Local Scenarios Research Articles

Rethinking Instructional Leadership in Pakistan’s Elite Schools: A Call for Indigenous Leadership Models

This opinion paper makes a case for the need to develop indigenous instructional leadership models through Pakistani elite schools that challenge the use of Western leadership models. This reveals some significant cultural misalignments between the imported leadership models and the local educational scenario, needing leadership techniques rooted in Pakistani norms and high academic standards. This paper presents the fundamental building blocks of indigenous models of leadership that are culturally, organisationally, and contextually relevant to Pakistan. We discuss an implementation framework that guides awareness of obstacles and stakeholder roles and underscores the importance of systematic evaluation mechanisms. The study proposes recommendations with respect to policymaking, research initiatives and capacity-building programmes that will facilitate effective transformation. The paper argues that indigenous leadership models could contribute to educational effectiveness by bringing academic excellence and cultural authenticity to educational leadership and potentially influencing educational leadership practices outside elite schools to the advantage of Pakistan’s educational system.

Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis

Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. Recently, LLM-based APR methods have shown promise in repairing real-world bugs. However, existing APR methods often utilize patches generated by LLMs without further optimization, resulting in reduced effectiveness due to the lack of program-specific knowledge. Furthermore, the evaluations of these APR methods have typically been conducted under the assumption of perfect fault localization, which may not accurately reflect their real-world effectiveness. To address these limitations, this paper introduces an innovative APR approach called GiantRepair . Our approach leverages the insight that LLM-generated patches, although not necessarily correct, offer valuable guidance for the patch generation process. Based on this insight, GiantRepair first constructs patch skeletons from LLM-generated patches to confine the patch space, and then generates high-quality patches tailored to specific programs through context-aware patch generation by instantiating the skeletons. To evaluate the performance of our approach, we conduct two large-scale experiments. The results demonstrate that GiantRepair not only effectively repairs more bugs (an average of 27.78% on Defects4J v1.2 and 23.40% on Defects4J v2.0) than using LLM-generated patches directly, but also outperforms state-of-the-art APR methods by repairing at least 42 and 7 more bugs under perfect and automated fault localization scenarios, respectively.

Brief communication: Stay local or go global? On the construction of plausible counterfactual scenarios to assess flash flood hazards

Abstract. Spatial counterfactuals are gaining attention to address the lack of robust flood frequency analysis in small catchments. However, the credibility of counterfactual scenarios decreases with the distance rain fields are transposed across space. We limit that distance by a local counterfactual search design and compare the corresponding scenarios to recently published results from long-distance transpositions. We then put all scenarios into context with 200-year return levels and with flood peaks simulated for the June 2024 flood event in southern Germany. We conclude that local counterfactual scenarios are transparent and credible and could complement the anticipation of low-probability events.

GAN-Assisted Road Segmentation from Satellite Imagery

Geo-information extraction from satellite imagery has become crucial to carry out large-scale ground surveys in a short amount of time. With the increasing number of commercial satellites launched into orbit in recent years, high-resolution RGB color remote sensing imagery has attracted a lot of attention. However, because of the high cost of image acquisition and even more complicated annotation procedures, there are limited high-resolution satellite datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc.). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. Instead of collecting and annotating more data, using synthetic images could be another solution to boost the performance of a model. This article proposes a GAN-assisted training scheme for road segmentation from high-resolution RGB color satellite images, which includes three critical components: (a) synthetic training sample generation, (b) synthetic training sample selection, and (c) assisted training strategy. Apart from the GeoPalette and cSinGAN image generators introduced in our prior work, this article explains in detail how to generate new training pairs using OpenStreetMap (OSM) and introduces a new set of evaluation metrics for selecting synthetic training pairs from a pool of generated samples. We conduct extensive quantitative and qualitative experiments to compare different image generators and training strategies. Our experiments on the downstream road segmentation task show that (1) our proposed metrics are more aligned with the trained model performance compared to commonly used GAN evaluation metrics such as the Fréchet inception distance (FID); and (2) by using synthetic data with the best training strategy, the model performance, mean Intersection over Union (mean IoU), is improved from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model that is standard-trained with 4,000 real images (64.59%), i.e., enabling a 4-fold reduction in real dataset size.

Research on 3D Localization of Indoor UAV Based on Wasserstein GAN and Pseudo Fingerprint Map

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average.

