Hasil untuk "Land use"

Fan Li, Chang Meng, Jiaqi Fu et al.

Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within the specific page. While the majority of research has been focusing on the sequential recommendation task that improves users' feedback in Stage II, there has been little investigation on how to achieve better page navigation in Stage I. To fill this gap, we formally define the task of Personalized Landing Page Modeling (PLPM) into the field of recommender systems: Given a user upon app entry, the goal of PLPM is to proactively select the most suitable landing page from a set of candidates (e.g., functional tabs, content channels, or aggregation pages) to optimize the short-term PDR metric and the long-term user engagement and satisfaction metrics, while adhering to industrial constraints. Additionally, we propose KLAN (Kuaishou Landing-page Adaptive Navigator), a hierarchical solution framework designed to provide personalized landing pages under the formulation of PLPM. KLAN comprises three key components: (1) KLAN-ISP captures inter-day static page preference; (2) KLAN-IIT captures intra-day dynamic interest transitions and (3) KLAN-AM adaptively integrates both components for optimal navigation decisions. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of KLAN, obtaining +0.205% and +0.192% improvements on in Daily Active Users (DAU) and user Lifetime (LT). Our KLAN is ultimately deployed on the online platform at full traffic, serving hundreds of millions of users. To promote further research in this important area, we will release our dataset and code upon paper acceptance.

Tian Xie, Menghui Jiang, Huanfeng Shen et al.

Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inverse problem, which becomes particularly severe when only a single band is available. In this paper, we propose a deeply coupled framework integrating mechanistic modeling and machine learning to enhance the accuracy and generalizability of single-channel LST retrieval. Training samples are generated using a physically-based radiative transfer model and a global collection of 5810 atmospheric profiles. A physics-informed machine learning framework is proposed to systematically incorporate the first principles from classical physical inversion models into the learning workflow, with optimization constrained by radiative transfer equations. Global validation demonstrated a 30% reduction in root-mean-square error versus standalone methods. Under extreme humidity, the mean absolute error decreased from 4.87 K to 2.29 K (53% improvement). Continental-scale tests across five continents confirmed the superior generalizability of this model.

I. V. Florinsky, S. O. Zharnova

Geomorphometric modeling and mapping of ice-free Antarctic areas is promising for obtaining new quantitative knowledge about the topography of these unique landscapes and for the further use of morphometric information in Antarctic research. Within the framework of a project of creating a physical geographical thematic scientific reference geomorphometric atlas of ice-free terrains of Antarctica, we performed geomorphometric modeling and mapping of key ice-free areas of the Sôya Coast (the east coast of Lützow-Holm Bay, Queen Maud Land, East Antarctica). These include the Flatvaer (Ongul) Islands, Langhovde Hills, Breidvågnipa, Skarvsnes Foreland, Skallen Hills, and Skallevikhalsen Hills. As input data for geomorphometric modeling and mapping, we used five fragments of the Reference Elevation Model of Antarctica. For the six ice-free areas and adjacent glaciers, we derived models and maps of eleven most scientifically important morphometric variables (i.e., slope, aspect, horizontal curvature, vertical curvature, minimal curvature, maximal curvature, catchment area, topographic wetness index, stream power index, total insolation, and wind exposition index). The obtained models and maps describe the ice-free topography of the Sôya Coast in a rigorous, quantitative, and reproducible manner. New morphometric data can be useful for further geological, geomorphological, glaciological, ecological, and hydrological studies of this region.

Anja Sheppard, Chris Reale, Katherine A. Skinner

Safely landing on the lunar surface is a challenging task, especially in the heavily-shadowed South Pole region where traditional vision-based hazard detection methods are not reliable. The potential existence of valuable resources at the lunar South Pole has made landing in that region a high priority for many space agencies and commercial companies. However, relying on a LiDAR for hazard detection during descent is risky, as this technology is fairly untested in the lunar environment. There exists a rich log of lunar surface data from the Lunar Reconnaissance Orbiter (LRO), which could be used to create informative prior maps of the surface before descent. In this work, we propose a method for generating stochastic elevation maps from LRO data using Gaussian processes (GPs), which are a powerful Bayesian framework for non-parametric modeling that produce accompanying uncertainty estimates. In high-risk environments such as autonomous spaceflight, interpretable estimates of terrain uncertainty are critical. However, no previous approaches to stochastic elevation mapping have taken LRO Digital Elevation Model (DEM) confidence maps into account, despite this data containing key information about the quality of the DEM in different areas. To address this gap, we introduce a two-stage GP model in which a secondary GP learns spatially varying noise characteristics from DEM confidence data. This heteroscedastic information is then used to inform the noise parameters for the primary GP, which models the lunar terrain. Additionally, we use stochastic variational GPs to enable scalable training. By leveraging GPs, we are able to more accurately model the impact of heteroscedastic sensor noise on the resulting elevation map. As a result, our method produces more informative terrain uncertainty, which can be used for downstream tasks such as hazard detection and safe landing site selection.

