Planning Tool Research Articles

Multi-modal 3-Dimensional Visualization of Pediatric Neuroblastoma: Aiding Surgical Planning Beyond Anatomical Information

BackgroundPatient-specific 3D models of neuroblastoma and relevant anatomy are useful tools for surgical planning. However, these models do not represent the heterogenous biology of neuroblastoma. This heterogeneity is visualized with the ADC and 123I-MIGB-SPECT-CT imaging. Combining these multi-modal data into preoperative 3D heatmaps, may allow differentiation of the areas of vital and non-vital tumor tissue. We developed a workflow to create multi-modal preoperative 3D models for neuroblastoma surgery. MethodsWe included 7 patients who underwent neuroblastoma surgery between 2022 and 2023. We developed 3D models based on the contrast enhanced T1-weighted MRI scans. Subsequently, we aligned the corresponding ADC and 123I-MIBG-SPECT-CT images using rigid transformation. We estimated registration precision using the Dice score and the target registration error (TRE). 3D heatmaps were computed based on ADC and 123I-MIBG uptake. ResultsThe registration algorithm had a median Dice score of 0.81 (0.75–0.90) for ADC and 0.77 (0.65–0.91) for 123I-MIBG-SPECT. For the ADC registration, the median TRE of renal vessels was 4.90 mm (0.86–10.18) and of the aorta 4.67 mm (1.59–12.20). For the 123I -MIBG-SPECT imaging the TRE of the renal vessels was 5.52 mm (1.71–10.97) and 5.28 mm (3.33–16.77) for the aorta. ConclusionsWe successfully developed a registration workflow to create multi-modal 3D models which allows the surgeon to visualize the tumor and its biological behavior in relation to the surrounding tissue. Future research will include linking of pathological results to imaging data, to validate these multi-modal 3D models. Level of EvidenceLevel IV. Type of StudyClinical Research.

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

An Estimation of the Available Spatial Intensity of Solar Energy in Urban Blocks in Wuhan, China

Urban form is an important factor affecting urban energy. However, the design of urban form and energy mostly belong to two separate disciplines and fields, and urban energy planning research rarely considers their mutual relationship. The available space intensity (ASI) of solar energy is formed on the basis of energy planning and urban design; the objective of this research is to evaluate the impact of urban form on the ASI of solar energy and to propose strategies for planning of the space that is available for solar energy so as to improve the efficiency of urban energy utilization and achieve sustainable urban development. Methodologically, this study firstly proposes a model to quantify the ASI of solar energy using three indicators: solar radiation intensity (SRI), solar installation intensity (SII), and solar generation intensity (SEGI). Then, we quantitatively calculate the solar ASI of nine types of typical urban blocks in a sub-center of Wuhan City, Nanhu. Correlation analysis and multiple linear regression analysis are then used to analyze the correlation between the form indicators and solar ASI, as well as the degree of influence. The results show that the differences in SRI, SII, and SEGI amongst the nine types of city blocks were as high as 114.61%, 162.50%, and 61.01%. The solar ASI was mainly affected by three form indicators: the building coverage ratio, the average building height, and the volume-to-area ratio. Reducing the building coverage ratio and increasing vertical development at the same time can effectively improve the ASI of solar energy. The results of this study and the established method provide an important reference and rapid calculation tool for urban energy planning and design, reducing the data and time usually required for solar analysis at the block scale.

Life cycle assessment as a prospective tool for sustainable agriculture and food planning at a local level

Owing to the far-reaching environmental consequences of agriculture and food systems, such as their contribution to climate change, there is an urgent need to reduce their impact. International and national governments set sustainability targets and implement corresponding measures. Nevertheless, critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues. Yet, at the sub-national level, local authorities rarely apply a systemic environmental assessment to enhance their action plans. This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning. The objective is to identify significant direct and indirect environmental hotspots, their origins, and formulate effective mitigation strategies. The methodology is applied to the administrative department of Finistere, a strategic agricultural region in North-Western France. Multiple environmental criteria including climate change, fossil resource scarcity, toxicity, and land use are modeled. The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources, such as livestock feed or diesel consumption. Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies, resulting in a 25% decrease in the climate change indicator. However, the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator. These results highlight the innovative application of life cycle assessment led at a local level, offering insights for the further advancement of systematic and prospective local agri-food assessment. Additionally, they provide guidance for local authorities to enhance the sustainability of planning strategies.

