Planning Model Research Articles

Early Results of a Randomized Trial on the Use of 3D-Printed Models for Endovascular Planning and Simulation in Patients Undergoing Peripheral Angioplasty

Early Results of a Randomized Trial on the Use of 3D-Printed Models for Endovascular Planning and Simulation in Patients Undergoing Peripheral Angioplasty

Mass evacuation planning for disasters management: A household evacuation route choice behavior analysis

This paper presents a multi-methodological approach to analyzing household evacuation route choice behaviors and the related decision-making for mass evacuation planning in disaster management. Specifically, the proposed multi-methodological approach integrates a syncretic risk perception conceptual model with a household-based group utility evolution model. The dynamics of household evacuation route choice are explored in two stages, namely the individual utility initiation and accommodation. The unique feature of the proposed approach is that it links individual risk perception, survival psychology, and group behavioral dynamics together to characterize household decision-making about choosing the evacuation routes. An empirical study coupled with experiments is conducted to help validate the proposed household evacuation route choice behavior model. Through various designed disaster scenarios, the applicability and strengths of the proposed model for mass evacuation planning and management are demonstrated. Particularly, the mean relative prediction errors of the proposed model with respect to household-based evacuation route switching probability and path flow splits are 3.79% and 6.88%, respectively, all within an acceptable range of ±10 %. This demonstrates that the proposed model and analytical results align closely with household choice behavior.

A framework and process for community-engaged, mixed-methods cancer needs assessments.

Community health needs assessments are required for most state and local public health agencies and non-profit hospitals. Typically based on community health improvement planning models, these assessments encompass overall community health and multiple diseases to inform program planning. National Cancer Institute (NCI)-designated Cancer Centers and community-based cancer-focused programs share the goal of reducing cancer burden in the catchment areas they serve. However, to date, no published models exist to guide cancer-specific needs assessments for a determined geographic area that can inform both public health and research initiatives. The purpose of this article is to outline a cancer needs assessment (CNA) framework and community-engaged, mixed-methods process, along with a case study of how we applied it in Kentucky. We convened a steering committee of key organizational partners to provide input throughout the process. We developed a conceptual framework of multi-level determinants affecting cancer-related outcomes. We incorporated both quantitative and qualitative data gathered through a variety of means, including a novel application of group concept mapping to guide definition of priorities. The resulting CNA has helped guide strategic planning and priorities for Kentucky's Cancer Action Plan, Markey Cancer Center, state agencies, and community-based organizations. This framework and process can be used collaboratively by cancer center Community Outreach and Engagement offices, public health agencies, oncology programs, and community partners to plan impactful cancer control programs and research in their catchment areas. Universities can also use them to inform the planning of community engagement and health equity research efforts.

Body Mass Index Greater Than 46 Associated With Increased Risk of 30 Day Complications Following Adult Tonsillectomy: A Retrospective Cohort Study.

Background: The study aimed to identify data-driven body mass index (BMI) thresholds that are associated with varying risk of 30 day complications following adult tonsillectomy. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was utilized to conduct a retrospective cohort analysis of patients undergoing adult tonsillectomy from 2005 to 2019. Stratum-specific likelihood ratio (SSLR) analysis was conducted to determine data-driven BMI strata that maximized the likelihood of 30 day complications following adult tonsillectomy. Patient demographics and clinical comorbidities were compared using chi-squared analysis and student t tests, where appropriate, for each stratum. Multivariable regression analysis was conducted to confirm association between identified data-driven strata with 30 day complication rates. Results: In total, 44,161 patients undergoing adult tonsillectomy were included in this study. SSLR analysis identified 2 BMI categories: 18 to 45 and 46+. Relative to the 18 to 45 BMI cohort, the 46+ BMI cohort was more likely to have 30 day all-cause complications after surgery [odds ratio (OR): 1.62, P = .007]. Specifically, the 46+ BMI cohort had significantly higher odds for 30 day major medical complications (OR: 2.86, P = .001), pulmonary domain complications (OR: 1.86, P = .041), unplanned reintubation (OR: 2.65, P = .033), and deep vein thrombosis (OR: 6.54, P = .026). Conclusions: We identified a BMI threshold of 46+ that was associated with a significantly increased risk of 30 day all-cause complications following adult tonsillectomy. These BMI strata can guide preoperative planning and risk-stratifying models for predicting 30 day complications in tonsillectomy surgery.

