Distant Stations Research Articles

Critical analysis of the ULF power depression as a possible Tohoku earthquake precursor

Among electromagnetic methods of short-term earthquake prediction, an approach is being actively developed based on the phenomenon of magnetic ultra-low-frequency (ULF) power depression occurring a few days before an earthquake. In particular, a nighttime geomagnetic power depression in the band 0.03–0.05 Hz was observed approximately 5 days before the catastrophic Tohoku 2011 earthquake. To verify the reliability of this method, we performed an extended analysis using data from magnetometer arrays JMA, MAGDAS, PWING, and INTERMAGNET. The selected stations included sites close to the epicenter (<300 km) and remote points (∼10000 km). The band-integrated spectral power of nighttime magnetic noise decreased significantly from March 6–9, several days before the earthquake. However, such variations occur simultaneously not only at nearby stations but also at distant stations. During this event, the ULF power depression was caused by low global geomagnetic activity, as evidenced by the planetary index SME. Thus, the depression of geomagnetic ULF noise cannot be considered a reliable short-term precursor.

Revolutionizing WBAN communication for public health: Effective Optimization strategy-driven health assessment

This study explores the optimization procedures that are critical to enhancing WBAN communication efficacy in an environment of public health initiatives. In order to observe someone's physiological characteristics continuously and non-invasively, wireless body area networks (WBANs), are an essential piece of innovation for distant health observation. The WBAN individual nodes, yet, might relocate and lose reception from a distant base station over time. Thus, an effective inter-WBAN interaction approach is required to address this issue. In order to lower cost associated with networks and maximize cluster lifespan towards the network's effectiveness, this research develops a clustering-driven routing approach that uses cluster head serving as a gateway. For efficient cluster head selection, a novel customized chicken swarm optimization (CCSO) technique was introduced. Various parameter adjustments are made in multiple trials to verify the suggested approach effectiveness. The research conduct comprehensive comparison analysis of suggested and existing algorithms, several tests are conducted. The results show that the approach technique can increase the effectiveness of WBAN engagement and create clusters more quickly than the other methods in the framework of public health initiatives.

Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

The Investigation of Tropospheric Changes with GNSS: A study on 6 February 2023 Kahramanmaraş Earthquake Sequence

The earthquakes that occurred in Kahramanmaraş on February 6, 2023, are among the significant seismic events in Turkey. Recorded at moment magnitudes of 7.8 and 7.6 in ten hours on East Anatolian Fault Zone (EAFZ), these earthquakes resulted in extensive destruction and loss of lives in the region. The effects of these earthquakes have been actively studied following the events, utilizing geodetic measurement techniques, particularly GNSS measurements, which are commonly employed in earthquake studies for determining tectonic movements and crustal deformations. As known, GNSS signals pass through significant atmospheric layers, namely the ionosphere and troposphere, before reaching the Earth's surface, and the influence of these atmospheric layers is evident in the results due to various error sources within these layers. One of the main limiting factors in studies such as determining crustal movements is the influence of the troposphere, as surface velocities are on the order of a few mm/yr and require high accuracy (at the mm level). In this study, changes in the troposphere during the earthquakes on February 6, 2023, were investigated using tropospheric zenith delays (Zenith Total Delay - ZTD) computed from GNSS observations. The results indicate the presence of zenith tropospheric delay anomalies at stations close to the fault rupture during and after the main shock, while no such anomalies were observed at distant stations from the fault rupture zone. This finding indicates a relationship between earthquakes and changes occurring in the troposphere.

Breaking a Combinatorial Symmetry Resolves the Paradox of Einstein-Podolsky-Rosen and Bell

We present a Monte Carlo model of Einstein–Podolsky–Rosen experiments that may be implemented on two independent computers and resembles the measurements of the Clauser–Aspect–Zeilinger-type which are performed in two distant stations SA and SB. Our computer model is local deterministic because we show that a theorist in station SB is able to conceive the products of the measurement outcomes of both stations, conditional to any possible equipment configuration in station SA. We show that the Monte Carlo model violates Bell-type inequalities and approaches the results of quantum theory for certain relationships between the number of measurements performed and the number of possible different physical properties of the entangled photon pairs. These relationships are clearly linked to Vorob’ev cyclicities, which always enforce Bell-type inequalities. The realization of this cyclicity depends, however, on combinatorial symmetry considerations that, in turn, depend on the mathematical properties of Einstein’s elements of physical reality. Because these mathematical properties have never been investigated and, therefore, may be free to be chosen in the models for all published experiments, Einstein’s physics does not contradict the experimental findings, instantaneous influences at a distance are put into question and the paradox of Einstein–Podolsky–Rosen and Bell is, thus, resolved.