Mid-term Results Following Reverse Shoulder Arthroplasty and the Role of Navigation in the Management of Glenoid Bone Loss.

Background Inaccurate positioning of the glenoid component has been well described as the most common cause of early failure following a reverse shoulder arthroplasty (RSA). Among the latest developments in operative technique, three-dimensional preoperative planning and navigation intraoperative systems have been developed to improve the accuracy of the baseplate positioning during RSA. The primary purpose of this retrospective analysis was to investigate the mid-term results of patients who underwent an elective RSA or for acute highly comminuted proximal humerus fractures. The secondary goal was to investigate the role of navigation in the execution of preoperative planning, especially in the management of glenoid bone loss. Methodology In total, 101 cases were included in this study. Patients were divided into the following two groups: 88 cases of RSA performed without the use of navigation (conventional RSA) and 13 cases performed using intraoperative navigation (navigated RSA). For all patients included in the study, preoperative planning software was employed. Patient demographics, gender, past medical history, indication of procedure, operated site, type of glenoid component used, length of baseplate screws, and clinical assessment scores (Oxford Shoulder Score, OSS) were reported for all patients. Cases of revision shoulder arthroplasty were excluded from this study. Results The postoperative clinical assessment of patients revealed that following RSA, all patients improved significantly with a consistently upward trend of the OSS noted for both groups (conventional and navigated RSA) throughout the postoperative assessment. Despite no statistically significant difference detected, the clinical scores of the navigated RSA group outperformed those of the conventional RSA group in the postoperative period. A higher incidence of augmented baseplate use was noted in the navigated RSA group than in the conventional group (23.07% vs. 5.68%, p < 0.001). Conclusions Our results indicate that the use of intraoperative navigation appears to be a valuable tool in preoperative planning, providing accurate positioning of the baseplate, a better understanding of the glenoid anatomy, and real-time monitoring of the length and direction of the baseplate screws. It is difficult to conclude if the use of navigation leads to superior clinical outcomes, and the cost-effectiveness of its use needs to be further analyzed. Prospective randomized trials are required to assess the cost-effectiveness of routine use of navigation in RSA.

A machine learning application to Google Maps Reviews as a participatory planning tool

ABSTRACT Public participation is vital to achieving successful plan outcomes, whereas meaningful participation requires at least informing and consulting the public. However, collecting frequent feedback from the public is a labour-intensive process. Information technologies like Google Maps Reviews offer to collect extensive public inputs by allowing users to share their feedback and ratings about services. Machine learning methods are ideal for analyzing these reviews to understand users’ experiences. This study proposes a machine learning application using Google Maps Reviews to examine feedback on the selected parks in Istanbul, Türkiye. Reviews provide insights into not only park features but also its social structure. The introduced method supports surveys and interview methods preferred by planners.

Computed tomographic assessment of bronchoesophageal artery hypertrophy and systemic‐to‐pulmonary shunt mimicking a persistent ductus arteriosus in a French Bulldog

AbstractA 5‐year‐old, female French bulldog was evaluated for left‐sided congestive heart failure. A diagnosis of bronchoesophageal artery hypertrophy with systemic‐to‐pulmonary shunt was made. Even though Doppler echocardiographic findings were in favour of a left‐to‐right shunting patent ductus arteriosus, the exploratory thoracotomy highlighted a more complex vascular anomaly. An angiographic computed tomography study allowed to establish the final diagnosis. Bronchial‐to‐pulmonary artery shunts can mimic the presence of a patent ductus arteriosus, therefore computed tomography is considered a valuable additional tool for diagnosis and surgical planning.

РАЗВИТИЕ ПРОГРАММНОГО УПРАВЛЕНИЯ РЕГИОНАЛЬНЫМИ АГРОПРОДОВОЛЬСТВЕННЫМИ СИСТЕМАМИ

Regional agro-food systems are important components of the national economic complex, ensuring the country's food security, development of rural areas, and improving the level and quality of life of the population. In modern conditions, government programs are becoming the main tool for strategic planning of industries, regions and the national economic system as a whole. The new system for managing state programs, approved in 2021, is aimed at eliminating many problems in the implementation of the program-target method, in particular, eliminating the ineffective use of budget resources and strengthening interdepartmental interactions. A new approach to the development, monitoring and implementation of state programs in the agro-food sector involves the addition of state programs with a number of parameters aimed at the fullest implementation of Russia’s national development goals. Proposals for improving the state programs of the Saratov region “Development of agriculture and regulation of markets for agricultural products, raw materials and food in the Saratov region” and “Integrated development of rural areas” are substantiated. The importance of transforming the redistribution model of financing the regional economy, which creates certain problems for regions when co-financing state programs, is emphasized.