Virtual reality modelling for planning of percutaneous first step palliation in a newborn with heterotaxy syndrome.

We report the first-stage percutaneous palliation in a newborn with a rare heterotaxy syndrome variant including interrupted inferior vena cava, partial anomalous pulmonary venous drainage, and restrictive interatrial communication. Virtual reality imaging aided visualisation, decision-making, and planning. Successful atrial septoplasty performed via the internal jugular vein and anomalous pulmonary vein was followed by stenting of ductus arteriosus.

Optimization of mixed cargo allocation and stowage with heterogeneous vehicles

In order to solve the problem of low efficiency and high loading costs in business logistics and distribution operations, this paper classifies non-standard cargoes based on their spatial and temporal characteristics. Based on this, a dual-layer planning model for multi-vehicle cargoes loading and transportation is constructed to jointly optimize the loading process. This paper designs a space-inspired heuristic algorithm to solve the model and verifies the feasibility of the model. The results show that compared to a single vehicle type, the joint optimization of multi-vehicle combinations for loading and transportation has stronger adaptability and can better accommodate changes in cargo volume. Compared to the single vehicle type, not only did the loading rate increase by 18%, but also effectively reduced the transportation cost by 3%. Different proportions of spatial and temporal characteristic cargoes have different effects on the loading and transportation. Adjusting the proportion of cargoes that can be placed on both upper and lower layers to 50% can better utilize transportation resources such as vehicles. Increasing the proportion of urgent cargoes can improve the economic efficiency of loading and transportation.

Validation of a 3D-printed model of cryptoglandular perianal fistulas.

Visualising the course of a complex perianal fistula on imaging can be difficult. It has been postulated that three-dimensional (3D) models of perianal fistulas improve understanding of the perianal pathology, contribute to surgical decision-making and might even improve future outcomes of surgical treatment. The aim of the current study is to investigate the accuracy of 3D-printed models of perianal fistulas compared with magnetic resonance imaging (MRI). MRI scans of 15 patients with transsphincteric and intersphincteric fistulas were selected and then assessed by an experienced abdominal and colorectal radiologist. A standardised method of creating a 3D-printed anatomical model of cryptoglandular perianal fistula was developed by a technical medical physicist and a surgeon in training with special interest in 3D printing. Manual segmentation of the fistula and external sphincter was performed by a trained technical medical physicist. The anatomical models were 3D printed in a 1:1 ratio and assessed by two colorectal surgeons. The 3D-printed models were then scanned with a 3D scanner. Volume of the 3D-printed model was compared with manual segmentation. Inter-rater reliability statistics were calculated for consistency between the radiologist who assessed the MRI scans and the surgeons who assessed the 3D-printed models. The assessment of the MRI was considered the 'gold standard'. Agreement between the two surgeons who assessed the 3D printed models was also determined. Consistency between the radiologist and the surgeons was almost perfect for classification (κ = 0.87, κ = 0.87), substantial for complexity (κ = 0.73, κ = 0.74) and location of the internal orifice (κ = 0.73, κ = 0.73) and moderate for the percentage of involved external anal sphincter in transsphincteric fistulas (ICC 0.63, ICC 0.52). Agreement between the two surgeons was substantial for classification (κ = 0.73), complexity (κ = 0.74), location of the internal orifice (κ = 0.75) and percentage of involved external anal sphincter in transsphincteric fistulas (ICC 0.77). Our 3D-printed anatomical models of perianal fistulas are an accurate reflection of the MRI. Further research is needed to determine the added value of 3D-printed anatomical models in preoperative planning and education.

