Search Results (1,212)

Consumer-accessible and user-friendly smart products such as unmanned aerial vehicles (UAVs), or drones, have become widely used, adaptable, and acceptable devices to observe, assess, measure, and explore urban and natural environments. A drone’s relatively low cost and flexibility in the level of expertise required to operate it has enabled users from novice to industry professionals to adapt a malleable technology to various disciplines. This review examines the academic literature and maps how drones are currently being used in 93 rural and regional city councils in New South Wales, Australia. Through a systematic review of the academic literature and scrutiny of current drone use in these councils using publicly available information found on council websites, findings reveal potential uses of drone technology for local governments who want to engage with smart technology devices. We looked at how drones were being used in the management of the council’s environment; health and safety initiatives; infrastructure; planning; social and community programmes; and waste and recycling. These findings suggest that drone technology is increasingly being utilised in rural and regional areas. While the focus is on rural and regional New South Wales, a review of the academic literature and local council websites provides a snapshot of drone use examples that holds global relevance for local councils in urban and remote areas seeking to incorporate drone technology into their daily practice of city, town, or region governance. Full article

Background: Population persistence for organisms to survive in a world with a rapidly changing climate will require either dispersal to suitable areas, evolutionary adaptation to altered conditions and/or sufficient phenotypic plasticity to withstand it. Given the slow growth and geographically isolated populations of many tree species, there is a high likelihood of local adaption or the acclimation of functional traits in these populations across the UK. Objectives: Given the slow growth and often isolated populations of Tilia cordata (lime tree), we hypothesised that there is a high likelihood of local adaptation or the acclimation of metabolic traits in these populations across the UK. Our aim was to test if the functional metabolomic traits of Tilia cordata (lime tree), collected in situ from natural populations, varied within and between populations and to compare this to neutral allele variation in the population. Methods: We used a metabolic fingerprinting approach to obtain a snapshot of the metabolic status of leaves collected from T. cordata from six populations across the UK. Environmental metadata, longer-term functional traits (specific leaf area) and neutral allelic variation in the population were also measured to assess the plastic capacity and local adaptation of the species. Results: The metabolic fingerprints derived from leaf material collected and fixed in situ from individuals in six populations of T. cordata across its UK range were similar, despite contrasting environmental conditions during sampling. Neutral allele frequencies showed almost no significant group structure, indicating low differentiation between populations. The specific leaf area did vary between sites. Conclusions: The low metabolic variation between UK populations of T. cordata despite contrasting environmental conditions during sampling indicates high levels of phenotypic plasticity. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

The mapping and monitoring of coastal regions are critical to ensure their sustainable use and viability in the long term. Delineation of coastlines is becoming increasingly important in the light of climate change and rising sea levels. However, many coastlines are highly dynamic; therefore, mono-temporal assessments of coastal ecosystems and coastlines are mere snapshots of limited practical value for space-based planning. Understanding of the spatio-temporal dynamics of coastal ecosystem boundaries is important to inform ecosystem management but also for a meaningful delineation of the high-water mark, which is used as a benchmark for coastal spatial planning in South Africa. This research aimed to use hyper-temporal Sentinel-2 imagery to extract ecological zones on the coast of KwaZulu-Natal, South Africa. A total of 613 images, collected between 2019 and 2023, were classified into four distinct coastal ecological zones—vegetation, bare, surf, and water—using a Random Forest model. Across all classifications, the percentage of each of the four classes’ occurrence per pixel over time was determined. This enabled the identification of ecosystem locations, spatially static ecosystem boundaries, and the occurrence of ecosystem boundaries with a more dynamic location over time, such as the non-permanent vegetation zone of the foredune area as well as the intertidal zone. The overall accuracy of the model was 98.13%, while the Kappa coefficient was 0.975, with user’s and producer’s accuracies ranging between 93.02% and 100%. These results indicate that cloud-based analysis of Sentinel-2 time series holds potential not just for delineating coastal ecosystem boundaries, but also for enhancing the understanding of spatio-temporal dynamics between them, to inform meaningful environmental management, spatial planning, and climate adaptation strategies. Full article

Idiopathic childhood nephrotic syndrome is a common glomerulopathy comprising proteinuria, hypoalbuminemia, and edema. Podocyte dysfunction is central to this disease process. Extracellular vesicles are released from stressed cells and can represent a molecular snapshot of the parent cell of origin. We previously showed that urinary large extracellular vesicles (LEVs) derived from podocytes are increased in patients with nephrotic syndrome relapse. Here, we investigated the role of mitochondrial DNA (mtDNA) within LEVs both in vitro and in vivo, revealing the novel finding that podocytes release LEVs containing mtDNA, driven by mitochondrial stress. A puromycin aminonucleoside nephrosis rat model showed foot process effacement on electron microscopy and urinary LEVs with significantly increased mtDNA. Prednisolone, which drives remission in nephrotic syndrome in children, attenuated mitochondrial stress and reduced the amount of mtDNA content within LEVs in vitro. Lastly, urinary LEVs from children with nephrotic syndrome also contain mtDNA, and it is the podocyte LEV-fraction which is preferentially enriched. Overall, these data support a potential mechanism of podocyte mitochondrial stress in non-genetic, idiopathic pediatric nephrotic syndrome. Full article

