the traditional project management approach, which uses static project baselines, the approach proposed in this study suggests learning from immediately prior experience to establish an evolving baseline for performance estimation. The principle of Pasteur’s quadrant is used to realize a highly practical solution, which extends the existing wisdom on leagile continuous planning. This study compares the accuracy of the proposed Bayesian approach with the traditional approach using real data. The results suggest that the evolving Bayesian baselines can generate a more realistic measure of performance than traditional baselines, enabling leagile projects and portfolios to be better managed in the continuously changing environments of today.

This article introduces a Bayesian learning approach for planning continuously evolving leagile project and portfolio baselines. Unlike the traditional project management approach, which uses static project baselines, the approach proposed in this study suggests learning from immediately prior experience to establish an evolving baseline for performance estimation. The principle of Pasteur’s quadrant is used to realize a highly practical solution, which extends the existing wisdom on leagile continuous planning. This study compares the accuracy of the proposed Bayesian approach with the traditional approach using real data. The results suggest that the evolving Bayesian baselines can generate a more realistic measure of performance than traditional baselines, enabling leagile projects and portfolios to be better managed in the continuously changing environments of today.

In summary, survey preparation should be more than preparing for an inspection. Preparation should result in improving patient care outcomes as identified through the system's mission and vision, and by all of the people working within the organization through education, planning, and defining quality.

Automating planning for large teams of heterogeneous robots is a growing challenge, as robot capabilities diversify and domain complexities are incorporated. Temporal and continuous features accurately model real-world constraints, but add computational complexity. Distributed planning methods, such as the Coalition Formation then Planning framework, allocate tasks to robot teams and plan each task separately to accelerate planning. However, the task decomposition limits cooperation between coalitions allocated to different tasks and results in lower quality plans that require more actions and time to complete. Task Fusion estimates couplings between tasks and fuses coupled coalition-task pairs to improve cooperation and produce higher quality plans. Task Fusion relies on existing heuristics, which were ineffective and often resulted in worse results than the baseline framework. This manuscript introduces new heuristics that outperform the existing methods in two complex heterogeneous multi-robot domains that incorporate temporal and continuous constraints. [ABSTRACT FROM AUTHOR]

The article studies one of the key business processes of the organization, i.e. planning. Planning cannot create added value (a product or service) for the customer, but it allows us to understand what values are required by the customer and to identify and distribute organization resources to meet their rising and changing needs. At the same time planning plays an important organizational role in the organization, as it coordinates the work of its structural bodies in order to attain the goals of the organization itself and its divisions. Planning gives flexibility to the organization in complicate changing conditions of its functioning and helps it to survive. The author points out that the important aspect of the planning process is raising personnel engagement and its motivation to reach organizational and group goals. At the same time planning should promote freedom of structural divisions of the organization. And finally, another important aspect of plan/forecast development is control over their execution.

Autonomous driving has long grappled with the need for precise absolute localization, making full autonomy elusive and raising the capital entry barriers for startups. This study delves into the feasibility of local trajectory planning for Level-2+ (L2+) semi-autonomous vehicles without the dependence on accurate absolute localization. Instead, emphasis is placed on estimating the pose change between consecutive planning timesteps from motion sensors and on integrating the relative locations of traffic objects into the local planning problem within the ego vehicle's local coordinate system, thereby eliminating the need for absolute localization. Without the availability of absolute localization for correction, the measurement errors of speed and yaw rate greatly affect the estimation accuracy of the relative pose change between timesteps. This paper proved that the stability of the continuous planning problem under such motion sensor errors can be guaranteed at certain defined conditions. This was achieved by formulating it as a Lyapunov-stability analysis problem. Moreover, a simulation pipeline was developed to further validate the proposed local planning method, which features adjustable driving environment with multiple lanes and dynamic traffic objects to replicate real-world conditions. Simulations were conducted at two traffic scenes with different sensor error settings for speed and yaw rate measurements. The results substantiate the proposed framework's functionality even under relatively inferior sensor errors distributions, i.e., speed error v err ∼ N (− 0.1 , 0.1) m/s and yaw rate error θ ˙ err ∼ N (0.57 , 1.72) deg/s. Experiments were also conducted to evaluate the stability limits of the planned results under abnormally larger motion sensor errors. The results provide a good match to the previous theoretical analysis. Our findings suggested that precise absolute localization may not be the sole path to achieving reliable trajectory planning, eliminating the necessity for high-accuracy dual-antenna Global Positioning System (GPS) as well as the pre-built high-fidelity (HD) maps for map-based localization. [ABSTRACT FROM AUTHOR]