Spatial-Temporal Dynamic Evolution of Land Deformation Driven by Hydrological Signals around Chaohu Lake.

The frequent occurrence of extreme climate events has a significant impact on people's lives. Heavy rainfall can lead to an increase of regional Terrestrial Water Storage (TWS), which will cause land subsidence due to the influence of hydrological load. At present, regional TWS is mostly obtained from Gravity Recovery and Climate Experiment (GRACE) data, but the method has limitations for small areas. This paper used water level and flow data as hydrological signals to study the land subsidence caused by heavy rainfall in the Chaohu Lake area of East China (June 2016-August 2016). Pearson's correlation coefficient was used to study the interconnection between water resource changes and Global Navigation Satellites System (GNSS) vertical displacement. Meanwhile, to address the reliability of the research results, combined with the Coefficient of determination method, the research findings were validated by using different institutional models. The results showed that: (1) During heavy rainfall, the vertical displacement caused by atmospheric load was larger than non-tidal oceanic load, and the influence trends of the two were opposite. (2) The rapidly increasing hydrologic load in the Chaohu Lake area resulted in greater subsidence displacement at the closer CORS station (CHCH station) than the more distant CORS station (LALA station). The Pearson correlation coefficients between the vertical displacement and water level were as high as -0.80 and -0.64, respectively. The phenomenon confirmed the elastic deformation principle of disc load. (3) Although there was a systematic bias between the different environmental load deformation models, the deformation trends were generally consistent with the GNSS monitoring results. The average Coefficients of determination between the different models and the GNSS results were 0.63 and 0.77, respectively. The results demonstrated the effectiveness of GNSS in monitoring short-term hydrological load. This study reveals the spatial-temporal evolution of land deformation during heavy rainfall around Chaohu Lake, which is of reference significance for water resource management and infrastructure maintenance in this area.

Plankton assessment across the distribution of West African hake and tuna based on eDNA metabarcoding

The richness of plankton communities determines the fish productivity in the ocean, including important resources that rely on extractive fisheries, such as hakes (genus Merluccius) and tunas (genus Thunnus). Their preys forage on zooplankton, and the latter feed on phytoplankton. Inventories of plankton communities for scientific advice to sustainable fishing are essential in this moment of climate change. Plankton is generally inventoried using conventional methodologies based on large water volumes and visual morphological analyses of samples. In this study, we have employed metabarcoding on environmental DNA (eDNA) samples extracted from small water volumes for plankton inventory from twelve distant sampling stations in the East Atlantic Ocean. Zones rich in hake and tuna prey were detected from eDNA, and multivariate multiple regression analysis was able to predict those zones from diatom-based indices and planktonic diversity based on functional groups. Salinity was negatively correlated with the proportion of diatoms in phytoplankton, highlighting expected impacts of current global change on marine plankton communities. The results emphasise the importance of the plankton richness for fish productivity and support the utility of environmental DNA as a tool to monitor plankton composition changes.

Another one bites the dust – Two street canyons studied with magnetic biomonitoring and OSPM modelling

Urban areas form a mosaic of microenvironments and structures, such as street canyons that are susceptible to elevated levels of traffic related pollutants. Street canyons are a relevant topic in air quality research and modelling since they are prone to reduced natural ventilation and when exposed to increased traffic emissions, can pose a serious risk to public health. We applied magnetic biomonitoring using moss bags to evaluate the quality of Operational Street Pollution Model (OSPM) and to study vertical distribution of modelled particulate matter (PM), magnetic PM and heavy metal pollution in a street canyon in Turku and Helsinki, Finland. Moss bags were attached on opposite sides of the streets at 3-, 6-, 9- and 12-m heights for 44 days in late autumn. Samples were analysed for mass-specific magnetic susceptibility (χ), hysteresis parameters and elemental components. High χ values and elemental concentrations of Fe, Al, Ti, Zn, Mn, Cu and Ba were found in both street canyons. Compared to measurements with airborne magnetic PM, the OSPM model underestimated the difference in PM concentrations between the opposite street canyon faces and overestimated the dilution of PM concentrations with altitude. In Turku the results between the OSPM model and magnetic measurements were incompatible. We suspect this is due to meteorological model input data from a distant weather station, street canyon features and a high slope angle contributing to non-uniform emissions in the street canyon. We demonstrate that the moss bag technique is a versatile tool to understand the small-scale variations of concentrations in a variety of complex urban environments.