Skeletal muscle regeneration requires a reliable source of myogenic progenitor cells capable of forming new fibers and creating a self-renewing satellite cell pool. Human induced pluripotent stem cell (hiPSC)-derived teratomas have emerged as a novel in vivo platform for generating skeletal myogenic progenitors, although in vivo studies to date have provided only an early single-time-point snapshot. In this study, we isolated a specific population of CD82⁺ ERBB3⁺ NGFR⁺ cells from human iPSC-derived teratomas and verified their long-term in vivo regenerative capacity following transplantation into NSG-mdx^4Cv mice. Transplanted cells engrafted, expanded, and generated human Dystrophin⁺ muscle fibers that increased in size over time and persisted stably long-term. A dynamic population of PAX7⁺ human satellite cells was established, initially expanding post-transplantation and declining moderately between 4 and 8 months as fibers matured. MyHC isoform analysis revealed a time-based shift from embryonic to neonatal and slow fiber types, indicating a slow progressive maturation of the graft. We further show that these progenitors can be cryopreserved and maintain their engraftment potential. Together, these findings give insight into the evolution of teratoma-derived human myogenic stem cell grafts, and highlight the long-term regenerative potential of teratoma-derived human skeletal myogenic progenitors. Full article

Respiratory pathogens are significant causes of morbidity and mortality worldwide. Since the emergence of SARS-CoV-2 in 2019 and the mitigation measures implemented to control the pandemic, other respiratory viruses’ transmission and circulation patterns were substantially disrupted. We leveraged the influenza hospitalization surveillance in Tanzania to understand the distribution of respiratory viruses shortly after nonpharmaceutical interventions (NPIs) were lifted. A total of 475 samples that tested negative for SARS-CoV-2 and influenza from March through May 2022 were included in this study. The samples were tested for 16 virus targets using Anyplex II RV16 multiplex assays. The findings indicate that most hospitalizations (74%) were among children under 15 years, with human bocavirus (HBoV) being the most prevalent (26.8%), followed by rhinovirus (RV, 12.3%), parainfluenza viruses (PIVs1–4, 10.2%), respiratory syncytial virus (RSV, 8.7%), adenovirus (AdV, 4.3%), and metapneumovirus (MPV, 2.9%). Notably, 54% of respiratory hospitalizations had no viruses detected. The findings highlight the broad circulation of respiratory viruses shortly after NPIs were lifted in Tanzania. Surveillance for respiratory pathogens beyond influenza and SARS-CoV-2 can inform public health officials of emerging threats in the country and should be considered an important pandemic preparedness measure at a global level. Full article

We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. Leveraging the difference coarray technique, the sparse array is transformed into a virtual uniform linear array (VULA) to enrich the spatial sampling; real-valued covariance matrices derived from the array measurements are used as the network’s input features. A final multi-layer perceptron (MLP) regression module then maps the learned representations to continuous DOA angle estimates. This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. By focusing on methodological innovation rather than bespoke architectural tuning, the framework shows promise for data-efficient DOA estimation in challenging practical conditions. Full article

An innovative high-order dimensionality reduction approach, which integrates a condensed finite-difference scheme with proper orthogonal decomposition techniques, has been explored for solving diffusion equations. The difference scheme with forth order accurate in both space and time is introduced through the idea of interpolation approximation. The quartic spline function and $(2,2)$ Padé approximation were utilized in space and time discretization, respectively. The stability and convergence were proven. Moreover, the dimensionality reduction formulas were derived using the proper orthogonal decomposition (POD) method, which is based on the matrix representation of the compact finite-difference scheme. The bases of the POD method were established by cumulative contribution rate of the eigenvalues of snapshot matrix that is different from the traditional ways in which the bases were established by the first eigenvalues. The method of cumulative contribution rate can optimize the degree of freedom. The error analysis of the reduced bases high-order POD finite-difference scheme was provided. Numerical experiments are conducted to validate the soundness and dependability of the reduced-order algorithm. The comparisons between the $(2,2)$ finite-difference method, the traditional POD method, and reduced dimensional method with cumulative contribution rate were discussed. Full article

The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing transit modeling tools. Unlike typical static GTFS feeds, this dataset provides empirically observed dwell times, run times, and travel times, offering a detailed snapshot of operational variability in urban bus systems. The dataset supports applications in machine learning–based travel time prediction, timetable optimization, and transit reliability analysis, especially in settings where live feeds are unavailable. By releasing this dataset publicly, we aim to promote transparent, data-driven transport research in emerging urban contexts. Full article

