Hasil untuk "Industries. Land use. Labor"

Junhao Wu, Aboagye-Ntow Stephen, Chuyuan Wang et al.

Ultra-high Spatial Resolution (UHSR) Land Cover Classification is increasingly important for urban analysis, enabling fine-scale planning, ecological monitoring, and infrastructure management. It identifies land cover types on sub-meter remote sensing imagery, capturing details such as building outlines, road networks, and distinct boundaries. However, most existing methods focus on 1 m imagery and rely heavily on large-scale annotations, while UHSR data remain scarce and difficult to annotate, limiting practical applicability. To address these challenges, we introduce Baltimore Atlas, a UHSR land cover classification framework that reduces reliance on large-scale training data and delivers high-accuracy results. Baltimore Atlas builds on three key ideas: (1) Baltimore Atlas Dataset, a 0.3 m resolution dataset based on aerial imagery of Baltimore City; (2) FreqWeaver Adapter, a parameter-efficient adapter that transfers SAM2 to this domain, leveraging foundation model knowledge to reduce training data needs while enabling fine-grained detail and structural modeling; (3) Uncertainty-Aware Teacher Student Framework, a semi-supervised framework that exploits unlabeled data to further reduce training dependence and improve generalization across diverse scenes. Using only 5.96% of total model parameters, our approach achieves a 1.78% IoU improvement over existing parameter-efficient tuning strategies and a 3.44% IoU gain compared to state-of-the-art high-resolution remote sensing segmentation methods on the Baltimore Atlas Dataset.

Xin Hong, Longchao Da, Hua Wei

Urban flooding in arid regions poses severe risks to infrastructure and communities. Accurate, fine-scale mapping of flood extents and recovery trajectories is therefore essential for improving emergency response and resilience planning. However, arid environments often exhibit limited spectral contrast between water and adjacent surfaces, rapid hydrological dynamics, and highly heterogeneous urban land covers, which challenge traditional flood-mapping approaches. High-resolution, daily PlanetScope imagery provides the temporal and spatial detail needed. In this work, we introduce FM-LC, a hierarchical framework for Flood Mapping by Land Cover identification, for this challenging task. Through a three-stage process, it first uses an initial multi-class U-Net to segment imagery into water, vegetation, built area, and bare ground classes. We identify that this method has confusion between spectrally similar categories (e.g., water vs. vegetation). Second, by early checking, the class with the major misclassified area is flagged, and a lightweight binary expert segmentation model is trained to distinguish the flagged class from the rest. Third, a Bayesian smoothing step refines boundaries and removes spurious noise by leveraging nearby pixel information. We validate the framework on the April 2024 Dubai storm event, using pre- and post-rainfall PlanetScope composites. Experimental results demonstrate average F1-score improvements of up to 29% across all land-cover classes and notably sharper flood delineations, significantly outperforming conventional single-stage U-Net baselines.

Guozhi Li, Yidan Yuan, Xunuo Chen et al.

Abstract To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management.

Peide Liu, Baoying Zhu, Mingyan Yang et al.

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

Jess Stephenson, Nathan T. Duncan, Melissa Greeff

Heterogeneous autonomous robot teams consisting of multirotor and uncrewed surface vessels (USVs) have the potential to enable various maritime applications, including advanced search-and-rescue operations. A critical requirement of these applications is the ability to land a multirotor on a USV for tasks such as recharging. This paper addresses the challenge of safely landing a multirotor on a cooperative USV in harsh open waters. To tackle this problem, we propose a novel sequential distributed model predictive control (MPC) scheme for cooperative multirotor-USV landing. Our approach combines standard tracking MPCs for the multirotor and USV with additional artificial intermediate goal locations. These artificial goals enable the robots to coordinate their cooperation without prior guidance. Each vehicle solves an individual optimization problem for both the artificial goal and an input that tracks it but only communicates the former to the other vehicle. The artificial goals are penalized by a suitable coupling cost. Furthermore, our proposed distributed MPC scheme utilizes a spatial-temporal wave model to coordinate in real-time a safer landing location and time the multirotor's landing to limit severe tilt of the USV.

Zhengyan Shi, Sander Land, Acyr Locatelli et al.

Direct Alignment Algorithms (DAAs), such as Direct Preference Optimisation (DPO) and Identity Preference Optimisation (IPO), have emerged as alternatives to online Reinforcement Learning from Human Feedback (RLHF) algorithms such as Proximal Policy Optimisation (PPO) for aligning language models to human preferences, without the need for explicit reward modelling. These methods generally aim to increase the likelihood of generating better (preferred) completions while discouraging worse (non-preferred) ones, while staying close to the original model's behaviour. In this work, we explore the relationship between completion likelihood and model performance in state-of-the-art DAAs, and identify a critical issue of likelihood over-optimisation. Contrary to expectations, we find that higher likelihood of better completions and larger margins between better and worse completion likelihoods do not necessarily lead to better performance, and may even degrade it. Our analysis reveals that while higher likelihood correlates with better memorisation of factual knowledge patterns, a slightly lower completion likelihood tends to improve output diversity, thus leading to better generalisation to unseen scenarios. Moreover, we identify two key indicators that signal when over-optimised output diversity begins to harm performance: Decreasing Entropy over Top-k Tokens and Diminishing Top-k Probability Mass. Our experimental results validate that these indicators are reliable signs of declining performance under different regularisations, helping prevent over-optimisation and improve alignment with human preferences.

Jess Stephenson, William S. Stewart, Melissa Greeff

Landing a multirotor unmanned aerial vehicle (UAV) on an uncrewed surface vessel (USV) extends the operational range and offers recharging capabilities for maritime and limnology applications, such as search-and-rescue and environmental monitoring. However, autonomous UAV landings on USVs are challenging due to the unpredictable tilt and motion of the vessel caused by waves. This movement introduces spatial and temporal uncertainties, complicating safe, precise landings. Existing autonomous landing techniques on unmanned ground vehicles (UGVs) rely on shared state information, often causing time delays due to communication limits. This paper introduces a learning-based distributed Model Predictive Control (MPC) framework for autonomous UAV landings on USVs in wave-like conditions. Each vehicle's MPC optimizes for an artificial goal and input, sharing only the goal with the other vehicle. These goals are penalized by coupling and platform tilt costs, learned as a Gaussian Process (GP). We validate our framework in comprehensive indoor experiments using a custom-designed platform attached to a UGV to simulate USV tilting motion. Our approach achieves a 53% increase in landing success compared to an approach that neglects the impact of tilt motion on landing.

Jeremy M. Wachter

I describe the assessment framework of labor-based contract grading (LBCG). In a labor-based grading scheme, the time and effort ("labor") a student spends on an assignment determines the credit they receive; the contract component requires students to design projects with clearly-defined goals and deliverables which must be satisfied to earn credit. LBCG is intended to promote student agency and engagement, and to provide a more equitable assessment framework given that students come with a wide range of prior experiences and preparation. I illustrate the LBCG framework within the context of an upper level physics course, using a particular assignment as an example; I also provide information on student experiences and engagement.

Gabriele Valvano, Antonino Agostino, Giovanni De Magistris et al.

Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments. Generative AI offers opportunities to enlarge small industrial datasets artificially, thus enabling the usage of state-of-the-art supervised approaches in the industry. Unfortunately, also good generative models need a lot of data to train, while industrial datasets are often tiny. Here, we propose a new approach for reusing general-purpose pre-trained generative models on industrial data, ultimately allowing the generation of self-labelled defective images. First, we let the model learn the new concept, entailing the novel data distribution. Then, we force it to learn to condition the generative process, producing industrial images that satisfy well-defined topological characteristics and show defects with a given geometry and location. To highlight the advantage of our approach, we use the synthetic dataset to optimise a crack segmentor for a real industrial use case. When the available data is small, we observe considerable performance increase under several metrics, showing the method's potential in production environments.

M. Gundall, J. Schneider, H. D. Schotten et al.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the "fourth industrial revolution", referred to as "Industrie 4.0". Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).

Eddy Soria Leyva, Aida Valls Mateu, Ana Beatriz Hernandez Lara

This book chapter conducts a comparative bibliometric analysis of literacies in the tourism labor market, drawing from the Web of Science (WoS) and Scopus databases. The objective is to assess scientific outputs and identify key patterns of scientific collaboration. Findings suggest a statistically significant difference between the two databases with an overlap level of 35.71%. However, there is a gradual and correlated increase in the number of publications over time. Scopus stands out for its broader impact and enduring citation relevance, suggesting its academic contributions have a longer-lasting effect. Conversely, WoS is characterized by a focus on more recent influential publications and exhibits a marginally more intense collaboration network.

Edoardo Severini, Monia Magri, Elisa Soana et al.

In the last decades, the intensification of agricultural practices has deeply altered nitrogen (N) and water cycles. Climate change and drought events are expected to further increase the human impacts on the hydrological and biogeochemical cycles, and these impacts are gaining the attention of the scientific community. Here we show how the Chiese River watershed (Lombardy Region, Italy) represents an interesting opportunity to analyse the effects of traditional irrigation practices on N contamination in the context of water scarcity. During summer, flood irrigation is mostly sustained by groundwater withdrawal. Additional water withdrawals from the river contribute to the dry out of the Chiese River. The use of wells for irrigation over permeable and fertilized soils and the percolation of nitrate (NO3-) from the vadose zone to groundwater result in the accumulation of NO3- in groundwater and limited N losses via denitrification due to dominant oxic conditions. These practices contrast other measures targeting the reduction of N excess over arable land. In the Chiese River watershed, the N surplus from Soil System Budget calculations decreased by 43% since the early 2000 s but NO3- concentration in groundwater remained high and stable (up to 98.0 mg NO3- L−1). The dried-out Chiese River gains groundwater and NO3- concentration at the river mouth approaches 32.2 mg NO3- L−1. Our results suggest how the mismanagement of the watershed (overabundant fertilization and flood irrigation using groundwater) increases the N concentration both in the river and groundwater, leading to the violation of both Nitrate and Water Framework directives. We anticipate our assay to be a starting point for the conversion of the northern Po Plain to more efficient irrigation and fertilization practices to contrast severe droughts driven by climate change like the one who struck the Po Plain in summer 2022.

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.

Guruh Ghifar Zalzalah, Deka Febriyanto

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.

Jana Kolassa, Manisha Ganeshan, Erica McGrath-Spangler et al.

Soil moisture conditions can influence the evolution of a tropical cyclone (TC) that is partially or completely over land. Hence, better constraining soil moisture initial conditions in a numerical weather prediction model can potentially improve predictions of TC evolution near or over land. This study examines the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). Two sets of retrospective forecasts of TC Idai are compared in an Observing System Experiment framework: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land. Results indicate that SMAP assimilation leads to pronounced improvements in the representation of TC Idai structure and prediction of its intensity and track. The wind speed radius (a measure for TC compactness) is reduced by up to 18% in the analysis with SMAP assimilation relative to the control experiment without SMAP assimilation. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. The forecast along-track error is reduced by up to 34%, indicating a more accurate propagation speed, while the impact of SMAP assimilation on the forecast cross-track error is neutral. These results provide a valuable demonstration that SMAP assimilation can have a highly beneficial impact on TC prediction in global weather forecast models.