Planning Model Research Articles

A community based intervention to modify preventive behaviors of cutaneous leishmaniasis in children: a randomized controlled trial based on PRECEDE PROCEED model

ObjectivesIran ranks among the top six countries globally with a significant incidence of Cutaneous Leishmaniasis (CL). Using planning models is one community-based intervention to promote preventive behaviors. The purpose of our study was to evaluate the effectiveness of the PRECEDE-PROCEED model (PPM) in modifying preventive behaviors related to CL in children through mother training in a community intervention.MethodsA randomized controlled trial based on the PPM model was conducted on 168 mothers (intervention (n = 84) and control group (n = 84) with 10 years old children in the rural areas of Iran. Mothers from 7 village areas were randomly allocated to the intervention (2 village) and control groups (5 village). The intervention group received a program comprising eight 90-minute training sessions and environmental interventions. In this study, we utilized the PPM as a framework to design the questionnaires on Leishmaniosis prevention behavior. Participants in both groups completed the questionnaires at baseline (before the intervention), immediately after the intervention, and at the 2-month follow-up. Analysis of the data was conducted utilizing SPSS20, with statistical significance set at p < 0.05.ResultsCompared to the control group, the intervention group showed significant increases in knowledge, enabling factors, reinforcing factors, attitude, and preventive behaviors related to Cutaneous Leishmaniasis over time from baseline to follow-up (P < 0.001). No significant differences (P > 0.05) were observed in the alterations of the PPM construct, knowledge, and preventive behaviors within the control group from pre-intervention to follow-up.ConclusionsCommunity (education and environmental) intervention based on PPM is feasible and acceptable to modify preventive behaviors of Cutaneous Leishmaniasis in children by increasing a mother’s knowledge and attitude as well as changing enabling and reinforcing factors.Trial registrationIRCT20160619028529N8.

Application of Computerized Surgical Planning in Craniosynostosis Surgery

AbstractCraniosynostosis, a medical condition characterized by premature fusion of one or multiple cranial sutures, has historically been treated through surgical correction. Computerized Surgical Planning (CSP) and three-dimensional (3D) modeling have gained significant popularity across craniofacial surgery. Through a collaborative effort between surgeons and engineers, it is now possible to virtually execute a surgical plan based on preoperative imaging using computed tomography scans. The CSP workflow involves several elements including virtual 3D modeling, CSP computer-aided surgical guide design, manufacturing of guides and templates, and intraoperative implementation. Through the gradual optimization of this workflow, it has been possible to achieve significant progress in the surgical process including improvements in the preoperative planning of complex craniosynostosis cases and reduction of intraoperative time. Furthermore, CSP and 3D modeling have had a positive impact on surgical simulation and residency training, along with patient education and counseling. This article summarizes the CSP workflow in the treatment of craniosynostosis and the implications of this treatment modality on medical trainee education and patient management.

Factors associated with family decision-making self-efficacy among family members of patients with advanced cancer in mainland China.

In Chinese culture, family members are the main decision maker on end-of-life (EoL) issues for patients with advanced cancer. Yet little is known about Chinese families' confidence in making EoL decisions and its associated factors. This study aims to investigate the status and associated factors of Chinese family members' confidence in making EoL decisions for patients with advanced cancer. This cross-sectional study used a convenience sample of 147 family members of patients with stage III or stage IV cancer from a tertiary cancer center in Guangzhou, China. The questionnaires included demographic information of patients and their family members, patients' EoL preferences, and the Chinese version of the Family Decision-Making Self-Efficacy (FDMSE) Scale. A total of145 family members (98.64%) completed the questionnaires. The average score of FDMSE was 3.92 ± 0.53. A multiple regression analysis showed that the factors associated with FDMSE included patients' duration of disease, health insurance, participation in EoL decision-making, the expression of unfilled wishes, and family members' employment status. Chinese family members were not confident enough in making EoL decisions for patients with advanced cancer. It is recommended to develop cultural-tailored advanced care planning models to clarify patient preferences and to enhance the family members' self-efficacy in making EoL decisions with or for patients with advanced cancer.

