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As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather.

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology.

• Rhizodeposition plays an important role in below-ground carbon (C) cycling. However, quantification of rhizodeposition in intact plant–soil systems has remained elusive due to methodological issues. • We used a 13C-CO₂ pulse-labelling method to quantify the contribution of rhizodeposition to below-ground respiration. Intact plant–soil cores were taken from a grassland field, and in half, shoots and roots were removed (unplanted cores). Both unplanted and planted cores were assigned to drought and nitrogen (N) treatments. Afterwards, shoots in planted cores were pulse labelled with 13C-CO₂ and then clipped to determine total below-ground respiration and its δ13C. Simultaneously, δ13C was measured for the respiration of live roots, soils with rhizodeposits, and unplanted treatments, and used as endmembers with which to determine root respiration and rhizodeposit C decomposition using two-source mixing models. • Rhizodeposit decomposition accounted for 7–31% of total below-ground respiration. Drought reduced decomposition of both rhizodeposits and soil organic carbon (SOC), while N addition increased root respiration but not the contribution of rhizodeposit C decomposition to below-ground respiration. • This study provides a new approach for the partitioning of below-ground respiration into different sources, and indicates that decomposition of rhizodeposit C is an important component of below-ground respiration that is sensitive to drought and N addition in grassland ecosystems.

The relationship between Mediterranean diet and obesity-related markers is a matter of debate. We investigated the association between adherence to the Mediterranean diet and anthropometric indices, body composition, and cardiometabolic risk factors in Iranian population. The cross-sectional study was performed on data of 3386 participants from Fasa PERSIAN cohort study. The Mediterranean diet score (MDS) was calculated based on consumption of 11 food groups (unrefined cereals, potatoes, fruits, vegetables, legumes, fish, red meat, poultry, dairy, olive oil, and alcoholic beverages). The association between MDS and cardiometabolic risk factors was examined by linear regression analysis. MDS was inversely associated with waist circumference (β  =   − 1.11; P = 0.033), waist-to-hip ratio (β =  − 0.007; P = 0.011), waist-to-height ratio (β =  − 0.009; P = 0.015), fasting glucose (β =  − 3.59; P = 0.001), and HDL-cholesterol (β =  − 0.96; P = 0.031) in unadjusted model. After adjusting for energy intake, the associations of MDS with markers of abdominal obesity and HDL-cholesterol disappeared. In fully adjusted model, MDS showed inverse relationships with waist-to-hip ratio (β =  − 0.005; P = 0.037) and fasting glucose (β =  − 2.71; P = 0.013). In conclusion, MDS showed an inverse relationship with fasting glucose and waist-to-hip ratio. Since energy intake increased along with increasing MDS, adherence to the Mediterranean diet may associate with lower abdominal obesity and better glycemic control if an energy-controlled Mediterranean diet is used.

Indoor positioning systems (IPSs) are increasingly applied in indoor settings where satellite-based GNSS signals are unavailable, including museums and other cultural heritage spaces. Within the META-MUSEUM project, we present a pilot study integrating an Ultra-Wideband (UWB) positioning system and an eye-tracking device to monitor and quantify visitor behavior in a real museum environment. The absence of common timestamps between the two systems, and the presence of UWB signal noise, have been the main challenges to address. A cross-correlation–based synchronization method was developed to align the two independent UWB and eye-tracking datasets. Data were collected from 100 visitors, of whom 7 different clusters were considered based on the characteristics of the visitors. The results demonstrate the system’s feasibility and provide two complementary metrics, Normalized Engagement and Collective Engagement, which are used to quantify the duration and spatial distribution of visitor engagement at specific exhibits. This work establishes a scalable multi-sensor foundation by addressing practical deployment challenges under real-world conditions. These findings form the basis for the project’s broader goal of linking spatial visitor behavior with neurophysiological responses, opening new possibilities for improving visitor engagement and supporting interactive cultural heritage experiences.

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world.

This study examines the vital role of environmental services in supporting human life and development in Teluk Bahang, Penang, Malaysia. Utilizing the Penang State Structure Plan 2020 (RSNPP2020) and the Draft Penang State Structure Plan 2030 (DRSNPP2030), it tracks land use patterns and changes over eight years, aiming for social, economic, and environmental sustainability. Data from the Malaysian Development Planning Department (MBPP) and the Penang Geographical Information System Center (PeGIS), along with external sources, inform the analysis. Employing Geospatial Information System (GIS) techniques, the study analyzes land use data from 2010, 2014, and 2018 using overlay and matrix methods. The results reveal a significant 18% increase in agricultural land use between 2014 and 2018 and notable shifts from agricultural to residential and commercial land uses. These changes highlight the dynamic transformation of land utilization in the region. The study recommends integrating environmental concerns into land use planning, emphasizing sustainable development strategies that balance economic growth with environmental conservation. These findings provide practical insights for policymakers to optimize land use while preserving the ecological integrity of Teluk Bahang.