Digital reactivation of territories through geospatial cultural itineraries: the VIDA-HTL web application

The significant imbalances between human settlements and their surrounding environments, stemming from sector-specific policies and urban regional planning tools, constitute a primary factor in the resilience and responsiveness of territories in crisis scenarios. Within this context, the promotion of territorial heritage values emerges as a crucial means for the reactivation of vulnerable areas. Furthermore, the utilisation of technological resources, such as Geographic Information Systems (GIS), has proven to be highly effective in managing cultural landscape areas. This contribution aims to enhance the valorisation of territorial heritage through the geolocation of a system of architectural and archaeological assets of public interest, and by enabling the flexible design of cultural itineraries within the framework of slow tourism. For this purpose, a replicable methodology for the construction of a WebGIS application has been proposed. This methodology has been put into practice in the territorial area of the lower Guadalquivir River in Spain, which encompasses 73 municipalities and one UNESCO World Heritage Site, all interconnected by rural areas. A four-stage plan has been followed for the construction of the VIDA-HTL web application: (1) conceptualisation; (2) development; (3) technical verification; and (4) social viability. The fundamental approach of the web application is based on the comprehensive integration of subsystems related to cultural heritage on one hand, and to tourist and environmental services on the other. Two main functions for the exploration of cultural assets and designing cultural routes have been designed, which complement other capabilities implemented in the web application. As a result of a detailed standardisation process, 981 cultural assets and 43 supplementary data layers have been georeferenced and included in the web application, with customised itineraries available for planning within the study area. Additionally, 49 thematic cultural itineraries are proposed. Finally, the web application is complemented with the development of a collaborative hub, which encourages its use and enhances its capabilities. It is concluded that digital tools, such as that proposed, foster knowledge and valorisation of territorial cultural heritage, while contributing to greater local development. In this way, they function as a counterbalance to the development of metropolises and the global economy, thereby improving levels of regional resilience.

Group sequential designs for pragmatic clinical trials with early outcomes: methods and guidance for planning and implementation

BackgroundGroup sequential designs are one of the most widely used methodologies for adaptive design in randomized clinical trials. In settings where early outcomes are available, they offer large gains in efficiency compared to a fixed design. However, such designs are underused and used predominantly in therapeutic areas where there is expertise and experience in implementation. One barrier to their greater use is the requirement to undertake simulation studies at the planning stage that require considerable knowledge, coding experience and additional costs. Based on some modest assumptions about the likely patterns of recruitment and the covariance structure of the outcomes, some simple analytic expressions are presented that negate the need to undertake simulations.MethodsA model for longitudinal outcomes with an assumed approximate multivariate normal distribution and three contrasting simple recruitment models are described, based on fixed, increasing and decreasing rates. For assumed uniform and exponential correlation models, analytic expressions for the variance of the treatment effect and the effects of the early outcomes on reducing this variance at the primary outcome time-point are presented. Expressions for the minimum and maximum values show how the correlations and timing of the early outcomes affect design efficiency.ResultsSimulations showed how patterns of information accrual varied between correlation and recruitment models, and consequentially to some general guidance for planning a trial. Using a previously reported group sequential trial as an exemplar, it is shown how the analytic expressions given here could have been used as a quick and flexible planning tool, avoiding the need for extensive simulation studies based on individual participant data.ConclusionsThe analytic expressions described can be routinely used at the planning stage of a putative trial, based on some modest assumptions about the likely number of outcomes and when they might occur and the expected recruitment patterns. Numerical simulations showed that these models behaved sensibly and allowed a range of design options to be explored in a way that would have been difficult and time-consuming if the previously described method of simulating individual trial participant data had been used.