Mobile learning and speech technology for language teachers’ professional development

This study aims to investigate the use of mobile learning to provide pronunciation training for lecturers of English as a Foreign Language (EFL) from Vietnamese provincial universities. Mobile learning offers a potential solution for the delivery of professional development to lecturers based outside major cities thanks to its capacity to enable learning anytime, anywhere. Mobile learning and speech technology are expected to facilitate lecturers’ self-direct learning to fulfil their professional development needs using their own devices. This paper reports results from a pilot study which serves as the first phase of an ongoing design-based research project. The pilot study was carried out to explore the feasibility of an online pronunciation course and identify potential problems for future course iterations in the context of participants living outside major cities in a developing country. The objectives of the project are to establish and test a set of fundamental principles for mobile learning to be an effective way of providing online professional development for lecturers based outside major cities and to shed light on the necessary adjustments in course design to make it a scalable model for future education planning. In this study, both qualitative and quantitative data were collected during two iterations of an online pronunciation professional development course for EFL lecturers from Vietnamese provincial universities.

Precise planning based on 3D-printed dry-laboratory models can reduce perioperative complications of laparoscopic surgery for complex hepatobiliary diseases: a preoperative cohort study

Background & aimsComplications after laparoscopic liver resection (LLR) are important factors affecting the prognosis of patients, especially for complex hepatobiliary diseases. The present study aimed to evaluate the value of a three-dimensional (3D) printed dry-laboratory model in the precise planning of LLR for complex hepatobiliary diseases.MethodsPatients with complex hepatobiliary diseases who underwent LLR were preoperatively enrolled, and divided into two groups according to whether using a 3D-printed dry-laboratory model (3D vs. control group). Clinical variables were assessed and complications were graded by the Clavien-Dindo classification. The Comprehensive Complication Index (CCI) scores were calculated and compared for each patient. Multivariable analysis was performed to determine the risk factors of postoperative complications.ResultsSixty-two patients with complex hepatobiliary diseases underwent the precise planning of LLR. Among them, thirty-one patients acquired the guidance of a 3D-printed dry-laboratory model, and others were only guided by traditional enhanced CT or MRI. The results showed no significant differences between the two groups in baseline characters. However, compared to the control group, the 3D group had a lower incidence of intraoperative blood loss, as well as postoperative 30-day and major complications, especially bile leakage (all P < 0.05). The median score on the CCI was 20.9 (range 8.7–51.8) in the control group and 8.7 (range 8.7–43.4) in the 3D group (mean difference, -12.2, P = 0.004). Multivariable analysis showed the 3D model was an independent protective factor in decreasing postoperative complications. Subgroup analysis also showed that a 3D model could decrease postoperative complications, especially for bile leakage in patients with intrahepatic cholelithiasis.ConclusionThe 3D-printed models can help reduce postoperative complications. The 3D-printed models should be recommended for patients with complex hepatobiliary diseases undergoing precise planning LLR.

AAPM Task Group Report 267: A joint AAPM GEC-ESTRO report on biophysical models and tools for the planning and evaluation of brachytherapy.

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit widely different spatial and temporal dose delivery patterns. Biophysical models, capable of modeling the key interacting effects of dose delivery patterns with the underlying cellular processes of the irradiated tissues, can be a potentially useful tool for elucidating the radiobiological effects of complex brachytherapy dose delivery patterns and for comparing their relative clinical effectiveness. While the biophysical models have been used largely in research settings by experts, it has also been used increasingly by clinical medical physicists over the last two decades. A good understanding of the potentials and limitations of the biophysical models and their intended use is critically important in the widespread use of these models. To facilitate meaningful and consistent use of biophysical models in brachytherapy, Task Group 267 (TG-267) was formed jointly with the American Association of Physics in Medicine (AAPM) and The Groupe Européen de Curiethérapie and the European Society for Radiotherapy & Oncology (GEC-ESTRO) to review the existing biophysical models, model parameters, and their use in selected brachytherapy modalities and to develop practice guidelines for clinical medical physicists regarding the selection, use, and interpretation of biophysical models. The report provides an overview of the clinical background and the rationale for the development of biophysical models in radiation oncology and, particularly, in brachytherapy; a summary of the results of literature review of the existing biophysical models that have been used in brachytherapy; a focused discussion of the applications of relevant biophysical models for five selected brachytherapy modalities; and the task group recommendations on the use, reporting, and implementation of biophysical models for brachytherapy treatment planning and evaluation. The report concludes with discussions on the challenges and opportunities in using biophysical models for brachytherapy and with an outlook for future developments.