Bayesian parameter calibration of a one-dimensional hemodynamic model

In this work, we couple a Bayesian optimization approach with a one-dimensional blood-flow model (known as ADAN86) to achieve a flexible and efficient strategy for the calibration of model parameters when in-vivo patient-specific data is available. The optimization step is addressed in the frequency domain, minimizing the discrepancies between the first harmonics of the in-vivo and predicted flow/pressure signals. The model parameters can be locally or globally perturbed, modifying the reference values in the same proportion for the whole system (global) or by region to account for the specificities of each location (local).To test the proposed strategy, two cases are considered. The first one involves parameter calibration in a synthetic patient, where some parameters are locally modified, and the optimization process is compared in local and global scenarios. The second case addresses parameter optimization in a patient with in-vivo flow/pressure data, comparing the local and global calibration approaches.The results show that the coupling between the Bayesian optimization process and the ADAN86 models yields an efficient strategy for the parameter calibration problem, which is naturally parallelizable and promising for data assimilation in computational hemodynamics.

Ab initio study of the laser-induced ultrafast spin dynamics on Ni[formula omitted]@C40H34 carbon cross

Miniaturizing magnetic logic is a significant challenge in spintronics. Magnetic molecules, with their complex properties, play a central role in this area. They offer encouraging prospects for today’s and future nanoscale magnetic applications. Here, we suggest a carbon cross system for spin-based logic operability with emphasis on laser-induced spin manipulation across carbon nodes. Using the spin density of the nickels, we accomplish six spin-transfer as well as several local-spin flip processes. All the spin dynamics scenarios can be accomplished in the subpicosecond regime. Varying the strength of the magnetic field can considerably alter the local spin-flip scenarios, while leaving the global spin-transfer processes mostly unchanged. Building all-spin-based functionality can be done with increased speed due to the controllable spin-transfer and spin-flip-transfer scenarios. Furthermore, reversible logic operability leads to reduced power consumption. Here, the reversed spin-transfer processes can be accomplished both with different and the same laser pulses. The impact of the angles of incidence of the laser pulse on the spin dynamics processes is examined. The whole spin dynamics is realized within the subpicosecond domain. The findings of our study offer important knowledge and new understanding on how to successfully control spin dynamics processes on microscopic molecular systems in order to attain a particular logic operation.

Framework for collaborative local climate adaptation scenario development- nexus between climate resilience, public health service and spatial planning

PurposeThe interaction between urban development and climate change significantly impacts local public health services. Unfortunately, cities and involved institutions often fail to prioritize and integrate spatial planning when dealing with these unprecedented future challenges. This study aims to offer Health Integrative Climate Resilience and Adaptation Future (HICRAF), an innovative planning framework that systematically operationalizes future climate risks and their impact on local public health services.Design/methodology/approachHICRAF is developed based on the intermix of explorative and normative scenario planning approaches. Mixed methods of quantitative and qualitative techniques were applied to develop and operationalize the local climate adaptation scenarios through stakeholder participation. The framework demonstrates how different methods and scales (spatial and temporal) can be linked to exhibit climate risk outcomes of different future pathways.FindingsThe practicality of HICRAF was demonstrated in Khon Kaen city, where it bridged the gaps between global climate trajectories and local climate adaptation scenarios. It also highlights the need to consider intertwining spatial and systemic risks in local infrastructure operations. Although HICRAF has gained political buy-in and fostered the establishment of stakeholder discourse on climate-resilient futures, further research is needed to enhance its robustness and replicability.Originality/valueThis paper proposes a novel planning framework, HICRAF, that can systematically operationalize the future challenges of unprecedented climate change and urban development changes for the local public health service. The demonstration of HICRAF in Khon Kaen city provides empirical evidence of its implementability and upscaling potential.

Simulating future cultivated land using a localized SSPs-RCPs framework: A case study in Yangtze River Economic Belt

Simulating cultivated land changes is vital for improving decision-making in sustainable agriculture. Compared to previous studies, the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs-RCPs) scenarios provide a comprehensive framework for integrating climate and socioeconomic factors, offering deeper insights into future cultivated land dynamics. Taking the Yangtze River Economic Belt (YREB) as the study area, we developed localized SSPs-RCPs scenarios and adopted Global Change Assessment Model (GCAM) and Future Land Use Simulation (FLUS) model to simulate cultivated land changes under multiple scenarios. The findings indicate that cultivated land is predicted to decrease under all scenarios, with the SSP1-2.6 and SSP3-7.0 scenarios showing the highest and lowest decrease of 39.24% and 8.32%, respectively. By 2050, the loss of cultivated land in the upper reaches is expected to be significantly greater than that in the middle and lower reaches. Furthermore, grasslands, forests, and urban areas are the main types of land that replacing cultivated land in the YREB. The hotspots of cultivated land loss are in mountainous areas such as Sichuan, Yunnan, and Guizhou. Additionally, the gravity center of cultivated land distribution moved along the southeast to northeast, with the center of gravity shifting from Guizhou to Hunan under scenarios SSP1-2.6 and SSP5-8.5. This study offers insights into the future development of cultivated land in the YREB and highlights the significance of implementing and improving policies for its protection.