A forecasting tool for a hospital to plan inbound transfers of COVID-19 patients from other regions

BackgroundIn April 2021, the province of Ontario, Canada, was at the peak of its third wave of the COVID-19 pandemic. Intensive Care Unit (ICU) capacity in the Toronto metropolitan area was insufficient to handle local COVID patients. As a result, some patients from the Toronto metropolitan area were transferred to other regions.MethodsA spreadsheet-based Monte Carlo simulation tool was built to help a large tertiary hospital plan and make informed decisions about the number of transfer patients it could accept from other hospitals. The model was implemented in Microsoft Excel to enable it to be widely distributed and easily used. The model estimates the probability that each ward will be overcapacity and percentiles of utilization daily for a one-week planning horizon.ResultsThe model was used from May 2021 to February 2022 to support decisions about the ability to accept transfers from other hospitals. The model was also used to ensure adequate inpatient bed capacity and human resources in response to various COVID-related scenarios, such as changes in hospital admission rates, managing the impact of intra-hospital outbreaks and balancing the COVID response with planned hospital activity.ConclusionsCoordination between hospitals was necessary due to the high stress on the health care system. A simple planning tool can help to understand the impact of patient transfers on capacity utilization and improve the confidence of hospital leaders when making transfer decisions. The model was also helpful in investigating other operational scenarios and may be helpful when preparing for future outbreaks or public health emergencies.

GNSS-supported direct georeferencing for UAV photogrammetry without GCP in Antarctica: a case study in Larsemann Hills

Unmanned aerial vehicle (UAV) has become an increasingly popular remote sensing platform in Antarctica. Due to the challenging natural conditions and lack of global navigation satellite system (GNSS) references in Antarctica, GNSS-supported direct georeferencing holds great potential for remote sensing applications in this region. Based on UAV surveys and GNSS observations we performed in Larsemann Hills, Antarctica, four GNSS-supported direct georeferencing schemes for UAV photogrammetry without ground control point (GCP) were designed and evaluated. Three of the schemes can generate high-accuracy photogrammetric products (horizontal accuracy: ∼0.7 ground sampling distance (GSD), vertical accuracy: ∼2.6 GSD). The fourth scheme, while exhibiting a lower accuracy at the meter-level, could offer high flexibility, and the accuracy of its derived products could be improved by post-flight transformation. The selection of an appropriate georeferencing scheme should be contingent upon the given application scenario, which can enhance the quality and accuracy of UAV photogrammetry in Antarctica. Potential applications of UAV remote sensing in Antarctica were discussed. It’s proven that UAV photogrammetry constitutes a reliable tool for Antarctic expedition path planning and ice morphology evolution monitoring. Our study demonstrated that direct georeferencing can generate high-accuracy UAV products in a reliable and feasible way in Antarctica.

Determination of optimal component positioning in THA using 3D preoperative planning.

Advancements in three-dimensional (3D) preoperative planning tools can offer surgeons and design engineers detailed feedback and additional opportunities for clinical advancements. The objective of this study is to use a 3D total hip arthroplasty preoperative planning tool to compare femoral component alignment for three different stem systems. The planning tool in this study used morphology data of femoral bones gathered from a CT database, seven from postoperative patients and 63 from statistical shape models (SSMs), to suggest specific implant sizes and optimal placements in 3D to match each specific bone model. Retrospective validations of predicted stem size and femoral version were first performed by comparing planner-chosen and surgeon-implanted stem sizes and version angles for the seven postoperative patients. Next, the alignment of three different stem systems was evaluated using bone models generated from SSMs, with a quantitative focus on component head positioning. In the validation study, the planner accurately selected stem size and orientation compared to the surgeon for all assessed subjects. In the stem evaluation, the three stem systems yielded different accuracies in component placement, with the newest stem system demonstrating the closest restoration of anatomical head center location. It is evident that new stem designs may have the potential for increased accuracy over their predecessors, demonstrating that new stem designs can offer improved intraoperative and postoperative alignment potential. The 3D preoperative planning tools can provide novel and reliable data to both surgeons and design engineers, which can ultimately improve clinical outcomes and future implant designs.

