Hasil untuk "Land use"

Xiaohui Yan, Shiqing Liu, Yongjian Su et al.

Free radicals are a class of reactive substances produced during the operation of proton exchange membrane fuel cells (PEMFCs), which have a great impact on the durability of PEMFCs. Previous research on the fuel cell degradation mechanism mainly focused on the degradation of the membrane electrode assembly (MEA) in high Pt loading PEMFCs, especially the chemical degradation of proton exchange membrane (PEM). However, there are significant differences in the characteristics and performance of PEMFCs with low and high Pt loading especially under the high current density, which is mainly due to the oxygen transport process in cathode catalyst layers (CCLs). Currently, few relevant research has explored the impact of chemical degradation on oxygen transport in the cathode of low-Pt PEMFCs. Therefore, this work investigates the effects of free radical attack on the structure of ionomer films, the local oxygen transport process and the evolution of the ionomer coated Pt/C structure in CCLs through physicochemical characterizations, electrochemical measurements and molecular dynamic simulations. Our research found that free radical attacks decreased the electrochemical active area of CCLs, but it also temporarily improved the cell performance at high current densities. Furthermore, molecular dynamics simulations demonstrated that the ionomer exhibited higher oxygen self-diffusion and a more relaxed structure after degradation.

Henning Schulte, Christian Ammon, Frauke Hagenkamp-Korth et al.

Previous studies have shown that ammonia emissions can be continuously reduced through the application of a urease inhibitor (UI) in cattle and pig farming. However, there is no information on whether the use of these inhibitors also has an effect on other emissions, and whether it leads to an increase or decrease in these emissions. In this study, carbon dioxide, ammonia, methane, nitrous oxide and odour emissions were measured in three mechanically ventilated, fully slatted pig fattening houses in Germany during 2019–2020. The UI was applied daily to compartments, and effects on emission values were comparatively analysed using four different calculation approaches: linear mixed model, direct case-control, case-control in time and a novel ratio-difference approach. As expected, a significant reduction in ammonia emissions of 22–24 % was observed across all four calculation approaches and all three farms, confirming the effectiveness of the UI; no decisive effects on carbon dioxide, methane or odour emissions were found. Effects on nitrous oxide emissions could not be reliably analysed due to low concentrations which were below the Fourier-transform infrared spectroscopy (FTIR) quantification limit. It is recommended to calculate the reduction effect using a combined approach so that over- and underestimation of the effect can be avoided. Two approaches are available for this purpose: the ratio-difference and linear mixed model. The ratio-difference approach has a simplicity of calculation and the ability to achieve results very similar to those of the linear mixed model.

Putri Mustika Sari, Agustinur Agustinur, Chairuddin Chairuddin

The production of shallots is fluctuating which means tends to decrease and result in harmony of farmers in terms of economy. One of the causes of pest attacks on aquaculture land, therefore there is a need for control. So this study aims to reduce the number of population of pest insects, and increase the diversity of natural enemy insects in the cultivation of red onion by utilizing the Refugia Zinnia elegans as a edge plant. The research method uses a non -factorial random group design with 5 treatments (Zinnea elegans), and 5 replications, data analysis by calculating the number of insect populations, predators, parasitoid Insects found in the Refugia Zinnia elegans plant are cultivated red onion. Observations are made every week since the plant is two weeks old until harvest. The results showed that the number of population of pest insects obtained in red onion plants with fewer refugia plants namely Gryllotalpa spp 12, Spodoptera exigua 18 tails, Spodoptera litura 26 tails, Liriomyza chinensis 20 tails, compared to the number of pest populations that do not use refugia plants as The edge plant namely Gryllotalpa spp 38 tails, Spodoptera exigua 28 tails, Spodoptera litura 56 tails, Liriomyza chinensis 50 tails. From the results of the results obtained show that by planting Zinnia Elegans around the area of red onion cultivation as a biological control, can reduce pest attacks on the red onion cultivation land, West Aceh. There are 5 ordo of natural enemy insects found in the planting area namely Araneae, Diptera, Coleoptera, Hymenoptera, and Dermaptera. The diversity index obtained is 1.92, with the category of moderate species diversity size (1 <h '<3), which means there is a balance and stability in the red onion cultivation ecosystem

Benjamín Castro-Cancino, Waldo Pérez-Martínez, Paulina Vidal-Páez et al.