Improving the stability of a planar tape-spring hyper-redundant manipulator

The planar tape-spring hyper-redundant manipulator presented in this paper is mainly constructed from tape springs, fixed-drive components, and mobile-drive components. It not only has high robustness and excellent transformability, but also high packaging efficiency. However, when the manipulator extends to a long range in motion experiments, some segments of the tape springs buckle. To address this drawback, a kinematic model of the planar tape-spring hyper-redundant manipulator is established, and, a configuration planning method based on a virtual spring model is proposed to solve the inverse kinematics problem. To enhance stability, the column stability is then incorporated into the configuration planning model. This approach relies on only configuration planning to prevent buckling. An alternative approach of adding auxiliary rods into the manipulator is also proposed. With this method, extra intermediate supports have been added to the manipulator. The effective column length of some segments is shortened, which effectively increases the critical buckling load of those segments of the tape spring. Finally, a prototype was subjected to motion and stability experiments to validate the presented approaches and analysis.

Innovative decision-making for solar photovoltaic system installation in residential energy hubs: A probabilistic dynamic planning approach

This paper introduces an innovative probabilistic dynamic planning model tailored to optimize photovoltaic (PV) system installation within Residential Energy Hubs (REHs). The REH amalgamates diverse components, including PV systems, Combined Heat and Power (CHP) units, and gas boilers, to holistically fulfill residential thermal and electrical energy demands. The model's primary objective is to achieve an optimal equilibrium between minimizing initial investment costs associated with PV systems and managing ongoing energy expenses within the REH framework, encompassing potential PV energy losses over the planning horizon. To augment the flexibility and benefits of PV systems for residential consumers, the model integrates several pioneering features. Particularly notable is the introduction of a heating energy storage system, which complements the REH's thermal requirements and adeptly reacts to fluctuations in gas and electricity prices, thereby maximizing PV system benefits and ensuring homeowners' cost efficiency. Moreover, the model addresses the inherent uncertainty tied to PV generation by employing a two-point estimation method, offering a more realistic depiction of system performance and potential outcomes. To resolve the two-objective problem, an initial conversion into a single-objective problem via a weighted summation method precedes the application of an optimization algorithm inspired by symbiotic organisms. The paper demonstrates the practical application and efficacy of the proposed model through extensive simulations spanning a 10-year planning horizon. The results underscore the model's effectiveness and versatility in guiding residential consumers to make informed and economically viable decisions regarding PV system installation within the broader spectrum of their energy requirements. This research represents a significant stride towards empowering homeowners with invaluable tools for embracing sustainable energy solutions.

Sustainability through the use of Building Information Modelling in infrastructure planning

This study examines the role of Building Information Modelling (BIM) in infrastructure planning from a sustainability perspective. It emphasises how BIM can contribute to the promotion of ecological, economic and social sustainability. Interviews with experts have shown that BIM can not only optimise the energy consumption of infrastructure projects, but also lead to precise material quantification and optimisation. Despite the benefits, the use of BIM in infrastructure planning is limited, especially in small and medium-sized projects and with local authorities. The study identifies challenges such as the complexity of BIM software systems and the additional costs of implementing them. Recommendations for action include training clients and engineering firms in the use of BIM for cost forecasting and extending the digital twin concept beyond the construction and planning process. The results show that widespread BIM application is only possible if both clients and planning offices share sustainability goals and are prepared to bear the additional costs for BIM planning. The work emphasises the need to use BIM beyond building construction for sustainable infrastructure development and provides insights into the practice of engineering firms in the infrastructure sector.

Cluster partition-based two-layer expansion planning of grid–resource–storage for distribution networks

In order to realize the optimal planning of grid–resource–storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning.

A multi-objective planning and scheduling model for elective and emergency cases in the operating room under uncertainty

Hospitals are paramount hubs for delivering healthcare services, with their Operating Rooms (ORs) as a pivotal and financially substantial component. Efficient surgery ward planning is crucial in healthcare institutions, aiming to improve medical service quality while reducing costs. This research delves into the intricacies of integrated OR planning and scheduling, focusing on elective and emergency patients in an uncertain environment. To address these challenges, a mixed integer programming (MIP) framework is developed to minimize inactivity and patient wait times while optimizing high-priority resource allocation. Both upstream and downstream units of the ward, the Pre-operative Holding Unit (PHU), Post Anesthesia Care Unit (PACU), and Intensive Care Unit (ICU) are included. The inherently uncertain aspects of surgery, including surgical duration, Length of Stay (LOS), and the influx of emergency patients, demand an intelligent optimization approach. Consequently, a robust optimization strategy is harnessed to effectively grapple with this pervasive uncertainty. A deterministic model is introduced and improved using an enhanced epsilon constraint method. The culmination of this analytical journey yields a collection of Pareto-optimal solutions. Empirical results, supported by managerial insights, highlight the superiority of the proposed method over the traditional weighting approach.