Air pollution removal with urban greenery – Introducing the Vegetation Impact Dynamic Assessment model (VIDA)

Urban greenery is identified as a potential tool in air pollution mitigation. However, the impact is still debated. This paper introduces the innovative VIDA (Vegetation Impact Dynamic Assessment) model, specifically designed to quantify air pollution removal through deposition on vegetation. The VIDA model offers an advanced representation of vegetation that could be integrated into urban air quality dispersion models in the future. Furthermore, the model serves as a valuable tool for exploring the intricate interactions among deposition, resuspension, and wash-off processes, as well as understanding how meteorological conditions and various leaf traits influence these processes.The current version of the model focuses on particulate matter (PM) and encompasses a range of processes, including deposition on vegetation surfaces, encapsulation within the waxy cuticle, wind-driven resuspension, and wash-off. Additionally, the model takes into consideration dynamic changes in PM concentrations on the leaf surface over time, incorporating factors such as PM size fractions, meteorological conditions, and leaf characteristics. This comprehensive approach allows for the evaluation of various species or species groups based on their distinct traits. The VIDA model effectively reproduces measured data, yet continued evaluation remains crucial as new data emerges. Notably, challenges were encountered due to data scarcity and the absence of standardized methods for characterizing vegetation traits. Addressing these challenges and refining the representation of wash-off process will enhance the VIDA model's utility in predicting the dynamic relationship between vegetation and air quality.The introduction of VIDA provides a significant advancement in modelling air pollution removal by deposition to vegetation at a relevant local scale and enables inclusion of urban greenery as tool in urban planning for air pollution mitigation.

Hubble Meets Webb: Image-to-Image Translation in Astronomy.

This work explores the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data. Comparative analysis encompasses the Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette methodologies, assessing the criticality of image registration in astronomy. While the focus of this study is not on the scientific evaluation of model fairness, we note that the techniques employed may bear some limitations and the translated images could include elements that are not present in actual astronomical phenomena. To mitigate this, uncertainty estimation is integrated into our methodology, enhancing the translation's integrity and assisting astronomers in distinguishing between reliable predictions and those of questionable certainty. The evaluation was performed using metrics including MSE, SSIM, PSNR, LPIPS, and FID. The paper introduces a novel approach to quantifying uncertainty within image translation, leveraging the stochastic nature of DDPMs. This innovation not only bolsters our confidence in the translated images but also provides a valuable tool for future astronomical experiment planning. By offering predictive insights when JWST data are unavailable, our approach allows for informed preparatory strategies for making observations with the upcoming JWST, potentially optimizing its precious observational resources. To the best of our knowledge, this work is the first attempt to apply image-to-image translation for astronomical sensor-to-sensor translation.

DNA framework signal amplification platform-based high-throughput systemic immune monitoring

Systemic immune monitoring is a crucial clinical tool for disease early diagnosis, prognosis and treatment planning by quantitative analysis of immune cells. However, conventional immune monitoring using flow cytometry faces huge challenges in large-scale sample testing, especially in mass health screenings, because of time-consuming, technical-sensitive and high-cost features. However, the lack of high-performance detection platforms hinders the development of high-throughput immune monitoring technology. To address this bottleneck, we constructed a generally applicable DNA framework signal amplification platform (DSAP) based on post-systematic evolution of ligands by exponential enrichment and DNA tetrahedral framework-structured probe design to achieve high-sensitive detection for diverse immune cells, including CD4+, CD8+ T-lymphocytes, and monocytes (down to 1/100 μl). Based on this advanced detection platform, we present a novel high-throughput immune-cell phenotyping system, DSAP, achieving 30-min one-step immune-cell phenotyping without cell washing and subset analysis and showing comparable accuracy with flow cytometry while significantly reducing detection time and cost. As a proof-of-concept, DSAP demonstrates excellent diagnostic accuracy in immunodeficiency staging for 107 HIV patients (AUC > 0.97) within 30 min, which can be applied in HIV infection monitoring and screening. Therefore, we initially introduced promising DSAP to achieve high-throughput immune monitoring and open robust routes for point-of-care device development.

An assessment framework for underground construction methods in the pre-construction stage: a technical perspective

The selection of an appropriate underground construction method plays a crucial role during the pre-construction phase as it must consider various aspects, such as safety, technical feasibility, and cost-effectiveness. However, there is still a lack of technical assessments for selecting suitable underground construction methods in the research field. Therefore, this study aims to build an assessment model to technically assess such methods in the pre-construction stage. Firstly, technical assessment factors were obtained from literature reviews, expert consultations and survey results. Factor analysis was performed to reveal the underlying significance of each factor. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) model was then constructed to assess the relative importance of each factor. Finally, the assessment model was validated by seeking experts’ opinions on its performance in a practical project. The study resulted in a Fuzzy AHP model established from a list of 8 groups of factors with 35 refined factors, covering many aspects from documents and resources to geology and hydrology. The validation results demonstrated the model’s effectiveness in assisting contractors to make informed decisions. This study has contributed to construction literature by introducing a novel framework for underground construction methods, offering a practical tool for pre-construction planning and decision-making.