The achievement of net-zero emissions is a major governmental challenge to ameliorate the adverse impacts of climate change, and to reduce the health risks associated with poor air quality. In Australia, the transport sector is a major contributor to particulate matter and greenhouse gas emissions, especially in urban areas. The Australian Capital Territory (ACT) Government recognises that one of the greatest sustainability challenges to achieving a carbon-neutral society with net-zero emissions is the transport sector because it contributes 60 percent of all emissions under its jurisdiction. The ACT is a suitable case study on climate change, energy policy, and transport emissions because its electricity is powered by renewable energy; its governance has included a continuous planning philosophy of integrating transport with land use. The methodology is based on identifying and summarising the international literature on net-zero emissions policy (n = 50), the relevant policy documents and reports by the Australian Government (n = 8) and by the ACT Government (n = 32). An appraisal of policy outcomes in the ACT is based on an analysis of quantitative and qualitative data. In a car-dependent city (77% of trips by private transport), the most realistic policies for achieving net-zero emissions are to encourage, through fiscal incentives and regulations, the adoption of electric vehicles for buses and private cars, electric or hydrogen vehicles for the commercial fleet, and regulations to phase out petrol- and diesel-powered vehicles. [ABSTRACT FROM AUTHOR]

In the ongoing scenario of Industry 4.0 and automation, every enterprise needs to look forward to digitalizing its ongoing production planning and scheduling methods by employing different platforms available in the market. In early industrialized nations, the transition to the 4th stage of industrialization is now influencing how industrial value is created. The study is focused on India's stainless steel manufacturing sector. The area of attention is the digitization of continuous planning and scheduling utilizing an automated model as opposed to a continuous system of planning which requires a significant amount of human labor. Consequently, information and technology are used to manage processes and quality. The research article discusses a problem with coil routing in cold rolling and proposes a solution using a multi-criteria decision-making technique called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS). The selection criteria considered economic, social, and environmental factors, and the research focuses on planning feasible routing options by considering constraints associated with cold rolling mills. The issue with the current routing process, which is determined by an Enterprise resource planning (ERP) system, is that it does not consider any mill-specific constraints and schedules all orders to the first mill by default, resulting in manual planning. The proposed solution involves creating a real-time data model that can consider the constraints associated with each mill, thus enabling automated routing and optimizing the process. This study focuses on addressing the routing problem in the steel industry, which considers the design constraints associated with various mills. To accomplish this, a multi-criteria decision-making model is employed, which considers the rolled coil's thickness, grade, and width as design criteria while also comparing mill-related restrictions. As a result, the model generates potential rolling alternatives for the coil. This approach contributes to sustainable steel production by reducing product quality deviations and minimizing the reliance on human planning, thereby promoting automation in the steel industry. Considering the limitations imposed by mills, a workable routing alternative is presented in this paper with the aid of information and technology. It will reduce the steel industry's reliance on human scheduling. This model is a first step towards rationalizing the scheduling and planning of a product like steel, which has quite a few characteristics. [ABSTRACT FROM AUTHOR]

Optimal facility layout design greatly reduces material handling cost, required floor area and dead space. The usual approaches do not consider material handling aspects and fail to provide an efficient manufacturing environment. This paper proposes a distinct methodology that integrates material handling equipment selection and facility layout design for a heavy manufacturing environment using knowledge-based and optimization technique. The algorithm was coded using C language. The applicability of the proposed hybrid methodology is simulated on a Pentium III, 550 MHz computer and applied to two projects involving 6 machines, 30 moves and 12 material handling equipment sets. [ABSTRACT FROM AUTHOR]