A data-driven approach to urban charging facility expansion based on bi-level optimization: A case study in a Chinese city

This paper addresses the optimization of urban charging facilities expansion in response to the growing charging demand and proposes a data-driven bi-level optimization framework. The proposed framework combines a mixed-integer programming model for siting and capacity planning, an improved sparrow search algorithm (ISSA) for hyperparameter optimization of the CNN-BiLSTM model, and the application of Benders decomposition with Lagrangian relaxation using Gurobi for solving the model. The proposed approach is empirically evaluated through a case study conducted in an administrative area of a Chinese city, using comprehensive charging records and other relevant information. The results demonstrate that the ISSA consistently outperforms other algorithms, showing an average improvement of 23.97 % in root mean square error, 19.25 % in average absolute percentage error, and 1.50 % in the determination coefficient. The modified Diebold-Mariano test confirms the significant advantage of ISSA. Based on the model analysis, the study recommends constructing 20 new charging stations in the case city, with more than half equipped with fast chargers. This strategy ensures that 95 % of the area can find a charging station within 5 min while also ensuring the operator's profitability. The research findings provide effective support for investment decisions related to charging facilities.

Exploring characteristics of and transitions between mental states within the Multi‐Action Plan model in high‐level judo

AbstractThe Multi‐Action Plan (MAP) model presents an idiosyncratic framework for human performance. MAP research has categorised four Performance Types (PTs; T1–T4) using self‐paced activities. Specifically, T1–4 present four distinct mental states experienced by athletes during performance, differing, among other aspects, in the level of effort and perceived control. However, transitions between PTs are yet to be empirically investigated. This study aimed to examine MAP in the hyperdynamic open‐skill combat context of judo. We explored how judoka experienced PTs and the transition processes between these mental states. Six British, high‐level judoka (n = 6; Mdnage = 19) were interviewed using a retrospective semi‐structured interview schedule. Interpretative phenomenological analysis indicated that judo presents a hyperdynamic difficult‐to‐predict performance context due to the opponent and fighting rhythm. Second, participants strongly related to T2 and T3 and less to T1 and T4. They also reported experiences ‘between’ T2 and T3. Lastly, participants reported common transition pathways (e.g., T2–T3), typical transition cues, and specific trainable strategies to optimise performance. Findings are discussed in relation to existing literature. Lastly, we present limitations, applied, and research considerations.

A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model’s practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost.

The planning method of new energy distribution network in plateau area based on local accommodation

Abstract The planning method of new energy distribution network in plateau area based on local accommodation is studied to improve the local accommodation capacity of new energy distribution network in plateau area. The framework of new energy distribution network planning in the plateau area is constructed, and the distribution network equipment suitable for the plateau environment is selected based on the harsh environment in the plateau area; in the framework of new energy distribution network planning in plateau area, a bi-level planning model considering multi-flexible resource coordinated scheduling and energy storage is established. The upper level planning model takes the maximum accommodation of new energy as the objective, and the lower level planning model takes the optimal daily operation benefit of energy storage under the given configuration as the optimization objective; the probabilistic power flow method and C-PSO are used to solve the bi-level model. The experiment shows that this method ensures the stability of the new energy distribution network in the plateau area, reduces the amount of abandoned wind energy and the operating cost, and improves the local accommodation capacity of the core higher than the distribution network.

City level water withdrawal and scarcity accounts of China

In the context of China’s freshwater crisis high-resolution data are critical for sustainable water management and economic growth. Yet there is a dearth of data on water withdrawal and scarcity regardless of whether total or subsector amount, for prefectural cities. In administrative and territorial scope, we accounted for water withdrawal of all 63 economic-socio-environmental sectors for all 343 prefectural cities in China, based on a general framework and 2015 data. Spatial and economic-sector resolution is improved compared with previous studies by partitioning general sectors into industrial and agricultural sub-sectors. Construction of these datasets was based on selection of 16 driving forces. We connected a size indicator with corresponding water-withdrawal efficiency. We further accounted for total blue-water withdrawal and quantitative water scarcity status. Then we compared different scopes and methods of official accounts and statistics from various water datasets. These disaggregated and complete data could be used in input-output models for municipal design and governmental planning to help gain in-depth insights into subsector water-saving priorities from local economic activities.

