Extensive Coverage Research Articles

Saving exposed titanium mesh cranioplasty using adipocutaneous anterolateral thigh flap: A case series.

Saving exposed titanium mesh cranioplasty using adipocutaneous anterolateral thigh flap: A case series.

Being a migrant woman during disasters: A phenomenological study to unveil experiences during the COVID-19 pandemic in Milan, Italy

Migrants and women were among the groups most severely affected by the COVID-19 pandemic disaster. By adopting an intersectional lens, it can be inferred that migrant women (MW) were particularly vulnerable to its impacts. This study aims to explore the multifaceted impact of the COVID-19 pandemic on MW living in Milan, Italy, investigating a broad spectrum of experiences. We conducted a phenomenological study using semi-structured interviews from September 2023 to January 2024. Interviews were transcribed and inductively analyzed. We interviewed 19 cisgender MW women coming from 10 different countries, with a median age of 43 years. At the pandemic’s onset, 12 were undocumented migrants, four were documented, while three had obtained Italian citizenship. Most held informal job positions, primarily as domestic workers, and were impacted by the economic crisis triggered by the pandemic. Both before and during the pandemic, non-governmental organizations were the preferred entry point to the healthcare systems. Their psychological well-being was compromised by distance from family members and the extensive COVID-19 media coverage. Despite skepticism, most MW adhered to the vaccination campaign due to its de-facto mandatory nature. Social isolation was not considered a major impact. Overall, MW did not perceive themselves as a particularly vulnerable group. Systemic interventions to address inequalities faced by MW should be incorporated throughout the entire disaster risk management cycle, and an intersectional approach should be integrated into all stages of public policy development. As distrust emerged as a particularly significant issue building trust before disasters is crucial for an effective response.

Detection of pine wood nematode infections in Chinese pine (Pinus tabuliformis) using hyperspectral drone images

AbstractBACKGROUNDThe pine wood nematode (PWN) has caused tremendous damage to pine forests in China. Accurately predicting the infestation stage of PWN is crucial for implementing appropriate management, such as chemically controlling early‐infested trees and felling and removing trees in the severe stages of infestation. Unmanned aerial vehicle (UAV)‐based hyperspectral technology can capture images with high spatial and spectral resolutions, facilitating more extensive coverage and enhanced detection efficiency. To date, few studies have used the correlation coefficient between full spectra and physiological traits to screen dual‐band vegetation indices (VIs). Moreover, there is a lack of comprehensive comparison between the screened VIs, feature wavelengths, and full spectra using various machine learning methods to predict the infection stage of PWN.RESULTSWe evaluated the abilities of screened VIs, feature wavelengths selected by successive projections algorithm (SPA), and full spectra in estimating PWN infection levels. Random forest (RF), artificial neural network (ANN), support vector machine (SVM), and three convolutional neural networks (CNN) were applied. Screened VIs performed the best (OA%: 76.03–80.99; Kappa: 0.68–0.74), and RF approach obtained highest classification accuracies (OA%: 72.73–80.99; Kappa: 0.63–0.74). In discriminating between healthy trees and PWN‐infected trees at an early stage, RF using screened VIs outperformed other approaches (healthy trees: PA% = 76.92, UA% = 76.92; early‐infested trees: PA% = 66.67, UA% = 72.00), and normalized difference spectral index (NDSI) selected by chlorophyll content was the most sensitive feature.CONCLUSIONWe propose the integration of RF with the screened VIs as a recommended approach for the early detection of PWN infections in Chinese Pine, which give reference to the management of PWN infections. © 2025 Society of Chemical Industry.