In the mountainous and foothill areas of Santiago, Chile, debris flows and debris floods have been recurrent over recent decades, triggered by short-duration, high-intensity summer rainfall events. These events have caused significant damage to infrastructure and have affected the population, including loss of human lives. This study assesses the susceptibility to debris flow and debris flood generation in the Arrayán and Gualtatas stream basins, located in the Metropolitan Region, using satellite and cartographic data. A Susceptibility Index (SI) was determined through the analysis of 14 conditioning factors, grouped into three main categories: geology, geomorphology, and soil conditions. The weighting and ranking of each factor’s importance were carried out using the Analytic Hierarchy Process (AHP). The results, presented in a susceptibility map, indicate that 60.78% of the study area exhibits low to very low susceptibility, 24.64% moderate susceptibility, and 14.58% high to very high susceptibility, concentrated in stream headwaters, steep slopes, and areas with unconsolidated deposits. Recent debris flow events that have reached urban areas coincide with high-susceptibility zones, validating the methodology and cartographic products, which can support land-use planning and risk management efforts.

Ruiyue Huang, Claire E. Heaney, Maarten van Reeuwijk

The Neural Physics approach is used to determine the parameters of a simple land-surface model using PyTorch's backpropagation engine to carry out the optimisation. In order to test the inverse model, a synthetic dataset is created by running the model in forward mode with known parameter values to create soil temperature time series that can be used as observations for the inverse model. We show that it is not possible to obtain a reliable parameter estimation using a time series of soil temperature observed at a single depth. Using measurements at two depths, reliable parameter estimates can be obtained although it is not possible to differentiate between latent and sensible heat fluxes. We apply the inverse model to urban flux tower data in Phoenix, United States, and show that the thermal conductivity, volumetric heat capacity and the combined sensible-latent heat transfer coefficient can be reliably estimated using an observed value for the effective surface albedo. The resulting model accurately predicts the outgoing longwave radiation, conductive soil fluxes and the combined sensible-latent heat fluxes.

Ziyun Zhang, Xinyi Liu, Xiaoyi Zhang et al.

External knowledge has played a crucial role in the recent development of computer use agents. We identify a critical knowledge-execution gap: retrieved knowledge often fails to translate into effective real-world task execution. Our analysis shows even 90% correct knowledge yields only 41% execution success rate. To bridge this gap, we propose UI-Evol, a plug-and-play module for autonomous GUI knowledge evolution. UI-Evol consists of two stages: a Retrace Stage that extracts faithful objective action sequences from actual agent-environment interactions, and a Critique Stage that refines existing knowledge by comparing these sequences against external references. We conduct comprehensive experiments on the OSWorld benchmark with the state-of-the-art Agent S2. Our results demonstrate that UI-Evol not only significantly boosts task performance but also addresses a previously overlooked issue of high behavioral standard deviation in computer use agents, leading to superior performance on computer use tasks and substantially improved agent reliability.

Wenfeng Zhan, Benjamin Bechtel, Huilin Du et al.

