Planning Approach Research Articles

Advancing Health Equity through 15-min Cities and Chrono-urbanism.

Implementing the 15-min city and chrono-urbanism aims to improve sustainability and quality of life by ensuring residents' proximity to essential services. The 15-min city model is gaining global traction, with localized adaptations to suit communities' needs. Beyond environmental motivations, 15-min cities can benefit public health through enhanced walkability, social cohesion, and universal accessibility. However, research examining the intersection of health and equity among chrono-urbanism and the 15-min city remains limited. This study aims to develop a framework to integrate health and equity into chrono-urbanism and 15-min city plans. We describe the potential benefits and risks of the 15-min approach for urban planning, daily behaviors, and health outcomes. Potential benefits of 15-min cities for health equity include proximity to destinations, increased physical activity, strengthened social capital, reduced emissions, and traffic calming. Risks that must be mitigated include gentrification, variable proximity definitions, infrastructure upgrades, and inadequate cultural sensitivity. Recommendations to integrate 15-min cities into planning activities include conducting comprehensive baseline assessments, aligning goals with sustainability, economic development, flexible zoning, inclusive public spaces, and diverse community engagement tactics. We recommend interventions targeting marginalized communities and developing standardized measurement tools for comparison, monitoring, and evaluation. A nuanced, equitable approach to implementing 15-min cities can help urban plans support health equity across diverse populations.

Soft vs. Hard Sustainability Approach in Marine Spatial Planning: Challenges and Solutions

Hitherto, over 50% of countries with marine waters have established MSPs or launched related legal actions. However, there are still conceptual and practical challenges to be overcome in the development of MSP. In this study, we investigate two main approaches in MSP (hard vs. soft sustainability) through reports, published manuscripts and meeting proceedings in seven pioneering countries (Belgium, Netherlands, Norway, Germany, United Kingdom, Australia, and Canada). We highlight the gaps, challenges, and solutions in each of these approaches. From our findings, there are four common challenges in both soft and hard sustainability approaches as follows: (i) the political framework and inconsistent support of MSP efforts, (ii) insufficient knowledge on social dimensions, (iii) insufficient stakeholder engagement in the diversity of stakeholder’s groups or in their contribution to the planning process from the initial steps, and (iv) finding a balance between environmental conservation and economic growth. We recommend that future studies should investigate how MSP can become more adaptive to long-term environmental and economic targets, how effective involving socioeconomic strata is in MSP, and how decision-making tools could help to cover the gaps in MSP. Furthermore, public forums are suggested to be developed to facilitate the systematic sharing of MSP experiences worldwide.

Planning for tourism under uncertainty: lessons from Macao, China (2002–2022)

ABSTRACT Drawing on the perspective of governance and planning uncertainties mentioned in Abbott 's 2005 and 2009 articles, namely, “Understanding and managing the unknown: The nature of uncertainty in planning” and “Planning for complex metropolitan regions: A better future or a more certain one?” , and several urban planning theories, this study examined the types of planning uncertainty that Macao (China), experienced from 2002 to 2020, and the planning approaches it adopted to tackle the uncertainties. A mixed research method was adopted. Fifteen qualitative face-to-face interviews with key informants were conducted, and secondary sources of data, such as government plans, reports, and statistics, were also reviewed. Results indicated that environmental, casual, value, organisational, and chance uncertainties emerged at three different stages of Macao's tourism development. To deal with these uncertainties, a variety of planning approaches were adopted, including guided flexible planning, comprehensive planning, communicative planning, and adaptive learning. This study contributed a novel perspective to examine tourism planning uncertainties over time. It emphasised the importance of a flexible planning approach in tackling uncertainties of growing complexity. The findings also shed light on how planners can strengthen their capacity for tackling uncertainty.