Deviation Aware Mask for Localizing Ground Penetrating Radar Data Alignment

Abstract This paper proposes a data align pipeline for LGPR (Localization Ground Penetrating Radar). LGPR achieves robot localization by matching prior underground data map, therefore high-precision maps are essential. Utilizing GPR in a time acquisition mode for robot localization enables the technology to generate maps with resolutions far superior to those produced by camera or lidar mapping methods. However, it also introduces several challenges. On one hand, the process of map generation heavily relies on velocity control of the moving platform, and non-uniform motion in the localization scenario can result in feature deformation of GPR images. On the other hand, in the absence of relative a priori position conditions, there exists a certain degree of spatial dimension offset between the multiple data acquisitions from array GPR systems, rendering existing time-series deformation correction schemes ineffective. To address these challenges, by designing a deviation-aware distance, we aim to enhance existing time-series regularization approaches without relying on additional sensor position information, thus achieving synchronized alignment of multiple rounds of ground-penetrating radar data for the generation of high-quality underground map data used to evaluate LGPR performance. In this work, we compare existing synchronization strategies of LGPR with those of other sensors and alignment schemes, and validate the effectiveness of our proposed approach.

A Base-Map-Guided Global Localization Solution for Heterogeneous Robots Using a Co-View Context Descriptor

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots.

Gamma Dose Rate Measurements in Northern Spain: Influence of Local Meteorological Scenarios on Radiological "False Alarms" in a Real-Time Radiological Monitoring Network.

The present study characterizes gamma dose rate (GDR) concentrations in Bilbao, located in the northern Iberian Peninsula, utilizing a comprehensive 10-min interval database spanning from 2009 to 2018. This station belongs to the radiological environmental monitoring of the Basque Country network. The daily average GDR was found to be 0.07624 ± 0.00004 µSv/h, with the daily 95th percentile averaging 0.08026 ± 0.00007 µSv/h throughout the entire period. Our analysis specifically addresses the impact of precipitation on GDR, revealing a higher correlation coefficient for daily 95th percentile values compared to daily averages. Additionally, the influence of the Galerna (GL) event, a natural meteorological phenomenon in this region, on GDR was investigated, noting that it can develop both with and without precipitation. Understanding the interaction between GDR and this meteorological scenario is vital for the development of more reliable radiological monitoring strategies and for safeguarding public health. For this purpose, 40 GL events were analyzed. The present results indicate that GDR values frequently exceed alarm levels when a GL is formed. These GDR peaks should be considered natural radiological events, necessitating the classification of such GDR peaks as false alarms within the radiological monitoring network. To explain them in detail, 10-min time series of precipitation and radon outdoor concentrations were analyzed. The results demonstrate that the GL event with precipitation is a meteorological scenario that can be associated with false alarms. The present analysis provides a distinct contrast in radon behavior under the same meteorological event in case of being developed with precipitation or without precipitation. The findings from this analysis are crucial for avoiding and understanding false radiological alarms triggered in the monitoring network, thereby enhancing the accuracy of radiological data interpretation and improving public safety measures.

Localizing Transitions via Interaction-Induced Flat Bands.

This Letter presents a theory of interaction-induced band-flattening in strongly correlated electron systems. We begin by illustrating an inherent connection between flat bands and index theorems, and presenting a generic prescription for constructing flat bands by periodically repeating local Hamiltonians with topological zero modes. Specifically, we demonstrate that a Dirac particle in an external, spatially periodic magnetic field can be cast in this form. We derive a condition on the field to produce perfectly flat bands and provide an exact analytical solution for the flat band wave functions. Furthermore, we explore an interacting model of Dirac fermions in a spatially inhomogeneous field. We show that certain Hubbard-Stratonovich configurations exist that "rectify" the field configuration, inducing band flattening. We present an explicit model where this localization scenario is energetically favorable-specifically in Dirac systems with nearly flat bands, where the energy cost of rectifying textures is quadratic in the order parameter, whereas the energy gain from flattening is linear. In conclusion, we discuss alternative symmetry-breaking channels, especially superconductivity, and propose that these interaction-induced band-flattening scenarios represent a generic nonperturbative mechanism for spontaneous symmetry breaking, pertinent to many strongly correlated electron systems.