We propo se an applied Bayesian learning approach for continuous planning and evolution of information system projects and portfolios. Unlike traditional project management approaches for information system, the proposed approach considers the cumulative effect of all past experiences to achieve continuous performance and reliability prediction. The results of quantitative comparisons with other common estimation approaches, such as non-learning point estimates and traditional Bayesian approach, using real case data indicate that the proposed approach can generate a more realistic metric to continuously plan and measure the performance of evolving LeAgile projects or portfolios. This study can support decision makers, engineering teams, and management by supplying a practical and scalable project performance prediction tool for continuous planning and system evolution. [ABSTRACT FROM AUTHOR]

Objectives With increasing life expectancy, aging in Iran becomes more feminine. This study aimed to prioritize the criteria affecting the health of Iranian elderly women. Methods & Materials In this descriptive-analytical study, factors affecting the health of the elderly women, with the opinion of 20 health-policy and health services management experts employed in governmental and non-governmental organizations, were approved. In the second phase, the weight and rank of these criteria were calculated and analyzed using Hierarchical Analysis Process. Excel software was used to analyze data. Results The analysis showed that the individual factor is the most important in terms of factors affecting the health of elderly women. In terms of the type of interventions, the provision of preventive health services, in terms of decision-making, executive interventions at the queue level, in terms of planning, integrated approach, and continuous planning with the participation of public and private sectors were ranked first. Conclusion Given the influence of the previous periods of life on healthy elderly, decision-makers should prioritize preventive services in all periods of life regarding age and gender context. Interventions should be presented with an integrated approach with continuous planning at the levels of government, organization, and queue, as well as with the participation of both public and private sectors. The existence of an information management system, supervision, and evaluation, provides the ground for continuous feedback and improvement of programs. Therefore, it is recommended that the government play a more significant role in ensuring the health of the elderly. [ABSTRACT FROM AUTHOR]

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Introduction Contemporary urban planning knowledge has undergone significant changes, shifting from natural and engineering sciences to interdisciplinary fields with a focus on social and human sciences. Urban development plans now emphasize public and private participation. As the second-largest city in Iran, Mashhad plays a vital role within the country due to its regional centrality, large population, and diversity. It is essential to prioritize social sustainability and encourage public participation in the planning process. Theoretical Framework Implementation is a critical component of the urban design process and can be interpreted in two ways: as the essence of the entire process or as an independent step within the process. Through a systematic study of various sources and analysis of urban design processes conducted by researchers, it is possible to categorize the urban design process involved in selecting the design group, defining initial objectives, assessing the existing conditions, clarifying goals, presenting design alternatives, evaluating options, optimizing, developing an implementation plan, implementing, and conducting post-implementation evaluations. Based on research synthesis, factors influencing the implementation of small-scale participatory projects can be categorized into five aspects: development stakeholders (including designers and implementers), management-planning system structures, economic considerations, public participation, and legal considerations. Methodology This paper adopts a qualitative approach and data-based method through semi-structured interviews to identify obstacles, challenges, and factors influencing the implementation of small-scale participatory projects (specifically, neighborhood units) in Mashhad. The study focuses on stakeholders including individuals, urban management, and facilitators involved in the “Mahalle Ma” project. Theoretical sampling was employed until saturation was reached. Data analysis utilized qualitative content analysis with a summative approach and MAXQDA 2018. Open and axial coding techniques were applied to identify the foundational concepts related to the implementation of the targeted projects. Results and Discussion The results of this study highlight the importance of effective collaboration and communication between stakeholders, including the people, urban management, and the facilitator team. Building trust, improving public participation, and addressing financial constraints are essential for the successful implementation of small-scale participatory projects. The relationship between the people and the facilitator: The quality of real participation by the people, as the most important stakeholder group, has a significant impact on achieving the goals of the plan. Negative experiences and a lack of trust in city management can present challenges for the project. To enhance citizens’ participation, the facilitator and designer can employ appropriate techniques to involve people in the process, clarify the project definition, and connect it with the needs assessment stage. Effective communication with the social council of the neighborhoods is also crucial, as the lack of it can hinder the quality of citizens’ participation. The relationship between the people and urban management: Urban management has initiated this plan with long-term goals in mind, including fostering a culture of participation. However, the inadequate allocation of funds and a lack of effective inter-organizational synergy have resulted in intangible short-term outcomes for the people. This situation raises concerns about increasing mistrust and decreasing satisfaction with the municipality’s performance. The relationship between urban management and the facilitator: Both urban management and the facilitator play crucial roles in promoting public satisfaction with the project. They should work towards achieving long-term goals by monitoring, improving, and maintaining a continuous planning process. This entails fostering a culture of participation through trust-building and capacity development. However, the frequent changes in design and planning approaches due to shifts in the urban management structure can affect the facilitator’s authority to achieve project goals. The facilitator team should also ensure a constant connection between the needs assessment stage, design, and implementation steps. Conclusion This study highlights the crucial role of urban management throughout the various stages of an urban development project. In the pre-implementation stage, urban management plays a fundamental role in selecting an expert design and implementation team. They also prioritize goals based on the type of management system in place. Recognizing the current situation requires the collaboration of the facilitator and the people to establish a shared understanding of the problem. The facilitator acts as a mediator, identifying the real demands of the people and conveying them to management levels to define the project in subsequent stages. Verification of goals and adjustments are made in consultation with citizens, academic experts, and local institutions. The presentation of design alternatives by the facilitator team ensures alignment with the real needs of the people. Evaluation and optimization of alternatives involve active participation from the people, who contribute to selecting the optimal alternative based on their genuine needs. Urban management also considers economic and technical factors in choosing the optimal option. During the implementation stage, urban management plays a critical role in ensuring the design team’s recommendations are executed properly by selecting the right contractors. The involvement of the social council, representing the people, is essential in monitoring the implementation process in line with the goals and needs assessment. In the post-implementation stage, continuous monitoring and modification are necessary, with direct input from the people through the facilitator team and urban management. This promotes the long-term continuity and success of the plan, fostering a culture of participation. [ABSTRACT FROM AUTHOR]