The novel SARS-CoV-2 outbreak was declared as pandemic by the World Health Organization (WHO) on March 11, 2020. Understanding the airborne route of SARS-CoV-2 transmission is essential for infection prevention and control. In this study, a total of 107 indoor air samples (45 SARS-CoV-2, 62 bacteria, and fungi) were collected from different wards of the Hajar Hospital in Shahrekord, Iran. Simultaneously, bacterial and fungal samples were also collected from the ambient air of hospital yard. Overall, 6 positive air samples were detected in the infectious 1 and infectious 2 wards, intensive care unit (ICU), computed tomography (CT) scan, respiratory patients’ clinic, and personal protective equipment (PPE) room. Also, airborne bacteria and fungi were simultaneously detected in the various wards of the hospital with concentrations ranging from 14 to 106 CFU m −3 and 18 to 141 CFU m −3 , respectively. The highest mean concentrations of bacteria and fungi were observed in respiratory patients’ clinics and ICU wards, respectively. Significant correlation ( p < 0.05) was found between airborne bacterial concentration and the presence of SARS-CoV-2, while no significant correlation was found between fungi concentration and the virus presence. This study provided an additional evidence about the presence of SARS-CoV-2 in the indoor air of a hospital that admitted COVID-19 patients. Moreover, it was revealed that the monitoring of microbial quality of indoor air in such hospitals is very important, especially during the COVID-19 pandemic, for controlling the nosocomial infections.

Coastal tourism is crucial for the development of sustainable coastal cities, since it depends on the interaction of natural resources and critical infrastructure. However, handling the complex issues brought on by changing coastal ecosystems and increased visitor expectations calls for a thorough grasp. The Sungai Karang area in Pahang, Malaysia, is used in this paper as a case study to analyze the development phases and transformational effects of coastal resort expansion. Our goal is a detailed assessment of land-use changes brought on by the growing coastline tourist industry. We examine data from 1966 to 2013 using a thorough technique that combines Geographic Information Systems (GIS) with patch analysis. The main findings show dramatic changes in land-use dynamics, with built-up areas increasing by 23.76% and forest cover increasing by 19.35%. Along with an increase in recreational places, utilities and infrastructure also saw a rise of 18.57%. Contrastingly, agricultural regions had a significant decline of 15.43%. These discoveries highlight the significant alteration of the local environment related to beach tourism. Planning effectively and developing coastal cities sustainably need careful observation of the wide-ranging effects that tourist expansion has on the environment, the economy, and society. In conclusion, our study promotes a comprehensive strategy for coastal city government that smoothly integrates with regional goals. This strategy emphasizes quantifiable goals, active community involvement, and preserving both natural and cultural heritage. It highlights the crucial part that careful planning plays in preventing environmental degradation while also acknowledging the difficulties brought on by the tourist industry's explosive growth. In the end, our research highlights the unavoidable necessity of supporting sustainable coastal city development among the varied effects of coastal tourism.

The exploration of Pb-Zn mineralization in carbonate complexes during field campaign is a challenging process that consumes high expenses and time to discover high prospective zones for a detailed exploration stage. In this study, multi-sensor remote-sensing imagery from Landsat-8, Sentinel-2, and ASTER were utilized for Pb-Zn mineralization prospectivity mapping in the Akhlamad carbonate complex area, Razavi Khorasan, NE Iran. Due to the presence of carbonate formations and various evidence of Pb-Zn mineralization, this area was selected. Band composition, band ratio, principal component analysis (PCA), and SAM techniques for mapping alteration minerals as well as lineament analysis were implemented. Subsequently, a fuzzy logic model for identifying the prospective zones of Pb-Zn mineralization using multi-sensor remote-sensing satellite images was designed. The weight of each exploratory layer was determined using the fuzzy hierarchical method and the integration process of the information layers was performed using fuzzy operators. Finally, the existing mineral indications were used to evaluate and validate the obtained mineral potential map. The outcome of this investigation suggested several high-potential zones for Pb-Zn exploration in the study area.