Environmental impacts and BIM: a Portuguese heritage case study

PurposeThis paper aims to compare the possible environmental impacts of different rehabilitation solutions proposed to a case study of a Portuguese vernacular building, bearing in mind the need to search for less impactful practices in accordance with global agendas and, simultaneously, the importance of preserving heritage assets.Design/methodology/approachThe two proposed solutions differ from each other in the selection of materials, varying from the exclusive use of conventional contemporary materials to the use of locally sourced and natural-based materials, considering the Portuguese and local scenario. Results obtained from Life Cycle Assessment procedures are the basis of comparison, which were developed using a Building Information Model (BIM)-integrated analytical plugin.FindingsProducing Heritage Building Information Model (HBIM) models was essential to the analysis, and therefore is a vital part of the used methodology; nevertheless, the modelling of heritage buildings itself plays an important part in preserving heritage assets once it provides precise documenting and managing tools. The results depict variation in values for different indicators in both cases, whose interpretation might need further development to get to deeper conclusions. It has been found that the software tools are still not well adapted to analyse heritage or historic buildings, and therefore adaptations had to take place to overcome these limitations.Originality/valueThe study distinguishes itself by involving the production of HBIM models and the use of BIM-integrated Life Cycle Assessment plugins as well as its analyses, to validate this approach.

When-to-Loop: Enhanced Loop Closure for LiDAR SLAM in Urban Environments Based on SCAN CONTEXT

Global Navigation Satellite Systems (GNSSs) frequently encounter challenges in providing reliable navigation and positioning within urban canyons due to signal obstruction. Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMUs) offers an alternative for autonomous navigation, but they are susceptible to accumulating errors. To mitigate these influences, a LiDAR-based Simultaneous Localization and Mapping (SLAM) system is often employed. However, these systems face challenges in drift and error accumulation over time. This paper presents a novel approach to loop closure detection within LiDAR-based SLAM, focusing on the identification of previously visited locations to correct time-accumulated errors. Specifically, the proposed method leverages the vehicular drivable area and IMU trajectory to identify significant environmental changes in keyframe selection. This approach differs from conventional methods that only rely on distance or time intervals. Furthermore, the proposed method extends the SCAN CONTEXT algorithm. This technique incorporates the overall distribution of point clouds within a region rather than solely relying on maximum height to establish more robust loop closure constraints. Finally, the effectiveness of the proposed method is validated through experiments conducted on the KITTI dataset with an enhanced accuracy of 6%, and the local scenarios exhibit a remarkable improvement in accuracy of 17%, demonstrating improved robustness in loop closure detection for LiDAR-based SLAM.

Neural Network Downscaling to Obtain Local Precipitation Scenarios in the Italian Alps: A Case Study

Neural Network Downscaling to Obtain Local Precipitation Scenarios in the Italian Alps: A Case Study

Study of integration processes in the pharmaceutical market in the context of the development of territorial pharmaceutical ecosystems