The transport of intensity equation enables quantitative phase imaging from only two axially displaced intensity images, facilitating the characterization of low-contrast samples like cells and microorganisms. However, the rapid selection of the correct defocused planes, crucial for real-time phase imaging of dynamic events, remains challenging. Additionally, the different images are normally acquired sequentially, further limiting phase-reconstruction speed. Here, we report on a system that addresses these issues and enables user-tuned defocusing with snapshot phase retrieval. Our approach is based on combining multi-color pulsed illumination with acousto-optic defocusing for microsecond-scale chromatic aberration control. By illuminating each plane with a different color and using a color camera, the information to reconstruct a phase map can be gathered in a single acquisition. We detail the fundamentals of our method, characterize its performance, and demonstrate live phase imaging of a freely moving microorganism at speeds of 150 phase reconstructions per second, limited only by the camera’s frame rate. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

Background: In September 2023, Storm Daniel triggered catastrophic flooding across Thessaly, in central Greece, leading to the deaths of approximately 483,476 animals and heightening concerns about zoonotic diseases, particularly Q fever caused by Coxiella burnetii. Sofades, a municipality in the Karditsa region that is severely impacted by the floods, emerged as a critical area for evaluating the risk of zoonotic disease transmission. This study aimed to determine the seroprevalence status of Coxiella burnetii Phase 1 IgA antibodies among residents in the rural area of Sofades after the Daniel floods. Methods: Serum samples were obtained from a convenient sample of residents with livestock exposure between 1 March and 31 March 2024. Enzyme-linked immunosorbent assay (ELISA) was used to detect Coxiella burnetii Phase 1 IgA antibodies. Descriptive analyses summarized demographic data, and logistic regression was employed to examine the association between gender, age, and positive ELISA results. Results: The overall seroprevalence was 16.66%. Males had a significantly higher positivity rate (28.57%) than females (6.25%). Seropositivity was more frequent among individuals aged 41–80 years, with peak prevalence observed in the 61–80 age group. Conclusions: This cross-sectional study offers a snapshot of Coxiella burnetii exposure in a high-risk rural population post-flood. The slightly higher seroprevalence in Sofades (16.66%) compared to Karditsa (16.1%) suggests limited influence of environmental factors on transmission. Despite limitations in causal inference, the findings highlight the need for enhanced surveillance and targeted public health measures. Longitudinal studies are needed to assess the long-term impact of environmental disasters on Q fever dynamics. Full article

In practical array signal processing applications, direction-of-arrival (DOA) estimation often suffers from degraded accuracy under low signal-to-noise ratio (SNR) and limited snapshot conditions. To address these challenges, we propose an off-grid DOA estimation method based on Fast Variational Bayesian Inference (OGFVBI). Within the variational Bayesian framework, we design a fixed-point criterion rooted in root-finding theory to accelerate the convergence of hyperparameter learning. We further introduce a grid fission and adaptive refinement strategy to dynamically adjust the sparse representation, effectively alleviating grid mismatch issues in traditional off-grid approaches. To address frequency dispersion in wideband signals, we develop an improved subspace focusing technique that transforms multi-frequency data into an equivalent narrowband model, enhancing compatibility with subspace DOA estimators. We demonstrate through simulations that OGFVBI achieves high estimation accuracy and resolution while significantly reducing computational time. Specifically, our method achieves more than 37.6% reduction in RMSE and at least 28.5% runtime improvement compared to other methods under low SNR and limited snapshot scenarios, indicating strong potential for real-time and resource-constrained applications. Full article

The enduring manufacturing goals are increasingly shifting toward ultra-precision manufacturing and micro-nano fabrication, driven by the demand for sophisticated products. Unconventional machining processes such as electrochemical jet machining (ECJM), electrical discharge machining (EDM), electrochemical machining (ECM), abrasive water jet machining (AWJM), and laser beam machining (LBM) have been widely adopted as feasible alternatives to traditional methods, enabling the production of high-quality engineering components with specific characteristics. ECJM, a non-contact machining technology, employs electrodes on the nozzle and workpiece to establish an electrical circuit via the jet. As a prominent special machining technology, ECJM has demonstrated significant advantages, such as rapid, non-thermal, and stress-free machining capabilities, in past research. This review is dedicated to outline the research progress of ECJM, focusing on its fundamental concepts, material processing capabilities, technological advancements, and its variants (e.g., ultrasonic-, laser-, abrasive-, and magnetism-assisted ECJM) along with their applications. Special attention is given to the application of ECJM in the semiconductor and biomedical fields, where the demand for ultra-precision components is most pronounced. Furthermore, this review explores recent innovations in process optimization, significantly boosting machining efficiency and quality. This review not only provides a snapshot of the current status of ECJM technology, but also discusses the current challenges and possible future improvements of the technology. Full article