Escalating urban heat, driven by the convergence of global warming and rapid urbanization, is a profound threat to billions of city dwellers. The science directing urban heat adaptation is strongly influenced by studies that use satellite-based land surface temperature (LST), which is readily available globally and address data gaps in cities, particularly in the Global South. LST, however, is a poor surrogate for near-surface air temperature, physiologically relevant human thermal comfort, or direct human heat exposure. This flawed practice leads to issues for several downstream use cases by inflating adaptation benefits, distorting the magnitude and variability of urban heat signals across scales, and thus misguiding urban adaptation policy. We argue that satellite-based LST must be treated as a distinct indicator of surface climate, which, though relevant to the urban surface energy budget, can be frequently decoupled from human-relevant thermal impacts especially during daytime. Only by a disciplined application of this variable, combined with complementary datasets, process-based and data-driven models, as well as interdisciplinary collaboration, can urban adaptation design and policy be effectively advanced.

Fangzhou Zhao, Yao Sun, Jianglin Lan et al.

The primary challenge in autonomous lunar landing missions lies in the unreliable local control system, which has limited capacity to handle high-dynamic conditions, severely affecting landing precision and safety. Recent advancements in lunar satellite communication make it possible to establish a wireless link between lunar orbit satellites and the lunar lander. This enables satellites to run high-performance autonomous landing algorithms, improving landing accuracy while reducing the lander's computational and storage load. Nevertheless, traditional communication paradigms are not directly applicable due to significant temperature fluctuations on the lunar surface, intense solar radiation, and severe interference caused by lunar dust on hardware. The emerging technique of semantic communication (SemCom) offers significant advantages in robustness and resource efficiency, particularly under harsh channel conditions. In this paper, we introduce a novel SemCom framework for transmitting images from the lander to satellites operating the remote landing control system. The proposed encoder-decoder dynamically adjusts the transmission strategy based on real-time feedback from the lander's control algorithm, ensuring the accurate delivery of critical image features and enhancing control reliability. We provide a rigorous theoretical analysis of the conditions that improve the accuracy of the control algorithm and reduce end-to-end transmission time under the proposed framework. Simulation results demonstrate that our SemCom method significantly enhances autonomous landing performance compared to traditional communication methods.

Neon Srinivasu, Amit Shivam, Nobin Paul

This paper develops a new guidance law for powered descent landing of a rocket-powered vehicle. The proposed law derives the acceleration command for a point mass model of the vehicle by expressing velocity as a dynamical system undergoing supercritical transcritical bifurcation with three bifurcation parameters. The parameters are designed such that the stable equilibrium points of the velocity dynamics correspond to the guided targeting state, that is, the landing point. Numerical simulations are performed to demonstrate the working of the proposed guidance law.

Zhuoyue Tan, Boyong He, Yuxiang Ji et al.

This paper presents VisLanding, a monocular 3D perception-based framework for safe UAV (Unmanned Aerial Vehicle) landing. Addressing the core challenge of autonomous UAV landing in complex and unknown environments, this study innovatively leverages the depth-normal synergy prediction capabilities of the Metric3D V2 model to construct an end-to-end safe landing zones (SLZ) estimation framework. By introducing a safe zone segmentation branch, we transform the landing zone estimation task into a binary semantic segmentation problem. The model is fine-tuned and annotated using the WildUAV dataset from a UAV perspective, while a cross-domain evaluation dataset is constructed to validate the model's robustness. Experimental results demonstrate that VisLanding significantly enhances the accuracy of safe zone identification through a depth-normal joint optimization mechanism, while retaining the zero-shot generalization advantages of Metric3D V2. The proposed method exhibits superior generalization and robustness in cross-domain testing compared to other approaches. Furthermore, it enables the estimation of landing zone area by integrating predicted depth and normal information, providing critical decision-making support for practical applications.

Wendong Yao, Binhua Huang, Soumyabrata Dev

Forecasting high-resolution land subsidence is a critical yet challenging task due to its complex, non-linear dynamics. While standard architectures like ConvLSTM often fail to model long-range dependencies, we argue that a more fundamental limitation of prior work lies in the uni-modal data paradigm. To address this, we propose the Multi-Modal Spatio-Temporal Transformer (MM-STT), a novel framework that fuses dynamic displacement data with static physical priors. Its core innovation is a joint spatio-temporal attention mechanism that processes all multi-modal features in a unified manner. On the public EGMS dataset, MM-STT establishes a new state-of-the-art, reducing the long-range forecast RMSE by an order of magnitude compared to all baselines, including SOTA methods like STGCN and STAEformer. Our results demonstrate that for this class of problems, an architecture's inherent capacity for deep multi-modal fusion is paramount for achieving transformative performance.