ExoPhoto: A Database of Temperature-Dependent Photodissociation Cross Sections

Abstract We present the ExoPhoto database https://exomol.com/exophoto/, an extension of the ExoMol database, specifically developed to address the growing need for high-accuracy, temperature-dependent photodissociation cross section data towards short-UV wavelengths. ExoPhoto combines theoretical models from three major computational databases (ExoMol, UGAMOP and PhoMol) and experimental datasets from two experimental groups, providing extensive wavelength and temperature coverage. ExoPhoto currently includes photodissociation data for 20 molecules: AlH, HCl, HF, MgH, OH, NaO, MgO, O2, AlCl, AlF, CS, HeH+, CO, CO2, H2O, SO2, C2H2, C2H4, H2CO, and NH3, derived from theoretical models and supported by experimental data from 5 databases. ExoPhoto also includes detailed data on branching ratios and quantum yields for selected datasets. The data structure of ExoPhoto follows the ExoMol framework, with a consistent naming convention and hierarchical JSON-based organization. Photodissociation cross sections are stored in a set of .photo files which provide data as a function of wavelength with one file for each target molecule temperature. Future developments aim to include more photodissociation cross section data and to provide data for molecules in non-local thermodynamic equilibrium (non-LTE). These will expand the utility of ExoPhoto for advanced astrophysical, planetary modeling and industrial applications.

The Impact of Urban Form on Carbon Emission Efficiency Under Public Transit-Oriented Development: Spatial Heterogeneity and Driving Forces

Urban form optimization is crucial for controlling carbon emissions. Taking Shenzhen as a case study with 2022 data, this research constructs a multidimensional indicator system covering land use, functional mix, transportation structure, and spatial layout. It incorporates both static (inventory-based) and dynamic (transit-based) carbon efficiency metrics to capture complementary urban emission patterns. We employed OLS, GWR, and quantile regression methods to identify key influencing factors, spatial variations, and their impact on carbon emission efficiency. Results show that (1) compact road infrastructure and dense transit systems in the southwestern core contribute to higher efficiency, whereas extensive green coverage in eastern areas facilitates carbon sequestration; (2) elevated population and building densities in central zones are linked with lower efficiency, implying the necessity for balanced spatial redistribution and peripheral infrastructure enhancement; (3) despite comprehensive transit electrification, further improvements in network density and accessibility are essential to enhance urban low-carbon outcomes. These results establish a basis for optimizing urban spatial layout and reducing carbon emissions.

Utilizing Drones and Satellite Imagery in Crop Monitoring and Precision Farming An Application of the WASPAS Method

Drones and satellite imagery have revolutionized crop monitoring and precision farming, offering innovative solutions for enhanced agricultural productivity. These technologies enable farmers to collect high-resolution data on crop health, soil conditions, and resource management. Drones provide real-time, localized insights, allowing for precise interventions, while satellite imagery offers extensive coverage and long-term trend analysis. By integrating these advanced technologies, farmers can monitor their fields more efficiently, reduce costs, and contribute to sustainable farming practices, ultimately ensuring food security in an increasingly challenging agricultural landscape. The significance of drones and satellite imagery in crop monitoring and precision farming lies in their potential to enhance agricultural efficiency and sustainability. They facilitate early detection of issues such as pests, diseases, and nutrient deficiencies, reducing waste and minimizing environmental impact. Furthermore, the integration of these tools supports precision agriculture practices, which are crucial for adapting to climate change and meeting the growing global food demand. Ultimately, their application promotes sustainable farming and contributes to food security. The methodology for utilizing drones and satellite imagery in crop monitoring and precision farming involves several key steps. First, high-resolution satellite images are obtained to assess the overall health and variability of crops across large areas. Drones are deployed for targeted monitoring, capturing detailed images and data at a finer scale. Data processing and analysis use advanced algorithms to extract insights, such as NDVI (Normalized Difference Vegetation Index) for assessing plant health. The findings inform precision interventions, such as variable rate irrigation or fertilization. Continuous monitoring allows for adaptive management, ensuring timely responses to emerging issues and optimizing agricultural practices. Alternative taken as Drone A, Drone B, Satelite A, Satelite B, Drone+Satelite. Evaluation preference taken as Cost Efficiency (USD/ha), Data Accuracy (%), Timeliness of Data, Coverage Area (ha). Drone A getting first place of the table and satellite B is getting last place of the table.