Restoration of Alouatta guariba populations: building a binational management strategy for the conservation of the endangered brown howler monkey of the Atlantic Forest

The brown howler monkey (Alouatta guariba) is endemic to the Atlantic Forest of eastern Brazil and northeastern Argentina, threatened by extinction due to habitat loss and fragmentation, and hunting. Its reduced and isolated populations dramatically decreased and suffered local extinctions after recent yellow fever outbreaks, recommending the species risk uplist from Vulnerable to Endangered. In Brazil, the species occurs along eight states in sparse populations, including some large protected areas, and is being uplisted to Endangered. Following the National Action Plan for Conservation of the Atlantic Forest Primates and the Maned-sloth, and applying the Guidelines for Reintroductions and other Conservation Translocations (IUCN/CTSG), the Ex Situ Guidelines (IUCN/CPSG) and the One Plan Approach (IUCN/CPSG), a workshop was held on August 2021 to evaluate the need, requisites and roles of an integrated Population Management Program for A. guariba. Such program was developed in June 2022 defining the objectives of (i) restore in situ populations from ex situ populations, prioritizing populations in higher risk to prevent local extinctions, and (ii) establish an ex situ insurance population. This in situ – ex situ integrated management program was approved by ICMBio in February 2023, with actions recommended for all states along the species distribution. In Argentina, the species is Critically Endangered, with a remaining population of 20-50 individuals. Following the National Plan for Primate Conservation in Argentina, in a process similar to that in Brazil, two workshops were held in 2022/2023 to evaluate and define management actions for the long-term recovery of A. guariba. The priorities indicated were (i) reintroduction in ten potential areas in Misiones – due to a lower risk when compared to reinforcement of remaining populations, and (ii) establishing an ex situ management program in Argentina – still non-existent. The progressive collaboration between the initiatives of both countries is promoting the exchange of experiences and the integration of strategies. Here we summarize the planning and management carried out in Brazil and Argentina, highlighting the need for integrated measures. We debate on the progress and challenges, proposing next steps for developing and implementing a binational population management program for the conservation of the brown howler monkey.

Drone path planning and dynamic arena-target allocation for crowd surveillance

The paper addresses the problem of surveillance of dynamic crowds in a bounded arena using multiple drones. The strategy proposes a prediction-based path planning approach for drones, where the target’s future position is predicted and used to generate a traverse path. The procedure for grouping numerous human beings in order to deal with a dense crowd scenario is also described. An algorithm to dynamically divide and allocate the arena to drones is also proposed to ensure equal workload distribution among all drones in the framework. The paper also discusses relevant concepts like detecting anomalous crowd activity and searching for any missing target in the arena. The proposed algorithms are validated on MATLAB environment numerically and ROS/Gazebo physics engine as a part of simulation-in-the-loop (SITL).

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.

Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer

BackgroundAdvanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration’s (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC.MethodsIn this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs).ResultsAnalyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001).ConclusionICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients’ responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient’s unique response situations.

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis.

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

Establishing subdivisions of M1 stage nasopharyngeal carcinoma based on decision tree classification: A multicenter retrospective study

ObjectivesTo meet the demand for personalized treatment, effective stratification of patients with metastatic nasopharyngeal carcinoma (mNPC) is essential. Hence, our study aimed to establish an M1 subdivision for prognostic prediction and treatment planning in patients with mNPC. Materials and methodsThis study included 1239 patients with mNPC from three medical centers divided into the synchronous mNPC cohort (smNPC, n = 556) to establish an M1 stage subdivision and the metachronous mNPC cohort (mmNPC, n = 683) to validate this subdivision. The primary endpoint was overall survival. Univariate and multivariate Cox analyses identified covariates for the decision-tree model, proposing an M1 subdivision. Model performance was evaluated using time-dependent receiver operating characteristic curves, Harrell’s concordance index, calibration plots, and decision curve analyses. ResultsThe proposed M1 subdivisions were M1a (≤5 metastatic lesions), M1b (>5 metastatic lesions + absent liver metastases), and M1c (>5 metastatic lesions + existing liver metastases) with median OS of 34, 22, and 13 months, respectively (p < 0.001). This M1 subdivision demonstrated superior discrimination (C-index = 0.698; 3-year AUC = 0.707) and clinical utility over those of existing staging systems. Calibration curves exhibited satisfactory agreement between predictions and actual observations. Internal and mmNPC cohort validation confirmed the robustness. Survival benefits from local metastatic treatment were observed in M1a, while immunotherapy improved survival in patients with M1b and M1c disease. ConclusionThis novel M1 staging strategy provides a refined approach for prognostic prediction and treatment planning in patients with mNPC, emphasizing the potential benefits of local and immunotherapeutic interventions based on individualized risk stratification.