Sebastian Schroder, Yao Deng, Alice James et al.

Uncrewed Aerial Vehicles (UAVs) have become a focal point of research, with both established companies and startups investing heavily in their development. This paper presents our iterative process in developing a robust autonomous marker-based landing system, highlighting the key challenges encountered and the solutions implemented. It reviews existing systems for autonomous landing processes, and through this aims to contribute to the community by sharing insights and challenges faced during development and testing.

Zainab Tahir, Muhammad Haseeb, Syed Amer Mahmood et al.

Abstract This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.

Olivia Emelie Engström, Hisayo Katsui, Lieketseng Ned

People who sustain traumatic brain injuries (TBIs) often experience unmet rehabilitation needs. The aim of our research was to explore how the invisible aspects of traumatic brain injury affect the experiences of survivors of TBI in accessing the necessary medical, social, and financial assistance. Using Giorgi’s descriptive phenomenological inquiry, we purposefully sampled 11 participants who had experienced TBI when aged 13–27 for interviews. The time since their injuries ranged from 7 to 37 years. Three key themes emerged: (1) lack of knowledge and guidance in medical services, (2) lack of social service assistance, and (3) battles with insurance companies. Our findings show that, due to the hidden nature of TBI-related disabilities and a general lack of societal knowledge about TBI outcomes, survivors face significant difficulties in accessing essential medical, social, and financial services. This study underscores the critical need to address the challenges faced by youth survivors of TBI, as their injuries occur during a pivotal developmental phase when they are developing psychosocial skills, pursuing education, and transitioning into the workforce. Delays or lack of proper medical, social, and financial support hinder rehabilitation and the successful reintegration of these youth into society.

Yi Liu, Chenhao Sun, Hao Ye et al.

Remote sensing image change detection (CD) is essential for analyzing land surface changes over time, with a significant challenge being the differentiation of actual changes from complex scenes while filtering out pseudo-changes. A primary contributor to this challenge is the intra-class dynamic changes due to phenological characteristics in natural areas. To overcome this, we introduce the InPhea model, which integrates phenological features into a remote sensing image CD framework. The model features a detector with a differential attention module for improved feature representation of change information, coupled with high-resolution feature extraction and spatial pyramid blocks to enhance performance. Additionally, a constrainer with four constraint modules and a multi-stage contrastive learning approach is employed to aid in the model's understanding of phenological characteristics. Experiments on the HRSCD, SECD, and PSCD-Wuhan datasets reveal that InPhea outperforms other models, confirming its effectiveness in addressing phenological pseudo-changes and its overall model superiority.

Kento Tomita, Koki Ho

Onboard terrain sensing and mapping for safe planetary landings often suffer from missed hazardous features, e.g., small rocks, due to the large observational range and the limited resolution of the obtained terrain data. To this end, this paper develops a novel real-time stochastic terrain mapping algorithm that accounts for topographic uncertainty between the sampled points, or the uncertainty due to the sparse 3D terrain measurements. We introduce a Gaussian digital elevation map that is efficiently constructed using the combination of Delauney triangulation and local Gaussian process regression. The geometric investigation of the lander-terrain interaction is exploited to efficiently evaluate the marginally conservative local slope and roughness while avoiding the costly computation of the local plane. The conservativeness is proved in the paper. The developed real-time uncertainty quantification pipeline enables stochastic landing safety evaluation under challenging operational conditions, such as a large observational range or limited sensor capability, which is a critical stepping stone for the development of predictive guidance algorithms for safe autonomous planetary landing. Detailed reviews on background and related works are also presented.

Jun Xiang, Drake Essick, Luiz Gonzalez Bautista et al.

Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing management due to the congested airspace near vertiports. In this paper, we propose a landing trajectory prediction model for UAS based on Generative Adversarial Network (GAN). The GAN is a prestigious neural network that has been developed for many years. In previous research, GAN has achieved many state-of-the-art results in many generation tasks. The GAN consists of one neural network generator and a neural network discriminator. Because of the learning capacity of the neural networks, the generator is capable to understand the features of the sample trajectory. The generator takes the previous trajectory as input and outputs some random status of a flight. According to the results of the experiences, the proposed model can output more accurate predictions than the baseline method(GMR) in various datasets. To evaluate the proposed model, we also create a real UAV landing dataset that includes more than 2600 trajectories of drone control manually by real pilots.

Mikhail Kistyarev, Xinhua Wang

Carrier landing of aircrafts is a challenge for control due to the existence of nonlinear wind disturbances and the requirements of changing reference trajectories. In this paper, a robust landing control system is presented for carrier landing of unmanned F/A-18 aircraft. In the control system, an augmented observer is applied to estimate the combined disturbances in the pitch dynamics of F/A-18 aircraft during carrier landing. Therefore, the control performance is improved through the control compensations from these estimations. Additionally, the controllers are designed to regulate the velocity, rate of descent and vertical position. A full model, including the nonlinear flight dynamics, controller, carrier deck motion, wind and measurement noise, is constructed numerically and implemented in software. Combining the observer with a proportional-derivative (PD) control, the proposed pitch control shows the better transient characteristics and stronger robustness than a proportional-integral-derivative (PID) controller. The simulations verify that the designed control system can make the aircraft quickly track a time-varying reference despite the existence of nonlinear disturbances and noise.

Sandeep Banik, Jinrae Kim, Naira Hovakimyan et al.

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their potential, the critical phases of take-off and landing in uncertain and dynamic environments pose significant safety challenges due to environmental uncertainties, sensor noise, and system-level interactions. This paper presents an integrated approach combining vision-based sensor fusion with System-Theoretic Process Analysis (STPA) to enhance the safety and robustness of VTOL UAV operations during take-off and landing. By incorporating fiducial markers, such as AprilTags, into the control architecture, and performing comprehensive hazard analysis, we identify unsafe control actions and propose mitigation strategies. Key contributions include developing the control structure with vision system capable of identifying a fiducial marker, multirotor controller and corresponding unsafe control actions and mitigation strategies. The proposed solution is expected to improve the reliability and safety of VTOL UAV operations, paving the way for resilient autonomous systems.

Rafiq Gheddar

As Islamic banks grow and evolve, pricing methods for their services have become essential to study and implement. This study highlights the significance of understanding the factors influencing Islamic banking service pricing in Algeria. The study aims to analyze how Islamic banks price their services, with a focus on cost, market, and value strategies. Additionally, it seeks to evaluate and recommend ways to enhance the current practices of banks operating in the national market. Algeria is experiencing rapid growth in Islamic banking, making it an ideal location to study this subject. The country is home to two Islamic banks, Al Baraka Bank and Al Salam Bank. Algeria was selected as a new market to allow the findings to be applicable to similar situations elsewhere. The research utilizes secondary data obtained from available information on Islamic bank service fees, comparing them with those of traditional banks. It also conducts financing simulations in both banks and compares them with the traditional theoretical framework. Data was gathered from various sources, including bank websites, annual reports, and previous studies. The research reveals that Algerian Islamic banks do not prioritize scientific methods in pricing their services. The results suggest that these banks operate within a traditional framework under the oversight of the central bank. The central bank's rules depend on the prices of services conventional banks offer. This shapes how customers perceive these banks as representatives of Islamic banking. Islamic banks can utilize the study's results to develop pricing strategies that are more effective and compliant with Islamic law. Regulators can utilize these findings to formulate enhanced policies to bolster the Islamic banking sector. The results also assist researchers in delving deeper into the realm of Islamic banking service pricing. This study refutes the hypothesis that Algerian Islamic banks have enhanced the efficiency of their service pricing by adopting models in line with Islamic finance principles, such as profit-sharing, while considering market conditions and service value. They should embrace more pragmatic and beneficial pricing strategies that align with Islamic law, cater to customer needs, and enhance their competitiveness and value in the national banking market.

