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Satellite imagery pre-processing and feature extraction for the mapping of coastal ecosystems using Google Earth Engine
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No data was used for the research described in the article.The use of Google Earth Engine (GEE) is increasingly common in geospatial analysis of satellite images for various environmental management purposes due to its easy accessibility and capabilities to support complex pre-processing and mining of geographic data. In the context of coastal management, GEE provides opportunities for cost-efficient mapping of coastal habitats and their ecosystem service potentials. Understanding the extent of coastal habitats and the spatial and temporal variabilities of their ecosystem services can be useful for management and intervention purposes. GEE is well-suited for this due to its user-friendliness, particularly for non-experts of programming languages, such as area managers and other practitioners. However, there is no specific methodological guideline for the pre-processing and feature extraction of satellite images in GEE that can be readily adopted by these practitioners. This study develops general methodological steps to perform those processes that can be adapted to different management needs. Highlights of this study:
• Steps detailed in this method paper will produce processed satellite images readily applicable for machine learning to classify coastal ecosystems.
• The development of this adaptable workflow can benefit and empower local area managers, particularly in low-resource settings, to conduct monitoring of their area.This work received funding from the Natural Environment Research Council (grant number NE/V006428/1) for “PISCES: A Systems Analysis Approach to Reduce Plastic Waste in Indonesian Societies” project
Introducing Repository Stability
Get PDFDrawing from engineering systems and control theory, we introduce a framework to understand repository stability, which is a repository activity capacity to return to equilibrium following disturbances - such as a sudden influx of bug reports, key contributor departures, or a spike in feature requests. The framework quantifies stability through four indicators: commit patterns, issue resolution, pull request processing, and community engagement, measuring development consistency, problem-solving efficiency, integration effectiveness, and sustainable participation, respectively. These indicators are synthesized into a Composite Stability Index (CSI) that provides a normalized measure of repository health proxied by its stability. Finally, the framework introduces several important theoretical properties that validate its usefulness as a measure of repository health and stability. At a conceptual phase and open to debate, our work establishes mathematical criteria for evaluating repository stability and proposes new ways to understand sustainable development practices. The framework bridges control theory concepts with modern collaborative software development, providing a foundation for future empirical validation
Stochastic data-driven NMPC for partially observable systems using Gaussian processes: a mineral flotation case study
Get PDFThis paper presents a nonlinear model predictive control (NMPC) strategy using Gaussian Processes (GPs) to control a froth flotation process under partial observability. The GP state-space model predicts future states for both observable and latent variables, using available data, while incorporating the probability distribution of these predictions into an optimization problem. This improves robustness against measurement noise and process disturbances and evaluates the impact of feed particle size, a typical process disturbance. We assessed the framework’s ability to maintain optimal process performance across varying operating conditions. The results demonstrate that the proposed GP-MPC framework improves process efficiency, even with frequent changes in particle size and measurement noise, confirming its potential for online control of partially observable systems
On preventing thermal damage in high-temperature joining applications of thermoplastic composites with metals
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Data will be made available on request.This paper addresses the critical need for a comprehensive investigation into the thermal limits of thermoplastic composites in thermal joining applications with metals. A numerical framework is employed to identify processing conditions that prevent thermal degradation in composite-metal joining, demonstrated through a case study of laser joining carbon fibre (CF) reinforced poly-ether-ether-ketone (PEEK) with a Ti6AL4V Titanium (Ti) alloy. The PEEK kinetics are integrated in the numerical solver and a coupled thermal-chemical analysis takes place that accounts for the heating rate effect on the material’s thermal response. To validate the model, an experimental investigation takes place where the two materials are joined with a varying laser power. To assess the extent of thermal degradation, the produced joints are examined with optical microscopy, scanning electron microscopy, and attenuated total reflection – Fourier transform infrared spectroscopy. To correlate the resulting thermal degradation