A reasonable evaluation of the ecological risk status of the landscape in the Yellow River source area is of practical significance for optimizing the regional landscape pattern and maintaining ecosystem function. To explore the regional heterogeneity of ecological risk in the watershed landscape, a landscape ecological risk evaluation model is constructed to evaluate the ecological risk status of the watershed for 20 years, and correlation analysis is used to further reveal the characteristics of the relationship between ecological risk and land use. The results show that the rapid expansion of urbanization and the increasing intensity of land development and use has caused significant changes in the Yellow River source area ecological environment and various land use types. The area of grassland decreased the most, by a total of 6160.04 km2, while the area of unused land increased the most, by a total of 2930.27 km2. A total of 12,453.11 km2 of land in the Yellow River source area was transformed, accounting for 9.52% of the total area. The most significant area of grassland was transferred out, accounting for 49.47% of the transferred area. During the study period, the proportion of area in the low-risk zone decreased from 54.75% to 36.35%, the proportion of area in the medium-low-risk zone increased from 21.75% to 31.74%, and the proportion of area in the medium-high-risk and high-risk zones increased from 10.63% to 14.38%. The high-risk areas are mainly located in areas with fragmented landscapes and are vulnerable to human activities. The mean ecological risk values in the study area show an increasing trend, and the spatial distribution shows a hierarchical distribution of "lower around the center and higher in the center". The global Moran's I index is higher than 0.68, which indicates that the ecological risk values have a significant positive correlation in space, the area of cold spots of ecological risk varies significantly, and the spatial pattern fluctuates frequently, while the spatial distribution of hot spots is relatively stable. Therefore, the landscape ecological risk in the Yellow River source area is rising, but the different risk levels and their spatial aggregation patterns and cold and hot spot areas continue to transform, which requires continuous planning of the landscape pattern to enhance the safety and stability of the regional ecosystem. [ABSTRACT FROM AUTHOR]