Neural digital twins: reconstructing complex medical environments for spatial planning in virtual reality.

Specialized robotic and surgical tools are increasing the complexity of operating rooms (ORs), requiring elaborate preparation especially when techniques or devices are to be used for the first time. Spatial planning can improve efficiency and identify procedural obstacles ahead of time, but real ORs offer little availability to optimize space utilization. Methods for creating reconstructions of physical setups, i.e., digital twins, are needed to enable immersive spatial planning of such complex environments in virtual reality. We present a neural rendering-based method to create immersive digital twins of complex medical environments and devices from casual video capture that enables spatial planning of surgical scenarios. To evaluate our approach we recreate two operating rooms and ten objects through neural reconstruction, then conduct a user study with 21 graduate students carrying out planning tasks in the resulting virtual environment. We analyze task load, presence, perceived utility, plus exploration and interaction behavior compared to low visual complexity versions of the same environments. Results show significantly increased perceived utility and presence using the neural reconstruction-based environments, combined with higher perceived workload and exploratory behavior. There's no significant difference in interactivity. We explore the feasibility of using modern reconstruction techniques to create digital twins of complex medical environments and objects. Without requiring expert knowledge or specialized hardware, users can create, explore and interact with objects in virtual environments. Results indicate benefits like high perceived utility while being technically approachable, which may indicate promise of this approach for spatial planning and beyond.

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.

Influence of Socioeconomic Factors on Access to Digital Resources for Education

Digital resources in education encompass a wide array of technological tools, online platforms, and digital content utilized to facilitate and enhance the learning process. They serve as invaluable supplements to traditional teaching methods, offering dynamic and diverse ways to impart knowledge, encourage engagement, and foster skill development among learners. This systematic review critically examines the influence of socioeconomic factors on access to digital resources for education by analyzing studies published between 2017 and 2024. The inclusion criteria prioritized empirical studies in English-language peer-reviewed journals or reputable conference proceedings, utilizing various research designs. The review involved a thorough search across databases like JSTOR, APA PsycNet, Wiley Online Library, ERIC, and Google Scholar. The study's analysis identified key themes, highlighting the profound influence of socioeconomic factors on technology access, engagement, and academic outcomes. The recurring theme of the digital divide emphasized disparities in device ownership, internet connectivity, and effective technology utilization. The nuanced exploration of parenting approaches, gender dynamics, and educational contexts added depth to understanding these influences. This study found the necessity for a holistic approach in educational planning, considering socioeconomic, cultural, and educational factors to effectively address challenges and disparities, ultimately fostering equitable access to digital resources in education.

Shopping trip recommendations: A novel deep learning–enhanced global planning approach

Brick-and-mortar shopping malls are embracing Artificial Intelligence (AI) technology and recommender systems to enhance the shopping experience and boost mall revenue. Echoing this trend, we formulate a new shopping trip recommendation problem, which aims to recommend a shopping trip (i.e., a list of stores) that matches customer preferences and has appropriate trip lengths. To solve this problem, we develop a novel deep learning–enhanced global planning (DeepGP) approach featuring three methodological novelties. First, we introduce a new shopping intensity term based on deep neural networks to capture the variation of trip lengths specific to different shopping contexts. Second, we innovatively formulate the learning and optimization objectives in a consistent form by balancing the shopping choice likelihood and the shopping intensity likelihood, thus resolving the inconsistency issue encountered by prior global planning methods. Third, to overcome the computational challenge caused by the nonlinear shopping intensity term, we design a new exact and efficient solution technique based on piecewise linear transformations. Using a real-world offline shopping dataset, we empirically demonstrate the superior performances of our approach compared to representative benchmarks in offering more accurate and relevant shopping trip recommendations. Through a simulation, we show the capacity of our approach to attract and balance customer traffic in practical deployments. Overall, our research highlights the efficacy of combining shopping choices and shopping intensity in a consistent learning and optimization framework for offline shopping trip recommendations.