Adaptive Resource Optimization for LoRa-Enabled LEO Satellite IoT System in High-Dynamic Environments

The integration of Low-Earth Orbit (LEO) satellites with Long Range Radio (LoRa)-based Internet of Things (IoT) systems for extensive wide-area coverage has gained traction in academia and industry, challenging traditional terrestrial resource optimization designed for semi-static single-base-station environments. This paper addresses LEO’s high dynamics and satellite-ground channel variability by introducing a beacon-triggered framework for LoRa-LEO IoT systems as a foundation for resource optimization. Then, in order to decouple the intertwined objectives of optimizing energy efficiency and maximizing the data extraction rate, an adaptive spreading factor (SF) allocation algorithm is proposed to mitigate collisions and resource waste, followed by a practical dynamic power control mechanism optimizing LoRa device power usage. Simulations validate that the proposed adaptive resource optimization outperforms conventional methods in dynamic, resource-constrained LEO environments, offering a robust solution for satellite IoT applications. In terms of energy efficiency and data extraction rate, the algorithm proposed in this paper outperforms other comparative algorithms. When the number of users reaches 3000, the energy efficiency is improved by at least 119%, and the data extraction rate is increased by at least 48%.

The Applicability of a Complete Archive of Keyhole Imagery for Land-Use Change Detection in China (1960-1984).

Declassified Keyhole imagery partially provides multi-temporal coverage that can support land-use change analysis. However, the volume of commercial (paid) Keyhole data is much larger than that of free imagery, and the extent to which commercial data can enhance the application of Keyhole imagery for land-use change analysis remains unknown. In this work, the full archive of Keyhole images for China was obtained from the USGS to identify regions with repeated coverage automatically by using the ArcPy library in Python. The years from 1960 to 1984 were divided into five 5-year periods (T1, 1960~1964; T2, 1965~1969; T3, 1970~1974; T4, 1975~1979; and T5, 1980~1984). The Keyhole images' metadata, including resolution, acquisition time, and image extent, were utilized to classify the images into meter level (C1), five-meter level (C2), and ten-meter level (C3). The spatial distributions of combinations of imagery at different resolutions for each period and the repeated coverage of imagery at each resolution across the five periods were investigated to extract repeated-coverage regions. The coverage proportions were nearly 100% for C1 imagery for the T3, T4, and T5 periods; C2 for T1 and T2; and C3 for T1 and T3. The T3 period featured extensive coverage at all three resolutions (66%). The T1 period was mainly covered by C2/C3 (93%), and T4 had C1/C3 coverage (68%). In contrast, T2 relied primarily on C2 imagery (100%), and T5 was only covered by C1 (96%). For C1 imagery, land-use changes in almost all areas in China in the T3/T4/T5 time span could be detected, and for C2 and C3 images, the corresponding time spans were T1/T2 and T1/T3. Although this study focused on repeated-coverage area detection within China, the methodology and Python codes provided allow for the implementation of an automated process for land-use change detection from the 1960s to the 1980s in other regions worldwide.

FIRST HUMAN EXPERIENCE WITH DUAL–LAYER MICROMESH STENT ROADSAVER TO TREAT ANEURISMATIC CORONARY ARTERY IN ACUTE CORONARY SYNDROME SCENARIO

FIRST HUMAN EXPERIENCE WITH DUAL–LAYER MICROMESH STENT ROADSAVER TO TREAT ANEURISMATIC CORONARY ARTERY IN ACUTE CORONARY SYNDROME SCENARIO

Point Cloud Fusion of Human Respiratory Motion Under Multi-View Time-of-Flight Camera System: Voxelization Method Using 2D Voxel Block Index.

Time-of-flight (ToF) 3D cameras can obtain a real-time point cloud of human respiratory motion in medical robot scenes. Through this point cloud, real-time displacement information can be provided for the medical robot to avoid the robot injuring the human body during the operation due to the positioning deviation. However, multi-camera deployments face a conflict between spatial coverage and measurement accuracy due to the limitations of different types of ToF modulation. To address this, we design a multi-camera acquisition system incorporating different modulation schemes and propose a multi-view voxelized point cloud fusion algorithm utilizing a two-dimensional voxel block index table. Our algorithm first constructs a voxelized scene from multi-view depth maps. Then, the two-dimensional voxel block index table estimates and reconstructs overlapping regions across views. Experimental results demonstrate that fusing multi-view point clouds from low-precision 3D cameras achieves accuracy comparable to high-precision systems while maintaining the extensive spatial coverage of multi-view configurations.

Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach

Mapping air pollution at high spatial resolution is essential for understanding, managing, and mitigating the adverse impacts of air pollution. Current air pollution monitoring approaches suffer from limited spatial coverage and resolution. Artificial intelligence holds great promise for tackling these challenges, yet its application in air pollution monitoring remains nascent, facing limited transferability regarding low-quality labeled and non-uniform spread data. Here, we introduce Height-Field Signed Distance Function (HF-SDF), an innovative 3D implicit representation, to reconstruct air pollution concentration maps from coarse, incomplete data, which achieves both extensive spatial coverage and fine-scale results with powerful transferability. HF-SDF learns a continuous and transferable mapping model that integrates an auto-decoder network with a geometric constraint, offering flexible resolution. The evaluation uses reanalysis data and satellite observations, reaching accuracy rates of 96% and 91%, respectively. HF-SDF reveals immense promise in advancing air pollution monitoring by offering insights into the spatial heterogeneity of pollution distributions.

Public engagement in polar science: perspectives from tourists and scientists on the SEES-2022 expedition to Svalbard

ABSTRACT Public engagement with polar regions is crucial for raising awareness and garnering support for these vulnerable environments. Cruise tourism offers unique opportunities for engagement through immersive experiences of polar landscapes and wildlife, lectures, and citizen science activities, deepening understanding of polar ecosystems and human impacts. This paper presents the case of the SEES-2022 Netherlands Scientific Expedition Edgeøya Svalbard, a distinctive public engagement activity. Fifty-two scientists joined an Arctic Academy tourism cruise alongside 35 tourists, seven policy representatives, one artist, and seven journalists. The Arctic Academy emphasised active tourist involvement in scientific projects to enhance outreach and support for the Arctic’s climate challenges. Extensive media coverage throughout SEES-2022 shared these experiences with the wider public. The study explores how integrating tourism and science may strengthen public engagement and awareness by examining perspectives from both tourists and scientists. Findings show that, while tourism-science combinations involve trade-offs, they enhance science outreach. Immersive tourism-science experiences can promote deeper understanding of human impacts on polar regions among tourists and encourage dialogue on sustainable tourism. Although SEES-2022 is a unique case, its findings can inspire broader discussions on developing effective public engagement in polar regions. Drawing on insights from the case study, six recommendations are proposed for future tourism-science blends that foster meaningful public engagement, raise awareness, and enhance knowledge of polar ecosystems.

Effects of Expanding Medicaid Dental Coverage on General Health Status of Low-Income Adults.

Oral health is considered a key component of general health. However, causal evidence examining the effects of dental coverage on general health is sparse. To examine the effects of the Affordable Care Act (ACA) Medicaid expansions with extensive dental benefits versus less generous dental benefits on the general health status of individuals with low income. A difference-in-differences design comparing states that expanded Medicaid eligibility in 2014 by whether they offered extensive or less generous dental benefits. Adults aged 18-64 years below 138% of the federal poverty level who participated in the 2011-2022 Behavioral Risk Factor Surveillance System surveys. Self-rated general health status and number of days not in good physical health or good mental health in the past 30 days. The likelihood of fair/poor rated health status declined with extensive dental benefits, including by 2.3 (95% CI: -3.90 to -0.69) percentage-points when aggregating 2014-2022, with declines first observed in 2015 and almost all years after. There were no statistically significant effects on days not in good physical or mental health when aggregating 2014-2022. There were fewer mentally unhealthy days with extensive dental benefits by 0.93 days in 2019 and 2021 (95% CI: -1.80 to -0.06 and -1.70 to -0.15, respectively). The findings suggest that extensive dental coverage improves self-rated general health status among low-income adults. There is suggestive evidence of improved mental health in 2 but not all years and no discernable effect on days not in good physical health.