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Driving in interactive dynamic traffic is a huge challenge for autonomous vehicles, especially for motion planning. The autonomous vehicle not only needs to predict the future states of the social vehicles to avoid a collision but also realizes the comfort and continuous plan. To deal with the problem, a spatio‐temporal decision‐making and motion planning framework with flexible constraints based on previous work is proposed. Improvements can be highlighted in three aspects. First, a neural network for trajectory prediction is trained and it is integrated into the framework, which can learn the social vehicles' reactions in the negotiation better and give more accurate predictions. Second, instead of setting the fixed local targets, the flexible‐constraint mechanism is introduced to increase the success rate of continuous planning, specifically, the local planning target is determined conditioned on the situation with an elastic factor. Third, two popular simulators with randomly disposed traffic participants are exploited, where vehicles are mutually influenced, more similar to real‐world traffic. The closed‐loop tests with zero crashes and the comparative experiments with traditional models demonstrate the superior performance and practical significance of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel affordance model, which combines object, action, and effect information in the latent space of a predictive neural network architecture that is built on Conditional Neural Processes. Our model allows us to make predictions of intermediate effects expected to be obtained during action executions and make multi-step plans that include partial actions. We first compared the prediction capability of our model using an existing interaction data set and showed that it outperforms a recurrent neural network-based model in predicting the effects of lever-up actions. Next, we showed that our model can generate accurate effect predictions for other actions, such as push and grasp actions. Our system was shown to generate successful multi-step plans to bring objects to desired positions using the traditional A* search algorithm. Furthermore, we realized a continuous planning method and showed that the proposed system generated more accurate and effective plans with sequences of partial action executions compared to plans that only consider full action executions using both planning algorithms. [ABSTRACT FROM AUTHOR]

The continuous-time service network design problem (CTSNDP) occurs widely in practice. It aims to minimize the total operational cost by optimizing the schedules of transportation services and the routes of shipments for dispatching, which can occur at any time point along a continuous planning horizon. In order to be cost-effective, shipments often wait to be consolidated, which incurs a holding cost. Despite its importance, the holding cost has not been taken into account in existing exact solution methods for the CTSNDP because introducing it significantly complicates the problem and makes solution development very challenging. To tackle this challenge, we develop a new dynamic discretization discovery algorithm, which can solve the CTSNDP with holding cost to exactly optimum. The algorithm is based on a novel relaxation model and several new optimization techniques. Results from extensive computational experiments validate the efficiency and effectiveness of the new algorithm and also demonstrate the benefits that can be gained by taking into account holding costs in solving the CTSNDP. In particular, we show that the significance of the benefits depends on the connectivity of the underlying physical network and the flexibility of the shipments' time requirements. Funding: This work was partially supported by National Natural Science Foundation of China [Grant 71831008] and the Hong Kong Polytechnic University [Project P0043872]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0104. [ABSTRACT FROM AUTHOR]

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Social responsibility is the most important thing to consider while working on a project. When deciding on a project or taking part in a bid, it is crucial to understand the nature and potential consequences of the risks involved. Attempting to implement projects with cutting-edge technologies appears to be necessary, necessitating up-to-date and continuous planning to implement the relevant matters in light of the ever-increasing growth of urban communities, the need to carry out tasks, and the rising standard of living. Due to the strong demand, these strategies try to improve quality while decreasing prices. In the beginning, Smarandache suggests using neutrosophic sets. These sets are an improvement above traditional fuzzy set theory in reflecting the uncertainty and fuzziness of real-world issues. Three decision-making states are considered: uncertainty, truthiness, and falseness. The fuzzy set degree in Zadeh's classic theory is merely the membership function. On the other hand, three membership functions are considered in a neutrosophic setting. An indeterminacy degree is considered, which is not the case with intuitionistic fuzzy sets. To express decision makers' perspectives on the truthiness (T), falsity (F), and indeterminacy (I) for a fuzzy set concurrently, this paper expands the usual neutrosophic TOPSIS approach to interval-valued neutrosophic. One example of how the suggested strategy might be put to use is to prioritize initiatives in the realm of corporate social responsibility using a combination of expert opinion and objective criteria. [ABSTRACT FROM AUTHOR]

This article introduces a Bayesian learning approach for planning continuously evolving leagile project and portfolio baselines. Unlike the traditional project management approach, which uses static project baselines, the approach proposed in this study suggests learning from immediately prior experience to establish an evolving baseline for performance estimation. The principle of Pasteur's quadrant is used to realize a highly practical solution, which extends the existing wisdom on leagile continuous planning. This study compares the accuracy of the proposed Bayesian approach with the traditional approach using real data. The results suggest that the evolving Bayesian baselines can generate a more realistic measure of performance than traditional baselines, enabling leagile projects and portfolios to be better managed in the continuously changing environments of today. [ABSTRACT FROM AUTHOR]