Integrating fuzzy analytic hierarchy process into ecosystem service-based spatial planning: A case study of the Shenyang metropolitan area, China

Incorporating ecosystem services (ESs) into spatial planning is crucial for effective environmental management and achieving sustainable development goals. While the Analytic Hierarchy Process (AHP) is a commonly employed method for multicriteria analysis of ESs, it has faced criticism for its limited ability to handle inherent imprecision and uncertainties. Consequently, we proposed an assessment framework for ESs using the Fuzzy Analytic Hierarchy Process (FAHP) and implemented it in the Shenyang Metropolitan Area (SMA), a key political and economic center in Northeast China. We quantified eight types of ESs (water yield, food supply, water regulation, carbon storage, waste regulation, soil retention, habitat quality, and outdoor recreation) within SMA. The FAHP was utilized to determine the weights of these ESs. Subsequently, we presented a normalized index to identify priority conservation and development areas. Our results indicated that the primary areas for conservation were mainly located in the southwest and east of Benxi City, the southeast of Fushun City, and the southeast of Anshan City, whereas areas for development were primarily in the northwest of Shenyang City. Through comparing various ES integration methods, our study underscores the effectiveness of FAHP in ES evaluation. The research also highlights the challenge of managing uncertainties in ES-based policy-making and emphasizes the necessity for resilient and comprehensive planning approaches.

Ganz femoral head reduction associated with coverage and containment procedures improve radiological and functional outcomes in Perthes' disease.

Ganz's studies made it possible to address joint deformities on both the femoral and acetabular side brought about by Perthes' disease. Femoral head reduction osteotomy (FHRO) was developed to improve joint congruency, along with periacetabular osteotomy (PAO), which may enhance coverage and containment. The purpose of this study is to show the clinical and morphological outcomes of the technique and the use of an implemented planning approach. From September 2015 to December 2021, 13 FHROs were performed on 11 patients for Perthes' disease in two centres. Of these, 11 hips had an associated PAO. A specific CT- and MRI-based protocol for virtual simulation of the corrections was developed. Outcomes were assessed with radiological parameters (sphericity index, extrusion index, integrity of the Shenton's line, lateral centre-edge angle (LCEA), Tönnis angle), and clinical parameters (range of motion, visual analogue scale (VAS) for pain, Merle d'Aubigné-Postel score, modified Harris Hip Score (mHHS), and EuroQol five-dimension five-level health questionnaire (EQ-5D-5L)). Early and late complications were reported. The mean follow-up was 39.7 months (standard deviation (SD) 26.4). The mean age at surgery was 11.4 years (SD 1.6). No major complications were recorded. One patient required a total hip arthroplasty. Mean femoral head sphericity increased from 46.8% (SD 9.34%) to 70.2% (SD 15.44; p < 0.001); mean LCEA from 19.2° (SD 9.03°) to 44° (SD 10.27°; p < 0.001); mean extrusion index from 37.8 (SD 8.70) to 7.5 (SD 9.28; p < 0.001); and mean Tönnis angle from 16.5° (SD 12.35°) to 4.8° (SD 4.05°; p = 0.100). The mean VAS improved from 3.55 (SD 3.05) to 1.22 (1.72; p = 0.06); mean Merle d'Aubigné-Postel score from 14.55 (SD 1.74) to 16 (SD 1.6; p = 0.01); and mean mHHS from 60.6 (SD 18.06) to 81 (SD 6.63; p = 0.021). The EQ-5D-5L also showed significant improvements. FHRO associated with periacetabular procedures is a safe technique that showed improved functional, clinical, and morphological outcomes in Perthes' disease. The newly introduced simulation and planning algorithm may help to further refine the technique.