David Rowe, Tibor Bece

In the 1990s Land Mobile Radio (LMR) systems evolved from analog frequency modulation (FM) to standardised digital systems. Both digital and analog FM systems now co-exist in various services and exhibit similar speech quality. The architecture of many digital radios retains the analog FM modulator and demodulator from legacy analog radios, but driven by a multi-level digital pulse train rather than an analog voice signal. We denote this architecture baseband FM (BBFM). In this paper we describe a modern machine learning approach that uses an autoencoder to send high quality, 8 kHz bandwidth speech over the BBFM channel. The speech quality is shown to be superior to analog FM over simulated LMR channels in the presence of fading, and a demonstration of the system running over commodity UHF radios is presented.

Edward Dodzi Amekah, Emmanuel Wendsongre Ramde, David Ato Quansah et al.

The shift towards renewable energy sources has heightened the interest in solar photovoltaic (SPV) systems, particularly in grid-connected configurations, to enhance energy security and reduce carbon emissions. Grid-tied SPVs face power quality challenges when specific grid codes are compromised. This study investigates and upgrades an integrated 90 kWp solar plant within a distribution network, leveraging data from Ghana's Energy Self-Sufficiency for Health Facilities (EnerSHelF) project. The research explores four scenarios for SPV placement optimization using dynamic programming and the Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA). A Python-based simulation identifies three scenarios, high load nodes, voltage drop nodes, and system loss nodes, as the points for placing PV for better performance. The analysis revealed 85 %, 82.88 %, and 100 % optimal SPV penetration levels for placing the SPV at high load, voltage drop, and loss nodes. System active power losses were reduced by 72.97 %, 71.52 %, and 70.15 %, and reactive power losses by 73.12 %, 71.86 %, and 68.11 %, respectively, by placing the SPV at the above three categories of nodes. The fourth scenario applies to CNAFTSSA, achieving 100 % SPV penetration and reducing active and reactive power losses by 72.33 % and 72.55 %, respectively. This approach optimizes the voltage regulation (VR) from 24.92 % to 4.16 %, outperforming the VR of PV placement at high load nodes, voltage drop nodes, and loss nodes, where the voltage regulations are 5.25 %, 9.36 %, and 9.64 %, respectively. The novel CNAFTSSA for optimal SPV placement demonstrates its effectiveness in achieving higher penetration levels and improving system losses and VR. The findings highlight the effectiveness of strategic SPV placement and offer a comprehensive methodology that can be adapted for similar power distribution systems.

Pallavi Jain, Dino Ienco, Roberto Interdonato et al.

Pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive zero-shot classification capabilities with free-form prompts and even show some generalization in specialized domains. However, their performance on satellite imagery is limited due to the underrepresentation of such data in their training sets, which predominantly consist of ground-level images. Existing prompting techniques for satellite imagery are often restricted to generic phrases like a satellite image of ..., limiting their effectiveness for zero-shot land-use and land-cover (LULC) mapping. To address these challenges, we introduce SenCLIP, which transfers CLIPs representation to Sentinel-2 imagery by leveraging a large dataset of Sentinel-2 images paired with geotagged ground-level photos from across Europe. We evaluate SenCLIP alongside other SOTA remote sensing VLMs on zero-shot LULC mapping tasks using the EuroSAT and BigEarthNet datasets with both aerial and ground-level prompting styles. Our approach, which aligns ground-level representations with satellite imagery, demonstrates significant improvements in classification accuracy across both prompt styles, opening new possibilities for applying free-form textual descriptions in zero-shot LULC mapping.

Seungyeop Han, Byeong-Un Jo, Koki Ho

This paper presents an innovative terminal landing guidance law that utilizes an analytic solution derived from the gravity turn trajectory. The characteristics of the derived solution are thoroughly investigated, and the solution is employed to generate a reference velocity vector that satisfies terminal landing conditions. A nonlinear control law is applied to effectively track the reference velocity vector within a finite time, and its robustness against disturbances is studied. Furthermore, the guidance law is expanded to incorporate ground collision avoidance by considering the shape of the gravity turn trajectory. The proposed method's fuel efficiency, robustness, and practicality are demonstrated through comprehensive numerical simulations, and its performance is compared with existing methods.

Hamed Javadi, Seyed Gholam Reza Moosavi, Nasrin Farahmandrad

IntroductionHarsh ecological conditions, including water scarcity, have limited vegetation life in desert areas. Consequently, the cultivation of drought-resistant plants compatible with desert areas and their expansion, while creating suitable vegetation, increases biodiversity, controls desertification and is oriented towards the sustainability of desert ecosystems. Cannabis is a drought-tolerant plant which, because of its great genetic diversity, has the ability to grow in different climates, particularly in semi-desert areas. Appropriate agricultural management enhances the vegetation, production and productivity of agricultural products. In this context, it is important to study planting date and plant density as factors impacting production. Planting dates must be chosen to allow sufficient time for each stage of growth and development. The use of optimal plant density may improve plant growth and increase plant yield by reducing intra-plant competition. Results of search on two densities of cannabis plants of 8 and 16 plants per m-2 in Birjand, the highest seed yield was obtained from a density of 16 plants per m-2. Finding on densities of 50, 150, and 250 plants per m-2 in Mashhad, and 30, 90, and 150 plants per m-2 in Shirvan reported that as the density of cannabis plants increased, the flowering date decreased in both regions. Given the arid and semi-arid climate of South Khorasan, planting plants compatible with the climate of the region, such as cannabis, can increase vegetation cover while producing an acceptable yield. The objectives of the current research are to study the effect of agricultural management on the growth characteristics of the forgotten cannabis plant in semi-arid climate of Birjand. Material and MethodsThe current research was carried out in Center of Agriculture and Natural Resources Research if South Khorasan, located at 59′ 13° east longitude and 53° 32′north latitude, and 1491m above sea level. South Khorasan province has a desert and semi-desert climate. Before preparing the soil to determine the required amount of chemical and organic fertilizers, the soil in the field was analyzed. Data on temperature changes and the total number of sunny hours of various months during the cannabis growing period were received from the Birjand weather station. The experiment was conducted as a split plot based on a randomized complete block design with three replications. Treatments investigated included planting date on three levels of May 12, May 27 and June 11 as the main plot and plant density at three levels of 22.2, 11.1 and 7.4 plants per m-2 as the sub plot. In this research, the phenological characteristics including the number of days to emergency, days to flowering, days to seed filling, days to physiological maturity, length of vegetative period, length of reproductive period, length of flowering period, and morphological characteristics including plant height, number of main stem branches, stem diameter and seed yield were investigated. Statistical analysis of the data was done using SAS software and the comparison of averages was done based on Duncan's 5% multiple range test. Results and DiscussionThe results showed that the impact of planting date on all morphophenological traits was significant, with the exception of stem diameter. The delay in planting between May 12 and June 11 significantly reduced the length of phenological stages, and vegetative growth of cannabis, and ultimately caused a 48% decrease in seed yield. Late cultivation, due to the increase in temperature, the plant completes its vegetative growth faster. The delay in planting by shortening the period of effective growth, reducing the photosynthetic potential of the plant, and coinciding with the period of seed filling with low temperatures and shortening of the day has led to a decrease in the quantity and the filling speed of the seeds, and subsequently the yield of the seeds decreases. It has been reported that a 20-day delay in seeding from 10 May led to a 46% decrease in seed yield under climatic conditions in Azerbaijan. The effect of plant density on morphological traits, number of days until flowering of female plants, days until seed set, days until physiological maturity, length of vegetative period, length of flowering period and seed yield were significant. The increase in density from 7.4 to 22.2 plants per m-2, while delaying flowering, increased seed yield by 15.4%. Increased plant density due to higher plant height and increased number of plants per unit area increased seed yield. Results of search on two densities of cannabis plants of 8 and 16 plants per m-2 in Birjand, the highest seed yield was obtained from a density of 16 plants per m-2. To achieve proper yield performance, and develop cannabis cultivation- as a plant compatible with the semi-desert region- the planting date of May 12 and the density of 22.2 plants per m-2 can be used.

Grace Nkansa Asante, Paul Owusu Takyi, Gideon Mensah

ABSTRACTIt is hypothesized that a well-functioning financial market is necessary but not sufficient condition to achieve the expected economic growth. Therefore, policy instruments of government aimed at streamlining financial sector activity in sub-Saharan Africa (SSA) are imperative. As a result, this paper explores the effect of financial development on economic growth by allowing the link between the two far variables to be mediated by the quality of institutions for the period 2000–2019. Using Twenty-nine (29) countries and the System-Generalized Method of Moments (system-GMM) estimation method, it is found that financial development has a positive and significant effect on economic growth. In addition, it is found that, when rule of law, political stability, and regulatory quality are highly effective, the positive effect of financial development on economic growth is magnified.

Farhan Zeb Khaskhelly, Ali Raza, Hemal Azhar et al.

Purpose The purpose of this study is to explore and analyze corporate social responsibility (CSR) as a helpful tool in solving significant societal concerns in countries where there is a greater desire for social and economic growth, such as Pakistan. Methodology In order to examine the current issues on supply chain collaboration for sustainability, this paper used a triangulation research method. In order to determine indicators in a CSR-intensive environment, data, and literature, the energy sector publications on EUR-Lex, international and European official papers, and the online site of the European Commission data sources were analyzed in this study. The indicators were divided into groups based on their sources (sets of standards and guidelines, council frameworks, document series, tools, and comprehensive legislation), as well as their intended uses (financial, social, and environmental). Findings The findings state that supply chain collaboration completely fulfills CSR for a viable economy. It focuses on three leading fashion brands and assesses their impact using open-source data, past research, and their official websites. It also highlights how, in comparison to global corporations, Pakistani business satisfies their corporate social responsibility.  Conclusion It is concluded that a supply chain can help companies minimize the environmental impact of their supply chain processes. Further, CSR is a part of the supply chain that helps businesses determine their social and economic responsibilities by focusing on environmental aspects to add to a more sustainable economy.

Haechan Mark Bong, Rongge Zhang, Antoine Robillard et al.

Safe landing is essential in robotics applications, from industrial settings to space exploration. As artificial intelligence advances, we have developed PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), a system that automatically generates and refines prompts for identifying landing zones in changing environments. Traditional approaches using fixed prompts for open-vocabulary models struggle with environmental changes and can lead to dangerous outcomes when conditions are not represented in the predefined prompts. PEACE addresses this limitation by dynamically adapting to shifting data distributions. Our key innovation is the dual segmentation of safe and unsafe landing zones, allowing the system to refine the results by removing unsafe areas from potential landing sites. Using only monocular cameras and image segmentation, PEACE can safely guide descent operations from 100 meters to altitudes as low as 20 meters. The testing shows that PEACE significantly outperforms the standard CLIP and CLIPSeg prompting methods, improving the successful identification of safe landing zones from 57% to 92%. We have also demonstrated enhanced performance when replacing CLIPSeg with FastSAM. The complete source code is available as an open-source software.

Tilemahos Mitroudas, Vasiliki Balaska, Athanasios Psomoulis et al.

Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.