with their mechanical response, lap-shear tests are performed. A good agreement is found between the two investigations: the model accurately identifies 500 W as the critical threshold where thermal degradation initiates (α ≈ 1.2%), leading to a 9% drop in joint strength. Optimal joint performance is achieved at 450 W - just below the degradation threshold - while higher powers result in severe thermal damage and porosities, causing performance losses of up to 76%. These findings demonstrate that the proposed methodology can effectively determine the thermal limits of CF/PEEK in fast heating applications where the exact temperature-time combination that would lead to thermal damage is elusive. Therefore, the model could be used to optimise a range of joining applications where high-temperature - short-duration processing is applied and thermal degradation is a potential issue.This publication was made possible by the sponsorship and support of TWI. The work was enabled through, and undertaken at, the National Structural Integrity Research Centre (NSIRC), a postgraduate engineering facility for industry-led research into structural integrity established and managed by TWI through a network of both national and international Universities
Big data show idiosyncratic patterns and rates of geomorphic river mobility
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The locational probability data generated in this study have been deposited in the NERC Environmental Information Data Centre (EIDC) along with supporting documentation (https://doi.org/10.5285/a2bcc66e-4dcc-4ed1-897d-cdf36dde246d).Code availability:
Google Earth Engine and MATLAB codes for processing the locational probability data have been deposited in the NERC Environmental Information Data Centre (EIDC) along with supporting documentation (https://doi.org/10.5285/a2bcc66e-4dcc-4ed1-897d-cdf36dde246d).Supplementary information is available online at: https://www.nature.com/articles/s41467-025-58427-9#Sec14 .Big data present unprecedented opportunities to test long-standing theories regarding patterns and rates of geomorphic river adjustments. Here, we use locational probabilities derived from Landsat imagery (1988-2019) to quantify the dynamics of 600 km2 of riverbed in 10 Philippine catchments. Analysis of lateral adjustments reveals spatially non-uniform variability in along-valley patterns of geomorphic river mobility, with zones of relative stability interspersed with zones of relative instability. Hotspots of mobility vary in magnitude, size and location between catchments. We could not identify monotonic relationships between local factors (active channel width, valley floor width and confinement ratio) and mobility. No relation between the channel pattern type and rates of adjustment was evident. We contend that satellite-derived locational probabilities provide a spatially continuous dynamic metric that can help unravel and contextualise forms and rates of geomorphic river adjustment, thereby helping to derive insights into idiosyncrasies of river behaviour in dynamic landscapes.This research was undertaken as part of a Natural Environment Research Council (NERC) and Department of Science and Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) – Newton Fund grant NE/S003312/1
Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning
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Data are contained within the article.The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions.This research received no external funding
Association between total daily sedentary time and cardiometabolic biomarkers in older adults: A systematic review and meta-analysis
Get PDFSupplementary Materials are available online at: https://journals.humankinetics.com/view/journals/jpah/22/9/article-p1086.xml?content=supplementary-materials .Background: Older adults engage in the highest levels of sedentary behavior across all age groups. Yet, the extent to which sedentary time is associated with cardiometabolic health in older adults is unclear. This systematic review and meta-analysis examined associations between daily sedentary time and cardiometabolic biomarkers in older adults. Methods: Peer-reviewed articles which studied the association between daily sedentary time and ≥1 cardiometabolic biomarker in participants aged ≥60 years were eligible. Five electronic databases (PubMed, CINAHL, MEDLINE, Web of Science, and PsycINFO) were searched. Screening, data extraction, and study quality were undertaken independently by 2 reviewers. Meta-analyses were undertaken using random-effects models based on correlation and regression coefficients. Methodological quality was assessed using the Joanna Briggs Institute checklist. Results: Twenty-eight articles were included with sample sizes ranging from 30 to 62,754 participants. Increasing daily sedentary time was adversely associated with body mass index (Hedge g: 0.32; P = .001), waist circumference (Hedge g: 0.45; P < .001), body fat percentage (Hedge g: 0.61; P = .012), and fat mass (Hedge g: 0.30; P = .018). There were also unfavorable associations with systolic blood pressure (Hedge g: 0.37; P = .047), blood glucose (Hedge g: 0.30; P = .044), triglycerides (Hedge g: 0.36; P = .039), and high-density lipoprotein cholesterol (Hedge g: 0.34; P = .034). Conclusions: Increased daily sedentary time is adversely associated with body composition, systolic blood pressure, and blood biomarkers in older adults. Therefore, limiting sedentary behavior should be considered an important target in this population group for improved cardiometabolic health