A conceptual framework of executive coaching in family business succession planning: the Malaysian context

ABSTRACT Family business succession planning receives much attention globally, but executive coaching remains underutilized. This applies to family-owned businesses; particularly challenging would be the survivorship of small and medium enterprises. Given the growing need for more effective succession planning, this paper aims to improve the existing executive coaching framework by applying it to family business succession planning in Malaysia. This conceptual article contains an accessible definition and presentation of executive coaching. Each framework component depicts the coach, coached person (‘coachee’), and organizational roles in the coaching, which leads to the possible leadership transfer outcome. This paper suggests a new succession planning direction for researchers and executive coaches. The planning direction aims to aid family business owners in understanding the executive coaching ‘what, why, and how’ and an alternative succession planning approach is suggested. The mechanism of executive coaching and the basis for novel family business research are discussed and established.

Managing uncertainties in fault avoidance zone of urban areas: a case study of Karaj, Iran

Purpose This study aims to develop the application of the strategic choice approach (SCA) in Karaj City (Iran) as the case study for facilitating the decision-making process concerning uncertainties associated with determining fault trace, fault buffer zone and fault setback, as well as inherent uncertainties related to urban issues – known as wicked problems – which make a clear definition unachievable for them. Design/methodology/approach The SCA was recycled in this study to manage uncertain situations in planning. It is designed to deal with problems caused by uncertainty, insufficient knowledge and unpredictable policy outcomes. In combination with the risk-based planning approach, the paper proposes a new model to deal with uncertainty and hazard risk at the same time. Findings The results indicated that such decision-making tools are helpful for urban planners who impose changes in the fault zone to save lives and properties where the process seems problematic and ambiguous. Further analysis from the SCA development not only shows that urban planning restrictions should be applied gradually and in pace with the completion of fault displacement geological data but also reveals that defining the implementation process and institutional structure is as important as the preparation of binding fault zoning documents. Originality/value The paper will push forward global research and practice because the results can be generalized for use in other earthquake-prone countries, as the considerations and obstacles have a universal character and more specifically because many earthquake-prone countries have not started to define fault avoidance zone restrictions up to now.

A Comprehensive Approach to Brain Tumor Classification in MRI: Unifying Classical Local Binary Patterns and Convolution Neural Networks

Brain tumors, among the most perilous neurological disorders affecting the human nervous system, are categorized into glioma, meningioma, and Pituitary types. The proposed system combines Classical Local Binary Patterns (CLBP), Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) to extract texture information from MRI images. The methodology comprises three key steps: image pre-processing, feature extraction, and classification. By evaluating a publicly accessible brain tumor dataset, the proposed approach attains an impressive accuracy of 96%. The findings highlight the potential uses of the CLBP+CNN approach in clinical diagnosis and treatment planning, demonstrating the possibility of accurate and efficient brain tumor classification. The proposal introduces future extension methods like CLBPs (DLBP, ӨLBP), where the DLBP approach incorporates a 'D' parameter specifying the distance between neighboring pixels. The ӨLBP Method evaluates the pixel value by changing the 'Ө' value, i.e., 150, 450, 900 and 1200. The classification of tumors involved the application of various classification methods, namely ANN (Artificial Neural Networks), AlDE (Ant Lion Optimizer Differential Evolution), and LDA (Linear Discriminant Analysis). This classification is executed using feature extractions derived from CLBP (DLBP &amp; ӨLBP) applied to MRI images within the dataset.

Optimal trajectory planning combining model-based and data-driven hybrid approaches

Trajectory planning aims at computing an optimal trajectory through the minimization of a cost function. This paper considers four different scenarios: (i) the first concerns a given trajectory on which a cost function is minimized by a acting on the velocity along it; (ii) the second considers trajectories expressed parametrically, from which an optimal path and the velocity along it are computed; (iii), the case in which only the departure and arrival points of the trajectory are known, and the optimal path must be determined; and finally, (iv) the case involving uncertainty in the environment in which the trajectory operates. When the considered cost functions are expressed analytically, the application of Euler–Lagrange equations constitutes an appealing option. However, in many applications, complex cost functions are learned by using black-box machine learning techniques, for instance deep neural networks. In such cases, a neural approach of the trajectory planning becomes an appealing alternative. Different numerical experiments will serve to illustrate the potential of the proposed methodologies on some selected use cases.

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings.