ISRO's terrain characterization and hazard mapping from Chandrayaan-2 Orbiter High Resolution Camera (OHRC) stereo imagery were central to the safe landing of Chandrayaan-3 - the first successful landing in the lunar south polar region. However, these elevation products were generated with a proprietary pipeline and have not been publicly released. We present a 0.30 m/pixel digital elevation model (DEM) of the Chandrayaan-3 Vikram landing site using a fully open workflow based on ISIS, the Ames Stereo Pipeline, and ALE, achieving sub-meter resolution comparable to mission-reported products. The reconstruction covers 2.18 x 2.24 km with 91.2% valid pixel coverage, 8.1 cm median triangulation error, and 40-50 cm relative vertical precision. The Vikram lander and Pragyan rover are individually resolved. Geodetic alignment to an LROC NAC stereo DEM achieves approximately 30 m horizontal accuracy; pixel-wise validation at 3 m resolution confirms negligible vertical bias (median dz = +0.28 m) and robust dispersion (NMAD = 2.88 m). Stable OHRC stereo convergence requires Community Sensor Model (CSM) camera models; the legacy ISIS camera model failed across two independent sites. At 0.30 m, these DEMs complement LROC NAC DTMs (approximately 1 m), resolving sub-meter hazards below the NAC detection threshold. Applied to the extensive OHRC south polar archive, this methodology provides independent capability for hazard mapping and landing site analysis for upcoming missions including Chandrayaan-4, LUPEX, and Artemis.
The electrification of vertical takeoff and landing aircraft demands high-fidelity battery management systems capable of predicting voltage response under aggressive power dynamics. While data-driven models offer high accuracy, they often require complex architectures and extensive training data. Conversely, equivalent circuit models (ECMs), such as the second-order model, offer physical interpretability but struggle with high C-rate non-linearities. This paper investigates the impact of integrating physics-based information into data-driven surrogate models. Specifically, we evaluate whether physics-informed features allow for the simplification of neural network architectures without compromising accuracy. Using the open-source electric vertical takeoff and landing (eVTOL) battery dataset, we compare pure data-driven models against physics-informed data models. Results demonstrate that physics-informed models achieve comparable accuracy to complex pure data-driven models while using up to 75% fewer trainable parameters, significantly reducing computational overhead for potential on-board deployment.
This study introduces a novel dataset of parking lot boundaries covering fifteen US cities. We generate this dataset using a deep learning segmentation model described in Qiam et al. (2025), and a subsequent post-processing workflow. The dataset, publicly available in shapefile format, enables spatial analysis of parking land use at both inter- and intra-city levels. To estimate the share of off-street land used for off-street parking, we link these polygons with tax parcel datasets, in order to exclude streets and public sidewalks. Off-street surface parking accounts for as little as 3.4% of parcel land in Oakland and as much as 10.7% in Anaheim, with central business districts ranging from 2.3% in Boston to 31.7% in Tulsa.
Transportation and communications, Urban groups. The city. Urban sociology
Abdelmoula Seqqam, Meryam Touirsi, Saliha Najib
et al.
Growing water scarcity, driven by climate change, population growth, and expanding human activities, poses a critical challenge to arid and semi-arid regions worldwide. In Morocco, the Khouribga region illustrates this stress, where limited recharge, recurrent droughts, and intensive groundwater abstraction threaten long-term water security. To address these pressures, this study applied an integrated framework combining remote sensing, Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to delineate groundwater potential zones (GWPZ). Eight hydrogeological parameters, namely rainfall, lithology, slope, lineament density, drainage density, land use and land cover, distance to rivers, and potential evapotranspiration, were weighted through AHP and integrated using the Weighted Linear Combination method. The resulting map shows low (24.97 %), moderate (49.94 %), high (24.81 %) and very high (0.25 %) potential areas. Validation with 72 wells and boreholes achieved 83.33 % concordance and R2 = 0.75, confirming model reliability. High-potential sectors in the north and northeast of Boujaad reflect favorable geological structures, fracture networks, and precipitation patterns. The results offer a practical basis for targeting drilling, designing artificial recharge systems, and protecting infiltration areas. Future work should incorporate higher-resolution hydrogeological data, extended climate series, and machine learning approaches to improve predictive performance and adaptability in other semi-arid contexts.
Le Thi Viet Ha, Pham Nhat Linh, Doan Duc Thanh
et al.
Employee innovation capability is a vital driver of business development and competitive advantage in today's dynamic markets. This study examines the role of corporate culture—specifically corporate vision, core values, customer orientation, and leadership—in shaping employees' innovation capability within enterprises. Drawing on data collected from 890 employees across various Vietnamese companies, this research utilizes the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, analyzed using Smart-PLS version 3.0 software, to test its hypotheses. The findings reveal that corporate vision, while not directly influencing innovation capability, has a significant indirect impact through customer orientation and leadership. Core values, in contrast, demonstrate both direct and indirect positive effects on innovation capability via these mediating factors. Additionally, the study highlights the moderating role of employee experience, showing that more experienced employees tend to be less responsive to visionary initiatives than their less experienced counterparts. This research contributes to the theoretical understanding of how corporate culture influences innovation by integrating Social Exchange Theory, Self-Determination Theory, and Social Learning Theory. It also provides practical implications for organizations seeking to foster innovation, emphasizing the importance of aligning vision and core values with leadership strategies and customer orientation while addressing the potential challenges posed by employee experience. By advancing insights into these dynamics, this study offers valuable contributions to the fields of organizational behavior and innovation management, with implications that extend beyond regional boundaries to global enterprises operating in complex and competitive environments.
Diego Ortiz Barbosa, Mohit Agrawal, Yash Malegaonkar
et al.
Autonomous drones must often respond to sudden events, such as alarms, faults, or unexpected changes in their environment, that require immediate and adaptive decision-making. Traditional approaches rely on safety engineers hand-coding large sets of recovery rules, but this strategy cannot anticipate the vast range of real-world contingencies and quickly becomes incomplete. Recent advances in embodied AI, powered by large visual language models, provide commonsense reasoning to assess context and generate appropriate actions in real time. We demonstrate this capability in a simulated urban benchmark in the Unreal Engine, where drones dynamically interpret their surroundings and decide on sudden maneuvers for safe landings. Our results show that embodied AI makes possible a new class of adaptive recovery and decision-making pipelines that were previously infeasible to design by hand, advancing resilience and safety in autonomous aerial systems.
High-quality marine economic development (HMED) is regarded as a new development pattern of the marine economy in China. This paper aims to examine the dynamic changes and improvement strategies of HMED from the perspective of the green total factor productivity (GTFP) growth. First, the GTFP growth of the marine economy in China’s coastal regions for the period 2007–2020 is calculated using the bootstrapped Malmquist index. Second, the dynamic changes and spatial impacts of the GTFP growth are characterized using kernel density estimation (KDE). Moreover, a novel analytical framework to study the improvement strategies of the GTFP is developed. Within this framework, the fuzzy set qualitative comparative analysis (fsQCA) method is used to explore the paths to achieve HMED. The findings show that: (i) the GTFP growth for coastal regions shows significant fluctuations, suggesting that a stable pattern of marine economic development has yet to be established; (ii) the regional distribution of GTFP growth varies significantly, with provinces with fast GTFP growth gathering resources from neighboring provinces, resulting in a siphon effect; (iii) for coastal provinces that lack certain development conditions, the combined effect of other advantageous factors can be used to achieve HMED. Finally, this study presents policy recommendations for achieving HMED, which can provide insights into the design of China’s future marine economic policies.
First published online 10 September 2024
Muhammad Salman, Salah Uddin Khan, Mansour Shrahili
Rotator cuff (RC) tendinopathy is the most debilitating musculoskeletal condition in general population and is considered to be the third commonly encountered musculoskeltal (MSK) disorder. After getting approval from ethical review committee (ERC) of Rawal Institute of Health Sciences, this Randomized control trail was initiated at Rawal General & Dental Hospital. The duration of this study was 6 months from March 10, 2023 to August 09, 2023. Forty patients of both genders between the age of 25 and 50 years who were suffering from RC tendinopathy were included in this study. Those who had any kind of cardiac complications, neurological disorders, or diabetes mellitus were excluded from this study. Two equal groups ( n = 20 each) were formed. Group A was given kinesio tape (KT) and group B was treated with dry needling (DN). Totally six sessions of each intervention were given to each patient at the rate of two sessions per week along with 10 min of interferential therapy and 10 min of moist packs to each patient. Statistical package for social science (SPSS) version 21 and Microsoft excel were used for the analysis of data. The mean ± standard deviation (SD) of age in group A was 35.30±8.07 and in group B it was 31.51 ± 2.46. The median and interquartile range (IQR) of SF-36 [quality of life (QoL)] at the baseline was 37.64 (1.75) in group A and 37.38 (1.31) in group B, respectively. Md (IQR) postinterventional improved with 91.31 (8.20) in group A, and in group B it was 90.37 (15.78) with P < 0.05. Within-group analysis showed a significant difference ( P < 0.05) in each group. Between-group analysis depicted a significant difference ( P < 0.05) on the Pain Numeric Scale score and an insignificant difference ( P > 0.05) on the basis of QoL (SF-36). It was revealed that KT is more effective in the reduction of disability in terms of pain as compared to DN whereas both interventions are equally effective in improving the QoL in RC tendinopathy.
Vocational rehabilitation. Employment of people with disabilities
This study intends to find out the impact of information quality, celebrity endorsements, and consumer attitudes on flash sale programs on purchase intention in the TikTok application, either partially or simultaneously. This research is a quantitative study using multiple linear regression analysis techniques which was carried out using the help of the SPSS 25 program. Data collection used a questionnaire, samples were consumers who are domiciled in the Special Region of Yogyakarta and used the Tiktok application with a total of 100 respondents. The results of this study concluded that partially the quality of information, celebrity endorsements, and consumer attitudes in flash sale programs have a positive impact on purchase intention.
Jonah Botvinick-Greenhouse, Robert Martin, Yunan Yang
We extend the methodology in [Yang et al., 2023] to learn autonomous continuous-time dynamical systems from invariant measures. The highlight of our approach is to reformulate the inverse problem of learning ODEs or SDEs from data as a PDE-constrained optimization problem. This shift in perspective allows us to learn from slowly sampled inference trajectories and perform uncertainty quantification for the forecasted dynamics. Our approach also yields a forward model with better stability than direct trajectory simulation in certain situations. We present numerical results for the Van der Pol oscillator and the Lorenz-63 system, together with real-world applications to Hall-effect thruster dynamics and temperature prediction, to demonstrate the effectiveness of the proposed approach.
Let T be a self-map on a metric space (X, d). Then T is called the Kannan map if there exists α, 0 < α< 1/2, such that d(T(x), T(y)) <= α[d(x, T(x)) + d(y, T(y))], for all x, y in X. This paper aims to introduce a new method to construct fractal functions using Kannan mappings. First, we give the rigorous construction of fractal functions with the help of the Kannan iterated function system (IFS). We also show the existence of a Borel probability measure supported on the attractor of the Kannan IFS satisfying the strong separation condition. Moreover, we study the smoothness of the constructed fractal functions. We end the paper with some examples and graphical illustrations.