Measurements of the Higgs boson production cross section in the four-lepton final state in proton-proton collisions at √ = 13.6 TeV
Get PDFA version of the article is available at arXiv:2501.14849v2 [hep-ex] (https://arxiv.org/abs/2501.14849). Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-24-013 (CMS Public Pages). Report number: CMS-HIG-24-013, CERN-EP-2024-336. Journal reference: JHEP 05 (2025) 079. Submission history: From: The CMS Collaboration: [v1] Fri, 24 Jan 2025 15:10:54 UTC (474 KB); [v2] Tue, 20 May 2025 17:41:38 UTC (474 KB).The measurements of the Higgs boson (H) production cross sections performed by the CMS Collaboration in the four-lepton (4ℓ, ℓ = e, μ) final state at a center-of-mass energy √ = 13.6 TeV are presented. These measurements are based on data collected with the CMS detector at the CERN LHC in 2022, corresponding to an integrated luminosity of 34.7 fb⁻¹. Cross sections are measured in a fiducial region closely matching the experimental acceptance, both inclusively and differentially, as a function of the transverse momentum and the absolute value of the rapidity of the four-lepton system. The H → ZZ → 4ℓ inclusive fiducial cross section is measured to be 2.89 -0.49+0.53 (stat) -0.21+0.29 (syst) fb, in agreement with the standard model expectation of 3.09 -0.27+0.24 fb.SCOAP³
Observation of the Charged-Particle Multiplicity Dependence of <sub>(2)</sub>/<sub>/</sub> in -Pb Collisions at 8.16 TeV
No full textA version of the article is available at arXiv:2503.02139v3 [nucl-ex] (https://arxiv.org/abs/2503.02139). Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/HIN-24-001 (CMS Public Pages). This version corrects the CERN preprint number. Report number: CMS-HIN-24-001, CERN-EP-2025-006. Journal reference: Phys. Rev. Lett. 135 (2025) 092301. Submission history: From: The CMS Collaboration: [v1] Tue, 4 Mar 2025 00:09:17 UTC (461 KB); [v2] Wed, 5 Mar 2025 13:37:38 UTC (461 KB); [v3] Mon, 8 Sep 2025 17:20:25 UTC (462 KB).Data availability—
Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, reuse and open access policy [86]. CMS data availability statement, 10.7483/OPENDATA.CMS.1BNU.8V1W .Bound states of charm and anticharm quarks, known as charmonia, have a rich spectroscopic structure that can be used to probe the dynamics of hadron production in high-energy hadron collisions. Here, the cross section ratio of excited ((2)) and ground state (/) vector mesons is measured as a function of the charged-particle multiplicity in proton-lead (Pb) collisions at a center-of-mass (CM) energy per nucleon pair of 8.16 TeV. The data corresponding to an integrated luminosity of 175 nb⁻¹ were collected using the CMS detector. The ratio is measured separately for prompt and nonprompt charmonia in the transverse momentum range 6.5 T CM < 1.935. For the first time, a statistically significant multiplicity dependence of the prompt cross section ratio is observed in proton-nucleus collisions. There is no clear rapidity dependence in the ratio. The prompt measurements are compared with a theoretical model which includes interactions with nearby particles during the evolution of the system. These results provide additional constraints on hadronization models of heavy quarks in nuclear collisions.SCOAP³
Enhancing Wind Energy Forecasting Efficiency Through Dense and Dropout Networks (DDN): Leveraging Grid Search Optimization
No full textThe wind power industry has experienced remarkable growth due to technological advancements and innovative business models. In 2020, the global installed wind power capacity reached 93 GW, marking a significant 52.96% increase compared to the previous year. This growth highlights the industry’s pivotal role in addressing energy needs and sustainability challenges. Timely wind energy forecasting is critical due to the nonlinear relationship between wind speed and power generation—however, the complexity and uncertainty of natural wind factors present challenges, necessitating effective forecasting methods. A deep learning-based approach named Dense and Dropout Networks (DDN) is introduced to address these challenges, employing Grid Search Optimization techniques. The model consists of eight dense layers for intricate data pattern recognition and a “ReLU” activation function. A dropout layer with a rate of 0.4 is integrated to enhance generalization and mitigate overfitting. The optimization process combines grid search with cross-validation to determine optimal hyperparameters. The actual “Texas Turbine” dataset evaluates the proposed DDN model based on Mean Squared Error (MSE) and Mean Absolute Error (MAE), revealing a significant improvement in accuracy with an enhanced MSE of 94.013% and an improved MAE of 76.947%. In conclusion, the optimized DDN model is a valuable and reliable tool for forecasting wind turbine energy production. Its impressive accuracy and potential for real-world implementation make it a noteworthy contribution to advancing renewable energy technologies and sustainable practices
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