Artificial neural network for safety information dissemination in vehicle‐to‐internet networks

AbstractIn vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle‐to‐internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic‐related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle‐to‐network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two‐hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle‐to‐internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

What is the market potential for on-demand services as a train station access mode?

On-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations. We estimate a joint access mode and train station choice model. Estimating a latent class choice model with different nesting structures, we uncover four distinct segments in the population. Two segments (∼50%) with a lower Willingness-to-Pay seem to be more likely to take-up FLEX. Ex-urban car drivers seem to be the most likely segment to adopt FLEX, showing great, since members of this segment are currently frequent users of the private car. Our case study also shows that while FLEX competes primarily with public transport when accessing local stations, it competes primarily with car for reaching distant stations.

Investigation of a river-tunnel effect on PM2.5 concentrations in New York City subway stations.

It is well-documented that subway stations exhibit high fine particulate matter (PM2.5) concentrations. Little is known about the potential of river-tunnels to increase PM2.5 concentrations in subways. We hypothesized a "river-tunnel" effect exists: Stations adjacent to poorly ventilated tunnels that travel beneath rivers exhibit higher PM2.5 concentrations than more distant stations. Accordingly, the PM2.5 concentrations were monitored at stations adjacent to and two- and three-stations distant from the river-tunnel. Multivariate linear regression analysis was conducted to disentangle how proximity to a river-tunnel and other factors (e.g., depth) influence concentrations. Stations adjacent to a river-tunnel had 80-130% higher PM2.5 concentrations than more distant stations. Moreover, distance from a river-tunnel was the strongest PM2.5-influencing factor This distance effect was not observed at underground stations adjacent to a river-bridge. The "river-tunnel" effect explains some of the inter-station variability in subway PM2.5 concentrations. These results support the need for improving ventilation systems in subways.

Retrieval of Peak Thunderstorm Wind Velocities Using WSR-88D Weather Radars

Abstract The current study develops a variant of the VAD method to retrieve thunderstorm peak event velocities using low-elevation WSR-88D radar scans. The main challenge pertains to the localized nature of thunderstorm winds, which complicates single-Doppler retrievals as it dictates the use of a limited spatial scale. Since VAD methods assume constant velocity in the fitted section, it is important that retrieved sections do not contain background flow. Accordingly, the current study proposes an image processing method to partition scans into regions, representing events and the background flows, that can be retrieved independently. The study compares the retrieved peak velocities to retrievals using another VAD method. The proposed technique is found to estimate peak event velocities that are closer to measured ASOS readings, making it more suitable for historical analysis. The study also compares the results of retrievals from over 2600 thunderstorm events from 19 radar–ASOS station combinations that are less than 10 km away from the radar. Comparisons of probability distributions of peak event velocities for ASOS readings and radar retrievals showed good agreement for stations within 4 km from the radar while more distant stations had a higher bias toward retrieved velocities compared to ASOS velocities. The mean absolute error for velocity magnitude increases with height ranging between 1.5 and 4.5 m s−1. A proposed correction based on the exponential trend of mean errors was shown to improve the probability distribution comparisons, especially for higher velocity magnitudes.

ВЛИЯНИЕ АТМОСФЕРЫ НА ИССЛЕДОВАНИЯ ПЕРЕСЕЧЕННОГО РЕЛЬЕФА МЕТОДАМИ РАДАРНОЙ ИНТЕРФЕРОМЕТРИИ

An influence of the atmosphere on the interferometric studies of the rugged terrain in the area of Tolbachik volcanic complex, Kamchatka, is discussed. Brief description of the radar interferometry technique and the components of the interferometric phase differences are presented. The two-component model of the troposphere is described, which assumes the decomposition of the refractive index into dry and wet components. An influence of the variations of the troposphere refractive index between radar observations on the phase measurements is simulated. The weather data of Klyuchi meteorological station were interpolated to the moments of the Canadian synthetic aperture radar «Radarsat-2» observations in summer-autumn 2013 and used as input data in the modeling. In the number of cases the calculated differential radar interferograms contain non-zero values of the phase difference that grow with the height of the relief. The non-zero phase differences observed on the interferograms are typically in the good agreement with simulation results. A noticeable discrepancy between interferometric measurements and simulation data occurs in the case of unstable weather with sharp alteration of relative humidity in the survey area, especially in the case the troposphere meteorological parameters vary in different directions during the radar observations. An assumption is made that the reason of the observed modeling errors is that the weather data of distant meteorological station do not match sometimes the actual weather conditions in the study area. The impact of variations of the refractive index between radar observations is less, the less the variations of the relief heights in the survey area.