Research on Optimization of International Multimodal Transportation Routes Based on Uncertainty Theory

Delays in international multimodal marine transport can result in subsequent connection delays, leading to prolonged transportation time and increased uncertainty. To identify more reliable and cost-effective robust transportation routes, this study establishes a constrained planning model for multimodal transportation routes. Employing uncertainty theory, the model is transformed into a deterministic equivalent class model and ultimately incorporates an adaptive differential evolution (ADE) algorithm. Results of a case study indicate that: (i) the proposed model, compared with deterministic models, exhibits greater robustness and better aligns with practical transportation scenarios, resulting in substantial reductions in actual transportation time and cost; (ii) the solution efficiency of the ADE algorithm surpasses that of the genetic algorithm and Dijkstra algorithm; (iii) the start time of transportation and the confidence level of uncertainty also play crucial roles in influencing route selection. Therefore, decision-makers should consider a multifaceted approach when formulating transportation routes.

Optimising Agricultural Waste Supply Chains for Sustainable Bioenergy Production: A Comprehensive Literature Review

One of the United Nations’ 17 Sustainable Development Goals is to “Ensure access to affordable, sustainable, and clean energy for all.” Additionally, the growing concerns about climate change and energy security have heightened the importance of exploring alternative energy sources to replace fossil fuels. The utilisation of agricultural waste for bioenergy production has acquired significant attention due to its potential to mitigate environmental impacts and provide renewable energy sources. However, the major obstacle to producing bioenergy is managing the supply chain while considering economic, environmental, and social factors in an optimal way. This paper presents a comprehensive overview of the literature on the management of agriculture waste supply chains, specifically related to the use of modelling and optimisation techniques for planning. The first section describes different stages of the supply chain and various technologies for converting biomass to bioenergy. This is followed by a synopsis of the literature reviewed based on decision levels, objective functions, modelling methodologies, and optimisation approaches. Finally, the review highlights limitations and gaps in current research and the areas with potential for further exploration.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

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

Abstract 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 management and 3 of them cerebrospinal leak management; 14 articles illustrated synthetic models, 1 article described animal models, and 16 articles outlined 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.

Research on Inventory Sustainable Development Strategy for Maximizing Cost-Effectiveness in Supply Chain

This study addresses the challenges of the expanding global online retail market by developing an innovative inventory management strategy that promotes sustainable development. We propose an integrated planning model that utilizes a newly enhanced Parallel Chicken Swarm Optimization (PCSO) algorithm alongside the established Economic Order Quantity (EOQ) model and the (s, S) inventory policy. Our model demonstrates its effectiveness through simulation experiments, showcasing its ability to dynamically adjust inventory thresholds based on daily forecasted demand, which significantly optimizes replenishment strategies. Notably, our model achieves a total cost reduction while maintaining a high service level of 0.761467 and reduces computational time to 82.32 s, highlighting its efficiency and adaptability. The innovation of this research lies in the application of the PCSO algorithm to dynamically manage inventory in real-time, aligning with sustainable inventory management practices. This study significantly contributes to the field by enhancing the economic and environmental sustainability of supply chain operations.

A Comprehensive Roadmap for Connecting Industry 4.0 Technologies to the Basic Model of Collaborative Planning, Forecasting, and Replenishment (CPFR)

A Comprehensive Roadmap for Connecting Industry 4.0 Technologies to the Basic Model of Collaborative Planning, Forecasting, and Replenishment (CPFR)

Modeling favorable locations for biogas plants that generate electricity from dairy and beef cattle manure through mixed integer linear programming

Mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fitting results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of Türkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m3 of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province.

Coupling planning models with models of spontaneous order

Coupling planning models with models of spontaneous order

Predictability of tooth rotations in patients treated with clear aligners

Clear aligners are employed daily for the treatment of several malocclusions. Previous clinical studies indicated low accuracy for the correction of tooth rotations. The aim of this study was to evaluate the predictability of tooth rotations with clear aligners. The sample comprised 390 teeth (190 mandibular; 200 maxillary), measured from the virtual models of 45 participants (21 men, 24 women; mean age: 29.2 ± 6.6 years old). For each patient, pre-treatment (T0) digital dental models (STL files), virtual plan (T1) and post-treatment digital dental models (T2) of both the mandibular and maxillary arches were imported onto Geomagic Control X, a 3D metrology software which allows angular measurements. Rotations were calculated by defining reproducible vectors for all teeth in each STL file and superimposing both T0 with T1 to determine the prescribed rotation, and T0 with T2 to determine the achieved rotation. Prescribed and achieved rotations were compared to assess movement’s accuracy. The Wilcoxon signed-rank test and paired t-test were used to assess differences between the prescribed and achieved movements (P < 0.05). The overall predictability of rotational movement was 78.6% for the mandibular arch and 75.0% for the maxillary arch. Second molar accuracy was the lowest in both arches. Clear aligners were not able to achieve 100% of the planned movements.