A concept that inspired social sciences as well as many other fields of science was developed from chaos theory. Disaster researchers are also trying to approach, understand, explain and manage disasters with this new concept. However, it has been observed that studies investigating disasters from this perspective do not focus on disaster sources. With this starting point, in this research, the chaotic factors that prepare the disaster formation were investigated with the example of earthquake. The study was carried out with the qualitative method. Data were collected by semi‐structured online interview. Participants were selected by purposive sampling method. The interview data obtained from a total of 10 participants were managed with the MAQXDA 2018 software package using the traditional content analysis method. All codes were classified into three themes and 11 categories. According to the findings, earthquakes can be dealt with in three different chaotic periods: a natural event, a natural event that turns into a disaster, and disaster response and recovery. Future research is recommended to explain the dynamics of occurrence of other disasters such as floods, storms, explosions, and epidemics. It is recommended that disaster management practitioners integrate the chaos management approach into disaster management. With this, it is hoped that disaster management will shift from a linear, bureaucratic, command‐control approach to a community‐based, flexible, continuous planning approach in which even the smallest occurrences are carefully monitored. [ABSTRACT FROM AUTHOR]

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Nowadays, autonomous robots are increasingly accomplishing tasks in the dynamic world where environment states may change unexpectedly and be partially observable. The robot tasks in dynamic environments generally expect the robot to continuously deliberate upon the task goal while effectively obtaining environmental information with sensor and actuator actions. Implementing the underlying robotic software for such tasks can be rather difficult and tedious. The software developers need to synthetically implement the decision-making issues of controlling and planning, as well as the interactions between robotic sensing and actuating components, which is much more challenging than general-purpose software development. The existing software engineering practices focus on the general-purpose software development issues of modularity and communication, without specialized architectural solutions for the implementation of robotic controlling and decision-making processes, which still limits the implementation efficiency of robotic software in dynamic environments. This paper proposes a general-purpose scheme of adjoint observation between robotic sensing and actuating components, which specifies the integral control loop of controlling, planning, and data flows. The adjoint observation scheme solves the problem of effectively exploring the environment for effective observations by the integral control loop. Then we utilize the Behavior Tree component software architecture for concrete implementation of adjoint observation schemes. More specifically, we propose the Parallel and Fallback tree structure for concrete implementation of adjoint control flows. We also extend the BT architecture with an online planning component and mutual data store mechanism, enabling continuous planning and efficient data communication between robotic sensing and actuating processes. In the experiment, we select the Classical BT approach and Pure ROS-based approach as baseline approaches, to validate the task effectiveness of the adjoint observation scheme and development efficiency of the supporting software architecture. [ABSTRACT FROM AUTHOR]

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How to adapt and still get the full potential of roadmapping to the context of Digital Entrepreneurship (DE)? To answer this question and design an agile roadmapping approach to support highly incremental planning done by startups, this article unfolded through a three-phase action research program that lasted 40 months and involved three startups and two startup accelerators. Typically, startups are not complex organizations but operate in turbulent markets—requiring constant changes in strategy, organization, and product decisions. Such an environment is notably adherent to the assumptions of agile methods, but not so to roadmapping, an approach typically tuned for larger companies. Thus, the DE context is explored to adapt the roadmapping approach to agile principles aiming at offering a nimble, living, and integrated process that can enhance business, product, and organizational decisions in startups. Finally, the resulting method helps to support 1) strategic to operational management by continuous planning of small teams dealing with constant changes; 2) better communication between investors, technical, and business members; 3) better absorption of roadmapping and its benefits by teams already proficient in agile approaches, and 4) better balance between plan and action in startups. [ABSTRACT FROM AUTHOR]