A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts

This study presents a proof-of-concept for a fully automated and adaptive observing system for coastal ocean ecosystems. Such systems present a viable future observational framework for oceanography, reducing the cost and carbon footprint of marine research. An autonomous ocean robot (an ocean glider) was deployed for 11 weeks in the western English Channel and navigated by exchanging information with operational forecasting models. It aimed to track the onset and development of the spring phytoplankton bloom in 2021. A stochastic prediction model combined the real-time glider data with forecasts from an operational numerical model, which in turn assimilated the glider observations and other environmental data, to create high-resolution probabilistic predictions of phytoplankton and its chlorophyll signature. A series of waypoints were calculated at regular time intervals, to navigate the glider to where the phytoplankton bloom was most likely to be found. The glider successfully tracked the spring bloom at unprecedented temporal resolution, and the adaptive sampling strategy was shown to be feasible in an operational context. Assimilating the real-time glider data clearly improved operational biogeochemical forecasts when validated against independent observations at a nearby time series station, with a smaller impact at a more distant neighboring station. Remaining issues to be addressed were identified, for instance relating to quality control of near-real time data, accounting for differences between remote sensing and in situ observations, and extension to larger geographic domains. Based on these, recommendations are made for the development of future smart observing systems.

Assessment of Hydrological Extremes for Arid Catchments: A Case Study in Wadi Al Jizzi, North-West Oman

The objective of this research was to analyse hydrological variability by conducting an intensive analysis of extreme events, under dry and wet conditions. Drought conditions were assessed using the Standard Precipitation Index (SPI) and Rainfall Anomaly Index (RAI), while the Soil Conservation Service (SCS) method was used to simulate flooding at four stations. The SPI results indicated that the amount of rainfall within the catchment area is near to normal, ranging from 64% to 75%, with some extremely wet exceptions which may cause flash floods. The RAI results also indicated that the amount of rainfall within the catchment area is near to normal, but the extremely wet category obtained the largest percentage (ranging from 36% to 50%) and the very wet category had the lowest percentage (ranging from 9% to 36%). The simulated flooding, using SCS, tended to slightly underestimate the observed streamflow, while the performance showed some weaknesses when the observed flooding was less than 1 m3/s. The Nash–Sutcliffe Efficiency showed higher performance at closer rainfall stations to the outlet, with values of 0.92 and 0.94. Distant stations simulated floods that showed a lower level of efficiency, with values of 0.77 and 0.81. Given the fact that hydrological extremes (dry and wet conditions) are connected, the findings of the two indices and the SCS method are consistent and suitable for monitoring drought and flood events under climate change.

Concurrence percolation threshold of large-scale quantum networks

Quantum networks describe communication networks that are based on quantum entanglement. A concurrence percolation theory has been recently developed to determine the required entanglement to enable communication between two distant stations in an arbitrary quantum network. Unfortunately, concurrence percolation has been calculated only for very small networks or large networks without loops. Here, we develop a set of mathematical tools for approximating the concurrence percolation threshold for unprecedented large-scale quantum networks by estimating the path-length distribution, under the assumption that all paths between a given pair of nodes have no overlap. We show that our approximate method agrees closely with analytical results from concurrence percolation theory. The numerical results we present include 2D square lattices of 2002 nodes and complex networks of up to 104 nodes. The entanglement percolation threshold of a quantum network is a crucial parameter for constructing a real-world communication network based on entanglement, and our method offers a significant speed-up for the intensive computations involved.