Mapping the landscape: A bibliometric analysis of AI and teacher collaboration in educational research.

This study intends to investigate the relationship between artificial intelligence and teachers' collaboration in educational research in response to the growing use of technologies and the current status of the field. A total of 62 publications were looked at through a systematic review that included data mining, analytics, and bibliometric methods. The study shows a steady increase in the field of artificial intelligence and teacher collaboration in educational research, especially in the last few years with the involvement of the USA, China, and India. Education and information technology are the main contributors to this field of study, followed by an international review of open and distance learning research. The Scopus database was chosen for this study because of its extensive coverage of high-quality, peer-reviewed literature and robust indexing system, making it a dependable source for conducting bibliometric analyses. Scopus offers substantial information, citations tracking, and multidisciplinary coverage, which are critical for spotting publication trends, significant articles, major themes, and keywords in the area. The findings show that education and information technology make the most significant contributions to this sector, followed by international studies on open and distance learning. Over a three-year period, the average citation value is 12.44%. The education system, learning, e-learning, sustainability, COVID-19 issues, team challenges, organizational conflicts, and digital transformation are just a few of the topics it significantly contributes to. The study acknowledges its limitations and considers potential avenues for additional research. The results also emphasize important gaps in the literature, highlighting the necessity for more research. This information can help develop strategic approaches to address issues and take advantage of opportunities relating to artificial intelligence and teacher collaboration in higher education and research. The study's ultimate goal is to offer guidance for tactics that promote teachers' cooperation in educational research and the development of artificial intelligence.

Establishment of a novel large-scale targeted metabolomics method based on NFSWI-DDA mode utilizing HRMS and TQ-MS.

Establishment of a novel large-scale targeted metabolomics method based on NFSWI-DDA mode utilizing HRMS and TQ-MS.

The influence of chronic periodontitis and type 2 diabetes mellitus on resistin levels of gingival crevicular fluid- a systematic review and meta-analysis.

The influence of chronic periodontitis and type 2 diabetes mellitus on resistin levels of gingival crevicular fluid- a systematic review and meta-analysis.

The Installation of a 24-hour Real-Time Surveillance System Using Wi-Fi Cameras, Solar Panels, and Laptops in the Female Hostels of the University of Cross River State

Due to the growing security risks in the University of Cross River State's female residence halls, an efficient surveillance system is required. The installation of a round-the-clock, real-time monitoring system utilizing computers, solar panels and Wi-Fi cameras is shown in this study. The system is designed to monitor the two female hostels to prevent harassment, theft and incursions. Four dual-lens Wi-Fi cameras are used in the suggested system; they are positioned thoughtfully to provide views of the half-walls and gates, which are the usual access points for criminals. Forty viewers are electronically attached to these cameras, guaranteeing accessibility and extensive coverage. Without depending on grid power, the approach improves security and guarantees uninterrupted functioning. The architecture, components, installation process, MATLAB Simulink representation of the connection, literature review and performance evaluation of the system are all covered in detail in this paper. The outcomes show a dependable and effective surveillance system with real-time recording and monitoring.

Improved consistency in solar-induced fluorescence retrievals from GOME-2A with the SIFTER v3 algorithm