The excessive use of pesticides and fertilizers during agricultural production causes water pollution, which is an important type of non-point source pollution (NSP). Large amounts of harmful substances, such as nitrogen and phosphorus, flow into surface water along with farmland runoff, leading to eutrophication and other problems. However, the pollutant discharge capacity of different types of cultivated land varies greatly. Areas sensitive to NSP are areas with rich crop types, large spatial differences in crop growth, and complex planting patterns. These factors can cause different amounts of fertilizer used in and absorbed by the crops to influence the emission intensity of pollutants. NSP intensity mapping can reflect the spatial distribution of lands’ pollutant discharge capacity and it can provide a basis for pollution control. However, when estimating NSP intensity, existing methods generally treat cultivated land as a category and ignore how complex crop conditions impact pollution intensity. Remote sensing technology enables the classification and monitoring of ground objects, which can provide rich geographical data for NSP intensity mapping. In this study, we used a phenology–GPP (gross primary productivity) method to extract the spatial distribution of crops in the Yuecheng reservoir catchment area from Sentinel-2 remote sensing images and the overall accuracy reached 85%. Moderate resolution imaging spectroradiometer (MODIS) GPP data were used to simulate the spatial distribution of crop growth. Finally, a new model that is more suitable for farmland was obtained by combining this large amount of remote sensing data with existing mapping models. The findings from this study highlight the differences in spatial distributions between total nitrogen and total phosphorous; they also provide the means to improve NSP intensity estimations.
We explore the potential of three-dimensional data assimilation for assimilating sparsely-distributed 2-metre temperature observations across the coupled atmosphere-land interface into the soil moisture. Using idealised twin experiments with the limited-area modelling platform TerrSysMP and synthetic observations, we avoid model biases and directly control errors in the initial conditions and observations. These experiments allow us to test hourly data assimilation with a localised ensemble Kalman filter, as often used for mesoscale data assimilation. We find here an error reduction of such an ensemble Kalman filter approach compared to daily-updating with a one-dimensional simplified extended Kalman filter. We attribute this improvement to the ensemble approximation of the sensitivities and the more frequent updates with the ensemble Kalman filter. The hourly updates result hereby into a positive assimilation impact during daytime and a neutral impact during night. With a three-dimensional ensemble Kalman filter, we can directly assimilate screen-level observations at their respective position into the soil moisture, skipping the otherwise needed spatial interpolation step. These findings suggest an emerging potential for the localised three-dimensional ensemble Kalman filter to hourly assimilate screen-level observations into coupled atmosphere-land models.
We propose to use agent-based simulation models for the development of statistical methods in Official Statistics, especially in relation with the new digital data sources. We present a mobile network data simulator which is managed through the simutils R package which provides geospatial representations of the simulated data. While the synthetic data are produced by an external tool, our simutils package allows an R user to parameterize and run this external simulation tool, to build geospatial data structures from the simulation output or to compute several aggregates. The geospatial data structures were designed with the purpose of using them in a visualization package too. Useful simulation models require the incorporation of real metadata from mobile telecommunication networks driving us to the inclusion of functionalities allowing the user to specify and validate them. All metadata are specified using XML file whose structure are defined in corresponding XSD files. Our R package includes example data sets and we show here how validate the metadata, how to run a simulation and how build the geospatial data structures and how to compute different aggregates.
In this paper, we consider the problem of formally verifying a Neural Network (NN) based autonomous landing system. In such a system, a NN controller processes images from a camera to guide the aircraft while approaching the runway. A central challenge for the safety and liveness verification of vision-based closed-loop systems is the lack of mathematical models that captures the relation between the system states (e.g., position of the aircraft) and the images processed by the vision-based NN controller. Another challenge is the limited abilities of state-of-the-art NN model checkers. Such model checkers can reason only about simple input-output robustness properties of neural networks. This limitation creates a gap between the NN model checker abilities and the need to verify a closed-loop system while considering the aircraft dynamics, the perception components, and the NN controller. To this end, this paper presents NNLander-VeriF, a framework to verify vision-based NN controllers used for autonomous landing. NNLander-VeriF addresses the challenges above by exploiting geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs to the NN controller. By converting this model into a NN (with manually assigned weights) and composing it with the NN controller, one can capture the relation between aircraft states and control actions using one augmented NN. Such an augmented NN model leads to a natural encoding of the closed-loop verification into several NN robustness queries, which state-of-the-art NN model checkers can handle. Finally, we evaluate our framework to formally verify the properties of a trained NN and we show its efficiency.