Leila HAGHSHENAS

When, in 1926, the renowned Edwardian poet and novelist Vita Sackville-West (1892-1962) travelled to Iran to visit her husband, Harold Nicolson, who was then serving as a diplomat in Teheran, she could not have imagined how disconnected her image of Persia was from reality. Despite her surprise and disappointment regarding some aspects of the country, Sackville-West’s Passenger to Teheran conveys a fantasized image of Persia. This paper aims to show that her text is influenced by inherited prejudices that are responsible for her picturing Persia as a land of exotic and romantic adventures. Instead of relying on historical facts, she deliberately invites fiction into her travelogue. Hence, despite the linear structure of the travelogue, the writer’s Edwardian representation of Persia is constantly shifted and pushed aside by the reality of life there. Building on Edward Said’s theories, this paper argues that Sackville-West’s subjective approach to her travelogue and her use of fictional elements contribute to the persistence of a mythical image of Persia despite the emergence of shifting lines and reliable information about the country.

Janani Venkatraman Jagatha, Christoph Schneider, Tobias Sauter

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods’ black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m³ to 3.6 µg/m³ and the root mean squared error (RMSE) from 9.86 µg/m³ to 4.23 µg/m³ when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM_2.5 concentrations with an MAE of less than 5 µg/m³ for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R² from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM_2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM_2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

J. Czarnecki, A. Bertoni, G. Goldoni et al.

We investigate the effective Lande factor in semiconductor nanowires with strong Rashba spin-orbit coupling. Using the $\mathbf{k}\cdot\mathbf{p}$ theory and the envelope function approach we derive a conduction band Hamiltonian where the tensor $g^*$ is explicitly related to the spin-orbit coupling constant $α_R$. Our model includes orbital effects from the Rashba spin-orbit term, leading to a significant enhancement of the effective Lande factor which is naturally anisotropic. For nanowires based on the low-gap, high spin-orbit coupled material InSb, we investigate the anisotropy of the effective Lande factor with respect to the magnetic field direction, exposing a twofold symmetry for the bottom gate architecture. The anisotropy results from the competition between the localization of the envelope function and the spin polarization of the electronic state, both determined by the magnetic field direction.

Jian Song, Hongruixuan Chen, Naoto Yokoya

Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages. We will release SyntheWorld to facilitate remote sensing image processing research.

Samuel David Vargas-Neira, Rodrigo Rodríguez-Cepeda

El presente artículo muestra los resultados de una intervención didáctica piloto, en la cual se diseñaron e implementaron algunos Trabajos Prácticos de Laboratorio (TPL) contextualizados, con el propósito de evaluar su incidencia en el aprendizaje significativo de parámetros fisicoquímicos de calidad en aceites comestibles. La intervención se realizó con un grupo de 14 estudiantes de ingeniería de alimentos, donde se analizaron las respuestas de los estudiantes a cuestionarios inicial y final, además de informes de trabajo práctico, desde un enfoque cualitativo. Se identificó que el diseño e implementación de TPL favorecen el aprendizaje significativo de los conceptos químicos asociados a la calidad de los aceites en los ingenieros en formación. En este sentido, los TPL fomentan criterios para evaluar la calidad de un producto en cuanto a aceptación o rechazo, lo cual permite desarrollar habilidades para la toma de decisiones.

Sofia McLeod, Gabriele Meoni, Dario Izzo et al.

Event sensing is a major component in bio-inspired flight guidance and control systems. We explore the usage of event cameras for predicting time-to-contact (TTC) with the surface during ventral landing. This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing. Our core contributions are a novel contrast maximisation formulation for event-based divergence estimation, and a branch-and-bound algorithm to exactly maximise contrast and find the optimal divergence value. GPU acceleration is conducted to speed up the global algorithm. Another contribution is a new dataset containing real event streams from ventral landing that was employed to test and benchmark our method. Owing to global optimisation, our algorithm is much more capable at recovering the true divergence, compared to other heuristic divergence estimators or event-based optic flow methods. With GPU acceleration, our method also achieves competitive runtimes.

董彦军, 王晓甜, 马红明 et al.

电力负荷具有非线性和时序性的特点，为了深入研究各特征变量对于电力负荷预测的重要性，进而获得更高的电力负荷预测精度，提出了基于随机森林（random forest，RF）算法及长短期记忆网络（long short-term memory，LSTM）的混合负荷预测模型。首先根据时间日期因素及气候因素建立高维特征数据集作为随机森林模型的输入，通过随机森林算法筛选出重要特征量，并使其与历史负荷结合作为LSTM模型的输入，经过粒子群算法对LSTM模型进行参数寻优后得到RF-LSTM混合模型及负荷预测结果。使用该方法对河北电网某台区的电力负荷进行预测，结果表明该混合模型的预测精度比未经特征变量筛选的传统单一的随机森林算法、LSTM模型以及BP神经网络更为理想。