Training in Endoscopic Endonasal Neurosurgical Procedures: a Systematic Review of Available Models

Background Proficiency in endoscopic endonasal neurosurgery (EEN) requires a unique skill set and is associated with a steep learning curve. Endoscopic simulation models present an opportunity for trainees to enhance their surgical skills and anatomical knowledge in a risk-free environment. To date, four main categories of training modalities have been recorded: cadaveric, synthetic, animal, and virtual reality (VR) models. Objective To analyze the features, advantages, and disadvantages of each EEN training model, describe the stages of simulation training, and propose future directions and an algorithm for EEN training. Methods A comprehensive search was performed including studies published before August 2023. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed in the search and selection of studies included. Terms for searching title and abstract were (“simulation”)AND(“endoscopic endonasal”)AND(“neurosurgery”). Articles were screened for duplicates. Results We identified 176 references, and 43 studies met the inclusion criteria. Twelve articles described cadaveric models, with 7 of them studying internal carotid artery injury (ICAI) management and 3 of them CSF leak management; fourteen articles illustrated synthetic models, one article described animal models, and sixteen articles outlined virtual reality (VR) simulations. Conclusion Cadaveric models provide realistic hands-on surgical training with acceptable cost and remain the gold standard method for training. Synthetic models are most effective for preoperative surgical planning and animal models are best suited for testing of hemostatic techniques. VR simulations may become the primary training tool for novices to acquire basic anatomical knowledge as their role in advanced surgical planning is evolving.

Optimizing competence in the service of collaboration

In order to efficiently divide labor with others, it’s important to understand what our collaborators can do (i.e., their competence). However, competence is not static—people get better at particular jobs the more often they perform them. This plasticity of competence creates a challenge for collaboration: For example, is it better to assign tasks to whoever is most competent now, or to the person who can be trained most efficiently “on-the-job”? We conducted four experiments (N=396) that examine how people make decisions about whom to train (Experiments 1 and 3) and whom to recruit (Experiments 2 and 4) to a collaborative task, based on the simulated collaborators’ starting expertise, the training opportunities available, and the goal of the task. We found that participants’ decisions were best captured by a planning model that attempts to maximize the returns from collaboration while minimizing the costs of hiring and training individual collaborators. This planning model outperformed alternative models that based these decisions on the agents’ current competence, or on how much agents stood to improve in a single training step, without considering whether this training would enable agents to succeed at the task in the long run. Our findings suggest that people do not recruit and train collaborators based solely on their current competence, nor solely on the opportunities for their collaborators to improve. Instead, people use an intuitive theory of competence to balance the costs of hiring and training others against the benefits to the collaboration.

Optimization method for renewable energy investment planning in the electricity carbon coupling market environment considering economy

China is developing rapidly in renewable energy development, electricity market reform, and carbon market construction, using market mechanisms for efficient planning and investment. The electricity and carbon markets are interrelated for market entities. This context has led to a dual-level optimization model for renewable energy investment planning, considering cost recovery and new power system goals. The model includes deriving electricity carbon coupling quotations for conventional units based on dynamic carbon emission characteristics and proposing an electricity spot market mechanism influenced by carbon quota trading. The model’s upper level maximizes renewable energy investment capacity, determining its size and location, while the lower level simulates electricity spot and voluntary emission reduction market trading to guide investment decisions. The model transformed into a mixed integer linear programming problem for optimal solutions was validated numerically, examining various factors affecting optimal renewable energy investment capacity.

Optimization-based planning of speech articulation using general Tau Theory

This paper presents a model of speech articulation planning and generation based on General Tau Theory and Optimal Control Theory. Because General Tau Theory assumes that articulatory targets are always reached, the model accounts for speech variation via context-dependent articulatory targets. Targets are chosen via the optimization of a composite objective function. This function models three different task requirements: maximal intelligibility, minimal articulatory effort and minimal utterance duration. The paper shows that systematic phonetic variability can be reproduced by adjusting the weights assigned to each task requirement. Weights can be adjusted globally to simulate different speech styles, and can be adjusted locally to simulate different levels of prosodic prominence. The solution of the optimization procedure contains Tau equation parameter values for each articulatory movement, namely position of the articulator at the movement offset, movement duration, and a parameter which relates to the shape of the movement’s velocity profile. The paper presents simulations which illustrate the ability of the model to predict or reproduce several well-known characteristics of speech. These phenomena include close-to-symmetric velocity profiles for articulatory movement, variation related to speech rate, centralization of unstressed vowels, lengthening of stressed vowels, lenition of unstressed lingual stop consonants, and coarticulation of stop consonants.