Optimization Model of Clothing Production Plan using Branch and Bound Method

The increasing number of clothing activities in Indonesia has resulted in companies having to increase their marketing and production activities in order to obtain maximum profit. One of the factors influencing the growth of basic clothing is the price of the middle class in Indonesia. This can be a strong foundation for a long-term clothing consumer base. One of the businesses engaged in the production of clothing is a garment production business. The problem that is often faced by companies is limited resources such as raw materials, capital, time, etc. Production planning is a process to produce goods in a certain period in accordance with those predicted through the organization of resources. Therefore, production optimization is one way to optimize profit and apparel production. This study aims to apply the Integer Linear Programming model and is completed using the Branch and Bound method to obtain the optimal number of clothing production in order to obtain maximum profit. The conclusion obtained from the research is the application of the Branch and Bound method to the problem of optimizing production planning at PT. GM Tactical to obtain more optimal results compared to the actual production carried out by the company.

Physician Workforce in Lithuania: Changes during Thirty Years of Independence.

Healthcare human resource planning is one of the biggest challenges facing the healthcare systems in many countries. Inadequate decisions in human resource planning can lead to an insufficient number of healthcare professionals then healthcare inequalities. One of the components of resource planning in the healthcare system is long-term data monitoring and the identification of potential trends. Since 1990, the number of physicians in Lithuania has decreased by 15.3% (-2266), but the decrease in the population has led to a 13.61% increase in the number of physicians per 10,000 inhabitants (5.32). During the analyzed period, the largest decrease in the number of physicians workforce by specialty was the number of medical physicians (-73.08%), epidemiology and hygiene (-69.30%), children's diseases (-49.08%), the most increased number was of family/general practitioners (GPs), geneticists, physical medicine, and rehabilitation specialists. Since 1992, the number of visits to physicians in Lithuania, which has been decreasing for a long time, began increasing, and in 2022 (9.3 visits) it has almost reached the number of visits (9.5) per capita as in 1991. The aim of this research was to collect long-term data from various databases, summarize them, and identify possible trends and the reasons for data changes. The study analyzed data from the Lithuanian healthcare system from the Declaration of Independence of Lithuania to the last 30 years. The data includes or affects the indicators of the healthcare system, changes in population and doctors, the number of visits to doctors, the number of medical students and residents, and data determining inequalities in the healthcare system. Long-term data analysis is useful for developing a model of healthcare human resource planning and for planning healthcare resources.

Action research and professional development in schools

This article examines practitioner action research in schools and how action research can enhance practice and generate knowledge through partnerships between academia and the education sector. In 2020, revisions to the Norwegian curriculum for primary and secondary education introduced guidelines for professional work. The changes in the curriculum framework entail teachers’ collaboration in knowledge production - utilizing research and their own experiences in the process. This changed the formal role of teachers from transmitting established knowledge to producing knowledge in professional development and research activities. The new regulations can be related to traditions within practitioner action research. The article explores how action research can be used as a methodological framework in the professional work of schools. The starting point for the article is a thematic analysis of reflections from an action research project named Deeper Learning – How?, in which six schools participate in developing a model for unit planning. Structured interviews were conducted with members of school development groups in the schools every six months over 3.5 years, totaling seven interviews per school. The participants' reflections on their professional work serve as the empirical basis for the article. Results from the thematic analysis demonstrate that participants employed the action research process as a methodological tool to cultivate new insights and enhance practice. These reflections enrich the schools' comprehension of their educational practices and lead to measures for improvements, actions, and change. The results also highlight that proactive leadership, agreed-upon measures, teamwork, and a consistent rhythm in professional work are vital for educational practice development and knowledge production.