Abstract. Space-based observations of solar-induced fluorescence (SIF) provide valuable insights into vegetation activity over time. The GOME-2A instrument, in particular, facilitates SIF retrievals with extensive global coverage and a record extending over 10 years. SIF retrievals, however, are sensitive to calibration issues, and instrument degradation complicates the construction of temporally consistent SIF records. This study introduces the improved Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval (SIFTER) v3 algorithm, designed to obtain a more accurate and reliable long-term SIF record from GOME-2A for the 2007–2017 period, building upon the previous SIFTER v2. The SIFTER v3 algorithm uses newly reprocessed level-1b Release 3 (R3) data, which provide a more homogenous record of the reflectances by eliminating spurious trends from changes in level-0 to level-1 processing. This improved consistency supports detailed analysis and correction of the reflectance degradation across the SIF retrieval window (734–758 nm). To address the reflectance degradation accurately, SIFTER v3 incorporates an advanced in-flight degradation correction that accounts for time, wavelength, and scan angle dependencies throughout the entire record. Additionally, algorithm revisions have consistently reduced the retrieval residuals by around 10 % and reduced sensitivity to water vapor absorption by better capturing the atmospheric and instrumental effects. A revised latitude bias adjustment resolves unrealistic values of GOME-2A SIF over desert areas. The SIFTER v3 dataset demonstrates improved robustness and consistency, both spatially and temporally, throughout the 2007–2017 record, and aligns closely with NASA GOME-2A SIF data and independent gross primary productivity (GPP) measurements from the global FluxSat and FLUXCOM-X products.

Near-Real-Time Global Thermospheric Density Variations Unveiled by Starlink Ephemeris

Previous efforts to retrieve thermospheric density using satellite payloads have been limited to a small number of satellites equipped with GNSS (Global Navigation Satellite System) receivers and accelerometers. These satellites are confined to a few orbital planes, and analysis can only be conducted after the data are processed and updated, resulting in sparse and delayed thermospheric density datasets. In recent years, the Starlink constellation, developed and deployed by SpaceX, has emerged as the world’s largest low Earth orbit (LEO) satellite constellation, with over 6000 satellites in operations as of October 2024. Through the strategic use of multiple orbital shells featuring various inclinations and altitudes, Starlink ensures continuous near-global coverage. Due to extensive coverage and frequent maneuvers, SpaceX has publicly released predicted ephemeris data for all Starlink satellites since May 2021, with updates approximately every 8 h. With the ephemeris data of Starlink satellites, we first apply a maneuver detection algorithm based on mean orbital elements to analyze their maneuvering behavior. The results indicate that Starlink satellites exhibit more frequent maneuvers during thermospheric disturbances. Then, we calculate the mechanical energy loss caused by non-conservative forces (primarily atmospheric drag) through precise dynamical models. The results demonstrate that, despite certain limitations in Starlink ephemeris data, the calculated mechanical energy loss still effectively captures thermospheric density variations during both quiet and disturbed geomagnetic periods. This finding is supported by comparisons with Swarm-B data, revealing that SpaceX incorporates the latest space environment conditions into its orbit extrapolation models during each ephemeris update. With a maximum lag of only 8 h, this approach enables near-real-time monitoring of thermospheric density variations using Starlink ephemeris.

Trust-driven approach to enhance early forest fire detection using machine learning

Forest fires pose significant threats to both natural ecosystems and human communities due to their unpredictable nature and capacity for widespread destruction. Identifying and mitigating fires in the trunk, ground, and canopy of forests is crucial for reducing their adverse effects on the ecosystem and climate. The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. This study presents a real-time Universal Trust Model (UTM) that is specifically designed for the early forest fires detection (FFD) using an intelligent wireless sensor network and machine learning approaches. Our method seeks to reduce fire detection time and improve the reliability of the detection process. This is achieved by employing environmental indicators and moisture levels to swiftly identify fires. The intelligent WSN functions by partitioning the forest into suitable clusters and intelligently positioning sensor nodes to guarantee extensive coverage and effective data transmission to the sink. The proposed UTM system’s core component is the computation of trust ratings for every sensor node. These ratings consider communication, energy, and data trust factors to evaluate the reliability of the data being delivered. This integrated trust model enhances the robustness and accuracy of fire detection, especially under difficult environmental conditions. Furthermore, a machine learning regression model is deployed at the base station to augment the precision of fire detection. This is accomplished by examining essential attributes such as temperature, humidity, and CO2 concentrations. We have conducted thorough experiments using actual datasets consisting of 7200 samples to confirm the efficacy of our proposed UTM in detecting forest fires at an early stage. The results suggest that our system obtains a high rate of data processing and a reduced time delay in comparison to existing systems. This renders it a promising solution for the imperative need to promptly detect and prevent forest fires. Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.