Re-Conceptualizing Vertical Subdivision Development for Sustainable, Affordable Housing Delivery

Research on sustainable, affordable housing is evolving. Yet, its conceptual efficacy in light of the changing needs of today’s cities and targeted low-to-middle-income households remains unknown. In today’s rapidly urbanizing world, understanding the conceptual relevance and importance of land use planning tools such as vertical subdivision to the delivery of sustainable housing is tenable. In response to this knowledge gap, this entry inquires, how can the delivery of affordable housing be configured in a manner that leverages the potential of a redefined vertical subdivision development to optimize densities and ensure that housing affordability is sustainable? Here, this entry re-defines vertical subdivision development as a housing planning and design tool that allows for the segregation of air spaces into individual volumetric land parcels that mimic the environmental features of the land-on-ground, such that housing construction within such volumetric spaces is a function of the contextually relevant needs of occupants. This entry demonstrates a paradigm shift from existing housing infrastructure planning models and narratives to one that responds to and addresses all three dimensions of sustainability: economic (sustainable affordability), environmental (sustainable densities), and social (occupant wellness) in the housing infrastructure planning and delivery process.

Results of robotic liver surgery in association with IWATE criteria — the first 100 cases

BackgroundAim of the current study was to present the results of the implementation phase of a robotic liver surgery program and to assess the validity of the IWATE difficulty score in predicting difficulty and postoperative complications in robotic liver surgery.MethodsBased on the prospective database of the Interdisciplinary Robotic Center of Ulm University Hospital, the first 100 robotic liver surgeries were identified and analyzed. Perioperative parameters (duration of surgery and blood loss) and postoperative parameters including morbidity, mortality, and length of hospital stay were assessed and the results were compared between different IWATE difficulty categories.ResultsFrom November 2020 until January 2023, 100 robotic liver surgeries were performed (41 female, 59 male; median age 60.6 years, median BMI 25.9 kg/m2). Median duration of surgery was 180 min (IQR: 128.7), and median blood loss was 300 ml (IQR: 550). Ninety-day mortality was 2%, and overall morbidity was 21%, with major complications occurring in 13% of patients (≥ grade 3 according to Clavien/Dindo). A clinically relevant postoperative biliary leakage was observed in 3 patients. Posthepatectomy liver failure occurred in 7% (4 Grade A, 3 Grade B). Duration of surgery (p < 0.001), blood loss (p < 0.001), CCI (p = 0.004), overall morbidity (p = 0.004), and length of hospital stay (p < 0.001) were significantly increased in the IWATE ‘expert’ category compared to lower categories.DiscussionRobotic surgery offers a minimally invasive approach for liver surgery with favorable clinical outcomes, even in the implementation phase. In the current study the IWATE difficulty score had the ability to predict both difficulty of surgery as well as postoperative outcomes when assessing the complexity of robotic liver surgery. Therefore, the role of the IWATE score in predicting these outcomes highlights its importance as a tool in surgical planning and decision-making.

The stand allocation model for aircraft MRO service provider

ABSTRACT In recent years, airlines have been expanding their fleets, but this has posed a challenge in terms of limited airport space for maintenance facilities. Maintenance, repair, and overhaul provider (MRO) enhances the efficiency of existing space utilization by reducing the distance between parked aircraft. However, it is often necessary to change the position of parked aircraft during maintenance and if there are other aircraft blocking this movement it is necessary to interrupt the original maintenance work to allow the aircraft to pass. Unnecessary relocation of aircraft could reduce maintenance efficiency and increase costs. This study adopts mathematical programming with a network flow technique to develop an MRO stand allocation model. The effectiveness of the proposed model is evaluated in a case study performed using the data collected from a Taiwan aircraft MRO service provider. The results demonstrate that the proposed model could be a useful and practical planning support tool.