Introduction. The ecosystem approach to the organization of business processes in the pharmaceutical market meets the prevailing trends, so the growth index of e-commerce in the retail sector of medicines for 5 years amounted to 1.87. The market distribution sector of medicines in the Russian Federation is characterized by a shift in corporate governance towards marketing cooperation based on aggregation electronic trading platforms (AETPS). Development of territorial pharmaceutical ecosystems (TFE) As a new integrative tool for managing the pharmaceutical development – production – distribution cycle, in order to achieve the target model of PHARMA 2030 at the territorial level, it requires the development of regulatory mechanisms for managing the supply of medicines. Objective: characteristics of integration processes in the industrial, wholesale and retail sectors of the pharmaceutical market to assess the potential competitiveness of territorial pharmaceutical ecosystems. Material and methods. The primary data for the analysis were: volumes of wholesale commodity residues of medicines produced in the Ural Federal District (461 registration certificates; proprietary Price and Distribution Test data for 230 trade names of LP EMIS data 2021–2022; Alpharm data 2021–2022; Datainsight 2022. In the course of the study, a structural analysis of the pharmaceutical market, a functional analysis of participants in the pharmaceutical product chain, and a marketing analysis were carried out. Results. It was found that the dynamics of the AETP segment development corresponds to the outstripping growth of highly integrated platforms: the short-term growth index of 5-pl AETP (Uteca) is 164%, 4-pl (Eapteca) is 55%, 3pl-AETP (averaged) is 36.5%. The absence of formal mechanisms of regional protectionism for the sale of finished dosage forms of local manufacturers in the Ural Federal District has been revealed. We have obtained the results of evaluating the effect of ecosystem integration of certain types of pharmaceutical market participants. For manufacturers of medicinal products, it was determined that the median coefficient of penetration of the local assortment of medicinal products into the pharmaceutical market in the context of ATX groups of level 2 is 0.67, which indicates a significant diversification of the assortment portfolio and the possibility of its aggregation without a barrier of internal competition. In the wholesale drugs sector, the potential for saving margins on medicines has been identified when implementing the scenario of localization of supplies in production regions in low and medium-low price ranges (30 – 550 rubles/unit), varying between 53–67%. It was found that the median share of regional distribution in the Ural Federal District (in packages) is 20.72%. It has been established that the Ural Federal District is one of the leaders in terms of the share of omnichannel consumers in the pharmaceutical market (63%), which is a significant incentive for the integration of organizations in the retail sector of the pharmaceutical market into the ecosystem 5pl platform. Conclusion. Situational analysis of the morphology of cooperative interactions in the pharmaceutical market allowed us to identify the prerequisites for the formation of ecosystem-organized management structures in the pharmaceutical market system – territorial pharmaceutical ecosystems. We have systematized incentives for ecosystem determination of medicines supplies, and found that: an additional channel for the market distribution of the industrial range of medicicnes in the Urals is not associated with a barrier to internal competition of pharmaceutical plants; the inclusion of regional suppliers in TFE will allow to form additional competitiveness in the “buyer's market” in conditions of market dominance of federal suppliers; the omnichannel approach to the distribution of the retail range of TFE in pharmacy organizations has a high projected conversion rate. A detailed assessment of integration processes in certain sectors of the pharmaceutical market did not reveal critical barriers to the introduction of TFE.

RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts.

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.

Developing a predictive method based on the vibration behavior of a naval ship hull model using hybrid fuzzy meta-heuristic algorithms

Ships are complex structures composed of various components with exclusive dynamic behaviors and natural frequencies, so assessing their vibration behavior is essential. Some situations in practical applications could alter the ship's dynamic characteristics and cause significant changes in its vibration behavior. Since the ship's mass is one of the most important dynamic parameters in determining vibration behavior, local mass change can lead to changes in its dynamic characteristics and must be considered. This study aims to develop a method to predict the effect of the location and magnitude of mass change on the ship hull's vibration behavior. It would be feasible to enhance the dynamic behavior and reduce undesirable noises by locating the mass change on the ship hull. In this regard, experimental and numerical modal analysis is performed on a scaled model of a naval ship hull. The baseline FE model is used to calculate the variation in frequencies of the model caused by different local mass change scenarios. Using these measurements a fuzzy system is generated and optimized by Genetic and Particle Swarm Optimization algorithms. Finally, the efficiency of the fuzzy-PSO is validated by different mass change scenarios foreseen on the physical model of the ship hull.

Avaliação da resiliência por indicadores de sustentabilidade em municípios da Bacia Hidrográfica Baixo Pardo/Grande

Population growth has created challenges in the urban environment for basic sanitation, health, education and urban mobility services. In this context, the use of indicators for the management of urban services stands out as a tool for analyzing quality of life, which motivated the proposal of 128 indicators by NBR ISO 37120:2021. The aim of this research was to assess the resilience of the Baixo Pardo/Grande Water Resources Management Unit (UGHRI 12) using sustainability indicators. The method consisted of selecting indicators with information available on a digital basis, standardizing indicators (scale 0 and 1) to assess local and regional sustainability and, finally, analyzing the potential for urban resilience based on sustainable development indicators. Thirteen indicators were selected in advance and, to identify sustainability, the arithmetic mean of the standardized values was calculated. So far, the main results show that the majority of municipalities (75.0%) have an overall score between 0.50 and 0.65, one (8.3%) is above 0.70 and two (16.7%) are below 0.50, which means that they may face the greatest challenge in tackling resilience associated with sustainability, which negates the central hypothesis. So far, the SDG with the most information available in a digital database has been SDG 6. The main contribution was to point out the local and regional scenario for achieving sustainability, in order to highlight the weak points in this river basin.