We examined inquiry practices and types of knowledge presented along with paths of logical associations between inquiry questions as part of an open inquiry process. The study analysed high school biology students' scientific summaries of open inquiry processes. We found three paths of logical associations between inquiry questions – preliminary to major, major to major and major to theoretical. Planning and design inquiry practices were implemented at similar rates in all paths. Analysis and interpretation practices were highest in the preliminary to major path and application inquiry practices were highest in the major to major path. In the preliminary to major path, procedural, logical, and strategic knowledge was used. Logical and situational knowledge were used in the major to major path, and situational knowledge was used in the major to theoretical path. Our results demonstrated that various inquiry practices and types of knowledge are used at different paths of formulating inquiry questions, specifically those that involve several stages of hands-on activity. Thus, we recommend that high school students should engage in different paths of scientific inquiry processes, specifically those that require them to engage in continuous planning and performing hands-on inquiry activity, to advance inquiry practices and their knowledge base. [ABSTRACT FROM AUTHOR]

Copyright of RECIEN: Revista Científica de Enfermagem is the property of Revista Cientifica de Enfermagem (RECIEN) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conceptual provisions of the importance of establishing management of economic sustainability of logistics enterprises are substantiated. The sequence of formation of a set of strategic priorities of the logistics enterprise to strengthen economic sustainability in a competitive environment is defined, providing for: conducting and generalizing the results of internal analysis of the state and dynamics of enterprise development, also the analysis of factors of the external competitive environment; formation of conceptual principles of the strategy for ensuring economic sustainability; development of strategic goals of the economic sustainability management system; development and implementation of tactical measures to strengthen economic sustainability. The characterizations of the main elements of the strategy for managing the economic sustainability of logistics enterprises are presented, i. e.: purpose; principles, goals, measures, subjects. It is determined that the main goal of managing the economic sustainability of logistics enterprises is to create conditions for safe functioning, with proper indicators of effectiveness and of the sustainable development ensured. Because its achieving depends on the ability to adhere to the principles of consistency, flexibility, continuity, complexity, balance, multivariateness, adaptation to changes in the external competitive environment. The main goals of managing the economic sustainability of logistics enterprises are associated with an increase in the provision of logistics services, ensuring an acceptable level of economic sustainability reserve, forming an insurance and reserve fund, implementing investments, and strengthening innovation. It is proposed to include in the measures of the economic sustainability management system of logistics enterprises the following: improving the quality and increasing the level of competitiveness of logistics services; continuous planning and monitoring of financial results; continuous planning and monitoring of economic sustainability indicators; planning, projecting, and gradual replacing of economic capacities; initiating the practice of cooperation with educational and research institutions.

Copyright of Revista Educación en Ingeniería is the property of Asociacion Colombiana de Facultades de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In many complex planning problems with factored, continuous state and action spaces such as Reservoir Control, Heating Ventilation and Air Conditioning (HVAC), and Navigation domains, it is difficult to obtain a model of the complex nonlinear dynamics that govern state evolution. However, the ubiquity of modern sensors allows us to collect large quantities of data from each of these complex systems and build accurate, nonlinear deep neural network models of their state transitions. But there remains one major problem for the task of control - how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to continuous domains? In this paper, we introduce two types of planning methods that can leverage deep neural network learned transition models: Hybrid Deep MILP Planner (HD-MILP-Plan) and Tensorflow Planner (TF-Plan). In HD-MILP-Plan, we make the critical observation that the Rectified Linear Unit (ReLU) transfer function for deep networks not only allows faster convergence of model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program (MILP) encoding. Further, we identify deep network specific optimizations for HD-MILP-Plan that improve performance over a base encoding and show that we can plan optimally with respect to the learned deep networks. In TF-Plan, we take advantage of the efficiency of auto-differentiation tools and GPU-based computation where we encode a subclass of purely continuous planning problems as Recurrent Neural Networks and directly optimize the actions through backpropagation. We compare both planners and show that TF-Plan is able to approximate the optimal plans found by HD-MILP-Plan in less computation time. Hence this article offers two novel planners for continuous state and action domains with learned deep neural net transition models: one optimal method (HD-MILP-Plan) and a scalable alternative for large-scale problems (TF-Plan). [ABSTRACT FROM AUTHOR]

It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs. [ABSTRACT FROM AUTHOR]