ONLINE COURSES FOR HIGH SCHOOL CHILDREN: ENROLLMENTS MATCHED BY ESCALATING ATTRITION RATES

Increased work at home opportunities, work place inaccessibility or difficulty in reaching distant work stations and the recent lock down have all converged to make online courses with its attendant spinoffs, an attractive alternative. In the age of knowledge revolution, the plethora of online courses on practically every conceivable subject seems to have spawned a deep seated hunger to expand horizons of knowledge and sharpen their skillsets so as to attain an unchallenged edge over their peers. This paper reviews the various online courses available in the market, the reasons why they take up these courses and also explores some of the reasons for the high rate of drop-offs and drop out of these courses. Three objectives have been set forth, and three hypothesis have been formulated which emerged from the literature review. A semi-structured questionnaire was created and tested both validity as well as reliability. Primary data obtained from the questionnaire was analysed using a combination of different tools of Statistical Package for Social Sciences. Both the null and alternative hypothesis were tested using the same package to arrive at certain cogent conclusions, summarised in the results and discussions section KEYWORDS: Pedagogy, drop-offs, dropouts, enrollment, disengagement, attrition

Microplastics shift macrobenthic community structures near a coastal nuclear power plant under construction in North East China

Coastal sediments are considered to be a potential sink for microplastics, which mainly derived from the land-based sources. This study investigated microplastic pollution in the sediments of 20 stations near a nuclear power plant under construction in North East China and analyzed its impacts on macrobenthic communities. The average abundance of microplastics in three stations close to the nuclear power plant was 0.33 items/g, which was approximately 10 times higher than those in the distant stations. The clustering of microplastic characteristics (sizes, shapes, and colors) showed that the three stations near the plant were in one group, and the distant stations were in another group. A total of 105 macroinvertebrate species belonging to 7 phyla were identified in all stations, and the dominant phyla were Annelida and Arthropoda. Spearman rank correlation showed that rare taxa (83 species with a contribution <1 %) were positively correlated with microplastics and As, and redundancy analysis demonstrated that the distribution patterns of macrobenthic communities were similar to those of microplastics. Moreover, co-occurrence networks showed that the rare taxa were positively correlated with microplastics. Therefore, microplastics released from the nuclear power plant under construction might shift the structure of macrobenthic communities, especially the rare taxa.

Validation of Repetitive Volcanoseismic Signals in Aso Volcano, Japan With Distant Stations: Implications of Source Characterization and Remote Sensing in Uninstrumented Volcanoes

AbstractRepetitive volcanoseismic signals, including very‐long‐period signals (VLP) and long‐period signals (LP), provide a unique probe of fluid transport processes inside magmatic‐plumbing system. While syneruptive signals are often detected and analyzed with regional or/and global seismic networks to retrieve eruption location and mechanism, repetitive noneruptive volcanoseismic signals are generally small, and they are typically detected with in situ stations near the volcanic edifices. Here, we show that repetitive VLP and synchronous deformation events in Aso volcano, Japan, can be detected in the high (15–30 s) and low (50–100 s) VLP bands, respectively, at seismic stations located ∼30–1,000 km away from their sources. Changes in the polarities, phases, and amplitudes of VLP and synchronous deformation events observed at the in situ stations can be verified by the seismic waves in the two VLP bands, respectively, at distant stations up to 150 km. Forward modeling of the amplitude decay in the two VLP bands against epicentral distance corroborates the source locations previously determined by the in situ data, whereas the joint data analysis of in situ and distant stations at high VLP band suggests the presence of single‐force component (i.e., force/moment ratio of 10−4 m−1) in the source of VLPs. We advocate that not only can systematic data mining against established global and regional seismic networks potentially expand the detection capability of repetitive volcanoseismic signals backward in time when in situ observations were unavailable, but it could also substantially improve the detection and monitoring capacity in otherwise uninstrumented volcanoes, complementary to remote sensing of ground deformation.

Quantum State Transfer over 1200km Assisted by Prior Distributed Entanglement.

Long-distance quantum state transfer (QST), which can be achieved with the help of quantum teleportation, is a core element of important quantum protocols. A typical situation for QST based on teleportation is one in which two remote communication partners (Alice and Bob) are far from the entanglement source (Charlie). Because of the atmospheric turbulence, it is challenging to implement the Bell-state measurement after photons propagate in atmospheric channels. In previous long-distance free-space experiments, Alice and Charlie always perform local Bell-state measurement before the entanglement distribution process is completed. Here, by developing a highly stable interferometer to project the photon into a hybrid path-polarization dimension and utilizing the satellite-borne entangled photon source, we demonstrate proof-of-principle QST at the distance of over 1200km assisted by prior quantum entanglement shared between two distant ground stations with the satellite Micius. The average fidelity of transferred six distinct quantum states is 0.82±0.01, exceeding the classical limit of 2/3 on a single copy of a qubit.