The timed-initiation paradigm developed by Ghez and colleagues (1997) has revealed two modes of motor planning: continuous and discrete. Continuous responding occurs when targets are separated by less than 60° of spatial angle, and discrete responding occurs when targets are separated by greater than 60°. Although these two modes are thought to reflect the operation of separable strategic planning systems, a new theory of movement preparation, the Dynamic Field Theory, suggests that two modes emerge flexibly from the same system. Experiment 1 replicated continuous and discrete performance using a task modified to allow for a critical test of the single system view. In Experiment 2, participants were allowed to correct their movements following movement initiation (the standard task does not allow corrections). Results showed continuous planning performance at large and small target separations. These results are consistent with the proposal that the two modes reflect the time-dependent "preshaping" of a single planning system. [ABSTRACT FROM AUTHOR]

Objectives With increasing life expectancy, aging in Iran becomes more feminine. This study aimed to prioritize the criteria affecting the health of Iranian elderly women. Methods & Materials In this descriptive-analytical study, factors affecting the health of the elderly women, with the opinion of 20 health-policy and health services management experts employed in governmental and non-governmental organizations, were approved. In the second phase, the weight and rank of these criteria were calculated and analyzed using Hierarchical Analysis Process. Excel software was used to analyze data. Results The analysis showed that the individual factor is the most important in terms of factors affecting the health of elderly women. In terms of the type of interventions, the provision of preventive health services, in terms of decision-making, executive interventions at the queue level, in terms of planning, integrated approach, and continuous planning with the participation of public and private sectors were ranked first. Conclusion Given the influence of the previous periods of life on healthy elderly, decision-makers should prioritize preventive services in all periods of life regarding age and gender context. Interventions should be presented with an integrated approach with continuous planning at the levels of government, organization, and queue, as well as with the participation of both public and private sectors. The existence of an information management system, supervision, and evaluation, provides the ground for continuous feedback and improvement of programs. Therefore, it is recommended that the government play a more significant role in ensuring the health of the elderly.

There are a vast number of large-scale ancient landslides in the east Tibetan plateau. However, these landslides have experienced reactivation in recent years and resulted in increasingly serious casualties and economic losses. To study the reactivation mechanism and early identification of ancient landslides on the eastern margin of the Tibetan Plateau, high-resolution remote-sensing interpretation, field survey, interferometric synthetic aperture radar (InSAR) monitoring, laboratory and in situ geotechnical tests, physical modeling tests, and numerical simulations were used, and the main results obtained are as follows. The development and distribution of ancient landslides on the eastern margin of the Tibetan Plateau were clarified, and an efficient identification method was proposed. Reactivation characteristics, triggering factors, and typical genesis patterns were analyzed. Second, the macroscopic mechanical properties of gravelly slip-zone soil and their strength evolution mechanisms at the mesoscale were revealed, and then the strength criterion of gravelly slip-zone soil is improved. Third, combined with typical cases, the reactivation mechanism of ancient landslides under different conditions is simulated and analyzed, and a multistage dynamic evolution model for the reactivation of ancient landslides is established by considering key factors such as geomorphic evolution, coupled endogenic and exogenic geological processes. Finally, an early identification method for ancient landslide reactivation was proposed, enabling rapid determination of the evolutionary stage of ancient landslide reactivation. These findings provide new theoretical and technical support for effectively preventing the risk of reactivation disasters of ancient landslides on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Stratospheric balloons serve as cost-effective platforms for wireless communication. However, these platforms encounter challenges stemming from their underactuation in the horizontal plane. Consequently, controllers must continually identify favorable wind conditions to optimize station-keeping performance while managing energy consumption. This study presents a receding horizon controller based on wind and balloon models. Two neural networks, PredRNN and ResNet, are utilized for short-term wind field forecast. Additionally, an online receding horizon controller, based on simultaneous optimistic optimization (SOO), is developed for action sequence planning and adapted to accommodate various constraints, which is especially suitable due to its gradient-free nature, high efficiency, and effectiveness in black-box function optimization. A reward function is formulated to balance power consumption and station-keeping performance. Simulations conducted across diverse positions and dates demonstrate the superior performance of the proposed method compared with traditional greedy and A* algorithms. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)